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rocksdb/db/db_bench.cc

4148 lines
143 KiB

// Copyright (c) 2013, Facebook, Inc. All rights reserved.
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree. An additional grant
// of patent rights can be found in the PATENTS file in the same directory.
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS
#endif
#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run rocksdb tools\n");
return 1;
}
#else
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
11 years ago
#ifdef NUMA
#include <numa.h>
#include <numaif.h>
#endif
#ifndef OS_WIN
#include <unistd.h>
#endif
#include <fcntl.h>
#include <inttypes.h>
#include <cstddef>
#include <sys/types.h>
#include <stdio.h>
#include <stdlib.h>
#include <gflags/gflags.h>
#include <atomic>
#include <condition_variable>
#include <mutex>
#include <thread>
#include <unordered_map>
#include "db/db_impl.h"
#include "db/version_set.h"
#include "rocksdb/options.h"
#include "rocksdb/cache.h"
#include "rocksdb/db.h"
#include "rocksdb/env.h"
#include "rocksdb/memtablerep.h"
#include "rocksdb/write_batch.h"
#include "rocksdb/slice.h"
Implement full filter for block based table. Summary: 1. Make filter_block.h a base class. Derive block_based_filter_block and full_filter_block. The previous one is the traditional filter block. The full_filter_block is newly added. It would generate a filter block that contain all the keys in SST file. 2. When querying a key, table would first check if full_filter is available. If not, it would go to the exact data block and check using block_based filter. 3. User could choose to use full_filter or tradional(block_based_filter). They would be stored in SST file with different meta index name. "filter.filter_policy" or "full_filter.filter_policy". Then, Table reader is able to know the fllter block type. 4. Some optimizations have been done for full_filter_block, thus it requires a different interface compared to the original one in filter_policy.h. 5. Actual implementation of filter bits coding/decoding is placed in util/bloom_impl.cc Benchmark: base commit 1d23b5c470844c1208301311f0889eca750431c0 Command: db_bench --db=/dev/shm/rocksdb --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --write_buffer_size=134217728 --max_write_buffer_number=2 --target_file_size_base=33554432 --max_bytes_for_level_base=1073741824 --verify_checksum=false --max_background_compactions=4 --use_plain_table=0 --memtablerep=prefix_hash --open_files=-1 --mmap_read=1 --mmap_write=0 --bloom_bits=10 --bloom_locality=1 --memtable_bloom_bits=500000 --compression_type=lz4 --num=393216000 --use_hash_search=1 --block_size=1024 --block_restart_interval=16 --use_existing_db=1 --threads=1 --benchmarks=readrandom —disable_auto_compactions=1 Read QPS increase for about 30% from 2230002 to 2991411. Test Plan: make all check valgrind db_test db_stress --use_block_based_filter = 0 ./auto_sanity_test.sh Reviewers: igor, yhchiang, ljin, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D20979
10 years ago
#include "rocksdb/filter_policy.h"
#include "rocksdb/rate_limiter.h"
#include "rocksdb/slice_transform.h"
#include "rocksdb/perf_context.h"
#include "rocksdb/utilities/flashcache.h"
#include "rocksdb/utilities/transaction.h"
#include "rocksdb/utilities/transaction_db.h"
#include "rocksdb/utilities/optimistic_transaction_db.h"
#include "port/port.h"
#include "port/stack_trace.h"
#include "util/crc32c.h"
10 years ago
#include "util/compression.h"
#include "util/histogram.h"
#include "util/mutexlock.h"
#include "util/random.h"
#include "util/string_util.h"
#include "util/statistics.h"
#include "util/testutil.h"
#include "util/xxhash.h"
#include "hdfs/env_hdfs.h"
#include "utilities/merge_operators.h"
#ifdef OS_WIN
#include <io.h> // open/close
#endif
using GFLAGS::ParseCommandLineFlags;
using GFLAGS::RegisterFlagValidator;
using GFLAGS::SetUsageMessage;
DEFINE_string(benchmarks,
"fillseq,"
"fillsync,"
"fillrandom,"
"overwrite,"
"readrandom,"
"newiterator,"
"newiteratorwhilewriting,"
"seekrandom,"
"seekrandomwhilewriting,"
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
"seekrandomwhilemerging,"
"readseq,"
"readreverse,"
"compact,"
"readrandom,"
"multireadrandom,"
"readseq,"
"readtocache,"
"readreverse,"
"readwhilewriting,"
"readwhilemerging,"
"readrandomwriterandom,"
"updaterandom,"
"randomwithverify,"
"fill100K,"
"crc32c,"
"xxhash,"
"compress,"
"uncompress,"
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
"acquireload,"
"fillseekseq,"
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
"randomtransaction,"
"randomreplacekeys",
"Comma-separated list of operations to run in the specified"
" order. Available benchmarks:\n"
"\tfillseq -- write N values in sequential key"
" order in async mode\n"
"\tfillrandom -- write N values in random key order in async"
" mode\n"
"\toverwrite -- overwrite N values in random key order in"
" async mode\n"
"\tfillsync -- write N/100 values in random key order in "
"sync mode\n"
"\tfill100K -- write N/1000 100K values in random order in"
" async mode\n"
"\tdeleteseq -- delete N keys in sequential order\n"
"\tdeleterandom -- delete N keys in random order\n"
"\treadseq -- read N times sequentially\n"
"\treadtocache -- 1 thread reading database sequentially\n"
"\treadreverse -- read N times in reverse order\n"
"\treadrandom -- read N times in random order\n"
"\treadmissing -- read N missing keys in random order\n"
"\treadwhilewriting -- 1 writer, N threads doing random "
"reads\n"
"\treadwhilemerging -- 1 merger, N threads doing random "
"reads\n"
"\treadrandomwriterandom -- N threads doing random-read, "
"random-write\n"
"\tprefixscanrandom -- prefix scan N times in random order\n"
"\tupdaterandom -- N threads doing read-modify-write for random "
"keys\n"
"\tappendrandom -- N threads doing read-modify-write with "
"growing values\n"
"\tmergerandom -- same as updaterandom/appendrandom using merge"
" operator. "
"Must be used with merge_operator\n"
"\treadrandommergerandom -- perform N random read-or-merge "
"operations. Must be used with merge_operator\n"
"\tnewiterator -- repeated iterator creation\n"
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
"\tseekrandom -- N random seeks, call Next seek_nexts times "
"per seek\n"
"\tseekrandomwhilewriting -- seekrandom and 1 thread doing "
"overwrite\n"
"\tseekrandomwhilemerging -- seekrandom and 1 thread doing "
"merge\n"
"\tcrc32c -- repeated crc32c of 4K of data\n"
"\txxhash -- repeated xxHash of 4K of data\n"
"\tacquireload -- load N*1000 times\n"
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
"\tfillseekseq -- write N values in sequential key, then read "
"them by seeking to each key\n"
"\trandomtransaction -- execute N random transactions and "
"verify correctness\n"
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
"\trandomreplacekeys -- randomly replaces N keys by deleting "
"the old version and putting the new version\n\n"
"Meta operations:\n"
"\tcompact -- Compact the entire DB\n"
"\tstats -- Print DB stats\n"
"\tlevelstats -- Print the number of files and bytes per level\n"
"\tsstables -- Print sstable info\n"
"\theapprofile -- Dump a heap profile (if supported by this"
" port)\n");
DEFINE_int64(num, 1000000, "Number of key/values to place in database");
DEFINE_int64(numdistinct, 1000,
"Number of distinct keys to use. Used in RandomWithVerify to "
"read/write on fewer keys so that gets are more likely to find the"
" key and puts are more likely to update the same key");
DEFINE_int64(merge_keys, -1,
"Number of distinct keys to use for MergeRandom and "
"ReadRandomMergeRandom. "
"If negative, there will be FLAGS_num keys.");
DEFINE_int32(num_column_families, 1, "Number of Column Families to use.");
DEFINE_int32(
num_hot_column_families, 0,
"Number of Hot Column Families. If more than 0, only write to this "
"number of column families. After finishing all the writes to them, "
"create new set of column families and insert to them. Only used "
"when num_column_families > 1.");
DEFINE_int64(reads, -1, "Number of read operations to do. "
"If negative, do FLAGS_num reads.");
DEFINE_int32(bloom_locality, 0, "Control bloom filter probes locality");
DEFINE_int64(seed, 0, "Seed base for random number generators. "
"When 0 it is deterministic.");
DEFINE_int32(threads, 1, "Number of concurrent threads to run.");
DEFINE_int32(duration, 0, "Time in seconds for the random-ops tests to run."
" When 0 then num & reads determine the test duration");
DEFINE_int32(value_size, 100, "Size of each value");
DEFINE_int32(seek_nexts, 0,
"How many times to call Next() after Seek() in "
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
"fillseekseq, seekrandom, seekrandomwhilewriting and "
"seekrandomwhilemerging");
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
DEFINE_bool(reverse_iterator, false,
"When true use Prev rather than Next for iterators that do "
"Seek and then Next");
DEFINE_bool(use_uint64_comparator, false, "use Uint64 user comparator");
DEFINE_int64(batch_size, 1, "Batch size");
static bool ValidateKeySize(const char* flagname, int32_t value) {
return true;
}
static bool ValidateUint32Range(const char* flagname, uint64_t value) {
if (value > std::numeric_limits<uint32_t>::max()) {
fprintf(stderr, "Invalid value for --%s: %lu, overflow\n", flagname,
(unsigned long)value);
return false;
}
return true;
}
DEFINE_int32(key_size, 16, "size of each key");
DEFINE_int32(num_multi_db, 0,
"Number of DBs used in the benchmark. 0 means single DB.");
DEFINE_double(compression_ratio, 0.5, "Arrange to generate values that shrink"
" to this fraction of their original size after compression");
DEFINE_double(read_random_exp_range, 0.0,
"Read random's key will be generated using distribution of "
"num * exp(-r) where r is uniform number from 0 to this value. "
"The larger the number is, the more skewed the reads are. "
"Only used in readrandom and multireadrandom benchmarks.");
DEFINE_bool(histogram, false, "Print histogram of operation timings");
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
11 years ago
DEFINE_bool(enable_numa, false,
"Make operations aware of NUMA architecture and bind memory "
"and cpus corresponding to nodes together. In NUMA, memory "
"in same node as CPUs are closer when compared to memory in "
"other nodes. Reads can be faster when the process is bound to "
"CPU and memory of same node. Use \"$numactl --hardware\" command "
"to see NUMA memory architecture.");
DEFINE_int64(db_write_buffer_size, rocksdb::Options().db_write_buffer_size,
"Number of bytes to buffer in all memtables before compacting");
Add monitoring for universal compaction and add counters for compaction IO Summary: Adds these counters { WAL_FILE_SYNCED, "rocksdb.wal.synced" } number of writes that request a WAL sync { WAL_FILE_BYTES, "rocksdb.wal.bytes" }, number of bytes written to the WAL { WRITE_DONE_BY_SELF, "rocksdb.write.self" }, number of writes processed by the calling thread { WRITE_DONE_BY_OTHER, "rocksdb.write.other" }, number of writes not processed by the calling thread. Instead these were processed by the current holder of the write lock { WRITE_WITH_WAL, "rocksdb.write.wal" }, number of writes that request WAL logging { COMPACT_READ_BYTES, "rocksdb.compact.read.bytes" }, number of bytes read during compaction { COMPACT_WRITE_BYTES, "rocksdb.compact.write.bytes" }, number of bytes written during compaction Per-interval stats output was updated with WAL stats and correct stats for universal compaction including a correct value for write-amplification. It now looks like: Compactions Level Files Size(MB) Score Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) RW-Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count Ln-stall Stall-cnt -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 0 7 464 46.4 281 3411 3875 3411 0 3875 2.1 12.1 13.8 621 0 240 240 628 0.0 0 Uptime(secs): 310.8 total, 2.0 interval Writes cumulative: 9999999 total, 9999999 batches, 1.0 per batch, 1.22 ingest GB WAL cumulative: 9999999 WAL writes, 9999999 WAL syncs, 1.00 writes per sync, 1.22 GB written Compaction IO cumulative (GB): 1.22 new, 3.33 read, 3.78 write, 7.12 read+write Compaction IO cumulative (MB/sec): 4.0 new, 11.0 read, 12.5 write, 23.4 read+write Amplification cumulative: 4.1 write, 6.8 compaction Writes interval: 100000 total, 100000 batches, 1.0 per batch, 12.5 ingest MB WAL interval: 100000 WAL writes, 100000 WAL syncs, 1.00 writes per sync, 0.01 MB written Compaction IO interval (MB): 12.49 new, 14.98 read, 21.50 write, 36.48 read+write Compaction IO interval (MB/sec): 6.4 new, 7.6 read, 11.0 write, 18.6 read+write Amplification interval: 101.7 write, 102.9 compaction Stalls(secs): 142.924 level0_slowdown, 0.000 level0_numfiles, 0.805 memtable_compaction, 0.000 leveln_slowdown Stalls(count): 132461 level0_slowdown, 0 level0_numfiles, 3 memtable_compaction, 0 leveln_slowdown Task ID: #3329644, #3301695 Blame Rev: Test Plan: Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14583
11 years ago
DEFINE_int64(write_buffer_size, rocksdb::Options().write_buffer_size,
"Number of bytes to buffer in memtable before compacting");
DEFINE_int32(max_write_buffer_number,
rocksdb::Options().max_write_buffer_number,
"The number of in-memory memtables. Each memtable is of size"
"write_buffer_size.");
DEFINE_int32(min_write_buffer_number_to_merge,
rocksdb::Options().min_write_buffer_number_to_merge,
"The minimum number of write buffers that will be merged together"
"before writing to storage. This is cheap because it is an"
"in-memory merge. If this feature is not enabled, then all these"
"write buffers are flushed to L0 as separate files and this "
"increases read amplification because a get request has to check"
" in all of these files. Also, an in-memory merge may result in"
" writing less data to storage if there are duplicate records "
" in each of these individual write buffers.");
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
10 years ago
DEFINE_int32(max_write_buffer_number_to_maintain,
rocksdb::Options().max_write_buffer_number_to_maintain,
"The total maximum number of write buffers to maintain in memory "
"including copies of buffers that have already been flushed. "
"Unlike max_write_buffer_number, this parameter does not affect "
"flushing. This controls the minimum amount of write history "
"that will be available in memory for conflict checking when "
"Transactions are used. If this value is too low, some "
"transactions may fail at commit time due to not being able to "
"determine whether there were any write conflicts. Setting this "
"value to 0 will cause write buffers to be freed immediately "
"after they are flushed. If this value is set to -1, "
"'max_write_buffer_number' will be used.");
DEFINE_int32(max_background_compactions,
rocksdb::Options().max_background_compactions,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_uint64(subcompactions, 1,
"Maximum number of subcompactions to divide L0-L1 compactions "
"into.");
static const bool FLAGS_subcompactions_dummy
__attribute__((unused)) = RegisterFlagValidator(&FLAGS_subcompactions,
&ValidateUint32Range);
DEFINE_int32(max_background_flushes,
rocksdb::Options().max_background_flushes,
"The maximum number of concurrent background flushes"
" that can occur in parallel.");
static rocksdb::CompactionStyle FLAGS_compaction_style_e;
DEFINE_int32(compaction_style, (int32_t) rocksdb::Options().compaction_style,
"style of compaction: level-based vs universal");
static rocksdb::CompactionPri FLAGS_compaction_pri_e;
DEFINE_int32(compaction_pri, (int32_t)rocksdb::Options().compaction_pri,
"priority of files to compaction: by size or by data age");
DEFINE_int32(universal_size_ratio, 0,
"Percentage flexibility while comparing file size"
" (for universal compaction only).");
DEFINE_int32(universal_min_merge_width, 0, "The minimum number of files in a"
" single compaction run (for universal compaction only).");
DEFINE_int32(universal_max_merge_width, 0, "The max number of files to compact"
" in universal style compaction");
DEFINE_int32(universal_max_size_amplification_percent, 0,
"The max size amplification for universal style compaction");
DEFINE_int32(universal_compression_size_percent, -1,
"The percentage of the database to compress for universal "
"compaction. -1 means compress everything.");
DEFINE_bool(universal_allow_trivial_move, false,
"Allow trivial move in universal compaction.");
DEFINE_int64(cache_size, -1, "Number of bytes to use as a cache of uncompressed"
"data. Negative means use default settings.");
DEFINE_bool(cache_index_and_filter_blocks, false,
"Cache index/filter blocks in block cache.");
DEFINE_int32(block_size,
static_cast<int32_t>(rocksdb::BlockBasedTableOptions().block_size),
"Number of bytes in a block.");
DEFINE_int32(block_restart_interval,
rocksdb::BlockBasedTableOptions().block_restart_interval,
"Number of keys between restart points "
"for delta encoding of keys.");
DEFINE_int64(compressed_cache_size, -1,
"Number of bytes to use as a cache of compressed data.");
DEFINE_int64(row_cache_size, 0,
"Number of bytes to use as a cache of individual rows"
" (0 = disabled).");
DEFINE_int32(open_files, rocksdb::Options().max_open_files,
"Maximum number of files to keep open at the same time"
" (use default if == 0)");
DEFINE_int32(file_opening_threads, rocksdb::Options().max_file_opening_threads,
"If open_files is set to -1, this option set the number of "
"threads that will be used to open files during DB::Open()");
DEFINE_int32(new_table_reader_for_compaction_inputs, true,
"If true, uses a separate file handle for compaction inputs");
DEFINE_int32(compaction_readahead_size, 0, "Compaction readahead size");
DEFINE_int32(random_access_max_buffer_size, 1024 * 1024,
"Maximum windows randomaccess buffer size");
DEFINE_int32(writable_file_max_buffer_size, 1024 * 1024,
"Maximum write buffer for Writable File");
DEFINE_int32(skip_table_builder_flush, false, "Skip flushing block in "
"table builder ");
DEFINE_int32(bloom_bits, -1, "Bloom filter bits per key. Negative means"
" use default settings.");
DEFINE_int32(memtable_bloom_bits, 0, "Bloom filter bits per key for memtable. "
"Negative means no bloom filter.");
DEFINE_bool(use_existing_db, false, "If true, do not destroy the existing"
" database. If you set this flag and also specify a benchmark that"
" wants a fresh database, that benchmark will fail.");
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
9 years ago
DEFINE_bool(show_table_properties, false,
"If true, then per-level table"
" properties will be printed on every stats-interval when"
" stats_interval is set and stats_per_interval is on.");
DEFINE_string(db, "", "Use the db with the following name.");
static bool ValidateCacheNumshardbits(const char* flagname, int32_t value) {
if (value >= 20) {
fprintf(stderr, "Invalid value for --%s: %d, must be < 20\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(cache_numshardbits, -1, "Number of shards for the block cache"
" is 2 ** cache_numshardbits. Negative means use default settings."
" This is applied only if FLAGS_cache_size is non-negative.");
DEFINE_bool(verify_checksum, false, "Verify checksum for every block read"
" from storage");
DEFINE_bool(statistics, false, "Database statistics");
static class std::shared_ptr<rocksdb::Statistics> dbstats;
DEFINE_int64(writes, -1, "Number of write operations to do. If negative, do"
" --num reads.");
DEFINE_bool(sync, false, "Sync all writes to disk");
DEFINE_bool(disable_data_sync, false, "If true, do not wait until data is"
" synced to disk.");
DEFINE_bool(use_fsync, false, "If true, issue fsync instead of fdatasync");
DEFINE_bool(disable_wal, false, "If true, do not write WAL for write.");
DEFINE_string(wal_dir, "", "If not empty, use the given dir for WAL");
DEFINE_int32(num_levels, 7, "The total number of levels");
DEFINE_int64(target_file_size_base, 2 * 1048576, "Target file size at level-1");
DEFINE_int32(target_file_size_multiplier, 1,
"A multiplier to compute target level-N file size (N >= 2)");
DEFINE_uint64(max_bytes_for_level_base, 10 * 1048576, "Max bytes for level-1");
DEFINE_bool(level_compaction_dynamic_level_bytes, false,
"Whether level size base is dynamic");
DEFINE_int32(max_bytes_for_level_multiplier, 10,
"A multiplier to compute max bytes for level-N (N >= 2)");
static std::vector<int> FLAGS_max_bytes_for_level_multiplier_additional_v;
DEFINE_string(max_bytes_for_level_multiplier_additional, "",
"A vector that specifies additional fanout per level");
DEFINE_int32(level0_stop_writes_trigger,
rocksdb::Options().level0_stop_writes_trigger,
"Number of files in level-0"
" that will trigger put stop.");
DEFINE_int32(level0_slowdown_writes_trigger,
rocksdb::Options().level0_slowdown_writes_trigger,
"Number of files in level-0"
" that will slow down writes.");
DEFINE_int32(level0_file_num_compaction_trigger,
rocksdb::Options().level0_file_num_compaction_trigger,
"Number of files in level-0"
" when compactions start");
static bool ValidateInt32Percent(const char* flagname, int32_t value) {
if (value <= 0 || value>=100) {
fprintf(stderr, "Invalid value for --%s: %d, 0< pct <100 \n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(readwritepercent, 90, "Ratio of reads to reads/writes (expressed"
" as percentage) for the ReadRandomWriteRandom workload. The "
"default value 90 means 90% operations out of all reads and writes"
" operations are reads. In other words, 9 gets for every 1 put.");
DEFINE_int32(mergereadpercent, 70, "Ratio of merges to merges&reads (expressed"
" as percentage) for the ReadRandomMergeRandom workload. The"
" default value 70 means 70% out of all read and merge operations"
" are merges. In other words, 7 merges for every 3 gets.");
DEFINE_int32(deletepercent, 2, "Percentage of deletes out of reads/writes/"
"deletes (used in RandomWithVerify only). RandomWithVerify "
"calculates writepercent as (100 - FLAGS_readwritepercent - "
"deletepercent), so deletepercent must be smaller than (100 - "
"FLAGS_readwritepercent)");
Speed up FindObsoleteFiles() Summary: There are two versions of FindObsoleteFiles(): * full scan, which is executed every 6 hours (and it's terribly slow) * no full scan, which is executed every time a background process finishes and iterator is deleted This diff is optimizing the second case (no full scan). Here's what we do before the diff: * Get the list of obsolete files (files with ref==0). Some files in obsolete_files set might actually be live. * Get the list of live files to avoid deleting files that are live. * Delete files that are in obsolete_files and not in live_files. After this diff: * The only files with ref==0 that are still live are files that have been part of move compaction. Don't include moved files in obsolete_files. * Get the list of obsolete files (which exclude moved files). * No need to get the list of live files, since all files in obsolete_files need to be deleted. I'll post the benchmark results, but you can get the feel of it here: https://reviews.facebook.net/D30123 This depends on D30123. P.S. We should do full scan only in failure scenarios, not every 6 hours. I'll do this in a follow-up diff. Test Plan: One new unit test. Made sure that unit test fails if we don't have a `if (!f->moved)` safeguard in ~Version. make check Big number of compactions and flushes: ./db_stress --threads=30 --ops_per_thread=20000000 --max_key=10000 --column_families=20 --clear_column_family_one_in=10000000 --verify_before_write=0 --reopen=15 --max_background_compactions=10 --max_background_flushes=10 --db=/fast-rocksdb-tmp/db_stress --prefixpercent=0 --iterpercent=0 --writepercent=75 --db_write_buffer_size=2000000 Reviewers: yhchiang, rven, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D30249
10 years ago
DEFINE_uint64(delete_obsolete_files_period_micros, 0,
"Ignored. Left here for backward compatibility");
#ifndef ROCKSDB_LITE
DEFINE_bool(optimistic_transaction_db, false,
"Open a OptimisticTransactionDB instance. "
"Required for randomtransaction benchmark.");
DEFINE_bool(transaction_db, false,
"Open a TransactionDB instance. "
"Required for randomtransaction benchmark.");
DEFINE_uint64(transaction_sets, 2,
"Number of keys each transaction will "
"modify (use in RandomTransaction only). Max: 9999");
DEFINE_bool(transaction_set_snapshot, false,
"Setting to true will have each transaction call SetSnapshot()"
" upon creation.");
DEFINE_int32(transaction_sleep, 0,
"Max microseconds to sleep in between "
"reading and writing a value (used in RandomTransaction only). ");
DEFINE_uint64(transaction_lock_timeout, 100,
"If using a transaction_db, specifies the lock wait timeout in"
" milliseconds before failing a transaction waiting on a lock");
#endif // ROCKSDB_LITE
DEFINE_bool(compaction_measure_io_stats, false,
"Measure times spents on I/Os while in compactions. ");
namespace {
enum rocksdb::CompressionType StringToCompressionType(const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "none"))
return rocksdb::kNoCompression;
else if (!strcasecmp(ctype, "snappy"))
return rocksdb::kSnappyCompression;
else if (!strcasecmp(ctype, "zlib"))
return rocksdb::kZlibCompression;
else if (!strcasecmp(ctype, "bzip2"))
return rocksdb::kBZip2Compression;
else if (!strcasecmp(ctype, "lz4"))
return rocksdb::kLZ4Compression;
else if (!strcasecmp(ctype, "lz4hc"))
return rocksdb::kLZ4HCCompression;
else if (!strcasecmp(ctype, "zstd"))
return rocksdb::kZSTDNotFinalCompression;
fprintf(stdout, "Cannot parse compression type '%s'\n", ctype);
return rocksdb::kSnappyCompression; //default value
}
std::string ColumnFamilyName(size_t i) {
if (i == 0) {
return rocksdb::kDefaultColumnFamilyName;
} else {
char name[100];
snprintf(name, sizeof(name), "column_family_name_%06zu", i);
return std::string(name);
}
}
} // namespace
DEFINE_string(compression_type, "snappy",
"Algorithm to use to compress the database");
static enum rocksdb::CompressionType FLAGS_compression_type_e =
rocksdb::kSnappyCompression;
DEFINE_int32(compression_level, -1,
"Compression level. For zlib this should be -1 for the "
"default level, or between 0 and 9.");
static bool ValidateCompressionLevel(const char* flagname, int32_t value) {
if (value < -1 || value > 9) {
fprintf(stderr, "Invalid value for --%s: %d, must be between -1 and 9\n",
flagname, value);
return false;
}
return true;
}
static const bool FLAGS_compression_level_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_compression_level, &ValidateCompressionLevel);
DEFINE_int32(min_level_to_compress, -1, "If non-negative, compression starts"
" from this level. Levels with number < min_level_to_compress are"
" not compressed. Otherwise, apply compression_type to "
"all levels.");
static bool ValidateTableCacheNumshardbits(const char* flagname,
int32_t value) {
if (0 >= value || value > 20) {
fprintf(stderr, "Invalid value for --%s: %d, must be 0 < val <= 20\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(table_cache_numshardbits, 4, "");
DEFINE_string(hdfs, "", "Name of hdfs environment");
// posix or hdfs environment
static rocksdb::Env* FLAGS_env = rocksdb::Env::Default();
DEFINE_int64(stats_interval, 0, "Stats are reported every N operations when "
"this is greater than zero. When 0 the interval grows over time.");
DEFINE_int64(stats_interval_seconds, 0, "Report stats every N seconds. This "
"overrides stats_interval when both are > 0.");
DEFINE_int32(stats_per_interval, 0, "Reports additional stats per interval when"
" this is greater than 0.");
DEFINE_int64(report_interval_seconds, 0,
"If greater than zero, it will write simple stats in CVS format "
"to --report_file every N seconds");
DEFINE_string(report_file, "report.csv",
"Filename where some simple stats are reported to (if "
"--report_interval_seconds is bigger than 0)");
DEFINE_int32(thread_status_per_interval, 0,
"Takes and report a snapshot of the current status of each thread"
" when this is greater than 0.");
DEFINE_int32(perf_level, 0, "Level of perf collection");
static bool ValidateRateLimit(const char* flagname, double value) {
const double EPSILON = 1e-10;
if ( value < -EPSILON ) {
fprintf(stderr, "Invalid value for --%s: %12.6f, must be >= 0.0\n",
flagname, value);
return false;
}
return true;
}
DEFINE_double(soft_rate_limit, 0.0, "DEPRECATED");
DEFINE_double(hard_rate_limit, 0.0, "DEPRECATED");
DEFINE_uint64(soft_pending_compaction_bytes_limit, 64ull * 1024 * 1024 * 1024,
"Slowdown writes if pending compaction bytes exceed this number");
DEFINE_uint64(hard_pending_compaction_bytes_limit, 128ull * 1024 * 1024 * 1024,
"Stop writes if pending compaction bytes exceed this number");
DEFINE_uint64(delayed_write_rate, 8388608u,
"Limited bytes allowed to DB when soft_rate_limit or "
"level0_slowdown_writes_trigger triggers");
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
DEFINE_bool(allow_concurrent_memtable_write, false,
"Allow multi-writers to update mem tables in parallel.");
DEFINE_bool(enable_write_thread_adaptive_yield, false,
"Use a yielding spin loop for brief writer thread waits.");
DEFINE_uint64(
write_thread_max_yield_usec, 100,
"Maximum microseconds for enable_write_thread_adaptive_yield operation.");
DEFINE_uint64(write_thread_slow_yield_usec, 3,
"The threshold at which a slow yield is considered a signal that "
"other processes or threads want the core.");
DEFINE_int32(rate_limit_delay_max_milliseconds, 1000,
"When hard_rate_limit is set then this is the max time a put will"
" be stalled.");
DEFINE_uint64(rate_limiter_bytes_per_sec, 0, "Set options.rate_limiter value.");
DEFINE_uint64(
benchmark_write_rate_limit, 0,
"If non-zero, db_bench will rate-limit the writes going into RocksDB. This "
"is the global rate in bytes/second.");
DEFINE_int32(max_grandparent_overlap_factor, 10, "Control maximum bytes of "
"overlaps in grandparent (i.e., level+2) before we stop building a"
" single file in a level->level+1 compaction.");
#ifndef ROCKSDB_LITE
DEFINE_bool(readonly, false, "Run read only benchmarks.");
#endif // ROCKSDB_LITE
DEFINE_bool(disable_auto_compactions, false, "Do not auto trigger compactions");
DEFINE_int32(source_compaction_factor, 1, "Cap the size of data in level-K for"
" a compaction run that compacts Level-K with Level-(K+1) (for"
" K >= 1)");
DEFINE_uint64(wal_ttl_seconds, 0, "Set the TTL for the WAL Files in seconds.");
DEFINE_uint64(wal_size_limit_MB, 0, "Set the size limit for the WAL Files"
" in MB.");
DEFINE_uint64(max_total_wal_size, 0, "Set total max WAL size");
DEFINE_bool(bufferedio, rocksdb::EnvOptions().use_os_buffer,
"Allow buffered io using OS buffers");
DEFINE_bool(mmap_read, rocksdb::EnvOptions().use_mmap_reads,
"Allow reads to occur via mmap-ing files");
DEFINE_bool(mmap_write, rocksdb::EnvOptions().use_mmap_writes,
"Allow writes to occur via mmap-ing files");
DEFINE_bool(advise_random_on_open, rocksdb::Options().advise_random_on_open,
"Advise random access on table file open");
DEFINE_string(compaction_fadvice, "NORMAL",
"Access pattern advice when a file is compacted");
static auto FLAGS_compaction_fadvice_e =
rocksdb::Options().access_hint_on_compaction_start;
DEFINE_bool(disable_flashcache_for_background_threads, false,
"Disable flashcache for background threads");
DEFINE_string(flashcache_dev, "", "Path to flashcache device");
DEFINE_bool(use_tailing_iterator, false,
"Use tailing iterator to access a series of keys instead of get");
DEFINE_bool(use_adaptive_mutex, rocksdb::Options().use_adaptive_mutex,
"Use adaptive mutex");
DEFINE_uint64(bytes_per_sync, rocksdb::Options().bytes_per_sync,
"Allows OS to incrementally sync SST files to disk while they are"
" being written, in the background. Issue one request for every"
" bytes_per_sync written. 0 turns it off.");
DEFINE_uint64(wal_bytes_per_sync, rocksdb::Options().wal_bytes_per_sync,
"Allows OS to incrementally sync WAL files to disk while they are"
" being written, in the background. Issue one request for every"
" wal_bytes_per_sync written. 0 turns it off.");
DEFINE_bool(filter_deletes, false, " On true, deletes use bloom-filter and drop"
" the delete if key not present");
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
DEFINE_bool(use_single_deletes, true,
"Use single deletes (used in RandomReplaceKeys only).");
DEFINE_double(stddev, 2000.0,
"Standard deviation of normal distribution used for picking keys"
" (used in RandomReplaceKeys only).");
DEFINE_int32(max_successive_merges, 0, "Maximum number of successive merge"
" operations on a key in the memtable");
static bool ValidatePrefixSize(const char* flagname, int32_t value) {
if (value < 0 || value>=2000000000) {
fprintf(stderr, "Invalid value for --%s: %d. 0<= PrefixSize <=2000000000\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(prefix_size, 0, "control the prefix size for HashSkipList and "
"plain table");
DEFINE_int64(keys_per_prefix, 0, "control average number of keys generated "
"per prefix, 0 means no special handling of the prefix, "
"i.e. use the prefix comes with the generated random number.");
DEFINE_bool(enable_io_prio, false, "Lower the background flush/compaction "
"threads' IO priority");
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
10 years ago
DEFINE_bool(identity_as_first_hash, false, "the first hash function of cuckoo "
"table becomes an identity function. This is only valid when key "
"is 8 bytes");
enum RepFactory {
kSkipList,
kPrefixHash,
kVectorRep,
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
11 years ago
kHashLinkedList,
kCuckoo
};
namespace {
enum RepFactory StringToRepFactory(const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "skip_list"))
return kSkipList;
else if (!strcasecmp(ctype, "prefix_hash"))
return kPrefixHash;
else if (!strcasecmp(ctype, "vector"))
return kVectorRep;
else if (!strcasecmp(ctype, "hash_linkedlist"))
return kHashLinkedList;
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
11 years ago
else if (!strcasecmp(ctype, "cuckoo"))
return kCuckoo;
fprintf(stdout, "Cannot parse memreptable %s\n", ctype);
return kSkipList;
}
} // namespace
static enum RepFactory FLAGS_rep_factory;
DEFINE_string(memtablerep, "skip_list", "");
DEFINE_int64(hash_bucket_count, 1024 * 1024, "hash bucket count");
DEFINE_bool(use_plain_table, false, "if use plain table "
"instead of block-based table format");
DEFINE_bool(use_cuckoo_table, false, "if use cuckoo table format");
DEFINE_double(cuckoo_hash_ratio, 0.9, "Hash ratio for Cuckoo SST table.");
DEFINE_bool(use_hash_search, false, "if use kHashSearch "
"instead of kBinarySearch. "
"This is valid if only we use BlockTable");
Implement full filter for block based table. Summary: 1. Make filter_block.h a base class. Derive block_based_filter_block and full_filter_block. The previous one is the traditional filter block. The full_filter_block is newly added. It would generate a filter block that contain all the keys in SST file. 2. When querying a key, table would first check if full_filter is available. If not, it would go to the exact data block and check using block_based filter. 3. User could choose to use full_filter or tradional(block_based_filter). They would be stored in SST file with different meta index name. "filter.filter_policy" or "full_filter.filter_policy". Then, Table reader is able to know the fllter block type. 4. Some optimizations have been done for full_filter_block, thus it requires a different interface compared to the original one in filter_policy.h. 5. Actual implementation of filter bits coding/decoding is placed in util/bloom_impl.cc Benchmark: base commit 1d23b5c470844c1208301311f0889eca750431c0 Command: db_bench --db=/dev/shm/rocksdb --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --write_buffer_size=134217728 --max_write_buffer_number=2 --target_file_size_base=33554432 --max_bytes_for_level_base=1073741824 --verify_checksum=false --max_background_compactions=4 --use_plain_table=0 --memtablerep=prefix_hash --open_files=-1 --mmap_read=1 --mmap_write=0 --bloom_bits=10 --bloom_locality=1 --memtable_bloom_bits=500000 --compression_type=lz4 --num=393216000 --use_hash_search=1 --block_size=1024 --block_restart_interval=16 --use_existing_db=1 --threads=1 --benchmarks=readrandom —disable_auto_compactions=1 Read QPS increase for about 30% from 2230002 to 2991411. Test Plan: make all check valgrind db_test db_stress --use_block_based_filter = 0 ./auto_sanity_test.sh Reviewers: igor, yhchiang, ljin, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D20979
10 years ago
DEFINE_bool(use_block_based_filter, false, "if use kBlockBasedFilter "
"instead of kFullFilter for filter block. "
"This is valid if only we use BlockTable");
DEFINE_string(merge_operator, "", "The merge operator to use with the database."
"If a new merge operator is specified, be sure to use fresh"
" database The possible merge operators are defined in"
" utilities/merge_operators.h");
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
DEFINE_int32(skip_list_lookahead, 0, "Used with skip_list memtablerep; try "
"linear search first for this many steps from the previous "
"position");
DEFINE_bool(report_file_operations, false, "if report number of file "
"operations");
static const bool FLAGS_soft_rate_limit_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_soft_rate_limit, &ValidateRateLimit);
static const bool FLAGS_hard_rate_limit_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_hard_rate_limit, &ValidateRateLimit);
static const bool FLAGS_prefix_size_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_prefix_size, &ValidatePrefixSize);
static const bool FLAGS_key_size_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_key_size, &ValidateKeySize);
static const bool FLAGS_cache_numshardbits_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_cache_numshardbits,
&ValidateCacheNumshardbits);
static const bool FLAGS_readwritepercent_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_readwritepercent, &ValidateInt32Percent);
DEFINE_int32(disable_seek_compaction, false,
"Not used, left here for backwards compatibility");
static const bool FLAGS_deletepercent_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_deletepercent, &ValidateInt32Percent);
static const bool FLAGS_table_cache_numshardbits_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_table_cache_numshardbits,
&ValidateTableCacheNumshardbits);
namespace rocksdb {
namespace {
struct ReportFileOpCounters {
std::atomic<int> open_counter_;
std::atomic<int> read_counter_;
std::atomic<int> append_counter_;
std::atomic<uint64_t> bytes_read_;
std::atomic<uint64_t> bytes_written_;
};
// A special Env to records and report file operations in db_bench
class ReportFileOpEnv : public EnvWrapper {
public:
explicit ReportFileOpEnv(Env* base) : EnvWrapper(base) { reset(); }
void reset() {
counters_.open_counter_ = 0;
counters_.read_counter_ = 0;
counters_.append_counter_ = 0;
counters_.bytes_read_ = 0;
counters_.bytes_written_ = 0;
}
Status NewSequentialFile(const std::string& f, unique_ptr<SequentialFile>* r,
const EnvOptions& soptions) override {
class CountingFile : public SequentialFile {
private:
unique_ptr<SequentialFile> target_;
ReportFileOpCounters* counters_;
public:
CountingFile(unique_ptr<SequentialFile>&& target,
ReportFileOpCounters* counters)
: target_(std::move(target)), counters_(counters) {}
virtual Status Read(size_t n, Slice* result, char* scratch) override {
counters_->read_counter_.fetch_add(1, std::memory_order_relaxed);
Status rv = target_->Read(n, result, scratch);
counters_->bytes_read_.fetch_add(result->size(),
std::memory_order_relaxed);
return rv;
}
virtual Status Skip(uint64_t n) override { return target_->Skip(n); }
};
Status s = target()->NewSequentialFile(f, r, soptions);
if (s.ok()) {
counters()->open_counter_.fetch_add(1, std::memory_order_relaxed);
r->reset(new CountingFile(std::move(*r), counters()));
}
return s;
}
Status NewRandomAccessFile(const std::string& f,
unique_ptr<RandomAccessFile>* r,
const EnvOptions& soptions) override {
class CountingFile : public RandomAccessFile {
private:
unique_ptr<RandomAccessFile> target_;
ReportFileOpCounters* counters_;
public:
CountingFile(unique_ptr<RandomAccessFile>&& target,
ReportFileOpCounters* counters)
: target_(std::move(target)), counters_(counters) {}
virtual Status Read(uint64_t offset, size_t n, Slice* result,
char* scratch) const override {
counters_->read_counter_.fetch_add(1, std::memory_order_relaxed);
Status rv = target_->Read(offset, n, result, scratch);
counters_->bytes_read_.fetch_add(result->size(),
std::memory_order_relaxed);
return rv;
}
};
Status s = target()->NewRandomAccessFile(f, r, soptions);
if (s.ok()) {
counters()->open_counter_.fetch_add(1, std::memory_order_relaxed);
r->reset(new CountingFile(std::move(*r), counters()));
}
return s;
}
Status NewWritableFile(const std::string& f, unique_ptr<WritableFile>* r,
const EnvOptions& soptions) override {
class CountingFile : public WritableFile {
private:
unique_ptr<WritableFile> target_;
ReportFileOpCounters* counters_;
public:
CountingFile(unique_ptr<WritableFile>&& target,
ReportFileOpCounters* counters)
: target_(std::move(target)), counters_(counters) {}
Status Append(const Slice& data) override {
counters_->append_counter_.fetch_add(1, std::memory_order_relaxed);
Status rv = target_->Append(data);
counters_->bytes_written_.fetch_add(data.size(),
std::memory_order_relaxed);
return rv;
}
Refactor to support file_reader_writer on Windows. Summary. A change https://reviews.facebook.net/differential/diff/224721/ Has attempted to move common functionality out of platform dependent code to a new facility called file_reader_writer. This includes: - perf counters - Buffering - RateLimiting However, the change did not attempt to refactor Windows code. To mitigate, we introduce new quering interfaces such as UseOSBuffer(), GetRequiredBufferAlignment() and ReaderWriterForward() for pure forwarding where required. Introduce WritableFile got a new method Truncate(). This is to communicate to the file as to how much data it has on close. - When space is pre-allocated on Linux it is filled with zeros implicitly, no such thing exist on Windows so we must truncate file on close. - When operating in unbuffered mode the last page is filled with zeros but we still want to truncate. Previously, Close() would take care of it but now buffer management is shifted to the wrappers and the file has no idea about the file true size. This means that Close() on the wrapper level must always include Truncate() as well as wrapper __dtor should call Close() and against double Close(). Move buffered/unbuffered write logic to the wrapper. Utilize Aligned buffer class. Adjust tests and implement Truncate() where necessary. Come up with reasonable defaults for new virtual interfaces. Forward calls for RandomAccessReadAhead class to avoid double buffering and locking (double locking in unbuffered mode on WIndows).
9 years ago
Status Truncate(uint64_t size) override { return target_->Truncate(size); }
Status Close() override { return target_->Close(); }
Status Flush() override { return target_->Flush(); }
Status Sync() override { return target_->Sync(); }
};
Status s = target()->NewWritableFile(f, r, soptions);
if (s.ok()) {
counters()->open_counter_.fetch_add(1, std::memory_order_relaxed);
r->reset(new CountingFile(std::move(*r), counters()));
}
return s;
}
// getter
ReportFileOpCounters* counters() { return &counters_; }
private:
ReportFileOpCounters counters_;
};
} // namespace
// Helper for quickly generating random data.
class RandomGenerator {
private:
std::string data_;
unsigned int pos_;
public:
RandomGenerator() {
// We use a limited amount of data over and over again and ensure
// that it is larger than the compression window (32KB), and also
// large enough to serve all typical value sizes we want to write.
Random rnd(301);
std::string piece;
while (data_.size() < (unsigned)std::max(1048576, FLAGS_value_size)) {
// Add a short fragment that is as compressible as specified
// by FLAGS_compression_ratio.
test::CompressibleString(&rnd, FLAGS_compression_ratio, 100, &piece);
data_.append(piece);
}
pos_ = 0;
}
Slice Generate(unsigned int len) {
assert(len <= data_.size());
if (pos_ + len > data_.size()) {
pos_ = 0;
}
pos_ += len;
return Slice(data_.data() + pos_ - len, len);
}
};
static void AppendWithSpace(std::string* str, Slice msg) {
if (msg.empty()) return;
if (!str->empty()) {
str->push_back(' ');
}
str->append(msg.data(), msg.size());
}
struct DBWithColumnFamilies {
std::vector<ColumnFamilyHandle*> cfh;
DB* db;
#ifndef ROCKSDB_LITE
OptimisticTransactionDB* opt_txn_db;
#endif // ROCKSDB_LITE
std::atomic<size_t> num_created; // Need to be updated after all the
// new entries in cfh are set.
size_t num_hot; // Number of column families to be queried at each moment.
// After each CreateNewCf(), another num_hot number of new
// Column families will be created and used to be queried.
port::Mutex create_cf_mutex; // Only one thread can execute CreateNewCf()
DBWithColumnFamilies()
: db(nullptr)
#ifndef ROCKSDB_LITE
, opt_txn_db(nullptr)
#endif // ROCKSDB_LITE
{
cfh.clear();
num_created = 0;
num_hot = 0;
}
DBWithColumnFamilies(const DBWithColumnFamilies& other)
: cfh(other.cfh),
db(other.db),
#ifndef ROCKSDB_LITE
opt_txn_db(other.opt_txn_db),
#endif // ROCKSDB_LITE
num_created(other.num_created.load()),
num_hot(other.num_hot) {}
void DeleteDBs() {
std::for_each(cfh.begin(), cfh.end(),
[](ColumnFamilyHandle* cfhi) { delete cfhi; });
cfh.clear();
#ifndef ROCKSDB_LITE
if (opt_txn_db) {
delete opt_txn_db;
opt_txn_db = nullptr;
} else {
delete db;
db = nullptr;
}
#else
delete db;
db = nullptr;
#endif // ROCKSDB_LITE
}
ColumnFamilyHandle* GetCfh(int64_t rand_num) {
assert(num_hot > 0);
return cfh[num_created.load(std::memory_order_acquire) - num_hot +
rand_num % num_hot];
}
// stage: assume CF from 0 to stage * num_hot has be created. Need to create
// stage * num_hot + 1 to stage * (num_hot + 1).
void CreateNewCf(ColumnFamilyOptions options, int64_t stage) {
MutexLock l(&create_cf_mutex);
if ((stage + 1) * num_hot <= num_created) {
// Already created.
return;
}
auto new_num_created = num_created + num_hot;
assert(new_num_created <= cfh.size());
for (size_t i = num_created; i < new_num_created; i++) {
Status s =
db->CreateColumnFamily(options, ColumnFamilyName(i), &(cfh[i]));
if (!s.ok()) {
fprintf(stderr, "create column family error: %s\n",
s.ToString().c_str());
abort();
}
}
num_created.store(new_num_created, std::memory_order_release);
}
};
// a class that reports stats to CSV file
class ReporterAgent {
public:
ReporterAgent(Env* env, const std::string& fname,
uint64_t report_interval_secs)
: env_(env),
total_ops_done_(0),
last_report_(0),
report_interval_secs_(report_interval_secs),
stop_(false) {
auto s = env_->NewWritableFile(fname, &report_file_, EnvOptions());
if (s.ok()) {
s = report_file_->Append(Header() + "\n");
}
if (s.ok()) {
s = report_file_->Flush();
}
if (!s.ok()) {
fprintf(stderr, "Can't open %s: %s\n", fname.c_str(),
s.ToString().c_str());
abort();
}
reporting_thread_ = std::thread([&]() { SleepAndReport(); });
}
~ReporterAgent() {
{
std::unique_lock<std::mutex> lk(mutex_);
stop_ = true;
stop_cv_.notify_all();
}
reporting_thread_.join();
}
// thread safe
void ReportFinishedOps(int64_t num_ops) {
total_ops_done_.fetch_add(num_ops);
}
private:
std::string Header() const { return "secs_elapsed,interval_qps"; }
void SleepAndReport() {
uint64_t kMicrosInSecond = 1000 * 1000;
auto time_started = env_->NowMicros();
while (true) {
{
std::unique_lock<std::mutex> lk(mutex_);
if (stop_ ||
stop_cv_.wait_for(lk, std::chrono::seconds(report_interval_secs_),
[&]() { return stop_; })) {
// stopping
break;
}
// else -> timeout, which means time for a report!
}
auto total_ops_done_snapshot = total_ops_done_.load();
// round the seconds elapsed
auto secs_elapsed =
(env_->NowMicros() - time_started + kMicrosInSecond / 2) /
kMicrosInSecond;
std::string report = ToString(secs_elapsed) + "," +
ToString(total_ops_done_snapshot - last_report_) +
"\n";
auto s = report_file_->Append(report);
if (s.ok()) {
s = report_file_->Flush();
}
if (!s.ok()) {
fprintf(stderr,
"Can't write to report file (%s), stopping the reporting\n",
s.ToString().c_str());
break;
}
last_report_ = total_ops_done_snapshot;
}
}
Env* env_;
std::unique_ptr<WritableFile> report_file_;
std::atomic<int64_t> total_ops_done_;
int64_t last_report_;
const uint64_t report_interval_secs_;
std::thread reporting_thread_;
std::mutex mutex_;
// will notify on stop
std::condition_variable stop_cv_;
bool stop_;
};
enum OperationType : unsigned char {
kRead = 0,
kWrite,
kDelete,
kSeek,
kMerge,
kUpdate,
kCompress,
kUncompress,
kCrc,
kHash,
kOthers
};
static std::unordered_map<OperationType, std::string, std::hash<unsigned char>>
OperationTypeString = {
{kRead, "read"},
{kWrite, "write"},
{kDelete, "delete"},
{kSeek, "seek"},
{kMerge, "merge"},
{kUpdate, "update"},
{kCompress, "compress"},
{kCompress, "uncompress"},
{kCrc, "crc"},
{kHash, "hash"},
{kOthers, "op"}
};
class Stats {
private:
int id_;
uint64_t start_;
uint64_t finish_;
double seconds_;
uint64_t done_;
uint64_t last_report_done_;
uint64_t next_report_;
uint64_t bytes_;
uint64_t last_op_finish_;
uint64_t last_report_finish_;
std::unordered_map<OperationType, HistogramImpl,
std::hash<unsigned char>> hist_;
std::string message_;
bool exclude_from_merge_;
ReporterAgent* reporter_agent_; // does not own
public:
Stats() { Start(-1); }
void SetReporterAgent(ReporterAgent* reporter_agent) {
reporter_agent_ = reporter_agent;
}
void Start(int id) {
id_ = id;
next_report_ = FLAGS_stats_interval ? FLAGS_stats_interval : 100;
last_op_finish_ = start_;
hist_.clear();
done_ = 0;
last_report_done_ = 0;
bytes_ = 0;
seconds_ = 0;
start_ = FLAGS_env->NowMicros();
finish_ = start_;
last_report_finish_ = start_;
message_.clear();
// When set, stats from this thread won't be merged with others.
exclude_from_merge_ = false;
}
void Merge(const Stats& other) {
if (other.exclude_from_merge_)
return;
for (auto it = other.hist_.begin(); it != other.hist_.end(); ++it) {
auto this_it = hist_.find(it->first);
if (this_it != hist_.end()) {
this_it->second.Merge(other.hist_.at(it->first));
} else {
hist_.insert({ it->first, it->second });
}
}
done_ += other.done_;
bytes_ += other.bytes_;
seconds_ += other.seconds_;
if (other.start_ < start_) start_ = other.start_;
if (other.finish_ > finish_) finish_ = other.finish_;
// Just keep the messages from one thread
if (message_.empty()) message_ = other.message_;
}
void Stop() {
finish_ = FLAGS_env->NowMicros();
seconds_ = (finish_ - start_) * 1e-6;
}
void AddMessage(Slice msg) {
AppendWithSpace(&message_, msg);
}
void SetId(int id) { id_ = id; }
void SetExcludeFromMerge() { exclude_from_merge_ = true; }
void PrintThreadStatus() {
std::vector<ThreadStatus> thread_list;
FLAGS_env->GetThreadList(&thread_list);
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
10 years ago
fprintf(stderr, "\n%18s %10s %12s %20s %13s %45s %12s %s\n",
"ThreadID", "ThreadType", "cfName", "Operation",
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
10 years ago
"ElapsedTime", "Stage", "State", "OperationProperties");
int64_t current_time = 0;
Env::Default()->GetCurrentTime(&current_time);
for (auto ts : thread_list) {
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
10 years ago
fprintf(stderr, "%18" PRIu64 " %10s %12s %20s %13s %45s %12s",
ts.thread_id,
ThreadStatus::GetThreadTypeName(ts.thread_type).c_str(),
ts.cf_name.c_str(),
ThreadStatus::GetOperationName(ts.operation_type).c_str(),
Report elapsed time in micros in ThreadStatus instead of start time. Summary: Report elapsed time of a thread operation in micros in ThreadStatus instead of start time of a thread operation in seconds since the Epoch, 1970-01-01 00:00:00 (UTC). Test Plan: ./db_bench --benchmarks=fillrandom --num=100000 --threads=40 \ --max_background_compactions=10 --max_background_flushes=3 \ --thread_status_per_interval=1000 --key_size=16 --value_size=1000 \ --num_column_families=10 Sample Output: ThreadID ThreadType cfName Operation ElapsedTime Stage State 140667724562496 High Pri column_family_name_000002 Flush 772.419 ms FlushJob::WriteLevel0Table 140667728756800 High Pri default Flush 617.845 ms FlushJob::WriteLevel0Table 140667732951104 High Pri column_family_name_000005 Flush 772.078 ms FlushJob::WriteLevel0Table 140667875557440 Low Pri column_family_name_000008 Compaction 1409.216 ms CompactionJob::Install 140667737145408 Low Pri 140667749728320 Low Pri 140667816837184 Low Pri column_family_name_000007 Compaction 1071.815 ms CompactionJob::ProcessKeyValueCompaction 140667787477056 Low Pri column_family_name_000009 Compaction 772.516 ms CompactionJob::ProcessKeyValueCompaction 140667741339712 Low Pri 140667758116928 Low Pri column_family_name_000004 Compaction 620.739 ms CompactionJob::ProcessKeyValueCompaction 140667753922624 Low Pri 140667842003008 Low Pri column_family_name_000006 Compaction 1260.079 ms CompactionJob::ProcessKeyValueCompaction 140667745534016 Low Pri Reviewers: sdong, igor, rven Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35769
10 years ago
ThreadStatus::MicrosToString(ts.op_elapsed_micros).c_str(),
Allow GetThreadList() to report operation stage. Summary: Allow GetThreadList() to report operation stage. Test Plan: ./thread_list_test ./db_bench --benchmarks=fillrandom --num=100000 --threads=40 \ --max_background_compactions=10 --max_background_flushes=3 \ --thread_status_per_interval=1000 --key_size=16 --value_size=1000 \ --num_column_families=10 export ROCKSDB_TESTS=ThreadStatus ./db_test Sample output ThreadID ThreadType cfName Operation OP_StartTime ElapsedTime Stage State 140116265861184 Low Pri 140116270055488 Low Pri 140116274249792 High Pri column_family_name_000005 Flush 2015/03/10-14:58:11 0 us FlushJob::WriteLevel0Table 140116400078912 Low Pri column_family_name_000004 Compaction 2015/03/10-14:58:11 0 us CompactionJob::FinishCompactionOutputFile 140116358135872 Low Pri column_family_name_000006 Compaction 2015/03/10-14:58:10 1 us CompactionJob::FinishCompactionOutputFile 140116341358656 Low Pri 140116295221312 High Pri default Flush 2015/03/10-14:58:11 0 us FlushJob::WriteLevel0Table 140116324581440 Low Pri column_family_name_000009 Compaction 2015/03/10-14:58:11 0 us CompactionJob::ProcessKeyValueCompaction 140116278444096 Low Pri 140116299415616 Low Pri column_family_name_000008 Compaction 2015/03/10-14:58:11 0 us CompactionJob::FinishCompactionOutputFile 140116291027008 High Pri column_family_name_000001 Flush 2015/03/10-14:58:11 0 us FlushJob::WriteLevel0Table 140116286832704 Low Pri column_family_name_000002 Compaction 2015/03/10-14:58:11 0 us CompactionJob::FinishCompactionOutputFile 140116282638400 Low Pri Reviewers: rven, igor, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D34683
10 years ago
ThreadStatus::GetOperationStageName(ts.operation_stage).c_str(),
ThreadStatus::GetStateName(ts.state_type).c_str());
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
10 years ago
auto op_properties = ThreadStatus::InterpretOperationProperties(
ts.operation_type, ts.op_properties);
for (const auto& op_prop : op_properties) {
fprintf(stderr, " %s %" PRIu64" |",
op_prop.first.c_str(), op_prop.second);
}
fprintf(stderr, "\n");
}
}
void ResetLastOpTime() {
// Set to now to avoid latency from calls to SleepForMicroseconds
last_op_finish_ = FLAGS_env->NowMicros();
}
void FinishedOps(DBWithColumnFamilies* db_with_cfh, DB* db, int64_t num_ops,
enum OperationType op_type = kOthers) {
if (reporter_agent_) {
reporter_agent_->ReportFinishedOps(num_ops);
}
if (FLAGS_histogram) {
uint64_t now = FLAGS_env->NowMicros();
uint64_t micros = now - last_op_finish_;
if (hist_.find(op_type) == hist_.end())
{
HistogramImpl hist_temp;
hist_.insert({op_type, hist_temp});
}
hist_[op_type].Add(micros);
if (micros > 20000 && !FLAGS_stats_interval) {
fprintf(stderr, "long op: %" PRIu64 " micros%30s\r", micros, "");
fflush(stderr);
}
last_op_finish_ = now;
}
done_ += num_ops;
if (done_ >= next_report_) {
if (!FLAGS_stats_interval) {
if (next_report_ < 1000) next_report_ += 100;
else if (next_report_ < 5000) next_report_ += 500;
else if (next_report_ < 10000) next_report_ += 1000;
else if (next_report_ < 50000) next_report_ += 5000;
else if (next_report_ < 100000) next_report_ += 10000;
else if (next_report_ < 500000) next_report_ += 50000;
else next_report_ += 100000;
fprintf(stderr, "... finished %" PRIu64 " ops%30s\r", done_, "");
} else {
uint64_t now = FLAGS_env->NowMicros();
int64_t usecs_since_last = now - last_report_finish_;
// Determine whether to print status where interval is either
// each N operations or each N seconds.
if (FLAGS_stats_interval_seconds &&
usecs_since_last < (FLAGS_stats_interval_seconds * 1000000)) {
// Don't check again for this many operations
next_report_ += FLAGS_stats_interval;
} else {
fprintf(stderr,
"%s ... thread %d: (%" PRIu64 ",%" PRIu64 ") ops and "
"(%.1f,%.1f) ops/second in (%.6f,%.6f) seconds\n",
FLAGS_env->TimeToString(now/1000000).c_str(),
id_,
done_ - last_report_done_, done_,
(done_ - last_report_done_) /
(usecs_since_last / 1000000.0),
done_ / ((now - start_) / 1000000.0),
(now - last_report_finish_) / 1000000.0,
(now - start_) / 1000000.0);
if (FLAGS_stats_per_interval) {
std::string stats;
if (db_with_cfh && db_with_cfh->num_created.load()) {
for (size_t i = 0; i < db_with_cfh->num_created.load(); ++i) {
if (db->GetProperty(db_with_cfh->cfh[i], "rocksdb.cfstats",
&stats))
fprintf(stderr, "%s\n", stats.c_str());
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
9 years ago
if (FLAGS_show_table_properties) {
for (int level = 0; level < FLAGS_num_levels; ++level) {
if (db->GetProperty(
db_with_cfh->cfh[i],
"rocksdb.aggregated-table-properties-at-level" +
ToString(level),
&stats)) {
if (stats.find("# entries=0") == std::string::npos) {
fprintf(stderr, "Level[%d]: %s\n", level,
stats.c_str());
}
}
}
}
}
} else if (db) {
if (db->GetProperty("rocksdb.stats", &stats)) {
fprintf(stderr, "%s\n", stats.c_str());
}
if (FLAGS_show_table_properties) {
for (int level = 0; level < FLAGS_num_levels; ++level) {
if (db->GetProperty(
"rocksdb.aggregated-table-properties-at-level" +
ToString(level),
&stats)) {
if (stats.find("# entries=0") == std::string::npos) {
fprintf(stderr, "Level[%d]: %s\n", level, stats.c_str());
}
}
}
}
}
}
Improve statistics Summary: This adds more statistics to be reported by GetProperty("leveldb.stats"). The new stats include time spent waiting on stalls in MakeRoomForWrite. This also includes the total amplification rate where that is: (#bytes of sequential IO during compaction) / (#bytes from Put) This also includes a lot more data for the per-level compaction report. * Rn(MB) - MB read from level N during compaction between levels N and N+1 * Rnp1(MB) - MB read from level N+1 during compaction between levels N and N+1 * Wnew(MB) - new data written to the level during compaction * Amplify - ( Write(MB) + Rnp1(MB) ) / Rn(MB) * Rn - files read from level N during compaction between levels N and N+1 * Rnp1 - files read from level N+1 during compaction between levels N and N+1 * Wnp1 - files written to level N+1 during compaction between levels N and N+1 * NewW - new files written to level N+1 during compaction * Count - number of compactions done for this level This is the new output from DB::GetProperty("leveldb.stats"). The old output stopped at Write(MB) Compactions Level Files Size(MB) Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count ------------------------------------------------------------------------------------------------------------------------------------- 0 3 6 33 0 576 0 0 576 -1.0 0.0 1.3 0 0 0 0 290 1 127 242 351 5316 5314 570 4747 567 17.0 12.1 12.1 287 2399 2685 286 32 2 161 328 54 822 824 326 496 328 4.0 1.9 1.9 160 251 411 160 161 Amplification: 22.3 rate, 0.56 GB in, 12.55 GB out Uptime(secs): 439.8 Stalls(secs): 206.938 level0_slowdown, 0.000 level0_numfiles, 24.129 memtable_compaction Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - (cherry picked from commit ecdeead38f86cc02e754d0032600742c4f02fec8) Reviewers: dhruba Differential Revision: https://reviews.facebook.net/D6153
12 years ago
next_report_ += FLAGS_stats_interval;
last_report_finish_ = now;
last_report_done_ = done_;
}
}
if (id_ == 0 && FLAGS_thread_status_per_interval) {
PrintThreadStatus();
}
fflush(stderr);
}
}
void AddBytes(int64_t n) {
bytes_ += n;
}
void Report(const Slice& name) {
// Pretend at least one op was done in case we are running a benchmark
// that does not call FinishedOps().
if (done_ < 1) done_ = 1;
std::string extra;
if (bytes_ > 0) {
// Rate is computed on actual elapsed time, not the sum of per-thread
// elapsed times.
double elapsed = (finish_ - start_) * 1e-6;
char rate[100];
snprintf(rate, sizeof(rate), "%6.1f MB/s",
(bytes_ / 1048576.0) / elapsed);
extra = rate;
}
AppendWithSpace(&extra, message_);
double elapsed = (finish_ - start_) * 1e-6;
double throughput = (double)done_/elapsed;
fprintf(stdout, "%-12s : %11.3f micros/op %ld ops/sec;%s%s\n",
name.ToString().c_str(),
elapsed * 1e6 / done_,
(long)throughput,
(extra.empty() ? "" : " "),
extra.c_str());
if (FLAGS_histogram) {
for (auto it = hist_.begin(); it != hist_.end(); ++it) {
fprintf(stdout, "Microseconds per %s:\n%s\n",
OperationTypeString[it->first].c_str(),
it->second.ToString().c_str());
}
}
if (FLAGS_report_file_operations) {
ReportFileOpEnv* env = static_cast<ReportFileOpEnv*>(FLAGS_env);
ReportFileOpCounters* counters = env->counters();
fprintf(stdout, "Num files opened: %d\n",
counters->open_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Read(): %d\n",
counters->read_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Append(): %d\n",
counters->append_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num bytes read: %" PRIu64 "\n",
counters->bytes_read_.load(std::memory_order_relaxed));
fprintf(stdout, "Num bytes written: %" PRIu64 "\n",
counters->bytes_written_.load(std::memory_order_relaxed));
env->reset();
}
fflush(stdout);
}
};
// State shared by all concurrent executions of the same benchmark.
struct SharedState {
port::Mutex mu;
port::CondVar cv;
int total;
int perf_level;
std::shared_ptr<RateLimiter> write_rate_limiter;
// Each thread goes through the following states:
// (1) initializing
// (2) waiting for others to be initialized
// (3) running
// (4) done
long num_initialized;
long num_done;
bool start;
SharedState() : cv(&mu), perf_level(FLAGS_perf_level) { }
};
// Per-thread state for concurrent executions of the same benchmark.
struct ThreadState {
int tid; // 0..n-1 when running in n threads
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
11 years ago
Random64 rand; // Has different seeds for different threads
Stats stats;
SharedState* shared;
/* implicit */ ThreadState(int index)
: tid(index),
rand((FLAGS_seed ? FLAGS_seed : 1000) + index) {
}
};
class Duration {
public:
Duration(uint64_t max_seconds, int64_t max_ops, int64_t ops_per_stage = 0) {
max_seconds_ = max_seconds;
max_ops_= max_ops;
ops_per_stage_ = (ops_per_stage > 0) ? ops_per_stage : max_ops;
ops_ = 0;
start_at_ = FLAGS_env->NowMicros();
}
int64_t GetStage() { return std::min(ops_, max_ops_ - 1) / ops_per_stage_; }
bool Done(int64_t increment) {
if (increment <= 0) increment = 1; // avoid Done(0) and infinite loops
ops_ += increment;
if (max_seconds_) {
// Recheck every appx 1000 ops (exact iff increment is factor of 1000)
if ((ops_/1000) != ((ops_-increment)/1000)) {
uint64_t now = FLAGS_env->NowMicros();
return ((now - start_at_) / 1000000) >= max_seconds_;
} else {
return false;
}
} else {
return ops_ > max_ops_;
}
}
private:
uint64_t max_seconds_;
int64_t max_ops_;
int64_t ops_per_stage_;
int64_t ops_;
uint64_t start_at_;
};
class Benchmark {
private:
std::shared_ptr<Cache> cache_;
std::shared_ptr<Cache> compressed_cache_;
std::shared_ptr<const FilterPolicy> filter_policy_;
const SliceTransform* prefix_extractor_;
DBWithColumnFamilies db_;
std::vector<DBWithColumnFamilies> multi_dbs_;
int64_t num_;
int value_size_;
int key_size_;
int prefix_size_;
int64_t keys_per_prefix_;
int64_t entries_per_batch_;
WriteOptions write_options_;
Options open_options_; // keep options around to properly destroy db later
int64_t reads_;
double read_random_exp_range_;
int64_t writes_;
int64_t readwrites_;
int64_t merge_keys_;
bool report_file_operations_;
int cachedev_fd_;
bool SanityCheck() {
if (FLAGS_compression_ratio > 1) {
fprintf(stderr, "compression_ratio should be between 0 and 1\n");
return false;
}
return true;
}
inline bool CompressSlice(const Slice& input, std::string* compressed) {
bool ok = true;
switch (FLAGS_compression_type_e) {
case rocksdb::kSnappyCompression:
ok = Snappy_Compress(Options().compression_opts, input.data(),
input.size(), compressed);
break;
case rocksdb::kZlibCompression:
ok = Zlib_Compress(Options().compression_opts, 2, input.data(),
input.size(), compressed);
break;
case rocksdb::kBZip2Compression:
ok = BZip2_Compress(Options().compression_opts, 2, input.data(),
input.size(), compressed);
break;
case rocksdb::kLZ4Compression:
ok = LZ4_Compress(Options().compression_opts, 2, input.data(),
input.size(), compressed);
break;
case rocksdb::kLZ4HCCompression:
ok = LZ4HC_Compress(Options().compression_opts, 2, input.data(),
input.size(), compressed);
break;
case rocksdb::kZSTDNotFinalCompression:
ok = ZSTD_Compress(Options().compression_opts, input.data(),
input.size(), compressed);
break;
default:
ok = false;
}
return ok;
}
void PrintHeader() {
PrintEnvironment();
fprintf(stdout, "Keys: %d bytes each\n", FLAGS_key_size);
fprintf(stdout, "Values: %d bytes each (%d bytes after compression)\n",
FLAGS_value_size,
static_cast<int>(FLAGS_value_size * FLAGS_compression_ratio + 0.5));
fprintf(stdout, "Entries: %" PRIu64 "\n", num_);
fprintf(stdout, "Prefix: %d bytes\n", FLAGS_prefix_size);
fprintf(stdout, "Keys per prefix: %" PRIu64 "\n", keys_per_prefix_);
fprintf(stdout, "RawSize: %.1f MB (estimated)\n",
((static_cast<int64_t>(FLAGS_key_size + FLAGS_value_size) * num_)
/ 1048576.0));
fprintf(stdout, "FileSize: %.1f MB (estimated)\n",
(((FLAGS_key_size + FLAGS_value_size * FLAGS_compression_ratio)
* num_)
/ 1048576.0));
fprintf(stdout, "Write rate: %" PRIu64 " bytes/second\n",
FLAGS_benchmark_write_rate_limit);
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
11 years ago
if (FLAGS_enable_numa) {
fprintf(stderr, "Running in NUMA enabled mode.\n");
#ifndef NUMA
fprintf(stderr, "NUMA is not defined in the system.\n");
exit(1);
#else
if (numa_available() == -1) {
fprintf(stderr, "NUMA is not supported by the system.\n");
exit(1);
}
#endif
}
auto compression = CompressionTypeToString(FLAGS_compression_type_e);
fprintf(stdout, "Compression: %s\n", compression.c_str());
switch (FLAGS_rep_factory) {
case kPrefixHash:
fprintf(stdout, "Memtablerep: prefix_hash\n");
break;
case kSkipList:
fprintf(stdout, "Memtablerep: skip_list\n");
break;
case kVectorRep:
fprintf(stdout, "Memtablerep: vector\n");
break;
case kHashLinkedList:
fprintf(stdout, "Memtablerep: hash_linkedlist\n");
break;
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
11 years ago
case kCuckoo:
fprintf(stdout, "Memtablerep: cuckoo\n");
break;
}
fprintf(stdout, "Perf Level: %d\n", FLAGS_perf_level);
PrintWarnings(compression.c_str());
fprintf(stdout, "------------------------------------------------\n");
}
void PrintWarnings(const char* compression) {
#if defined(__GNUC__) && !defined(__OPTIMIZE__)
fprintf(stdout,
"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n"
);
#endif
#ifndef NDEBUG
fprintf(stdout,
"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
#endif
if (FLAGS_compression_type_e != rocksdb::kNoCompression) {
// The test string should not be too small.
const int len = FLAGS_block_size;
std::string input_str(len, 'y');
std::string compressed;
bool result = CompressSlice(Slice(input_str), &compressed);
if (!result) {
fprintf(stdout, "WARNING: %s compression is not enabled\n",
compression);
} else if (compressed.size() >= input_str.size()) {
fprintf(stdout, "WARNING: %s compression is not effective\n",
compression);
}
}
}
// Current the following isn't equivalent to OS_LINUX.
#if defined(__linux)
static Slice TrimSpace(Slice s) {
unsigned int start = 0;
while (start < s.size() && isspace(s[start])) {
start++;
}
unsigned int limit = static_cast<unsigned int>(s.size());
while (limit > start && isspace(s[limit-1])) {
limit--;
}
return Slice(s.data() + start, limit - start);
}
#endif
void PrintEnvironment() {
fprintf(stderr, "LevelDB: version %d.%d\n",
kMajorVersion, kMinorVersion);
#if defined(__linux)
time_t now = time(nullptr);
char buf[52];
// Lint complains about ctime() usage, so replace it with ctime_r(). The
// requirement is to provide a buffer which is at least 26 bytes.
fprintf(stderr, "Date: %s",
ctime_r(&now, buf)); // ctime_r() adds newline
FILE* cpuinfo = fopen("/proc/cpuinfo", "r");
if (cpuinfo != nullptr) {
char line[1000];
int num_cpus = 0;
std::string cpu_type;
std::string cache_size;
while (fgets(line, sizeof(line), cpuinfo) != nullptr) {
const char* sep = strchr(line, ':');
if (sep == nullptr) {
continue;
}
Slice key = TrimSpace(Slice(line, sep - 1 - line));
Slice val = TrimSpace(Slice(sep + 1));
if (key == "model name") {
++num_cpus;
cpu_type = val.ToString();
} else if (key == "cache size") {
cache_size = val.ToString();
}
}
fclose(cpuinfo);
fprintf(stderr, "CPU: %d * %s\n", num_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#endif
}
public:
Benchmark()
: cache_(
FLAGS_cache_size >= 0
? (FLAGS_cache_numshardbits >= 1
? NewLRUCache(FLAGS_cache_size, FLAGS_cache_numshardbits)
: NewLRUCache(FLAGS_cache_size))
: nullptr),
compressed_cache_(FLAGS_compressed_cache_size >= 0
? (FLAGS_cache_numshardbits >= 1
? NewLRUCache(FLAGS_compressed_cache_size,
FLAGS_cache_numshardbits)
: NewLRUCache(FLAGS_compressed_cache_size))
: nullptr),
filter_policy_(FLAGS_bloom_bits >= 0
? NewBloomFilterPolicy(FLAGS_bloom_bits,
FLAGS_use_block_based_filter)
: nullptr),
prefix_extractor_(NewFixedPrefixTransform(FLAGS_prefix_size)),
num_(FLAGS_num),
value_size_(FLAGS_value_size),
key_size_(FLAGS_key_size),
prefix_size_(FLAGS_prefix_size),
keys_per_prefix_(FLAGS_keys_per_prefix),
entries_per_batch_(1),
reads_(FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads),
read_random_exp_range_(0.0),
writes_(FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes),
readwrites_(
(FLAGS_writes < 0 && FLAGS_reads < 0)
? FLAGS_num
: ((FLAGS_writes > FLAGS_reads) ? FLAGS_writes : FLAGS_reads)),
merge_keys_(FLAGS_merge_keys < 0 ? FLAGS_num : FLAGS_merge_keys),
report_file_operations_(FLAGS_report_file_operations),
cachedev_fd_(-1) {
if (report_file_operations_) {
if (!FLAGS_hdfs.empty()) {
fprintf(stderr,
"--hdfs and --report_file_operations cannot be enabled "
"at the same time");
exit(1);
}
FLAGS_env = new ReportFileOpEnv(rocksdb::Env::Default());
}
if (FLAGS_prefix_size > FLAGS_key_size) {
fprintf(stderr, "prefix size is larger than key size");
exit(1);
}
std::vector<std::string> files;
FLAGS_env->GetChildren(FLAGS_db, &files);
for (size_t i = 0; i < files.size(); i++) {
if (Slice(files[i]).starts_with("heap-")) {
FLAGS_env->DeleteFile(FLAGS_db + "/" + files[i]);
}
}
if (!FLAGS_use_existing_db) {
Options options;
if (!FLAGS_wal_dir.empty()) {
options.wal_dir = FLAGS_wal_dir;
}
DestroyDB(FLAGS_db, options);
}
}
~Benchmark() {
db_.DeleteDBs();
delete prefix_extractor_;
if (cache_.get() != nullptr) {
// this will leak, but we're shutting down so nobody cares
cache_->DisownData();
}
if (FLAGS_disable_flashcache_for_background_threads && cachedev_fd_ != -1) {
// Dtor for this env should run before cachedev_fd_ is closed
flashcache_aware_env_ = nullptr;
close(cachedev_fd_);
}
}
Slice AllocateKey(std::unique_ptr<const char[]>* key_guard) {
char* data = new char[key_size_];
const char* const_data = data;
key_guard->reset(const_data);
return Slice(key_guard->get(), key_size_);
}
// Generate key according to the given specification and random number.
// The resulting key will have the following format (if keys_per_prefix_
// is positive), extra trailing bytes are either cut off or paddd with '0'.
// The prefix value is derived from key value.
// ----------------------------
// | prefix 00000 | key 00000 |
// ----------------------------
// If keys_per_prefix_ is 0, the key is simply a binary representation of
// random number followed by trailing '0's
// ----------------------------
// | key 00000 |
// ----------------------------
void GenerateKeyFromInt(uint64_t v, int64_t num_keys, Slice* key) {
char* start = const_cast<char*>(key->data());
char* pos = start;
if (keys_per_prefix_ > 0) {
int64_t num_prefix = num_keys / keys_per_prefix_;
int64_t prefix = v % num_prefix;
int bytes_to_fill = std::min(prefix_size_, 8);
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (prefix >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&prefix), bytes_to_fill);
}
if (prefix_size_ > 8) {
// fill the rest with 0s
memset(pos + 8, '0', prefix_size_ - 8);
}
pos += prefix_size_;
}
int bytes_to_fill = std::min(key_size_ - static_cast<int>(pos - start), 8);
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (v >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&v), bytes_to_fill);
}
pos += bytes_to_fill;
if (key_size_ > pos - start) {
memset(pos, '0', key_size_ - (pos - start));
}
}
std::string GetDbNameForMultiple(std::string base_name, size_t id) {
return base_name + ToString(id);
}
void Run() {
if (!SanityCheck()) {
exit(1);
}
PrintHeader();
Open(&open_options_);
std::stringstream benchmark_stream(FLAGS_benchmarks);
std::string name;
while (std::getline(benchmark_stream, name, ',')) {
// Sanitize parameters
num_ = FLAGS_num;
reads_ = (FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads);
writes_ = (FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes);
value_size_ = FLAGS_value_size;
key_size_ = FLAGS_key_size;
entries_per_batch_ = FLAGS_batch_size;
write_options_ = WriteOptions();
read_random_exp_range_ = FLAGS_read_random_exp_range;
if (FLAGS_sync) {
write_options_.sync = true;
}
write_options_.disableWAL = FLAGS_disable_wal;
void (Benchmark::*method)(ThreadState*) = nullptr;
void (Benchmark::*post_process_method)() = nullptr;
bool fresh_db = false;
int num_threads = FLAGS_threads;
if (name == "fillseq") {
fresh_db = true;
method = &Benchmark::WriteSeq;
} else if (name == "fillbatch") {
fresh_db = true;
entries_per_batch_ = 1000;
method = &Benchmark::WriteSeq;
} else if (name == "fillrandom") {
fresh_db = true;
method = &Benchmark::WriteRandom;
} else if (name == "filluniquerandom") {
fresh_db = true;
if (num_threads > 1) {
fprintf(stderr,
"filluniquerandom multithreaded not supported"
", use 1 thread");
num_threads = 1;
}
method = &Benchmark::WriteUniqueRandom;
} else if (name == "overwrite") {
method = &Benchmark::WriteRandom;
} else if (name == "fillsync") {
fresh_db = true;
num_ /= 1000;
write_options_.sync = true;
method = &Benchmark::WriteRandom;
} else if (name == "fill100K") {
fresh_db = true;
num_ /= 1000;
value_size_ = 100 * 1000;
method = &Benchmark::WriteRandom;
} else if (name == "readseq") {
method = &Benchmark::ReadSequential;
} else if (name == "readtocache") {
method = &Benchmark::ReadSequential;
num_threads = 1;
reads_ = num_;
} else if (name == "readreverse") {
method = &Benchmark::ReadReverse;
} else if (name == "readrandom") {
method = &Benchmark::ReadRandom;
} else if (name == "readrandomfast") {
method = &Benchmark::ReadRandomFast;
} else if (name == "multireadrandom") {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
method = &Benchmark::MultiReadRandom;
} else if (name == "readmissing") {
++key_size_;
method = &Benchmark::ReadRandom;
} else if (name == "newiterator") {
method = &Benchmark::IteratorCreation;
} else if (name == "newiteratorwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::IteratorCreationWhileWriting;
} else if (name == "seekrandom") {
method = &Benchmark::SeekRandom;
} else if (name == "seekrandomwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::SeekRandomWhileWriting;
} else if (name == "seekrandomwhilemerging") {
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
num_threads++; // Add extra thread for merging
method = &Benchmark::SeekRandomWhileMerging;
} else if (name == "readrandomsmall") {
reads_ /= 1000;
method = &Benchmark::ReadRandom;
} else if (name == "deleteseq") {
method = &Benchmark::DeleteSeq;
} else if (name == "deleterandom") {
method = &Benchmark::DeleteRandom;
} else if (name == "readwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileWriting;
} else if (name == "readwhilemerging") {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileMerging;
} else if (name == "readrandomwriterandom") {
method = &Benchmark::ReadRandomWriteRandom;
} else if (name == "readrandommergerandom") {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.c_str());
exit(1);
}
method = &Benchmark::ReadRandomMergeRandom;
} else if (name == "updaterandom") {
method = &Benchmark::UpdateRandom;
} else if (name == "appendrandom") {
method = &Benchmark::AppendRandom;
} else if (name == "mergerandom") {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.c_str());
exit(1);
}
method = &Benchmark::MergeRandom;
} else if (name == "randomwithverify") {
method = &Benchmark::RandomWithVerify;
} else if (name == "fillseekseq") {
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
method = &Benchmark::WriteSeqSeekSeq;
} else if (name == "compact") {
method = &Benchmark::Compact;
} else if (name == "crc32c") {
method = &Benchmark::Crc32c;
} else if (name == "xxhash") {
method = &Benchmark::xxHash;
} else if (name == "acquireload") {
method = &Benchmark::AcquireLoad;
} else if (name == "compress") {
method = &Benchmark::Compress;
} else if (name == "uncompress") {
method = &Benchmark::Uncompress;
#ifndef ROCKSDB_LITE
} else if (name == "randomtransaction") {
method = &Benchmark::RandomTransaction;
post_process_method = &Benchmark::RandomTransactionVerify;
#endif // ROCKSDB_LITE
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
} else if (name == "randomreplacekeys") {
fresh_db = true;
method = &Benchmark::RandomReplaceKeys;
} else if (name == "stats") {
PrintStats("rocksdb.stats");
} else if (name == "levelstats") {
PrintStats("rocksdb.levelstats");
} else if (name == "sstables") {
PrintStats("rocksdb.sstables");
} else if (!name.empty()) { // No error message for empty name
fprintf(stderr, "unknown benchmark '%s'\n", name.c_str());
exit(1);
}
if (fresh_db) {
if (FLAGS_use_existing_db) {
fprintf(stdout, "%-12s : skipped (--use_existing_db is true)\n",
name.c_str());
method = nullptr;
} else {
if (db_.db != nullptr) {
db_.DeleteDBs();
DestroyDB(FLAGS_db, open_options_);
}
for (size_t i = 0; i < multi_dbs_.size(); i++) {
delete multi_dbs_[i].db;
DestroyDB(GetDbNameForMultiple(FLAGS_db, i), open_options_);
}
multi_dbs_.clear();
}
Open(&open_options_); // use open_options for the last accessed
}
if (method != nullptr) {
fprintf(stdout, "DB path: [%s]\n", FLAGS_db.c_str());
RunBenchmark(num_threads, name, method);
}
if (post_process_method != nullptr) {
(this->*post_process_method)();
}
}
if (FLAGS_statistics) {
fprintf(stdout, "STATISTICS:\n%s\n", dbstats->ToString().c_str());
}
}
private:
std::unique_ptr<Env> flashcache_aware_env_;
struct ThreadArg {
Benchmark* bm;
SharedState* shared;
ThreadState* thread;
void (Benchmark::*method)(ThreadState*);
};
static void ThreadBody(void* v) {
ThreadArg* arg = reinterpret_cast<ThreadArg*>(v);
SharedState* shared = arg->shared;
ThreadState* thread = arg->thread;
{
MutexLock l(&shared->mu);
shared->num_initialized++;
if (shared->num_initialized >= shared->total) {
shared->cv.SignalAll();
}
while (!shared->start) {
shared->cv.Wait();
}
}
SetPerfLevel(static_cast<PerfLevel> (shared->perf_level));
thread->stats.Start(thread->tid);
(arg->bm->*(arg->method))(thread);
thread->stats.Stop();
{
MutexLock l(&shared->mu);
shared->num_done++;
if (shared->num_done >= shared->total) {
shared->cv.SignalAll();
}
}
}
void RunBenchmark(int n, Slice name,
void (Benchmark::*method)(ThreadState*)) {
SharedState shared;
shared.total = n;
shared.num_initialized = 0;
shared.num_done = 0;
shared.start = false;
if (FLAGS_benchmark_write_rate_limit > 0) {
shared.write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
std::unique_ptr<ReporterAgent> reporter_agent;
if (FLAGS_report_interval_seconds > 0) {
reporter_agent.reset(new ReporterAgent(FLAGS_env, FLAGS_report_file,
FLAGS_report_interval_seconds));
}
ThreadArg* arg = new ThreadArg[n];
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
11 years ago
for (int i = 0; i < n; i++) {
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
11 years ago
#ifdef NUMA
if (FLAGS_enable_numa) {
// Performs a local allocation of memory to threads in numa node.
int n_nodes = numa_num_task_nodes(); // Number of nodes in NUMA.
numa_exit_on_error = 1;
int numa_node = i % n_nodes;
bitmask* nodes = numa_allocate_nodemask();
numa_bitmask_clearall(nodes);
numa_bitmask_setbit(nodes, numa_node);
// numa_bind() call binds the process to the node and these
// properties are passed on to the thread that is created in
// StartThread method called later in the loop.
numa_bind(nodes);
numa_set_strict(1);
numa_free_nodemask(nodes);
}
#endif
arg[i].bm = this;
arg[i].method = method;
arg[i].shared = &shared;
arg[i].thread = new ThreadState(i);
arg[i].thread->stats.SetReporterAgent(reporter_agent.get());
arg[i].thread->shared = &shared;
FLAGS_env->StartThread(ThreadBody, &arg[i]);
}
shared.mu.Lock();
while (shared.num_initialized < n) {
shared.cv.Wait();
}
shared.start = true;
shared.cv.SignalAll();
while (shared.num_done < n) {
shared.cv.Wait();
}
shared.mu.Unlock();
// Stats for some threads can be excluded.
Stats merge_stats;
for (int i = 0; i < n; i++) {
merge_stats.Merge(arg[i].thread->stats);
}
merge_stats.Report(name);
for (int i = 0; i < n; i++) {
delete arg[i].thread;
}
delete[] arg;
}
void Crc32c(ThreadState* thread) {
// Checksum about 500MB of data total
const int size = 4096;
const char* label = "(4K per op)";
std::string data(size, 'x');
int64_t bytes = 0;
uint32_t crc = 0;
while (bytes < 500 * 1048576) {
crc = crc32c::Value(data.data(), size);
thread->stats.FinishedOps(nullptr, nullptr, 1, kCrc);
bytes += size;
}
// Print so result is not dead
fprintf(stderr, "... crc=0x%x\r", static_cast<unsigned int>(crc));
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(label);
}
void xxHash(ThreadState* thread) {
// Checksum about 500MB of data total
const int size = 4096;
const char* label = "(4K per op)";
std::string data(size, 'x');
int64_t bytes = 0;
unsigned int xxh32 = 0;
while (bytes < 500 * 1048576) {
xxh32 = XXH32(data.data(), size, 0);
thread->stats.FinishedOps(nullptr, nullptr, 1, kHash);
bytes += size;
}
// Print so result is not dead
fprintf(stderr, "... xxh32=0x%x\r", static_cast<unsigned int>(xxh32));
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(label);
}
void AcquireLoad(ThreadState* thread) {
int dummy;
std::atomic<void*> ap(&dummy);
int count = 0;
void *ptr = nullptr;
thread->stats.AddMessage("(each op is 1000 loads)");
while (count < 100000) {
for (int i = 0; i < 1000; i++) {
ptr = ap.load(std::memory_order_acquire);
}
count++;
thread->stats.FinishedOps(nullptr, nullptr, 1, kOthers);
}
if (ptr == nullptr) exit(1); // Disable unused variable warning.
}
void Compress(ThreadState *thread) {
RandomGenerator gen;
Slice input = gen.Generate(FLAGS_block_size);
int64_t bytes = 0;
int64_t produced = 0;
bool ok = true;
std::string compressed;
// Compress 1G
while (ok && bytes < int64_t(1) << 30) {
compressed.clear();
ok = CompressSlice(input, &compressed);
produced += compressed.size();
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1, kCompress);
}
if (!ok) {
thread->stats.AddMessage("(compression failure)");
} else {
char buf[100];
snprintf(buf, sizeof(buf), "(output: %.1f%%)",
(produced * 100.0) / bytes);
thread->stats.AddMessage(buf);
thread->stats.AddBytes(bytes);
}
}
void Uncompress(ThreadState *thread) {
RandomGenerator gen;
Slice input = gen.Generate(FLAGS_block_size);
std::string compressed;
bool ok = CompressSlice(input, &compressed);
int64_t bytes = 0;
int decompress_size;
while (ok && bytes < 1024 * 1048576) {
char *uncompressed = nullptr;
switch (FLAGS_compression_type_e) {
case rocksdb::kSnappyCompression: {
// get size and allocate here to make comparison fair
size_t ulength = 0;
if (!Snappy_GetUncompressedLength(compressed.data(),
compressed.size(), &ulength)) {
ok = false;
break;
}
uncompressed = new char[ulength];
ok = Snappy_Uncompress(compressed.data(), compressed.size(),
uncompressed);
break;
}
case rocksdb::kZlibCompression:
10 years ago
uncompressed = Zlib_Uncompress(compressed.data(), compressed.size(),
&decompress_size, 2);
ok = uncompressed != nullptr;
break;
case rocksdb::kBZip2Compression:
10 years ago
uncompressed = BZip2_Uncompress(compressed.data(), compressed.size(),
&decompress_size, 2);
ok = uncompressed != nullptr;
break;
case rocksdb::kLZ4Compression:
10 years ago
uncompressed = LZ4_Uncompress(compressed.data(), compressed.size(),
&decompress_size, 2);
ok = uncompressed != nullptr;
break;
case rocksdb::kLZ4HCCompression:
10 years ago
uncompressed = LZ4_Uncompress(compressed.data(), compressed.size(),
&decompress_size, 2);
ok = uncompressed != nullptr;
break;
case rocksdb::kZSTDNotFinalCompression:
uncompressed = ZSTD_Uncompress(compressed.data(), compressed.size(),
&decompress_size);
ok = uncompressed != nullptr;
break;
default:
ok = false;
}
delete[] uncompressed;
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1, kUncompress);
}
if (!ok) {
thread->stats.AddMessage("(compression failure)");
} else {
thread->stats.AddBytes(bytes);
}
}
void Open(Options* opts) {
Options& options = *opts;
assert(db_.db == nullptr);
options.create_if_missing = !FLAGS_use_existing_db;
options.create_missing_column_families = FLAGS_num_column_families > 1;
options.db_write_buffer_size = FLAGS_db_write_buffer_size;
options.write_buffer_size = FLAGS_write_buffer_size;
options.max_write_buffer_number = FLAGS_max_write_buffer_number;
options.min_write_buffer_number_to_merge =
FLAGS_min_write_buffer_number_to_merge;
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
10 years ago
options.max_write_buffer_number_to_maintain =
FLAGS_max_write_buffer_number_to_maintain;
options.max_background_compactions = FLAGS_max_background_compactions;
options.max_subcompactions = static_cast<uint32_t>(FLAGS_subcompactions);
options.max_background_flushes = FLAGS_max_background_flushes;
options.compaction_style = FLAGS_compaction_style_e;
options.compaction_pri = FLAGS_compaction_pri_e;
if (FLAGS_prefix_size != 0) {
options.prefix_extractor.reset(
NewFixedPrefixTransform(FLAGS_prefix_size));
}
if (FLAGS_use_uint64_comparator) {
options.comparator = test::Uint64Comparator();
if (FLAGS_key_size != 8) {
fprintf(stderr, "Using Uint64 comparator but key size is not 8.\n");
exit(1);
}
}
options.memtable_prefix_bloom_bits = FLAGS_memtable_bloom_bits;
options.bloom_locality = FLAGS_bloom_locality;
options.max_open_files = FLAGS_open_files;
options.max_file_opening_threads = FLAGS_file_opening_threads;
options.new_table_reader_for_compaction_inputs =
FLAGS_new_table_reader_for_compaction_inputs;
options.compaction_readahead_size = FLAGS_compaction_readahead_size;
options.random_access_max_buffer_size = FLAGS_random_access_max_buffer_size;
options.writable_file_max_buffer_size = FLAGS_writable_file_max_buffer_size;
options.statistics = dbstats;
if (FLAGS_enable_io_prio) {
FLAGS_env->LowerThreadPoolIOPriority(Env::LOW);
FLAGS_env->LowerThreadPoolIOPriority(Env::HIGH);
}
if (FLAGS_disable_flashcache_for_background_threads &&
cachedev_fd_ == -1) {
// Avoid creating the env twice when an use_existing_db is true
cachedev_fd_ = open(FLAGS_flashcache_dev.c_str(), O_RDONLY);
if (cachedev_fd_ < 0) {
fprintf(stderr, "Open flash device failed\n");
exit(1);
}
flashcache_aware_env_ = NewFlashcacheAwareEnv(FLAGS_env, cachedev_fd_);
if (flashcache_aware_env_.get() == nullptr) {
fprintf(stderr, "Failed to open flashcache device at %s\n",
FLAGS_flashcache_dev.c_str());
std::abort();
}
options.env = flashcache_aware_env_.get();
} else {
options.env = FLAGS_env;
}
options.disableDataSync = FLAGS_disable_data_sync;
options.use_fsync = FLAGS_use_fsync;
options.wal_dir = FLAGS_wal_dir;
options.num_levels = FLAGS_num_levels;
options.target_file_size_base = FLAGS_target_file_size_base;
options.target_file_size_multiplier = FLAGS_target_file_size_multiplier;
options.max_bytes_for_level_base = FLAGS_max_bytes_for_level_base;
options.level_compaction_dynamic_level_bytes =
FLAGS_level_compaction_dynamic_level_bytes;
options.max_bytes_for_level_multiplier =
FLAGS_max_bytes_for_level_multiplier;
options.filter_deletes = FLAGS_filter_deletes;
if (FLAGS_row_cache_size) {
if (FLAGS_cache_numshardbits >= 1) {
options.row_cache =
NewLRUCache(FLAGS_row_cache_size, FLAGS_cache_numshardbits);
} else {
options.row_cache = NewLRUCache(FLAGS_row_cache_size);
}
}
if ((FLAGS_prefix_size == 0) && (FLAGS_rep_factory == kPrefixHash ||
FLAGS_rep_factory == kHashLinkedList)) {
fprintf(stderr, "prefix_size should be non-zero if PrefixHash or "
"HashLinkedList memtablerep is used\n");
exit(1);
}
switch (FLAGS_rep_factory) {
case kSkipList:
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
options.memtable_factory.reset(new SkipListFactory(
FLAGS_skip_list_lookahead));
break;
#ifndef ROCKSDB_LITE
case kPrefixHash:
options.memtable_factory.reset(
NewHashSkipListRepFactory(FLAGS_hash_bucket_count));
break;
case kHashLinkedList:
options.memtable_factory.reset(NewHashLinkListRepFactory(
FLAGS_hash_bucket_count));
break;
case kVectorRep:
options.memtable_factory.reset(
new VectorRepFactory
);
break;
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
11 years ago
case kCuckoo:
options.memtable_factory.reset(NewHashCuckooRepFactory(
options.write_buffer_size, FLAGS_key_size + FLAGS_value_size));
break;
#else
default:
fprintf(stderr, "Only skip list is supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
}
if (FLAGS_use_plain_table) {
#ifndef ROCKSDB_LITE
if (FLAGS_rep_factory != kPrefixHash &&
FLAGS_rep_factory != kHashLinkedList) {
fprintf(stderr, "Waring: plain table is used with skipList\n");
}
if (!FLAGS_mmap_read && !FLAGS_mmap_write) {
fprintf(stderr, "plain table format requires mmap to operate\n");
exit(1);
}
int bloom_bits_per_key = FLAGS_bloom_bits;
if (bloom_bits_per_key < 0) {
bloom_bits_per_key = 0;
}
PlainTableOptions plain_table_options;
plain_table_options.user_key_len = FLAGS_key_size;
plain_table_options.bloom_bits_per_key = bloom_bits_per_key;
plain_table_options.hash_table_ratio = 0.75;
options.table_factory = std::shared_ptr<TableFactory>(
NewPlainTableFactory(plain_table_options));
#else
fprintf(stderr, "Plain table is not supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
} else if (FLAGS_use_cuckoo_table) {
#ifndef ROCKSDB_LITE
if (FLAGS_cuckoo_hash_ratio > 1 || FLAGS_cuckoo_hash_ratio < 0) {
fprintf(stderr, "Invalid cuckoo_hash_ratio\n");
exit(1);
}
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
10 years ago
rocksdb::CuckooTableOptions table_options;
table_options.hash_table_ratio = FLAGS_cuckoo_hash_ratio;
table_options.identity_as_first_hash = FLAGS_identity_as_first_hash;
options.table_factory = std::shared_ptr<TableFactory>(
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
10 years ago
NewCuckooTableFactory(table_options));
#else
fprintf(stderr, "Cuckoo table is not supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
} else {
BlockBasedTableOptions block_based_options;
if (FLAGS_use_hash_search) {
if (FLAGS_prefix_size == 0) {
fprintf(stderr,
"prefix_size not assigned when enable use_hash_search \n");
exit(1);
}
block_based_options.index_type = BlockBasedTableOptions::kHashSearch;
} else {
block_based_options.index_type = BlockBasedTableOptions::kBinarySearch;
}
if (cache_ == nullptr) {
block_based_options.no_block_cache = true;
}
block_based_options.cache_index_and_filter_blocks =
FLAGS_cache_index_and_filter_blocks;
block_based_options.block_cache = cache_;
block_based_options.block_cache_compressed = compressed_cache_;
block_based_options.block_size = FLAGS_block_size;
block_based_options.block_restart_interval = FLAGS_block_restart_interval;
block_based_options.filter_policy = filter_policy_;
block_based_options.skip_table_builder_flush =
FLAGS_skip_table_builder_flush;
block_based_options.format_version = 2;
options.table_factory.reset(
NewBlockBasedTableFactory(block_based_options));
}
if (FLAGS_max_bytes_for_level_multiplier_additional_v.size() > 0) {
if (FLAGS_max_bytes_for_level_multiplier_additional_v.size() !=
(unsigned int)FLAGS_num_levels) {
fprintf(stderr, "Insufficient number of fanouts specified %d\n",
(int)FLAGS_max_bytes_for_level_multiplier_additional_v.size());
exit(1);
}
options.max_bytes_for_level_multiplier_additional =
FLAGS_max_bytes_for_level_multiplier_additional_v;
}
options.level0_stop_writes_trigger = FLAGS_level0_stop_writes_trigger;
Improve statistics Summary: This adds more statistics to be reported by GetProperty("leveldb.stats"). The new stats include time spent waiting on stalls in MakeRoomForWrite. This also includes the total amplification rate where that is: (#bytes of sequential IO during compaction) / (#bytes from Put) This also includes a lot more data for the per-level compaction report. * Rn(MB) - MB read from level N during compaction between levels N and N+1 * Rnp1(MB) - MB read from level N+1 during compaction between levels N and N+1 * Wnew(MB) - new data written to the level during compaction * Amplify - ( Write(MB) + Rnp1(MB) ) / Rn(MB) * Rn - files read from level N during compaction between levels N and N+1 * Rnp1 - files read from level N+1 during compaction between levels N and N+1 * Wnp1 - files written to level N+1 during compaction between levels N and N+1 * NewW - new files written to level N+1 during compaction * Count - number of compactions done for this level This is the new output from DB::GetProperty("leveldb.stats"). The old output stopped at Write(MB) Compactions Level Files Size(MB) Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count ------------------------------------------------------------------------------------------------------------------------------------- 0 3 6 33 0 576 0 0 576 -1.0 0.0 1.3 0 0 0 0 290 1 127 242 351 5316 5314 570 4747 567 17.0 12.1 12.1 287 2399 2685 286 32 2 161 328 54 822 824 326 496 328 4.0 1.9 1.9 160 251 411 160 161 Amplification: 22.3 rate, 0.56 GB in, 12.55 GB out Uptime(secs): 439.8 Stalls(secs): 206.938 level0_slowdown, 0.000 level0_numfiles, 24.129 memtable_compaction Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - (cherry picked from commit ecdeead38f86cc02e754d0032600742c4f02fec8) Reviewers: dhruba Differential Revision: https://reviews.facebook.net/D6153
12 years ago
options.level0_file_num_compaction_trigger =
FLAGS_level0_file_num_compaction_trigger;
options.level0_slowdown_writes_trigger =
FLAGS_level0_slowdown_writes_trigger;
options.compression = FLAGS_compression_type_e;
options.compression_opts.level = FLAGS_compression_level;
options.WAL_ttl_seconds = FLAGS_wal_ttl_seconds;
options.WAL_size_limit_MB = FLAGS_wal_size_limit_MB;
options.max_total_wal_size = FLAGS_max_total_wal_size;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
12 years ago
if (FLAGS_min_level_to_compress >= 0) {
assert(FLAGS_min_level_to_compress <= FLAGS_num_levels);
options.compression_per_level.resize(FLAGS_num_levels);
for (int i = 0; i < FLAGS_min_level_to_compress; i++) {
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
12 years ago
options.compression_per_level[i] = kNoCompression;
}
for (int i = FLAGS_min_level_to_compress;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
12 years ago
i < FLAGS_num_levels; i++) {
options.compression_per_level[i] = FLAGS_compression_type_e;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
12 years ago
}
}
options.soft_rate_limit = FLAGS_soft_rate_limit;
options.hard_rate_limit = FLAGS_hard_rate_limit;
options.soft_pending_compaction_bytes_limit =
FLAGS_soft_pending_compaction_bytes_limit;
options.hard_pending_compaction_bytes_limit =
FLAGS_hard_pending_compaction_bytes_limit;
options.delayed_write_rate = FLAGS_delayed_write_rate;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
9 years ago
options.allow_concurrent_memtable_write =
FLAGS_allow_concurrent_memtable_write;
options.enable_write_thread_adaptive_yield =
FLAGS_enable_write_thread_adaptive_yield;
options.write_thread_max_yield_usec = FLAGS_write_thread_max_yield_usec;
options.write_thread_slow_yield_usec = FLAGS_write_thread_slow_yield_usec;
options.rate_limit_delay_max_milliseconds =
FLAGS_rate_limit_delay_max_milliseconds;
options.table_cache_numshardbits = FLAGS_table_cache_numshardbits;
options.max_grandparent_overlap_factor =
FLAGS_max_grandparent_overlap_factor;
options.disable_auto_compactions = FLAGS_disable_auto_compactions;
options.source_compaction_factor = FLAGS_source_compaction_factor;
// fill storage options
options.allow_os_buffer = FLAGS_bufferedio;
options.allow_mmap_reads = FLAGS_mmap_read;
options.allow_mmap_writes = FLAGS_mmap_write;
options.advise_random_on_open = FLAGS_advise_random_on_open;
options.access_hint_on_compaction_start = FLAGS_compaction_fadvice_e;
options.use_adaptive_mutex = FLAGS_use_adaptive_mutex;
options.bytes_per_sync = FLAGS_bytes_per_sync;
options.wal_bytes_per_sync = FLAGS_wal_bytes_per_sync;
// merge operator options
options.merge_operator = MergeOperators::CreateFromStringId(
FLAGS_merge_operator);
if (options.merge_operator == nullptr && !FLAGS_merge_operator.empty()) {
fprintf(stderr, "invalid merge operator: %s\n",
FLAGS_merge_operator.c_str());
exit(1);
}
options.max_successive_merges = FLAGS_max_successive_merges;
options.compaction_measure_io_stats = FLAGS_compaction_measure_io_stats;
// set universal style compaction configurations, if applicable
if (FLAGS_universal_size_ratio != 0) {
options.compaction_options_universal.size_ratio =
FLAGS_universal_size_ratio;
}
if (FLAGS_universal_min_merge_width != 0) {
options.compaction_options_universal.min_merge_width =
FLAGS_universal_min_merge_width;
}
if (FLAGS_universal_max_merge_width != 0) {
options.compaction_options_universal.max_merge_width =
FLAGS_universal_max_merge_width;
}
if (FLAGS_universal_max_size_amplification_percent != 0) {
options.compaction_options_universal.max_size_amplification_percent =
FLAGS_universal_max_size_amplification_percent;
}
if (FLAGS_universal_compression_size_percent != -1) {
options.compaction_options_universal.compression_size_percent =
FLAGS_universal_compression_size_percent;
}
options.compaction_options_universal.allow_trivial_move =
FLAGS_universal_allow_trivial_move;
if (FLAGS_thread_status_per_interval > 0) {
options.enable_thread_tracking = true;
}
if (FLAGS_rate_limiter_bytes_per_sec > 0) {
options.rate_limiter.reset(
NewGenericRateLimiter(FLAGS_rate_limiter_bytes_per_sec));
}
#ifndef ROCKSDB_LITE
if (FLAGS_readonly && FLAGS_transaction_db) {
fprintf(stderr, "Cannot use readonly flag with transaction_db\n");
exit(1);
}
#endif // ROCKSDB_LITE
if (FLAGS_num_multi_db <= 1) {
OpenDb(options, FLAGS_db, &db_);
} else {
multi_dbs_.clear();
multi_dbs_.resize(FLAGS_num_multi_db);
for (int i = 0; i < FLAGS_num_multi_db; i++) {
OpenDb(options, GetDbNameForMultiple(FLAGS_db, i), &multi_dbs_[i]);
}
}
if (FLAGS_min_level_to_compress >= 0) {
options.compression_per_level.clear();
}
}
void OpenDb(const Options& options, const std::string& db_name,
DBWithColumnFamilies* db) {
Status s;
// Open with column families if necessary.
if (FLAGS_num_column_families > 1) {
size_t num_hot = FLAGS_num_column_families;
if (FLAGS_num_hot_column_families > 0 &&
FLAGS_num_hot_column_families < FLAGS_num_column_families) {
num_hot = FLAGS_num_hot_column_families;
} else {
FLAGS_num_hot_column_families = FLAGS_num_column_families;
}
std::vector<ColumnFamilyDescriptor> column_families;
for (size_t i = 0; i < num_hot; i++) {
column_families.push_back(ColumnFamilyDescriptor(
ColumnFamilyName(i), ColumnFamilyOptions(options)));
}
#ifndef ROCKSDB_LITE
if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, column_families,
&db->cfh, &db->db);
} else if (FLAGS_optimistic_transaction_db) {
s = OptimisticTransactionDB::Open(options, db_name, column_families,
&db->cfh, &db->opt_txn_db);
if (s.ok()) {
db->db = db->opt_txn_db->GetBaseDB();
}
} else if (FLAGS_transaction_db) {
TransactionDB* ptr;
TransactionDBOptions txn_db_options;
s = TransactionDB::Open(options, txn_db_options, db_name,
column_families, &db->cfh, &ptr);
if (s.ok()) {
db->db = ptr;
}
} else {
s = DB::Open(options, db_name, column_families, &db->cfh, &db->db);
}
#else
s = DB::Open(options, db_name, column_families, &db->cfh, &db->db);
#endif // ROCKSDB_LITE
db->cfh.resize(FLAGS_num_column_families);
db->num_created = num_hot;
db->num_hot = num_hot;
#ifndef ROCKSDB_LITE
} else if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, &db->db);
} else if (FLAGS_optimistic_transaction_db) {
s = OptimisticTransactionDB::Open(options, db_name, &db->opt_txn_db);
if (s.ok()) {
db->db = db->opt_txn_db->GetBaseDB();
}
} else if (FLAGS_transaction_db) {
TransactionDB* ptr;
TransactionDBOptions txn_db_options;
s = TransactionDB::Open(options, txn_db_options, db_name, &ptr);
if (s.ok()) {
db->db = ptr;
}
#endif // ROCKSDB_LITE
} else {
s = DB::Open(options, db_name, &db->db);
}
if (!s.ok()) {
fprintf(stderr, "open error: %s\n", s.ToString().c_str());
exit(1);
}
}
enum WriteMode {
RANDOM, SEQUENTIAL, UNIQUE_RANDOM
};
void WriteSeq(ThreadState* thread) {
DoWrite(thread, SEQUENTIAL);
}
void WriteRandom(ThreadState* thread) {
DoWrite(thread, RANDOM);
}
void WriteUniqueRandom(ThreadState* thread) {
DoWrite(thread, UNIQUE_RANDOM);
}
class KeyGenerator {
public:
KeyGenerator(Random64* rand, WriteMode mode,
uint64_t num, uint64_t num_per_set = 64 * 1024)
: rand_(rand),
mode_(mode),
num_(num),
next_(0) {
if (mode_ == UNIQUE_RANDOM) {
// NOTE: if memory consumption of this approach becomes a concern,
// we can either break it into pieces and only random shuffle a section
// each time. Alternatively, use a bit map implementation
// (https://reviews.facebook.net/differential/diff/54627/)
values_.resize(num_);
for (uint64_t i = 0; i < num_; ++i) {
values_[i] = i;
}
std::shuffle(
values_.begin(), values_.end(),
std::default_random_engine(static_cast<unsigned int>(FLAGS_seed)));
}
}
uint64_t Next() {
switch (mode_) {
case SEQUENTIAL:
return next_++;
case RANDOM:
return rand_->Next() % num_;
case UNIQUE_RANDOM:
return values_[next_++];
}
assert(false);
return std::numeric_limits<uint64_t>::max();
}
private:
Random64* rand_;
WriteMode mode_;
const uint64_t num_;
uint64_t next_;
std::vector<uint64_t> values_;
};
DB* SelectDB(ThreadState* thread) {
return SelectDBWithCfh(thread)->db;
}
DBWithColumnFamilies* SelectDBWithCfh(ThreadState* thread) {
return SelectDBWithCfh(thread->rand.Next());
}
DBWithColumnFamilies* SelectDBWithCfh(uint64_t rand_int) {
if (db_.db != nullptr) {
return &db_;
} else {
return &multi_dbs_[rand_int % multi_dbs_.size()];
}
}
void DoWrite(ThreadState* thread, WriteMode write_mode) {
const int test_duration = write_mode == RANDOM ? FLAGS_duration : 0;
const int64_t num_ops = writes_ == 0 ? num_ : writes_;
size_t num_key_gens = 1;
if (db_.db == nullptr) {
num_key_gens = multi_dbs_.size();
}
std::vector<std::unique_ptr<KeyGenerator>> key_gens(num_key_gens);
int64_t max_ops = num_ops * num_key_gens;
int64_t ops_per_stage = max_ops;
if (FLAGS_num_column_families > 1 && FLAGS_num_hot_column_families > 0) {
ops_per_stage = (max_ops - 1) / (FLAGS_num_column_families /
FLAGS_num_hot_column_families) +
1;
}
Duration duration(test_duration, max_ops, ops_per_stage);
for (size_t i = 0; i < num_key_gens; i++) {
key_gens[i].reset(new KeyGenerator(&(thread->rand), write_mode, num_,
ops_per_stage));
}
if (num_ != FLAGS_num) {
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " ops)", num_);
thread->stats.AddMessage(msg);
}
RandomGenerator gen;
WriteBatch batch;
Status s;
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
int64_t stage = 0;
while (!duration.Done(entries_per_batch_)) {
if (duration.GetStage() != stage) {
stage = duration.GetStage();
if (db_.db != nullptr) {
db_.CreateNewCf(open_options_, stage);
} else {
for (auto& db : multi_dbs_) {
db.CreateNewCf(open_options_, stage);
}
}
}
size_t id = thread->rand.Next() % num_key_gens;
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(id);
batch.Clear();
if (thread->shared->write_rate_limiter.get() != nullptr) {
thread->shared->write_rate_limiter->Request(
entries_per_batch_ * (value_size_ + key_size_),
Env::IO_HIGH);
// Set time at which last op finished to Now() to hide latency and
// sleep from rate limiter. Also, do the check once per batch, not
// once per write.
thread->stats.ResetLastOpTime();
}
for (int64_t j = 0; j < entries_per_batch_; j++) {
int64_t rand_num = key_gens[id]->Next();
GenerateKeyFromInt(rand_num, FLAGS_num, &key);
if (FLAGS_num_column_families <= 1) {
batch.Put(key, gen.Generate(value_size_));
} else {
// We use same rand_num as seed for key and column family so that we
// can deterministically find the cfh corresponding to a particular
// key while reading the key.
batch.Put(db_with_cfh->GetCfh(rand_num), key,
gen.Generate(value_size_));
}
bytes += value_size_ + key_size_;
}
s = db_with_cfh->db->Write(write_options_, &batch);
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db,
entries_per_batch_, kWrite);
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
}
thread->stats.AddBytes(bytes);
}
void ReadSequential(ThreadState* thread) {
if (db_.db != nullptr) {
ReadSequential(thread, db_.db);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
ReadSequential(thread, db_with_cfh.db);
}
}
}
void ReadSequential(ThreadState* thread, DB* db) {
ReadOptions options(FLAGS_verify_checksum, true);
options.tailing = FLAGS_use_tailing_iterator;
Iterator* iter = db->NewIterator(options);
int64_t i = 0;
int64_t bytes = 0;
for (iter->SeekToFirst(); i < reads_ && iter->Valid(); iter->Next()) {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(nullptr, db, 1, kRead);
++i;
}
delete iter;
thread->stats.AddBytes(bytes);
}
void ReadReverse(ThreadState* thread) {
if (db_.db != nullptr) {
ReadReverse(thread, db_.db);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
ReadReverse(thread, db_with_cfh.db);
}
}
}
void ReadReverse(ThreadState* thread, DB* db) {
Iterator* iter = db->NewIterator(ReadOptions(FLAGS_verify_checksum, true));
int64_t i = 0;
int64_t bytes = 0;
for (iter->SeekToLast(); i < reads_ && iter->Valid(); iter->Prev()) {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(nullptr, db, 1, kRead);
++i;
}
delete iter;
thread->stats.AddBytes(bytes);
}
void ReadRandomFast(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
int64_t nonexist = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::string value;
DB* db = SelectDBWithCfh(thread)->db;
int64_t pot = 1;
while (pot < FLAGS_num) {
pot <<= 1;
}
Duration duration(FLAGS_duration, reads_);
do {
for (int i = 0; i < 100; ++i) {
int64_t key_rand = thread->rand.Next() & (pot - 1);
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
++read;
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
} else if (!status.IsNotFound()) {
10 years ago
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
}
if (key_rand >= FLAGS_num) {
++nonexist;
}
}
thread->stats.FinishedOps(nullptr, db, 100, kRead);
} while (!duration.Done(100));
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found, "
"issued %" PRIu64 " non-exist keys)\n",
found, read, nonexist);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > 0) {
thread->stats.AddMessage(perf_context.ToString());
}
}
int64_t GetRandomKey(Random64* rand) {
uint64_t rand_int = rand->Next();
int64_t key_rand;
if (read_random_exp_range_ == 0) {
key_rand = rand_int % FLAGS_num;
} else {
const uint64_t kBigInt = static_cast<uint64_t>(1U) << 62;
long double order = -static_cast<long double>(rand_int % kBigInt) /
static_cast<long double>(kBigInt) *
read_random_exp_range_;
long double exp_ran = std::exp(order);
uint64_t rand_num =
static_cast<int64_t>(exp_ran * static_cast<long double>(FLAGS_num));
// Map to a different number to avoid locality.
const uint64_t kBigPrime = 0x5bd1e995;
// Overflow is like %(2^64). Will have little impact of results.
key_rand = static_cast<int64_t>((rand_num * kBigPrime) % FLAGS_num);
}
return key_rand;
}
void ReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
int64_t bytes = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::string value;
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
// We use same key_rand as seed for key and column family so that we can
// deterministically find the cfh corresponding to a particular key, as it
// is done in DoWrite method.
int64_t key_rand = GetRandomKey(&thread->rand);
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
read++;
Status s;
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Get(options, db_with_cfh->GetCfh(key_rand), key,
&value);
} else {
s = db_with_cfh->db->Get(options, key, &value);
}
if (s.ok()) {
found++;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value.size();
} else if (!s.IsNotFound()) {
10 years ago
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n",
found, read);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > 0) {
thread->stats.AddMessage(perf_context.ToString());
}
}
// Calls MultiGet over a list of keys from a random distribution.
// Returns the total number of keys found.
void MultiReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::vector<Slice> keys;
std::vector<std::unique_ptr<const char[]> > key_guards;
std::vector<std::string> values(entries_per_batch_);
while (static_cast<int64_t>(keys.size()) < entries_per_batch_) {
key_guards.push_back(std::unique_ptr<const char[]>());
keys.push_back(AllocateKey(&key_guards.back()));
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
for (int64_t i = 0; i < entries_per_batch_; ++i) {
GenerateKeyFromInt(GetRandomKey(&thread->rand), FLAGS_num, &keys[i]);
}
std::vector<Status> statuses = db->MultiGet(options, keys, &values);
assert(static_cast<int64_t>(statuses.size()) == entries_per_batch_);
read += entries_per_batch_;
for (int64_t i = 0; i < entries_per_batch_; ++i) {
if (statuses[i].ok()) {
++found;
} else if (!statuses[i].IsNotFound()) {
fprintf(stderr, "MultiGet returned an error: %s\n",
statuses[i].ToString().c_str());
abort();
}
}
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)",
found, read);
thread->stats.AddMessage(msg);
}
void IteratorCreation(ThreadState* thread) {
Duration duration(FLAGS_duration, reads_);
ReadOptions options(FLAGS_verify_checksum, true);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
Iterator* iter = db->NewIterator(options);
delete iter;
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
}
}
void IteratorCreationWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
IteratorCreation(thread);
} else {
BGWriter(thread, kWrite);
}
}
void SeekRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
int64_t bytes = 0;
ReadOptions options(FLAGS_verify_checksum, true);
options.tailing = FLAGS_use_tailing_iterator;
Iterator* single_iter = nullptr;
std::vector<Iterator*> multi_iters;
if (db_.db != nullptr) {
single_iter = db_.db->NewIterator(options);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
multi_iters.push_back(db_with_cfh.db->NewIterator(options));
}
}
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
Duration duration(FLAGS_duration, reads_);
char value_buffer[256];
while (!duration.Done(1)) {
if (!FLAGS_use_tailing_iterator) {
if (db_.db != nullptr) {
delete single_iter;
single_iter = db_.db->NewIterator(options);
} else {
for (auto iter : multi_iters) {
delete iter;
}
multi_iters.clear();
for (const auto& db_with_cfh : multi_dbs_) {
multi_iters.push_back(db_with_cfh.db->NewIterator(options));
}
}
}
// Pick a Iterator to use
Iterator* iter_to_use = single_iter;
if (single_iter == nullptr) {
iter_to_use = multi_iters[thread->rand.Next() % multi_iters.size()];
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
iter_to_use->Seek(key);
read++;
if (iter_to_use->Valid() && iter_to_use->key().compare(key) == 0) {
found++;
}
for (int j = 0; j < FLAGS_seek_nexts && iter_to_use->Valid(); ++j) {
// Copy out iterator's value to make sure we read them.
Slice value = iter_to_use->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += iter_to_use->key().size() + iter_to_use->value().size();
if (!FLAGS_reverse_iterator) {
iter_to_use->Next();
} else {
iter_to_use->Prev();
}
assert(iter_to_use->status().ok());
}
thread->stats.FinishedOps(&db_, db_.db, 1, kSeek);
}
delete single_iter;
for (auto iter : multi_iters) {
delete iter;
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n",
found, read);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > 0) {
thread->stats.AddMessage(perf_context.ToString());
}
}
void SeekRandomWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
void SeekRandomWhileMerging(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread, kMerge);
}
}
void DoDelete(ThreadState* thread, bool seq) {
WriteBatch batch;
Duration duration(seq ? 0 : FLAGS_duration, num_);
int64_t i = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
while (!duration.Done(entries_per_batch_)) {
DB* db = SelectDB(thread);
batch.Clear();
for (int64_t j = 0; j < entries_per_batch_; ++j) {
const int64_t k = seq ? i + j : (thread->rand.Next() % FLAGS_num);
GenerateKeyFromInt(k, FLAGS_num, &key);
batch.Delete(key);
}
auto s = db->Write(write_options_, &batch);
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kDelete);
if (!s.ok()) {
fprintf(stderr, "del error: %s\n", s.ToString().c_str());
exit(1);
}
i += entries_per_batch_;
}
}
void DeleteSeq(ThreadState* thread) {
DoDelete(thread, true);
}
void DeleteRandom(ThreadState* thread) {
DoDelete(thread, false);
}
void ReadWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
void ReadWhileMerging(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGWriter(thread, kMerge);
}
}
void BGWriter(ThreadState* thread, enum OperationType write_merge) {
// Special thread that keeps writing until other threads are done.
RandomGenerator gen;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
int64_t bytes = 0;
std::unique_ptr<RateLimiter> write_rate_limiter;
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
// Don't merge stats from this thread with the readers.
thread->stats.SetExcludeFromMerge();
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
while (true) {
DB* db = SelectDB(thread);
{
MutexLock l(&thread->shared->mu);
if (thread->shared->num_done + 1 >= thread->shared->num_initialized) {
// Other threads have finished
break;
}
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Status s;
if (write_merge == kWrite) {
s = db->Put(write_options_, key, gen.Generate(value_size_));
} else {
s = db->Merge(write_options_, key, gen.Generate(value_size_));
}
if (!s.ok()) {
fprintf(stderr, "put or merge error: %s\n", s.ToString().c_str());
exit(1);
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value_size_;
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter->Request(
entries_per_batch_ * (value_size_ + key_size_),
Env::IO_HIGH);
}
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
thread->stats.AddBytes(bytes);
}
// Given a key K and value V, this puts (K+"0", V), (K+"1", V), (K+"2", V)
// in DB atomically i.e in a single batch. Also refer GetMany.
Status PutMany(DB* db, const WriteOptions& writeoptions, const Slice& key,
const Slice& value) {
std::string suffixes[3] = {"2", "1", "0"};
std::string keys[3];
WriteBatch batch;
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Put(keys[i], value);
}
s = db->Write(writeoptions, &batch);
return s;
}
// Given a key K, this deletes (K+"0", V), (K+"1", V), (K+"2", V)
// in DB atomically i.e in a single batch. Also refer GetMany.
Status DeleteMany(DB* db, const WriteOptions& writeoptions,
const Slice& key) {
std::string suffixes[3] = {"1", "2", "0"};
std::string keys[3];
WriteBatch batch;
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Delete(keys[i]);
}
s = db->Write(writeoptions, &batch);
return s;
}
// Given a key K and value V, this gets values for K+"0", K+"1" and K+"2"
// in the same snapshot, and verifies that all the values are identical.
// ASSUMES that PutMany was used to put (K, V) into the DB.
Status GetMany(DB* db, const ReadOptions& readoptions, const Slice& key,
std::string* value) {
std::string suffixes[3] = {"0", "1", "2"};
std::string keys[3];
Slice key_slices[3];
std::string values[3];
ReadOptions readoptionscopy = readoptions;
readoptionscopy.snapshot = db->GetSnapshot();
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
key_slices[i] = keys[i];
s = db->Get(readoptionscopy, key_slices[i], value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
values[i] = "";
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (s.IsNotFound()) {
values[i] = "";
} else {
values[i] = *value;
}
}
db->ReleaseSnapshot(readoptionscopy.snapshot);
if ((values[0] != values[1]) || (values[1] != values[2])) {
fprintf(stderr, "inconsistent values for key %s: %s, %s, %s\n",
key.ToString().c_str(), values[0].c_str(), values[1].c_str(),
values[2].c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
}
return s;
}
// Differs from readrandomwriterandom in the following ways:
// (a) Uses GetMany/PutMany to read/write key values. Refer to those funcs.
// (b) Does deletes as well (per FLAGS_deletepercent)
// (c) In order to achieve high % of 'found' during lookups, and to do
// multiple writes (including puts and deletes) it uses upto
// FLAGS_numdistinct distinct keys instead of FLAGS_num distinct keys.
// (d) Does not have a MultiGet option.
void RandomWithVerify(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
int get_weight = 0;
int put_weight = 0;
int delete_weight = 0;
int64_t gets_done = 0;
int64_t puts_done = 0;
int64_t deletes_done = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
for (int64_t i = 0; i < readwrites_; i++) {
DB* db = SelectDB(thread);
if (get_weight == 0 && put_weight == 0 && delete_weight == 0) {
// one batch completed, reinitialize for next batch
get_weight = FLAGS_readwritepercent;
delete_weight = FLAGS_deletepercent;
put_weight = 100 - get_weight - delete_weight;
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_numdistinct,
FLAGS_numdistinct, &key);
if (get_weight > 0) {
// do all the gets first
Status s = GetMany(db, options, key, &value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "getmany error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
found++;
}
get_weight--;
gets_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kRead);
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Status s = PutMany(db, write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "putmany error: %s\n", s.ToString().c_str());
exit(1);
}
put_weight--;
puts_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
} else if (delete_weight > 0) {
Status s = DeleteMany(db, write_options_, key);
if (!s.ok()) {
fprintf(stderr, "deletemany error: %s\n", s.ToString().c_str());
exit(1);
}
delete_weight--;
deletes_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kDelete);
}
}
char msg[100];
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
11 years ago
snprintf(msg, sizeof(msg),
"( get:%" PRIu64 " put:%" PRIu64 " del:%" PRIu64 " total:%" \
PRIu64 " found:%" PRIu64 ")",
gets_done, puts_done, deletes_done, readwrites_, found);
thread->stats.AddMessage(msg);
}
// This is different from ReadWhileWriting because it does not use
// an extra thread.
void ReadRandomWriteRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
int get_weight = 0;
int put_weight = 0;
int64_t reads_done = 0;
int64_t writes_done = 0;
Duration duration(FLAGS_duration, readwrites_);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
if (get_weight == 0 && put_weight == 0) {
// one batch completed, reinitialize for next batch
get_weight = FLAGS_readwritepercent;
put_weight = 100 - get_weight;
}
if (get_weight > 0) {
// do all the gets first
Status s = db->Get(options, key, &value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
found++;
}
get_weight--;
reads_done++;
thread->stats.FinishedOps(nullptr, db, 1, kRead);
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Status s = db->Put(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
put_weight--;
writes_done++;
thread->stats.FinishedOps(nullptr, db, 1, kWrite);
}
}
char msg[100];
snprintf(msg, sizeof(msg), "( reads:%" PRIu64 " writes:%" PRIu64 \
" total:%" PRIu64 " found:%" PRIu64 ")",
reads_done, writes_done, readwrites_, found);
thread->stats.AddMessage(msg);
}
//
// Read-modify-write for random keys
void UpdateRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
int64_t bytes = 0;
Duration duration(FLAGS_duration, readwrites_);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value.size();
} else if (!status.IsNotFound()) {
10 years ago
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
}
Status s = db->Put(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value_size_;
thread->stats.FinishedOps(nullptr, db, 1, kUpdate);
}
char msg[100];
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
11 years ago
snprintf(msg, sizeof(msg),
"( updates:%" PRIu64 " found:%" PRIu64 ")", readwrites_, found);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read-modify-write for random keys.
// Each operation causes the key grow by value_size (simulating an append).
// Generally used for benchmarking against merges of similar type
void AppendRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// The number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value.size();
} else if (!status.IsNotFound()) {
10 years ago
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
} else {
// If not existing, then just assume an empty string of data
value.clear();
}
// Update the value (by appending data)
Slice operand = gen.Generate(value_size_);
if (value.size() > 0) {
// Use a delimiter to match the semantics for StringAppendOperator
value.append(1,',');
}
value.append(operand.data(), operand.size());
// Write back to the database
Status s = db->Put(write_options_, key, value);
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value.size();
thread->stats.FinishedOps(nullptr, db, 1, kUpdate);
}
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 " found:%" PRIu64 ")",
readwrites_, found);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read-modify-write for random keys (using MergeOperator)
// The merge operator to use should be defined by FLAGS_merge_operator
// Adjust FLAGS_value_size so that the keys are reasonable for this operator
// Assumes that the merge operator is non-null (i.e.: is well-defined)
//
// For example, use FLAGS_merge_operator="uint64add" and FLAGS_value_size=8
// to simulate random additions over 64-bit integers using merge.
//
// The number of merges on the same key can be controlled by adjusting
// FLAGS_merge_keys.
void MergeRandom(ThreadState* thread) {
RandomGenerator gen;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// The number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % merge_keys_, merge_keys_, &key);
Status s = db->Merge(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
bytes += key.size() + value_size_;
thread->stats.FinishedOps(nullptr, db, 1, kMerge);
}
// Print some statistics
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 ")", readwrites_);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
10 years ago
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read and merge random keys. The amount of reads and merges are controlled
// by adjusting FLAGS_num and FLAGS_mergereadpercent. The number of distinct
// keys (and thus also the number of reads and merges on the same key) can be
// adjusted with FLAGS_merge_keys.
//
// As with MergeRandom, the merge operator to use should be defined by
// FLAGS_merge_operator.
void ReadRandomMergeRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t num_hits = 0;
int64_t num_gets = 0;
int64_t num_merges = 0;
size_t max_length = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % merge_keys_, merge_keys_, &key);
bool do_merge = int(thread->rand.Next() % 100) < FLAGS_mergereadpercent;
if (do_merge) {
Status s = db->Merge(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
num_merges++;
thread->stats.FinishedOps(nullptr, db, 1, kMerge);
} else {
Status s = db->Get(options, key, &value);
if (value.length() > max_length)
max_length = value.length();
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
num_hits++;
}
num_gets++;
thread->stats.FinishedOps(nullptr, db, 1, kRead);
}
}
char msg[100];
snprintf(msg, sizeof(msg),
"(reads:%" PRIu64 " merges:%" PRIu64 " total:%" PRIu64
" hits:%" PRIu64 " maxlength:%" ROCKSDB_PRIszt ")",
num_gets, num_merges, readwrites_, num_hits, max_length);
thread->stats.AddMessage(msg);
}
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
void WriteSeqSeekSeq(ThreadState* thread) {
writes_ = FLAGS_num;
DoWrite(thread, SEQUENTIAL);
// exclude writes from the ops/sec calculation
thread->stats.Start(thread->tid);
DB* db = SelectDB(thread);
std::unique_ptr<Iterator> iter(
db->NewIterator(ReadOptions(FLAGS_verify_checksum, true)));
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
for (int64_t i = 0; i < FLAGS_num; ++i) {
GenerateKeyFromInt(i, FLAGS_num, &key);
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
for (int j = 0; j < FLAGS_seek_nexts && i + 1 < FLAGS_num; ++j) {
if (!FLAGS_reverse_iterator) {
iter->Next();
} else {
iter->Prev();
}
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
GenerateKeyFromInt(++i, FLAGS_num, &key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
}
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
10 years ago
}
}
#ifndef ROCKSDB_LITE
// This benchmark stress tests Transactions. For a given --duration (or
// total number of --writes, a Transaction will perform a read-modify-write
// to increment the value of a key in each of N(--transaction-sets) sets of
// keys (where each set has --num keys). If --threads is set, this will be
// done in parallel.
//
// To test transactions, use --transaction_db=true. Not setting this
// parameter
// will run the same benchmark without transactions.
//
// RandomTransactionVerify() will then validate the correctness of the results
// by checking if the sum of all keys in each set is the same.
void RandomTransaction(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
Duration duration(FLAGS_duration, readwrites_);
ReadOptions read_options(FLAGS_verify_checksum, true);
std::string value;
DB* db = db_.db;
uint64_t transactions_done = 0;
uint64_t transactions_aborted = 0;
Status s;
uint64_t num_prefix_ranges = FLAGS_transaction_sets;
if (num_prefix_ranges == 0 || num_prefix_ranges > 9999) {
fprintf(stderr, "invalid value for transaction_sets\n");
abort();
}
if (FLAGS_num_multi_db > 1) {
fprintf(stderr,
"Cannot run RandomTransaction benchmark with "
"FLAGS_multi_db > 1.");
abort();
}
while (!duration.Done(1)) {
Transaction* txn = nullptr;
WriteBatch* batch = nullptr;
if (FLAGS_optimistic_transaction_db) {
txn = db_.opt_txn_db->BeginTransaction(write_options_);
assert(txn);
} else if (FLAGS_transaction_db) {
TransactionDB* txn_db = reinterpret_cast<TransactionDB*>(db_.db);
TransactionOptions txn_options;
txn_options.lock_timeout = FLAGS_transaction_lock_timeout;
txn = txn_db->BeginTransaction(write_options_, txn_options);
assert(txn);
} else {
batch = new WriteBatch();
}
if (txn && FLAGS_transaction_set_snapshot) {
txn->SetSnapshot();
}
// pick a random number to use to increment a key in each set
uint64_t incr = (thread->rand.Next() % 100) + 1;
bool failed = false;
// For each set, pick a key at random and increment it
for (uint8_t i = 0; i < num_prefix_ranges; i++) {
uint64_t int_value;
char prefix_buf[5];
// key format: [SET#][random#]
std::string rand_key = ToString(thread->rand.Next() % FLAGS_num);
Slice base_key(rand_key);
// Pad prefix appropriately so we can iterate over each set
snprintf(prefix_buf, sizeof(prefix_buf), "%04d", i + 1);
std::string full_key = std::string(prefix_buf) + base_key.ToString();
Slice key(full_key);
if (txn) {
s = txn->GetForUpdate(read_options, key, &value);
} else {
s = db->Get(read_options, key, &value);
}
if (s.ok()) {
int_value = std::stoull(value);
if (int_value == 0 || int_value == ULONG_MAX) {
fprintf(stderr, "Get returned unexpected value: %s\n",
value.c_str());
abort();
}
} else if (s.IsNotFound()) {
int_value = 0;
} else if (!(s.IsBusy() || s.IsTimedOut() || s.IsTryAgain())) {
fprintf(stderr, "Get returned an unexpected error: %s\n",
s.ToString().c_str());
abort();
} else {
failed = true;
break;
}
if (FLAGS_transaction_sleep > 0) {
FLAGS_env->SleepForMicroseconds(thread->rand.Next() %
FLAGS_transaction_sleep);
}
std::string sum = ToString(int_value + incr);
if (txn) {
s = txn->Put(key, sum);
if (!s.ok()) {
// Since we did a GetForUpdate, Put should not fail.
fprintf(stderr, "Put returned an unexpected error: %s\n",
s.ToString().c_str());
abort();
}
} else {
batch->Put(key, sum);
}
}
if (txn) {
if (failed) {
transactions_aborted++;
txn->Rollback();
s = Status::OK();
} else {
s = txn->Commit();
}
} else {
s = db->Write(write_options_, batch);
}
if (!s.ok()) {
failed = true;
// Ideally, we'd want to run this stress test with enough concurrency
// on a small enough set of keys that we get some failed transactions
// due to conflicts.
if (FLAGS_optimistic_transaction_db &&
(s.IsBusy() || s.IsTimedOut() || s.IsTryAgain())) {
transactions_aborted++;
} else if (FLAGS_transaction_db && s.IsExpired()) {
transactions_aborted++;
} else {
fprintf(stderr, "Unexpected write error: %s\n", s.ToString().c_str());
abort();
}
}
delete txn;
delete batch;
if (!failed) {
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
}
transactions_done++;
}
char msg[100];
if (FLAGS_optimistic_transaction_db || FLAGS_transaction_db) {
snprintf(msg, sizeof(msg),
"( transactions:%" PRIu64 " aborts:%" PRIu64 ")",
transactions_done, transactions_aborted);
} else {
snprintf(msg, sizeof(msg), "( batches:%" PRIu64 " )", transactions_done);
}
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > 0) {
thread->stats.AddMessage(perf_context.ToString());
}
}
// Verifies consistency of data after RandomTransaction() has been run.
// Since each iteration of RandomTransaction() incremented a key in each set
// by the same value, the sum of the keys in each set should be the same.
void RandomTransactionVerify() {
if (!FLAGS_transaction_db && !FLAGS_optimistic_transaction_db) {
// transactions not used, nothing to verify.
return;
}
uint64_t prev_total = 0;
// For each set of keys with the same prefix, sum all the values
for (uint32_t i = 0; i < FLAGS_transaction_sets; i++) {
char prefix_buf[5];
snprintf(prefix_buf, sizeof(prefix_buf), "%04u", i + 1);
uint64_t total = 0;
Iterator* iter = db_.db->NewIterator(ReadOptions());
for (iter->Seek(Slice(prefix_buf, 4)); iter->Valid(); iter->Next()) {
Slice key = iter->key();
// stop when we reach a different prefix
if (key.ToString().compare(0, 4, prefix_buf) != 0) {
break;
}
Slice value = iter->value();
uint64_t int_value = std::stoull(value.ToString());
if (int_value == 0 || int_value == ULONG_MAX) {
fprintf(stderr, "Iter returned unexpected value: %s\n",
value.ToString().c_str());
abort();
}
total += int_value;
}
delete iter;
if (i > 0) {
if (total != prev_total) {
fprintf(stderr,
"RandomTransactionVerify found inconsistent totals. "
"Set[%" PRIu32 "]: %" PRIu64 ", Set[%" PRIu32 "]: %" PRIu64
" \n",
i - 1, prev_total, i, total);
abort();
}
}
prev_total = total;
}
fprintf(stdout, "RandomTransactionVerify Success!\n");
}
#endif // ROCKSDB_LITE
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
// Writes and deletes random keys without overwriting keys.
//
// This benchmark is intended to partially replicate the behavior of MyRocks
// secondary indices: All data is stored in keys and updates happen by
// deleting the old version of the key and inserting the new version.
void RandomReplaceKeys(ThreadState* thread) {
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::vector<uint32_t> counters(FLAGS_numdistinct, 0);
size_t max_counter = 50;
RandomGenerator gen;
Status s;
DB* db = SelectDB(thread);
for (int64_t i = 0; i < FLAGS_numdistinct; i++) {
GenerateKeyFromInt(i * max_counter, FLAGS_num, &key);
s = db->Put(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "Operation failed: %s\n", s.ToString().c_str());
exit(1);
}
}
db->GetSnapshot();
std::default_random_engine generator;
std::normal_distribution<double> distribution(FLAGS_numdistinct / 2.0,
FLAGS_stddev);
Duration duration(FLAGS_duration, FLAGS_num);
while (!duration.Done(1)) {
int64_t rnd_id = static_cast<int64_t>(distribution(generator));
int64_t key_id = std::max(std::min(FLAGS_numdistinct - 1, rnd_id),
static_cast<int64_t>(0));
GenerateKeyFromInt(key_id * max_counter + counters[key_id], FLAGS_num,
&key);
s = FLAGS_use_single_deletes ? db->SingleDelete(write_options_, key)
: db->Delete(write_options_, key);
if (s.ok()) {
counters[key_id] = (counters[key_id] + 1) % max_counter;
GenerateKeyFromInt(key_id * max_counter + counters[key_id], FLAGS_num,
&key);
s = db->Put(write_options_, key, Slice());
}
if (!s.ok()) {
fprintf(stderr, "Operation failed: %s\n", s.ToString().c_str());
exit(1);
}
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
9 years ago
}
char msg[200];
snprintf(msg, sizeof(msg),
"use single deletes: %d, "
"standard deviation: %lf\n",
FLAGS_use_single_deletes, FLAGS_stddev);
thread->stats.AddMessage(msg);
}
void Compact(ThreadState* thread) {
DB* db = SelectDB(thread);
db->CompactRange(CompactRangeOptions(), nullptr, nullptr);
}
void PrintStats(const char* key) {
if (db_.db != nullptr) {
PrintStats(db_.db, key, false);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStats(db_with_cfh.db, key, true);
}
}
void PrintStats(DB* db, const char* key, bool print_header = false) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
std::string stats;
if (!db->GetProperty(key, &stats)) {
stats = "(failed)";
}
fprintf(stdout, "\n%s\n", stats.c_str());
}
};
} // namespace rocksdb
int main(int argc, char** argv) {
rocksdb::port::InstallStackTraceHandler();
SetUsageMessage(std::string("\nUSAGE:\n") + std::string(argv[0]) +
" [OPTIONS]...");
ParseCommandLineFlags(&argc, &argv, true);
[RocksDB] Add stacktrace signal handler Summary: This diff provides the ability to print out a stacktrace when the process receives certain signals. Currently, we enable this for the following signals (program error related): SIGILL SIGSEGV SIGBUS SIGABRT Application simply #include "util/stack_trace.h" and call leveldb::InstallStackTraceHandler() during initialization, if signal handler is needed. It's not done automatically when openning db, because it's the application(process)'s responsibility to install signal handler and some applications might already have their own (like fbcode). Sample output: Received signal 11 (Segmentation fault) #0 0x408ff0 ./signal_test() [0x408ff0] /home/haobo/rocksdb/util/signal_test.cc:4 #1 0x40827d ./signal_test() [0x40827d] /home/haobo/rocksdb/util/signal_test.cc:24 #2 0x7f8bb183172e /usr/local/fbcode/gcc-4.7.1-glibc-2.14.1/lib/libc.so.6(__libc_start_main+0x10e) [0x7f8bb183172e] ??:0 #3 0x408ebc ./signal_test() [0x408ebc] /home/engshare/third-party/src/glibc/glibc-2.14.1/glibc-2.14.1/csu/../sysdeps/x86_64/elf/start.S:113 Segmentation fault (core dumped) For each frame, we print the raw pointer, the symbol provided by backtrace_symbols (still not good enough), and the source file/line. Note that address translation is done by directly shell out to addr2line. ??:0 means addr2line fails to do the translation. Hacky, but I think it's good for now. Test Plan: signal_test.cc Reviewers: dhruba, MarkCallaghan Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D10173
12 years ago
FLAGS_compaction_style_e = (rocksdb::CompactionStyle) FLAGS_compaction_style;
if (FLAGS_statistics) {
dbstats = rocksdb::CreateDBStatistics();
}
FLAGS_compaction_pri_e = (rocksdb::CompactionPri)FLAGS_compaction_pri;
std::vector<std::string> fanout = rocksdb::StringSplit(
FLAGS_max_bytes_for_level_multiplier_additional, ',');
for (size_t j = 0; j < fanout.size(); j++) {
FLAGS_max_bytes_for_level_multiplier_additional_v.push_back(
#ifndef CYGWIN
std::stoi(fanout[j]));
#else
stoi(fanout[j]));
#endif
}
FLAGS_compression_type_e =
StringToCompressionType(FLAGS_compression_type.c_str());
if (!FLAGS_hdfs.empty()) {
FLAGS_env = new rocksdb::HdfsEnv(FLAGS_hdfs);
}
if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "NONE"))
FLAGS_compaction_fadvice_e = rocksdb::Options::NONE;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "NORMAL"))
FLAGS_compaction_fadvice_e = rocksdb::Options::NORMAL;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "SEQUENTIAL"))
FLAGS_compaction_fadvice_e = rocksdb::Options::SEQUENTIAL;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "WILLNEED"))
FLAGS_compaction_fadvice_e = rocksdb::Options::WILLNEED;
else {
fprintf(stdout, "Unknown compaction fadvice:%s\n",
FLAGS_compaction_fadvice.c_str());
}
FLAGS_rep_factory = StringToRepFactory(FLAGS_memtablerep.c_str());
// The number of background threads should be at least as much the
// max number of concurrent compactions.
FLAGS_env->SetBackgroundThreads(FLAGS_max_background_compactions);
FLAGS_env->SetBackgroundThreads(FLAGS_max_background_flushes,
rocksdb::Env::Priority::HIGH);
// Choose a location for the test database if none given with --db=<path>
if (FLAGS_db.empty()) {
std::string default_db_path;
rocksdb::Env::Default()->GetTestDirectory(&default_db_path);
default_db_path += "/dbbench";
FLAGS_db = default_db_path;
}
if (FLAGS_stats_interval_seconds > 0) {
// When both are set then FLAGS_stats_interval determines the frequency
// at which the timer is checked for FLAGS_stats_interval_seconds
FLAGS_stats_interval = 1000;
}
rocksdb::Benchmark benchmark;
benchmark.Run();
return 0;
}
#endif // GFLAGS