Summary:
For performance purposes, the lower level routines were changed to use a SystemClock* instead of a std::shared_ptr<SystemClock>. The shared ptr has some performance degradation on certain hardware classes.
For most of the system, there is no risk of the pointer being deleted/invalid because the shared_ptr will be stored elsewhere. For example, the ImmutableDBOptions stores the Env which has a std::shared_ptr<SystemClock> in it. The SystemClock* within the ImmutableDBOptions is essentially a "short cut" to gain access to this constant resource.
There were a few classes (PeriodicWorkScheduler?) where the "short cut" property did not hold. In those cases, the shared pointer was preserved.
Using db_bench readrandom perf_level=3 on my EC2 box, this change performed as well or better than 6.17:
6.17: readrandom : 28.046 micros/op 854902 ops/sec; 61.3 MB/s (355999 of 355999 found)
6.18: readrandom : 32.615 micros/op 735306 ops/sec; 52.7 MB/s (290999 of 290999 found)
PR: readrandom : 27.500 micros/op 871909 ops/sec; 62.5 MB/s (367999 of 367999 found)
(Note that the times for 6.18 are prior to revert of the SystemClock).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/8033
Reviewed By: pdillinger
Differential Revision: D27014563
Pulled By: mrambacher
fbshipit-source-id: ad0459eba03182e454391b5926bf5cdd45657b67
Summary:
For dictionary compression, we need to collect some representative samples of the data to be compressed, which we use to either generate or train (when `CompressionOptions::zstd_max_train_bytes > 0`) a dictionary. Previously, the strategy was to buffer all the data blocks during flush, and up to the target file size during compaction. That strategy allowed us to randomly pick samples from as wide a range as possible that'd be guaranteed to land in a single output file.
However, some users try to make huge files in memory-constrained environments, where this strategy can cause OOM. This PR introduces an option, `CompressionOptions::max_dict_buffer_bytes`, that limits how much data blocks are buffered before we switch to unbuffered mode (which means creating the per-SST dictionary, writing out the buffered data, and compressing/writing new blocks as soon as they are built). It is not strict as we currently buffer more than just data blocks -- also keys are buffered. But it does make a step towards giving users predictable memory usage.
Related changes include:
- Changed sampling for dictionary compression to select unique data blocks when there is limited availability of data blocks
- Made use of `BlockBuilder::SwapAndReset()` to save an allocation+memcpy when buffering data blocks for building a dictionary
- Changed `ParseBoolean()` to accept an input containing characters after the boolean. This is necessary since, with this PR, a value for `CompressionOptions::enabled` is no longer necessarily the final component in the `CompressionOptions` string.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7970
Test Plan:
- updated `CompressionOptions` unit tests to verify limit is respected (to the extent expected in the current implementation) in various scenarios of flush/compaction to bottommost/non-bottommost level
- looked at jemalloc heap profiles right before and after switching to unbuffered mode during flush/compaction. Verified memory usage in buffering is proportional to the limit set.
Reviewed By: pdillinger
Differential Revision: D26467994
Pulled By: ajkr
fbshipit-source-id: 3da4ef9fba59974e4ef40e40c01611002c861465
Summary:
The patch adds the configuration options of the new BlobDB implementation
to `db_bench` and adjusts the help messages of the old (`StackableDB`-based)
BlobDB's options to make it clear which implementation they pertain to.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7956
Test Plan: Ran `make check` and `db_bench` with the new options.
Reviewed By: jay-zhuang
Differential Revision: D26384808
Pulled By: ltamasi
fbshipit-source-id: b4405bb2c56cfd3506d4c32e3329c08dfdf69c94
Summary:
Currently, db_bench cleanup only deletes the main DB, if there's one.
Multiple DBs that are opened when --num_multi_db is specified are not
deleted, which can lead to crashes due to running compaction threads on
process exit.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7891
Test Plan: Run regression test
Reviewed By: jay-zhuang
Differential Revision: D26049914
Pulled By: anand1976
fbshipit-source-id: acef2821001ca5e208a96a6a273c724e56353316
Summary:
The multireadrandom benchmark, when run for a specific number of reads (--reads argument), should base the duration on the actual number of keys read rather than number of batches.
Tests:
Run db_bench multireadrandom benchmark
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7817
Reviewed By: zhichao-cao
Differential Revision: D25717230
Pulled By: anand1976
fbshipit-source-id: 13f4d8162268cf9a34918655e60302d0aba3864b
Summary:
Primarily this change refactors the optimize_filters_for_memory
code for Bloom filters, based on malloc_usable_size, to also work for
Ribbon filters.
This change also replaces the somewhat slow but general
BuiltinFilterBitsBuilder::ApproximateNumEntries with
implementation-specific versions for Ribbon (new) and Legacy Bloom
(based on a recently deleted version). The reason is to emphasize
speed in ApproximateNumEntries rather than 100% accuracy.
Justification: ApproximateNumEntries (formerly CalculateNumEntry) is
only used by RocksDB for range-partitioned filters, called each time we
start to construct one. (In theory, it should be possible to reuse the
estimate, but the abstractions provided by FilterPolicy don't really
make that workable.) But this is only used as a heuristic estimate for
hitting a desired partitioned filter size because of alignment to data
blocks, which have various numbers of unique keys or prefixes. The two
factors lead us to prioritize reasonable speed over 100% accuracy.
optimize_filters_for_memory adds extra complication, because precisely
calculating num_entries for some allowed number of bytes depends on state
with optimize_filters_for_memory enabled. And the allocator-agnostic
implementation of optimize_filters_for_memory, using malloc_usable_size,
means we would have to actually allocate memory, many times, just to
precisely determine how many entries (keys) could be added and stay below
some size budget, for the current state. (In a draft, I got this
working, and then realized the balance of speed vs. accuracy was all
wrong.)
So related to that, I have made CalculateSpace, an internal-only API
only used for testing, non-authoritative also if
optimize_filters_for_memory is enabled. This simplifies some code.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7774
Test Plan:
unit test updated, and for FilterSize test, range of tested
values is greatly expanded (still super fast)
Also tested `db_bench -benchmarks=fillrandom,stats -bloom_bits=10 -num=1000000 -partition_index_and_filters -format_version=5 [-optimize_filters_for_memory] [-use_ribbon_filter]` with temporary debug output of generated filter sizes.
Bloom+optimize_filters_for_memory:
1 Filter size: 197 (224 in memory)
134 Filter size: 3525 (3584 in memory)
107 Filter size: 4037 (4096 in memory)
Total on disk: 904,506
Total in memory: 918,752
Ribbon+optimize_filters_for_memory:
1 Filter size: 3061 (3072 in memory)
110 Filter size: 3573 (3584 in memory)
58 Filter size: 4085 (4096 in memory)
Total on disk: 633,021 (-30.0%)
Total in memory: 634,880 (-30.9%)
Bloom (no offm):
1 Filter size: 261 (320 in memory)
1 Filter size: 3333 (3584 in memory)
240 Filter size: 3717 (4096 in memory)
Total on disk: 895,674 (-1% on disk vs. +offm; known tolerable overhead of offm)
Total in memory: 986,944 (+7.4% vs. +offm)
Ribbon (no offm):
1 Filter size: 2949 (3072 in memory)
1 Filter size: 3381 (3584 in memory)
167 Filter size: 3701 (4096 in memory)
Total on disk: 624,397 (-30.3% vs. Bloom)
Total in memory: 690,688 (-30.0% vs. Bloom)
Note that optimize_filters_for_memory is even more effective for Ribbon filter than for cache-local Bloom, because it can close the unused memory gap even tighter than Bloom filter, because of 16 byte increments for Ribbon vs. 64 byte increments for Bloom.
Reviewed By: jay-zhuang
Differential Revision: D25592970
Pulled By: pdillinger
fbshipit-source-id: 606fdaa025bb790d7e9c21601e8ea86e10541912
Summary:
db_bench currently does not allow overriding the default `arena_block_size `calculation ([memtable size/8](https://github.com/facebook/rocksdb/blob/master/db/column_family.cc#L216)). For memtables whose size is in gigabytes, the `arena_block_size` defaults to hundreds of megabytes (affecting performance).
Exposing this option in db_bench would allow us to test the workloads with various `arena_block_size` values.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7654
Reviewed By: jay-zhuang
Differential Revision: D24996812
Pulled By: ajkr
fbshipit-source-id: a5e3d2c83d9f89e1bb8382f2e8dd476c79e33bef
Summary:
This is a PR generated **semi-automatically** by an internal tool to remove unused includes and `using` statements.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7604
Test Plan: make check
Reviewed By: ajkr
Differential Revision: D24579392
Pulled By: riversand963
fbshipit-source-id: c4bfa6c6b08da1de186690d37eb73d8fff45aecd
Summary:
The patch introduces a helper method in `util/compression.h` called `UncompressData`
that dispatches calls to the correct uncompression method based on type, and changes
`UncompressBlockContentsForCompressionType` and `Benchmark::Uncompress` in
`db_bench` so they are implemented in terms of the new method. This eliminates
some code duplication. (`Benchmark::Compress` is also updated to use the previously
introduced `CompressData` helper.)
In addition, the patch brings the implementation of `Snappy_Uncompress` into sync with
the other uncompression methods by making the method compute the buffer size and allocate
the buffer itself. Finally, the patch eliminates some potentially risky back-and-forth conversions
between various unsigned and signed integer types by exposing the size of the allocated buffer
as a `size_t` instead of an `int`.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7434
Test Plan:
`make check`
`./db_bench -benchmarks=compress,uncompress --compression_type ...`
Reviewed By: riversand963
Differential Revision: D23900011
Pulled By: ltamasi
fbshipit-source-id: b25df63ceec4639889be94acb22eb53e530c54e0
Summary:
Update db_bench so that we can run it with user-defined timestamp.
Currently, only 64-bit timestamp is supported, while others are disabled by assertion.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7389
Test Plan: ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readsequential,....., -user_timestamp_size=8
Reviewed By: ltamasi
Differential Revision: D23720830
Pulled By: riversand963
fbshipit-source-id: 486eacbb82de9a5441e79a61bfa9beef6581608a
Summary:
This PR merges the functionality of making the ColumnFamilyOptions, TableFactory, and DBOptions into Configurable into a single PR, resolving any merge conflicts
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5753
Reviewed By: ajkr
Differential Revision: D23385030
Pulled By: zhichao-cao
fbshipit-source-id: 8b977a7731556230b9b8c5a081b98e49ee4f160a
Summary:
Also enables a pull request to trigger all the Travis
configurations by writing FULL_CI in the commit message. (See what I did
there?)
First issue
make: *** No rule to make target 'jl/util/crc32c_ppc_asm.o', needed by 'rocksdbjava'. Stop.
Second issue
tools/db_bench_tool.cc:5514:38: error: ‘gen_exp.rocksdb::Benchmark::GenerateTwoTermExpKeys::keyrange_size_’ may be used uninitialized in this function
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7359
Test Plan: CI
Reviewed By: zhichao-cao
Differential Revision: D23582132
Pulled By: pdillinger
fbshipit-source-id: 06d794673fd522ba11cf6398385387e6bd97ef89
Summary:
Delete database instances to make sure there are no loose threads
running before exit(). This fixes segfaults seen when running
workloads through CompositeEnvs with custom file systems.
For further background on the issues arising when using CompositeEnvs, see the discussion in:
https://github.com/facebook/rocksdb/pull/6878
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7327
Reviewed By: cheng-chang
Differential Revision: D23433244
Pulled By: ajkr
fbshipit-source-id: 4e19cf2067e3fe68c2a3fe1823f24b4091336bbe
Summary:
This pull request adds the parameter --fs_uri to db_bench and db_stress, creating a composite env combining the default env with a specified registered rocksdb file system.
This makes it easier to develop and test new RocksDB FileSystems.
The pull request also registers the posix file system for testing purposes.
Examples:
```
$./db_bench --fs_uri=posix:// --benchmarks=fillseq
$./db_stress --fs_uri=zenfs://nullb1
```
zenfs is a RocksDB FileSystem I'm developing to add support for zoned block devices, and in that case the zoned block device is specified in the uri (a zoned null block device in the above example).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6878
Reviewed By: siying
Differential Revision: D23023063
Pulled By: ajkr
fbshipit-source-id: 8b3fe7193ce45e683043b021779b7a4d547af247
Summary:
Adds compaction statistics (total bytes read and written) for compactions that occur for delete-triggered, periodic, and TTL compaction reasons.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7165
Test Plan:
TTL and periodic can be checked by runnning db_bench with the options activated:
/db_bench --benchmarks="fillrandom,stats" --statistics --num=10000000 -base_background_compactions=16 -periodic_compaction_seconds=1
./db_bench --benchmarks="fillrandom,stats" --statistics --num=10000000 -base_background_compactions=16 -fifo_compaction_ttl=1
Setting the time to one second causes non-zero bytes read/written for those compaction reasons. Disabling them or setting them to times longer than the test run length causes the stats to return to zero as expected.
Delete-triggered compaction counting is tested in DBTablePropertiesTest.DeletionTriggeredCompactionMarking
Reviewed By: ajkr
Differential Revision: D22693050
Pulled By: akabcenell
fbshipit-source-id: d15cef4d94576f703015c8942d5f0d492f69401d
Summary:
New experimental option BBTO::optimize_filters_for_memory builds
filters that maximize their use of "usable size" from malloc_usable_size,
which is also used to compute block cache charges.
Rather than always "rounding up," we track state in the
BloomFilterPolicy object to mix essentially "rounding down" and
"rounding up" so that the average FP rate of all generated filters is
the same as without the option. (YMMV as heavily accessed filters might
be unluckily lower accuracy.)
Thus, the option near-minimizes what the block cache considers as
"memory used" for a given target Bloom filter false positive rate and
Bloom filter implementation. There are no forward or backward
compatibility issues with this change, though it only works on the
format_version=5 Bloom filter.
With Jemalloc, we see about 10% reduction in memory footprint (and block
cache charge) for Bloom filters, but 1-2% increase in storage footprint,
due to encoding efficiency losses (FP rate is non-linear with bits/key).
Why not weighted random round up/down rather than state tracking? By
only requiring malloc_usable_size, we don't actually know what the next
larger and next smaller usable sizes for the allocator are. We pick a
requested size, accept and use whatever usable size it has, and use the
difference to inform our next choice. This allows us to narrow in on the
right balance without tracking/predicting usable sizes.
Why not weight history of generated filter false positive rates by
number of keys? This could lead to excess skew in small filters after
generating a large filter.
Results from filter_bench with jemalloc (irrelevant details omitted):
(normal keys/filter, but high variance)
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9
Build avg ns/key: 29.6278
Number of filters: 5516
Total size (MB): 200.046
Reported total allocated memory (MB): 220.597
Reported internal fragmentation: 10.2732%
Bits/key stored: 10.0097
Average FP rate %: 0.965228
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
Build avg ns/key: 30.5104
Number of filters: 5464
Total size (MB): 200.015
Reported total allocated memory (MB): 200.322
Reported internal fragmentation: 0.153709%
Bits/key stored: 10.1011
Average FP rate %: 0.966313
(very few keys / filter, optimization not as effective due to ~59 byte
internal fragmentation in blocked Bloom filter representation)
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9
Build avg ns/key: 29.5649
Number of filters: 162950
Total size (MB): 200.001
Reported total allocated memory (MB): 224.624
Reported internal fragmentation: 12.3117%
Bits/key stored: 10.2951
Average FP rate %: 0.821534
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
Build avg ns/key: 31.8057
Number of filters: 159849
Total size (MB): 200
Reported total allocated memory (MB): 208.846
Reported internal fragmentation: 4.42297%
Bits/key stored: 10.4948
Average FP rate %: 0.811006
(high keys/filter)
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9
Build avg ns/key: 29.7017
Number of filters: 164
Total size (MB): 200.352
Reported total allocated memory (MB): 221.5
Reported internal fragmentation: 10.5552%
Bits/key stored: 10.0003
Average FP rate %: 0.969358
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
Build avg ns/key: 30.7131
Number of filters: 160
Total size (MB): 200.928
Reported total allocated memory (MB): 200.938
Reported internal fragmentation: 0.00448054%
Bits/key stored: 10.1852
Average FP rate %: 0.963387
And from db_bench (block cache) with jemalloc:
$ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false
$ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false
$ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }'
17063835
$ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }'
17430747
$ #^ 2.1% additional filter storage
$ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000
rocksdb.block.cache.index.add COUNT : 33
rocksdb.block.cache.index.bytes.insert COUNT : 8440400
rocksdb.block.cache.filter.add COUNT : 33
rocksdb.block.cache.filter.bytes.insert COUNT : 21087528
rocksdb.bloom.filter.useful COUNT : 4963889
rocksdb.bloom.filter.full.positive COUNT : 1214081
rocksdb.bloom.filter.full.true.positive COUNT : 1161999
$ #^ 1.04 % observed FP rate
$ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000
rocksdb.block.cache.index.add COUNT : 33
rocksdb.block.cache.index.bytes.insert COUNT : 8448592
rocksdb.block.cache.filter.add COUNT : 33
rocksdb.block.cache.filter.bytes.insert COUNT : 18220328
rocksdb.bloom.filter.useful COUNT : 5360933
rocksdb.bloom.filter.full.positive COUNT : 1321315
rocksdb.bloom.filter.full.true.positive COUNT : 1262999
$ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters
(Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.)
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427
Test Plan: unit test added, 'make check' with gcc, clang and valgrind
Reviewed By: siying
Differential Revision: D22124374
Pulled By: pdillinger
fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
Summary:
Mostly uninitialized values: some probably written before use, but some seem like bugs. Also, destructor needs to be virtual, and possible use-after-free in test
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6935
Test Plan: make check
Reviewed By: siying
Differential Revision: D21885484
Pulled By: pdillinger
fbshipit-source-id: e2e7cb0a0cf196f2b55edd16f0634e81f6cc8e08
Summary:
The implementation of GetApproximateSizes was inconsistent in
its treatment of the size of non-data blocks of SST files, sometimes
including and sometimes now. This was at its worst with large portion
of table file used by filters and querying a small range that crossed
a table boundary: the size estimate would include large filter size.
It's conceivable that someone might want only to know the size in terms
of data blocks, but I believe that's unlikely enough to ignore for now.
Similarly, there's no evidence the internal function AppoximateOffsetOf
is used for anything other than a one-sided ApproximateSize, so I intend
to refactor to remove redundancy in a follow-up commit.
So to fix this, GetApproximateSizes (and implementation details
ApproximateSize and ApproximateOffsetOf) now consistently include in
their returned sizes a portion of table file metadata (incl filters
and indexes) based on the size portion of the data blocks in range. In
other words, if a key range covers data blocks that are X% by size of all
the table's data blocks, returned approximate size is X% of the total
file size. It would technically be more accurate to attribute metadata
based on number of keys, but that's not computationally efficient with
data available and rarely a meaningful difference.
Also includes miscellaneous comment improvements / clarifications.
Also included is a new approximatesizerandom benchmark for db_bench.
No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784
Test Plan:
Test added to DBTest.ApproximateSizesFilesWithErrorMargin.
Old code running new test...
[ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin
db/db_test.cc:1562: Failure
Expected: (size) <= (11 * 100), actual: 9478 vs 1100
Other tests updated to reflect consistent accounting of metadata.
Reviewed By: siying
Differential Revision: D21334706
Pulled By: pdillinger
fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
Summary:
Fix issues for reproducing synthetic ZippyDB workloads in the FAST20' paper using db_bench. Details changes as follows.
1, add a separate random mode in MixGraph to produce all_random workload.
2, fix power inverse function for generating prefix_dist workload.
3, make sure key_offset in prefix mode is always unsigned.
note: Need to carefully choose key_dist_a/b to avoid aliasing. Power inverse function range should be close to overall key space.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6795
Reviewed By: akankshamahajan15
Differential Revision: D21371095
Pulled By: zhichao-cao
fbshipit-source-id: 80744381e242392c8c7cf8ac3d68fe67fe876048
Summary:
This commit adds an `compression_parallel_threads` option in
db_stress. It also fixes the naming of parallel compression
option in db_bench to keep it aligned with others.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6722
Reviewed By: pdillinger
Differential Revision: D21091385
fbshipit-source-id: c9ba8c4e5cc327ff9e6094a6dc6a15fcff70f100
Summary:
The dynamic_cast in the filter benchmark causes release mode to fail due to
no-rtti. Replace with static_cast_with_check.
Signed-off-by: Derrick Pallas <derrick@pallas.us>
Addition by peterd: Remove unnecessary 2nd template arg on all static_cast_with_check
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6732
Reviewed By: ltamasi
Differential Revision: D21304260
Pulled By: pdillinger
fbshipit-source-id: 6e8eb437c4ca5a16dbbfa4053d67c4ad55f1608c
Summary:
Based on https://github.com/facebook/rocksdb/issues/6648 (CLA Signed), but heavily modified / extended:
* Implicit capture of this via [=] deprecated in C++20, and [=,this] not standard before C++20 -> now using explicit capture lists
* Implicit copy operator deprecated in gcc 9 -> add explicit '= default' definition
* std::random_shuffle deprecated in C++17 and removed in C++20 -> migrated to a replacement in RocksDB random.h API
* Add the ability to build with different std version though -DCMAKE_CXX_STANDARD=11/14/17/20 on the cmake command line
* Minimal rebuild flag of MSVC is deprecated and is forbidden with /std:c++latest (C++20)
* Added MSVC 2019 C++11 & MSVC 2019 C++20 in AppVeyor
* Added GCC 9 C++11 & GCC9 C++20 in Travis
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6697
Test Plan: make check and CI
Reviewed By: cheng-chang
Differential Revision: D21020318
Pulled By: pdillinger
fbshipit-source-id: 12311be5dbd8675a0e2c817f7ec50fa11c18ab91
Summary:
New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM.
The new allocator provided in this PR uses the memkind library to allocate memory on different media.
**Performance**
We tested the new allocator using db_bench.
- For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database).
- The database is filled sequentially. Throughput is then measured with a readrandom benchmark.
- We use a uniform distribution as a worst-case scenario.
The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator.
For all tests, p99 latency is below 500 us.
**Changes**
- Add MemkindKmemAllocator
- Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator)
- Add detection of memkind library with KMEM DAX support
- Add test for MemkindKmemAllocator
**Minimum Requirements**
- kernel 5.3.12
- ndctl v67 - https://github.com/pmem/ndctl
- memkind v1.10.0 - https://github.com/memkind/memkind
**Memory Configuration**
The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly.
Note on memory allocation with NVDIMM memory exposed as system memory.
- The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind).
- The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node.
**Usage**
When creating an LRU cache, pass a MemkindKmemAllocator object as argument.
For example (replace capacity with the desired value in bytes):
```
#include "rocksdb/cache.h"
#include "memory/memkind_kmem_allocator.h"
NewLRUCache(
capacity /*size_t*/,
6 /*cache_numshardbits*/,
false /*strict_capacity_limit*/,
false /*cache_high_pri_pool_ratio*/,
std::make_shared<MemkindKmemAllocator>());
```
Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214
Reviewed By: cheng-chang
Differential Revision: D19292435
fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
Summary:
This commit is fixing a bug that readrandom test returns many NotFound in db_bench from Version 6.2.
Pull Request resolved: https://github.com/facebook/rocksdb/issues/6664
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6665
Reviewed By: cheng-chang
Differential Revision: D20911298
Pulled By: ajkr
fbshipit-source-id: c2658d4dbb35798ccbf67dff6e64923fb731ef81
Summary:
This PR adds support for pipelined & parallel compression optimization for `BlockBasedTableBuilder`. This optimization makes block building, block compression and block appending a pipeline, and uses multiple threads to accelerate block compression. Users can set `CompressionOptions::parallel_threads` greater than 1 to enable compression parallelism.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6262
Reviewed By: ajkr
Differential Revision: D20651306
fbshipit-source-id: 62125590a9c15b6d9071def9dc72589c1696a4cb
Summary:
I start to see following failures:
tools/db_bench_tool.cc: In constructor ‘rocksdb::NormalDistribution::NormalDistribution(unsigned int, unsigned int)’:
tools/db_bench_tool.cc:1528:58: error: declaration of ‘max’ shadows a member of 'this' [-Werror=shadow]
NormalDistribution(unsigned int min, unsigned int max) :
^
tools/db_bench_tool.cc:1528:58: error: declaration of ‘min’ shadows a member of 'this' [-Werror=shadow]
tools/db_bench_tool.cc: In constructor ‘rocksdb::UniformDistribution::UniformDistribution(unsigned int, unsigned int)’:
tools/db_bench_tool.cc:1546:59: error: declaration of ‘max’ shadows a member of 'this' [-Werror=shadow]
UniformDistribution(unsigned int min, unsigned int max) :
^
tools/db_bench_tool.cc:1546:59: error: declaration of ‘min’ shadows a member of 'this' [-Werror=shadow]
when I build from GCC 4.8. Rename those variables to fix the problem.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6537
Test Plan: make all with the compiler that used to show the failure.
Differential Revision: D20448741
fbshipit-source-id: 18bcf012dbe020f22f79038a9b08f447befa2574
Summary:
Some combinatino of --index_with_first_key and --index_shortening_mode can signifcantly improve performance for large values. Expose them in db_bench.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5859
Test Plan: Run them with the new options and observe the behavior.
Differential Revision: D20104434
fbshipit-source-id: 21d48a732a9caf20b82312c7d7557d747ea3c304
Summary:
When dynamically linking two binaries together, different builds of RocksDB from two sources might cause errors. To provide a tool for user to solve the problem, the RocksDB namespace is changed to a flag which can be overridden in build time.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6433
Test Plan: Build release, all and jtest. Try to build with ROCKSDB_NAMESPACE with another flag.
Differential Revision: D19977691
fbshipit-source-id: aa7f2d0972e1c31d75339ac48478f34f6cfcfb3e
Summary:
Right, when reading from option files, no readahead is used and 8KB buffer is used. It might introduce high latency if the file system provide high latency and doesn't do readahead. Instead, introduce a readahead to the file. When calling inside DB, infer the value from options.log_readahead. Otherwise, a default 512KB readahead size is used.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6372
Test Plan: Add --log_readahead_size in db_bench. Run it with several options and observe read size from option files using strace.
Differential Revision: D19727739
fbshipit-source-id: e6d8053b0a64259abc087f1f388b9cd66fa8a583
Summary:
We see some odd errors complaining math. However, it doesn't seem that it is needed to be included. Remove the include of math.h. Just removing it from db_bench doesn't seem to break anything. Replacing sqrt from std::sqrt seems to work for histogram.cc
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6373
Test Plan: Watch Travis and appveyor to run.
Differential Revision: D19730068
fbshipit-source-id: d3ad41defcdd9f51c2da1a3673fb258f5dfacf47
Summary:
The patch makes it possible to set the BlobDB configuration option
`garbage_collection_cutoff` on the command line. In addition, it changes
the `db_bench` code so that the default values of BlobDB related
parameters are taken from the defaults of the actual BlobDB
configuration options (note: this changes the the default of
`blob_db_bytes_per_sync`).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6211
Test Plan: Ran `db_bench` with various values of the new parameter.
Differential Revision: D19166895
Pulled By: ltamasi
fbshipit-source-id: 305ccdf0123b9db032b744715810babdc3e3b7d5
Summary:
In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters.
For example:
`./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48`
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953
Test Plan: run db_bench with different parameters and checked the results.
Differential Revision: D18053527
Pulled By: zhichao-cao
fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
Summary:
Since we already parse env_uri from command line and creates custom Env
accordingly, we should invoke the methods of such Envs instead of using
Env::Default().
Test Plan (on devserver):
```
$make db_bench db_stress
$./db_bench -benchmarks=fillseq
./db_stress
```
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5943
Differential Revision: D18018550
Pulled By: riversand963
fbshipit-source-id: 03b61329aaae0dfd914a0b902cc677f570f102e3
Summary:
In the current trace replay, all the queries are serialized and called by single threads. It may not simulate the original application query situations closely. The multi-threads replay is implemented in this PR. Users can set the number of threads to replay the trace. The queries generated according to the trace records are scheduled in the thread pool job queue.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5934
Test Plan: test with make check and real trace replay.
Differential Revision: D17998098
Pulled By: zhichao-cao
fbshipit-source-id: 87eecf6f7c17a9dc9d7ab29dd2af74f6f60212c8
Summary:
Currently, db_bench only supports PutWithTTL operations for BlobDB but
not regular Puts. The patch adds support for regular (non-TTL) Puts and also
changes the default for blob_db_max_ttl_range to zero, which corresponds
to no TTL.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5921
Test Plan:
make check
./db_bench -benchmarks=fillrandom -statistics -stats_interval_seconds=1
-duration=90 -num=500000 -use_blob_db=1 -blob_db_file_size=1000000
-target_file_size_base=1000000 (issues Put operations with no TTL)
./db_bench -benchmarks=fillrandom -statistics -stats_interval_seconds=1
-duration=90 -num=500000 -use_blob_db=1 -blob_db_file_size=1000000
-target_file_size_base=1000000 -blob_db_max_ttl_range=86400 (issues
PutWithTTL operations with random TTLs in the [0, blob_db_max_ttl_range)
interval, as before)
Differential Revision: D17919798
Pulled By: ltamasi
fbshipit-source-id: b946c3522b836b92b4c157ffbad24f92ba2b0a16
Summary:
Further apply formatter to more recent commits.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5830
Test Plan: Run all existing tests.
Differential Revision: D17488031
fbshipit-source-id: 137458fd94d56dd271b8b40c522b03036943a2ab
Summary:
MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory.
We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one.
The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming.
In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022
Differential Revision: D14394062
Pulled By: miasantreble
fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
Summary:
This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases:
1. Update subset of columns and read subset of columns -
Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU.
2. Updating very few attributes in a value which is a JSON-like document -
Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge.
----------------------------------------------------------------------------------------------------
API :
Status GetMergeOperands(
const ReadOptions& options, ColumnFamilyHandle* column_family,
const Slice& key, PinnableSlice* merge_operands,
GetMergeOperandsOptions* get_merge_operands_options,
int* number_of_operands)
Example usage :
int size = 100;
int number_of_operands = 0;
std::vector<PinnableSlice> values(size);
GetMergeOperandsOptions merge_operands_info;
db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands);
Description :
Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion.
merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604
Test Plan:
Added unit test and perf test in db_bench that can be run using the command:
./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist
Differential Revision: D16657366
Pulled By: vjnadimpalli
fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
Summary:
The ObjectRegistry class replaces the Registrar and NewCustomObjects. Objects are registered with the registry by Type (the class must implement the static const char *Type() method).
This change is necessary for a few reasons:
- By having a class (rather than static template instances), the class can be passed between compilation units, meaning that objects could be registered and shared from a dynamic library with an executable.
- By having a class with instances, different units could have different objects registered. This could be useful if, for example, one Option allowed for a dynamic library and one did not.
When combined with some other PRs (being able to load shared libraries, a Configurable interface to configure objects to/from string), this code will allow objects in external shared libraries to be added to a RocksDB image at run-time, rather than requiring every new extension to be built into the main library and called explicitly by every program.
Test plan (on riversand963's devserver)
```
$COMPILE_WITH_ASAN=1 make -j32 all && sleep 1 && make check
```
All tests pass.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5293
Differential Revision: D16363396
Pulled By: riversand963
fbshipit-source-id: fbe4acb615bfc11103eef40a0b288845791c0180
Summary:
Sometimes it is helpful to fetch the whole history of stats after benchmark runs. Add such an option
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5532
Test Plan: Run the benchmark manually and observe the output is as expected.
Differential Revision: D16097764
fbshipit-source-id: 10b5b735a22a18be198b8f348be11f11f8806904
mrambacher
