Summary:
We have recently added caching support to BlobDB, and separately,
implemented an optimization where reading blobs from the cache
results in the cache handle being transferred to the target `PinnableSlice`
(as opposed to the contents getting copied). With these changes,
it makes sense to reset the `PinnableSlice` storing the blob value in
`DBIter` as soon as we move to a different iterator position to prevent
us from holding on to the cache handle any longer than necessary.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10490
Test Plan: `make check`
Reviewed By: akankshamahajan15
Differential Revision: D38473630
Pulled By: ltamasi
fbshipit-source-id: 84c045ffac76436c6152fd0f5775b007f4051386
Summary:
Change tiered compaction feature from `bottommost_temperture` to
`last_level_temperture`. The old option is kept for migration purpose only,
which is behaving the same as `last_level_temperture` and it will be removed in
the next release.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10471
Test Plan: CI
Reviewed By: siying
Differential Revision: D38450621
Pulled By: jay-zhuang
fbshipit-source-id: cc1cdf8bad409376fec0152abc0a64fb72a91527
Summary:
Current universal compaction picker may cause extra size amplification
compaction if there're more hot data on penultimate level. Improve the picker
to skip the last level for size amp calculation if tiered compaction is
enabled, which can
1. avoid extra unnecessary size amp compaction;
2. typically cold tier (the last level) is not size constrained, so skip size
amp for cold tier is intended;
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10467
Test Plan: CI and added unittest
Reviewed By: siying
Differential Revision: D38391350
Pulled By: jay-zhuang
fbshipit-source-id: 103c0731c05e0a7e8f267e9e829d022328be25d2
Summary:
lambda function dynamicly allocates memory from heap if it needs to
capture multiple values, which could be expensive.
Switch to explictly use local functor from stack.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10453
Test Plan:
CI
db_bench shows ~2-3% read improvement:
```
# before the change
TEST_TMPDIR=/tmp/dbbench4 ./db_bench_main --benchmarks=filluniquerandom,readrandom -compression_type=none -max_background_jobs=12 -num=10000000
readrandom : 8.528 micros/op 117265 ops/sec 85.277 seconds 10000000 operations; 13.0 MB/s (10000000 of 10000000 found)
# after the change
TEST_TMPDIR=/tmp/dbbench5 ./db_bench_new --benchmarks=filluniquerandom,readrandom -compression_type=none -max_background_jobs=12 -num=10000000
readrandom : 8.263 micros/op 121015 ops/sec 82.634 seconds 10000000 operations; 13.4 MB/s (10000000 of 10000000 found)
```
details: https://gist.github.com/jay-zhuang/5ac0628db8fc9cbcb499e056d4cb5918
Micro-benchmark shows a similar improvement ~1-2%:
before the change:
https://gist.github.com/jay-zhuang/9dc0ebf51bbfbf4af82f6193d43cf75b
after the change:
https://gist.github.com/jay-zhuang/fc061f1813cd8f441109ad0b0fe7c185
Reviewed By: ajkr
Differential Revision: D38345056
Pulled By: jay-zhuang
fbshipit-source-id: f3597aeeee338a804d37bf2e81386d5a100665e0
Summary:
- Right now each read fragments the memtable range tombstones https://github.com/facebook/rocksdb/issues/4808. This PR explores the idea of fragmenting memtable range tombstones in the write path and reads can just read this cached fragmented tombstone without any fragmenting cost. This PR only does the caching for immutable memtable, and does so right before a memtable is added to an immutable memtable list. The fragmentation is done without holding mutex to minimize its performance impact.
- db_bench is updated to print out the number of range deletions executed if there is any.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10380
Test Plan:
- CI, added asserts in various places to check whether a fragmented range tombstone list should have been constructed.
- Benchmark: as this PR only optimizes immutable memtable path, the number of writes in the benchmark is chosen such an immutable memtable is created and range tombstones are in that memtable.
```
single thread:
./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=500000 --reads=100000 --max_num_range_tombstones=100
multi_thread
./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=15000 --reads=20000 --threads=32 --max_num_range_tombstones=100
```
Commit 99cdf16464a057ca44de2f747541dedf651bae9e is included in benchmark result. It was an earlier attempt where tombstones are fragmented for each write operation. Reader threads share it using a shared_ptr which would slow down multi-thread read performance as seen in benchmark results.
Results are averaged over 5 runs.
Single thread result:
| Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR |
| ------------- | ------------- |------------- |------------- |------------- |------------- |------------- |
| 0 |6.68 |6.57 |6.72 |4.72 |4.79 |4.54 |
| 1 |6.67 |6.58 |6.62 |5.41 |4.74 |4.72 |
| 10 |6.59 |6.5 |6.56 |7.83 |4.69 |4.59 |
| 100 |6.62 |6.75 |6.58 |29.57 |5.04 |5.09 |
| 1000 |6.54 |6.82 |6.61 |320.33 |5.22 |5.21 |
32-thread result: note that "Max # tombstones" is per thread.
| Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR |
| ------------- | ------------- |------------- |------------- |------------- |------------- |------------- |
| 0 |234.52 |260.25 |239.42 |5.06 |5.38 |5.09 |
| 1 |236.46 |262.0 |231.1 |19.57 |22.14 |5.45 |
| 10 |236.95 |263.84 |251.49 |151.73 |21.61 |5.73 |
| 100 |268.16 |296.8 |280.13 |2308.52 |22.27 |6.57 |
Reviewed By: ajkr
Differential Revision: D37916564
Pulled By: cbi42
fbshipit-source-id: 05d6d2e16df26c374c57ddcca13a5bfe9d5b731e
Summary:
Currently, `SetIsInSecondaryCache` is after `Promote`. After `Promote`, a handle can be accessed and its flags can be set. This causes data race.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10472
Test Plan:
unit tests
stress tests
Reviewed By: pdillinger
Differential Revision: D38403991
Pulled By: gitbw95
fbshipit-source-id: 0aaa2d2edeaf5bc799fcce605648fe49eb7119c2
Summary:
### **Summary:**
To minimize the internal fragmentation caused by the variable size of the compressed blocks, the original block is split according to the jemalloc bin size in `Insert()` and then merged back in `Lookup()`. Based on the analysis of the results of the following tests, from the overall internal fragmentation perspective, this PR does mitigate the internal fragmentation issue.
_Do more myshadow tests with the latest commit. I finished several myshadow AB Testing and the results are promising. For the config of 4GB primary cache and 3GB secondary cache, Jemalloc resident stats shows consistently ~0.15GB memory saving; the allocated and active stats show similar memory savings. The CPU usage is almost the same before and after this PR._
To evaluate the issue of memory fragmentations and the benefits of this PR, I conducted two sets of local tests as follows.
**T1**
Keys: 16 bytes each (+ 0 bytes user-defined timestamp)
Values: 100 bytes each (50 bytes after compression)
Entries: 90000000
RawSize: 9956.4 MB (estimated)
FileSize: 5664.8 MB (estimated)
| Test Name | Primary Cache Size (MB) | Compressed Secondary Cache Size (MB) |
| - | - | - |
| T1_3 | 4000 | 4000 |
| T1_4 | 2000 | 3000 |
Populate the DB:
./db_bench --benchmarks=fillrandom --num=90000000 -db=/mem_fragmentation/db_bench_1
Overwrite it to a stable state:
./db_bench --benchmarks=overwrite --num=90000000 -use_existing_db -db=/mem_fragmentation/db_bench_1
Run read tests with differnt cache setting:
T1_3:
MALLOC_CONF="prof:true,prof_stats:true" ../rocksdb/db_bench --benchmarks=seekrandom --threads=16 --num=90000000 -use_existing_db --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=4000000000 -compressed_secondary_cache_size=4000000000 -use_compressed_secondary_cache -db=/mem_fragmentation/db_bench_1 --print_malloc_stats=true > ~/temp/mem_frag/20220710/jemalloc_stats_json_T1_3_20220710 -duration=1800 &
T1_4:
MALLOC_CONF="prof:true,prof_stats:true" ../rocksdb/db_bench --benchmarks=seekrandom --threads=16 --num=90000000 -use_existing_db --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=2000000000 -compressed_secondary_cache_size=3000000000 -use_compressed_secondary_cache -db=/mem_fragmentation/db_bench_1 --print_malloc_stats=true > ~/temp/mem_frag/20220710/jemalloc_stats_json_T1_4_20220710 -duration=1800 &
For T1_3 and T1_4, I also conducted the tests before and after this PR. The following table show the important jemalloc stats.
| Test Name | T1_3 | T1_3 after mem defrag | T1_4 | T1_4 after mem defrag |
| - | - | - | - | - |
| allocated (MB) | 8728 | 8076 | 5518 | 5043 |
| available (MB) | 8753 | 8092 | 5536 | 5051 |
| external fragmentation rate | 0.003 | 0.002 | 0.003 | 0.0016 |
| resident (MB) | 8956 | 8365 | 5655 | 5235 |
**T2**
Keys: 32 bytes each (+ 0 bytes user-defined timestamp)
Values: 256 bytes each (128 bytes after compression)
Entries: 40000000
RawSize: 10986.3 MB (estimated)
FileSize: 6103.5 MB (estimated)
| Test Name | Primary Cache Size (MB) | Compressed Secondary Cache Size (MB) |
| - | - | - |
| T2_3 | 4000 | 4000 |
| T2_4 | 2000 | 3000 |
Create DB (10GB):
./db_bench -benchmarks=fillrandom -use_direct_reads=true -num=40000000 -key_size=32 -value_size=256 -db=/mem_fragmentation/db_bench_2
Overwrite it to a stable state:
./db_bench --benchmarks=overwrite --num=40000000 -use_existing_db -key_size=32 -value_size=256 -db=/mem_fragmentation/db_bench_2
Run read tests with differnt cache setting:
T2_3:
MALLOC_CONF="prof:true,prof_stats:true" ./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=4000000000 -compressed_secondary_cache_size=4000000000 -use_compressed_secondary_cache -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=1000 -sine_b=0.000073 -sine_d=400000 -reads=80000000 -num=40000000 -key_size=32 -value_size=256 -use_existing_db=true -db=/mem_fragmentation/db_bench_2 --print_malloc_stats=true > ~/temp/mem_frag/jemalloc_stats_T2_3 -duration=1800 &
T2_4:
MALLOC_CONF="prof:true,prof_stats:true" ./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=2000000000 -compressed_secondary_cache_size=3000000000 -use_compressed_secondary_cache -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=1000 -sine_b=0.000073 -sine_d=400000 -reads=80000000 -num=40000000 -key_size=32 -value_size=256 -use_existing_db=true -db=/mem_fragmentation/db_bench_2 --print_malloc_stats=true > ~/temp/mem_frag/jemalloc_stats_T2_4 -duration=1800 &
For T2_3 and T2_4, I also conducted the tests before and after this PR. The following table show the important jemalloc stats.
| Test Name | T2_3 | T2_3 after mem defrag | T2_4 | T2_4 after mem defrag |
| - | - | - | - | - |
| allocated (MB) | 8425 | 8093 | 5426 | 5149 |
| available (MB) | 8489 | 8138 | 5435 | 5158 |
| external fragmentation rate | 0.008 | 0.0055 | 0.0017 | 0.0017 |
| resident (MB) | 8676 | 8392 | 5541 | 5321 |
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10287
Test Plan: Unit tests.
Reviewed By: anand1976
Differential Revision: D37743362
Pulled By: gitbw95
fbshipit-source-id: 0010c5af08addeacc5ebbc4ffe5be882fb1d38ad
Summary:
TL;DR: due to a recent change, if you drop a column family,
often that DB will no longer fsync after writing new SST files
to remaining or new column families, which could lead to data
loss on power loss.
More bug detail:
The intent of https://github.com/facebook/rocksdb/issues/10049 was to Close FSDirectory objects at
DB::Close time rather than waiting for DB object destruction.
Unfortunately, it also closes shared FSDirectory objects on
DropColumnFamily (& destroy remaining handles), which can lead
to use-after-Close on FSDirectory shared with remaining column
families. Those "uses" are only Fsyncs (or redundant Closes). In
the default Posix filesystem, an Fsync on a closed FSDirectory is a
quiet no-op. Consequently (under most configurations), if you drop
a column family, that DB will no longer fsync after writing new SST
files to column families sharing the same directory (true under most
configurations).
More fix detail:
Basically, this removes unnecessary Close ops on destroying
ColumnFamilyData. We let `shared_ptr` take care of calling the
destructor at the right time. If the intent was to require Close be
called before destroying FSDirectory, that was not made clear by the
author of FileSystem and was not at all enforced by https://github.com/facebook/rocksdb/issues/10049, which
could have added `assert(fd_ == -1)` to `~PosixDirectory()` but did
not. To keep this fix simple, we relax the unit test for https://github.com/facebook/rocksdb/issues/10049 to allow
timely destruction of FSDirectory to suffice as Close (in
CountedFileSystem). Added a TODO to revisit that.
Also in this PR:
* Added a TODO to share FSDirectory instances between DB and its column
families. (Already shared among column families.)
* Made DB::Close attempt to close all its open FSDirectory objects even
if there is a failure in closing one. Also code clean-up around this
logic.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10460
Test Plan:
add an assert to check for use-after-Close. With that
existing tests can detect the misuse. With fix, tests pass (except noted
relaxing of unit test for https://github.com/facebook/rocksdb/issues/10049)
Reviewed By: ajkr
Differential Revision: D38357922
Pulled By: pdillinger
fbshipit-source-id: d42079cadbedf0a969f03389bf586b3b4e1f9137
Summary:
During compaction, blobs are currently read using the default
`ReadOptions`, which has the `fill_cache` flag set to true. Earlier,
this didn't make any difference since we didn't have a blob cache;
however, now we have to explicitly set this flag to false to avoid
polluting the cache during compaction.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10457
Test Plan: `make check`
Reviewed By: riversand963
Differential Revision: D38333528
Pulled By: ltamasi
fbshipit-source-id: 5b4d49a1e39543bee73c7df2aa9194fb101875e2
Summary:
FileMetaData::[min|max]_timestamp are not currently being used or
tracked by RocksDB, even when user-defined timestamp is enabled. Each of
them is a std::string which can occupy 32 bytes. Remove them for now.
They may be added back when we have a pressing need for them. When we do
add them back, consider store them in a more compact way, e.g. one
boolean flag and a byte array of size 16.
Per file min/max timestamp bounds are available as table properties.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10443
Test Plan: make check
Reviewed By: pdillinger
Differential Revision: D38292275
Pulled By: riversand963
fbshipit-source-id: 841dc4e855ad8f8481c80cb020603de9607c9c94
Summary:
EnvLogger was built to replace PosixLogger that supports multiple Envs. Make FileSystem use EnvLogger by default, remove Posix FS specific implementation and remove PosixLogger code,
Some hacky changes are made to make sure iostats are not polluted by logging, in order to pass existing unit tests.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10436
Test Plan: Run db_bench and watch info log files.
Reviewed By: anand1976
Differential Revision: D38259855
fbshipit-source-id: 67d65874bfba7a33535b6d0dd0ed92cbbc9888b8
Summary:
The subcompaction logic currently picks file boundaries as subcompaction boundaries. This is not compatible with user-defined timestamps because of two issues.
Issue1: ReadOptions.iterate_lower_bound and ReadOptions.iterate_upper_bound contains timestamps which results in assertion failure as BlockBasedTableIterator expects bounds to be without timestamps. As result, because of wrong comparison end key is returned as user_key resulting in assertion failure.
Issue2: Since it might result in two keys that only differ by user timestamp getting processed by two different subcompactions (and thus two different CompactionIterator state machines), which in turn can cause data correction issues.
This PR provide support to reenable subcompactions with user-defined timestamps.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10344
Test Plan:
Added new unit test
- Without fix for Issue1 unit test MultipleSubCompactions fails with error:
```
db_with_timestamp_compaction_test: ./db/compaction/clipping_iterator.h:247: void rocksdb::ClippingIterat│
or::AssertBounds(): Assertion `!valid_ || !end_ || cmp_->Compare(key(), *end_) < 0' failed.
Received signal 6 (Aborted) │
#0 /usr/local/fbcode/platform009/lib/libc.so.6(gsignal+0x100) [0x7f8fbbbfe530] db_with_timestamp_compaction_test: ./db/compaction/clipping_iterator.h:247: void rocksdb::ClippingIterator::AssertBounds(): Assertion `!valid_ || !end_ || cmp_->Compare(key(), *end_) < 0' failed.
Aborted (core dumped)
```
Ran stress test
`make crash_test_with_ts -j32`
Reviewed By: riversand963
Differential Revision: D38220841
Pulled By: akankshamahajan15
fbshipit-source-id: 5d5cae2bd37fcaeba1e77fce0a69070ad4158ccb
Summary:
This PR changes the default value of
`CompactRangeOptions::exclusive_manual_compaction` from true to false so
manual `CompactRange()`s can run in parallel with other compactions. I
believe no artificial parallelism restriction is the intuitive behavior
so feel the old default value is a trap, which I have fallen into
several times, including yesterday.
`CompactRangeOptions::exclusive_manual_compaction == false` has been
used in both our correctness test and in production for years so should
be reasonably safe.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10317
Reviewed By: jay-zhuang
Differential Revision: D37659392
Pulled By: ajkr
fbshipit-source-id: 504915e978bbe300b79483d064070c75e93d91e5
Summary:
RocksDB's `Cache` abstraction currently supports two priority levels for items: high (used for frequently accessed/highly valuable SST metablocks like index/filter blocks) and low (used for SST data blocks). Blobs are typically lower-value targets for caching than data blocks, since 1) with BlobDB, data blocks containing blob references conceptually form an index structure which has to be consulted before we can read the blob value, and 2) cached blobs represent only a single key-value, while cached data blocks generally contain multiple KVs. Since we would like to make it possible to use the same backing cache for the block cache and the blob cache, it would make sense to add a new, lower-than-low cache priority level (bottom level) for blobs so data blocks are prioritized over them.
This task is a part of https://github.com/facebook/rocksdb/issues/10156
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10309
Reviewed By: ltamasi
Differential Revision: D38211655
Pulled By: gangliao
fbshipit-source-id: 65ef33337db4d85277cc6f9782d67c421ad71dd5
Summary:
If WAL compression is enabled, WAL fragment decompression results are concatenated together in `log::Reader::ReadPhysicalRecord()`. This PR adds checksum handshake to protect memory corruption during the copying process.
`checksum` is renamed to `record_checksum` in `ReadRecord()` to differentiate it from `checksum_` flag that specifies whether CRC32C checksum is verified.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10339
Test Plan: added checksum verification in log_test.cc, `make check -j32`.
Reviewed By: ajkr
Differential Revision: D37763734
Pulled By: cbi42
fbshipit-source-id: c4faa7c76b9ff1df35026edf31adfe4b47ae3154
Summary:
In hash linked list, with a bucket of only one record, following sequence can cause users to temporarily miss a record:
Thread 1: Fetch the structure bucket x points too, which would be a Node n1 for a key, with next pointer to be null
Thread 2: Insert a key to bucket x that is larger than the existing key. This will make n1->next points to a new node n2, and update bucket x to point to n1.
Thread 1: see n1->next is not null, so it thinks it is a header of linked list and ignore the key of n1.
Fix it by refetch structure that bucket x points to when it sees n1->next is not null. This should work because if n1->next is not null, bucket x should already point to a linked list or skip list header.
A related change is to revert th order of testing for linked list and skip list. This is because after refetching the bucket, it might end up with a skip list, rather than linked list.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10401
Test Plan: Run existing tests and make sure at least it doesn't regress.
Reviewed By: jay-zhuang
Differential Revision: D38064471
fbshipit-source-id: 142bb85e1546c803f47e3357aef3e76debccd8df
Summary:
Unit tests still haven't been fixed. Also need to add more tests. But I ran some simple fillrandom db_bench and the partitioning feels reasonable.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10393
Test Plan:
1. Make sure existing tests pass. This should cover some basic sub compaction logic to be correct and the partitioning result is reasonable;
2. Add a new unit test to ApproximateKeyAnchors()
3. Run some db_bench with max_subcompaction = 4 and watch the compaction is indeed partitioned evenly.
Reviewed By: jay-zhuang
Differential Revision: D38043783
fbshipit-source-id: 085008e0f85f9b7c5abff7800307618320efb19f
Summary:
## Problem Summary
RocksDB will acquire the global mutex of db instance for every time when user calls `Write`. When RocksDB schedules a lot of compaction jobs, it will compete the mutex with write thread and it will hurt the write performance.
## Problem Solution:
I want to use log_write_mutex to replace the global mutex in most case so that we do not acquire it in write-thread unless there is a write-stall event or a write-buffer-full event occur.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7516
Test Plan:
1. make check
2. CI
3. COMPILE_WITH_TSAN=1 make db_stress
make crash_test
make crash_test_with_multiops_wp_txn
make crash_test_with_multiops_wc_txn
make crash_test_with_atomic_flush
Reviewed By: siying
Differential Revision: D36908702
Pulled By: riversand963
fbshipit-source-id: 59b13881f4f5c0a58fd3ca79128a396d9cd98efe
Summary:
Made locking strict for all accesses of `GenericRateLimiter` internal state.
`SetBytesPerSecond()` was the main problem since it had no locking, while the two updates it makes need to be done as one atomic operation.
The test case, "ConfigOptionsTest.ConfiguringOptionsDoesNotRevertRateLimiterBandwidth", is for the issue fixed in https://github.com/facebook/rocksdb/issues/10378, but I forgot to include the test there.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10374
Reviewed By: pdillinger
Differential Revision: D37906367
Pulled By: ajkr
fbshipit-source-id: ccde620d2a7f96d1401bdafd2bdb685cbefbafa5
Summary:
To help service owners to manage their memory budget effectively, we have been working towards counting all major memory users inside RocksDB towards a single global memory limit (see e.g. https://github.com/facebook/rocksdb/wiki/Write-Buffer-Manager#cost-memory-used-in-memtable-to-block-cache). The global limit is specified by the capacity of the block-based table's block cache, and is technically implemented by inserting dummy entries ("reservations") into the block cache. The goal of this task is to support charging the memory usage of the new blob cache against this global memory limit when the backing cache of the blob cache and the block cache are different.
This PR is a part of https://github.com/facebook/rocksdb/issues/10156
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10321
Reviewed By: ltamasi
Differential Revision: D37913590
Pulled By: gangliao
fbshipit-source-id: eaacf23907f82dc7d18964a3f24d7039a2937a72
Summary:
(PR created for informational/testing purposes only.)
- Fixes lost dynamic updates to GenericRateLimiter bandwidth using `SetBytesPerSecond()`
- Benefit over #10374 is eliminating race conditions with Configurable framework.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10378
Reviewed By: pdillinger
Differential Revision: D37914865
fbshipit-source-id: d4f566d60ec9726d26932388c61671adf0ee0f30
Summary:
Many workloads have temporal locality, where recently written items are read back in a short period of time. When using remote file systems, this is inefficient since it involves network traffic and higher latencies. Because of this, we would like to support prepopulating the blob cache during flush.
This task is a part of https://github.com/facebook/rocksdb/issues/10156
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10298
Reviewed By: ltamasi
Differential Revision: D37908743
Pulled By: gangliao
fbshipit-source-id: 9feaed234bc719d38f0c02975c1ad19fa4bb37d1
Summary:
RocksDB supports a two-level cache hierarchy (see https://rocksdb.org/blog/2021/05/27/rocksdb-secondary-cache.html), where items evicted from the primary cache can be spilled over to the secondary cache, or items from the secondary cache can be promoted to the primary one. We have a CacheLib-based non-volatile secondary cache implementation that can be used to improve read latencies and reduce the amount of network bandwidth when using distributed file systems. In addition, we have recently implemented a compressed secondary cache that can be used as a replacement for the OS page cache when e.g. direct I/O is used. The goals of this task are to add support for using a secondary cache with the blob cache and to measure the potential performance gains using `db_bench`.
This task is a part of https://github.com/facebook/rocksdb/issues/10156
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10349
Reviewed By: ltamasi
Differential Revision: D37896773
Pulled By: gangliao
fbshipit-source-id: 7804619ce4a44b73d9e11ad606640f9385969c84
Summary:
Which will be used for tiered storage to preclude hot data from
compacting to the cold tier (the last level).
Internally, adding seqno to time mapping. A periodic_task is scheduled
to record the current_seqno -> current_time in certain cadence. When
memtable flush, the mapping informaiton is stored in sstable property.
During compaction, the mapping information are merged and get the
approximate time of sequence number, which is used to determine if a key
is recently inserted or not and preclude it from the last level if it's
recently inserted (within the `preclude_last_level_data_seconds`).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10338
Test Plan: CI
Reviewed By: siying
Differential Revision: D37810187
Pulled By: jay-zhuang
fbshipit-source-id: 6953be7a18a99de8b1cb3b162d712f79c2b4899f
Summary:
Some items are misplaced to 7.4 but they are unreleased.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10362
Reviewed By: jay-zhuang
Differential Revision: D37859426
fbshipit-source-id: e2ad099227309ed2e0f3ca450a9a43986d681c7c
Summary:
InternalKeyComparator is an internal class which is a simple wrapper of Comparator. https://github.com/facebook/rocksdb/pull/8336 made Comparator customizeable. As a side effect, internal key comparator was made configurable too. This introduces overhead to this simple wrapper. For example, every InternalKeyComparator will have an std::vector attached to it, which consumes memory and possible allocation overhead too.
We remove InternalKeyComparator from being customizable by making InternalKeyComparator not a subclass of Comparator.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10342
Test Plan: Run existing CI tests and make sure it doesn't fail
Reviewed By: riversand963
Differential Revision: D37771351
fbshipit-source-id: 917256ee04b2796ed82974549c734fb6c4d8ccee
Summary:
Enabled zstd checksum flag in StreamingCompress so that WAL (de)compreression is protected by a checksum per compression frame.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10319
Test Plan:
- `make check`
- WAL perf: average ops/sec over 10 runs is 161226 pre PR and 159635 post PR (1% drop).
```
sudo TEST_TMPDIR=/dev/shm/memtable_write ./db_bench_checksum -benchmarks=fillseq -max_write_buffer_number=100 -num=1000000 -min_write_buffer_number_to_merge=10 -wal_compression=zstd
```
Reviewed By: ajkr
Differential Revision: D37673311
Pulled By: cbi42
fbshipit-source-id: 9f34a3bfc2a82e5c80b1ec63bb339a7465108ec9
Summary:
ClockCache is still in experimental stage, and currently fails some pre-release fbcode tests. See https://www.internalfb.com/diff/D37772011. API calls to construct ClockCache are done via the function NewClockCache. For now, NewClockCache calls will return an LRUCache (with appropriate arguments), which is stable.
The idea that NewClockCache returns nullptr was also floated, but this would be interpreted as unsupported cache, and a default LRUCache would be constructed instead, potentially causing a performance regression that is harder to identify.
A new version of the NewClockCache function was created for our internal tests.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10351
Test Plan: ``make -j24 check`` and re-run the pre-release tests.
Reviewed By: pdillinger
Differential Revision: D37802685
Pulled By: guidotag
fbshipit-source-id: 0a8d10612ff21e576f7360cb13e20bc36e244972
Summary:
Add `ReserveThreads` and `ReleaseThreads` functions in thread pool to support reservation in for a specific thread pool. With this feature, a thread will be blocked if the number of waiting threads (noted by `num_waiting_threads_`) equals the number of reserved threads (noted by `reserved_threads_`), normally `reserved_threads_` is upper bounded by `num_waiting_threads_`; in rare cases (e.g. `SetBackgroundThreadsInternal` is called when some threads are already reserved), `num_waiting_threads_` can be less than `reserved_threads`.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10278
Test Plan: Add `ReserveThreads` unit test in `env_test`. Update the unit test `SimpleColumnFamilyInfoTest` in `thread_list_test` with adding `ReserveThreads` related assertions.
Reviewed By: hx235
Differential Revision: D37640946
Pulled By: littlepig2013
fbshipit-source-id: 4d691f6b9a433569f96ab52d52c3defe5b065367
Summary:
I noticed it would clean up some things to have Cache::Insert()
return our MemoryLimit Status instead of Incomplete for the case in
which the capacity limit is reached. I suspect this fixes some existing but
unknown bugs where this Incomplete could be confused with other uses
of Incomplete, especially no_io cases. This is the most suspicious case I
noticed, but was not able to reproduce a bug, in part because the existing
code is not covered by unit tests (FIXME added): 57adbf0e91/table/get_context.cc (L397)
I audited all the existing uses of IsIncomplete and updated those that
seemed relevant.
HISTORY updated with a clear warning to users of strict_capacity_limit=true
to update uses of `IsIncomplete()`
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10262
Test Plan: updated unit tests
Reviewed By: hx235
Differential Revision: D37473155
Pulled By: pdillinger
fbshipit-source-id: 4bd9d9353ccddfe286b03ebd0652df8ce20f99cb
Summary:
In leveled compaction, try to trivial move more than one files if possible, up to 4 files or max_compaction_bytes. This is to allow higher write throughput for some use cases where data is loaded in sequential order, where appying compaction results is the bottleneck.
When pick up a file to compact and it doesn't have overlapping files in the next level, try to expand to the next file if there is still no overlapping.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10190
Test Plan:
Add some unit tests.
For performance, Try to run
./db_bench_multi_move --benchmarks=fillseq --compression_type=lz4 --write_buffer_size=5000000 --num=100000000 --value_size=1000 -level_compaction_dynamic_level_bytes
Together with https://github.com/facebook/rocksdb/pull/10188 , stalling will be eliminated in this benchmark.
Reviewed By: jay-zhuang
Differential Revision: D37230647
fbshipit-source-id: 42b260f545c46abc5d90335ac2bbfcd09602b549
Summary:
If dbname and db_log_dir are at different filesystems (one
local and one remote), creation of dbname will fail because that path
doesn't exist wrt to db_log_dir.
This patch will ignore the error returned on creation of dbname. If they
are on same filesystem, db_log_dir creation will automatically return
the error in case there is any error in creation of dbname.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10292
Test Plan: Existing unit tests
Reviewed By: riversand963
Differential Revision: D37567773
Pulled By: akankshamahajan15
fbshipit-source-id: 005d28c536208d4c126c8cb8e196d1d85b881100
Summary:
In leveled compaction, L0->L1 trivial move will allow more than one file to be moved in one compaction. This would allow L0 files to be moved down faster when data is loaded in sequential order, making slowdown or stop condition harder to hit. Also seek L0->L1 trivial move when only some files qualify.
1. We always try to find L0->L1 trivial move from the oldest files. Keep including newer files, until adding a new file won't trigger a trivial move
2. Modify the trivial move condition so that this compaction would be tagged as trivial move.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10188
Test Plan:
See throughput improvements with db_bench with fast fillseq benchmark and small L0 files:
./db_bench_l0_move --benchmarks=fillseq --compression_type=lz4 --write_buffer_size=5000000 --num=100000000 --value_size=1000 -level_compaction_dynamic_level_bytes
The throughput improved by about 50%. Stalling still happens though.
Reviewed By: jay-zhuang
Differential Revision: D37224743
fbshipit-source-id: 8958d97f22e12bdfc14d2e85930f6fa0070e9659
Summary:
The current level targets for dynamical leveling has a problem: the target level size will dramatically change after a L0->L1 compaction. When there are many L0 bytes, lower level compactions are delayed, but they will be resumed after the L0->L1 compaction finishes, so the expected write amplification benefits might not be realized. The proposal here is to revert the level targetting size, but instead relying on adjusting score for each level to prioritize levels that need to compact most.
Basic idea:
(1) target level size isn't adjusted, but score is adjusted. The reasoning is that with parallel compactions, holding compactions from happening might not be desirable, but we would like the compactions are scheduled from the level we feel most needed. For example, if we have a extra-large L2, we would like all compactions are scheduled for L2->L3 compactions, rather than L4->L5. This gets complicated when a large L0->L1 compaction is going on. Should we compact L2->L3 or L4->L5. So the proposal for that is:
(2) the score is calculated by actual level size / (target size + estimated upper bytes coming down). The reasoning is that if we have a large amount of pending L0/L1 bytes coming down, compacting L2->L3 might be more expensive, as when the L0 bytes are compacted down to L2, the actual L2->L3 fanout would change dramatically. On the other hand, when the amount of bytes coming down to L5, the impacts to L5->L6 fanout are much less. So when calculating target score, we can adjust it by adding estimated downward bytes to the target level size.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10057
Test Plan: Repurpose tests VersionStorageInfoTest.MaxBytesForLevelDynamicWithLargeL0_* tests to cover this scenario.
Reviewed By: ajkr
Differential Revision: D37539742
fbshipit-source-id: 9c154cbfe92023f918cf5d80875d8776ad4831a4
Summary:
Currently, when installing a new super version, when stalling condition triggers, we compare estimated compaction bytes to previously, and if the new value is larger or equal to the previous one, we reduce the slowdown write rate. However, if concurrent compactions happen, the same value might be used. The result is that, although some compactions reduce estimated compaction bytes, we treat them as a signal for further slowing down. In some cases, it causes slowdown rate drops all the way to the minimum, far lower than needed.
Fix the bug by not triggering a re-calculation if a new super version doesn't have Version or a memtable change. With this fix, number of compaction finishes are still undercounted in this algorithm, but it is still better than the current bug where they are negatively counted.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10270
Test Plan: Run a benchmark where the slowdown rate is dropped to minimal unnessarily and see it is back to a normal value.
Reviewed By: ajkr
Differential Revision: D37497327
fbshipit-source-id: 9bca961cc38fed965c3af0fa6c9ca0efaa7637c4
Summary:
Resolves https://github.com/facebook/rocksdb/issues/9761
With this PR, applications can create an iterator with the following
```cpp
ReadOptions read_opts;
read_opts.timestamp = &ts_ub;
read_opts.iter_start_ts = &ts_lb;
auto* it = db->NewIterator(read_opts);
it->SeekToLast();
// or it->SeekForPrev("foo");
it->Prev();
...
```
The application can access different versions of the same user key via `key()`, `value()`, and `timestamp()`.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10200
Test Plan: make check
Reviewed By: ltamasi
Differential Revision: D37258074
Pulled By: riversand963
fbshipit-source-id: 3f0b866ade50dcff7ef60d506397a9dd6ec91565
Summary:
Currently SortFileByOverlappingRatio() is O(nlogn). It is usually OK but When there are a lot of files in an LSM-tree, SortFileByOverlappingRatio() can take non-trivial amount of time. The problem is severe when the user is loading keys in sorted order, where compaction is only trivial move and this operation becomes the bottleneck and limit the total throughput. This commit makes SortFileByOverlappingRatio() only find the top 50 files based on score. 50 files are usually enough for the parallel compactions needed for the level, and in case it is not enough, we would fall back to random, which should be acceptable.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10161
Test Plan:
Run a fillseq that generates a lot of files, and observe throughput improved (although stall is not yet eliminated). The command ran:
TEST_TMPDIR=/dev/shm/ ./db_bench_sort --benchmarks=fillseq --compression_type=lz4 --write_buffer_size=5000000 --num=100000000 --value_size=1000
The throughput improved by 11%.
Reviewed By: jay-zhuang
Differential Revision: D37129469
fbshipit-source-id: 492da2ef5bfc7cdd6daa3986b50d2ff91f88542d
Summary:
Resolves https://github.com/facebook/rocksdb/issues/10129
I extracted this fix from https://github.com/facebook/rocksdb/issues/7516 since it's also already a bug in main branch, and we want to
separate it from the main part of the PR.
There can be a race condition between two threads. Thread 1 executes
`DBImpl::FindObsoleteFiles()` while thread 2 executes `GetSortedWals()`.
```
Time thread 1 thread 2
| mutex_.lock
| read disable_delete_obsolete_files_
| ...
| wait on log_sync_cv and release mutex_
| mutex_.lock
| ++disable_delete_obsolete_files_
| mutex_.unlock
| mutex_.lock
| while (pending_purge_obsolete_files > 0) { bg_cv.wait;}
| wake up with mutex_ locked
| compute WALs tracked by MANIFEST
| mutex_.unlock
| wake up with mutex_ locked
| ++pending_purge_obsolete_files_
| mutex_.unlock
|
| delete obsolete WAL
| WAL missing but tracked in MANIFEST.
V
```
The fix proposed eliminates the possibility of the above by increasing
`pending_purge_obsolete_files_` before `FindObsoleteFiles()` can possibly release the mutex.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10187
Test Plan: make check
Reviewed By: ltamasi
Differential Revision: D37214235
Pulled By: riversand963
fbshipit-source-id: 556ab1b58ae6d19150169dfac4db08195c797184
Summary:
Add suggest_compact_range() and suggest_compact_range_cf() to C API.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10175
Test Plan:
As verifying the result requires SyncPoint, which is not available in the c_test.c,
the test is currently done by invoking the functions and making sure it does not crash.
Reviewed By: jay-zhuang
Differential Revision: D37305191
Pulled By: ajkr
fbshipit-source-id: 0fe257b45914f6c9aeb985d8b1820dafc57a20db
Summary:
**Summary**
Make the mempurge option flag a Mutable Column Family option flag. Therefore, the mempurge feature can be dynamically toggled.
**Motivation**
RocksDB users prefer having the ability to switch features on and off without having to close and reopen the DB. This is particularly important if the feature causes issues and needs to be turned off. Dynamically changing a DB option flag does not seem currently possible.
Moreover, with this new change, the MemPurge feature can be toggled on or off independently between column families, which we see as a major improvement.
**Content of this PR**
This PR includes removal of the `experimental_mempurge_threshold` flag as a DB option flag, and its re-introduction as a `MutableCFOption` flag. I updated the code to handle dynamic changes of the flag (in particular inside the `FlushJob` file). Additionally, this PR includes a new test to demonstrate the capacity of the code to toggle the MemPurge feature on and off, as well as the addition in the `db_stress` module of 2 different mempurge threshold values (0.0 and 1.0) that can be randomly changed with the `set_option_one_in` flag. This is useful to stress test the dynamic changes.
**Benchmarking**
I will add numbers to prove that there is no performance impact within the next 12 hours.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10011
Reviewed By: pdillinger
Differential Revision: D36462357
Pulled By: bjlemaire
fbshipit-source-id: 5e3d63bdadf085c0572ecc2349e7dd9729ce1802
Levi Tamasi