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18 Commits (99d86252b6514d0fe3b848bd39bda94642c14faf)
| Author | SHA1 | Message | Date |
|---|---|---|---|
Peter Dillinger
|
68a9c186d0 |
FilterPolicy API changes for 7.0 (#9501)
Summary: * Inefficient block-based filter is no longer customizable in the public API, though (for now) can still be enabled. * Removed deprecated FilterPolicy::CreateFilter() and FilterPolicy::KeyMayMatch() * Removed `rocksdb_filterpolicy_create()` from C API * Change meaning of nullptr return from GetBuilderWithContext() from "use block-based filter" to "generate no filter in this case." This is a cleaner solution to the proposal in https://github.com/facebook/rocksdb/issues/8250. * Also, when user specifies bits_per_key < 0.5, we now round this down to "no filter" because we expect a filter with >= 80% FP rate is unlikely to be worth the CPU cost of accessing it (esp with cache_index_and_filter_blocks=1 or partition_filters=1). * bits_per_key >= 0.5 and < 1.0 is still rounded up to 1.0 (for 62% FP rate) * This also gives us some support for configuring filters from OPTIONS file as currently saved: `filter_policy=rocksdb.BuiltinBloomFilter`. Opening from such an options file will enable reading filters (an improvement) but not writing new ones. (See Customizable follow-up below.) * Also removed deprecated functions * FilterBitsBuilder::CalculateNumEntry() * FilterPolicy::GetFilterBitsBuilder() * NewExperimentalRibbonFilterPolicy() * Remove default implementations of * FilterBitsBuilder::EstimateEntriesAdded() * FilterBitsBuilder::ApproximateNumEntries() * FilterPolicy::GetBuilderWithContext() * Remove support for "filter_policy=experimental_ribbon" configuration string. * Allow "filter_policy=bloomfilter:n" without bool to discourage use of block-based filter. Some pieces for https://github.com/facebook/rocksdb/issues/9389 Likely follow-up (later PRs): * Refactoring toward FilterPolicy Customizable, so that we can generate filters with same configuration as before when configuring from options file. * Remove support for user enabling block-based filter (ignore `bool use_block_based_builder`) * Some months after this change, we could even remove read support for block-based filter, because it is not critical to DB data preservation. * Make FilterBitsBuilder::FinishV2 to avoid `using FilterBitsBuilder::Finish` mess and add support for specifying a MemoryAllocator (for cache warming) Pull Request resolved: https://github.com/facebook/rocksdb/pull/9501 Test Plan: A number of obsolete tests deleted and new tests or test cases added or updated. Reviewed By: hx235 Differential Revision: D34008011 Pulled By: pdillinger fbshipit-source-id: a39a720457c354e00d5b59166b686f7f59e392aa |
2 years ago |
|
Hui Xiao
|
920386f2b7 |
Detect (new) Bloom/Ribbon Filter construction corruption (#9342)
Summary: Note: rebase on and merge after https://github.com/facebook/rocksdb/pull/9349, https://github.com/facebook/rocksdb/pull/9345, (optional) https://github.com/facebook/rocksdb/pull/9393 **Context:** (Quoted from pdillinger) Layers of information during new Bloom/Ribbon Filter construction in building block-based tables includes the following: a) set of keys to add to filter b) set of hashes to add to filter (64-bit hash applied to each key) c) set of Bloom indices to set in filter, with duplicates d) set of Bloom indices to set in filter, deduplicated e) final filter and its checksum This PR aims to detect corruption (e.g, unexpected hardware/software corruption on data structures residing in the memory for a long time) from b) to e) and leave a) as future works for application level. - b)'s corruption is detected by verifying the xor checksum of the hash entries calculated as the entries accumulate before being added to the filter. (i.e, `XXPH3FilterBitsBuilder::MaybeVerifyHashEntriesChecksum()`) - c) - e)'s corruption is detected by verifying the hash entries indeed exists in the constructed filter by re-querying these hash entries in the filter (i.e, `FilterBitsBuilder::MaybePostVerify()`) after computing the block checksum (except for PartitionFilter, which is done right after each `FilterBitsBuilder::Finish` for impl simplicity - see code comment for more). For this stage of detection, we assume hash entries are not corrupted after checking on b) since the time interval from b) to c) is relatively short IMO. Option to enable this feature of detection is `BlockBasedTableOptions::detect_filter_construct_corruption` which is false by default. **Summary:** - Implemented new functions `XXPH3FilterBitsBuilder::MaybeVerifyHashEntriesChecksum()` and `FilterBitsBuilder::MaybePostVerify()` - Ensured hash entries, final filter and banding and their [cache reservation ](https://github.com/facebook/rocksdb/issues/9073) are released properly despite corruption - See [Filter.construction.artifacts.release.point.pdf ](https://github.com/facebook/rocksdb/files/7923487/Design.Filter.construction.artifacts.release.point.pdf) for high-level design - Bundled and refactored hash entries's related artifact in XXPH3FilterBitsBuilder into `HashEntriesInfo` for better control on lifetime of these artifact during `SwapEntires`, `ResetEntries` - Ensured RocksDB block-based table builder calls `FilterBitsBuilder::MaybePostVerify()` after constructing the filter by `FilterBitsBuilder::Finish()` - When encountering such filter construction corruption, stop writing the filter content to files and mark such a block-based table building non-ok by storing the corruption status in the builder. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9342 Test Plan: - Added new unit test `DBFilterConstructionCorruptionTestWithParam.DetectCorruption` - Included this new feature in `DBFilterConstructionReserveMemoryTestWithParam.ReserveMemory` as this feature heavily touch ReserveMemory's impl - For fallback case, I run `./filter_bench -impl=3 -detect_filter_construct_corruption=true -reserve_table_builder_memory=true -strict_capacity_limit=true -quick -runs 10 | grep 'Build avg'` to make sure nothing break. - Added to `filter_bench`: increased filter construction time by **30%**, mostly by `MaybePostVerify()` - FastLocalBloom - Before change: `./filter_bench -impl=2 -quick -runs 10 | grep 'Build avg'`: **28.86643s** - After change: - `./filter_bench -impl=2 -detect_filter_construct_corruption=false -quick -runs 10 | grep 'Build avg'` (expect a tiny increase due to MaybePostVerify is always called regardless): **27.6644s (-4% perf improvement might be due to now we don't drop bloom hash entry in `AddAllEntries` along iteration but in bulk later, same with the bypassing-MaybePostVerify case below)** - `./filter_bench -impl=2 -detect_filter_construct_corruption=true -quick -runs 10 | grep 'Build avg'` (expect acceptable increase): **34.41159s (+20%)** - `./filter_bench -impl=2 -detect_filter_construct_corruption=true -quick -runs 10 | grep 'Build avg'` (by-passing MaybePostVerify, expect minor increase): **27.13431s (-6%)** - Standard128Ribbon - Before change: `./filter_bench -impl=3 -quick -runs 10 | grep 'Build avg'`: **122.5384s** - After change: - `./filter_bench -impl=3 -detect_filter_construct_corruption=false -quick -runs 10 | grep 'Build avg'` (expect a tiny increase due to MaybePostVerify is always called regardless - verified by removing MaybePostVerify under this case and found only +-1ns difference): **124.3588s (+2%)** - `./filter_bench -impl=3 -detect_filter_construct_corruption=true -quick -runs 10 | grep 'Build avg'`(expect acceptable increase): **159.4946s (+30%)** - `./filter_bench -impl=3 -detect_filter_construct_corruption=true -quick -runs 10 | grep 'Build avg'`(by-passing MaybePostVerify, expect minor increase) : **125.258s (+2%)** - Added to `db_stress`: `make crash_test`, `./db_stress --detect_filter_construct_corruption=true` - Manually smoke-tested: manually corrupted the filter construction in some db level tests with basic PUT and background flush. As expected, the error did get returned to users in subsequent PUT and Flush status. Reviewed By: pdillinger Differential Revision: D33746928 Pulled By: hx235 fbshipit-source-id: cb056426be5a7debc1cd16f23bc250f36a08ca57 |
2 years ago |
|
Peter Dillinger
|
2a383f21f4 |
Add Bloom/Ribbon hybrid API support (#8679)
Summary: This is essentially resurrection and fixing of the part of https://github.com/facebook/rocksdb/issues/8198 that was reverted in https://github.com/facebook/rocksdb/issues/8212, using data added in https://github.com/facebook/rocksdb/issues/8246. Basically, when configuring Ribbon filter, you can specify an LSM level before which Bloom will be used instead of Ribbon. But Bloom is only considered for Leveled and Universal compaction styles and file going into a known LSM level. This way, SST file writer, FIFO compaction, etc. use Ribbon filter as you would expect with NewRibbonFilterPolicy. So that this can be controlled with a single int value and so that flushes can be distinguished from intra-L0, we consider flush to go to level -1 for the purposes of this option. (Explained in API comment.) I also expect the most common and recommended Ribbon configuration to use Bloom during flush, to minimize slowing down writes and because according to my estimates, Ribbon only pays off if the structure lives in memory for more than an hour. Thus, I have changed the default for NewRibbonFilterPolicy to be this mild hybrid configuration. I don't really want to add something like NewHybridFilterPolicy because at least the mild hybrid configuration (Bloom for flush, Ribbon otherwise) should be considered a natural choice. C APIs also updated, but because they don't support overloading, rocksdb_filterpolicy_create_ribbon is kept pure ribbon for clarity and rocksdb_filterpolicy_create_ribbon_hybrid must be called for a hybrid configuration. While touching C API, I changed bits per key options from int to double. BuiltinFilterPolicy is needed so that LevelThresholdFilterPolicy doesn't inherit unused fields from BloomFilterPolicy. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8679 Test Plan: new + updated tests, including crash test Reviewed By: jay-zhuang Differential Revision: D30445797 Pulled By: pdillinger fbshipit-source-id: 6f5aeddfd6d79f7e55493b563c2d1d2d568892e1 |
3 years ago |
|
Peter Dillinger
|
95f6add746 |
Revert Ribbon starting level support from #8198 (#8212)
Summary:
This partially reverts commit
|
3 years ago |
|
Peter Dillinger
|
10196d7edc |
Ribbon long-term support, starting level support (#8198)
Summary: Since the Ribbon filter schema seems good (compatible back to 6.15.0), this change commits to long term support of the SST schema, even though we expect the API for enabling Ribbon to change (still called NewExperimentalRibbonFilterPolicy). This also adds support for "hybrid" configuration in which some levels use Bloom (higher levels, lower numbered) for speed and the rest use Ribbon (lower levels, higher numbered) for memory space efficiency. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8198 Test Plan: unit test added, crash test support Reviewed By: jay-zhuang Differential Revision: D27831232 Pulled By: pdillinger fbshipit-source-id: 90e528677689474d293ed6710b42ba89fbd5b5ab |
3 years ago |
|
Peter Dillinger
|
239d17a19c |
Support optimize_filters_for_memory for Ribbon filter (#7774)
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 |
4 years ago |
|
Peter Dillinger
|
003e72b201 |
Use size_t for filter APIs, protect against overflow (#7726)
Summary: Deprecate CalculateNumEntry and replace with ApproximateNumEntries (better name) using size_t instead of int and uint32_t, to minimize confusing casts and bad overflow behavior (possible though probably not realistic). Bloom sizes are now explicitly capped at max size supported by implementations: just under 4GiB for fv=5 Bloom, and just under 512MiB for fv<5 Legacy Bloom. This hardening could help to set up for fuzzing. Also, since RocksDB only uses this information as an approximation for trying to hit certain sizes for partitioned filters, it's more important that the function be reasonably fast than for it to be completely accurate. It's hard enough to be 100% accurate for Ribbon (currently reversing CalculateSpace) that adding optimize_filters_for_memory into the mix is just not worth trying to be 100% accurate for num entries for bytes. Also: - Cleaned up filter_policy.h to remove MSVC warning handling and potentially unsafe use of exception for "not implemented" - Correct the number of entries limit beyond which current Ribbon implementation falls back on Bloom instead. - Consistently use "num_entries" rather than "num_entry" - Remove LegacyBloomBitsBuilder::CalculateNumEntry as it's essentially obsolete from general implementation BuiltinFilterBitsBuilder::CalculateNumEntries. - Fix filter_bench to skip some tests that don't make sense when only one or a small number of filters has been generated. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7726 Test Plan: expanded existing unit tests for CalculateSpace / ApproximateNumEntries. Also manually used filter_bench to verify Legacy and fv=5 Bloom size caps work (much too expensive for unit test). Note that the actual bits per key is below requested due to space cap. $ ./filter_bench -impl=0 -bits_per_key=20 -average_keys_per_filter=256000000 -vary_key_count_ratio=0 -m_keys_total_max=256 -allow_bad_fp_rate ... Total size (MB): 511.992 Bits/key stored: 16.777 ... $ ./filter_bench -impl=2 -bits_per_key=20 -average_keys_per_filter=2000000000 -vary_key_count_ratio=0 -m_keys_total_max=2000 ... Total size (MB): 4096 Bits/key stored: 17.1799 ... $ Reviewed By: jay-zhuang Differential Revision: D25239800 Pulled By: pdillinger fbshipit-source-id: f94e6d065efd31e05ec630ae1a82e6400d8390c4 |
4 years ago |
|
Peter Dillinger
|
60af964372 |
Experimental (production candidate) SST schema for Ribbon filter (#7658)
Summary:
Added experimental public API for Ribbon filter:
NewExperimentalRibbonFilterPolicy(). This experimental API will
take a "Bloom equivalent" bits per key, and configure the Ribbon
filter for the same FP rate as Bloom would have but ~30% space
savings. (Note: optimize_filters_for_memory is not yet implemented
for Ribbon filter. That can be added with no effect on schema.)
Internally, the Ribbon filter is configured using a "one_in_fp_rate"
value, which is 1 over desired FP rate. For example, use 100 for 1%
FP rate. I'm expecting this will be used in the future for configuring
Bloom-like filters, as I expect people to more commonly hold constant
the filter accuracy and change the space vs. time trade-off, rather than
hold constant the space (per key) and change the accuracy vs. time
trade-off, though we might make that available.
### Benchmarking
```
$ ./filter_bench -impl=2 -quick -m_keys_total_max=200 -average_keys_per_filter=100000 -net_includes_hashing
Building...
Build avg ns/key: 34.1341
Number of filters: 1993
Total size (MB): 238.488
Reported total allocated memory (MB): 262.875
Reported internal fragmentation: 10.2255%
Bits/key stored: 10.0029
----------------------------
Mixed inside/outside queries...
Single filter net ns/op: 18.7508
Random filter net ns/op: 258.246
Average FP rate %: 0.968672
----------------------------
Done. (For more info, run with -legend or -help.)
$ ./filter_bench -impl=3 -quick -m_keys_total_max=200 -average_keys_per_filter=100000 -net_includes_hashing
Building...
Build avg ns/key: 130.851
Number of filters: 1993
Total size (MB): 168.166
Reported total allocated memory (MB): 183.211
Reported internal fragmentation: 8.94626%
Bits/key stored: 7.05341
----------------------------
Mixed inside/outside queries...
Single filter net ns/op: 58.4523
Random filter net ns/op: 363.717
Average FP rate %: 0.952978
----------------------------
Done. (For more info, run with -legend or -help.)
```
168.166 / 238.488 = 0.705 -> 29.5% space reduction
130.851 / 34.1341 = 3.83x construction time for this Ribbon filter vs. lastest Bloom filter (could make that as little as about 2.5x for less space reduction)
### Working around a hashing "flaw"
bloom_test discovered a flaw in the simple hashing applied in
StandardHasher when num_starts == 1 (num_slots == 128), showing an
excessively high FP rate. The problem is that when many entries, on the
order of number of hash bits or kCoeffBits, are associated with the same
start location, the correlation between the CoeffRow and ResultRow (for
efficiency) can lead to a solution that is "universal," or nearly so, for
entries mapping to that start location. (Normally, variance in start
location breaks the effective association between CoeffRow and
ResultRow; the same value for CoeffRow is effectively different if start
locations are different.) Without kUseSmash and with num_starts > 1 (thus
num_starts ~= num_slots), this flaw should be completely irrelevant. Even
with 10M slots, the chances of a single slot having just 16 (or more)
entries map to it--not enough to cause an FP problem, which would be local
to that slot if it happened--is 1 in millions. This spreadsheet formula
shows that: =1/(10000000*(1 - POISSON(15, 1, TRUE)))
As kUseSmash==false (the setting for Standard128RibbonBitsBuilder) is
intended for CPU efficiency of filters with many more entries/slots than
kCoeffBits, a very reasonable work-around is to disallow num_starts==1
when !kUseSmash, by making the minimum non-zero number of slots
2*kCoeffBits. This is the work-around I've applied. This also means that
the new Ribbon filter schema (Standard128RibbonBitsBuilder) is not
space-efficient for less than a few hundred entries. Because of this, I
have made it fall back on constructing a Bloom filter, under existing
schema, when that is more space efficient for small filters. (We can
change this in the future if we want.)
TODO: better unit tests for this case in ribbon_test, and probably
update StandardHasher for kUseSmash case so that it can scale nicely to
small filters.
### Other related changes
* Add Ribbon filter to stress/crash test
* Add Ribbon filter to filter_bench as -impl=3
* Add option string support, as in "filter_policy=experimental_ribbon:5.678;"
where 5.678 is the Bloom equivalent bits per key.
* Rename internal mode BloomFilterPolicy::kAuto to kAutoBloom
* Add a general BuiltinFilterBitsBuilder::CalculateNumEntry based on
binary searching CalculateSpace (inefficient), so that subclasses
(especially experimental ones) don't have to provide an efficient
implementation inverting CalculateSpace.
* Minor refactor FastLocalBloomBitsBuilder for new base class
XXH3pFilterBitsBuilder shared with new Standard128RibbonBitsBuilder,
which allows the latter to fall back on Bloom construction in some
extreme cases.
* Mostly updated bloom_test for Ribbon filter, though a test like
FullBloomTest::Schema is a next TODO to ensure schema stability
(in case this becomes production-ready schema as it is).
* Add some APIs to ribbon_impl.h for configuring Ribbon filters.
Although these are reasonably covered by bloom_test, TODO more unit
tests in ribbon_test
* Added a "tool" FindOccupancyForSuccessRate to ribbon_test to get data
for constructing the linear approximations in GetNumSlotsFor95PctSuccess.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/7658
Test Plan:
Some unit tests updated but other testing is left TODO. This
is considered experimental but laying down schema compatibility as early
as possible in case it proves production-quality. Also tested in
stress/crash test.
Reviewed By: jay-zhuang
Differential Revision: D24899349
Pulled By: pdillinger
fbshipit-source-id: 9715f3e6371c959d923aea8077c9423c7a9f82b8
|
4 years ago |
|
Peter Dillinger
|
5b2bbacb6f |
Minimize memory internal fragmentation for Bloom filters (#6427)
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
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4 years ago |
|
sdong
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fdf882ded2 |
Replace namespace name "rocksdb" with ROCKSDB_NAMESPACE (#6433)
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 |
4 years ago |
|
Peter Dillinger
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8aa99fc71e |
Warn on excessive keys for legacy Bloom filter with 32-bit hash (#6317)
Summary: With many millions of keys, the old Bloom filter implementation for the block-based table (format_version <= 4) would have excessive FP rate due to the limitations of feeding the Bloom filter with a 32-bit hash. This change computes an estimated inflated FP rate due to this effect and warns in the log whenever an SST filter is constructed (almost certainly a "full" not "partitioned" filter) that exceeds 1.5x FP rate due to this effect. The detailed condition is only checked if 3 million keys or more have been added to a filter, as this should be a lower bound for common bits/key settings (< 20). Recommended remedies include smaller SST file size, using format_version >= 5 (for new Bloom filter), or using partitioned filters. This does not change behavior other than generating warnings for some constructed filters using the old implementation. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6317 Test Plan: Example with warning, 15M keys @ 15 bits / key: (working_mem_size_mb is just to stop after building one filter if it's large) $ ./filter_bench -quick -impl=0 -working_mem_size_mb=1 -bits_per_key=15 -average_keys_per_filter=15000000 2>&1 | grep 'FP rate' [WARN] [/block_based/filter_policy.cc:292] Using legacy SST/BBT Bloom filter with excessive key count (15.0M @ 15bpk), causing estimated 1.8x higher filter FP rate. Consider using new Bloom with format_version>=5, smaller SST file size, or partitioned filters. Predicted FP rate %: 0.766702 Average FP rate %: 0.66846 Example without warning (150K keys): $ ./filter_bench -quick -impl=0 -working_mem_size_mb=1 -bits_per_key=15 -average_keys_per_filter=150000 2>&1 | grep 'FP rate' Predicted FP rate %: 0.422857 Average FP rate %: 0.379301 $ With more samples at 15 bits/key: 150K keys -> no warning; actual: 0.379% FP rate (baseline) 1M keys -> no warning; actual: 0.396% FP rate, 1.045x 9M keys -> no warning; actual: 0.563% FP rate, 1.485x 10M keys -> warning (1.5x); actual: 0.564% FP rate, 1.488x 15M keys -> warning (1.8x); actual: 0.668% FP rate, 1.76x 25M keys -> warning (2.4x); actual: 0.880% FP rate, 2.32x At 10 bits/key: 150K keys -> no warning; actual: 1.17% FP rate (baseline) 1M keys -> no warning; actual: 1.16% FP rate 10M keys -> no warning; actual: 1.32% FP rate, 1.13x 25M keys -> no warning; actual: 1.63% FP rate, 1.39x 35M keys -> warning (1.6x); actual: 1.81% FP rate, 1.55x At 5 bits/key: 150K keys -> no warning; actual: 9.32% FP rate (baseline) 25M keys -> no warning; actual: 9.62% FP rate, 1.03x 200M keys -> no warning; actual: 12.2% FP rate, 1.31x 250M keys -> warning (1.5x); actual: 12.8% FP rate, 1.37x 300M keys -> warning (1.6x); actual: 13.4% FP rate, 1.43x The reason for the modest inaccuracy at low bits/key is that the assumption of independence between a collision between 32-hash values feeding the filter and an FP in the filter is not quite true for implementations using "simple" logic to compute indices from the stock hash result. There's math on this in my dissertation, but I don't think it's worth the effort just for these extreme cases (> 100 million keys and low-ish bits/key). Differential Revision: D19471715 Pulled By: pdillinger fbshipit-source-id: f80c96893a09bf1152630ff0b964e5cdd7e35c68 |
5 years ago |
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Peter Dillinger
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4b86fe1123 |
Log warning for high bits/key in legacy Bloom filter (#6312)
Summary: Help users that would benefit most from new Bloom filter implementation by logging a warning that recommends the using format_version >= 5. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6312 Test Plan: $ (for BPK in 10 13 14 19 20 50; do ./filter_bench -quick -impl=0 -bits_per_key=$BPK -m_queries=1 2>&1; done) | grep 'its/key' Bits/key actual: 10.0647 Bits/key actual: 13.0593 [WARN] [/block_based/filter_policy.cc:546] Using legacy Bloom filter with high (14) bits/key. Significant filter space and/or accuracy improvement is available with format_verion>=5. Bits/key actual: 14.0581 [WARN] [/block_based/filter_policy.cc:546] Using legacy Bloom filter with high (19) bits/key. Significant filter space and/or accuracy improvement is available with format_verion>=5. Bits/key actual: 19.0542 [WARN] [/block_based/filter_policy.cc:546] Using legacy Bloom filter with high (20) bits/key. Dramatic filter space and/or accuracy improvement is available with format_verion>=5. Bits/key actual: 20.0584 [WARN] [/block_based/filter_policy.cc:546] Using legacy Bloom filter with high (50) bits/key. Dramatic filter space and/or accuracy improvement is available with format_verion>=5. Bits/key actual: 50.0577 Differential Revision: D19457191 Pulled By: pdillinger fbshipit-source-id: 073d94cde5c70e03a160f953e1100c15ea83eda4 |
5 years ago |
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Peter Dillinger
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ca3b6c28c9 |
Expose and elaborate FilterBuildingContext (#6088)
Summary: This change enables custom implementations of FilterPolicy to wrap a variety of NewBloomFilterPolicy and select among them based on contextual information such as table level and compaction style. * Moves FilterBuildingContext to public API and elaborates it with more useful data. (It would be nice to put more general options-like data, but at the time this object is constructed, we are using internal APIs ImmutableCFOptions and MutableCFOptions and don't have easy access to ColumnFamilyOptions that I can tell.) * Renames BloomFilterPolicy::GetFilterBitsBuilderInternal to GetBuilderWithContext, because it's now public. * Plumbs through the table's "level_at_creation" for filter building context. * Simplified some tests by adding GetBuilder() to MockBlockBasedTableTester. * Adds test as DBBloomFilterTest.ContextCustomFilterPolicy, including sample wrapper class LevelAndStyleCustomFilterPolicy. * Fixes a cross-test bug in DBBloomFilterTest.OptimizeFiltersForHits where it does not reset perf context. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6088 Test Plan: make check, valgrind on db_bloom_filter_test Differential Revision: D18697817 Pulled By: pdillinger fbshipit-source-id: 5f987a2d7b07cc7a33670bc08ca6b4ca698c1cf4 |
5 years ago |
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Peter Dillinger
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57f3032285 |
Allow fractional bits/key in BloomFilterPolicy (#6092)
Summary: There's no technological impediment to allowing the Bloom filter bits/key to be non-integer (fractional/decimal) values, and it provides finer control over the memory vs. accuracy trade-off. This is especially handy in using the format_version=5 Bloom filter in place of the old one, because bits_per_key=9.55 provides the same accuracy as the old bits_per_key=10. This change not only requires refining the logic for choosing the best num_probes for a given bits/key setting, it revealed a flaw in that logic. As bits/key gets higher, the best num_probes for a cache-local Bloom filter is closer to bpk / 2 than to bpk * 0.69, the best choice for a standard Bloom filter. For example, at 16 bits per key, the best num_probes is 9 (FP rate = 0.0843%) not 11 (FP rate = 0.0884%). This change fixes and refines that logic (for the format_version=5 Bloom filter only, just in case) based on empirical tests to find accuracy inflection points between each num_probes. Although bits_per_key is now specified as a double, the new Bloom filter converts/rounds this to "millibits / key" for predictable/precise internal computations. Just in case of unforeseen compatibility issues, we round to the nearest whole number bits / key for the legacy Bloom filter, so as not to unlock new behaviors for it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6092 Test Plan: unit tests included Differential Revision: D18711313 Pulled By: pdillinger fbshipit-source-id: 1aa73295f152a995328cb846ef9157ae8a05522a |
5 years ago |
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Peter Dillinger
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f059c7d9b9 |
New Bloom filter implementation for full and partitioned filters (#6007)
Summary:
Adds an improved, replacement Bloom filter implementation (FastLocalBloom) for full and partitioned filters in the block-based table. This replacement is faster and more accurate, especially for high bits per key or millions of keys in a single filter.
Speed
The improved speed, at least on recent x86_64, comes from
* Using fastrange instead of modulo (%)
* Using our new hash function (XXH3 preview, added in a previous commit), which is much faster for large keys and only *slightly* slower on keys around 12 bytes if hashing the same size many thousands of times in a row.
* Optimizing the Bloom filter queries with AVX2 SIMD operations. (Added AVX2 to the USE_SSE=1 build.) Careful design was required to support (a) SIMD-optimized queries, (b) compatible non-SIMD code that's simple and efficient, (c) flexible choice of number of probes, and (d) essentially maximized accuracy for a cache-local Bloom filter. Probes are made eight at a time, so any number of probes up to 8 is the same speed, then up to 16, etc.
* Prefetching cache lines when building the filter. Although this optimization could be applied to the old structure as well, it seems to balance out the small added cost of accumulating 64 bit hashes for adding to the filter rather than 32 bit hashes.
Here's nominal speed data from filter_bench (200MB in filters, about 10k keys each, 10 bits filter data / key, 6 probes, avg key size 24 bytes, includes hashing time) on Skylake DE (relatively low clock speed):
$ ./filter_bench -quick -impl=2 -net_includes_hashing # New Bloom filter
Build avg ns/key: 47.7135
Mixed inside/outside queries...
Single filter net ns/op: 26.2825
Random filter net ns/op: 150.459
Average FP rate %: 0.954651
$ ./filter_bench -quick -impl=0 -net_includes_hashing # Old Bloom filter
Build avg ns/key: 47.2245
Mixed inside/outside queries...
Single filter net ns/op: 63.2978
Random filter net ns/op: 188.038
Average FP rate %: 1.13823
Similar build time but dramatically faster query times on hot data (63 ns to 26 ns), and somewhat faster on stale data (188 ns to 150 ns). Performance differences on batched and skewed query loads are between these extremes as expected.
The only other interesting thing about speed is "inside" (query key was added to filter) vs. "outside" (query key was not added to filter) query times. The non-SIMD implementations are substantially slower when most queries are "outside" vs. "inside". This goes against what one might expect or would have observed years ago, as "outside" queries only need about two probes on average, due to short-circuiting, while "inside" always have num_probes (say 6). The problem is probably the nastily unpredictable branch. The SIMD implementation has few branches (very predictable) and has pretty consistent running time regardless of query outcome.
Accuracy
The generally improved accuracy (re: Issue https://github.com/facebook/rocksdb/issues/5857) comes from a better design for probing indices
within a cache line (re: Issue https://github.com/facebook/rocksdb/issues/4120) and improved accuracy for millions of keys in a single filter from using a 64-bit hash function (XXH3p). Design details in code comments.
Accuracy data (generalizes, except old impl gets worse with millions of keys):
Memory bits per key: FP rate percent old impl -> FP rate percent new impl
6: 5.70953 -> 5.69888
8: 2.45766 -> 2.29709
10: 1.13977 -> 0.959254
12: 0.662498 -> 0.411593
16: 0.353023 -> 0.0873754
24: 0.261552 -> 0.0060971
50: 0.225453 -> ~0.00003 (less than 1 in a million queries are FP)
Fixes https://github.com/facebook/rocksdb/issues/5857
Fixes https://github.com/facebook/rocksdb/issues/4120
Unlike the old implementation, this implementation has a fixed cache line size (64 bytes). At 10 bits per key, the accuracy of this new implementation is very close to the old implementation with 128-byte cache line size. If there's sufficient demand, this implementation could be generalized.
Compatibility
Although old releases would see the new structure as corrupt filter data and read the table as if there's no filter, we've decided only to enable the new Bloom filter with new format_version=5. This provides a smooth path for automatic adoption over time, with an option for early opt-in.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6007
Test Plan: filter_bench has been used thoroughly to validate speed, accuracy, and correctness. Unit tests have been carefully updated to exercise new and old implementations, as well as the logic to select an implementation based on context (format_version).
Differential Revision: D18294749
Pulled By: pdillinger
fbshipit-source-id: d44c9db3696e4d0a17caaec47075b7755c262c5f
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5 years ago |
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Peter Dillinger
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685e895652 |
Prepare filter tests for more implementations (#5967)
Summary: This change sets up for alternate implementations underlying BloomFilterPolicy: * Refactor BloomFilterPolicy and expose in internal .h file so that it's easy to iterate over / select implementations for testing, regardless of what the best public interface will look like. Most notably updated db_bloom_filter_test to use this. * Hide FullFilterBitsBuilder from unit tests (alternate derived classes planned); expose the part important for testing (CalculateSpace), as abstract class BuiltinFilterBitsBuilder. (Also cleaned up internally exposed interface to CalculateSpace.) * Rename BloomTest -> BlockBasedBloomTest for clarity (despite ongoing confusion between block-based table and block-based filter) * Assert that block-based filter construction interface is only used on BloomFilterPolicy appropriately constructed. (A couple of tests updated to add ", true".) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5967 Test Plan: make check Differential Revision: D18138704 Pulled By: pdillinger fbshipit-source-id: 55ef9273423b0696309e251f50b8c1b5e9ec7597 |
5 years ago |
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Peter Dillinger
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013babc685 |
Clean up some filter tests and comments (#5960)
Summary: Some filtering tests were unfriendly to new implementations of FilterBitsBuilder because of dynamic_cast to FullFilterBitsBuilder. Most of those have now been cleaned up, worked around, or at least changed from crash on dynamic_cast failure to individual test failure. Also put some clarifying comments on filter-related APIs. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5960 Test Plan: make check Differential Revision: D18121223 Pulled By: pdillinger fbshipit-source-id: e83827d9d5d96315d96f8e25a99cd70f497d802c |
5 years ago |
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Peter Dillinger
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dd19014a7a |
FilterPolicy consolidation, part 1/2 (#5963)
Summary: The parts that are used to implement FilterPolicy / NewBloomFilterPolicy and not used other than for the block-based table should be consolidated under table/block_based/filter_policy*. I don't foresee sharing these APIs with e.g. the Plain Table because they don't expose hashes for reuse in indexing. This change is step 1 of 2: (a) mv table/full_filter_bits_builder.h to table/block_based/filter_policy_internal.h which I expect to expand soon to internally reveal more implementation details for testing. (b) consolidate eventual contents of table/block_based/filter_policy.cc in util/bloom.cc, which has the most elaborate revision history (see step 2 ...) Step 2 soon to follow: mv util/bloom.cc table/block_based/filter_policy.cc This gets its own PR so that git has the best chance of following the rename for blame purposes. Note that low-level shared implementation details of Bloom filters are in util/bloom_impl.h. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5963 Test Plan: make check Differential Revision: D18121199 Pulled By: pdillinger fbshipit-source-id: 8f21732c3d8909777e3240e4ac3123d73140326a |
5 years ago |
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Peter Dillinger
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68626249c3 |
Refactor/consolidate legacy Bloom implementation details (#5784)
Summary: Refactoring to consolidate implementation details of legacy Bloom filters. This helps to organize and document some related, obscure code. Also added make/cpp var TEST_CACHE_LINE_SIZE so that it's easy to compile and run unit tests for non-native cache line size. (Fixed a related test failure in db_properties_test.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5784 Test Plan: make check, including Recently added Bloom schema unit tests (in ./plain_table_db_test && ./bloom_test), and including with TEST_CACHE_LINE_SIZE=128U and TEST_CACHE_LINE_SIZE=256U. Tested the schema tests with temporary fault injection into new implementations. Some performance testing with modified unit tests suggest a small to moderate improvement in speed. Differential Revision: D17381384 Pulled By: pdillinger fbshipit-source-id: ee42586da996798910fc45ac0b6289147f16d8df |
5 years ago |
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Peter Dillinger
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d3a6726f02 |
Revert changes from PR#5784 accidentally in PR#5780 (#5810)
Summary: This will allow us to fix history by having the code changes for PR#5784 properly attributed to it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5810 Differential Revision: D17400231 Pulled By: pdillinger fbshipit-source-id: 2da8b1cdf2533cfedb35b5526eadefb38c291f09 |
5 years ago |
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Peter Dillinger
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aa2486b23c |
Refactor some confusing logic in PlainTableReader
Summary: Pull Request resolved: https://github.com/facebook/rocksdb/pull/5780 Test Plan: existing plain table unit test Differential Revision: D17368629 Pulled By: pdillinger fbshipit-source-id: f25409cdc2f39ebe8d5cbb599cf820270e6b5d26 |
5 years ago |
|
Shylock Hg
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9eb3e1f77d |
Use delete to disable automatic generated methods. (#5009)
Summary: Use delete to disable automatic generated methods instead of private, and put the constructor together for more clear.This modification cause the unused field warning, so add unused attribute to disable this warning. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5009 Differential Revision: D17288733 fbshipit-source-id: 8a767ce096f185f1db01bd28fc88fef1cdd921f3 |
5 years ago |
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Maysam Yabandeh
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bc0da4b512 |
Skip duplicate bloom keys when whole_key and prefix are mixed
Summary: Currently we rely on FilterBitsBuilder to skip the duplicate keys. It does that by comparing that hash of the key to the hash of the last added entry. This logic breaks however when we have whole_key_filtering mixed with prefix blooms as their addition to FilterBitsBuilder will be interleaved. The patch fixes that by comparing the last whole key and last prefix with the whole key and prefix of the new key respectively and skip the call to FilterBitsBuilder if it is a duplicate. Closes https://github.com/facebook/rocksdb/pull/3764 Differential Revision: D7744413 Pulled By: maysamyabandeh fbshipit-source-id: 15df73bbbafdfd754d4e1f42ea07f47b03bc5eb8 |
6 years ago |
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Siying Dong
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3c327ac2d0 |
Change RocksDB License
Summary: Closes https://github.com/facebook/rocksdb/pull/2589 Differential Revision: D5431502 Pulled By: siying fbshipit-source-id: 8ebf8c87883daa9daa54b2303d11ce01ab1f6f75 |
7 years ago |
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Maysam Yabandeh
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45b9bb0331 |
Cut filter partition based on metadata_block_size
Summary: Currently metadata_block_size controls only index partition size. With this patch a partition is cut after any of index or filter partitions reaches metadata_block_size. Closes https://github.com/facebook/rocksdb/pull/2452 Differential Revision: D5275651 Pulled By: maysamyabandeh fbshipit-source-id: 5057e4424b4c8902043782e6bf8c38f0c4f25160 |
7 years ago |