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Author SHA1 Message Date
Richard Barnes fc25ac0f3b Remove extra semi colon from internal_repo_rocksdb/repo/util/ribbon_impl.h (#12269) 
Summary:
Pull Request resolved: https://github.com/facebook/rocksdb/pull/12269

`-Wextra-semi` or `-Wextra-semi-stmt`

If the code compiles, this is safe to land.

Reviewed By: jaykorean

Differential Revision: D52969093

fbshipit-source-id: 0520085819fa785679c859b63b877931d3f71f2c
 2024-01-24 08:20:50 -08:00
Hui Xiao 74544d582f Account Bloom/Ribbon filter construction memory in global memory limit (#9073) 
Summary:
Note: This PR is the 4th part of a bigger PR stack (https://github.com/facebook/rocksdb/pull/9073) and will rebase/merge only after the first three PRs (https://github.com/facebook/rocksdb/pull/9070, https://github.com/facebook/rocksdb/pull/9071, https://github.com/facebook/rocksdb/pull/9130) merge.

**Context:**
Similar to https://github.com/facebook/rocksdb/pull/8428, this PR is to track memory usage during (new) Bloom Filter (i.e,FastLocalBloom) and Ribbon Filter (i.e, Ribbon128) construction, moving toward the goal of [single global memory limit using block cache capacity](https://github.com/facebook/rocksdb/wiki/Projects-Being-Developed#improving-memory-efficiency). It also constrains the size of the banding portion of Ribbon Filter during construction by falling back to Bloom Filter if that banding is, at some point, larger than the available space in the cache under `LRUCacheOptions::strict_capacity_limit=true`.

The option to turn on this feature is `BlockBasedTableOptions::reserve_table_builder_memory = true` which by default is set to `false`. We [decided](https://github.com/facebook/rocksdb/pull/9073#discussion_r741548409) not to have separate option for separate memory user in table building therefore their memory accounting are all bundled under one general option.

**Summary:**
- Reserved/released cache for creation/destruction of three main memory users with the passed-in `FilterBuildingContext::cache_res_mgr` during filter construction:
   - hash entries (i.e`hash_entries`.size(), we bucket-charge hash entries during insertion for performance),
   - banding (Ribbon Filter only, `bytes_coeff_rows` +`bytes_result_rows` + `bytes_backtrack`),
   - final filter (i.e, `mutable_buf`'s size).
      - Implementation details: in order to use `CacheReservationManager::CacheReservationHandle` to account final filter's memory, we have to store the `CacheReservationManager` object and `CacheReservationHandle` for final filter in `XXPH3BitsFilterBuilder` as well as  explicitly delete the filter bits builder when done with the final filter in block based table.
- Added option fo run `filter_bench` with this memory reservation feature

Pull Request resolved: https://github.com/facebook/rocksdb/pull/9073

Test Plan:
- Added new tests in `db_bloom_filter_test` to verify filter construction peak cache reservation under combination of  `BlockBasedTable::Rep::FilterType` (e.g, `kFullFilter`, `kPartitionedFilter`), `BloomFilterPolicy::Mode`(e.g, `kFastLocalBloom`, `kStandard128Ribbon`, `kDeprecatedBlock`) and `BlockBasedTableOptions::reserve_table_builder_memory`
  - To address the concern for slow test: tests with memory reservation under `kFullFilter` + `kStandard128Ribbon` and `kPartitionedFilter` take around **3000 - 6000 ms** and others take around **1500 - 2000 ms**, in total adding **20000 - 25000 ms** to the test suit running locally
- Added new test in `bloom_test` to verify Ribbon Filter fallback on large banding in FullFilter
- Added test in `filter_bench` to verify that this feature does not significantly slow down Bloom/Ribbon Filter construction speed. Local result averaged over **20** run as below:
   - FastLocalBloom
      - baseline `./filter_bench -impl=2 -quick -runs 20 | grep 'Build avg'`:
         - **Build avg ns/key: 29.56295** (DEBUG_LEVEL=1), **29.98153** (DEBUG_LEVEL=0)
      - new feature (expected to be similar as above)`./filter_bench -impl=2 -quick -runs 20 -reserve_table_builder_memory=true | grep 'Build avg'`:
         - **Build avg ns/key: 30.99046** (DEBUG_LEVEL=1), **30.48867** (DEBUG_LEVEL=0)
      - new feature of RibbonFilter with fallback  (expected to be similar as above) `./filter_bench -impl=2 -quick -runs 20 -reserve_table_builder_memory=true -strict_capacity_limit=true | grep 'Build avg'` :
         - **Build avg ns/key: 31.146975** (DEBUG_LEVEL=1), **30.08165** (DEBUG_LEVEL=0)

    - Ribbon128
       - baseline `./filter_bench -impl=3 -quick -runs 20 | grep 'Build avg'`:
           - **Build avg ns/key: 129.17585** (DEBUG_LEVEL=1), **130.5225** (DEBUG_LEVEL=0)
       - new feature  (expected to be similar as above) `./filter_bench -impl=3 -quick -runs 20 -reserve_table_builder_memory=true | grep 'Build avg' `:
           - **Build avg ns/key: 131.61645** (DEBUG_LEVEL=1), **132.98075** (DEBUG_LEVEL=0)
       - new feature of RibbonFilter with fallback (expected to be a lot faster than above due to fallback) `./filter_bench -impl=3 -quick -runs 20 -reserve_table_builder_memory=true -strict_capacity_limit=true | grep 'Build avg'` :
          - **Build avg ns/key: 52.032965** (DEBUG_LEVEL=1), **52.597825** (DEBUG_LEVEL=0)
          - And the warning message of `"Cache reservation for Ribbon filter banding failed due to cache full"` is indeed logged to console.

Reviewed By: pdillinger

Differential Revision: D31991348

Pulled By: hx235

fbshipit-source-id: 9336b2c60f44d530063da518ceaf56dac5f9df8e
 2021-11-18 09:42:20 -08:00
Peter Dillinger 22161b7547 Upgrade xxhash, add Hash128 (#8634) 
Summary:
With expected use for a 128-bit hash, xxhash library is
upgraded to current dev (2c611a76f914828bed675f0f342d6c4199ffee1e)
as of Aug 6 so that we can use production version of XXH3_128bits
as new Hash128 function (added in hash128.h).

To make this work, however, we have to carve out the "preview" version
of XXH3 that is used in new SST Bloom and Ribbon filters, since that
will not get maintenance in xxhash releases. I have consolidated all the
relevant code into xxph3.h and made it "inline only" (no .cc file). The
working name for this hash function is changed from XXH3p to XXPH3
(XX Preview Hash) because the latter is easier to get working with no
symbol name conflicts between the headers.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/8634

Test Plan:
no expected change in existing functionality. For Hash128,
added some unit tests based on those for Hash64 to ensure some basic
properties and that the values do not change accidentally.

Reviewed By: zhichao-cao

Differential Revision: D30173490

Pulled By: pdillinger

fbshipit-source-id: 06aa542a7a28b353bc2c865b9b2f8bdfe44158e4
 2021-08-20 18:41:51 -07:00
Peter Dillinger a8b3b9a20c Refine Ribbon configuration, improve testing, add Homogeneous (#7879) 
Summary:
This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings.

This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing).

Homogenous Ribbon:
This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate.

Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit.

Other misc item specifics:
* Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%.
  * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting.
  * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool.
* Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision)
* Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB
* Add some missing 'const' to member functions
* Small optimization to 128-bit BitParity
* Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon
* CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.)
* 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879

Test Plan: unit test updates included

Reviewed By: jay-zhuang

Differential Revision: D26371245

Pulled By: pdillinger

fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
 2021-02-26 08:50:42 -08:00
Peter Dillinger e4f1e64c30 Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) 
Summary:
Adds support for prefetching data in Ribbon queries,
which especially optimizes batched Ribbon queries for MultiGet
(~222ns/key to ~97ns/key) but also single key queries on cold memory
(~333ns to ~226ns) because many queries span more than one cache line.

This required some refactoring of the query algorithm, and there
does not appear to be a noticeable regression in "hot memory" query
times (perhaps from 48ns to 50ns).

Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889

Test Plan:
existing unit tests, plus performance validation with
filter_bench:

Each data point is the best of two runs. I saturated the machine
CPUs with other filter_bench runs in the background.

Before:

    $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50
    WARNING: Assertions are enabled; benchmarks unnecessarily slow
    Building...
    Build avg ns/key: 125.86
    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
    Prelim FP rate %: 0.951827
    ----------------------------
    Mixed inside/outside queries...
      Single filter net ns/op: 48.0111
      Batched, prepared net ns/op: 222.384
      Batched, unprepared net ns/op: 343.908
      Skewed 50% in 1% net ns/op: 252.916
      Skewed 80% in 20% net ns/op: 320.579
      Random filter net ns/op: 332.957

After:

    $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50
    WARNING: Assertions are enabled; benchmarks unnecessarily slow
    Building...
    Build avg ns/key: 128.117
    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
    Prelim FP rate %: 0.951827
    ----------------------------
    Mixed inside/outside queries...
      Single filter net ns/op: 49.8812
      Batched, prepared net ns/op: 97.1514
      Batched, unprepared net ns/op: 222.025
      Skewed 50% in 1% net ns/op: 197.48
      Skewed 80% in 20% net ns/op: 212.457
      Random filter net ns/op: 226.464

Bloom comparison, for reference:

    $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50
    WARNING: Assertions are enabled; benchmarks unnecessarily slow
    Building...
    Build avg ns/key: 35.3042
    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
    Prelim FP rate %: 0.965327
    ----------------------------
    Mixed inside/outside queries...
      Single filter net ns/op: 9.09931
      Batched, prepared net ns/op: 34.21
      Batched, unprepared net ns/op: 88.8564
      Skewed 50% in 1% net ns/op: 139.75
      Skewed 80% in 20% net ns/op: 181.264
      Random filter net ns/op: 173.88

Reviewed By: jay-zhuang

Differential Revision: D26378710

Pulled By: pdillinger

fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
 2021-02-10 21:04:56 -08:00
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
 2020-12-18 14:31:03 -08:00
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
 2020-11-12 20:46:14 -08:00
Peter Dillinger 8b8a2e9f05 Ribbon: major re-work of hashing, seeds, and more (#7635) 
Summary:
* Fully optimized StandardHasher, in terms of efficiently generating Start, CoeffRow, and ResultRow from a stock hash value, with sufficient independence between them to have no measurably degraded behavior. (Degraded behavior would be an FP rate higher than explainable by 2^-b and, if using a 32-bit stock hash function, expected stock hash collisions.) Details in code comments.
* Our standard 64-bit and 32-bit hash functions do not exhibit sufficient independence on sequential seeds (for one Ribbon construction attempt to have independent probability from the next). I have worked around this in the Ribbon code by "pre-mixing" "ordinal seeds," sequentially tried and appropriate for storage in persisted metadata, into "raw seeds," ready for application and appropriate for in-memory storage. This way the pre-mixing step (though fast) is only applied on loading or configuring the structure, not on each query or banding add.
* Fix a subtle flaw in which backtracking not clearing ResultRow data could lead to elevated FP rate on keys that were backtracked on and should (for generality) exhibit the same FP rate as novel keys.
* Added a basic test for PhsfQuery and construction algorithms (map or "retrieval structure" rather than set or filter), and made a few trivial related fixes.
* Better random configuration generation in unit tests
* Some other minor cleanup / clarification / etc.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/7635

Test Plan: unit tests included

Reviewed By: jay-zhuang

Differential Revision: D24738978

Pulled By: pdillinger

fbshipit-source-id: f9d03599d9e2ca3e30e9d3e7d81cd936b56f76f0
 2020-11-07 17:22:54 -08:00
Peter Dillinger 746909ceda Ribbon: InterleavedSolutionStorage (#7598) 
Summary:
The core algorithms for InterleavedSolutionStorage and the
implementation SerializableInterleavedSolution make Ribbon fast for
filter queries. Example output from new unit test:

    Simple      outside query, hot, incl hashing, ns/key: 117.796
    Interleaved outside query, hot, incl hashing, ns/key: 42.2655
    Bloom       outside query, hot, incl hashing, ns/key: 24.0071

Also includes misc cleanup of previous Ribbon code and comments.

Some TODOs and FIXMEs remain for futher work / investigation.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/7598

Test Plan: unit tests included (integration work and tests coming later)

Reviewed By: jay-zhuang

Differential Revision: D24559209

Pulled By: pdillinger

fbshipit-source-id: fea483cd354ba782aea3e806f2bc96e183d59441
 2020-11-03 12:46:36 -08:00
Peter Dillinger 25d54c799c Ribbon: initial (general) algorithms and basic unit test (#7491) 
Summary:
This is intended as the first commit toward a near-optimal alternative to static Bloom filters for SSTs. Stephan Walzer and I have agreed upon the name "Ribbon" for a PHSF based on his linear system construction in "Efficient Gauss Elimination for Near-Quadratic Matrices with One Short Random Block per Row, with Applications" ("SGauss") and my much faster "on the fly" algorithm for gaussian elimination (or for this linear system, "banding"), which can be faster than peeling while also more compact and flexible. See util/ribbon_alg.h for more detailed introduction and background. RIBBON = Rapid Incremental Boolean Banding ON-the-fly

This commit just adds generic (templatized) core algorithms and a basic unit test showing some features, including the ability to construct structures within 2.5% space overhead vs. information theoretic lower bound. (Compare to cache-local Bloom filter's ~50% space overhead -> ~30% reduction anticipated.) This commit does not include the storage scheme necessary to make queries fast, especially for filter queries, nor fractional "result bits", but there is some description already and those implementations will come soon. Nor does this commit add FilterPolicy support, for use in SST files, but that will also come soon.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/7491

Reviewed By: jay-zhuang

Differential Revision: D24517954

Pulled By: pdillinger

fbshipit-source-id: 0119ee597e250d7e0edd38ada2ba50d755606fa7
 2020-10-25 20:44:49 -07:00