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
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
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
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
