4 Commits (e4f1e64c3021180a4b57f92f48c1059ce62193fc)

Author	SHA1	Message	Date
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	4 years ago
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	4 years ago
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	4 years ago
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	4 years ago