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rocksdb/util/ribbon_impl.h

870 lines
35 KiB

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
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
#pragma once
#include <cmath>
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
#include "port/port.h" // for PREFETCH
#include "util/ribbon_alg.h"
namespace ROCKSDB_NAMESPACE {
namespace ribbon {
// RIBBON PHSF & RIBBON Filter (Rapid Incremental Boolean Banding ON-the-fly)
//
// ribbon_impl.h: templated (parameterized) standard implementations
//
// Ribbon is a Perfect Hash Static Function construction useful as a compact
// static Bloom filter alternative. See ribbon_alg.h for core algorithms
// and core design details.
//
// TODO: more details on trade-offs and practical issues.
// Ribbon implementations in this file take these parameters, which must be
// provided in a class/struct type with members expressed in this concept:
// concept TypesAndSettings {
// // See RibbonTypes and *Hasher in ribbon_alg.h, except here we have
// // the added constraint that Hash be equivalent to either uint32_t or
// // uint64_t.
// typename Hash;
// typename CoeffRow;
// typename ResultRow;
// typename Index;
// typename Key;
// static constexpr bool kFirstCoeffAlwaysOne;
//
// // An unsigned integer type for identifying a hash seed, typically
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
// // uint32_t or uint64_t. Importantly, this is the amount of data
// // stored in memory for identifying a raw seed. See StandardHasher.
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
// typename Seed;
//
// // When true, the PHSF implements a static filter, expecting just
// // keys as inputs for construction. When false, implements a general
// // PHSF and expects std::pair<Key, ResultRow> as inputs for
// // construction.
// static constexpr bool kIsFilter;
//
// // When true, adds a tiny bit more hashing logic on queries and
// // construction to improve utilization at the beginning and end of
// // the structure. Recommended when CoeffRow is only 64 bits (or
// // less), so typical num_starts < 10k.
// static constexpr bool kUseSmash;
//
// // When true, allows number of "starts" to be zero, for best support
// // of the "no keys to add" case by always returning false for filter
// // queries. (This is distinct from the "keys added but no space for
// // any data" case, in which a filter always returns true.) The cost
// // supporting this is a conditional branch (probably predictable) in
// // queries.
// static constexpr bool kAllowZeroStarts;
//
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
// // A seedable stock hash function on Keys. All bits of Hash must
// // be reasonably high quality. XXH functions recommended, but
// // Murmur, City, Farm, etc. also work.
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
// static Hash HashFn(const Key &, Seed raw_seed);
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
// };
// A bit of a hack to automatically construct the type for
// AddInput based on a constexpr bool.
template <typename Key, typename ResultRow, bool IsFilter>
struct AddInputSelector {
// For general PHSF, not filter
using T = std::pair<Key, ResultRow>;
};
template <typename Key, typename ResultRow>
struct AddInputSelector<Key, ResultRow, true /*IsFilter*/> {
// For Filter
using T = Key;
};
// To avoid writing 'typename' everywhere that we use types like 'Index'
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
#define IMPORT_RIBBON_TYPES_AND_SETTINGS(TypesAndSettings) \
using CoeffRow = typename TypesAndSettings::CoeffRow; \
using ResultRow = typename TypesAndSettings::ResultRow; \
using Index = typename TypesAndSettings::Index; \
using Hash = typename TypesAndSettings::Hash; \
using Key = typename TypesAndSettings::Key; \
using Seed = typename TypesAndSettings::Seed; \
\
/* Some more additions */ \
using QueryInput = Key; \
using AddInput = typename ROCKSDB_NAMESPACE::ribbon::AddInputSelector< \
Key, ResultRow, TypesAndSettings::kIsFilter>::T; \
static constexpr auto kCoeffBits = \
static_cast<Index>(sizeof(CoeffRow) * 8U); \
\
/* Export to algorithm */ \
static constexpr bool kFirstCoeffAlwaysOne = \
TypesAndSettings::kFirstCoeffAlwaysOne; \
\
static_assert(sizeof(CoeffRow) + sizeof(ResultRow) + sizeof(Index) + \
sizeof(Hash) + sizeof(Key) + sizeof(Seed) + \
sizeof(QueryInput) + sizeof(AddInput) + kCoeffBits + \
kFirstCoeffAlwaysOne > \
0, \
"avoid unused warnings, semicolon expected after macro call")
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
#ifdef _MSC_VER
#pragma warning(push)
#pragma warning(disable : 4309) // cast truncating constant
#pragma warning(disable : 4307) // arithmetic constant overflow
#endif
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
// StandardHasher: A standard implementation of concepts RibbonTypes,
// PhsfQueryHasher, FilterQueryHasher, and BandingHasher from ribbon_alg.h.
//
// This implementation should be suitable for most all practical purposes
// as it "behaves" across a wide range of settings, with little room left
// for improvement. The key functionality in this hasher is generating
// CoeffRows, starts, and (for filters) ResultRows, which could be ~150
// bits of data or more, from a modest hash of 64 or even just 32 bits, with
// enough uniformity and bitwise independence to be close to "the best you
// can do" with available hash information in terms of FP rate and
// compactness. (64 bits recommended and sufficient for PHSF practical
// purposes.)
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
//
// Another feature of this hasher is a minimal "premixing" of seeds before
// they are provided to TypesAndSettings::HashFn in case that function does
// not provide sufficiently independent hashes when iterating merely
// sequentially on seeds. (This for example works around a problem with the
// preview version 0.7.2 of XXH3 used in RocksDB, a.k.a. XXH3p or Hash64, and
// MurmurHash1 used in RocksDB, a.k.a. Hash.) We say this pre-mixing step
// translates "ordinal seeds," which we iterate sequentially to find a
// solution, into "raw seeds," with many more bits changing for each
// iteration. The translation is an easily reversible lightweight mixing,
// not suitable for hashing on its own. An advantage of this approach is that
// StandardHasher can store just the raw seed (e.g. 64 bits) for fast query
// times, while from the application perspective, we can limit to a small
// number of ordinal keys (e.g. 64 in 6 bits) for saving in metadata.
//
// The default constructor initializes the seed to ordinal seed zero, which
// is equal to raw seed zero.
//
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
template <class TypesAndSettings>
class StandardHasher {
public:
IMPORT_RIBBON_TYPES_AND_SETTINGS(TypesAndSettings);
inline Hash GetHash(const Key& key) const {
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
return TypesAndSettings::HashFn(key, raw_seed_);
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
};
// For when AddInput == pair<Key, ResultRow> (kIsFilter == false)
inline Hash GetHash(const std::pair<Key, ResultRow>& bi) const {
return GetHash(bi.first);
};
inline Index GetStart(Hash h, Index num_starts) const {
// This is "critical path" code because it's required before memory
// lookup.
//
// FastRange gives us a fast and effective mapping from h to the
// appropriate range. This depends most, sometimes exclusively, on
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
// upper bits of h.
//
if (TypesAndSettings::kUseSmash) {
// Extra logic to "smash" entries at beginning and end, for
// better utilization. For example, without smash and with
// kFirstCoeffAlwaysOne, there's about a 30% chance that the
// first slot in the banding will be unused, and worse without
// kFirstCoeffAlwaysOne. The ending slots are even less utilized
// without smash.
//
// But since this only affects roughly kCoeffBits of the slots,
// it's usually small enough to be ignorable (less computation in
// this function) when number of slots is roughly 10k or larger.
//
// The best values for these smash weights might depend on how
// densely you're packing entries, but this seems to work well for
// 2% overhead and roughly 50% success probability.
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
//
constexpr auto kFrontSmash = kCoeffBits / 3;
constexpr auto kBackSmash = kCoeffBits / 3;
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
Index start = FastRangeGeneric(h, num_starts + kFrontSmash + kBackSmash);
start = std::max(start, kFrontSmash);
start -= kFrontSmash;
start = std::min(start, num_starts - 1);
return start;
} else {
// For query speed, we allow small number of initial and final
// entries to be under-utilized.
// NOTE: This call statically enforces that Hash is equivalent to
// either uint32_t or uint64_t.
return FastRangeGeneric(h, num_starts);
}
}
inline CoeffRow GetCoeffRow(Hash h) const {
// This is not so much "critical path" code because it can be done in
// parallel (instruction level) with memory lookup.
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
//
// We do not need exhaustive remixing for CoeffRow, but just enough that
// (a) every bit is reasonably independent from Start.
// (b) every Hash-length bit subsequence of the CoeffRow has full or
// nearly full entropy from h.
// (c) if nontrivial bit subsequences within are correlated, it needs to
// be more complicated than exact copy or bitwise not (at least without
// kFirstCoeffAlwaysOne), or else there seems to be a kind of
// correlated clustering effect.
// (d) the CoeffRow is not zero, so that no one input on its own can
// doom construction success. (Preferably a mix of 1's and 0's if
// satisfying above.)
// First, establish sufficient bitwise independence from Start, with
// multiplication by a large random prime.
// Note that we cast to Hash because if we use product bits beyond
// original input size, that's going to correlate with Start (FastRange)
// even with a (likely) different multiplier here.
Hash a = h * kCoeffAndResultFactor;
// If that's big enough, we're done. If not, we have to expand it,
// maybe up to 4x size.
uint64_t b = a;
static_assert(
sizeof(Hash) == sizeof(uint64_t) || sizeof(Hash) == sizeof(uint32_t),
"Supported sizes");
if (sizeof(Hash) < sizeof(uint64_t)) {
// Almost-trivial hash expansion (OK - see above), favoring roughly
// equal number of 1's and 0's in result
b = (b << 32) ^ b ^ kCoeffXor32;
}
Unsigned128 c = b;
static_assert(sizeof(CoeffRow) == sizeof(uint64_t) ||
sizeof(CoeffRow) == sizeof(Unsigned128),
"Supported sizes");
if (sizeof(uint64_t) < sizeof(CoeffRow)) {
// Almost-trivial hash expansion (OK - see above), favoring roughly
// equal number of 1's and 0's in result
c = (c << 64) ^ c ^ kCoeffXor64;
}
auto cr = static_cast<CoeffRow>(c);
// Now ensure the value is non-zero
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
if (kFirstCoeffAlwaysOne) {
cr |= 1;
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
} else if (sizeof(CoeffRow) == sizeof(Hash)) {
// Still have to ensure some bit is non-zero
cr |= (cr == 0) ? 1 : 0;
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
} else {
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
// (We did trivial expansion with constant xor, which ensures some
// bits are non-zero.)
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
}
return cr;
}
inline ResultRow GetResultRowMask() const {
// TODO: will be used with InterleavedSolutionStorage?
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
// For now, all bits set (note: might be a small type so might need to
// narrow after promotion)
return static_cast<ResultRow>(~ResultRow{0});
}
inline ResultRow GetResultRowFromHash(Hash h) const {
if (TypesAndSettings::kIsFilter) {
// This is not so much "critical path" code because it can be done in
// parallel (instruction level) with memory lookup.
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
//
// There is no evidence that ResultRow needs to be independent from
// CoeffRow, so we draw from the same bits computed for CoeffRow,
// which are reasonably independent from Start. (Inlining and common
// subexpression elimination with GetCoeffRow should make this
// a single shared multiplication in generated code.)
Hash a = h * kCoeffAndResultFactor;
// The bits here that are *most* independent of Start are the highest
// order bits (as in Knuth multiplicative hash). To make those the
// most preferred for use in the result row, we do a bswap here.
auto rr = static_cast<ResultRow>(EndianSwapValue(a));
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
return rr & GetResultRowMask();
} else {
// Must be zero
return 0;
}
}
// For when AddInput == Key (kIsFilter == true)
inline ResultRow GetResultRowFromInput(const Key&) const {
// Must be zero
return 0;
}
// For when AddInput == pair<Key, ResultRow> (kIsFilter == false)
inline ResultRow GetResultRowFromInput(
const std::pair<Key, ResultRow>& bi) const {
// Simple extraction
return bi.second;
}
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
// Seed tracking APIs - see class comment
void SetRawSeed(Seed seed) { raw_seed_ = seed; }
Seed GetRawSeed() { return raw_seed_; }
void SetOrdinalSeed(Seed count) {
// A simple, reversible mixing of any size (whole bytes) up to 64 bits.
// This allows casting the raw seed to any smaller size we use for
// ordinal seeds without risk of duplicate raw seeds for unique ordinal
// seeds.
// Seed type might be smaller than numerical promotion size, but Hash
// should be at least that size, so we use Hash as intermediate type.
static_assert(sizeof(Seed) <= sizeof(Hash),
"Hash must be at least size of Seed");
// Multiply by a large random prime (one-to-one for any prefix of bits)
Hash tmp = count * kToRawSeedFactor;
// Within-byte one-to-one mixing
static_assert((kSeedMixMask & (kSeedMixMask >> kSeedMixShift)) == 0,
"Illegal mask+shift");
tmp ^= (tmp & kSeedMixMask) >> kSeedMixShift;
raw_seed_ = static_cast<Seed>(tmp);
// dynamic verification
assert(GetOrdinalSeed() == count);
}
Seed GetOrdinalSeed() {
Hash tmp = raw_seed_;
// Within-byte one-to-one mixing (its own inverse)
tmp ^= (tmp & kSeedMixMask) >> kSeedMixShift;
// Multiply by 64-bit multiplicative inverse
static_assert(kToRawSeedFactor * kFromRawSeedFactor == Hash{1},
"Must be inverses");
return static_cast<Seed>(tmp * kFromRawSeedFactor);
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
}
protected:
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
// For expanding hash:
// large random prime
static constexpr Hash kCoeffAndResultFactor =
static_cast<Hash>(0xc28f82822b650bedULL);
// random-ish data
static constexpr uint32_t kCoeffXor32 = 0xa6293635U;
static constexpr uint64_t kCoeffXor64 = 0xc367844a6e52731dU;
// For pre-mixing seeds
static constexpr Hash kSeedMixMask = static_cast<Hash>(0xf0f0f0f0f0f0f0f0ULL);
static constexpr unsigned kSeedMixShift = 4U;
static constexpr Hash kToRawSeedFactor =
static_cast<Hash>(0xc78219a23eeadd03ULL);
static constexpr Hash kFromRawSeedFactor =
static_cast<Hash>(0xfe1a137d14b475abULL);
// See class description
Seed raw_seed_ = 0;
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
};
// StandardRehasher (and StandardRehasherAdapter): A variant of
// StandardHasher that uses the same type for keys as for hashes.
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
// This is primarily intended for building a Ribbon filter
// from existing hashes without going back to original inputs in
// order to apply a different seed. This hasher seeds a 1-to-1 mixing
// transformation to apply a seed to an existing hash. (Untested for
// hash-sized keys that are not already uniformly distributed.) This
// transformation builds on the seed pre-mixing done in StandardHasher.
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
//
// Testing suggests essentially no degradation of solution success rate
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
// vs. going back to original inputs when changing hash seeds. For example:
// Average re-seeds for solution with r=128, 1.02x overhead, and ~100k keys
// is about 1.10 for both StandardHasher and StandardRehasher.
//
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
// StandardRehasher is not really recommended for general PHSFs (not
// filters) because a collision in the original hash could prevent
// construction despite re-seeding the Rehasher. (Such collisions
// do not interfere with filter construction.)
//
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
// concept RehasherTypesAndSettings: like TypesAndSettings but
// does not require Key or HashFn.
template <class RehasherTypesAndSettings>
class StandardRehasherAdapter : public RehasherTypesAndSettings {
public:
using Hash = typename RehasherTypesAndSettings::Hash;
using Key = Hash;
using Seed = typename RehasherTypesAndSettings::Seed;
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
static Hash HashFn(const Hash& input, Seed raw_seed) {
// Note: raw_seed is already lightly pre-mixed, and this multiplication
// by a large prime is sufficient mixing (low-to-high bits) on top of
// that for good FastRange results, which depends primarily on highest
// bits. (The hashed CoeffRow and ResultRow are less sensitive to
// mixing than Start.)
// Also note: did consider adding ^ (input >> some) before the
// multiplication, but doesn't appear to be necessary.
return (input ^ raw_seed) * kRehashFactor;
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
}
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
private:
static constexpr Hash kRehashFactor =
static_cast<Hash>(0x6193d459236a3a0dULL);
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
};
// See comment on StandardRehasherAdapter
template <class RehasherTypesAndSettings>
using StandardRehasher =
StandardHasher<StandardRehasherAdapter<RehasherTypesAndSettings>>;
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
#ifdef _MSC_VER
#pragma warning(pop)
#endif
// Especially with smaller hashes (e.g. 32 bit), there can be noticeable
// false positives due to collisions in the Hash returned by GetHash.
// This function returns the expected FP rate due to those collisions,
// which can be added to the expected FP rate from the underlying data
// structure. (Note: technically, a + b is only a good approximation of
// 1-(1-a)(1-b) == a + b - a*b, if a and b are much closer to 0 than to 1.)
// The number of entries added can be a double here in case it's an
// average.
template <class Hasher, typename Numerical>
double ExpectedCollisionFpRate(const Hasher& hasher, Numerical added) {
// Standardize on the 'double' specialization
return ExpectedCollisionFpRate(hasher, 1.0 * added);
}
template <class Hasher>
double ExpectedCollisionFpRate(const Hasher& /*hasher*/, double added) {
// Technically, there could be overlap among the added, but ignoring that
// is typically close enough.
return added / std::pow(256.0, sizeof(typename Hasher::Hash));
}
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
// StandardBanding: a canonical implementation of BandingStorage and
// BacktrackStorage, with convenience API for banding (solving with on-the-fly
// Gaussian elimination) with and without backtracking.
template <class TypesAndSettings>
class StandardBanding : public StandardHasher<TypesAndSettings> {
public:
IMPORT_RIBBON_TYPES_AND_SETTINGS(TypesAndSettings);
StandardBanding(Index num_slots = 0, Index backtrack_size = 0) {
Reset(num_slots, backtrack_size);
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
}
void Reset(Index num_slots, Index backtrack_size = 0) {
if (num_slots == 0) {
// Unusual (TypesAndSettings::kAllowZeroStarts) or "uninitialized"
num_starts_ = 0;
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
} else {
// Normal
assert(num_slots >= kCoeffBits);
if (num_slots > num_slots_allocated_) {
coeff_rows_.reset(new CoeffRow[num_slots]());
// Note: don't strictly have to zero-init result_rows,
// except possible information leakage ;)
result_rows_.reset(new ResultRow[num_slots]());
num_slots_allocated_ = num_slots;
} else {
for (Index i = 0; i < num_slots; ++i) {
coeff_rows_[i] = 0;
// Note: don't strictly have to zero-init result_rows
result_rows_[i] = 0;
}
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
}
num_starts_ = num_slots - kCoeffBits + 1;
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
}
EnsureBacktrackSize(backtrack_size);
}
void EnsureBacktrackSize(Index backtrack_size) {
if (backtrack_size > backtrack_size_) {
backtrack_.reset(new Index[backtrack_size]);
backtrack_size_ = backtrack_size;
}
}
// ********************************************************************
// From concept BandingStorage
inline bool UsePrefetch() const {
// A rough guesstimate of when prefetching during construction pays off.
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
// TODO: verify/validate
return num_starts_ > 1500;
}
inline void Prefetch(Index i) const {
PREFETCH(&coeff_rows_[i], 1 /* rw */, 1 /* locality */);
PREFETCH(&result_rows_[i], 1 /* rw */, 1 /* locality */);
}
inline CoeffRow* CoeffRowPtr(Index i) { return &coeff_rows_[i]; }
inline ResultRow* ResultRowPtr(Index i) { return &result_rows_[i]; }
inline Index GetNumStarts() const { return num_starts_; }
// from concept BacktrackStorage, for when backtracking is used
inline bool UseBacktrack() const { return true; }
inline void BacktrackPut(Index i, Index to_save) { backtrack_[i] = to_save; }
inline Index BacktrackGet(Index i) const { return backtrack_[i]; }
// ********************************************************************
// Some useful API, still somewhat low level. Here an input is
// a Key for filters, or std::pair<Key, ResultRow> for general PHSF.
// Adds a range of inputs to the banding, returning true if successful.
// False means none or some may have been successfully added, so it's
// best to Reset this banding before any further use.
//
// Adding can fail even before all the "slots" are completely "full".
//
template <typename InputIterator>
bool AddRange(InputIterator begin, InputIterator end) {
assert(num_starts_ > 0 || TypesAndSettings::kAllowZeroStarts);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual. Can't add any in this case.
return begin == end;
}
// Normal
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
return BandingAddRange(this, *this, begin, end);
}
// Adds a range of inputs to the banding, returning true if successful,
// or if unsuccessful, rolls back to state before this call and returns
// false. Caller guarantees that the number of inputs in this batch
// does not exceed `backtrack_size` provided to Reset.
//
// Adding can fail even before all the "slots" are completely "full".
//
template <typename InputIterator>
bool AddRangeOrRollBack(InputIterator begin, InputIterator end) {
assert(num_starts_ > 0 || TypesAndSettings::kAllowZeroStarts);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual. Can't add any in this case.
return begin == end;
}
// else Normal
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
return BandingAddRange(this, this, *this, begin, end);
}
// Adds a single input to the banding, returning true if successful.
// If unsuccessful, returns false and banding state is unchanged.
//
// Adding can fail even before all the "slots" are completely "full".
//
bool Add(const AddInput& input) {
// Pointer can act as iterator
return AddRange(&input, &input + 1);
}
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
// Return the number of "occupied" rows (with non-zero coefficients stored).
Index GetOccupiedCount() const {
Index count = 0;
if (num_starts_ > 0) {
const Index num_slots = num_starts_ + kCoeffBits - 1;
for (Index i = 0; i < num_slots; ++i) {
if (coeff_rows_[i] != 0) {
++count;
}
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
}
}
return count;
}
// ********************************************************************
// High-level API
// Iteratively (a) resets the structure for `num_slots`, (b) attempts
// to add the range of inputs, and (c) if unsuccessful, chooses next
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
// hash seed, until either successful or unsuccessful with all the
// allowed seeds. Returns true if successful. In that case, use
// GetOrdinalSeed() or GetRawSeed() to get the successful seed.
//
// The allowed sequence of hash seeds is determined by
// `starting_ordinal_seed,` the first ordinal seed to be attempted
// (see StandardHasher), and `ordinal_seed_mask,` a bit mask (power of
// two minus one) for the range of ordinal seeds to consider. The
// max number of seeds considered will be ordinal_seed_mask + 1.
// For filters we suggest `starting_ordinal_seed` be chosen randomly
// or round-robin, to minimize false positive correlations between keys.
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
//
// If unsuccessful, how best to continue is going to be application
// specific. It should be possible to choose parameters such that
// failure is extremely unlikely, using max_seed around 32 to 64.
// (TODO: APIs to help choose parameters) One option for fallback in
// constructing a filter is to construct a Bloom filter instead.
// Increasing num_slots is an option, but should not be used often
// unless construction maximum latency is a concern (rather than
// average running time of construction). Instead, choose parameters
// appropriately and trust that seeds are independent. (Also,
// increasing num_slots without changing hash seed would have a
// significant correlation in success, rather than independence.)
template <typename InputIterator>
bool ResetAndFindSeedToSolve(Index num_slots, InputIterator begin,
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
InputIterator end,
Seed starting_ordinal_seed = 0U,
Seed ordinal_seed_mask = 63U) {
// power of 2 minus 1
assert((ordinal_seed_mask & (ordinal_seed_mask + 1)) == 0);
// starting seed is within mask
assert((starting_ordinal_seed & ordinal_seed_mask) ==
starting_ordinal_seed);
starting_ordinal_seed &= ordinal_seed_mask; // if not debug
Seed cur_ordinal_seed = starting_ordinal_seed;
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
do {
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
StandardHasher<TypesAndSettings>::SetOrdinalSeed(cur_ordinal_seed);
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
Reset(num_slots);
bool success = AddRange(begin, end);
if (success) {
return true;
}
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
cur_ordinal_seed = (cur_ordinal_seed + 1) & ordinal_seed_mask;
} while (cur_ordinal_seed != starting_ordinal_seed);
// Reached limit by circling around
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
return false;
}
protected:
// TODO: explore combining in a struct
std::unique_ptr<CoeffRow[]> coeff_rows_;
std::unique_ptr<ResultRow[]> result_rows_;
// We generally store "starts" instead of slots for speed of GetStart(),
// as in StandardHasher.
Index num_starts_ = 0;
Index num_slots_allocated_ = 0;
std::unique_ptr<Index[]> backtrack_;
Index backtrack_size_ = 0;
};
// Implements concept SimpleSolutionStorage, mostly for demonstration
// purposes. This is "in memory" only because it does not handle byte
// ordering issues for serialization.
template <class TypesAndSettings>
class InMemSimpleSolution {
public:
IMPORT_RIBBON_TYPES_AND_SETTINGS(TypesAndSettings);
void PrepareForNumStarts(Index num_starts) {
if (TypesAndSettings::kAllowZeroStarts && num_starts == 0) {
// Unusual
num_starts_ = 0;
} else {
// Normal
const Index num_slots = num_starts + kCoeffBits - 1;
assert(num_slots >= kCoeffBits);
if (num_slots > num_slots_allocated_) {
// Do not need to init the memory
solution_rows_.reset(new ResultRow[num_slots]);
num_slots_allocated_ = num_slots;
}
num_starts_ = num_starts;
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
}
}
Index GetNumStarts() const { return num_starts_; }
ResultRow Load(Index slot_num) const { return solution_rows_[slot_num]; }
void Store(Index slot_num, ResultRow solution_row) {
solution_rows_[slot_num] = solution_row;
}
// ********************************************************************
// High-level API
template <typename BandingStorage>
void BackSubstFrom(const BandingStorage& bs) {
if (TypesAndSettings::kAllowZeroStarts && bs.GetNumStarts() == 0) {
// Unusual
PrepareForNumStarts(0);
} else {
// Normal
SimpleBackSubst(this, bs);
}
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
}
template <typename PhsfQueryHasher>
ResultRow PhsfQuery(const Key& input, const PhsfQueryHasher& hasher) {
assert(!TypesAndSettings::kIsFilter);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual
return 0;
} else {
// Normal
return SimplePhsfQuery(input, hasher, *this);
}
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
}
template <typename FilterQueryHasher>
bool FilterQuery(const Key& input, const FilterQueryHasher& hasher) {
assert(TypesAndSettings::kIsFilter);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual. Zero starts presumes no keys added -> always false
return false;
} else {
// Normal, or upper_num_columns_ == 0 means "no space for data" and
// thus will always return true.
return SimpleFilterQuery(input, hasher, *this);
}
}
double ExpectedFpRate() {
assert(TypesAndSettings::kIsFilter);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual, but we don't have FPs if we always return false.
return 0.0;
}
// else Normal
// Each result (solution) bit (column) cuts FP rate in half
return std::pow(0.5, 8U * sizeof(ResultRow));
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
}
protected:
// We generally store "starts" instead of slots for speed of GetStart(),
// as in StandardHasher.
Index num_starts_ = 0;
Index num_slots_allocated_ = 0;
std::unique_ptr<ResultRow[]> solution_rows_;
};
// Implements concept InterleavedSolutionStorage always using little-endian
// byte order, so easy for serialization/deserialization. This implementation
// fully supports fractional bits per key, where any number of segments
// (number of bytes multiple of sizeof(CoeffRow)) can be used with any number
// of slots that is a multiple of kCoeffBits.
//
// The structure is passed an externally allocated/de-allocated byte buffer
// that is optionally pre-populated (from storage) for answering queries,
// or can be populated by BackSubstFrom.
//
template <class TypesAndSettings>
class SerializableInterleavedSolution {
public:
IMPORT_RIBBON_TYPES_AND_SETTINGS(TypesAndSettings);
// Does not take ownership of `data` but uses it (up to `data_len` bytes)
// throughout lifetime
SerializableInterleavedSolution(char* data, size_t data_len)
: data_(data), data_len_(data_len) {}
void PrepareForNumStarts(Index num_starts) {
assert(num_starts == 0 || (num_starts % kCoeffBits == 1));
num_starts_ = num_starts;
InternalConfigure();
}
Index GetNumStarts() const { return num_starts_; }
Index GetNumBlocks() const {
const Index num_slots = num_starts_ + kCoeffBits - 1;
return num_slots / kCoeffBits;
}
Index GetUpperNumColumns() const { return upper_num_columns_; }
Index GetUpperStartBlock() const { return upper_start_block_; }
Index GetNumSegments() const {
return static_cast<Index>(data_len_ / sizeof(CoeffRow));
}
CoeffRow LoadSegment(Index segment_num) const {
assert(data_ != nullptr); // suppress clang analyzer report
return DecodeFixedGeneric<CoeffRow>(data_ + segment_num * sizeof(CoeffRow));
}
void StoreSegment(Index segment_num, CoeffRow val) {
assert(data_ != nullptr); // suppress clang analyzer report
EncodeFixedGeneric(data_ + segment_num * sizeof(CoeffRow), val);
}
// ********************************************************************
// High-level API
template <typename BandingStorage>
void BackSubstFrom(const BandingStorage& bs) {
if (TypesAndSettings::kAllowZeroStarts && bs.GetNumStarts() == 0) {
// Unusual
PrepareForNumStarts(0);
} else {
// Normal
InterleavedBackSubst(this, bs);
}
}
template <typename PhsfQueryHasher>
ResultRow PhsfQuery(const Key& input, const PhsfQueryHasher& hasher) {
assert(!TypesAndSettings::kIsFilter);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual
return 0;
} else {
// Normal
return InterleavedPhsfQuery(input, hasher, *this);
}
}
template <typename FilterQueryHasher>
bool FilterQuery(const Key& input, const FilterQueryHasher& hasher) {
assert(TypesAndSettings::kIsFilter);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual. Zero starts presumes no keys added -> always false
return false;
} else {
// Normal, or upper_num_columns_ == 0 means "no space for data" and
// thus will always return true.
return InterleavedFilterQuery(input, hasher, *this);
}
}
double ExpectedFpRate() {
assert(TypesAndSettings::kIsFilter);
if (TypesAndSettings::kAllowZeroStarts && num_starts_ == 0) {
// Unusual. Zero starts presumes no keys added -> always false
return 0.0;
}
// else Normal
// Note: Ignoring smash setting; still close enough in that case
double lower_portion =
(upper_start_block_ * kCoeffBits * 1.0) / num_starts_;
// Each result (solution) bit (column) cuts FP rate in half. Weight that
// for upper and lower number of bits (columns).
return lower_portion * std::pow(0.5, upper_num_columns_ - 1) +
(1.0 - lower_portion) * std::pow(0.5, upper_num_columns_);
}
protected:
void InternalConfigure() {
const Index num_blocks = GetNumBlocks();
Index num_segments = GetNumSegments();
if (num_blocks == 0) {
// Exceptional
upper_num_columns_ = 0;
upper_start_block_ = 0;
} else {
// Normal
upper_num_columns_ =
(num_segments + /*round up*/ num_blocks - 1) / num_blocks;
upper_start_block_ = upper_num_columns_ * num_blocks - num_segments;
// Unless that's more columns than supported by ResultRow data type
if (upper_num_columns_ > 8U * sizeof(ResultRow)) {
// Use maximum columns (there will be space unused)
upper_num_columns_ = static_cast<Index>(8U * sizeof(ResultRow));
upper_start_block_ = 0;
num_segments = num_blocks * upper_num_columns_;
}
}
// Update data_len_ for correct rounding and/or unused space
// NOTE: unused space stays gone if we PrepareForNumStarts again.
// We are prioritizing minimizing the number of fields over making
// the "unusued space" feature work well.
data_len_ = num_segments * sizeof(CoeffRow);
}
Index num_starts_ = 0;
Index upper_num_columns_ = 0;
Index upper_start_block_ = 0;
char* const data_;
size_t data_len_;
};
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
} // namespace ribbon
} // namespace ROCKSDB_NAMESPACE
// For convenience working with templates
#define IMPORT_RIBBON_IMPL_TYPES(TypesAndSettings) \
using Hasher = ROCKSDB_NAMESPACE::ribbon::StandardHasher<TypesAndSettings>; \
using Banding = \
ROCKSDB_NAMESPACE::ribbon::StandardBanding<TypesAndSettings>; \
using SimpleSoln = \
ROCKSDB_NAMESPACE::ribbon::InMemSimpleSolution<TypesAndSettings>; \
using InterleavedSoln = \
ROCKSDB_NAMESPACE::ribbon::SerializableInterleavedSolution< \
TypesAndSettings>; \
static_assert(sizeof(Hasher) + sizeof(Banding) + sizeof(SimpleSoln) + \
sizeof(InterleavedSoln) > \
0, \
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
"avoid unused warnings, semicolon expected after macro call")