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rocksdb/util/ribbon_alg.h

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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 <array>
#include <memory>
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
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
#include "rocksdb/rocksdb_namespace.h"
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 "util/math128.h"
namespace ROCKSDB_NAMESPACE {
namespace ribbon {
// RIBBON PHSF & RIBBON Filter (Rapid Incremental Boolean Banding ON-the-fly)
//
// ribbon_alg.h: generic versions of core algorithms.
//
// Ribbon is a Perfect Hash Static Function construction useful as a compact
// static Bloom filter alternative. It combines (a) a boolean (GF(2)) linear
// system construction that approximates a Band Matrix with hashing,
// (b) an incremental, on-the-fly Gaussian Elimination algorithm that is
// remarkably efficient and adaptable at constructing an upper-triangular
// band matrix from a set of band-approximating inputs from (a), and
// (c) a storage layout that is fast and adaptable as a filter.
//
// Footnotes: (a) "Efficient Gauss Elimination for Near-Quadratic Matrices
// with One Short Random Block per Row, with Applications" by Stefan
// Walzer and Martin Dietzfelbinger ("DW paper")
// (b) developed by Peter C. Dillinger, though not the first on-the-fly
// GE algorithm. See "On the fly Gaussian Elimination for LT codes" by
// Bioglio, Grangetto, Gaeta, and Sereno.
// (c) see "interleaved" solution storage below.
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 ribbon_impl.h for high-level behavioral summary. This file focuses
// on the core design details.
//
// ######################################################################
// ################# PHSF -> static filter reduction ####################
//
// A Perfect Hash Static Function is a data structure representing a
// map from anything hashable (a "key") to values of some fixed size.
// Crucially, it is allowed to return garbage values for anything not in
// the original set of map keys, and it is a "static" structure: entries
// cannot be added or deleted after construction. PHSFs representing n
// mappings to b-bit values (assume uniformly distributed) require at least
// n * b bits to represent, or at least b bits per entry. We typically
// describe the compactness of a PHSF by typical bits per entry as some
// function of b. For example, the MWHC construction (k=3 "peeling")
// requires about 1.0222*b and a variant called Xor+ requires about
// 1.08*b + 0.5 bits per entry.
//
// With more hashing, a PHSF can over-approximate a set as a Bloom filter
// does, with no FN queries and predictable false positive (FP) query
// rate. Instead of the user providing a value to map each input key to,
// a hash function provides the value. Keys in the original set will
// return a positive membership query because the underlying PHSF returns
// the same value as hashing the key. When a key is not in the original set,
// the PHSF returns a "garbage" value, which is only equal to the key's
// hash with (false positive) probability 1 in 2^b.
//
// For a matching false positive rate, standard Bloom filters require
// 1.44*b bits per entry. Cache-local Bloom filters (like bloom_impl.h)
// require a bit more, around 1.5*b bits per entry. Thus, a Bloom
// alternative could save up to or nearly 1/3rd of memory and storage
// that RocksDB uses for SST (static) Bloom filters. (Memtable Bloom filter
// is dynamic.)
//
// Recommended reading:
// "Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters"
// by Graf and Lemire
// First three sections of "Fast Scalable Construction of (Minimal
// Perfect Hash) Functions" by Genuzio, Ottaviano, and Vigna
//
// ######################################################################
// ################## PHSF vs. hash table vs. Bloom #####################
//
// You can think of traditional hash tables and related filter variants
// such as Cuckoo filters as utilizing an "OR" construction: a hash
// function associates a key with some slots and the data is returned if
// the data is found in any one of those slots. The collision resolution
// is visible in the final data structure and requires extra information.
// For example, Cuckoo filter uses roughly 1.05b + 2 bits per entry, and
// Golomb-Rice code (aka "GCS") as little as b + 1.5. When the data
// structure associates each input key with data in one slot, the
// structure implicitly constructs a (near-)minimal (near-)perfect hash
// (MPH) of the keys, which requires at least 1.44 bits per key to
// represent. This is why approaches with visible collision resolution
// have a fixed + 1.5 or more in storage overhead per entry, often in
// addition to an overhead multiplier on b.
//
// By contrast Bloom filters utilize an "AND" construction: a query only
// returns true if all bit positions associated with a key are set to 1.
// There is no collision resolution, so Bloom filters do not suffer a
// fixed bits per entry overhead like the above structures.
//
// PHSFs typically use a bitwise XOR construction: the data you want is
// not in a single slot, but in a linear combination of several slots.
// For static data, this gives the best of "AND" and "OR" constructions:
// avoids the +1.44 or more fixed overhead by not approximating a MPH and
// can do much better than Bloom's 1.44 factor on b with collision
// resolution, which here is done ahead of time and invisible at query
// time.
//
// ######################################################################
// ######################## PHSF construction ###########################
//
// For a typical PHSF, construction is solving a linear system of
// equations, typically in GF(2), which is to say that values are boolean
// and XOR serves both as addition and subtraction. We can use matrices to
// represent the problem:
//
// C * S = R
// (n x m) (m x b) (n x b)
// where C = coefficients, S = solution, R = results
// and solving for S given C and R.
//
// Note that C and R each have n rows, one for each input entry for the
// PHSF. A row in C is given by a hash function on the PHSF input key,
// and the corresponding row in R is the b-bit value to associate with
// that input key. (In a filter, rows of R are given by another hash
// function on the input key.)
//
// On solving, the matrix S (solution) is the final PHSF data, as it
// maps any row from the original C to its corresponding desired result
// in R. We just have to hash our query inputs and compute a linear
// combination of rows in S.
//
// In theory, we could chose m = n and let a hash function associate
// each input key with random rows in C. A solution exists with high
// probability, and uses essentially minimum space, b bits per entry
// (because we set m = n) but this has terrible scaling, something
// like O(n^2) space and O(n^3) time during construction (Gaussian
// elimination) and O(n) query time. But computational efficiency is
// key, and the core of this is avoiding scanning all of S to answer
// each query.
//
// The traditional approach (MWHC, aka Xor filter) starts with setting
// only some small fixed number of columns (typically k=3) to 1 for each
// row of C, with remaining entries implicitly 0. This is implemented as
// three hash functions over [0,m), and S can be implemented as a vector
// vector of b-bit values. Now, a query only involves looking up k rows
// (values) in S and computing their bitwise XOR. Additionally, this
// construction can use a linear time algorithm called "peeling" for
// finding a solution in many cases of one existing, but peeling
// generally requires a larger space overhead factor in the solution
// (m/n) than is required with Gaussian elimination.
//
// Recommended reading:
// "Peeling Close to the Orientability Threshold – Spatial Coupling in
// Hashing-Based Data Structures" by Stefan Walzer
//
// ######################################################################
// ##################### Ribbon PHSF construction #######################
//
// Ribbon constructs coefficient rows essentially the same as in the
// Walzer/Dietzfelbinger paper cited above: for some chosen fixed width
// r (kCoeffBits in code), each key is hashed to a starting column in
// [0, m - r] (GetStart() in code) and an r-bit sequence of boolean
// coefficients (GetCoeffRow() in code). If you sort the rows by start,
// the C matrix would look something like this:
//
// [####00000000000000000000]
// [####00000000000000000000]
// [000####00000000000000000]
// [0000####0000000000000000]
// [0000000####0000000000000]
// [000000000####00000000000]
// [000000000####00000000000]
// [0000000000000####0000000]
// [0000000000000000####0000]
// [00000000000000000####000]
// [00000000000000000000####]
//
// where each # could be a 0 or 1, chosen uniformly by a hash function.
// (Except we typically set the start column value to 1.) This scheme
// uses hashing to approximate a band matrix, and it has a solution iff
// it reduces to an upper-triangular boolean r-band matrix, like this:
//
// [1###00000000000000000000]
// [01##00000000000000000000]
// [000000000000000000000000]
// [0001###00000000000000000]
// [000000000000000000000000]
// [000001##0000000000000000]
// [000000000000000000000000]
// [00000001###0000000000000]
// [000000001###000000000000]
// [0000000001##000000000000]
// ...
// [00000000000000000000001#]
// [000000000000000000000001]
//
// where we have expanded to an m x m matrix by filling with rows of
// all zeros as needed. As in Gaussian elimination, this form is ready for
// generating a solution through back-substitution.
//
// The awesome thing about the Ribbon construction (from the DW paper) is
// how row reductions keep each row representable as a start column and
// r coefficients, because row reductions are only needed when two rows
// have the same number of leading zero columns. Thus, the combination
// of those rows, the bitwise XOR of the r-bit coefficient rows, cancels
// out the leading 1s, so starts (at least) one column later and only
// needs (at most) r - 1 coefficients.
//
// ######################################################################
// ###################### Ribbon PHSF scalability #######################
//
// Although more practical detail is in ribbon_impl.h, it's worth
// understanding some of the overall benefits and limitations of the
// Ribbon PHSFs.
//
// High-end scalability is a primary issue for Ribbon PHSFs, because in
// a single Ribbon linear system with fixed r and fixed m/n ratio, the
// solution probability approaches zero as n approaches infinity.
// For a given n, solution probability improves with larger r and larger
// m/n.
//
// By contrast, peeling-based PHSFs have somewhat worse storage ratio
// or solution probability for small n (less than ~1000). This is
// especially true with spatial-coupling, where benefits are only
// notable for n on the order of 100k or 1m or more.
//
// To make best use of current hardware, r=128 seems to be closest to
// a "generally good" choice for Ribbon, at least in RocksDB where SST
// Bloom filters typically hold around 10-100k keys, and almost always
// less than 10m keys. r=128 ribbon has a high chance of encoding success
// (with first hash seed) when storage overhead is around 5% (m/n ~ 1.05)
// for roughly 10k - 10m keys in a single linear system. r=64 only scales
// up to about 10k keys with the same storage overhead. Construction and
// access times for r=128 are similar to r=64. r=128 tracks nearly
// twice as much data during construction, but in most cases we expect
// the scalability benefits of r=128 vs. r=64 to make it preferred.
//
// A natural approach to scaling Ribbon beyond ~10m keys is splitting
// (or "sharding") the inputs into multiple linear systems with their
// own hash seeds. This can also help to control peak memory consumption.
// TODO: much more to come
//
// ######################################################################
// #################### Ribbon on-the-fly banding #######################
//
// "Banding" is what we call the process of reducing the inputs to an
// upper-triangular r-band matrix ready for finishing a solution with
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
// back-substitution. Although the DW paper presents an algorithm for
// this ("SGauss"), the awesome properties of their construction enable
// an even simpler, faster, and more backtrackable algorithm. In simplest
// terms, the SGauss algorithm requires sorting the inputs by start
// columns, but it's possible to make Gaussian elimination resemble hash
// table insertion!
//
// The enhanced algorithm is based on these observations:
// - When processing a coefficient row with first 1 in column j,
// - If it's the first at column j to be processed, it can be part of
// the banding at row j. (And that decision never overwritten, with
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
// no loss of generality!)
// - Else, it can be combined with existing row j and re-processed,
// which will look for a later "empty" row or reach "no solution".
//
// We call our banding algorithm "incremental" and "on-the-fly" because
// (like hash table insertion) we are "finished" after each input
// processed, with respect to all inputs processed so far. Although the
// band matrix is an intermediate step to the solution structure, we have
// eliminated intermediate steps and unnecessary data tracking for
// banding.
//
// Building on "incremental" and "on-the-fly", the banding algorithm is
// easily backtrackable because no (non-empty) rows are overwritten in
// the banding. Thus, if we want to "try" adding an additional set of
// inputs to the banding, we only have to record which rows were written
// in order to efficiently backtrack to our state before considering
// the additional set. (TODO: how this can mitigate scalability and
// reach sub-1% overheads)
//
// Like in a linear-probed hash table, as the occupancy approaches and
// surpasses 90-95%, collision resolution dominates the construction
// time. (Ribbon doesn't usually pay at query time; see solution
// storage below.) This means that we can speed up construction time
// by using a higher m/n ratio, up to negative returns around 1.2.
// At m/n ~= 1.2, which still saves memory substantially vs. Bloom
// filter's 1.5, construction speed (including back-substitution) is not
// far from sorting speed, but still a few times slower than cache-local
// Bloom construction speed.
//
// Back-substitution from an upper-triangular boolean band matrix is
// especially fast and easy. All the memory accesses are sequential or at
// least local, no random. If the number of result bits (b) is a
// compile-time constant, the back-substitution state can even be tracked
// in CPU registers. Regardless of the solution representation, we prefer
// column-major representation for tracking back-substitution state, as
// r (the band width) will typically be much larger than b (result bits
// or columns), so better to handle r-bit values b times (per solution
// row) than b-bit values r times.
//
// ######################################################################
// ##################### Ribbon solution storage ########################
//
// Row-major layout is typical for boolean (bit) matrices, including for
// MWHC (Xor) filters where a query combines k b-bit values, and k is
// typically smaller than b. Even for k=4 and b=2, at least k=4 random
// look-ups are required regardless of layout.
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 PHSFs are quite different, however, because
// (a) all of the solution rows relevant to a query are within a single
// range of r rows, and
// (b) the number of solution rows involved (r/2 on average, or r if
// avoiding conditional accesses) is typically much greater than
// b, the number of solution columns.
//
// Row-major for Ribbon PHSFs therefore tends to incur undue CPU overhead
// by processing (up to) r entries of b bits each, where b is typically
// less than 10 for filter applications.
//
// Column-major layout has poor locality because of accessing up to b
// memory locations in different pages (and obviously cache lines). Note
// that negative filter queries do not typically need to access all
// solution columns, as they can return when a mismatch is found in any
// result/solution column. This optimization doesn't always pay off on
// recent hardware, where the penalty for unpredictable conditional
// branching can exceed the penalty for unnecessary work, but the
// optimization is essentially unavailable with row-major layout.
//
// The best compromise seems to be interleaving column-major on the small
// scale with row-major on the large scale. For example, let a solution
// "block" be r rows column-major encoded as b r-bit values in sequence.
// Each query accesses (up to) 2 adjacent blocks, which will typically
// span 1-3 cache lines in adjacent memory. We get very close to the same
// locality as row-major, but with much faster reconstruction of each
// result column, at least for filter applications where b is relatively
// small and negative queries can return early.
//
// ######################################################################
// ###################### Fractional result bits ########################
//
// Bloom filters have great flexibility that alternatives mostly do not
// have. One of those flexibilities is in utilizing any ratio of data
// structure bits per key. With a typical memory allocator like jemalloc,
// this flexibility can save roughly 10% of the filters' footprint in
// DRAM by rounding up and down filter sizes to minimize memory internal
// fragmentation (see optimize_filters_for_memory RocksDB option).
//
// At first glance, PHSFs only offer a whole number of bits per "slot"
// (m rather than number of keys n), but coefficient locality in the
// Ribbon construction makes fractional bits/key quite possible and
// attractive for filter applications. This works by a prefix of the
// structure using b-1 solution columns and the rest using b solution
// columns. See InterleavedSolutionStorage below for more detail.
//
// Because false positive rates are non-linear in bits/key, this approach
// is not quite optimal in terms of information theory. In common cases,
// we see additional space overhead up to about 1.5% vs. theoretical
// optimal to achieve the same FP rate. We consider this a quite acceptable
// overhead for very efficiently utilizing space that might otherwise be
// wasted.
//
// This property of Ribbon even makes it "elastic." A Ribbon filter and
// its small metadata for answering queries can be adapted into another
// Ribbon filter filling any smaller multiple of r bits (plus small
// metadata), with a correspondingly higher FP rate. None of the data
// thrown away during construction needs to be recalled for this reduction.
// Similarly a single Ribbon construction can be separated (by solution
// column) into two or more structures (or "layers" or "levels") with
// independent filtering ability (no FP correlation, just as solution or
// result columns in a single structure) despite being constructed as part
// of a single linear system. (TODO: implement)
// See also "ElasticBF: Fine-grained and Elastic Bloom Filter Towards
// Efficient Read for LSM-tree-based KV Stores."
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
//
// ######################################################################
// ################### CODE: Ribbon core algorithms #####################
// ######################################################################
//
// These algorithms are templatized for genericity but near-maximum
// performance in a given application. The template parameters
// adhere to informal class/struct type concepts outlined below. (This
// code is written for C++11 so does not use formal C++ concepts.)
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
// Rough architecture for these algorithms:
//
// +-----------+ +---+ +-----------------+
// | AddInputs | --> | H | --> | BandingStorage |
// +-----------+ | a | +-----------------+
// | s | |
// | h | Back substitution
// | e | V
// +-----------+ | r | +-----------------+
// | Query Key | --> | | >+< | SolutionStorage |
// +-----------+ +---+ | +-----------------+
// V
// Query result
// Common to other concepts
// concept RibbonTypes {
// // An unsigned integer type for an r-bit subsequence of coefficients.
// // r (or kCoeffBits) is taken to be sizeof(CoeffRow) * 8, as it would
// // generally only hurt scalability to leave bits of CoeffRow unused.
// typename CoeffRow;
// // An unsigned integer type big enough to hold a result row (b bits,
// // or number of solution/result columns).
// // In many applications, especially filters, the number of result
// // columns is decided at run time, so ResultRow simply needs to be
// // big enough for the largest number of columns allowed.
// typename ResultRow;
// // An unsigned integer type sufficient for representing the number of
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
// // rows in the solution structure, and at least the arithmetic
// // promotion size (usually 32 bits). uint32_t recommended because a
// // single Ribbon construction doesn't really scale to billions of
// // entries.
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 Index;
// };
// ######################################################################
// ######################## Hashers and Banding #########################
// Hasher concepts abstract out hashing details.
// concept PhsfQueryHasher extends RibbonTypes {
// // Type for a lookup key, which is hashable.
// typename Key;
//
// // Type for hashed summary of a Key. uint64_t is recommended.
// typename Hash;
//
// // Compute a hash value summarizing a Key
// Hash GetHash(const Key &) const;
//
// // Given a hash value and a number of columns that can start an
// // r-sequence of coefficients (== m - r + 1), return the start
// // column to associate with that hash value. (Starts can be chosen
// // uniformly or "smash" extra entries into the beginning and end for
// // better utilization at those extremes of the structure. Details in
// // ribbon.impl.h)
// Index GetStart(Hash, Index num_starts) const;
//
// // Given a hash value, return the r-bit sequence of coefficients to
// // associate with it. It's generally OK if
// // sizeof(CoeffRow) > sizeof(Hash)
// // as long as the hash itself is not too prone to collisions for the
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
// // applications and the CoeffRow is generated uniformly from
// // available hash data, but relatively independent of the start.
// //
// // Must be non-zero, because that's required for a solution to exist
// // when mapping to non-zero result row. (Note: BandingAdd could be
// // modified to allow 0 coeff row if that only occurs with 0 result
// // row, which really only makes sense for filter implementation,
// // where both values are hash-derived. Or BandingAdd could reject 0
// // coeff row, forcing next seed, but that has potential problems with
// // generality/scalability.)
// CoeffRow GetCoeffRow(Hash) const;
// };
// concept FilterQueryHasher extends PhsfQueryHasher {
// // For building or querying a filter, this returns the expected
// // result row associated with a hashed input. For general PHSF,
// // this must return 0.
// //
// // Although not strictly required, there's a slightly better chance of
// // solver success if result row is masked down here to only the bits
// // actually needed.
// ResultRow GetResultRowFromHash(Hash) const;
// }
// concept BandingHasher extends FilterQueryHasher {
// // For a filter, this will generally be the same as Key.
// // For a general PHSF, it must either
// // (a) include a key and a result it maps to (e.g. in a std::pair), or
// // (b) GetResultRowFromInput looks up the result somewhere rather than
// // extracting it.
// typename AddInput;
//
// // Instead of requiring a way to extract a Key from an
// // AddInput, we require getting the hash of the Key part
// // of an AddInput, which is trivial if AddInput == Key.
// Hash GetHash(const AddInput &) const;
//
// // For building a non-filter PHSF, this extracts or looks up the result
// // row to associate with an input. For filter PHSF, this must return 0.
// ResultRow GetResultRowFromInput(const AddInput &) const;
//
// // Whether the solver can assume the lowest bit of GetCoeffRow is
// // always 1. When true, it should improve solver efficiency slightly.
// static bool kFirstCoeffAlwaysOne;
// }
// Abstract storage for the the result of "banding" the inputs (Gaussian
// elimination to an upper-triangular boolean band matrix). Because the
// banding is an incremental / on-the-fly algorithm, this also represents
// all the intermediate state between input entries.
//
// concept BandingStorage extends RibbonTypes {
// // Tells the banding algorithm to prefetch memory associated with
// // the next input before processing the current input. Generally
// // recommended iff the BandingStorage doesn't easily fit in CPU
// // cache.
// bool UsePrefetch() const;
//
// // Prefetches (e.g. __builtin_prefetch) memory associated with a
// // slot index i.
// void Prefetch(Index i) const;
//
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
// // Load or store CoeffRow and ResultRow for slot index i.
// // (Gaussian row operations involve both sides of the equation.)
// // Bool `for_back_subst` indicates that customizing values for
// // unconstrained solution rows (cr == 0) is allowed.
// void LoadRow(Index i, CoeffRow *cr, ResultRow *rr, bool for_back_subst)
// const;
// void StoreRow(Index i, CoeffRow cr, ResultRow rr);
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
//
// // Returns the number of columns that can start an r-sequence of
// // coefficients, which is the number of slots minus r (kCoeffBits)
// // plus one. (m - r + 1)
// Index GetNumStarts() const;
// };
// Optional storage for backtracking data in banding a set of input
// entries. It exposes an array structure which will generally be
// used as a stack. It must be able to accommodate as many entries
// as are passed in as inputs to `BandingAddRange`.
//
// concept BacktrackStorage extends RibbonTypes {
// // If false, backtracking support will be disabled in the algorithm.
// // This should preferably be an inline compile-time constant function.
// bool UseBacktrack() const;
//
// // Records `to_save` as the `i`th backtrack entry
// void BacktrackPut(Index i, Index to_save);
//
// // Recalls the `i`th backtrack entry
// Index BacktrackGet(Index i) const;
// }
// Adds a single entry to BandingStorage (and optionally, BacktrackStorage),
// returning true if successful or false if solution is impossible with
// current hasher (and presumably its seed) and number of "slots" (solution
// or banding rows). (A solution is impossible when there is a linear
// dependence among the inputs that doesn't "cancel out".)
//
// Pre- and post-condition: the BandingStorage represents a band matrix
// ready for back substitution (row echelon form except for zero rows),
// augmented with result values such that back substitution would give a
// solution satisfying all the cr@start -> rr entries added.
template <bool kFirstCoeffAlwaysOne, typename BandingStorage,
typename BacktrackStorage>
bool BandingAdd(BandingStorage *bs, typename BandingStorage::Index start,
typename BandingStorage::ResultRow rr,
typename BandingStorage::CoeffRow cr, BacktrackStorage *bts,
typename BandingStorage::Index *backtrack_pos) {
using CoeffRow = typename BandingStorage::CoeffRow;
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
using ResultRow = typename BandingStorage::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
using Index = typename BandingStorage::Index;
Index i = start;
if (!kFirstCoeffAlwaysOne) {
// Requires/asserts that cr != 0
int tz = CountTrailingZeroBits(cr);
i += static_cast<Index>(tz);
cr >>= tz;
}
for (;;) {
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
assert((cr & 1) == 1);
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
CoeffRow cr_at_i;
ResultRow rr_at_i;
bs->LoadRow(i, &cr_at_i, &rr_at_i, /* for_back_subst */ false);
if (cr_at_i == 0) {
bs->StoreRow(i, cr, rr);
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
bts->BacktrackPut(*backtrack_pos, i);
++*backtrack_pos;
return true;
}
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
assert((cr_at_i & 1) == 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
// Gaussian row reduction
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
cr ^= cr_at_i;
rr ^= rr_at_i;
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 (cr == 0) {
// Inconsistency or (less likely) redundancy
break;
}
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
// Find relative offset of next non-zero coefficient.
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
int tz = CountTrailingZeroBits(cr);
i += static_cast<Index>(tz);
cr >>= tz;
}
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
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
// Failed, unless result row == 0 because e.g. a duplicate input or a
// stock hash collision, with same result row. (For filter, stock hash
// collision implies same result row.) Or we could have a full equation
// equal to sum of other equations, which is very possible with
// small range of values for result row.
return rr == 0;
}
// Adds a range of entries to BandingStorage returning true if successful
// or false if solution is impossible with current hasher (and presumably
// its seed) and number of "slots" (solution or banding rows). (A solution
// is impossible when there is a linear dependence among the inputs that
// doesn't "cancel out".) Here "InputIterator" is an iterator over AddInputs.
//
// If UseBacktrack in the BacktrackStorage, this function call rolls back
// to prior state on failure. If !UseBacktrack, some subset of the entries
// will have been added to the BandingStorage, so best considered to be in
// an indeterminate state.
//
template <typename BandingStorage, typename BacktrackStorage,
typename BandingHasher, typename InputIterator>
bool BandingAddRange(BandingStorage *bs, BacktrackStorage *bts,
const BandingHasher &bh, InputIterator begin,
InputIterator end) {
using CoeffRow = typename BandingStorage::CoeffRow;
using Index = typename BandingStorage::Index;
using ResultRow = typename BandingStorage::ResultRow;
using Hash = typename BandingHasher::Hash;
static_assert(IsUnsignedUpTo128<CoeffRow>::value, "must be unsigned");
static_assert(IsUnsignedUpTo128<Index>::value, "must be unsigned");
static_assert(IsUnsignedUpTo128<ResultRow>::value, "must be unsigned");
constexpr bool kFCA1 = BandingHasher::kFirstCoeffAlwaysOne;
if (begin == end) {
// trivial
return true;
}
const Index num_starts = bs->GetNumStarts();
InputIterator cur = begin;
Index backtrack_pos = 0;
if (!bs->UsePrefetch()) {
// Simple version, no prefetch
for (;;) {
Hash h = bh.GetHash(*cur);
Index start = bh.GetStart(h, num_starts);
ResultRow rr =
bh.GetResultRowFromInput(*cur) | bh.GetResultRowFromHash(h);
CoeffRow cr = bh.GetCoeffRow(h);
if (!BandingAdd<kFCA1>(bs, start, rr, cr, bts, &backtrack_pos)) {
break;
}
if ((++cur) == end) {
return true;
}
}
} else {
// Pipelined w/prefetch
// Prime the pipeline
Hash h = bh.GetHash(*cur);
Index start = bh.GetStart(h, num_starts);
ResultRow rr = bh.GetResultRowFromInput(*cur);
bs->Prefetch(start);
// Pipeline
for (;;) {
rr |= bh.GetResultRowFromHash(h);
CoeffRow cr = bh.GetCoeffRow(h);
if ((++cur) == end) {
if (!BandingAdd<kFCA1>(bs, start, rr, cr, bts, &backtrack_pos)) {
break;
}
return true;
}
Hash next_h = bh.GetHash(*cur);
Index next_start = bh.GetStart(next_h, num_starts);
ResultRow next_rr = bh.GetResultRowFromInput(*cur);
bs->Prefetch(next_start);
if (!BandingAdd<kFCA1>(bs, start, rr, cr, bts, &backtrack_pos)) {
break;
}
h = next_h;
start = next_start;
rr = next_rr;
}
}
// failed; backtrack (if implemented)
if (bts->UseBacktrack()) {
while (backtrack_pos > 0) {
--backtrack_pos;
Index i = bts->BacktrackGet(backtrack_pos);
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
// Clearing the ResultRow is not strictly required, but is required
// for good FP rate on inputs that might have been backtracked out.
// (We don't want anything we've backtracked on to leak into final
// result, as that might not be "harmless".)
bs->StoreRow(i, 0, 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
}
}
return false;
}
// Adds a range of entries to BandingStorage returning true if successful
// or false if solution is impossible with current hasher (and presumably
// its seed) and number of "slots" (solution or banding rows). (A solution
// is impossible when there is a linear dependence among the inputs that
// doesn't "cancel out".) Here "InputIterator" is an iterator over AddInputs.
//
// On failure, some subset of the entries will have been added to the
// BandingStorage, so best considered to be in an indeterminate state.
//
template <typename BandingStorage, typename BandingHasher,
typename InputIterator>
bool BandingAddRange(BandingStorage *bs, const BandingHasher &bh,
InputIterator begin, InputIterator end) {
using Index = typename BandingStorage::Index;
struct NoopBacktrackStorage {
bool UseBacktrack() { return false; }
void BacktrackPut(Index, Index) {}
Index BacktrackGet(Index) {
assert(false);
return 0;
}
} nbts;
return BandingAddRange(bs, &nbts, bh, begin, end);
}
// ######################################################################
// ######################### Solution Storage ###########################
// Back-substitution and query algorithms unfortunately depend on some
// details of data layout in the final data structure ("solution"). Thus,
// there is no common SolutionStorage covering all the reasonable
// possibilities.
// ###################### SimpleSolutionStorage #########################
// SimpleSolutionStorage is for a row-major storage, typically with no
// unused bits in each ResultRow. This is mostly for demonstration
// purposes as the simplest solution storage scheme. It is relatively slow
// for filter queries.
// concept SimpleSolutionStorage extends RibbonTypes {
// // This is called at the beginning of back-substitution for the
// // solution storage to do any remaining configuration before data
// // is stored to it. If configuration is previously finalized, this
// // could be a simple assertion or even no-op. Ribbon algorithms
// // only call this from back-substitution, and only once per call,
// // before other functions here.
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 PrepareForNumStarts(Index num_starts) const;
// // Must return num_starts passed to PrepareForNumStarts, or the most
// // recent call to PrepareForNumStarts if this storage object can be
// // reused. Note that num_starts == num_slots - kCoeffBits + 1 because
// // there must be a run of kCoeffBits slots starting from each start.
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;
// // Load the solution row (type ResultRow) for a slot
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
// ResultRow Load(Index slot_num) const;
// // Store the solution row (type ResultRow) for a slot
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 Store(Index slot_num, ResultRow data);
// };
// Back-substitution for generating a solution from BandingStorage to
// SimpleSolutionStorage.
template <typename SimpleSolutionStorage, typename BandingStorage>
void SimpleBackSubst(SimpleSolutionStorage *sss, const BandingStorage &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
using CoeffRow = typename BandingStorage::CoeffRow;
using Index = typename BandingStorage::Index;
using ResultRow = typename BandingStorage::ResultRow;
static_assert(sizeof(Index) == sizeof(typename SimpleSolutionStorage::Index),
"must be same");
static_assert(
sizeof(CoeffRow) == sizeof(typename SimpleSolutionStorage::CoeffRow),
"must be same");
static_assert(
sizeof(ResultRow) == sizeof(typename SimpleSolutionStorage::ResultRow),
"must be same");
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 kCoeffBits = static_cast<Index>(sizeof(CoeffRow) * 8U);
constexpr auto kResultBits = static_cast<Index>(sizeof(ResultRow) * 8U);
// A column-major buffer of the solution matrix, containing enough
// recently-computed solution data to compute the next solution row
// (based also on banding data).
std::array<CoeffRow, kResultBits> state;
state.fill(0);
const Index num_starts = bs.GetNumStarts();
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
sss->PrepareForNumStarts(num_starts);
const Index num_slots = num_starts + kCoeffBits - 1;
for (Index i = num_slots; i > 0;) {
--i;
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
CoeffRow cr;
ResultRow rr;
bs.LoadRow(i, &cr, &rr, /* for_back_subst */ true);
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
// solution row
ResultRow sr = 0;
for (Index j = 0; j < kResultBits; ++j) {
// Compute next solution bit at row i, column j (see derivation below)
CoeffRow tmp = state[j] << 1;
bool bit = (BitParity(tmp & cr) ^ ((rr >> j) & 1)) != 0;
tmp |= bit ? CoeffRow{1} : CoeffRow{0};
// Now tmp is solution at column j from row i for next kCoeffBits
// more rows. Thus, for valid solution, the dot product of the
// solution column with the coefficient row has to equal the result
// at that column,
// BitParity(tmp & cr) == ((rr >> j) & 1)
// Update state.
state[j] = tmp;
// add to solution row
sr |= (bit ? ResultRow{1} : ResultRow{0}) << j;
}
sss->Store(i, sr);
}
}
// Common functionality for querying a key (already hashed) in
// SimpleSolutionStorage.
template <typename SimpleSolutionStorage>
typename SimpleSolutionStorage::ResultRow SimpleQueryHelper(
typename SimpleSolutionStorage::Index start_slot,
typename SimpleSolutionStorage::CoeffRow cr,
const SimpleSolutionStorage &sss) {
using CoeffRow = typename SimpleSolutionStorage::CoeffRow;
using ResultRow = typename SimpleSolutionStorage::ResultRow;
constexpr unsigned kCoeffBits = static_cast<unsigned>(sizeof(CoeffRow) * 8U);
ResultRow result = 0;
for (unsigned i = 0; i < kCoeffBits; ++i) {
// Bit masking whole value is generally faster here than 'if'
result ^= sss.Load(start_slot + i) &
(ResultRow{0} - (static_cast<ResultRow>(cr >> i) & ResultRow{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 result;
}
// General PHSF query a key from SimpleSolutionStorage.
template <typename SimpleSolutionStorage, typename PhsfQueryHasher>
typename SimpleSolutionStorage::ResultRow SimplePhsfQuery(
const typename PhsfQueryHasher::Key &key, const PhsfQueryHasher &hasher,
const SimpleSolutionStorage &sss) {
const typename PhsfQueryHasher::Hash hash = hasher.GetHash(key);
static_assert(sizeof(typename SimpleSolutionStorage::Index) ==
sizeof(typename PhsfQueryHasher::Index),
"must be same");
static_assert(sizeof(typename SimpleSolutionStorage::CoeffRow) ==
sizeof(typename PhsfQueryHasher::CoeffRow),
"must be same");
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 SimpleQueryHelper(hasher.GetStart(hash, sss.GetNumStarts()),
hasher.GetCoeffRow(hash), sss);
}
// Filter query a key from SimpleSolutionStorage.
template <typename SimpleSolutionStorage, typename FilterQueryHasher>
bool SimpleFilterQuery(const typename FilterQueryHasher::Key &key,
const FilterQueryHasher &hasher,
const SimpleSolutionStorage &sss) {
const typename FilterQueryHasher::Hash hash = hasher.GetHash(key);
const typename SimpleSolutionStorage::ResultRow expected =
hasher.GetResultRowFromHash(hash);
static_assert(sizeof(typename SimpleSolutionStorage::Index) ==
sizeof(typename FilterQueryHasher::Index),
"must be same");
static_assert(sizeof(typename SimpleSolutionStorage::CoeffRow) ==
sizeof(typename FilterQueryHasher::CoeffRow),
"must be same");
static_assert(sizeof(typename SimpleSolutionStorage::ResultRow) ==
sizeof(typename FilterQueryHasher::ResultRow),
"must be same");
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 expected ==
SimpleQueryHelper(hasher.GetStart(hash, sss.GetNumStarts()),
hasher.GetCoeffRow(hash), sss);
}
// #################### InterleavedSolutionStorage ######################
// InterleavedSolutionStorage is row-major at a high level, for good
// locality, and column-major at a low level, for CPU efficiency
// especially in filter queries or relatively small number of result bits
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
// (== solution columns). The storage is a sequence of "blocks" where a
// block has one CoeffRow-sized segment for each solution column. Each
// query spans at most two blocks; the starting solution row is typically
// in the row-logical middle of a block and spans to the middle of the
// next block. (See diagram below.)
//
// InterleavedSolutionStorage supports choosing b (number of result or
// solution columns) at run time, and even supports mixing b and b-1 solution
// columns in a single linear system solution, for filters that can
// effectively utilize any size space (multiple of CoeffRow) for minimizing
// FP rate for any number of added keys. To simplify query implementation
// (with lower-index columns first), the b-bit portion comes after the b-1
// portion of the structure.
//
// Diagram (=== marks logical block boundary; b=4; ### is data used by a
// query crossing the b-1 to b boundary, each Segment has type CoeffRow):
// ...
// +======================+
// | S e g m e n t col=0 |
// +----------------------+
// | S e g m e n t col=1 |
// +----------------------+
// | S e g m e n t col=2 |
// +======================+
// | S e g m e n #########|
// +----------------------+
// | S e g m e n #########|
// +----------------------+
// | S e g m e n #########|
// +======================+ Result/solution columns: above = 3, below = 4
// |#############t col=0 |
// +----------------------+
// |#############t col=1 |
// +----------------------+
// |#############t col=2 |
// +----------------------+
// | S e g m e n t col=3 |
// +======================+
// | S e g m e n t col=0 |
// +----------------------+
// | S e g m e n t col=1 |
// +----------------------+
// | S e g m e n t col=2 |
// +----------------------+
// | S e g m e n t col=3 |
// +======================+
// ...
//
// InterleavedSolutionStorage will be adapted by the algorithms from
// simple array-like segment storage. That array-like storage is templatized
// in part so that an implementation may choose to handle byte ordering
// at access time.
//
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 InterleavedSolutionStorage extends RibbonTypes {
// // This is called at the beginning of back-substitution for the
// // solution storage to do any remaining configuration before data
// // is stored to it. If configuration is previously finalized, this
// // could be a simple assertion or even no-op. Ribbon algorithms
// // only call this from back-substitution, and only once per call,
// // before other functions here.
// void PrepareForNumStarts(Index num_starts) const;
// // Must return num_starts passed to PrepareForNumStarts, or the most
// // recent call to PrepareForNumStarts if this storage object can be
// // reused. Note that num_starts == num_slots - kCoeffBits + 1 because
// // there must be a run of kCoeffBits slots starting from each start.
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;
// // The larger number of solution columns used (called "b" above).
// Index GetUpperNumColumns() const;
// // If returns > 0, then block numbers below that use
// // GetUpperNumColumns() - 1 columns per solution row, and the rest
// // use GetUpperNumColumns(). A block represents kCoeffBits "slots",
// // where all but the last kCoeffBits - 1 slots are also starts. And
// // a block contains a segment for each solution column.
// // An implementation may only support uniform columns per solution
// // row and return constant 0 here.
// Index GetUpperStartBlock() const;
//
// // ### "Array of segments" portion of API ###
// // The number of values of type CoeffRow used in this solution
// // representation. (This value can be inferred from the previous
// // three functions, but is expected at least for sanity / assertion
// // checking.)
// Index GetNumSegments() const;
// // Load an entry from the logical array of segments
// CoeffRow LoadSegment(Index segment_num) const;
// // Store an entry to the logical array of segments
// void StoreSegment(Index segment_num, CoeffRow data);
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 helper for InterleavedBackSubst.
template <typename BandingStorage>
inline void BackSubstBlock(typename BandingStorage::CoeffRow *state,
typename BandingStorage::Index num_columns,
const BandingStorage &bs,
typename BandingStorage::Index start_slot) {
using CoeffRow = typename BandingStorage::CoeffRow;
using Index = typename BandingStorage::Index;
using ResultRow = typename BandingStorage::ResultRow;
constexpr auto kCoeffBits = static_cast<Index>(sizeof(CoeffRow) * 8U);
for (Index i = start_slot + kCoeffBits; i > start_slot;) {
--i;
Refine Ribbon configuration, improve testing, add Homogeneous (#7879) Summary: This change only affects non-schema-critical aspects of the production candidate Ribbon filter. Specifically, it refines choice of internal configuration parameters based on inputs. The changes are minor enough that the schema tests in bloom_test, some of which depend on this, are unaffected. There are also some minor optimizations and refactorings. This would be a schema change for "smash" Ribbon, to fix some known issues with small filters, but "smash" Ribbon is not accessible in public APIs. Unit test CompactnessAndBacktrackAndFpRate updated to test small and medium-large filters. Run with --thoroughness=100 or so for much better detection power (not appropriate for continuous regression testing). Homogenous Ribbon: This change adds internally a Ribbon filter variant we call Homogeneous Ribbon, in collaboration with Stefan Walzer. The expected "result" value for every key is zero, instead of computed from a hash. Entropy for queries not to be false positives comes from free variables ("overhead") in the solution structure, which are populated pseudorandomly. Construction is slightly faster for not tracking result values, and never fails. Instead, FP rate can jump up whenever and whereever entries are packed too tightly. For small structures, we can choose overhead to make this FP rate jump unlikely, as seen in updated unit test CompactnessAndBacktrackAndFpRate. Unlike standard Ribbon, Homogeneous Ribbon seems to scale to arbitrary number of keys when accepting an FP rate penalty for small pockets of high FP rate in the structure. For example, 64-bit ribbon with 8 solution columns and 10% allocated space overhead for slots seems to achieve about 10.5% space overhead vs. information-theoretic minimum based on its observed FP rate with expected pockets of degradation. (FP rate is close to 1/256.) If targeting a higher FP rate with fewer solution columns, Homogeneous Ribbon can be even more space efficient, because the penalty from degradation is relatively smaller. If targeting a lower FP rate, Homogeneous Ribbon is less space efficient, as more allocated overhead is needed to keep the FP rate impact of degradation relatively under control. The new OptimizeHomogAtScale tool in ribbon_test helps to find these optimal allocation overheads for different numbers of solution columns. And Ribbon widths, with 128-bit Ribbon apparently cutting space overheads in half vs. 64-bit. Other misc item specifics: * Ribbon APIs in util/ribbon_config.h now provide configuration data for not just 5% construction failure rate (95% success), but also 50% and 0.1%. * Note that the Ribbon structure does not exhibit "threshold" behavior as standard Xor filter does, so there is a roughly fixed space penalty to cut construction failure rate in half. Thus, there isn't really an "almost sure" setting. * Although we can extrapolate settings for large filters, we don't have a good formula for configuring smaller filters (< 2^17 slots or so), and efforts to summarize with a formula have failed. Thus, small data is hard-coded from updated FindOccupancy tool. * Enhances ApproximateNumEntries for public API Ribbon using more precise data (new API GetNumToAdd), thus a more accurate but not perfect reversal of CalculateSpace. (bloom_test updated to expect the greater precision) * Move EndianSwapValue from coding.h to coding_lean.h to keep Ribbon code easily transferable from RocksDB * Add some missing 'const' to member functions * Small optimization to 128-bit BitParity * Small refactoring of BandingStorage in ribbon_alg.h to support Homogeneous Ribbon * CompactnessAndBacktrackAndFpRate now has an "expand" test: on construction failure, a possible alternative to re-seeding hash functions is simply to increase the number of slots (allocated space overhead) and try again with essentially the same hash values. (Start locations will be different roundings of the same scaled hash values--because fastrange not mod.) This seems to be as effective or more effective than re-seeding, as long as we increase the number of slots (m) by roughly m += m/w where w is the Ribbon width. This way, there is effectively an expansion by one slot for each ribbon-width window in the banding. (This approach assumes that getting "bad data" from your hash function is as unlikely as it naturally should be, e.g. no adversary.) * 32-bit and 16-bit Ribbon configurations are added to ribbon_test for understanding their behavior, e.g. with FindOccupancy. They are not considered useful at this time and not tested with CompactnessAndBacktrackAndFpRate. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7879 Test Plan: unit test updates included Reviewed By: jay-zhuang Differential Revision: D26371245 Pulled By: pdillinger fbshipit-source-id: da6600d90a3785b99ad17a88b2a3027710b4ea3a
4 years ago
CoeffRow cr;
ResultRow rr;
bs.LoadRow(i, &cr, &rr, /* for_back_subst */ true);
for (Index j = 0; j < num_columns; ++j) {
// Compute next solution bit at row i, column j (see derivation below)
CoeffRow tmp = state[j] << 1;
int bit = BitParity(tmp & cr) ^ ((rr >> j) & 1);
tmp |= static_cast<CoeffRow>(bit);
// Now tmp is solution at column j from row i for next kCoeffBits
// more rows. Thus, for valid solution, the dot product of the
// solution column with the coefficient row has to equal the result
// at that column,
// BitParity(tmp & cr) == ((rr >> j) & 1)
// Update state.
state[j] = tmp;
}
}
}
// Back-substitution for generating a solution from BandingStorage to
// InterleavedSolutionStorage.
template <typename InterleavedSolutionStorage, typename BandingStorage>
void InterleavedBackSubst(InterleavedSolutionStorage *iss,
const BandingStorage &bs) {
using CoeffRow = typename BandingStorage::CoeffRow;
using Index = typename BandingStorage::Index;
static_assert(
sizeof(Index) == sizeof(typename InterleavedSolutionStorage::Index),
"must be same");
static_assert(
sizeof(CoeffRow) == sizeof(typename InterleavedSolutionStorage::CoeffRow),
"must be same");
constexpr auto kCoeffBits = static_cast<Index>(sizeof(CoeffRow) * 8U);
const Index num_starts = bs.GetNumStarts();
// Although it might be nice to have a filter that returns "always false"
// when no key is added, we aren't specifically supporting that here
// because it would require another condition branch in the query.
assert(num_starts > 0);
iss->PrepareForNumStarts(num_starts);
const Index num_slots = num_starts + kCoeffBits - 1;
assert(num_slots % kCoeffBits == 0);
const Index num_blocks = num_slots / kCoeffBits;
const Index num_segments = iss->GetNumSegments();
// For now upper, then lower
Index num_columns = iss->GetUpperNumColumns();
const Index upper_start_block = iss->GetUpperStartBlock();
if (num_columns == 0) {
// Nothing to do, presumably because there's not enough space for even
// a single segment.
assert(num_segments == 0);
// When num_columns == 0, a Ribbon filter query will always return true,
// or a PHSF query always 0.
return;
}
// We should be utilizing all available segments
assert(num_segments == (upper_start_block * (num_columns - 1)) +
((num_blocks - upper_start_block) * num_columns));
// TODO: consider fixed-column specializations with stack-allocated state
// A column-major buffer of the solution matrix, containing enough
// recently-computed solution data to compute the next solution row
// (based also on banding data).
std::unique_ptr<CoeffRow[]> state{new CoeffRow[num_columns]()};
Index block = num_blocks;
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
Index segment_num = num_segments;
while (block > upper_start_block) {
--block;
BackSubstBlock(state.get(), num_columns, bs, block * kCoeffBits);
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
segment_num -= num_columns;
for (Index i = 0; i < num_columns; ++i) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
iss->StoreSegment(segment_num + i, state[i]);
}
}
// Now (if applicable), region using lower number of columns
// (This should be optimized away if GetUpperStartBlock() returns
// constant 0.)
--num_columns;
while (block > 0) {
--block;
BackSubstBlock(state.get(), num_columns, bs, block * kCoeffBits);
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
segment_num -= num_columns;
for (Index i = 0; i < num_columns; ++i) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
iss->StoreSegment(segment_num + i, state[i]);
}
}
// Verify everything processed
assert(block == 0);
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
assert(segment_num == 0);
}
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
// Prefetch memory for a key in InterleavedSolutionStorage.
template <typename InterleavedSolutionStorage, typename PhsfQueryHasher>
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
inline void InterleavedPrepareQuery(
const typename PhsfQueryHasher::Key &key, const PhsfQueryHasher &hasher,
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
const InterleavedSolutionStorage &iss,
typename PhsfQueryHasher::Hash *saved_hash,
typename InterleavedSolutionStorage::Index *saved_segment_num,
typename InterleavedSolutionStorage::Index *saved_num_columns,
typename InterleavedSolutionStorage::Index *saved_start_bit) {
using Hash = typename PhsfQueryHasher::Hash;
using CoeffRow = typename InterleavedSolutionStorage::CoeffRow;
using Index = typename InterleavedSolutionStorage::Index;
static_assert(sizeof(Index) == sizeof(typename PhsfQueryHasher::Index),
"must be same");
const Hash hash = hasher.GetHash(key);
const Index start_slot = hasher.GetStart(hash, iss.GetNumStarts());
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
constexpr auto kCoeffBits = static_cast<Index>(sizeof(CoeffRow) * 8U);
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
const Index upper_start_block = iss.GetUpperStartBlock();
Index num_columns = iss.GetUpperNumColumns();
Index start_block_num = start_slot / kCoeffBits;
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
Index segment_num = start_block_num * num_columns -
std::min(start_block_num, upper_start_block);
// Change to lower num columns if applicable.
// (This should not compile to a conditional branch.)
num_columns -= (start_block_num < upper_start_block) ? 1 : 0;
Index start_bit = start_slot % kCoeffBits;
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
Index segment_count = num_columns + (start_bit == 0 ? 0 : num_columns);
iss.PrefetchSegmentRange(segment_num, segment_num + segment_count);
*saved_hash = hash;
*saved_segment_num = segment_num;
*saved_num_columns = num_columns;
*saved_start_bit = start_bit;
}
// General PHSF query from InterleavedSolutionStorage, using data for
// the query key from InterleavedPrepareQuery
template <typename InterleavedSolutionStorage, typename PhsfQueryHasher>
inline typename InterleavedSolutionStorage::ResultRow InterleavedPhsfQuery(
typename PhsfQueryHasher::Hash hash,
typename InterleavedSolutionStorage::Index segment_num,
typename InterleavedSolutionStorage::Index num_columns,
typename InterleavedSolutionStorage::Index start_bit,
const PhsfQueryHasher &hasher, const InterleavedSolutionStorage &iss) {
using CoeffRow = typename InterleavedSolutionStorage::CoeffRow;
using Index = typename InterleavedSolutionStorage::Index;
using ResultRow = typename InterleavedSolutionStorage::ResultRow;
static_assert(sizeof(Index) == sizeof(typename PhsfQueryHasher::Index),
"must be same");
static_assert(sizeof(CoeffRow) == sizeof(typename PhsfQueryHasher::CoeffRow),
"must be same");
constexpr auto kCoeffBits = static_cast<Index>(sizeof(CoeffRow) * 8U);
const CoeffRow cr = hasher.GetCoeffRow(hash);
ResultRow sr = 0;
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
const CoeffRow cr_left = cr << static_cast<unsigned>(start_bit);
for (Index i = 0; i < num_columns; ++i) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
sr ^= BitParity(iss.LoadSegment(segment_num + i) & cr_left) << i;
}
if (start_bit > 0) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
segment_num += num_columns;
const CoeffRow cr_right =
cr >> static_cast<unsigned>(kCoeffBits - start_bit);
for (Index i = 0; i < num_columns; ++i) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
sr ^= BitParity(iss.LoadSegment(segment_num + i) & cr_right) << i;
}
}
return sr;
}
// Filter query a key from InterleavedFilterQuery.
template <typename InterleavedSolutionStorage, typename FilterQueryHasher>
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
inline bool InterleavedFilterQuery(
typename FilterQueryHasher::Hash hash,
typename InterleavedSolutionStorage::Index segment_num,
typename InterleavedSolutionStorage::Index num_columns,
typename InterleavedSolutionStorage::Index start_bit,
const FilterQueryHasher &hasher, const InterleavedSolutionStorage &iss) {
using CoeffRow = typename InterleavedSolutionStorage::CoeffRow;
using Index = typename InterleavedSolutionStorage::Index;
using ResultRow = typename InterleavedSolutionStorage::ResultRow;
static_assert(sizeof(Index) == sizeof(typename FilterQueryHasher::Index),
"must be same");
static_assert(
sizeof(CoeffRow) == sizeof(typename FilterQueryHasher::CoeffRow),
"must be same");
static_assert(
sizeof(ResultRow) == sizeof(typename FilterQueryHasher::ResultRow),
"must be same");
constexpr auto kCoeffBits = static_cast<Index>(sizeof(CoeffRow) * 8U);
const CoeffRow cr = hasher.GetCoeffRow(hash);
const ResultRow expected = hasher.GetResultRowFromHash(hash);
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
// TODO: consider optimizations such as
// * get rid of start_bit == 0 condition with careful fetching & shifting
if (start_bit == 0) {
for (Index i = 0; i < num_columns; ++i) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
if (BitParity(iss.LoadSegment(segment_num + i) & cr) !=
(static_cast<int>(expected >> i) & 1)) {
return false;
}
}
} else {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
const CoeffRow cr_left = cr << static_cast<unsigned>(start_bit);
const CoeffRow cr_right =
cr >> static_cast<unsigned>(kCoeffBits - start_bit);
for (Index i = 0; i < num_columns; ++i) {
Add prefetching (batched MultiGet) for experimental Ribbon filter (#7889) Summary: Adds support for prefetching data in Ribbon queries, which especially optimizes batched Ribbon queries for MultiGet (~222ns/key to ~97ns/key) but also single key queries on cold memory (~333ns to ~226ns) because many queries span more than one cache line. This required some refactoring of the query algorithm, and there does not appear to be a noticeable regression in "hot memory" query times (perhaps from 48ns to 50ns). Pull Request resolved: https://github.com/facebook/rocksdb/pull/7889 Test Plan: existing unit tests, plus performance validation with filter_bench: Each data point is the best of two runs. I saturated the machine CPUs with other filter_bench runs in the background. Before: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 125.86 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 48.0111 Batched, prepared net ns/op: 222.384 Batched, unprepared net ns/op: 343.908 Skewed 50% in 1% net ns/op: 252.916 Skewed 80% in 20% net ns/op: 320.579 Random filter net ns/op: 332.957 After: $ ./filter_bench -impl=3 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 128.117 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 Prelim FP rate %: 0.951827 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 49.8812 Batched, prepared net ns/op: 97.1514 Batched, unprepared net ns/op: 222.025 Skewed 50% in 1% net ns/op: 197.48 Skewed 80% in 20% net ns/op: 212.457 Random filter net ns/op: 226.464 Bloom comparison, for reference: $ ./filter_bench -impl=2 -m_keys_total_max=200 -average_keys_per_filter=100000 -m_queries=50 WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 35.3042 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 Prelim FP rate %: 0.965327 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 9.09931 Batched, prepared net ns/op: 34.21 Batched, unprepared net ns/op: 88.8564 Skewed 50% in 1% net ns/op: 139.75 Skewed 80% in 20% net ns/op: 181.264 Random filter net ns/op: 173.88 Reviewed By: jay-zhuang Differential Revision: D26378710 Pulled By: pdillinger fbshipit-source-id: 058428967c55ed763698284cd3b4bbe3351b6e69
4 years ago
CoeffRow soln_data =
(iss.LoadSegment(segment_num + i) & cr_left) ^
(iss.LoadSegment(segment_num + num_columns + i) & cr_right);
if (BitParity(soln_data) != (static_cast<int>(expected >> i) & 1)) {
return false;
}
}
}
// otherwise, all match
return true;
}
// TODO: refactor Interleaved*Query so that queries can be "prepared" by
// prefetching memory, to hide memory latency for multiple queries in a
// single thread.
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