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authorVictor Costan <costan@google.com>2019-12-02 13:37:34 -0800
committerVictor Costan <pwnall@chromium.org>2019-12-02 13:51:20 -0800
commit58a89bbcb28d02d5704c5fff7aeb6e72f7ca2431 (patch)
tree739302b5de05554a2fb5e30192787323ef671755 /util/env_posix_test.cc
parentc0d43142ff190b25fd1cd30c26927982bb75b499 (diff)
downloadleveldb-58a89bbcb28d02d5704c5fff7aeb6e72f7ca2431.tar.gz
Add WITHOUT ROWID to SQLite benchmark.
The SQLite-specific schema feature is documented at https://www.sqlite.org/withoutrowid.html and https://www.sqlite.org/rowidtable.html. By default, SQLite stores each table in a B-tree keyed by an integer, called the ROWID. Any index, including the PRIMARY KEY index, is a separate B-tree mapping index keys to ROWIDs. Tables without ROWIDs are stored in a B-tree keyed by the primary key. Additional indexes (the PRIMARY KEY index is implicitly built into the table) are stored as B-trees mapping index keys to row primary keys. This CL introduces a boolean --use-rowids flag to db_bench_sqlite. When the flag is false (default), the schema of the test table includes WITHOUT ROWID. The test table uses a primary key, so adding WITHOUT ROWID to the schema reduces the number of B-trees used by the benchmark from 2 to 1. This brings SQLite's disk usage closer to LevelDB. When WITHOUT ROWID is used, SQLite fares better (than today) on benchmarks with small (16-byte) keys, and worse on benchmarks with large (100kb) keys. Baseline results: fillseq : 21.310 micros/op; 5.2 MB/s fillseqsync : 146.377 micros/op; 0.8 MB/s (10000 ops) fillseqbatch : 2.065 micros/op; 53.6 MB/s fillrandom : 34.767 micros/op; 3.2 MB/s fillrandsync : 159.943 micros/op; 0.7 MB/s (10000 ops) fillrandbatch : 15.055 micros/op; 7.3 MB/s overwrite : 43.660 micros/op; 2.5 MB/s overwritebatch : 27.691 micros/op; 4.0 MB/s readrandom : 12.725 micros/op; readseq : 2.602 micros/op; 36.7 MB/s fillrand100K : 606.333 micros/op; 157.3 MB/s (1000 ops) fillseq100K : 657.457 micros/op; 145.1 MB/s (1000 ops) readseq : 46.523 micros/op; 2049.9 MB/s readrand100K : 54.943 micros/op; Results after this CL: fillseq : 16.231 micros/op; 6.8 MB/s fillseqsync : 147.460 micros/op; 0.8 MB/s (10000 ops) fillseqbatch : 2.294 micros/op; 48.2 MB/s fillrandom : 27.871 micros/op; 4.0 MB/s fillrandsync : 141.979 micros/op; 0.8 MB/s (10000 ops) fillrandbatch : 16.087 micros/op; 6.9 MB/s overwrite : 26.829 micros/op; 4.1 MB/s overwritebatch : 19.014 micros/op; 5.8 MB/s readrandom : 11.657 micros/op; readseq : 0.155 micros/op; 615.0 MB/s fillrand100K : 816.812 micros/op; 116.8 MB/s (1000 ops) fillseq100K : 754.689 micros/op; 126.4 MB/s (1000 ops) readseq : 47.112 micros/op; 2024.3 MB/s readrand100K : 287.679 micros/op; Results after this CL, with --use-rowids=1 fillseq : 20.655 micros/op; 5.4 MB/s fillseqsync : 146.408 micros/op; 0.8 MB/s (10000 ops) fillseqbatch : 2.045 micros/op; 54.1 MB/s fillrandom : 34.080 micros/op; 3.2 MB/s fillrandsync : 154.582 micros/op; 0.7 MB/s (10000 ops) fillrandbatch : 14.404 micros/op; 7.7 MB/s overwrite : 42.928 micros/op; 2.6 MB/s overwritebatch : 27.829 micros/op; 4.0 MB/s readrandom : 12.835 micros/op; readseq : 2.483 micros/op; 38.4 MB/s fillrand100K : 603.265 micros/op; 158.1 MB/s (1000 ops) fillseq100K : 662.473 micros/op; 144.0 MB/s (1000 ops) readseq : 45.478 micros/op; 2097.0 MB/s readrand100K : 54.439 micros/op; PiperOrigin-RevId: 283407101
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