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
58a89bbcb2