# Set filter benchmarks

The data and the timed cells of [docs/SET-FILTERS.md](../../docs/SET-FILTERS.md): a fact table of events, a dimension table of entities and a membership table of groups, all synthetic and deterministic.

```sh
psql -d sets -v np=200000 -v nr=3000000 -v sch=sets -f bench/sets/gen.sql     # about 4 minutes
psql -d sets -v sch=sets -f bench/sets/build.sql                               # the fact table, its btrees and the sift_col index
```

`run.sh` runs `run.py` in a systemd scope (`MemoryMax=12G`, `CPUQuota=300%`) and `run.py` takes the host lock `/tmp/sift-perf.lock` (blocking `flock`) for the whole run;
the connection is the libpq environment, the database `sets` unless `PGDATABASE` says another. A cell is one query, one plan mode and one session; the cells interleave in
random order, the first pass is the warm-up, and the median of the timed runs is the figure. The answer of every cell is compared with the one of the normal plan.

Designs and their cells (`run.py --design N`, `--list`): 1 the array parameter (generic, custom and literal plans), 2 the semi and anti join byte table, 4 the lookup load (`siftagg-lookup-full`,
`siftagg-lookup-pushdown`, `siftagg-semi-table-full`, default), 5 the attribute copied into the fact table (`denorm.sql` builds `events_dn_u` and `events_dn`; `denorm.py` measures the write path:
WAL of an attribute change, of an insert, and the units a late batch adds). `types.sql` and `types.py` time the array parameter on key columns of each type (`int2`, `int4`, `int8`, `text`, `date`,
`timestamp`, `timestamptz`) with 1,000, 2,000 and 5,000 elements.