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c6172dae47
tests are based on self-hosted runner which is physically close to our staging deployment in aws, currently tests consist of various configurations of pgbenchi runs. Also these changes rework benchmark fixture by removing globals and allowing to collect reports with desired metrics and dump them to json for further analysis. This is also applicable to usual performance tests which use local zenith binaries.
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
import os
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from contextlib import closing
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from fixtures.benchmark_fixture import MetricReport
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from fixtures.zenith_fixtures import ZenithEnv
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from fixtures.log_helper import log
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pytest_plugins = ("fixtures.zenith_fixtures", "fixtures.benchmark_fixture")
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#
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# Test buffering GisT build. It WAL-logs the whole relation, in 32-page chunks.
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# As of this writing, we're duplicate those giant WAL records for each page,
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# which makes the delta layer about 32x larger than it needs to be.
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#
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def test_gist_buffering_build(zenith_simple_env: ZenithEnv, zenbenchmark):
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env = zenith_simple_env
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# Create a branch for us
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env.zenith_cli(["branch", "test_gist_buffering_build", "empty"])
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pg = env.postgres.create_start('test_gist_buffering_build')
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log.info("postgres is running on 'test_gist_buffering_build' branch")
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# Open a connection directly to the page server that we'll use to force
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# flushing the layers to disk
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psconn = env.pageserver.connect()
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pscur = psconn.cursor()
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# Get the timeline ID of our branch. We need it for the 'do_gc' command
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with closing(pg.connect()) as conn:
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with conn.cursor() as cur:
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cur.execute("SHOW zenith.zenith_timeline")
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timeline = cur.fetchone()[0]
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# Create test table.
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cur.execute("create table gist_point_tbl(id int4, p point)")
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cur.execute(
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"insert into gist_point_tbl select g, point(g, g) from generate_series(1, 1000000) g;"
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)
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# Build the index.
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with zenbenchmark.record_pageserver_writes(env.pageserver, 'pageserver_writes'):
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with zenbenchmark.record_duration('build'):
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cur.execute(
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"create index gist_pointidx2 on gist_point_tbl using gist(p) with (buffering = on)"
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)
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# Flush the layers from memory to disk. This is included in the reported
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# time and I/O
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pscur.execute(f"do_gc {env.initial_tenant} {timeline} 1000000")
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# Record peak memory usage
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zenbenchmark.record("peak_mem",
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zenbenchmark.get_peak_mem(env.pageserver) / 1024,
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'MB',
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report=MetricReport.LOWER_IS_BETTER)
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# Report disk space used by the repository
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timeline_size = zenbenchmark.get_timeline_size(env.repo_dir,
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env.initial_tenant,
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timeline)
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zenbenchmark.record('size',
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timeline_size / (1024 * 1024),
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'MB',
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report=MetricReport.LOWER_IS_BETTER)
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