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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.
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
from contextlib import closing
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from fixtures.zenith_fixtures import ZenithEnv
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from fixtures.log_helper import log
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from fixtures.benchmark_fixture import MetricReport, ZenithBenchmarker
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pytest_plugins = ("fixtures.zenith_fixtures", "fixtures.benchmark_fixture")
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#
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# Run bulk INSERT test.
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#
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# Collects metrics:
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#
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# 1. Time to INSERT 5 million rows
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# 2. Disk writes
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# 3. Disk space used
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# 4. Peak memory usage
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#
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def test_bulk_insert(zenith_simple_env: ZenithEnv, zenbenchmark: ZenithBenchmarker):
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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_bulk_insert", "empty"])
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pg = env.postgres.create_start('test_bulk_insert')
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log.info("postgres is running on 'test_bulk_insert' 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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cur.execute("create table huge (i int, j int);")
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# Run INSERT, recording the time and I/O it takes
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with zenbenchmark.record_pageserver_writes(env.pageserver, 'pageserver_writes'):
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with zenbenchmark.record_duration('insert'):
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cur.execute("insert into huge values (generate_series(1, 5000000), 0);")
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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} 0")
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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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