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6cfebc096f
Part of #1467 This PR adds several performance tests that compare the [PG statistics](https://www.postgresql.org/docs/current/monitoring-stats.html) obtained when running PG benchmarks against Neon and vanilla PG to measure the read/write throughput of the DB.
137 lines
5.1 KiB
Python
137 lines
5.1 KiB
Python
from contextlib import closing
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from fixtures.neon_fixtures import PgBin, VanillaPostgres, NeonEnv, profiling_supported
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from fixtures.compare_fixtures import PgCompare, VanillaCompare, NeonCompare
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from fixtures.benchmark_fixture import PgBenchRunResult, MetricReport, NeonBenchmarker
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from fixtures.log_helper import log
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from pathlib import Path
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import pytest
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from datetime import datetime
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import calendar
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import os
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import timeit
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def utc_now_timestamp() -> int:
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return calendar.timegm(datetime.utcnow().utctimetuple())
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def init_pgbench(env: PgCompare, cmdline):
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# calculate timestamps and durations separately
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# timestamp is intended to be used for linking to grafana and logs
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# duration is actually a metric and uses float instead of int for timestamp
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init_start_timestamp = utc_now_timestamp()
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t0 = timeit.default_timer()
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with env.record_pageserver_writes('init.pageserver_writes'):
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env.pg_bin.run_capture(cmdline)
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env.flush()
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init_duration = timeit.default_timer() - t0
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init_end_timestamp = utc_now_timestamp()
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env.zenbenchmark.record("init.duration",
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init_duration,
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unit="s",
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report=MetricReport.LOWER_IS_BETTER)
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env.zenbenchmark.record("init.start_timestamp",
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init_start_timestamp,
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'',
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MetricReport.TEST_PARAM)
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env.zenbenchmark.record("init.end_timestamp", init_end_timestamp, '', MetricReport.TEST_PARAM)
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def run_pgbench(env: PgCompare, prefix: str, cmdline):
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with env.record_pageserver_writes(f'{prefix}.pageserver_writes'):
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run_start_timestamp = utc_now_timestamp()
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t0 = timeit.default_timer()
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out = env.pg_bin.run_capture(cmdline, )
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run_duration = timeit.default_timer() - t0
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run_end_timestamp = utc_now_timestamp()
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env.flush()
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stdout = Path(f"{out}.stdout").read_text()
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res = PgBenchRunResult.parse_from_stdout(
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stdout=stdout,
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run_duration=run_duration,
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run_start_timestamp=run_start_timestamp,
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run_end_timestamp=run_end_timestamp,
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)
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env.zenbenchmark.record_pg_bench_result(prefix, res)
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#
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# Initialize a pgbench database, and run pgbench against it.
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#
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# This makes runs two different pgbench workloads against the same
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# initialized database, and 'duration' is the time of each run. So
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# the total runtime is 2 * duration, plus time needed to initialize
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# the test database.
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#
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# Currently, the # of connections is hardcoded at 4
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def run_test_pgbench(env: PgCompare, scale: int, duration: int):
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# Record the scale and initialize
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env.zenbenchmark.record("scale", scale, '', MetricReport.TEST_PARAM)
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init_pgbench(env, ['pgbench', f'-s{scale}', '-i', env.pg.connstr()])
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# Run simple-update workload
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run_pgbench(env,
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"simple-update",
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['pgbench', '-N', '-c4', f'-T{duration}', '-P2', '-Mprepared', env.pg.connstr()])
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# Run SELECT workload
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run_pgbench(env,
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"select-only",
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['pgbench', '-S', '-c4', f'-T{duration}', '-P2', '-Mprepared', env.pg.connstr()])
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env.report_size()
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def get_durations_matrix(default: int = 45):
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durations = os.getenv("TEST_PG_BENCH_DURATIONS_MATRIX", default=str(default))
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return list(map(int, durations.split(",")))
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def get_scales_matrix(default: int = 10):
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scales = os.getenv("TEST_PG_BENCH_SCALES_MATRIX", default=str(default))
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return list(map(int, scales.split(",")))
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# Run the pgbench tests against vanilla Postgres and neon
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@pytest.mark.parametrize("scale", get_scales_matrix())
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@pytest.mark.parametrize("duration", get_durations_matrix())
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def test_pgbench(neon_with_baseline: PgCompare, scale: int, duration: int):
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run_test_pgbench(neon_with_baseline, scale, duration)
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# Run the pgbench tests, and generate a flamegraph from it
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# This requires that the pageserver was built with the 'profiling' feature.
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#
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# TODO: If the profiling is cheap enough, there's no need to run the same test
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# twice, with and without profiling. But for now, run it separately, so that we
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# can see how much overhead the profiling adds.
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@pytest.mark.parametrize("scale", get_scales_matrix())
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@pytest.mark.parametrize("duration", get_durations_matrix())
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def test_pgbench_flamegraph(zenbenchmark, pg_bin, neon_env_builder, scale: int, duration: int):
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neon_env_builder.num_safekeepers = 1
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neon_env_builder.pageserver_config_override = '''
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profiling="page_requests"
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'''
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if not profiling_supported():
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pytest.skip("pageserver was built without 'profiling' feature")
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env = neon_env_builder.init_start()
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env.neon_cli.create_branch("empty", "main")
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run_test_pgbench(NeonCompare(zenbenchmark, env, pg_bin, "pgbench"), scale, duration)
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# Run the pgbench tests against an existing Postgres cluster
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@pytest.mark.parametrize("scale", get_scales_matrix())
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@pytest.mark.parametrize("duration", get_durations_matrix())
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@pytest.mark.remote_cluster
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def test_pgbench_remote(remote_compare: PgCompare, scale: int, duration: int):
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run_test_pgbench(remote_compare, scale, duration)
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