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Instead of having a lot of separate fixtures for setting up the page server, the compute nodes, the safekeepers etc., have one big ZenithEnv object that encapsulates the whole environment. Every test either uses a shared "zenith_simple_env" fixture, which contains the default setup of a pageserver with no authentication, and no safekeepers. Tests that want to use safekeepers or authentication set up a custom test-specific ZenithEnv fixture. Gathering information about the whole environment into one object makes some things simpler. For example, when a new compute node is created, you no longer need to pass the 'wal_acceptors' connection string as argument to the 'postgres.create_start' function. The 'create_start' function fetches that information directly from the ZenithEnv object.
58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
import os
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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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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", zenbenchmark.get_peak_mem(env.pageserver) / 1024, 'MB')
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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', timeline_size / (1024 * 1024), 'MB')
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