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61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
import os
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from contextlib import closing
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from fixtures.zenith_fixtures import PostgresFactory, ZenithPageserver
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pytest_plugins = ("fixtures.zenith_fixtures", "fixtures.benchmark_fixture")
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def get_timeline_size(repo_dir: str, tenantid: str, timelineid: str):
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path = "{}/tenants/{}/timelines/{}".format(repo_dir, tenantid, timelineid)
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totalbytes = 0
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for root, dirs, files in os.walk(path):
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for name in files:
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totalbytes += os.path.getsize(os.path.join(root, name))
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if 'wal' in dirs:
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dirs.remove('wal') # don't visit 'wal' subdirectory
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return totalbytes
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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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#
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def test_bulk_insert(postgres: PostgresFactory, pageserver: ZenithPageserver, pg_bin, zenith_cli, zenbenchmark, repo_dir: str):
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# Create a branch for us
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zenith_cli.run(["branch", "test_bulk_insert", "empty"])
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pg = postgres.create_start('test_bulk_insert')
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print("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 = 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(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 {pageserver.initial_tenant} {timeline} 0")
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# Report disk space used by the repository
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timeline_size = get_timeline_size(repo_dir, pageserver.initial_tenant, timeline)
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zenbenchmark.record('size', timeline_size / (1024*1024), 'MB')
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