Files
neon/test_runner/performance/test_bulk_insert.py
2021-09-21 13:25:46 +03:00

61 lines
2.2 KiB
Python

import os
from contextlib import closing
from fixtures.zenith_fixtures import PostgresFactory, ZenithPageserver
pytest_plugins = ("fixtures.zenith_fixtures", "fixtures.benchmark_fixture")
def get_timeline_size(repo_dir: str, tenantid: str, timelineid: str):
path = "{}/tenants/{}/timelines/{}".format(repo_dir, tenantid, timelineid)
totalbytes = 0
for root, dirs, files in os.walk(path):
for name in files:
totalbytes += os.path.getsize(os.path.join(root, name))
if 'wal' in dirs:
dirs.remove('wal') # don't visit 'wal' subdirectory
return totalbytes
#
# Run bulk INSERT test.
#
# Collects metrics:
#
# 1. Time to INSERT 5 million rows
# 2. Disk writes
# 3. Disk space used
#
def test_bulk_insert(postgres: PostgresFactory, pageserver: ZenithPageserver, pg_bin, zenith_cli, zenbenchmark, repo_dir: str):
# Create a branch for us
zenith_cli.run(["branch", "test_bulk_insert", "empty"])
pg = postgres.create_start('test_bulk_insert')
print("postgres is running on 'test_bulk_insert' branch")
# Open a connection directly to the page server that we'll use to force
# flushing the layers to disk
psconn = pageserver.connect();
pscur = psconn.cursor()
# Get the timeline ID of our branch. We need it for the 'do_gc' command
with closing(pg.connect()) as conn:
with conn.cursor() as cur:
cur.execute("SHOW zenith.zenith_timeline")
timeline = cur.fetchone()[0]
cur.execute("create table huge (i int, j int);")
# Run INSERT, recording the time and I/O it takes
with zenbenchmark.record_pageserver_writes(pageserver, 'pageserver_writes'):
with zenbenchmark.record_duration('insert'):
cur.execute("insert into huge values (generate_series(1, 5000000), 0);")
# Flush the layers from memory to disk. This is included in the reported
# time and I/O
pscur.execute(f"do_gc {pageserver.initial_tenant} {timeline} 0")
# Report disk space used by the repository
timeline_size = get_timeline_size(repo_dir, pageserver.initial_tenant, timeline)
zenbenchmark.record('size', timeline_size / (1024*1024), 'MB')