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72ef59c378
The introducing paragraph README could use some more love, but let's at least fix the typos.
84 lines
4.3 KiB
Python
84 lines
4.3 KiB
Python
# Demonstrate Write Amplification with naive oldest-first layer checkpointing
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# algorithm.
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#
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# In each iteration of the test, we create a new table that's slightly under 10
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# MB in size (10 MB is the current "segment size" used by the page server). Then
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# we make a tiny update to all the tables already created. This creates a WAL
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# pattern where you have a lot of updates on one segment (the newly created
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# one), alternating with a small updates on all relations. This is the worst
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# case scenario for the naive checkpointing policy where we write out the layers
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# in LSN order, writing the oldest layer first. That creates a new 10 MB image
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# layer to be created for each of those small updates. This is the Write
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# Amplification problem at its finest.
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import os
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from contextlib import closing
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from fixtures.benchmark_fixture import MetricReport
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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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def test_write_amplification(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_write_amplification", "empty"])
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pg = env.postgres.create_start('test_write_amplification')
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log.info("postgres is running on 'test_write_amplification' 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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with closing(pg.connect()) as conn:
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with conn.cursor() as cur:
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# Get the timeline ID of our branch. We need it for the 'do_gc' command
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cur.execute("SHOW zenith.zenith_timeline")
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timeline = cur.fetchone()[0]
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with zenbenchmark.record_pageserver_writes(env.pageserver, 'pageserver_writes'):
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with zenbenchmark.record_duration('run'):
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# NOTE: Because each iteration updates every table already created,
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# the runtime and write amplification is O(n^2), where n is the
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# number of iterations.
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for i in range(25):
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cur.execute(f'''
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CREATE TABLE tbl{i} AS
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SELECT g as i, 'long string to consume some space' || g as t
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FROM generate_series(1, 100000) g
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''')
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cur.execute(f"create index on tbl{i} (i);")
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for j in range(1, i):
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cur.execute(f"delete from tbl{j} where i = {i}")
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# Force checkpointing. As of this writing, we don't have
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# a back-pressure mechanism, and the page server cannot
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# keep up digesting and checkpointing the WAL at the
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# rate that it is generated. If we don't force a
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# checkpoint, the WAL will just accumulate in memory
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# until you hit OOM error. So in effect, we use much
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# more memory to hold the incoming WAL, and write them
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# out in larger batches than we'd really want. Using
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# more memory hides the write amplification problem this
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# test tries to demonstrate.
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#
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# The write amplification problem is real, and using
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# more memory isn't the right solution. We could
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# demonstrate the effect also by generating the WAL
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# slower, adding some delays in this loop. But forcing
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# the checkpointing and GC makes the test go faster,
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# with the same total I/O effect.
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pscur.execute(f"do_gc {env.initial_tenant} {timeline} 0")
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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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