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34fa34d15c
The layer map json is an interesting file for that test, so dump it to make debugging easier.
89 lines
4.0 KiB
Python
89 lines
4.0 KiB
Python
import json
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import pytest
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from fixtures.benchmark_fixture import MetricReport, NeonBenchmarker
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from fixtures.log_helper import log
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from fixtures.neon_fixtures import NeonEnvBuilder
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@pytest.mark.timeout(10000)
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def test_gc_feedback(neon_env_builder: NeonEnvBuilder, zenbenchmark: NeonBenchmarker):
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"""
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Test that GC is able to collect all old layers even if them are forming
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"stairs" and there are not three delta layers since last image layer.
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Information about image layers needed to collect old layers should
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be propagated by GC to compaction task which should take in in account
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when make a decision which new image layers needs to be created.
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NB: this test demonstrates the problem. The source tree contained the
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`gc_feedback` mechanism for about 9 months, but, there were problems
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with it and it wasn't enabled at runtime.
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This PR removed the code: https://github.com/neondatabase/neon/pull/6863
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"""
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env = neon_env_builder.init_start()
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client = env.pageserver.http_client()
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tenant_id, _ = env.neon_cli.create_tenant(
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conf={
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# disable default GC and compaction
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"gc_period": "1000 m",
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"compaction_period": "0 s",
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"gc_horizon": f"{1024 ** 2}",
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"checkpoint_distance": f"{1024 ** 2}",
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"compaction_target_size": f"{1024 ** 2}",
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# set PITR interval to be small, so we can do GC
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"pitr_interval": "10 s",
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# "compaction_threshold": "3",
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# "image_creation_threshold": "2",
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}
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)
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endpoint = env.endpoints.create_start("main", tenant_id=tenant_id)
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timeline_id = endpoint.safe_psql("show neon.timeline_id")[0][0]
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n_steps = 10
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n_update_iters = 100
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step_size = 10000
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with endpoint.cursor() as cur:
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cur.execute("SET statement_timeout='1000s'")
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cur.execute(
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"CREATE TABLE t(step bigint, count bigint default 0, payload text default repeat(' ', 100)) with (fillfactor=50)"
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)
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cur.execute("CREATE INDEX ON t(step)")
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# In each step, we insert 'step_size' new rows, and update the newly inserted rows
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# 'n_update_iters' times. This creates a lot of churn and generates lots of WAL at the end of the table,
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# without modifying the earlier parts of the table.
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for step in range(n_steps):
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cur.execute(f"INSERT INTO t (step) SELECT {step} FROM generate_series(1, {step_size})")
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for _ in range(n_update_iters):
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cur.execute(f"UPDATE t set count=count+1 where step = {step}")
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cur.execute("vacuum t")
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# cur.execute("select pg_table_size('t')")
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# logical_size = cur.fetchone()[0]
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logical_size = client.timeline_detail(tenant_id, timeline_id)["current_logical_size"]
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log.info(f"Logical storage size {logical_size}")
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client.timeline_checkpoint(tenant_id, timeline_id)
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# Do compaction and GC
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client.timeline_gc(tenant_id, timeline_id, 0)
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client.timeline_compact(tenant_id, timeline_id)
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# One more iteration to check that no excessive image layers are generated
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client.timeline_gc(tenant_id, timeline_id, 0)
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client.timeline_compact(tenant_id, timeline_id)
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physical_size = client.timeline_detail(tenant_id, timeline_id)["current_physical_size"]
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log.info(f"Physical storage size {physical_size}")
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MB = 1024 * 1024
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zenbenchmark.record("logical_size", logical_size // MB, "Mb", MetricReport.LOWER_IS_BETTER)
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zenbenchmark.record("physical_size", physical_size // MB, "Mb", MetricReport.LOWER_IS_BETTER)
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zenbenchmark.record(
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"physical/logical ratio", physical_size / logical_size, "", MetricReport.LOWER_IS_BETTER
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)
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layer_map_path = env.repo_dir / "layer-map.json"
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log.info(f"Writing layer map to {layer_map_path}")
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with layer_map_path.open("w") as f:
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f.write(json.dumps(client.timeline_layer_map_info(tenant_id, timeline_id)))
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