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53f438a8a8
We use the term "endpoint" in for compute Postgres nodes in the web UI
and user-facing documentation now. Adjust the nomenclature in the code.
This changes the name of the "neon_local pg" command to "neon_local
endpoint". Also adjust names of classes, variables etc. in the python
tests accordingly.
This also changes the directory structure so that endpoints are now
stored in:
.neon/endpoints/<endpoint id>
instead of:
.neon/pgdatadirs/tenants/<tenant_id>/<endpoint (node) name>
The tenant ID is no longer part of the path. That means that you
cannot have two endpoints with the same name/ID in two different
tenants anymore. That's consistent with how we treat endpoints in the
real control plane and proxy: the endpoint ID must be globally unique.
59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
from contextlib import closing
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import pytest
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from fixtures.compare_fixtures import NeonCompare
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from fixtures.neon_fixtures import wait_for_last_flush_lsn
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#
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# Test compaction and image layer creation performance.
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#
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# This creates a few tables and runs some simple INSERTs and UPDATEs on them to generate
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# some delta layers. Then it runs manual compaction, measuring how long it takes.
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#
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@pytest.mark.timeout(1000)
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def test_compaction(neon_compare: NeonCompare):
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env = neon_compare.env
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pageserver_http = env.pageserver.http_client()
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tenant_id, timeline_id = env.neon_cli.create_tenant(
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conf={
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# Disable background GC and compaction, we'll run compaction manually.
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"gc_period": "0s",
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"compaction_period": "0s",
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# Make checkpoint distance somewhat smaller than default, to create
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# more delta layers quicker, to trigger compaction.
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"checkpoint_distance": "25000000", # 25 MB
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# Force image layer creation when we run compaction.
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"image_creation_threshold": "1",
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}
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)
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neon_compare.tenant = tenant_id
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neon_compare.timeline = timeline_id
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# Create some tables, and run a bunch of INSERTs and UPDATes on them,
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# to generate WAL and layers
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endpoint = env.endpoints.create_start(
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"main", tenant_id=tenant_id, config_lines=["shared_buffers=512MB"]
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)
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with closing(endpoint.connect()) as conn:
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with conn.cursor() as cur:
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for i in range(100):
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cur.execute(f"create table tbl{i} (i int, j int);")
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cur.execute(f"insert into tbl{i} values (generate_series(1, 1000), 0);")
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for j in range(100):
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cur.execute(f"update tbl{i} set j = {j};")
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wait_for_last_flush_lsn(env, endpoint, tenant_id, timeline_id)
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# First compaction generates L1 layers
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with neon_compare.zenbenchmark.record_duration("compaction"):
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pageserver_http.timeline_compact(tenant_id, timeline_id)
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# And second compaction triggers image layer creation
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with neon_compare.zenbenchmark.record_duration("image_creation"):
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pageserver_http.timeline_compact(tenant_id, timeline_id)
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neon_compare.report_size()
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