test(pageserver): add test_gc_feedback_with_snapshots

Signed-off-by: Alex Chi Z <[email protected]>

test_runner/performance/test_gc_feedback.py

@@ -20,6 +20,9 @@ def test_gc_feedback(neon_env_builder: NeonEnvBuilder, zenbenchmark: NeonBenchma
     `gc_feedback` mechanism for about 9 months, but, there were problems
     with it and it wasn't enabled at runtime.
     This PR removed the code: https://github.com/neondatabase/neon/pull/6863
+
+    And the bottom-most GC-compaction epic resolves the problem.
+    https://github.com/neondatabase/neon/issues/8002
     """
     env = neon_env_builder.init_start()
     client = env.pageserver.http_client()
@@ -149,3 +152,144 @@ def test_gc_feedback(neon_env_builder: NeonEnvBuilder, zenbenchmark: NeonBenchma
     log.info(f"Writing layer map to {layer_map_path}")
     with layer_map_path.open("w") as f:
         f.write(json.dumps(client.timeline_layer_map_info(tenant_id, timeline_id)))
+
+
+@pytest.mark.timeout(10000)
+def test_gc_feedback_with_snapshots(neon_env_builder: NeonEnvBuilder, zenbenchmark: NeonBenchmarker):
+    """
+    Compared with `test_gc_feedback`, we create a branch in the middle of the benchmark, and the
+    bottom-most compaction should collect as much garbage as possible below the GC horizon. Ideally,
+    there should be images (in an image layer) covering the full range at the branch point, and
+    images covering the full key range (in a delta layer) at the GC horizon.
+    """
+    env = neon_env_builder.init_start()
+    client = env.pageserver.http_client()
+
+    tenant_id, _ = env.neon_cli.create_tenant(
+        conf={
+            # disable default GC and compaction
+            "gc_period": "1000 m",
+            "compaction_period": "0 s",
+            "gc_horizon": f"{1024 ** 2}",
+            "checkpoint_distance": f"{1024 ** 2}",
+            "compaction_target_size": f"{1024 ** 2}",
+            # set PITR interval to be small, so we can do GC
+            "pitr_interval": "60 s",
+            # "compaction_threshold": "3",
+            # "image_creation_threshold": "2",
+        }
+    )
+    endpoint = env.endpoints.create_start("main", tenant_id=tenant_id)
+    timeline_id = endpoint.safe_psql("show neon.timeline_id")[0][0]
+    n_steps = 10
+    n_update_iters = 100
+    step_size = 10000
+    with endpoint.cursor() as cur:
+        cur.execute("SET statement_timeout='1000s'")
+        cur.execute(
+            "CREATE TABLE t(step bigint, count bigint default 0, payload text default repeat(' ', 100))  with (fillfactor=50)"
+        )
+        cur.execute("CREATE INDEX ON t(step)")
+        # In each step, we insert 'step_size' new rows, and update the newly inserted rows
+        # 'n_update_iters' times. This creates a lot of churn and generates lots of WAL at the end of the table,
+        # without modifying the earlier parts of the table.
+        for step in range(n_steps):
+            cur.execute(f"INSERT INTO t (step) SELECT {step} FROM generate_series(1, {step_size})")
+            for _ in range(n_update_iters):
+                cur.execute(f"UPDATE t set count=count+1 where step = {step}")
+                cur.execute("vacuum t")
+
+            # cur.execute("select pg_table_size('t')")
+            # logical_size = cur.fetchone()[0]
+            logical_size = client.timeline_detail(tenant_id, timeline_id)["current_logical_size"]
+            log.info(f"Logical storage size  {logical_size}")
+
+            client.timeline_checkpoint(tenant_id, timeline_id)
+
+            # Do compaction and GC
+            client.timeline_gc(tenant_id, timeline_id, 0)
+            client.timeline_compact(tenant_id, timeline_id)
+            # One more iteration to check that no excessive image layers are generated
+            client.timeline_gc(tenant_id, timeline_id, 0)
+            client.timeline_compact(tenant_id, timeline_id)
+
+            physical_size = client.timeline_detail(tenant_id, timeline_id)["current_physical_size"]
+            log.info(f"Physical storage size {physical_size}")
+
+            if step == n_steps / 2:
+                env.neon_cli.create_branch("child")
+
+    max_num_of_deltas_above_image = 0
+    max_total_num_of_deltas = 0
+    for key_range in client.perf_info(tenant_id, timeline_id):
+        max_total_num_of_deltas = max(max_total_num_of_deltas, key_range["total_num_of_deltas"])
+        max_num_of_deltas_above_image = max(
+            max_num_of_deltas_above_image, key_range["num_of_deltas_above_image"]
+        )
+
+    MB = 1024 * 1024
+    zenbenchmark.record("logical_size", logical_size // MB, "Mb", MetricReport.LOWER_IS_BETTER)
+    zenbenchmark.record("physical_size", physical_size // MB, "Mb", MetricReport.LOWER_IS_BETTER)
+    zenbenchmark.record(
+        "physical/logical ratio", physical_size / logical_size, "", MetricReport.LOWER_IS_BETTER
+    )
+    zenbenchmark.record(
+        "max_total_num_of_deltas", max_total_num_of_deltas, "", MetricReport.LOWER_IS_BETTER
+    )
+    zenbenchmark.record(
+        "max_num_of_deltas_above_image",
+        max_num_of_deltas_above_image,
+        "",
+        MetricReport.LOWER_IS_BETTER,
+    )
+
+    client.timeline_compact(tenant_id, timeline_id, enhanced_gc_bottom_most_compaction=True)
+    tline_detail = client.timeline_detail(tenant_id, timeline_id)
+    logical_size = tline_detail["current_logical_size"]
+    physical_size = tline_detail["current_physical_size"]
+
+    max_num_of_deltas_above_image = 0
+    max_total_num_of_deltas = 0
+    for key_range in client.perf_info(tenant_id, timeline_id):
+        max_total_num_of_deltas = max(max_total_num_of_deltas, key_range["total_num_of_deltas"])
+        max_num_of_deltas_above_image = max(
+            max_num_of_deltas_above_image, key_range["num_of_deltas_above_image"]
+        )
+    zenbenchmark.record(
+        "logical_size_after_bottom_most_compaction",
+        logical_size // MB,
+        "Mb",
+        MetricReport.LOWER_IS_BETTER,
+    )
+    zenbenchmark.record(
+        "physical_size_after_bottom_most_compaction",
+        physical_size // MB,
+        "Mb",
+        MetricReport.LOWER_IS_BETTER,
+    )
+    zenbenchmark.record(
+        "physical/logical ratio after bottom_most_compaction",
+        physical_size / logical_size,
+        "",
+        MetricReport.LOWER_IS_BETTER,
+    )
+    zenbenchmark.record(
+        "max_total_num_of_deltas_after_bottom_most_compaction",
+        max_total_num_of_deltas,
+        "",
+        MetricReport.LOWER_IS_BETTER,
+    )
+    zenbenchmark.record(
+        "max_num_of_deltas_above_image_after_bottom_most_compaction",
+        max_num_of_deltas_above_image,
+        "",
+        MetricReport.LOWER_IS_BETTER,
+    )
+
+    with endpoint.cursor() as cur:
+        cur.execute("SELECT * FROM t")  # ensure data is not corrupted
+
+    layer_map_path = env.repo_dir / "layer-map.json"
+    log.info(f"Writing layer map to {layer_map_path}")
+    with layer_map_path.open("w") as f:
+        f.write(json.dumps(client.timeline_layer_map_info(tenant_id, timeline_id)))