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https://github.com/hanzoai/datastore.git
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Alexey MilovidovandClaude Opus 4.8
295ced78d1
Follow-up to #105171, which added `ruff` with a deliberately narrow, bug-class-only rule set and left the unused-name and star-import rules off pending a dedicated cleanup pass. This enables that second batch in `pyproject.toml` and fixes every violation across the tree (the bulk in integration tests): - `F401` unused-import and `F841` unused-variable: removed, preserving any side-effecting right-hand side (e.g. `result = node.query(...)` becomes `node.query(...)`). Two `try: import ...` feature probes are kept with `# noqa: F401`. - `F541` f-string-missing-placeholders: dropped the redundant `f` prefix. - `F403`/`F405` star imports: every `from module import *` is expanded into explicit imports of the names actually used, taken from each name's real origin, so stdlib/third-party names import from their real source rather than through a test "convenience bundle". The now-empty `tests/integration/helpers/kafka/common_direct.py` bundle is removed and its three remaining consumers import `_VarintBytes`/`KafkaAdminClient` directly. Eliminating the star imports surfaced a real bug the wildcard had masked from `F821`: `generate_presets.py` in `test_prometheus_protocols` called an undefined `execute_instant_query_with_http_api`; fixed to the existing `execute_query_via_http_api`. No `black`/`isort` reformatting was applied (neither is enforced in the style check), so the diff is limited to the lint fixes themselves. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
116 lines
4.0 KiB
Python
116 lines
4.0 KiB
Python
import re
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import uuid
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import pytest
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from helpers.cluster import ClickHouseCluster
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cluster = ClickHouseCluster(__file__)
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nodes = [
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cluster.add_instance(
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f"node{num}",
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main_configs=["configs/remote_servers.xml"],
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with_zookeeper=True,
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macros={"replica": f"node{num}", "shard": "shard"},
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)
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for num in range(3)
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]
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def _create_tables(table_name):
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nodes[0].query(
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f"DROP TABLE IF EXISTS {table_name} ON CLUSTER 'parallel_replicas'",
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settings={"database_atomic_wait_for_drop_and_detach_synchronously": True},
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)
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# big number of granules + low total size in bytes = super tiny granules = big min_marks_per_task
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# => big mark_segment_size will be chosen. it is not required to be big, just not equal to the default
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nodes[0].query(
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f"""
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CREATE TABLE {table_name} ON CLUSTER 'parallel_replicas' (value Int64)
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Engine=ReplicatedMergeTree('/test_parallel_replicas/shard/{table_name}', '{{replica}}')
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ORDER BY ()
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SETTINGS index_granularity = 1
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"""
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)
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nodes[0].query(f"INSERT INTO {table_name} SELECT 42 FROM numbers(1000)")
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nodes[0].query(f"SYSTEM SYNC REPLICA ON CLUSTER 'parallel_replicas' {table_name}")
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table_name = "t"
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@pytest.fixture(scope="module", autouse=True)
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def start_cluster():
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try:
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cluster.start()
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_create_tables(table_name)
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yield cluster
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finally:
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cluster.shutdown()
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# now mark_segment_size is part of the protocol and is communicated to the initiator.
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# let's check that the correct value is actually used by the coordinator
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@pytest.mark.parametrize("local_plan", [0, 1])
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@pytest.mark.parametrize("index_analysis_only_on_coordinator", [0, 1])
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def test_mark_segment_size_communicated_correctly(
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start_cluster, local_plan, index_analysis_only_on_coordinator
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):
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query_id = f"query_id_{str(uuid.uuid4())}"
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nodes[0].query(
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f"SELECT sum(value) FROM {table_name}",
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settings={
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"allow_experimental_parallel_reading_from_replicas": 2,
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"max_parallel_replicas": 100,
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"cluster_for_parallel_replicas": "parallel_replicas",
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"parallel_replicas_mark_segment_size": 0,
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"parallel_replicas_local_plan": local_plan,
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"query_id": query_id,
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"parallel_replicas_index_analysis_only_on_coordinator": index_analysis_only_on_coordinator,
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},
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)
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nodes[0].query("SYSTEM FLUSH LOGS")
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log_line = nodes[0].grep_in_log(f"{query_id}.*Reading state is fully initialized")
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assert re.search(r"mark_segment_size: (\d+)", log_line).group(1) == "16384"
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def test_final_on_parallel_replicas(start_cluster):
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nodes[0].query("DROP TABLE IF EXISTS t0")
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nodes[0].query(
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"CREATE TABLE t0 (c0 Int) ENGINE = SummingMergeTree() ORDER BY tuple()",
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)
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nodes[0].query("INSERT INTO t0 VALUES (1)")
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nodes[0].query("OPTIMIZE TABLE t0 FINAL")
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resp = nodes[0].query(
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"SELECT 1 FROM t0 RIGHT JOIN (SELECT c0 FROM t0) tx ON TRUE GROUP BY c0",
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settings={
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"allow_experimental_parallel_reading_from_replicas": 2,
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"cluster_for_parallel_replicas": "parallel_replicas",
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},
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)
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assert resp == "1\n"
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nodes[0].query("DROP TABLE t0")
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def test_insert_cluster_parallel_replicas(start_cluster):
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nodes[0].query("DROP TABLE IF EXISTS t0 ON CLUSTER 'test_cluster_two_shards'")
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nodes[0].query(
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"CREATE TABLE t0 ON CLUSTER 'test_cluster_two_shards' (c0 Int) ENGINE = Log()"
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)
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nodes[0].query(
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"INSERT INTO TABLE FUNCTION cluster('test_cluster_two_shards', 'default', 't0') (c0) SELECT c0 FROM generateRandom('c0 Int', 1) LIMIT 3",
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settings={
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"insert_distributed_one_random_shard": 1,
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"allow_experimental_parallel_reading_from_replicas": 2,
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"min_insert_block_size_bytes": 1,
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"optimize_trivial_insert_select": 1,
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"max_insert_threads": 3,
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},
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)
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nodes[0].query("DROP TABLE t0 ON CLUSTER 'test_cluster_two_shards'")
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