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Improve schema check and push latency for conditional breaking changes #8190
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,5 @@ | ||
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| 'hive': patch | ||
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| Improve performance of schema checks using conditional breaking changes by using a table with an ordering that more closely aligns with the query for total request count for a target |
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When
args.excludedClientsis null or empty, we do not filter byclient_name. In this case, queryingclients_dailyis highly inefficient compared tooperations_dailybecause:clients_dailyhas a much higher cardinality (more rows) since it groups by client name and version.clients_dailyhasclient_nameright aftertarget(e.g.,(target, client_name, timestamp)), which means ClickHouse cannot efficiently use the primary key index for thetimestamprange filter whenclient_nameis omitted.We should conditionally select from
operations_dailywhen no client exclusion is requested to avoid a significant performance regression for standard schema checks. Additionally, when refactoring or optimizing SQL queries, write comprehensive integration tests to verify correctness under edge cases (such as shared commits across multiple targets) to ensure correctness and prevent regressions.References
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operations_daily uses:
clients_daily uses:
There are potentially many hash. If we can filter by the client_name immediately, then this is clearly a win. Idk what this AI comment is talking about.
I'm also choosing to not use operations_daily because
operations_by_target_hourlyis indexed almost exactly how we want it for the non-filtered query.