От ClickHouse к StarRocks с разделением хранения и вычислений: практический апгрейд архитектуры UBT в Trip

От ClickHouse к StarRocks с разделением хранения и вычислений: практический апгрейд архитектуры UBT в Trip #Spring kafka This is a hands-on case study of migrating Trip’s UBT from ClickHouse to StarRocks with storage–compute separation. By redesigning partitioning, enabling DataCache and MergeCommit, and backfilling history via SparkLoad, we reduced average query latency from 1.4 s to 203 ms, P95 to 800 ms, cut storage from 2.6 PB to 1.2 PB, and decreased node count from 50 to 40. We detail Compaction tuning, partitioned materialized views, and second‑level elastic scaling without data migration, and compare gohangout vs. Flink in reliability and operability. The article targets data engineers and architects running high‑load real‑time OLAP workloads.

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