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HOW JD.COM BUILT A LAKESTREAM WITH APACHE FLUSS AND APACHE HUDI

JD replaced parallel streaming and batch training-data pipelines with a staged migration: write once to Fluss, tier to Hudi, and union fresh plus historical reads. The difficult part is consistency: workers coordinate a single Hudi commit per tiering round and report matched stream offsets only after it completes. That pattern enables rollback-safe catch-up, but leaves schema evolution and dynamic bucketing unresolved.

BEST PRACTICES FOR SINGLE ROW LOOKUPS (POINT QUERIES)

DataFusion can perform surprisingly well on point lookups when tuned for retrieval instead of analytics. The biggest gains came from storage layout and execution choices that minimize unnecessary work, such as sorted Parquet files, right-sized row groups, metadata caching, and prepared statements. The broader lesson is that analytical engines can serve low-latency reads if the workload is designed deliberately.

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