[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-06-24。"],[[["Use the Apache Beam Bigtable I/O connector to read data from Bigtable to Dataflow, considering Google-provided Dataflow templates as an alternative depending on your specific use case."],["Parallelism in reading Bigtable data is governed by the number of nodes in the Bigtable cluster, with each node managing key ranges."],["Performance metrics for Bigtable read operations on one `e2-standard2` worker using Apache Beam SDK 2.48.0 for Java, show a throughput of 180 MBps or 170,000 elements per second for 100M records, 1 kB, and 1 column, noting that real-world pipeline performance may vary."],["For new pipelines, use the `BigtableIO` connector instead of `CloudBigtableIO`, and create separate app profiles for each pipeline type for better traffic differentiation and tracking."],["Best practices for pipeline optimization include monitoring Bigtable node resources, adjusting timeouts as needed, considering Bigtable autoscaling or resizing, and potentially using replication to separate batch and streaming pipelines for improved performance."]]],[]]