[[["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)：2025-04-03。"],[[["Arm VMs, including Tau T2A and C4A machine series, can be used as workers for Dataflow batch and streaming jobs, offering improved price-performance for certain workloads due to their power efficiency."],["Arm VM support requires specific Apache Beam SDK versions (2.50.0 or later for Java, Python, and Go), availability in select regions, use of Runner v2, and Streaming Engine for streaming jobs."],["Running Dataflow jobs on Arm VMs requires setting the `workerMachineType` (Java) or `machine_type`/`worker_machine_type` (Python/Go) pipeline option and specifying an ARM machine type."],["There are several limitations to consider, such as unsupported GPUs, Cloud Profiler, Dataflow Prime, worker VM metrics, and container image pre-building, in addition to the limitations that also apply to Tau T2A and C4A machines."],["Using custom containers require multi-architecture images, to ensure they match the architecture of the worker VMs."]]],[]]
