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Nesta página, explicamos como usar VMs ARM como workers para jobs em lote e de streaming do Dataflow.
É possível usar a
série de máquinas Tau T2A
e a série de máquinas C4A
(pré-lançamento) de
processadores Arm para executar jobs do Dataflow. Como a arquitetura ARM é otimizada para ter eficiência energética, essas VMs proporcionam a melhor relação entre preço e desempenho para algumas cargas de trabalho. Para mais informações sobre VMs ARM, consulte
VMs ARM no Compute.
Requisitos
Os seguintes SDKs do Apache Beam são compatíveis com VMs ARM:
SDK do Apache Beam para Java versão 2.50.0 ou mais recente
SDK do Apache Beam para Python versão 2.50.0 ou mais recente
SDK do Apache Beam para Go versões 2.50.0 ou mais recentes
Selecione uma região onde as máquinas Tau T2A ou C4A estão disponíveis. Para mais
informações, consulte
Regiões e zonas disponíveis.
Se você usar um contêiner personalizado no Dataflow, ele precisará corresponder à arquitetura das VMs de worker. Se você planeja usar um contêiner
personalizado em VMs ARM, recomendamos criar uma imagem de multiarquitetura. Para mais informações, consulte
Criar uma imagem de contêiner de várias arquiteturas.
Preços
Você vai receber cobranças pelos recursos de computação do Dataflow.
O preço do Dataflow não depende da família de tipos de máquina. Para mais informações, consulte Preços do Dataflow.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[[["\u003cp\u003eArm 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.\u003c/p\u003e\n"],["\u003cp\u003eArm 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.\u003c/p\u003e\n"],["\u003cp\u003eRunning Dataflow jobs on Arm VMs requires setting the \u003ccode\u003eworkerMachineType\u003c/code\u003e (Java) or \u003ccode\u003emachine_type\u003c/code\u003e/\u003ccode\u003eworker_machine_type\u003c/code\u003e (Python/Go) pipeline option and specifying an ARM machine type.\u003c/p\u003e\n"],["\u003cp\u003eThere 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.\u003c/p\u003e\n"],["\u003cp\u003eUsing custom containers require multi-architecture images, to ensure they match the architecture of the worker VMs.\u003c/p\u003e\n"]]],[],null,["# Use Arm VMs on Dataflow\n\nThis page explains how to use Arm VMs as workers for batch and streaming\nDataflow jobs.\n\nYou can use the\n[Tau T2A machine series](/compute/docs/general-purpose-machines#t2a_machines)\nand [C4A machine series](/compute/docs/general-purpose-machines#c4a_series)\n([Preview](/products#product-launch-stages)) of\nArm processors to run Dataflow jobs. Because Arm architecture is\noptimized for power efficiency, using these VMs yields better price for\nperformance for some workloads. For more information about Arm VMs, see\n[Arm VMs on Compute](/compute/docs/instances/arm-on-compute).\n\nRequirements\n------------\n\n- The following Apache Beam SDKs support Arm VMs:\n - Apache Beam Java SDK versions 2.50.0 or later\n - Apache Beam Python SDK versions 2.50.0 or later\n - Apache Beam Go SDK versions 2.50.0 or later\n- Select a region where Tau T2A or C4A machines are available. For more information, see [Available regions and zones](/compute/docs/regions-zones#available).\n- Use [Runner v2](/dataflow/docs/runner-v2) to run the job.\n- Streaming jobs must use [Streaming Engine](/dataflow/docs/streaming-engine).\n\nLimitations\n-----------\n\n- All [Tau T2A limitations](/compute/docs/general-purpose-machines#t2a_limitations) and [C4A limitations](/compute/docs/general-purpose-machines#supported_disk_types_for_c4a) apply.\n- [GPUs](/dataflow/docs/gpu) are not supported.\n- [Cloud Profiler](/dataflow/docs/guides/profiling-a-pipeline) is not supported.\n- [Dataflow Prime](/dataflow/docs/guides/enable-dataflow-prime) is not supported.\n- Receiving worker VM metrics from [Cloud Monitoring](/dataflow/docs/guides/using-cloud-monitoring#receive_worker_vm_metrics_from_the_agent) is not supported.\n- [Container image pre-building](/dataflow/docs/guides/build-container-image#prebuild) is not supported.\n\nRun a job using Arm VMs\n-----------------------\n\nTo use Arm VMs, set the following pipeline option. \n\n### Java\n\nSet the `workerMachineType` pipeline option and specify an\n[ARM machine type](/compute/docs/instances/arm-on-compute).\n\nFor more information about setting pipeline options, see\n[Set Dataflow pipeline options](/dataflow/docs/guides/setting-pipeline-options).\n\n### Python\n\nSet the `machine_type` pipeline option and specify an\n[ARM machine type](/compute/docs/instances/arm-on-compute).\n\nFor more information about setting pipeline options, see\n[Set Dataflow pipeline options](/dataflow/docs/guides/setting-pipeline-options).\n\n### Go\n\nSet the `worker_machine_type` pipeline option and specify an\n[ARM machine type](/compute/docs/instances/arm-on-compute).\n\nFor more information about setting pipeline options, see\n[Set Dataflow pipeline options](/dataflow/docs/guides/setting-pipeline-options).\n\nUse multi-architecture container images\n---------------------------------------\n\nIf you use a custom container in Dataflow, the container must\nmatch the architecture of the worker VMs. If you plan to use a custom\ncontainer on ARM VMs, we recommend building a multi-architecture image. For more\ninformation, see\n[Build a multi-architecture container image](/dataflow/docs/guides/multi-architecture-container).\n\nPricing\n-------\n\nYou are billed for Dataflow compute resources.\nDataflow pricing is independent of the machine type family. For\nmore information, see [Dataflow pricing](/dataflow/pricing).\n\nWhat's next\n-----------\n\n- [Set Dataflow pipeline options](/dataflow/docs/guides/setting-pipeline-options)\n- [Use custom containers in Dataflow](/dataflow/docs/guides/using-custom-containers)"]]
