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Para que os recursos da VM estejam disponíveis quando os jobs do Dataflow precisarem
deles, use as reservas do Compute Engine. As reservas fornecem um nível
alto de garantia da capacidade dos recursos zonais do Compute
Engine.
Para usar as reservas do Compute Engine com o Dataflow, siga
estas etapas:
Crie uma reserva do Compute Engine. Pode ser uma reserva de projeto
único ou compartilhada. Confira mais informações nestes
documentos:
Ao enviar o job do Dataflow, transmita uma das seguintes
opções de serviço, dependendo da versão do SDK do Beam que você estiver usando:
Versão do Beam anterior à 2.29: --experiments=skip_gce_quota_verification
Versão do Beam 2.29 ou mais recente: --dataflow_service_options=automatically_use_created_reservation
Para evitar que as cargas de trabalho de baixa prioridade no mesmo projeto concorram por
reservas com o Dataflow, defina a afinidade de reserva como
none ao criar VMs para essas cargas de trabalho. Saiba mais em
Consumir instâncias reservadas.
Para usar a reserva, os workers do Dataflow precisam corresponder
à configuração da reserva. Talvez seja necessário definir o tipo de máquina do worker para
o job. Saiba mais em
Workers.
Limitações
Todas as limitações das reservas do Compute Engine se aplicam quando
os workers do Dataflow consomem as reservas. Veja Como funcionam as reservas.
O Dataflow depende da
ordem de consumo padrão
no Compute Engine. Como resultado, as seguintes limitações se aplicam:
O Dataflow não consome uma reserva criada com a
sinalização --require-specific-reservation.
Outras cargas de trabalho no mesmo projeto ou organização que não especificarem a sinalização
--reservation podem competir com as cargas de trabalho do Dataflow por
reservas compartilhadas ou específicas do projeto.
Os jobs do Dataflow Prime não consomem reservas do Compute Engine.
Preços
As VMs reservadas do Compute Engine são cobradas pelo Dataflow enquanto
o job dele está em execução. Quando as VMs não
estão sendo usadas pelo Dataflow, elas são faturadas pelo
Compute Engine.
[[["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 2025-08-18 UTC."],[[["\u003cp\u003eCompute Engine reservations can be used to ensure VM resources are available for Dataflow jobs.\u003c/p\u003e\n"],["\u003cp\u003eTo utilize reservations, create a Compute Engine reservation and pass the appropriate service option when submitting a Dataflow job, dependent on the Beam SDK version used.\u003c/p\u003e\n"],["\u003cp\u003eSetting the reservation affinity to \u003ccode\u003enone\u003c/code\u003e for low-priority workloads prevents competition for reservations with Dataflow jobs.\u003c/p\u003e\n"],["\u003cp\u003eDataflow worker configurations must match the reservation's configuration to successfully consume the reserved resources, which may require adjustments to the worker machine type.\u003c/p\u003e\n"],["\u003cp\u003eCompute Engine reservations used with Dataflow are billed by Dataflow while the job runs and by Compute Engine when idle, and they are not eligible for Compute Engine committed use discounts.\u003c/p\u003e\n"]]],[],null,["# Use Compute Engine reservations with Dataflow\n\nTo ensure that VM resources are available when your Dataflow jobs need\nthem, you can use Compute Engine reservations. Reservations provide a high\nlevel of assurance in obtaining capacity for Compute Engine zonal\nresources.\n\nTo use Compute Engine reservations with Dataflow, perform the\nfollowing steps:\n\n1. Create a Compute Engine reservation. It can be a single-project\n reservation or a shared reservation. For more information, see the following\n documents:\n\n - [Create a reservation for a single project](/compute/docs/instances/reservations-single-project)\n - [Create a shared reservation](/compute/docs/instances/reservations-shared)\n\n The reservation can include GPU accelerators.\n2. When you submit your Dataflow job, pass one of the following\n service options, depending on which version of the Beam SDK you are using:\n\n - Beam version \\\u003c 2.29: `--experiments=skip_gce_quota_verification`\n - Beam version \\\u003e= 2.29: `--dataflow_service_options=automatically_use_created_reservation`\n\nTo prevent low-priority workloads in the same project from competing for\nreservations with Dataflow, set the reservation affinity to\n`none` when you create VMs for those workloads. For more information, see\n[Consuming reserved instances](/compute/docs/instances/reserving-zonal-resources#consuming_reserved_instances).\n\nIn order to use the reservation, the Dataflow workers must match\nthe reservation configuration. You might need to set the worker machine type for\nthe job. For more information, see\n[Workers](/dataflow/docs/request-quotas#workers).\n\nLimitations\n-----------\n\n- All limitations of Compute Engine reservations apply when\n Dataflow workers consume reservations. See\n [How reservations work](/compute/docs/instances/reservations-overview#how-reservations-work).\n\n- Dataflow relies on the\n [default consumption order](/compute/docs/instances/reservations-overview#consumption-order)\n in Compute Engine. As a result, the following limitations apply:\n\n - Dataflow does not consume a reservation created with the `--require-specific-reservation` flag.\n - Other workloads in the same project or Organization that do not specify the `--reservation` flag might compete with Dataflow workloads for project-specific or shared reservations.\n- Dataflow Prime jobs don't consume Compute Engine reservations.\n\nPricing\n-------\n\nReserved Compute Engine VMs are billed by Dataflow while\nthe Dataflow job is running, and are billed by\nCompute Engine when the VMs are not being used by\nDataflow.\n\nIf you use your Compute Engine reservations with\nDataflow, then those reserved resources aren't eligible for\n[Compute Engine committed use discounts](/compute/vm-instance-pricing#committed_use).\nUsage is billed by using the\n[Dataflow pricing model](/dataflow/pricing).\n\nWhat's next\n-----------\n\nTo learn more about Compute Engine reservations, see\n[Reservations of Compute Engine zonal resources](/compute/docs/instances/reservations-overview)."]]
