Menggunakan reservasi Compute Engine dengan Dataflow
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Simpan dan kategorikan konten berdasarkan preferensi Anda.
Untuk memastikan bahwa resource VM tersedia saat tugas Dataflow Anda membutuhkannya, Anda dapat menggunakan reservasi Compute Engine. Pemesanan memberikan tingkat
jaminan yang tinggi dalam mendapatkan kapasitas untuk resource zona Compute Engine.
Untuk menggunakan reservasi Compute Engine dengan Dataflow, lakukan
langkah-langkah berikut:
Buat reservasi Compute Engine. Pemesanan dapat berupa pemesanan satu project atau pemesanan bersama. Untuk informasi selengkapnya, lihat dokumen
berikut:
Saat Anda mengirimkan tugas Dataflow, teruskan salah satu opsi layanan berikut, bergantung pada versi Beam SDK yang Anda gunakan:
Versi Beam < 2.29: --experiments=skip_gce_quota_verification
Versi Beam >= 2.29: --dataflow_service_options=automatically_use_created_reservation
Untuk mencegah workload prioritas rendah di project yang sama bersaing untuk
pemesanan dengan Dataflow, tetapkan afinitas reservasi ke
none saat Anda membuat VM untuk workload tersebut. Untuk informasi selengkapnya, lihat
Menggunakan instance yang direservasi.
Untuk menggunakan reservasi, pekerja Dataflow harus cocok dengan konfigurasi reservasi. Anda mungkin perlu menetapkan jenis mesin pekerja untuk
tugas. Untuk informasi selengkapnya, lihat
Pekerja.
Batasan
Semua batasan reservasi Compute Engine berlaku saat pekerja Dataflow menggunakan reservasi. Lihat
Cara kerja pemesanan.
Dataflow mengandalkan urutan penggunaan default di Compute Engine. Oleh karena itu, batasan berikut berlaku:
Dataflow tidak menggunakan reservasi yang dibuat dengan flag --require-specific-reservation.
Workload lain dalam project atau Organisasi yang sama yang tidak menentukan
flag --reservation dapat bersaing dengan workload Dataflow untuk
reservasi khusus project atau bersama.
Tugas Dataflow Prime tidak menggunakan reservasi Compute Engine.
Harga
VM Compute Engine yang direservasi ditagih oleh Dataflow saat tugas Dataflow berjalan, dan ditagih oleh Compute Engine saat VM tidak digunakan oleh Dataflow.
Jika Anda menggunakan reservasi Compute Engine dengan
Dataflow, resource yang direservasi tersebut tidak memenuhi syarat untuk
diskon abonemen Compute Engine.
Penggunaan ditagih menggunakan model harga Dataflow.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 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)."]]
