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Saat Anda mengaktifkan penyiapan cache cluster Dataproc, cluster akan menyimpan data Cloud Storage yang sering diakses oleh tugas Spark Anda.
Manfaat
Peningkatan performa: Caching dapat meningkatkan performa tugas dengan mengurangi jumlah waktu yang dihabiskan untuk mengambil data dari penyimpanan.
Biaya penyimpanan yang lebih rendah: Karena data aktif di-cache di disk lokal, lebih sedikit panggilan API yang dilakukan ke penyimpanan untuk mengambil data.
Penerapan tugas Spark: Jika caching cluster diaktifkan di cluster, caching ini berlaku untuk semua tugas Spark yang dijalankan di cluster, baik yang dikirimkan ke layanan Dataproc maupun yang dijalankan secara independen di cluster.
Batasan dan persyaratan
Caching hanya berlaku untuk tugas Spark Dataproc.
Hanya data Cloud Storage yang di-cache.
Caching hanya berlaku untuk cluster yang memenuhi persyaratan berikut:
Fitur ini tersedia di Dataproc di Compute Engine
versi image2.0.72+, 2.1.20+, dan 2.2.0+.
Setiap node cluster harus memiliki
SSD lokal
yang terpasang dengan
antarmuka NVME (Non-Volatile Memory Express) (Persistent Disk (PD) tidak didukung). Data di-cache hanya di SSD lokal NVME.
[[["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 2025-09-04 UTC."],[[["\u003cp\u003eEnabling Dataproc cluster caching improves Spark job performance by caching frequently accessed Cloud Storage data on local SSDs, reducing data retrieval time and storage costs.\u003c/p\u003e\n"],["\u003cp\u003eCluster caching applies to all Spark jobs on the cluster, whether submitted to the Dataproc service or run independently, and this applies only to Cloud Storage data.\u003c/p\u003e\n"],["\u003cp\u003eCluster caching is only compatible with clusters meeting specific criteria, such as having one master and \u003ccode\u003en\u003c/code\u003e workers, supported image versions (\u003ccode\u003e2.0.72+\u003c/code\u003e, \u003ccode\u003e2.1.20+\u003c/code\u003e, \u003ccode\u003e2.2.0+\u003c/code\u003e), NVME local SSDs, and the default VM service account.\u003c/p\u003e\n"],["\u003cp\u003eYou can enable cluster caching during Dataproc cluster creation through the Google Cloud console, gcloud CLI, or the Dataproc API, using the property \u003ccode\u003edataproc:dataproc.cluster.caching.enabled=true\u003c/code\u003e.\u003c/p\u003e\n"]]],[],null,["When you enable Dataproc cluster caching, the cluster caches\nCloud Storage data frequently accessed by your Spark jobs.\n\nBenefits\n\n- **Improved performance:** Caching can improve job performance by reducing the amount of time spent retrieving data from storage.\n- **Reduced storage costs:** Since hot data is cached on local disk, fewer API calls are made to storage to retrieve data.\n- **Spark job applicability**: When cluster caching is enabled on a cluster, it applies to all Spark jobs run on the cluster, whether submitted to the Dataproc service or run independently on the cluster.\n\nLimitations and requirements\n\n- Caching applies to Dataproc Spark jobs only.\n- Only Cloud Storage data is cached.\n- Caching only applies to clusters that meet the following requirements:\n - The cluster has one master and `n` workers ([High Availability (HA)](/dataproc/docs/concepts/configuring-clusters/high-availability) and [single node](/dataproc/docs/concepts/configuring-clusters/single-node-clusters) clusters are not supported).\n - This feature is available in Dataproc on Compute Engine [image versions](/dataproc/docs/concepts/versioning/dataproc-version-clusters#supported-dataproc-image-versions) `2.0.72+`, `2.1.20+`, and `2.2.0+`.\n - Each cluster node must have [local SSDs](/dataproc/docs/concepts/compute/dataproc-local-ssds) attached with the [NVME (Non-Volatile Memory Express)](/compute/docs/disks/local-ssd#nvme) interface (Persistent Disks (PDs) are not supported). Data is cached on NVME local SSDs only.\n - The cluster uses the [default VM service account](/dataproc/docs/concepts/configuring-clusters/service-accounts#VM_service_account) for authentication. [Custom VM service accounts](/dataproc/docs/concepts/configuring-clusters/service-accounts#create_a_cluster_with_a_custom_vm_service_account) are not supported.\n\nEnable cluster caching\n\nYou can enable cluster caching when you create a Dataproc cluster\nusing the Google Cloud console, Google Cloud CLI, or the Dataproc API. \n\nGoogle Cloud console\n\n- Open the Dataproc [**Create a cluster on Compute Engine**](https://console.cloud.google.com/dataproc/clustersAdd) page in the Google Cloud console.\n- The **Set up cluster** panel is selected. In the **Spark performance enhancements** section, select **Enable Google Cloud Storage caching**.\n- After confirming and specifying cluster details in the cluster create panels, click **Create**.\n\ngcloud CLI\n\nRun the [gcloud dataproc clusters create](/sdk/gcloud/reference/dataproc/clusters/create)\ncommand locally in a terminal window or in\n[Cloud Shell](https://console.cloud.google.com/?cloudshell=true)\nusing the `dataproc:dataproc.cluster.caching.enabled=true`\n[cluster property](/dataproc/docs/concepts/configuring-clusters/cluster-properties#dataproc_service_properties_table).\n\nExample: \n\n```\ngcloud dataproc clusters create CLUSTER_NAME \\\n --region=REGION \\\n --properties dataproc:dataproc.cluster.caching.enabled=true \\\n --num-master-local-ssds=2 \\\n --master-local-ssd-interface=NVME \\\n --num-worker-local-ssds=2 \\\n --worker-local-ssd-interface=NVME \\\n other args ...\n \n```\n\nREST API\n\nSet [SoftwareConfig.properties](/static/dataproc/docs/reference/rest/v1/ClusterConfig#SoftwareConfig.FIELDS.properties)\nto include the `\"dataproc:dataproc.cluster.caching.enabled\": \"true\"`\n[cluster property](/dataproc/docs/concepts/configuring-clusters/cluster-properties#dataproc_service_properties_table)\nas part of a\n[clusters.create](/dataproc/docs/reference/rest/v1/projects.regions.clusters/create)\nrequest."]]
