[[["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"]],[],[[["\u003cp\u003eDataproc pricing is calculated based on the total number of virtual CPUs (vCPUs) in the cluster and the duration of cluster usage, with a formula of \u003ccode\u003e$0.010 * # of vCPUs * hourly duration\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eDataproc billing is done per-second, with a 1-minute minimum, and usage is expressed in fractional hours to apply hourly rates to second-by-second use.\u003c/p\u003e\n"],["\u003cp\u003eIn addition to Dataproc charges, users will also be billed for Compute Engine resources and other Google Cloud services like Persistent Disk and Monitoring that are utilized by Dataproc clusters.\u003c/p\u003e\n"],["\u003cp\u003eDataproc on GKE has the same pricing formula as Dataproc on Compute Engine and the pricing is applied to the aggregate number of vCPUs, but it also includes additional charges for the user-managed GKE cluster.\u003c/p\u003e\n"],["\u003cp\u003eThere are separate charges for Dataproc serverless and the information is available in the Dataproc Serverless Pricing page.\u003c/p\u003e\n"]]],[],null,["Dataproc pricing \n\u003cbr /\u003e\n\nDataproc \\| [Serverless for Apache Spark](/dataproc-serverless/pricing \"View this page for Serverless for Apache Spark\") \\| [Dataproc Metastore](/dataproc-metastore/pricing \"View this page for Dataproc Metastore\")\n\n\u003cbr /\u003e\n\n- [Dataproc on Compute Engine pricing](#on_pricing)\n- [Dataproc on GKE pricing](#on_gke_pricing)\n- [Serverless for Apache Spark pricing](#serverless_pricing)\n\nDataproc on Compute Engine pricing\n\n[Dataproc on Compute Engine](/dataproc)\npricing is based on the size of Dataproc clusters and the duration\nof time that they run. The size of a cluster is based on the aggregate number of\n[virtual CPUs (vCPUs)](/compute/docs/machine-types)\nacross the entire cluster, including the master and worker nodes. The duration\nof a cluster is the length of time between cluster creation and cluster stopping\nor deletion.\n\nThe Dataproc pricing formula is: `$0.010 * # of vCPUs * hourly duration`.\n\nAlthough the pricing formula is expressed as an hourly rate,\nDataproc is billed by the second, and all Dataproc\nclusters are billed in one-second clock-time increments, subject to a 1-minute\nminimum billing. Usage is stated in fractional hours (for example, 30 minutes\nis expressed as 0.5 hours) in order to apply hourly pricing to second-by-second\nuse.\n\nDataproc pricing is in addition to the\n[Compute Engine per-instance price](/compute/pricing) for each virtual machine\n(see [Use of other Google Cloud resources](#use_of_other_google_cloud_resources)).\n| [Preemptible secondary VMs](/compute/docs/instances/preemptible). can be used to lower your Compute Engine costs for Dataproc clusters.\n| **Note:** See [Supported machine types](/dataproc/docs/concepts/compute/supported-machine-types) for information on the predefined and custom machine types you can use in Dataproc clusters.\n\nAccrued Charges\n\nThe following Dataproc operations and scenarios cause charges to\naccrue:\n\n- [**Scaling**](/dataproc/docs/concepts/configuring-clusters/scaling-clusters) and\n [**autoscaling**](/dataproc/docs/concepts/configuring-clusters/autoscaling):\n When VMs are added to the cluster, charges accrue while the VMs are\n active. These accrued charges continue until the VMs are removed.\n\n- **Clusters in an Error state**: When a Dataproc cluster is in an\n error state, cluster VMs remain active and charges continue to accrue.\n These accrued charges continue until the cluster is deleted.\n\nPricing example\n\nAs an example, consider a cluster (with master and worker nodes) that has\nthe following configuration:\n\n| Item | Machine Type | Virtual CPUs | Attached persistent disk | Number in cluster |\n|--------------|---------------|--------------|--------------------------|-------------------|\n| Master Node | n1-standard-4 | 4 | 500 GB | 1 |\n| Worker Nodes | n1-standard-4 | 4 | 500 GB | 5 |\n\nThis Dataproc cluster has 24 virtual CPUs, 4 for the master and\n20 spread across the workers. For Dataproc billing purposes,\nthe pricing for this cluster would be based on those 24 virtual CPUs and the\nlength of time the cluster ran (assuming no nodes are scaled down or\npreempted). If the cluster runs for 2 hours,\nthe Dataproc pricing would use the following formula:\n\n`Dataproc charge = # of vCPUs * hours * Dataproc price = 24 * 2 * $0.01 = $0.48`\n\nIn this example, the cluster would also incur charges for Compute Engine\nand Standard Persistent Disk Provisioned Space in addition to the\nDataproc charge (see\n[Use of other Google Cloud resources](#use_of_other_google_cloud_resources)).\nThe [billing calculator](/products/calculator)\ncan be used to determine separate Google Cloud resource costs.\n\nUse of other Google Cloud resources\n\nAs a managed and integrated solution, Dataproc is built on top of other\nGoogle Cloud technologies. Dataproc clusters consume the following\nresources, each billed at its own pricing:\n\n- [Compute Engine](/compute)---All Compute Engine instances for a Dataproc cluster have a 1-minute clock-time minimum, and are billed based on per-second billing increments and [sustained use price rules](/compute/docs/sustained-use-discounts).\n- [Standard Persistent Disk](/compute/docs/disks#pdspecs) provisioned space\n- [Cloud Monitoring](/monitoring)---see [Google Cloud Observability Pricing](/stackdriver/pricing)\n\nDataproc clusters can optionally utilize the following resources, each\nbilled at its own pricing, including but not limited to:\n\n- [Cloud Storage](/storage)\n- [Global Networking](/products/networking)\n- [BigQuery](/bigquery)\n- [Bigtable](/bigtable)\n\nDataproc on GKE pricing\n\nThis section explains the charges that apply only to the virtual\nDataproc cluster that runs on a user-managed GKE.\nSee [GKE pricing](/kubernetes-engine/pricing)\nto learn about the added charges that apply to the user-managed GKE\ncluster.\n\nThe [Dataproc on GKE](/dataproc/docs/guides/dpgke/dataproc-gke-overview) pricing\nformula, `$0.010 * # of vCPUs * hourly duration`, is the same as the\n[Dataproc on Compute Engine](#on_pricing) pricing formula, and\nis applied to the aggregate number of virtual CPUs running in VMs instances in\n[Dataproc-created node pools](/dataproc/docs/guides/dpgke/dataproc-gke-nodepools)\nin the cluster. The duration of a virtual machine instance is the length of time\nfrom its creation to its deletion. As with Dataproc on Compute Engine,\nDataproc on GKE is billed by the second, subject to a 1-minute minimum billing\nper virtual machine instance. [Other Google Cloud charges](#use_of_other_google_cloud_resources)\nare applied in addition to Dataproc charges.\n\nDataproc-created node pools continue to exist after deletion of the\nDataproc cluster since they may be shared by multiple clusters. If you\n[delete the node pools](/kubernetes-engine/docs/how-to/node-pools#deleting_a_node_pool) or\n[scale node pools](/kubernetes-engine/docs/how-to/node-pools#resizing_a_node_pool)\ndown to zero instances, continued Dataproc charges will not be\nincurred. **Any remaining node pool VMs will continue to incur charges\nuntil you delete them.**\n\nServerless for Apache Spark pricing\n\nSee [Serverless for Apache Spark pricing](/dataproc-serverless/pricing).\n\nWhat's next\n\n- Read the [Dataproc documentation](/dataproc/docs).\n- Get started with [Dataproc](/dataproc/docs/quickstarts).\n- Try the [Pricing calculator](/products/calculator).\n- Learn about [Dataproc solutions and use cases](/architecture?text=Dataproc).\n\nRequest a custom quote \nWith Google Cloud's pay-as-you-go pricing, you only pay for the services you use. Connect with our sales team to get a custom quote for your organization.\n[Contact sales](/contact?direct=true)"]]
