This page describes pricing for Dataflow. To see the pricing for other products, read the Pricing documentation.
While the rate for pricing is based on the hour, Dataflow service usage is billed in per second increments, on a per job basis. Usage is stated in hours (30 minutes is 0.5 hours, for example) in order to apply hourly pricing to second-by-second use. Workers and jobs may consume resources as described in the following sections. Dataflow logs are not billed.
Workers and worker resources
Each Dataflow job uses at least one Dataflow worker. The Dataflow service provides two worker types: batch and streaming. There are separate service charges for batch and streaming workers.
Dataflow workers consume the following resources, each billed on a per second basis.
- Storage: Persistent Disk
Batch and streaming workers are specialized resources that use Compute Engine. However, a Dataflow job will not emit Compute Engine billing for Compute Engine resources managed by the Dataflow service. Instead, Dataflow service charges will encompass the use of these Compute Engine resources.
You can override the default worker count for a job. If you are using autoscaling, you can specify the maximum number of workers to be allocated to a job. Workers and respective resources will be added and removed automatically based on autoscaling actuation.
In addition, you can use pipeline options to override the default resource settings (machine type, disk type, and disk size) that are allocated to each worker.
The Dataflow Shuffle operation partitions and groups data by key in a scalable, efficient, fault-tolerant manner. By default, Dataflow uses a shuffle implementation that runs entirely on worker virtual machines and consumes worker CPU, memory, and Persistent Disk storage.
Dataflow also provides an optional highly scalable feature, Dataflow Shuffle, which is available only for batch pipelines and shuffles data outside of workers. Shuffle charges by the volume of data processed. You can instruct Dataflow to use Shuffle by specifying the Shuffle pipeline parameter.
Similar to Shuffle, the Dataflow Streaming Engine moves streaming shuffle and state processing out of the worker VMs and into the Dataflow service backend. You instruct Dataflow to use the Streaming Engine for your streaming pipelines by specifying the Streaming Engine pipeline parameter. Streaming Engine usage is billed by the volume of streaming data processed, which depends on the volume of data ingested into your streaming pipeline and the complexity and number of pipeline stages. Examples of what counts as a byte processed include input flows from data sources, flows of data from one fused pipeline stage to another fused stage, flows of data persisted in user-defined state or used for windowing, and output messages to data sinks, such as to Pub/Sub or BigQuery.
Dataflow also provides an option with discounted CPU and memory pricing for batch processing. Flexible Resource Scheduling (FlexRS) combines regular and preemptible VMs in a single Dataflow worker pool, giving users access to cheaper processing resources. FlexRS also delays the execution of a batch Dataflow job within a 6-hour window to identify the best point in time to start the job based on available resources. While Dataflow uses a combination of workers to execute a FlexRS job, you are billed a uniform discounted rate compared to regular Dataflow prices, regardless of the worker type. You instruct Dataflow to use FlexRS for your autoscaled batch pipelines by specifying the FlexRS parameter.
To help you manage the reliability of your streaming pipelines, Dataflow
snapshots allow you to save and restore your pipeline state.
Snapshot usage is billed by the volume of data stored, which depends on the volume
of data ingested into your streaming pipeline, your windowing logic, and the number
of pipeline stages. You can take a snapshot of your streaming job using the Dataflow
Web UI or the
gcloud command-line tool. There is no additional charge for creating a job from your snapshot
to restore your pipeline's state. For more information, see Using Dataflow snapshots.
Additional job resources
In addition to worker resource usage, a job might consume the following resources, each billed at its own pricing, including but not limited to:
Future releases of Dataflow may have different service charges and/or bundling of related services.
See the Compute Engine Regions and Zones page for more information about the available regions and their zones.
It will become available in other regions in the future.
Dataflow Shuffle pricing is based on volume adjustments applied to the amount of data processed during read and write operations while shuffling your dataset. For more information, see Dataflow Shuffle pricing details.
Dataflow Shuffle pricing details
Charges are calculated per Dataflow job through volume adjustments applied to the total amount of data processed during Dataflow Shuffle operations. Your actual bill for the Dataflow Shuffle data processed is equivalent to being charged full price for a smaller amount of data than the amount processed by a Dataflow job. This difference results in the billable Dataflow Shuffle data metric being smaller than the total Dataflow Shuffle data metric.
The following table explains how these adjustments are applied:
|Data Processed by a job||Billing Adjustment|
|First 250 GB||75% reduction|
|Next 4870 GB||50% reduction|
|Remaining data over 5120 GB (5 TB)||none|
For example, if your pipeline results in 1024 GB (1 TB) of total Dataflow Shuffle data processed, the billable amount is calculated as follows: 250 GB * 25% + 774 GB * 50% = 449.5 GB * regional Dataflow Shuffle data processing rate. If your pipeline results in 10240 GB (10 TB) of total Dataflow Shuffle data processed, the billable amount of data is 250 GB * 25% + 4870 GB * 50% + 5120 GB = 7617.5 GB.
Dataflow snapshots will become available in other regions upon General Availability.
You can view the total vCPU, memory, and Persistent Disk resources associated with a job either in the Google Cloud Console or via the gcloud command line tool. You can track both the actual and chargeable Shuffle Data Processed and Streaming Data Processed metrics in the Dataflow Monitoring Interface. You can use the actual Shuffle Data Processed to evaluate the performance of your pipeline and the chargeable Shuffle Data Processed to determine the costs of the Dataflow job. For Streaming Data Processed, the actual and chargeable metrics are identical.
Use the Google Cloud Pricing Calculator to help you understand how your bill is calculated.
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Last updated 2021-01-14 UTC.