Use Streaming Engine for streaming jobs

Dataflow's Streaming Engine moves pipeline execution out of the worker virtual machines (VMs) and into the Dataflow service backend. When you don't use Streaming Engine for streaming jobs, the Dataflow runner executes the steps of your streaming pipeline entirely on worker VMs, consuming worker CPU, memory, and Persistent Disk storage.

Streaming Engine is enabled by default for the following pipelines:

• Streaming pipelines that use the Apache Beam Python SDK version 2.21.0 or later and Python 3.
• Streaming pipelines that use the Apache Beam Go SDK version 2.33.0 or later.

To learn more about the implementation of Streaming Engine, see Streaming Engine: Execution Model for Highly-Scalable, Low-Latency Data Processing.

Benefits

The Streaming Engine model has the following benefits:

• Reduced CPU, memory, and Persistent Disk storage resource usage on the worker VMs. Streaming Engine works best with smaller worker machine types (n1-standard-2 instead of n1-standard-4). It doesn't require Persistent Disk beyond a small worker boot disk, leading to less resource and quota consumption.
• More responsive Horizontal Autoscaling in response to variations in incoming data volume. Streaming Engine offers smoother, more granular scaling of workers.
• Improved supportability, because you don't need to redeploy your pipelines to apply service updates.

Most of the reduction in worker resources comes from offloading the work to the Dataflow service. For that reason, there is a charge associated with the use of Streaming Engine.

Support and limitations

• For the Java SDK, Streaming Engine requires the Apache Beam SDK version 2.10.0 or later.
• For the Python SDK, Streaming Engine requires the Apache Beam SDK version 2.16.0 or later.
• For the Go SDK, Streaming Engine requires the Apache Beam SDK version 2.33.0 or later.
• You can't update pipelines that are already running to use Streaming Engine. If your pipeline is running in production without Streaming Engine and you want to use Streaming Engine, stop your pipeline by using the Dataflow Drain option. Then, specify the Streaming Engine parameter, and rerun your pipeline.
• For jobs that use Streaming Engine, the aggregated input data for the open windows has a limit of 60 GB per key. Aggregated input data includes both buffered elements and custom state. When a pipeline exceeds this limit, the pipeline becomes stuck with high system lag, and a message in the job log indicates that the limit has been exceeded. As a best practice, avoid pipeline designs that result in large keys. For more information, see Writing Dataflow pipelines with scalability in mind.
• Supports customer-managed encryption keys (CMEK)

Use Streaming Engine

This feature is available in all regions where Dataflow is supported. To see available locations, read Dataflow locations.

Java

Streaming Engine requires the Apache Beam Java SDK version 2.10.0 or later.

To use Streaming Engine for your streaming pipelines, specify the following parameter:

• --enableStreamingEngine if you're using Apache Beam SDK for Java versions 2.11.0 or later.
• --experiments=enable_streaming_engine if you're using Apache Beam SDK for Java version 2.10.0.

If you use Dataflow Streaming Engine for your pipeline, don't specify the --zone parameter. Instead, specify the --region parameter and set the value to a supported region. Dataflow auto-selects the zone in the region you specified. If you do specify the --zone parameter and set it to a zone outside of the available regions, Dataflow reports an error.

Streaming Engine works best with smaller core worker machine types. Use the job type to determine whether to use a high memory worker machine type. Example machine types that we recommend include --workerMachineType=n1-standard-2 and --workerMachineType=n1-highmem-2. You can also set --diskSizeGb=30 because Streaming Engine only needs space for the worker boot image and local logs. These values are the default values.

Python

Streaming Engine requires the Apache Beam Python SDK version 2.16.0 or later.

Streaming Engine is enabled by default for new Dataflow streaming pipelines when the following conditions are met:

• Pipelines use the Apache Beam Python SDK version 2.21.0 or later and Python 3.
• Customer-managed encryption keys are not used.
• Dataflow workers are in the same region as your Dataflow job.

In Python SDK version 2.45.0 or later, you cannot disable Streaming Engine for streaming pipelines. In Python SDK version 2.44.0 or earlier, to disable Streaming Engine, specify the following parameter:

--experiments=disable_streaming_engine

If you use Python 2, to enable Streaming Engine, specify the following parameter:

--enable_streaming_engine

Caution: As of October 7, 2020, Dataflow no longer supports pipelines that use Python 2. Read more information about Python 2 support on Google Cloud.

If you use Dataflow Streaming Engine in your pipeline, don't specify the --zone parameter. Instead, specify the --region parameter and set the value to a supported region. Dataflow auto-selects the zone in the region you specified. If you specify the --zone parameter and set it to a zone outside of the available regions, Dataflow reports an error.

Streaming Engine works best with smaller core worker machine types. Use the job type to determine whether to use a high memory worker machine type. Example machine types that we recommend include --workerMachineType=n1-standard-2 and --workerMachineType=n1-highmem-2. You can also set --disk_size_gb=30 because Streaming Engine only needs space for the worker boot image and local logs. These values are the default values.

Go

Streaming Engine requires the Apache Beam Go SDK version 2.33.0 or later.

Streaming Engine is enabled by default for new Dataflow streaming pipelines that use the Apache Beam Go SDK.

If you want to disable Streaming Engine in your Go streaming pipeline, specify the following parameter. This parameter must be specified everytime you want to disable Streaming Engine.

--experiments=disable_streaming_engine

If you use Dataflow Streaming Engine in your pipeline, don't specify the --zone parameter. Instead, specify the --region parameter and set the value to a supported region. Dataflow auto-selects the zone in the region you specified. If you specify the --zone parameter and set it to a zone outside of the available regions, Dataflow reports an error.

Streaming Engine works best with smaller core worker machine types. Use the job type to determine whether to use a high memory worker machine type. Example machine types that we recommend include --workerMachineType=n1-standard-2 and --workerMachineType=n1-highmem-2. You can also set --disk_size_gb=30 because Streaming Engine only needs space for the worker boot image and local logs. These values are the default values.

gcloud CLI

When you run your pipeline by using the gcloud dataflow jobs run command or the gcloud dataflow flex-template run command, to enable Streaming Engine, use the following flag:

--enable-streaming-engine

To disable streaming engine, use the following flag:

--additional-experiments=disable_streaming_engine

REST

When you run your pipeline by using the projects.locations.jobs.create method in the REST API, use the Job resource to enable or disable Streaming Engine. To enable Streaming Engine, under environment, set the experiments field to enable_streaming_engine:

"environment": {
          "experiments": "enable_streaming_engine"
      }

To disable Streaming Engine, under environment, set the experiments field to disable_streaming_engine:

"environment": {
          "experiments": "disable_streaming_engine"
      }

Pricing

Dataflow Streaming Engine offers a resource-based billing model where you're billed for the total resources that are consumed by your job. With resource-based billing, the Streaming Engine resources consumed by your job are metered and measured in Streaming Engine Compute Units. You're billed for worker CPU, worker memory, and Streaming Engine Compute Units.

Use resource-based billing

To use resource-based billing, when you start or update your job, include the following Dataflow service option.

Java

--dataflowServiceOptions=enable_streaming_engine_resource_based_billing

Python

--dataflow_service_options=enable_streaming_engine_resource_based_billing

Go

--dataflow_service_options=enable_streaming_engine_resource_based_billing

Data-processed billing (legacy)

Unless you enable resource-based billing, your jobs are billed by using the legacy data-processed billing.

Verify the billing model

Unless you're using Dataflow Prime, when you have jobs that use resource-based billing, the bill includes the SKU Streaming Engine Compute Unit. When you have jobs that use data-processed billing, the bill includes the SKU Streaming Engine data processed. If you have some jobs that use resource-based billing and other jobs that use data-processed billing, the bill includes both SKUs.

When you use Dataflow Prime with resource-based billing, the Data Compute Unit (DCU) SKU is used.

To see which pricing model your job uses, in the Dataflow monitoring interface, select your job. If your job uses resource-based billing, the Job info side panel includes a Streaming Engine Compute Units field.

If you have questions about your billing, contact Cloud Customer Care.