Using Dataflow SQL

The page explains how to use Dataflow SQL and create Dataflow SQL jobs.

To create a Dataflow SQL job, you must write and run a Dataflow SQL query.

Using the Dataflow SQL editor

The Dataflow SQL editor is a page in the Google Cloud Console where you write and run queries for creating Dataflow SQL jobs.

To access the Dataflow SQL editor, follow these steps:

In the Cloud Console, go to the Dataflow SQL Editor page.

Go to Dataflow SQL editor

You can also access the Dataflow SQL editor from the Dataflow monitoring interface by following these steps:

In the Cloud Console, go to the Dataflow Jobs page.

Go to Jobs
Select the SQL Workspace tab from the left menu.

Writing Dataflow SQL queries

Dataflow SQL queries use the Dataflow SQL query syntax. The Dataflow SQL query syntax is similar to BigQuery standard SQL.

You can use the Dataflow SQL streaming extensions to aggregate data from continuously updating Dataflow sources like Pub/Sub.

For example, the following query counts the passengers in a Pub/Sub stream of taxi rides every minute:

SELECT
  TUMBLE_START('INTERVAL 1 MINUTE') as period_start,
  SUM(passenger_count) AS pickup_count
FROM pubsub.topic.`pubsub-public-data`.`taxirides-realtime`
WHERE
  ride_status = "pickup"
GROUP BY
  TUMBLE(event_timestamp, 'INTERVAL 1 MINUTE')

Running Dataflow SQL queries

When you run a Dataflow SQL query, Dataflow turns the query into an Apache Beam pipeline and runs the pipeline.

You can run a Dataflow SQL query using the Cloud Console or gcloud command-line tool.

Console

To run a Dataflow SQL query, use the Dataflow SQL editor:

Go to the Dataflow SQL Editor page.

Go to Dataflow SQL editor
Enter the Dataflow SQL query into the query editor.
Click Create job to open a panel of job options.
Optional: In the Job name field, enter a unique job name.
For the Regional endpoint field, select a value from the drop-down menu.
Optional: Click Show optional parameters, and then enter values for the provided Dataflow pipeline options.
In the Destination section, select an Output type, and then enter values for the provided fields.
Optional: In the SQL query parameters section, add parameters and then enter values in the provided fields.
Click Create.

gcloud

To run a Dataflow SQL query, use the gcloud dataflow sql query command. The following is an example SQL query that creates

gcloud dataflow sql query \
  --job-name=JOB_NAME \
  --region=REGION \
  --bigquery-table=BIGQUERY_TABLE \
  --bigquery-dataset=BIGQUERY_DATASET \
  --bigquery-project=BIGQUERY_PROJECT \
'SQL_QUERY'

Replace the following:

JOB_NAME: a name for your Dataflow SQL job
REGION: the regional endpoint for deploying your Dataflow job
BIGQUERY_TABLE: the name of the BigQuery table to which you want to write the output
BIGQUERY_DATASET: the BigQuery dataset ID that contains the output table
BIGQUERY_PROJECT: the Cloud project ID that contains the output BigQuery table
SQL_QUERY: your Dataflow SQL query

For more information about querying data and writing Dataflow SQL query results, see Using data sources and destinations.

Setting pipeline options

You can set Dataflow pipeline options for Dataflow SQL jobs. Dataflow pipeline options are execution parameters that configure how and where to run Dataflow SQL queries.

To set Dataflow pipeline options for Dataflow SQL jobs, specify the following parameters when you run a Dataflow SQL query.

Console

Parameter	Type	Description	Default value
Regional endpoint	`String`	The region to run the query in. Dataflow SQL queries can be run in regions that have a Dataflow regional endpoint.	If not set, defaults to `us-central1.`
Max workers	`int`	The maximum number of Compute Engine instances available to your pipeline during execution.	If unspecified, Dataflow automatically service determines an appropriate number of workers.
Worker region	`String`	The Compute Engine region for launching worker instances to run your pipeline. The Compute Engine worker region can be in a different region than the Dataflow regional endpoint.	If not set, defaults to the specified Dataflow regional endpoint.
Worker zone	`String`	The Compute Engine zone for launching worker instances to run your pipeline. The Compute Engine zone can be in a different region than the Dataflow regional endpoint.	If not set, defaults to a zone in the worker region. If the worker region is not set, defaults to a zone in the specified Dataflow regional endpoint.
Service account email	`String`	The email address of the controller service account with which to run the pipeline. The email address must be in the form `my-service-account-name@<project-id>.iam.gserviceaccount.com`.	If not set, Dataflow workers use the Compute Engine service account of the current project as the controller service account.
Machine type	`String`	The Compute Engine machine type that Dataflow uses when starting workers. You can use any of the available Compute Engine machine type families as well as custom machine types. For best results, use `n1` machine types. Shared core machine types, such as `f1` and `g1` series workers, are not supported under the Dataflow Service Level Agreement. Note that Dataflow bills by the number of vCPUs and GB of memory in workers. Billing is independent of the machine type family.	If not set, Dataflow automatically chooses the machine type.
Additional experiments	`String`	The experiments to enable. An experiment can be a value, like `enable_streaming_engine`, or a key-value pair, such as `shuffle_mode=service`. The experiments must be in a comma-separated list.	If unspecified, no experiments are enabled.
Worker IP Address Configuration	`String`	Specifies whether Dataflow workers use public IP addresses. If the value is set to `Private`, Dataflow workers use private IP addresses for all communication. The specified `Network` or `Subnetwork` must have Private Google Access enabled. If the value is set to `Private` and the `Subnetwork` option is specified, the `Network` option is ignored.	If not set, defaults to `Public`.
Network	`String`	The Compute Engine network to which workers are assigned.	If not set, defaults to the network `default`.
Subnetwork	`String`	The Compute Engine subnetwork to which workers are assigned. The subnetwork must be in the form `regions/region/subnetworks/subnetwork`.	If not set, Dataflow automatically determines subnetwork.

gcloud

Flag	Type	Description	Default value
`‑‑region`	`String`	The region to run the query in. Dataflow SQL queries can be run in regions that have a Dataflow regional endpoint.	If not set, throws an error.
`‑‑max‑workers`	`int`	The maximum number of Compute Engine instances available to your pipeline during execution.	If unspecified, Dataflow automatically determines an appropriate number of workers.
`‑‑num‑workers`	`int`	The initial number of Compute Engine instances to use when executing your pipeline. This parameter determines how many workers Dataflow starts up when your job begins.	If unspecified, Dataflow automatically determines an appropriate number of workers.
`‑‑worker‑region`	`String`	The Compute Engine region for launching worker instances to run your pipeline. The Compute Engine worker region can be in a different region than the Dataflow regional endpoint. You can specify one of `‑‑worker‑region` or `‑‑worker‑zone`.	If not set, defaults to the specified Dataflow regional endpoint.
`‑‑worker‑zone`	`String`	The Compute Engine zone for launching worker instances to run your pipeline. The Compute Engine zone can be in a different region than the Dataflow regional endpoint. You can specify one of `‑‑worker‑region` or `‑‑worker‑zone`.	If not set, defaults to a zone in the specified Dataflow regional endpoint.
`‑‑worker‑machine‑type`	`String`	The Compute Engine machine type that Dataflow uses when starting workers. You can use any of the available Compute Engine machine type families as well as custom machine types. For best results, use `n1` machine types. Shared core machine types, such as `f1` and `g1` series workers, are not supported under the Dataflow Service Level Agreement. Note that Dataflow bills by the number of vCPUs and GB of memory in workers. Billing is independent of the machine type family.	If not set, Dataflow automatically chooses the machine type.
`‑‑service‑account‑email`	`String`	The email address of the controller service account with which to run the pipeline. The email address must be in the form `my-service-account-name@<project-id>.iam.gserviceaccount.com`.	If not set, Dataflow workers use the Compute Engine service account of the current project as the controller service account.
`‑‑disable‑public‑ips`	`boolean`	Specifies whether Dataflow workers use public IP addresses. If set, Dataflow workers use private IP addresses for all communication.	If not set, Dataflow workers use public IP addresses.
`‑‑network`	`String`	The Compute Engine network to which workers are assigned.	If not set, defaults to the network `default`.
`‑‑subnetwork`	`String`	The Compute Engine subnetwork to which workers are assigned. The subnetwork must be in the form `regions/region/subnetworks/subnetwork`.	If not set, Dataflow automatically determines subnetwork.
`‑‑dataflow‑kms‑key`	`String`	The customer-managed encryption key (CMEK) used to encrypt data at rest. You can control the encryption key through Cloud KMS. The key must be in the same location as the job.	If unspecified, Dataflow uses the default Google Cloud encryption instead of a CMEK.

For more information, see the gcloud dataflow sql query command reference.

Stopping Dataflow SQL jobs

To stop a Dataflow SQL job, you must cancel it. Stopping a Dataflow SQL job with the drain option is not supported.

Pricing

Dataflow SQL uses the standard Dataflow pricing; it does not have separate pricing. You are billed for the resources consumed by the Dataflow jobs that you create based on your SQL statements. The charges for these resource are the standard Dataflow charges for vCPU, memory, Persistent Disk, Streaming Engine, and Dataflow Shuffle.

A Dataflow SQL job might consume additional resources such as Pub/Sub and BigQuery, each billed at their own pricing.

For more information about Dataflow pricing, see Dataflow pricing.

What's next

Walk through the Joining streaming data with Dataflow SQL tutorial.
Read about using data sources and destinations.
Explore the gcloud command-line tool for Dataflow SQL.