Subscribe to Google Pub/Sub

Use the built-in connector to subscribe to Google Pub/Sub. This connector provides exactly-once processing semantics for records from the subscriber.

note

Pub/Sub might publish duplicate records, or records might arrive to the subscriber out of order. Write code to handle duplicate and out-of-order records.

Configure a Pub/Sub stream

The following code example demonstrates the basic syntax for configuring a Structured Streaming read from Pub/Sub.

Python

Python

auth_options = {
    "clientId": client_id,
    "clientEmail": client_email,
    "privateKey": private_key,
    "privateKeyId": private_key_id
}

query = (spark.readStream
  .format("pubsub")
  .option("subscriptionId", "mysub")
  .option("topicId", "mytopic")
  .option("projectId", "myproject")
  .options(auth_options)
  .load()
)

SQL

SQL

CREATE OR REFRESH STREAMING TABLE pubsub_raw
AS SELECT * FROM STREAM read_pubsub(
  subscriptionId => 'mysub',
  projectId => 'myproject',
  topicId => 'mytopic',
  clientEmail => secret('pubsub-scope', 'clientEmail'),
  clientId => secret('pubsub-scope', 'clientId'),
  privateKeyId => secret('pubsub-scope', 'privateKeyId'),
  privateKey => secret('pubsub-scope', 'privateKey')
);

Scala

Scala

val authOptions: Map[String, String] =
  Map("clientId" -> clientId,
      "clientEmail" -> clientEmail,
      "privateKey" -> privateKey,
      "privateKeyId" -> privateKeyId)

val query = spark.readStream
  .format("pubsub")
  // Creates a Pub/Sub subscription if one does not already exist with this ID
  .option("subscriptionId", "mysub")
  .option("topicId", "mytopic")
  .option("projectId", "myproject")
  .options(authOptions)
  .load()

For more configuration options, see Configure options for Pub/Sub streaming read.

Configure access to Pub/Sub

The credentials you configure must have the following roles.

Roles	Required or optional	How role is used
roles/pubsub.viewer or roles/viewer	Required	Checks if subscription exists and gets subscription.
roles/pubsub.subscriber	Required	Fetches data from a subscription.
roles/pubsub.editor or roles/editor	Optional	Enables creation of a subscription if one doesn't exist and enables use of the deleteSubscriptionOnStreamStop to delete subscriptions on stream termination.

Databricks recommends using secrets when providing authorization options. The following options are required to authorize a connection:

• clientEmail
• clientId
• privateKey
• privateKeyId

Understand the Pub/Sub schema

The schema for the stream matches the records that are fetched from Pub/Sub, as described in the following table.

Field	Type
messageId	StringType
payload	ArrayType[ByteType]
attributes	StringType
publishTimestampInMillis	LongType

Configure options for Pub/Sub streaming read

The following table describes the options supported for Pub/Sub. All options are configured as part of a Structured Streaming read using .option("<optionName>", "<optionValue>") syntax.

note

Some Pub/Sub configuration options use the concept of fetches instead of micro-batches. This reflects internal implementation details, and options work similarly to corollaries in other Structured Streaming connectors, except that records are fetched and then processed.

Option	Default value	Description
numFetchPartitions	Set to one half of the number of executors present at stream initialization.	The number of parallel Spark tasks that fetch records from a subscription.
deleteSubscriptionOnStreamStop	false	If true, the subscription passed to the stream is deleted when the streaming job ends.
maxBytesPerTrigger	none	A soft limit for the batch size to be processed during each triggered micro-batch.
maxRecordsPerFetch	1000	The number of records to fetch per task before processing records.
maxFetchPeriod	10s	The time duration for each task to fetch before processing records. Accepts a duration string, for example, 1s for 1 second or 1m for 1 minute. Databricks recommends using the default value.

Use incremental batch processing with Pub/Sub

You can use Trigger.AvailableNow to consume available records from the Pub/Sub sources as an incremental batch.

Databricks records the timestamp when you begin a read with the Trigger.AvailableNow setting. Records processed by the batch include all previously fetched data and any newly published records with a timestamp less than the recorded stream start timestamp. For more information, see AvailableNow: Incremental batch processing.

Monitor Pub/Sub streaming metrics

Structured Streaming progress metrics report the number of records fetched and ready to process, the size of the records fetched and ready to process, and the number of duplicates seen since stream start. The following is an example of these metrics:

JSON

"metrics" : {
  "numDuplicatesSinceStreamStart" : "1",
  "numRecordsReadyToProcess" : "1",
  "sizeOfRecordsReadyToProcess" : "8"
}

Limitations

Pub/Sub does not support speculative execution (spark.speculation).