tPubSubInputAvro properties for Apache Spark Streaming

Google PubSub

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These properties are used to configure tPubSubInputAvro running in the Spark Streaming Job framework.

The Spark Streaming tPubSubInputAvro component belongs to the Messaging family.

The component in this framework is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

Define a Goolge Cloud configuration component

If you are using Dataproc as your Spark cluster, clear this check box.

Otherwise, select this check box to allow the Pub/Sub component to use the Google Cloud configuration information provided by a tGoogleCloudConfiguration component.

Schema and Edit schema

A schema is a row description. It defines the number of fields (columns) to Repository. When you create a Spark Job, avoid the reserved word line when naming the fields.

Note that the schema defined here must correspond exactly to the binary Avro schema of the messages received on Pub/Sub. To guarantee this, you can create another Spark Streaming Job to define a given schema using tWriteAvroFields and write the messages with this schema using tPubSubOutput.

Topic name

Enter the name of topic from which you want to consume the messages.

Subscription name

Enter the name of the subscription that needs to consume the specified topic.

If the subscription exists, it must be connected to the given topic; if the subscription does not exist, it is created and connected to the given topic at runtime.

Advanced settings

Storage level

From the Storage level drop-down list that is displayed, select how the cached RDDs are stored, such as in memory only or in memory and on disk.

For further information about each of the storage level, see

Use hierarchical mode

Select this check box to map the binary (including hierarchical) Avro schema to the flat schema defined in the schema editor of the current component. If the Avro message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameter(s):

  • Local path to the avro schema: browse to the file which defines the schema of the Avro data to be processed.

  • Mapping: create the map between the schema columns of the current component and the data stored in the hierarchical Avro message to be handled. In the Node column, you need to enter the JSON path pointing to the data to be read from the Avro message.


Usage rule

This component is used as a start component and requires an output link.

Spark Connection

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files:
  • Yarn mode: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory.

  • Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.

PubSub access permissions

When you use Pub/Sub with a Dataproc cluster, ensure that this cluster has the appropriate permissions to access the Pub/Sub service.

To do this, you can create the Dataproc cluster by checking Allow API access to all Google Cloud services in the same project in the advanced options on Google Cloud Platform, or via the command line, assigning the scopes explicitly (the following example is for a low-resource test cluster):
gcloud beta dataproc clusters create CLUSTER_ID \
    --zone europe-west1-b \
    --master-machine-type n1-standard-2 \
    --master-boot-disk-size 50 \
    --num-workers 2 \
    --worker-machine-type n1-standard-2 \
    --worker-boot-disk-size 50 \
    --scopes '' \
    --project PROJECT_ID