Google Pub/Sub Lite Spark Connector Client for Java

Java idiomatic client for Pub/Sub Lite Spark Connector.

• Product Documentation
• Client Library Documentation

Quickstart

If you are using Maven, add this to your pom.xml file:

<dependency>
  <groupId>com.google.cloud</groupId>
  <artifactId>pubsublite-spark-sql-streaming</artifactId>
  <version>1.0.0</version>
</dependency>

If you are using Gradle without BOM, add this to your dependencies:

implementation 'com.google.cloud:pubsublite-spark-sql-streaming:1.0.0'

If you are using SBT, add this to your dependencies:

libraryDependencies += "com.google.cloud" % "pubsublite-spark-sql-streaming" % "1.0.0"

Authentication

See the Authentication section in the base directory's README.

Authorization

The client application making API calls must be granted authorization scopes required for the desired Pub/Sub Lite Spark Connector APIs, and the authenticated principal must have the IAM role(s) required to access GCP resources using the Pub/Sub Lite Spark Connector API calls.

Getting Started

Prerequisites

You will need a Google Cloud Platform Console project with the Pub/Sub Lite Spark Connector API enabled. You will need to enable billing to use Google Pub/Sub Lite Spark Connector. Follow these instructions to get your project set up. You will also need to set up the local development environment by installing the Google Cloud SDK and running the following commands in command line: gcloud auth login and gcloud config set project [YOUR PROJECT ID].

Installation and setup

You'll need to obtain the pubsublite-spark-sql-streaming library. See the Quickstart section to add pubsublite-spark-sql-streaming as a dependency in your code.

About Pub/Sub Lite Spark Connector

Google Cloud Pub/Sub Lite is a zonal, real-time messaging service that lets you send and receive messages between independent applications. You can manually configure the throughput and storage capacity for Pub/Sub Lite systems.

The Pub/Sub Lite Spark connector supports Pub/Sub Lite as an input source to Apache Spark Structured Streaming in both the default micro-batch processing mode and the experimental continous processing mode. The connector works in all Apache Spark distributions, including Google Cloud Dataproc and manual Spark installations.

Requirements

Creating a new subscription or using an existing subscription

Follow the instruction to create a new subscription or use an existing subscription. If using an existing subscription, the connector will read from the oldest unacknowledged message in the subscription.

Creating a Google Cloud Dataproc cluster (Optional)

If you do not have an Apache Spark environment, you can create a Cloud Dataproc cluster with pre-configured auth. The following examples assume you are using Cloud Dataproc, but you can use spark-submit on any cluster.

MY_CLUSTER=...
gcloud dataproc clusters create "$MY_CLUSTER"

Downloading and Using the Connector

The latest version of the connector is publicly available from the Maven Central repository. You can download and pass it in the --jars option when using the spark-submit command.

Compatibility

Connector version	Spark version
≤0.3.4	2.4.X
Current	3.X.X

Usage

Samples

There are 3 java samples (word count, simple write, simple read) under samples that shows using the connector inside Dataproc.

Reading data from Pub/Sub Lite

Here is an example in Python:

df = spark.readStream \
  .format("pubsublite") \
  .option("pubsublite.subscription", "projects/$PROJECT_NUMBER/locations/$LOCATION/subscriptions/$SUBSCRIPTION_ID") \
  .load

Here is an example in Java:

Dataset<Row> df = spark
  .readStream()
  .format("pubsublite")
  .option("pubsublite.subscription", "projects/$PROJECT_NUMBER/locations/$LOCATION/subscriptions/$SUBSCRIPTION_ID")
  .load();

Note that the connector supports both MicroBatch Processing and Continuous Processing.

Writing data to Pub/Sub Lite

Here is an example in Python:

df.writeStream \
  .format("pubsublite") \
  .option("pubsublite.topic", "projects/$PROJECT_NUMBER/locations/$LOCATION/topics/$TOPIC_ID") \
  .option("checkpointLocation", "path/to/HDFS/dir")
  .outputMode("complete") \
  .trigger(processingTime="2 seconds") \
  .start()

Here is an example in Java:

df.writeStream()
  .format("pubsublite")
  .option("pubsublite.topic", "projects/$PROJECT_NUMBER/locations/$LOCATION/topics/$TOPIC_ID")
  .option("checkpointLocation", "path/to/HDFS/dir")
  .outputMode(OutputMode.Complete())
  .trigger(Trigger.ProcessingTime(2, TimeUnit.SECONDS))
  .start();

Properties

When reading from Pub/Sub Lite, the connector supports a number of configuration options:

Option	Type	Required	Default Value	Meaning
pubsublite.subscription	String	Y		Full subscription path that the connector will read from.
pubsublite.flowcontrol.byteoutstandingperpartition	Long	N	50_000_000	Max number of bytes per partition that will be cached in workers before Spark processes the messages.
pubsublite.flowcontrol.messageoutstandingperpartition	Long	N	Long.MAX	Max number of messages per partition that will be cached in workers before Spark processes the messages.
pubsublite.flowcontrol.maxmessagesperbatch	Long	N	Long.MAX	Max number of messages in micro batch.
gcp.credentials.key	String	N	Application Default Credentials	Service account JSON in base64.

When writing to Pub/Sub Lite, the connector supports a number of configuration options:

Option	Type	Required	Default Value	Meaning
pubsublite.topic	String	Y		Full topic path that the connector will write to.
gcp.credentials.key	String	N	Application Default Credentials	Service account JSON in base64.

Data Schema

When reading from Pub/Sub Lite, the connector has a fixed data schema as follows:

Data Field	Spark Data Type	Notes
subscription	StringType	Full subscription path
partition	LongType
offset	LongType
key	BinaryType
data	BinaryType
attributes	MapType[StringType, ArrayType[BinaryType]]
publish_timestamp	TimestampType
event_timestamp	TimestampType	Nullable

When writing to Pub/Sub Lite, the connetor matches the following data field and data types as follows:

Data Field	Spark Data Type	Required
key	BinaryType	N
data	BinaryType	N
attributes	MapType[StringType, ArrayType[BinaryType]]	N
event_timestamp	TimestampType	N

Note that when a data field is present in the table but the data type mismatches, the connector will throw IllegalArgumentException that terminates the query.

Building the Connector

The connector is built using Maven. Following command creates a JAR file with shaded dependencies:

mvn package

FAQ

What is the cost for the Pub/Sub Lite?

See the Pub/Sub Lite pricing documentation.

Can I configure the number of Spark partitions?

No, the number of Spark partitions is set to be the number of Pub/Sub Lite partitions of the topic that the subscription is attached to.

How do I authenticate outside Cloud Compute Engine / Cloud Dataproc?

Use a service account JSON key and GOOGLE_APPLICATION_CREDENTIALS as described here.

Credentials can be provided with gcp.credentials.key option, it needs to be passed in as a base64-encoded string.

Example:

spark.readStream.format("pubsublite").option("gcp.credentials.key", "<SERVICE_ACCOUNT_JSON_IN_BASE64>")

Samples

Samples are in the samples/ directory.

Sample	Source Code	Try it
Admin Utils	source code
Common Utils	source code
Publish Words	source code
Read Results	source code
Simple Read	source code
Simple Write	source code
Word Count	source code

Troubleshooting

To get help, follow the instructions in the shared Troubleshooting document.

Transport

Pub/Sub Lite Spark Connector uses gRPC for the transport layer.

Supported Java Versions

Java 8 or above is required for using this client.

Google's Java client libraries, Google Cloud Client Libraries and Google Cloud API Libraries, follow the Oracle Java SE support roadmap (see the Oracle Java SE Product Releases section).

For new development

In general, new feature development occurs with support for the lowest Java LTS version covered by Oracle's Premier Support (which typically lasts 5 years from initial General Availability). If the minimum required JVM for a given library is changed, it is accompanied by a semver major release.

Java 11 and (in September 2021) Java 17 are the best choices for new development.

Keeping production systems current

Google tests its client libraries with all current LTS versions covered by Oracle's Extended Support (which typically lasts 8 years from initial General Availability).

Legacy support

Google's client libraries support legacy versions of Java runtimes with long term stable libraries that don't receive feature updates on a best efforts basis as it may not be possible to backport all patches.

Google provides updates on a best efforts basis to apps that continue to use Java 7, though apps might need to upgrade to current versions of the library that supports their JVM.

Where to find specific information

The latest versions and the supported Java versions are identified on the individual GitHub repository github.com/GoogleAPIs/java-SERVICENAME and on google-cloud-java.

Versioning

This library follows Semantic Versioning.

Contributing

Contributions to this library are always welcome and highly encouraged.

See CONTRIBUTING for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Code of Conduct for more information.

License

Apache 2.0 - See LICENSE for more information.

CI Status

Java Version	Status
Java 8
Java 8 OSX
Java 8 Windows
Java 11

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