pulsar-spark

Unified data processing with Apache Pulsar and Apache Spark.

Prerequisites

Java 8 or later
Spark 2.4.0 or later
Pulsar 2.4.0 or later

Preparations

Link

Client library

For Scala/Java applications using SBT/Maven project definitions, link your application with the following artifact:

    groupId = io.streamnative.connectors
    artifactId = pulsar-spark-connector_{{SCALA_BINARY_VERSION}}
    version = {{PULSAR_SPARK_VERSION}}

Currently, the artifact is available in Bintray Maven repository of StreamNative. For Maven project, you can add the repository to your pom.xml as follows:

  <repositories>
    <repository>
      <id>central</id>
      <layout>default</layout>
      <url>https://repo1.maven.org/maven2</url>
    </repository>
    <repository>
      <id>bintray-streamnative-maven</id>
      <name>bintray</name>
      <url>https://dl.bintray.com/streamnative/maven</url>
    </repository>
  </repositories>

Deploy

Client library

As with any Spark applications, spark-submit is used to launch your application.
pulsar-spark-connector_{{SCALA_BINARY_VERSION}} and its dependencies can be directly added to spark-submit using --packages.

Example

$ ./bin/spark-submit 
  --packages io.streamnative.connectors:pulsar-spark-connector_{{SCALA_BINARY_VERSION}}:{{PULSAR_SPARK_VERSION}}
  --repositories https://dl.bintray.com/streamnative/maven
  ...

CLI

For experimenting on spark-shell (or pyspark for Python), you can also use --packages to add pulsar-spark-connector_{{SCALA_BINARY_VERSION}} and its dependencies directly.

Example

$ ./bin/spark-shell 
  --packages io.streamnative.connectors:pulsar-spark-connector_{{SCALA_BINARY_VERSION}}:{{PULSAR_SPARK_VERSION}}
  --repositories https://dl.bintray.com/streamnative/maven
  ...

When locating an artifact or library, --packages option checks the following repositories in order:

Local maven repository
Maven central repository
Other repositories specified by --repositories

The format for the coordinates should be groupId:artifactId:version.

For more information about submitting applications with external dependencies, see Application Submission Guide.

Usage

Read data from Pulsar

Create a Pulsar source for streaming queries

The following examples are in Scala.

// Subscribe to 1 topic
val df = spark
  .readStream
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topic", "topic1")
  .load()
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

// Subscribe to multiple topics
val df = spark
  .readStream
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topics", "topic1,topic2")
  .load()
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

// Subscribe to a pattern
val df = spark
  .readStream
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topicsPattern", "topic.*")
  .load()
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

Tip

For more information on how to use other language bindings for Spark Structured Streaming, see Structured Streaming Programming Guide.

Create a Pulsar source for batch queries

If you have a use case that is better suited to batch processing, you can create a Dataset/DataFrame for a defined range of offsets.

The following examples are in Scala.


// Subscribe to 1 topic defaults to the earliest and latest offsets
val df = spark
  .read
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topic", "topic1")
  .load()
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

// Subscribe to multiple topics, specifying explicit Pulsar offsets
import org.apache.spark.sql.pulsar.JsonUtils._
val startingOffsets = topicOffsets(Map("topic1" -> messageId1, "topic2" -> messageId2))
val endingOffsets = topicOffsets(...)
val df = spark
  .read
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topics", "topic1,topic2")
  .option("startingOffsets", startingOffsets)
  .option("endingOffsets", endingOffsets)
  .load()
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

// Subscribe to a pattern, at the earliest and latest offsets
val df = spark
  .read
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topicsPattern", "topic.*")
  .option("startingOffsets", "earliest")
  .option("endingOffsets", "latest")
  .load()
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

The following options must be set for the Pulsar source for both batch and streaming queries.

Option	Value	Description
`topic`	A topic name string	The topic to be consumed. Only one of `topic`, `topics` or `topicsPattern` options can be specified for Pulsar source.
`topics`	A comma-separated list of topics	The topic list to be consumed. Only one of `topic`, `topics` or `topicsPattern` options can be specified for Pulsar source.
`topicsPattern`	A Java regex string	The pattern used to subscribe to topic(s). Only one of `topic`, `topics` or `topicsPattern` options can be specified for Pulsar source.
`service.url`	A service URL of your Pulsar cluster	The Pulsar `serviceUrl` configuration.
`admin.url`	A service HTTP URL of your Pulsar cluster	The Pulsar `serviceHttpUrl` configuration.

The following configurations are optional.

Option	Value	Default	Query Type	Description
`startingOffsets`	The following are valid values: * "earliest"(streaming and batch queries) * "latest" (streaming query) * A JSON string Example """ {"topic-1":[8,11,16,101,24,1,32,1],"topic-5":[8,15,16,105,24,5,32,5]} """	* "earliest"（batch query) * "latest"（streaming query)	Streaming and batch queries	`startingOffsets` option controls where a reader reads data from. * "earliest": lacks a valid offset, the reader reads all the data in the partition, starting from the very beginning. * "latest": lacks a valid offset, the reader reads from the newest records written after the reader starts running. * A JSON string: specifies a starting offset for each Topic. You can use `org.apache.spark.sql.pulsar.JsonUtils.topicOffsets(Map[String, MessageId])` to convert a message offset to a JSON string. Note: * For batch query, "latest" is not allowed, either implicitly specified or use `MessageId.latest ([8,-1,-1,-1,-1,-1,-1,-1,-1,127,16,-1,-1,-1,-1,-1,-1,-1,-1,127])` in JSON. * For streaming query, "latest" only applies when a new query is started, and the resuming will always pick up from where the query left off. Newly discovered partitions during a query will start at "earliest".
`endingOffsets`	The following are valid values: * "latest" (batch query) * A JSON string Example {"topic-1":[8,12,16,102,24,2,32,2],"topic-5":[8,16,16,106,24,6,32,6]}	"latest"	Batch query	`endingOffsets` option controls where a reader stops reading data. * "latest": the reader stops reading data at the latest record. * A JSON string: specifies an ending offset for each topic. Note: `MessageId.earliest ([8,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,16,-1,-1,-1,-1,-1,-1,-1,-1,-1,1])` is not allowed.
`failOnDataLoss`	The following are valid values: * true * false	true	Streaming query	`failOnDataLoss` option controls whether to fail a query when data is lost (for example, topics are deleted, or messages are deleted because of retention policy). This may cause a false alarm. You can set it to `false` when it doesn't work as you expected. A batch query always fails if it fails to read any data from the provided offsets due to data loss.

Schema of Pulsar source

For topics without schema or with primitive schema in Pulsar, messages' payload is loaded to a value column with the corresponding type with Pulsar schema.
For topics with Avro or JSON schema, their field names and field types are kept in the result rows.

Besides, each row in the source has the following metadata fields as well.

Column	Type
`__key`	Binary
`__topic`	String
`__messageId`	Binary
`__publishTime`	Timestamp
`__eventTime`	Timestamp

Example

The topic of AVRO schema s in Pulsar is as below:

  case class Foo(i: Int, f: Float, bar: Bar)
  case class Bar(b: Boolean, s: String)
  val s = Schema.AVRO(Foo.getClass)

has the following schema as a DataFrame/DataSet in Spark:

root
 |-- i: integer (nullable = false)
 |-- f: float (nullable = false)
 |-- bar: struct (nullable = true)
 |    |-- b: boolean (nullable = false)
 |    |-- s: string (nullable = true)
 |-- __key: binary (nullable = true)
 |-- __topic: string (nullable = true)
 |-- __messageId: binary (nullable = true)
 |-- __publishTime: timestamp (nullable = true)
 |-- __eventTime: timestamp (nullable = true)

For Pulsar topic with Schema.DOUBLE, it's schema as a DataFrame is:

 root
 |-- value: double (nullable = false)
 |-- __key: binary (nullable = true)
 |-- __topic: string (nullable = true)
 |-- __messageId: binary (nullable = true)
 |-- __publishTime: timestamp (nullable = true)
 |-- __eventTime: timestamp (nullable = true)

Write data to Pulsar

The DataFrame written to Pulsar can have arbitrary schema, since each record in DataFrame is transformed as one message sent to Pulsar, fields of DataFrame are divided into two groups: __key and __eventTime fields are encoded as metadata of Pulsar message; other fields are grouped and encoded using AVRO and put in value():

producer.newMessage().key(__key).value(avro_encoded_fields).eventTime(__eventTime)

Create a Pulsar sink for streaming queries

The following examples are in Scala.


// Write key-value data from a DataFrame to a specific Pulsar topic specified in an option
val ds = df
  .selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .writeStream
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topic", "topic1")
  .start()

// Write key-value data from a DataFrame to Pulsar using a topic specified in the data
val ds = df
  .selectExpr("__topic", "CAST(__key AS STRING)", "CAST(value AS STRING)")
  .writeStream
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .start()

Write the output of batch queries to Pulsar

The following examples are in Scala.


// Write key-value data from a DataFrame to a specific Pulsar topic specified in an option
df.selectExpr("CAST(__key AS STRING)", "CAST(value AS STRING)")
  .write
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .option("topic", "topic1")
  .save()

// Write key-value data from a DataFrame to Pulsar using a topic specified in the data
df.selectExpr("__topic", "CAST(__key AS STRING)", "CAST(value AS STRING)")
  .write
  .format("pulsar")
  .option("service.url", "pulsar://localhost:6650")
  .option("admin.url", "http://localhost:8080")
  .save()

Limitations

Currently, we provide at-least-once semantic. Consequently, when writing either streaming queries or batch queries to Pulsar, some records may be duplicated. A possible solution to remove duplicates when reading the written data could be to introduce a primary (unique) key that can be used to perform de-duplication when reading.

Pulsar specific configurations

Client/producer/reader configurations of Pulsar can be set via DataStreamReader.option with pulsar.client./pulsar.producer./pulsar.reader. prefix, e.g, stream.option("pulsar.reader.receiverQueueSize", "1000000"). For possible Pulsar parameters, check docs at Pulsar client libraries.

Build Spark Pulsar Connector

If you want to build a Spark-Pulsar connector reading data from Pulsar and writing results to Pulsar, follow the steps below.

Checkout the source code.

$ git clone https://github.com/streamnative/pulsar-spark.git
$ cd pulsar-spark

Install Docker.

Pulsar-spark connector is using Testcontainers for integration tests. In order to run the integration tests, make sure you have installed Docker.

Set a Scala version.

Change scala.version and scala.binary.version in pom.xml.

Note

Scala version should be consistent with the Scala version of Spark you use.
Build the project.

$ mvn clean install -DskipTests

Run the tests.

$ mvn clean install

Once the installation is finished, there is a fat jar generated under both local maven repo and target directory.

pulsar-spark

Prerequisites

Preparations

Link

Client library

Deploy

Client library

CLI

Usage

Read data from Pulsar

Create a Pulsar source for streaming queries

Tip

Create a Pulsar source for batch queries

Schema of Pulsar source

Write data to Pulsar

Create a Pulsar sink for streaming queries

Write the output of batch queries to Pulsar

Limitations

Pulsar specific configurations

Build Spark Pulsar Connector

Note

License