HPE Ezmeral Data Fabric 6.1.x is In Maintenance and transitions to "End of Maintenance" in June 2024. Please see the latest documentation.

About MapR 6.1
This site contains the main documentation for Version 6.1 of the MapR Converged Data Platform, including installation, configuration, administration, and reference information.
6.1 Installation
This section contains information about installing and upgrading MapR software. It also contains information about how to migrate data and applications from an Apache Hadoop cluster to a MapR cluster.
6.1 MapR Data Platform
MapR Data Platform is the industry-leading data platform for AI and analytics that solves enterprise business needs.
6.1 Administration
This section describes how to manage the nodes and services that make up a cluster.
6.1 Development
This section contains information related to application development for Ezmeral ecosystem components and MapR Data Platform products, including the file system, Database (Key-Value and JSON), and Event Streams.
- Application Development Process
  Before you start developing applications on the MapR Data Platform platform, consider how you will get the data into the platform, the storage format of the data, the type of processing or modeling that is required, and how the data will be accessed.
- MapR XD and Apps
  The following sections provide information about accessing the MapR XD with C and Java applications.
- MapR Database and Apps
  This section contains information about developing client applications for JSON and key-value tables.
- MapR Event Store For Apache Kafka and Apps
  MapR Event Store For Apache Kafka brings integrated publish and subscribe messaging to MapR Data Platform.
- MapReduce and Apps
  This section contains information associated with developing YARN applications.
- MapR Data Science Refinery
  The MapR Data Science Refinery product is an easy-to-deploy and scalable data science toolkit with native access to all platform assets and superior out-of-the-box security.
- MapR Data Fabric for Kubernetes
  This section describes how to leverage the capabilities of the MapR Data Fabric for Kubernetes.
- Ecosystem Components
  The following sections provide information about each open-source project that is supported by the MapR Data Platform.
  - Ezmeral Ecosystem Packs
  - AsyncHBase
  - Cascading
  - Apache Drill
  - Flume
  - HBase
  - HBase Client and MapR Database Binary Tables
  - HCatalog
  - Hive
  - HttpFS
  - Hue
  - Impala
  - Livy
    Apache Livy is primarily used to provide integration between Hue and Spark.
  - MapR Event Store For Apache Kafka Clients and Tools
    Describes the supported MapR Event Store For Apache Kafka tools and clients.
  - S3 Gateway
    The S3 gateway is a service that provides an S3-compatible interface to expose data in MapR Data Platform as objects. The S3 gateway manages all inbound S3 API requests to put data into and get data out of cloud storage.
  - Myriad
  - Oozie
  - Pig
  - Sentry
  - Apache Spark
    - Getting Started with Spark Interactive Shell
      After you have a basic understanding of Apache Spark and have it installed and running on your cluster, you can use it to load datasets, apply schemas, and query data from the Spark interactive shell.
    - Apache Spark Feature Support
      MapR Data Platform supports most Apache Spark features. However, there are some exceptions.
    - Spark Standalone
    - Spark on YARN
    - Spark configure.sh
      Starting in the EEP 4.0 release, run configure.sh -R to complete your Spark configuration when manually installing Spark or upgrading to a new version.
    - Spark SQL Thrift Server
      Spark SQL Thrift (Spark Thrift) was developed from Apache Hive HiveServer2 and operates like HiveSever2 Thrift server.
    - Spark History Server SSL
      Describes how to enable SSL for Spark History Server.
    - MapR Database Connectors for Apache Spark
      This section describes the MapR Database connectors that you can use with Apache Spark.
    - Integrating Spark
      This section includes the following topics about configuring Spark to work with other ecosystem components.
      - Integrate Spark-SQL (Spark 2.3.1 and later) with Avro
        You integrate Spark-SQL with Avro when you want to read and write Avro data. This information is for Spark 2.3.0 or later users.
      - Integrate Spark-SQL (Spark 1.6.1) with Avro
        You integrate Spark-SQL with Avro when you want to read and write Avro data. This information is for Spark 1.6.1 or earlier users.
      - Integrate Spark with HBase
        Integrate Spark with HBase or MapR Database when you want to run Spark jobs on HBase or MapR Database tables.
      - Integrate Spark-SQL (Spark 2.0.1 and later) with Hive
        You integrate Spark-SQL with Hive when you want to run Spark-SQL queries on Hive tables. This information is for Spark 2.0.1 or later users.
      - Integrate Spark-SQL (Spark 1.6.1) with Hive
        You integrate Spark-SQL with Hive when you want to run Spark-SQL queries on Hive tables. This information is for Spark 1.6.1 or earlier users.
      - Integrate Spark with MapR Event Store For Apache Kafka
        Integrate Spark with MapR Streams to enable Spark to query MapR Event Store For Apache Kafka for new messages at a given interval, process any new messages that are available, and also publish messages into MapR Event Store For Apache Kafka.
        Configure Spark 2.2.1 and later to Consume MapR Event Store For Apache Kafka Messages
        Using the Kafka 0.9 API, you can configure a Spark application to query MapR Event Store For Apache Kafka for new messages at a given interval. This information is for Spark 2.2.1 and later users.
        Configure Spark to Produce MapR Event Store For Apache Kafka Messages
        Using the Kafka 0.9 API, you can configure a Spark application to produce MapR Streams messages.
      - Integrate Spark with R
        You integrate Spark with R when you want to run R programs as Spark jobs.
      - Integrate Spark with Kafka
        From EEP-5.0.0, Spark can be integrated with Kafka-1.0. You can configure a Spark application to produce Kafka messages.
    - Spark JDBC and ODBC Drivers
      MapR provides JDBC and ODBC drivers so you can write SQL queries that access the Apache Spark data-processing engine. This section describes how to download the drivers, and install and configure them.
    - Spark API Changes
      This topic describes the public API changes that occurred for specific Spark versions.
    - Structured Streaming in Spark
      Starting in EEP 5.0.0, structured streaming is supported in Spark.
    - PAM Authentication for Spark
      Spark supports PAM authentication on secure MapR clusters.
    - Read or Write LZO Compressed Data for Spark
      This topic provides details for reading or writing LZO compressed data for Spark.
    - Ports Used by Spark
      To run a Spark job from a client node, ephemeral ports should be opened in the cluster for the client from which you are running the Spark job.
    - ACL Configuration for Spark
      Starting in the EEP 6.0 release, the ACL configuration for Spark is disabled by default.
  - Sqoop
  - YARN
  - Zeppelin
- Maven and MapR
  This section discusses topics associated with Maven and MapR.
- Developer's Reference
  This section contains in-depth information for the developer.
- API Documentation
  MapR Data Platform supports public APIs for MapR File System, MapR Database, and MapR Event Store For Apache Kafka. These APIs are available for application-development purposes.
Other Docs
This section contains release-independent information, including: MapR Installer documentation, Ecosystem release notes, interoperability matrices, security vulnerabilities, and links to other MapR version documentation.
Glossary
Definitions for commonly used terms in MapR Converged Data Platform environments.

Configure Spark to Produce MapR Event Store For Apache Kafka Messages

Using the Kafka 0.9 API, you can configure a Spark application to produce MapR Streams messages.

Procedure

Add the following dependency:
```
groupId = org.apache.spark
artifactId = spark-streaming-kafka-producer_2.11
version = <spark_version>-mapr-<mapr_eco_version>
```
NOTE If you would like to use Streaming Producer Examples, you must add the appropriate Spark streaming Kafka producer jar from the MapR Maven repository to the Spark classpath (/opt/mapr/spark/spark-<spark_version>/jars/.
When you write the Spark program, import and use classes from org.apache.spark.streaming.kafka.producer._ and org.apache.spark.streaming.dstream.
The import of org.apache.spark.streaming.stream.DStream adds the following method from DStream:
```
sendToKafka(topic: String, conf: ProducerConf)
```
In the code below, calling sendToKafka will send numMessages messages to the set of topics specified by the topics parameter.
```
val producerConf = new ProducerConf(bootstrapServers = kafkaBrokers.split(",").toList)
   .withKeySerializer("org.apache.kafka.common.serialization.ByteArraySerializer")
   .withValueSerializer("org.apache.kafka.common.serialization.StringSerializer")
                        
val items = (0 until numMessages.toInt).map(i => Item(i, i).toString)
val defaultRDD: RDD[String] = ssc.sparkContext.parallelize(items)
val dStream: DStream[String] = new ConstantInputDStream[String](ssc, defaultRDD)
                        
dStream.foreachRDD(_.sendToKafka(topics, producerConf))
dStream.count().print()
```
The org.apache.kafka.common.serialization.ByteArraySerializer and org.apache.kafka.common.serialization.StringSerializer properties are used by default, and in case you do not want to use another serializer, withKeySerializer and withValueSerializer methods are not necessary.

Example

Source code for a sample producer program can be found at https://github.com/mapr/spark/blob/2.2.1-mapr-1803/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaProducerExample.scala