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
    - Configuring Flume
      - Configure a Secure HBase Sink
      - Configure a Secure File System Sink
        When writing to the file system on a secure MapR cluster, you must configure Flume agents to use either a user ticket or a Kerberos ticket.
      - Flume Thrift Security Parameters
        On a secure cluster, SSL for Flume Thrift Source and Sink is automatically configured.
      - Configure User Impersonation for Flume
        Configure impersonation for Flume agents to allow one user (the mapr super user) to access data and submit jobs on behalf of another user.
      - Integrate Flume with MapR Event Store For Apache Kafka
        Integrate Flume 1.6 with MapR Event Store For Apache Kafka when you want to write Flume source data to a MapR Event Store For Apache Kafka topic or when you want to write MapR Event Store For Apache Kafka topics to a Flume sink.
        Read and Write MapR Event Store For Apache Kafka Messages With Flume
        Read MapR Event Store For Apache Kafka Using a Kafka Source
        Read or Write to MapR Event Store For Apache Kafka using a Kafka Channel
        Write to MapR Event Store For Apache Kafka using a Kafka Sink
        Example Agents Files: Flume 1.7 or Above and MapR Event Store For Apache Kafka Integration
        Example Agents Files: Flume 1.6 or Lower and MapR Event Store For Apache Kafka Integration
    - Using Flume
    - Flume 1.7.0 API
  - 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
  - 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.

Write to MapR Event Store For Apache Kafka using a Kafka Sink

IMPORTANT This component is deprecated. Hewlett Packard Enterprise recommends using an alternate product. For more information, see Discontinued Ecosystem Components.

The following parameters are required when configuring Kafka sink to write to a MapR Stream:

Property Name Description

type

This property must be set to

org.apache.flume.sink.kafka.KafkaSink

kafka.topic

A topic specified with the volume path and stream name. For example:

/streaming_data/flume_stream:topic1

NOTE It is critical that the path to the topic starts with a slash(/), as the slash is what distinguishes the topic as a MapR Event Store For Apache Kafka topic. The default is "default-flume-topic".

NOTE If the header for a given message contains a "topic" field, the message will be published to that topic instead of the topic that you configure here.

For additional properties that you may want to configure for the Kafka sink, see the Flume documentation.

NOTE kafka.flumeBatchSize sets the number of messages to process in a batch and the default is 100. To ensure that the events are written in the original order, set kafka.flumeBatchSize=1 and specify a topic with a single partition. If maintaining the order is not required, you can increase the value. However, note that larger batches improve throughput while also adding latency.