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.
  - Getting Started with MapR Event Store For Apache Kafka
    If you have a basic understanding of MapR Event Store For Apache Kafka components and the typical flow of messages from producers to consumers, you can get started.
  - Consuming CDC Records
    The OJAI changelog interfaces are used to consume changed data records (propagated by the Change Data Capture feature).
  - Consuming Audit Logs
    Audit Streaming (available from v6.0.1) provides a way to process the audit data in real-time.
  - MapR Event Store For Apache Kafka Java Applications
    This section contains information on developing client applications with Java including information about the MapR Event Store For Apache Kafka and Apache Kafka Java APIs, configuration parameters, and compiling and running producers and consumers.
  - MapR Event Store For Apache Kafka C Applications
    You can develop C applications for MapR Event Store For Apache Kafka. The MapR Event Store For Apache Kafka C Client is a distribution of librdkafka that works with MapR Event Store For Apache Kafka. The MapR Event Store For Apache Kafka C Client is available in Ezmeral Ecosystem Pack (EEP) 3.0 and later.
  - MapR Event Store For Apache Kafka Python Applications
    As of MapR Data Platform 5.2.1, you can create python applications for MapR Event Store For Apache Kafka using the MapR Event Store For Apache Kafka Python client. The MapR Event Store For Apache Kafka Python client is a binding for librdkafka and the MapR Event Store For Apache Kafka C Client is a distribution of librdkafka that works with MapR Event Store For Apache Kafka.
    - Developing MapR Event Store For Apache Kafka Python Applications
      This topic includes basic information about how to develop a MapR Event Store For Apache Kafka Python application and an example program that you can run.
    - Migrating Kafka Python Applications to MapR Event Store For Apache Kafka
      With some modification, you can use existing confluent-kafka python applications to consume and produce topics in MapR Event Store For Apache Kafka. The MapR Event Store For Apache Kafka Python Client is a binding for Apache librdkafka that works with MapR Event Store For Apache Kafka.
    - API for MapR Event Store For Apache Kafka Python Client
      MapR Event Store For Apache Kafka Python Client is a binding for librdkafka and it supports the following APIs.
    - Configuration Properties for MapR Event Store For Apache Kafka Python Client
      In the instance constructor of a MapR Event Store For Apache Kafka Python application, you can use a dictionary to set the following configuration properties. MapR Event Store For Apache Kafka Python client supports a superset of the configuration properties supported by the MapR Event Store For Apache Kafka C client.
  - MapR Event Store For Apache Kafka C#/.NET Applications
    As of MapR Data Platform 6.0.1/EEP5.0, you can create C#/.NET applications for MapR Event Store For Apache Kafka using the MapR Event Store For Apache Kafka C#/.NET client. The MapR Event Store For Apache Kafka C#/.NET client is a binding for librdkafka and the MapR Event Store For Apache Kafka C Client is a distribution of librdkafka that works with MapR Event Store For Apache Kafka.
  - Utilities for MapR Event Store For Apache Kafka
    MapR Event Store For Apache Kafka provides the utilities for operating on streams and topics.
  - Configuring Properties for Message Size
    Describes the message.max.bytes and receive.message.max.bytes properties for configuring message size.
- 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.
- 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.

MapR Event Store For Apache Kafka Python Applications

As of MapR Data Platform 5.2.1, you can create python applications for MapR Event Store For Apache Kafka using the MapR Event Store For Apache Kafka Python client. The MapR Event Store For Apache Kafka Python client is a binding for librdkafka and the MapR Event Store For Apache Kafka C Client is a distribution of librdkafka that works with MapR Event Store For Apache Kafka.

The MapR Event Store For Apache Kafka Python client is available in a Ezmeral Ecosystem Pack (EEP) starting with EEP 3.0.

The following Apache Kafka librdkafka versions are supported:

Table 1. Supported Apache Kafka librdkafka versions
Core release	EEP Release	Kafka librdkafka version
As of MapR Data Platform 6.0.1	As of 5.0	0.11.3
As of MapR Data Platform 5.2.1 through 6.0.0	As of 3.0	0.9.0

NOTE Because the MapR Event Store For Apache Kafka Python Client is dependent on the MapR Event Store For Apache Kafka C Client, the MapR Event Store For Apache Kafka C Client must be configured before using the MapR Event Store For Apache Kafka Python Client.

When developing and running MapR Event Store For Apache Kafka Python applications, note the following points:

You can create producers and high-level consumers. Low-level consumers are not supported.
Consuming or producing topics in a Kafka cluster is not supported.
MapR Event Store For Apache Kafka offset values start at 1, not 0.
MapR Data Platform security is supported including ACLs and ACEs for authorization. The unique Kafka security features that are part of Apache Kafka are not supported. See Security for more information about MapR Data Platform security features.
User impersonation is not supported.