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.
      - Understanding the MapR Database OJAI Connector for Spark
        Using the MapR Database OJAI connector for Spark enables you build real-time and batch pipelines between your data and MapR Database JSON. Before getting started, it is important that you understand Spark terminology and workflow, system requirements and support, and OJAI connector and API features.
        Configuring the MapR Database OJAI Connector for Apache Spark
        Before using the MapR Database OJAI Connector for Apache Spark, you must edit the pom.xml file for your project.
        Loading Data from MapR Database Using the MapR Database OJAI Connector for Apache Spark
        The MapR Database OJAI Connector for Apache Spark supports loading data as an Apache Spark RDD. Starting in the EEP 4.0 release, the connector introduces support for Apache Spark DataFrames and Datasets. DataFrames and Datasets perform better than RDDs. Whether you load your MapR Database data as a DataFrame or Dataset depends on the APIs you prefer to use. It is also possible to convert an RDD to a DataFrame.
        Working with Complex JSON Document Types
        The MapR Database OJAI Connector for Apache Spark provides APIs to process JSON documents loaded from MapR Database.
        Saving Data to a MapR Database JSON Table
        The MapR Database OJAI Connector for Apache Spark provides an API to save an Apache Spark RDD to a MapR Database JSON table. Starting in the EEP 4.0 release, the connector introduces support for saving Apache Spark DataFrames and DStreams to MapR Database JSON tables.
        Saving an Apache Spark RDD to a MapR Database JSON Table
        Saving an Apache Spark DataFrame to a MapR Database JSON Table
        Inserting an Apache Spark DataFrame into a MapR Database JSON Table
        Starting in the EEP 4.1.0 release, you can use the insertToMapRDB API to insert an Apache Spark DataFrame into a MapR Database JSON table in Python. The insertToMapRDB API throws an exception if a row with the same ID already exists.
        Using Alternate Write Modes for MapR Database OJAI Connector
        You can use alternate write modes supported by MapR Database OJAI Connector for Apache Spark to save an Apache Spark DataFrame to a MapR Database JSON table.
        Saving an Apache Spark DStream to a MapR Database JSON Table
        Saving an Apache Spark Dataset to a MapR Database JSON Table
        Using Serialization with the MapR Database OJAI Connector for Apache Spark
        In the context of the MapR Database OJAI Connector for Apache Spark, serialization refers to the methods that read and write objects into bytes. This section describes how to configure your application to use a more efficient serializer.
      - MapR Database Binary Connector for Apache Spark
        This section describes the three main interaction points between Spark and HBase APIs and provides examples for each interaction point.
    - Integrating Spark
      This section includes the following topics about configuring Spark to work with other ecosystem components.
    - 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.

Inserting an Apache Spark DataFrame into a MapR Database JSON Table

Starting in the EEP 4.1.0 release, you can use the insertToMapRDB API to insert an Apache Spark DataFrame into a MapR Database JSON table in Python. The insertToMapRDB API throws an exception if a row with the same ID already exists.

PySpark supports only DataFrame(Dataset<Row>):

sparkSession.insertToMapRDB(df, tableName, idFieldPath, bulkInsert)