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6.0 Development
This section contains information related to application development for ecosystem components and MapR products including MapR-DB (binary and JSON), MapR-FS, and MapR Streams.
Ecosystem Components
The following sections provide information about each open source project that MapR supports.
Spark
MapR-DB Connectors for Apache Spark
This section describes the MapR-DB connectors that you can use with Apache Spark.
Understanding the MapR-DB OJAI Connector for Spark
Using the MapR-DB OJAI connector for Spark enables you build real-time and batch pipelines between your data and MapR-DB JSON. Before getting started, it is important that you understand Spark terminology and workflow, system requirements and support, and OJAI connector and API features.

MapR 6.0 Documentation

6.0 Development
This section contains information related to application development for ecosystem components and MapR products including MapR-DB (binary and JSON), MapR-FS, and MapR Streams.
- Application Development Process
  Before you start developing applications on MapR’s Converged Data Platform, consider how you will get the data onto the platform, the format it will be stored in, the type of processing or modeling that is required, and how the data will be accessed.
- MapR-FS and Apps
  The following sections provide information about accessing MapR-FS with C and Java applications.
- MapR-DB and Apps
  This section contains information about developing client applications for JSON and binary tables.
- MapR-ES and Apps
  MapR-ES brings integrated publish and subscribe messaging to the MapR Converged Data Platform.
- MapReduce and Apps
  This section contains information associated with developing YARN applications.
- MapR Data Science Refinery
  The MapR Data Science Refinery 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 FlexVolume Driver
  This section describes how to use and troubleshoot the MapR Data Fabric for Kubernetes FlexVolume Driver.
- Ecosystem Components
  The following sections provide information about each open source project that MapR supports.
  - MapR Ecosystem Packs
    A MapR Ecosystem Pack (MEP) provides a set of ecosystem components that work together on one or more MapR cluster versions. Only one version of each ecosystem component is available in each MEP. For example, only one version of Hive and one version of Spark is supported in a MEP.
  - AsyncHBase
  - Cascading
  - Drill
  - Flume
  - HBase Client and MapR-DB Binary Tables
  - HCatalog
  - Hive
  - HttpFS
  - Hue
  - Impala
  - MapR-ES Clients and Tools
  - Myriad
  - OpenStack Manila
  - Oozie
  - Pig
  - Sentry
  - Spark
    - Getting Started with Spark Interactive Shell
      After you have a basic understanding of Apache Spark and have it installed and running on your MapR cluster, you can use it to load datasets, apply schemas, and query data from the Spark interactive shell.
    - Spark Feature Support
      MapR supports most Spark features. However, there a few exceptions.
    - Spark Standalone
    - Spark on YARN
    - Spark on Mesos
    - Spark configure.sh
      Starting in the MEP 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-DB Connectors for Apache Spark
      This section describes the MapR-DB connectors that you can use with Apache Spark.
      - Understanding the MapR-DB OJAI Connector for Spark
        Using the MapR-DB OJAI connector for Spark enables you build real-time and batch pipelines between your data and MapR-DB 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-DB OJAI Connector for Apache Spark
        Before using the MapR-DB OJAI Connector for Apache Spark, you must edit the pom.xml file for your project.
        Loading Data from MapR-DB Using the MapR-DB OJAI Connector for Apache Spark
        The MapR-DB OJAI Connector for Apache Spark supports loading data as an Apache Spark RDD. Starting in the MEP 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-DB 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-DB OJAI Connector for Apache Spark provides APIs to process JSON documents loaded from MapR-DB.
        Saving Data to a MapR-DB JSON Table
        The MapR-DB OJAI Connector for Apache Spark provides an API to save an Apache Spark RDD to a MapR-DB JSON table. Starting in the MEP 4.0 release, the connector introduces support for saving Apache Spark DataFrames and DStreams to MapR-DB JSON tables.
        Using Serialization with the MapR-DB OJAI Connector for Apache Spark
        In the context of the MapR-DB 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-DB 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 provides instructions on 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.
    - Using Structured Streaming in Spark
      Starting in MEP 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.
  - Sqoop
  - Third Party Solutions
- 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 supports public APIs for MapR-FS, MapR-DB, and MapR-ES. These APIs are available for application development purposes.

Understanding the MapR-DB OJAI Connector for Spark

Using the MapR-DB OJAI connector for Spark enables you build real-time and batch pipelines between your data and MapR-DB JSON. Before getting started, it is important that you understand Spark terminology and workflow, system requirements and support, and OJAI connector and API features.

The MapR-DB OJAI connector includes a set of APIs that enable you to write applications that consume MapR-DB JSON tables and use them in Spark. The MapR-DB OJAI Connector for Apache Spark is a companion to the MapR-DB Binary Connector for Apache Spark, which provides the equivalent functionality for MapR-DB Binary tables.

MapR-DB OJAI Connector with Spark Workflow

You can use the MapR-DB OJAI Connector to extract data from MapR-DB or MapR-FS and transform that data using either Spark or Spark SQL, and then load it into MapR-DB JSON:

MapR-DB OJAI Connector for Apache Spark Features

Principal features of the MapR-DB OJAI Connector for Apache Spark include the following:

Support for Scala and, beginning with MEP 4.1, Java and Python APIs

This matrix shows the programming languages and features supported:

	Scala	Java	Python
RDD	Yes	Yes	No
DataFrame	Yes	Yes	Yes
Dataset	Yes	Yes	No
DStream	Yes	No	No

APIs that enable you to load data from a MapR-DB JSON table to an Apache Spark RDD, DataFrame, or Dataset
Projection and filter pushdown for better performance
Custom partitioner for RDDs that enables you to partition data for better performance
APIs that save an Apache Spark RDD, DataFrame, or DStream to a MapR-DB JSON table using either normal or bulk insert
Support for Scala and Java bean classes
Support for data locality

The following features are not supported:

MapR-DB Binary tables
Only MapR-DB JSON tables are supported; access to MapR-DB binary tables is provided through the MapR-DB Binary Connector.
Secondary indexes

Supported Product Versions and System Requirements

To use the MapR-DB OJAI Connector for Apache Spark, you must have the following minimum software versions:

MapR: 5.2.1 or later
MEP 3.0 or later
Spark 2.1.0 or later
Scala 2.11 or later
Java 8 or later

Support for DataFrames and Datasets is available starting in the MEP 4.0 release.

OJAI API

The MapR-DB OJAI Connector for Apache Spark uses the OJAI API internally to access MapR-DB JSON tables.

(Topic last modified: 2018-10-31)