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.
    - 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.

Apache Spark Feature Support

MapR Data Platform supports most Apache Spark features. However, there are some exceptions.

Spark SQL and Apache Derby Support on Spark

If you are using Spark SQL with Derby database without Hive or Hive Metastore installation, you will see the following exception:

java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient

Add the hive-service-2.3.*.jar and log4j2 jars to /opt/mapr/spark/spark-3.x.x/jars location to use Spark SQL with Derby Database without Hive or Hive Metastore installation.

The log4j2 jars are located at /opt/mapr/lib/log4j2/log4j-*.jar location.

Spark 3.1.2 and Spark 3.2.0 does not support log4j1.2 logging on HPE Ezmeral Data Fabric.

Symlink Support on Spark 2.4.4

For full Symlink support on Spark 2.4.4, request the patch. See Applying a Patch.

Spark Thrift JDBC/ODBC Server Support

Running the Spark Thrift JDBC/ODBC Server on a secure cluster is supported only on Spark 2.1.0 or later.

You can run the Spark Thrift JDBC/ODBC Server to enable connections to Hive 1.2.1 using Beeline; however, you can connect only to Hive versions supported by your Spark version.

Spark SQL and Hive Support for Spark 2.1.0

Spark 2.1.0 is able to connect to Hive 2.1 Metastore; however, only features of Hive 1.2 are supported.

Spark SQL and Hive Support for Spark 2.0.1

Spark SQL is supported, but it is not fully compatible with Hive. For details, see the Apache Spark documentation.

The following Hive functions are not supported in Spark SQL:

Tables with buckets
UNION type
Unique join
Column statistics collecting
Output formats: File format (for CLI), Hadoop Archive
Block-level bitmap indexes and virtual columns
Automatic determination of the number of reducers for JOIN and GROUP BY
Metadata-only query
Skew data flag
STREAMTABLE hint in JOIN
Merging of multiple small files for query results

Spark SQL and Hive Support for Spark 1.6.1

Spark SQL is supported, but it is not fully compatible with Hive. For details, see the Apache Spark documentation. The following Spark SQL operations support the following Hive table formats:

	Hive 1.2 Table Format
Spark SQL Operations	AVRO	ORC	Parquet	RC	default
create	Yes	Yes	Yes	Yes	Yes
drop	Yes	Yes	Yes	Yes	Yes
insert into	Yes	Yes	Yes	Yes	Yes
insert overwrite	Yes	Yes	Yes	Yes	Yes
select	Yes	Yes	Yes	Yes	Yes
load data	Yes	Yes	Yes	Yes	Yes