Home
6.2 Administration
This section describes how to manage the nodes and services that make up a cluster.
Administering Tables
Administration of the HPE Ezmeral Data Fabric Database is done primarily via the command line (maprcli) or with the MapR Control System (MCS). Regardless of whether the HPE Ezmeral Data Fabric Database table is used for binary files or JSON documents, the same types of commands are used with slightly different parameter options. HPE Ezmeral Data Fabric Database administration is associated with tables, columns and column families, and table regions.
Managing Tables
HPE Ezmeral Data Fabric Database supports two types of table: binary tables and JSON tables. This section covers how to create, edit, and delete tables, as well as how to set parameter values, display parameter values, grant permissions and access, replicate tables, and more using the Control System and the CLI.
Accessing HPE Ezmeral Data Fabric Database Binary Tables Using HBase APIs
This section describes how to access HPE Ezmeral Data Fabric Database binary tables via HBase APIs, HBase Shell, and MapReduce applications.

HPE Ezmeral Data Fabric 6.2 Documentation

6.2 Administration
This section describes how to manage the nodes and services that make up a cluster.
- Administering Users and Clusters
  Lists topics that help manage a data-fabric cluster.
- Administering Nodes
  Provides a synopsis of managing nodes in a cluster.
- Administering Volumes
  This section provide information about how to organize and manage data using volumes, a unique feature of HPE Ezmeral Data Fabric clusters.
- Administering Files and Directories
- Administering Tables
  Administration of the HPE Ezmeral Data Fabric Database is done primarily via the command line (maprcli) or with the MapR Control System (MCS). Regardless of whether the HPE Ezmeral Data Fabric Database table is used for binary files or JSON documents, the same types of commands are used with slightly different parameter options. HPE Ezmeral Data Fabric Database administration is associated with tables, columns and column families, and table regions.
  - Managing Tables
    HPE Ezmeral Data Fabric Database supports two types of table: binary tables and JSON tables. This section covers how to create, edit, and delete tables, as well as how to set parameter values, display parameter values, grant permissions and access, replicate tables, and more using the Control System and the CLI.
    - Creating a New Table
      Explains how to create both binary tables and JSON tables using either the Control System, the CLI, or the REST API.
    - Configuring Maximum Row Sizes Using the CLI
      The default maximum row size at installation is 32MB. You can configure this maximum by changing the value of the mfs.db.max.rowsize.kb parameter with the maprcli config save command.
    - Editing Tables
      Explains how to edit binary and JSON tables using either the Control System, the CLI, or the REST API.
    - Removing a Table
      Use either the Control System or the maprcli table delete command to drop a HPE Ezmeral Data Fabric Database table.
    - Defining ACEs Using the Access Control Expression Builder
      Indicates how to build access control expressions (ACEs) using the Expression Builder.
    - Viewing the List of Tables
      Describes how to view the list of tables using either the Control System or the CLI.
    - Viewing Table Information
      Explains how to view table information using either the Control System, the CLI or the REST API.
    - Loading Documents into JSON Tables
      There are three command-line utilities for loading documents into JSON tables: mapr copytable, mapr importtable (which works in conjunction with the mapr exporttable utility), and mapr importJSON
    - Accessing HPE Ezmeral Data Fabric Database Binary Tables Using HBase APIs
      This section describes how to access HPE Ezmeral Data Fabric Database binary tables via HBase APIs, HBase Shell, and MapReduce applications.
    - Loading Data into Binary Tables
      Bulkload operations can be performed as a full bulkload or as an incremental bulkload.
    - Performing Filesystem Operations on HPE Ezmeral Data Fabric Database Tables
  - Managing Column Families and Columns
    This section covers overviews of column families in binary tables and JSON tables, how to create column families, alter them, delete them, set permissions on them, and set and display parameter values.
  - Managing Table Replication
    This section contains topics about setting up table replication and administering existing replicas.
  - Managing Secondary Indexes
    Describes how to manage secondary indexes, including adding, deleting, and listing indexes, setting up your cluster for querying, and troubleshooting index usage.
  - Administering Change Data Capture
    This topic covers the Control System and maprcli tools for managing the Change Data Capture (CDC) feature.
  - Indexing HPE Ezmeral Data Fabric Database Binary Tables with Elasticsearch
    As of with MapR 6.0, HPE Ezmeral Data Fabric Database Elastic Search integration capability is deprecated and no longer available in the HPE Ezmeral Data Fabric Database product.
- Administering Streams
- Administering Data Fabric Gateways
  A data-fabric gateway mediates one-way communication between a source data-fabric cluster and a destination cluster. You can replicate HPE Ezmeral Data Fabric Database tables (binary and JSON) and HPE Ezmeral Data Fabric Event Store streams. Data Fabric gateways also apply updates from JSON tables to their secondary indexes and propagate Change Data Capture (CDC) logs.
- Administering Services
- Monitoring the Cluster
  This section describes how to monitor the health and performance of a MapR cluster.
- Configuring Security
  Describes how to configure MapR security and manage secure clusters.
- Managing Secure Clusters
  Provides procedures that will enable you to use MapR clusters securely.
- Administering the Data Access Gateway
  The HPE Ezmeral Data Fabric Data Access Gateway is a service that acts as a proxy and gateway for translating requests between lightweight client applications and the HPE Ezmeral Data Fabric cluster. This section describes considerations when upgrading the service, how to modify configuration settings, and how to administer and manage the service.
- Planning for High Availability
- Administrator's Reference
  This section contains in-depth reference information for the administrator.
- Troubleshooting Cluster Administration
  Lists the common errors and their solutions.
- Best Practices for Backing Up HPE Ezmeral Data Fabric Information
  Lists the best practices and performance considerations to follow when backing up HPE Ezmeral Data Fabric information.

Accessing HPE Ezmeral Data Fabric Database Binary Tables Using HBase APIs

This section describes how to access HPE Ezmeral Data Fabric Database binary tables via HBase APIs, HBase Shell, and MapReduce applications.

MapR Core extends the HBase component to handle access to HPE Ezmeral Data Fabric Database binary tables. HPE Ezmeral Data Fabric Database binary tables do not support low-level HBase API calls that are used to manipulate the state of an Apache HBase cluster.

For a full list of supported HBase Java APIs, see Creating Java Apps - Binary Tables.

For a full list of supported commands in the HBase shell, see HBase Shell for HPE Ezmeral Data Fabric Database.

To enable the HBase API and hbase shell access, install the mapr-hbase package on every node in the cluster. The HBase component of the MapR distribution for Hadoop is typically installed under/opt/mapr/hbase. Install the mapr-hbase package that provides the version that corresponds with the current MEP and MapR version you are running.

For MapR installation procedures, see Installing Core and Ecosystem Components.

Note: The version of HBase provided by MapR has been modified to work with HPE Ezmeral Data Fabric Database binary tables. Do not download and install stock Apache HBase on a MapR cluster that uses MapR tables.

Note: If you use fat JARs to deploy your application as a single JAR including all dependencies, be aware that the fat JAR may contain versions of HBase that override the installed MapR versions, leading to problems. Check your fat JARs for the presence of stock HBase to prevent this problem.

(Topic last modified: 2019-10-21)