MapR 5.1 is at End of Life (EOL) and no longer supported. Please see the latest documentation. This documentation is not being updated.

5.1 DB & Streams
This section covers the MapR-DB, MapR-Streams, and MapR-Gateways features. Each feature may contain both administrator and developer topics.
- MapR-DB
  - Architecture of MapR-DB
    MapR-DB's architecture gives it a large of advantages over other NoSQL databases.
  - Performance Enhancements for SSD Based MapR-DB Deployments
  - MapR-DB as a Document Database
    MapR-DB supports OJAI documents as a native data store. OJAI documents are stored in a compact binary format, not as plain ASCII text.
  - MapR-DB as a Wide-Column Database
    - Logical Model of MapR-DB Binary Tables
    - Filesystem Operations on MapR-DB Tables
    - Migrating Between Apache HBase Tables and MapR-DB Binary Tables
    - Designing Row Keys for MapR-DB Binary Tables
      A row key identifies a row in a MapR-DB binary table.
    - Designing Column Families for MapR-DB Binary Tables
      Scanning an entire table for matches can be very performance-intensive. Column families enable you to group related sets of data and restrict queries to a defined subset, leading to better performance. When you design a column family, think about what kinds of queries are going to be used the most often, and group your columns accordingly.
    - Configuring Maximum Row Sizes for Binary Tables
      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.
    - Enabling Access to MapR Tables via HBase APIs, hbase shell, and MapReduce Jobs
    - Creating MapR-DB Binary Tables
    - Displaying Table Region Information for MapR-DB Tables
    - Creating Applications That Access MapR-DB Binary Tables
    - Bulk Loading and MapR-DB Binary Tables
    - Replicating MapR-DB Binary Tables
    - Indexing MapR-DB Binary Tables with Elasticsearch
      - Uses of Indexing MapR-DB Binary Tables with Elasticsearch
      - Elasticsearch entities
      - Registering Elasticsearch Clusters with MapR Clusters
        To register an Elasticsearch cluster with a MapR cluster, you run a script that copies the Elasticsearch cluster’s configuration file (elasticsearch.yml), JAR files, and plugin JAR files into MapR-FS on the MapR cluster where you run the script from. When you run the script, you provide the IP address or hostname of the Elasticsearch node to copy the files from.
      - Configuring Replication to Elasticsearch Types
        When you index a MapR-DB binary table, you set up replication from that table to a type in an Elasticsearch cluster. After an initial load of data from the source table into the type, updates to the source table are replicated immediately to the type. Updates are not replicated in batches but as they happen. Replication of data to Elasticsearch indexes is asynchronous. MapR-DB does not wait to receive confirmation that changes have been replicated before it notifies client applications that requested operations on the MapR-DB database are complete.
      - Mapping MapR-DB Data to Elasticsearch Data Types Other Than String
      - Tuning the Performance of Data Indexing
    - Impersonation via the HBase REST Gateway
- MapR Streams
  MapR Streams brings integrated publish/subscribe messaging to the MapR Converged Data Platform.
- MapR Gateways
  A MapR gateway mediates one-way communication between a source MapR cluster and a destination cluster. MapR-DB binary tables and MapR Streams streams can be replicated.

Uses of Indexing MapR-DB Binary Tables with Elasticsearch

Indexing MapR-DB binary tables with Elasticsearch makes possible these use cases:

Full-text searches

Client applications can run queries against text documents to search for specific words.

For example, a web-based travel agency could allow customers to search for destinations based on keywords. The travel agency indexes text documents that describe hotels, their location, their accommodations, and nearby attractions. So, a customer could search on the terms “Costa Rica”, “spa”, and “monkeys” to try finding a hotel in Costa Rica that offers a spa and is near businesses that give jungle tours in which the customer can see monkeys.

To enable full-text search in your existing MapR-DB applications, you do not need to add complex code for indexing data. Instead, you add code for searching with Elasticsearch and let the integration of MapR-DB with Elasticsearch handle indexing.

Geospatial searches

Enable geospatial searches by indexing location information (latitude and longitude). For example, indexed Elasticsearch documents might represent restaurants. If those documents contained location information, end users could use query filters to search for all restaurants within a certain radius of their current location.