Getting Started with JSON Tables

This section provides a tutorial that will help you to build a Java application by using the new MapR-DB JSON Java API library.

This tutorial is an implementation of the Open JSON Application Interface (OJAI) API library. Basic information about using the mapr dbshell utility for JSON tables is also included in this document.

About the OJAI

The MapR-DB JSON Java API library leverages MapR-DB support for OJAI in many areas, including:

  • Tables
  • Sub-documents
  • Efficient access to data
  • Large document support
  • Security

Key features of the API library include:

  • APIs for manipulating OJAI documents
  • Extensions to the Hadoop ecosystem:
    • Efficient parsing of large files
    • MapReduce integration

JSON Support

MapR-DB, in addition to its support of the key-value model (column family), now offers a Document Model. This means that applications can use a JSON document to represent data. JSON is built on two structures:

  • A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.
  • An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence.
A document looks like this:
{
    "_id" : "001",
    "first_name" : "John",
    "last_name" : "Doe",
    "age" : 45,
    "email" : "jd@mydoc.com",
    "interests" : ["sports", "movies"],
    "address" : {
            "street" : "1015 Main Street",
            "city" : "San Jose",
            "state" : "CA",
            "zip" : "95106"
        }
}

MapR-DB stores the documents in tables. An interesting aspect of MapR-DB JSON is that inside a table, documents can have a different structure.

Documents inside MapR-DB must have a unique identifier stored in the _id field.

MapR-DB does not store the documents as a whole in a single location. Instead, MapR-DB creates fields for each attribute and nested documents/attributes. This allows MapR-DB to access the information very efficiently. When you read, for example using projection, or when you edit a document, only the necessary fields will be modified. MapR-DB can store very large documents, for example, multi-GB documents, if the application requires it.

Prerequisites