Objects in an application are seldom just a simple list of keys and values. More often than not, they are complex data structures that may contain dates, geo locations, other objects, or arrays of values.

Sooner or later you’re going to want to store these objects in a database. Trying to do this with the rows and columns of a relational database is the equivalent of trying to squeeze your rich, expressive objects into a very big spreadsheet: you have to flatten the object to fit the table schema—​usually one field per column—​and then have to reconstruct it every time you retrieve it.

Elasticsearch is document oriented, meaning that it stores entire objects or documents. It not only stores them, but also indexes the contents of each document in order to make them searchable. In Elasticsearch, you index, search, sort, and filter documents—​not rows of columnar data. This is a fundamentally different way of thinking about data and is one of the reasons Elasticsearch can perform complex full-text search.

{
    "email":      "john@smith.com",
    "first_name": "John",
    "last_name":  "Smith",
    "info": {
        "bio":         "Eco-warrior and defender of the weak",
        "age":         25,
        "interests": [ "dolphins", "whales" ]
    },
    "join_date": "2014/05/01"
}