The first order of business is storing employee data. This will take the form of an employee document: a single document represents a single employee. The act of storing data in Elasticsearch is called indexing, but before we can index a document, we need to decide where to store it.

An Elasticsearch cluster can contain multiple indices, which in turn contain multiple types. These types hold multiple documents, and each document has multiple fields.

So for our employee directory, we are going to do the following:

In practice, this is easy (even though it looks like a lot of steps). We can perform all of those actions in a single command:

PUT /megacorp/employee/1
{
    "first_name" : "John",
    "last_name" :  "Smith",
    "age" :        25,
    "about" :      "I love to go rock climbing",
    "interests": [ "sports", "music" ]
}

Notice that the path /megacorp/employee/1 contains three pieces of information:

The request body—​the JSON document—​contains all the information about this employee. His name is John Smith, he’s 25, and enjoys rock climbing.

Simple! There was no need to perform any administrative tasks first, like creating an index or specifying the type of data that each field contains. We could just index a document directly. Elasticsearch ships with defaults for everything, so all the necessary administration tasks were taken care of in the background, using default values.

Before moving on, let’s add a few more employees to the directory:

PUT /megacorp/employee/2
{
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}

PUT /megacorp/employee/3
{
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}