Most entities or objects in most applications can be serialized into a JSON object, with keys and values. A key is the name of a field or property, and a value can be a string, a number, a Boolean, another object, an array of values, or some other specialized type such as a string representing a date or an object representing a geolocation:
{
"name": "John Smith",
"age": 42,
"confirmed": true,
"join_date": "2014-06-01",
"home": {
"lat": 51.5,
"lon": 0.1
},
"accounts": [
{
"type": "facebook",
"id": "johnsmith"
},
{
"type": "twitter",
"id": "johnsmith"
}
]
}Often, we use the terms object and document interchangeably. However, there is a distinction. An object is just a JSON object—similar to what is known as a hash, hashmap, dictionary, or associative array. Objects may contain other objects. In Elasticsearch, the term document has a specific meaning. It refers to the top-level, or root object that is serialized into JSON and stored in Elasticsearch under a unique ID.
A document doesn’t consist only of its data. It also has metadata—information about the document. The three required metadata elements are as follows:
_index-
Where the document lives
_type-
The class of object that the document represents
_id-
The unique identifier for the document
An index is like a database in a relational database; it’s the place we store and index related data.
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Tip
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Actually, in Elasticsearch, our data is stored and indexed in shards, while an index is just a logical namespace that groups together one or more shards. However, this is an internal detail; our application shouldn’t care about shards at all. As far as our application is concerned, our documents live in an index. Elasticsearch takes care of the details. |
We cover how to create and manage indices ourselves in [index-management],
but for now we will let Elasticsearch create the index for us. All we have to
do is choose an index name. This name must be lowercase, cannot begin with an
underscore, and cannot contain commas. Let’s use website as our index name.
In applications, we use objects to represent things such as a user, a blog
post, a comment, or an email. Each object belongs to a class that defines
the properties or data associated with an object. Objects in the user class
may have a name, a gender, an age, and an email address.
In a relational database, we usually store objects of the same class in the same table, because they share the same data structure. For the same reason, in Elasticsearch we use the same type for documents that represent the same class of thing, because they share the same data structure.
Every type has its own mapping or schema definition, which defines the data structure for documents of that type, much like the columns in a database table. Documents of all types can be stored in the same index, but the mapping for the type tells Elasticsearch how the data in each document should be indexed.
We show how to specify and manage mappings in [mapping], but for now we will rely on Elasticsearch to detect our document’s data structure automatically.
A _type name can be lowercase or uppercase, but shouldn’t begin with an
underscore or contain commas. We will use blog for our type name.
The ID is a string that, when combined with the _index and _type,
uniquely identifies a document in Elasticsearch. When creating a new document,
you can either provide your own _id or let Elasticsearch generate one for
you.
There are several other metadata elements, which are presented in [mapping]. With the elements listed previously, we are already able to store a document in Elasticsearch and to retrieve it by ID—in other words, to use Elasticsearch as a document store.