Elasticsearch is a distributed document store. Instead of storing information as rows of columnar data, Elasticsearch stores complex data structures that have been serialized as JSON documents. When you have multiple Elasticsearch nodes in a cluster, stored documents are distributed across the cluster and can be accessed immediately from any node.
When a document is stored, it is indexed and fully searchable in near real-time--within 1 second. Elasticsearch uses a data structure called an inverted index that supports very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in.
An index can be thought of as an optimized collection of documents and each document is a collection of fields, which are the key-value pairs that contain your data. By default, Elasticsearch indexes all data in every field and each indexed field has a dedicated, optimized data structure. For example, text fields are stored in inverted indices, and numeric and geo fields are stored in BKD trees. The ability to use the per-field data structures to assemble and return search results is what makes Elasticsearch so fast.
Elasticsearch also has the ability to be schema-less, which means that documents can be indexed without explicitly specifying how to handle each of the different fields that might occur in a document. When dynamic mapping is enabled, Elasticsearch automatically detects and adds new fields to the index. This default behavior makes it easy to index and explore your data—just start indexing documents and Elasticsearch will detect and map booleans, floating point and integer values, dates, and strings to the appropriate Elasticsearch datatypes.
Ultimately, however, you know more about your data and how you want to use it than Elasticsearch can. You can define rules to control dynamic mapping and explicitly define mappings to take full control of how fields are stored and indexed.
Defining your own mappings enables you to:
- Distinguish between full-text string fields and exact value string fields
- Perform language-specific text analysis
- Optimize fields for partial matching
- Use custom date formats
Use data types such as
geo_shapethat cannot be automatically detected
It’s often useful to index the same field in different ways for different purposes. For example, you might want to index a string field as both a text field for full-text search and as a keyword field for sorting or aggregating your data. Or, you might choose to use more than one language analyzer to process the contents of a string field that contains user input.
The analysis chain that is applied to a full-text field during indexing is also used at search time. When you query a full-text field, the query text undergoes the same analysis before the terms are looked up in the index.