The core strength of Elasticsearch lies in its text-processing capabilities. Elasticsearch is great at searching, especially full-text searches. Let's understand what a full-text search is:

Full-text search means searching through all the terms of all the documents available in the database. This requires the entire contents of all documents to be parsed and stored beforehand. When you hear full-text search, think of Google Search. You can enter any search term and Google looks through all of the web pages on the internet to find the best-matching web pages. This is quite different from simple SQL queries run against columns of type string in relational databases. Normal SQL queries with a WHERE clause and an equals (=) or LIKE clause try to do an exact or wildcard match with underlying data. SQL queries can, at best, just match the search term to a sub-string within the text column.

When you want to perform a search similar to a Google search on your own data, Elasticsearch is your best bet. You can index emails, text documents, PDF files, web pages, or practically any unstructured text documents and search across all your documents with search terms.

At a high level, Elasticsearch breaks up text data into terms and makes every term searchable by building Lucene indexes. You can build your own fast and flexible Google-like search for your application.

In addition to supporting text data, Elasticsearch also supports other data types such as numbers, dates, geolocations, IP addresses, and many more. We will take an in-depth look at searching in Chapter 3, Searching-What is Relevant.