Elasticsearch uses a structure called an inverted index, which is designed to allow very fast full-text searches. An inverted index consists of a list of all the unique words that appear in any document, and for each word, a list of the documents in which it appears.

For example, let’s say we have two documents, each with a content field containing the following:

To create an inverted index, we first split the content field of each document into separate words (which we call terms, or tokens), create a sorted list of all the unique terms, and then list in which document each term appears. The result looks something like this:

Term      Doc_1  Doc_2
-------------------------
Quick   |       |  X
The     |   X   |
brown   |   X   |  X
dog     |   X   |
dogs    |       |  X
fox     |   X   |
foxes   |       |  X
in      |       |  X
jumped  |   X   |
lazy    |   X   |  X
leap    |       |  X
over    |   X   |  X
quick   |   X   |
summer  |       |  X
the     |   X   |
------------------------

Now, if we want to search for quick brown, we just need to find the documents in which each term appears:

Term      Doc_1  Doc_2
-------------------------
brown   |   X   |  X
quick   |   X   |
------------------------
Total   |   2   |  1

Both documents match, but the first document has more matches than the second. If we apply a naive similarity algorithm that just counts the number of matching terms, then we can say that the first document is a better match—​is more relevant to our query—​than the second document.

But there are a few problems with our current inverted index:

With the preceding index, a search for +Quick +fox wouldn’t match any documents. (Remember, a preceding + means that the word must be present.) Both the term Quick and the term fox have to be in the same document in order to satisfy the query, but the first doc contains quick fox and the second doc contains Quick foxes.

Our user could reasonably expect both documents to match the query. We can do better.

If we normalize the terms into a standard format, then we can find documents that contain terms that are not exactly the same as the user requested, but are similar enough to still be relevant. For instance:

Now the index looks like this:

Term      Doc_1  Doc_2
-------------------------
brown   |   X   |  X
dog     |   X   |  X
fox     |   X   |  X
in      |       |  X
jump    |   X   |  X
lazy    |   X   |  X
over    |   X   |  X
quick   |   X   |  X
summer  |       |  X
the     |   X   |  X
------------------------

But we’re not there yet. Our search for +Quick +fox would still fail, because we no longer have the exact term Quick in our index. However, if we apply the same normalization rules that we used on the content field to our query string, it would become a query for +quick +fox, which would match both documents!

This process of tokenization and normalization is called analysis, which we discuss in the next section.