Exact_vs_full_text

Data in Elasticsearch can be broadly divided into two types: exact values and full text.

Exact values are exactly what they sound like. Examples would be a date or a user ID, but can also include exact strings like a username or an email address. The exact value "Foo" is not the same as the exact value "foo". The exact value 2014 is not the same as the exact value 2014-09-15.

Full text, on the other hand, refers to textual data -- usually written in some human language -- like the text of a tweet or the body of an email.

Full text is often referred to as ``unstructured data'', which is a misnomer -- natural language is highly structured. The problem is that the rules of natural languages are complex which makes them difficult for computers to parse correctly. For instance, consider this sentence:

May is fun but June bores me.

Does it refer to months or to people?

Exact values are easy to query. The decision is binary -- a value either matches the query, or it doesn't. This kind of query is easy to express with SQL:

WHERE name    = "John Smith"
  AND user_id = 2
  AND date    > "2014-09-15"

Querying full text data is much more subtle. We are not just asking Does this document match the query'', butHow well does this document match the query?'' In other words, how relevant is this document to the given query?

We seldom want to match the whole full text field exactly. Instead, we want to search within text fields. Not only that, but we expect search to understand our intent:

• a search for "UK" should also return documents mentioning the "United Kingdom"

• a search for "jump" should also match "jumped", "jumps", "jumping" and perhaps even "leap"

• "johnny walker" should match "Johnnie Walker" and "johnnie depp" should match "Johnny Depp"

• "fox news hunting" should return stories about hunting on Fox News, while "fox hunting news" should return news stories about fox hunting.

In order to facilitate these types of queries on full text fields, Elasticsearch first analyzes the text, then uses the results to build an inverted index. We will discuss the inverted index and the analysis process in the next two sections.