Data in Elasticsearch can be broadly divided into two types: exact values and full text.
Exact values are exactly what they sound like. Examples are a date or a
user ID, but can also include exact strings such as a username or an email
address. The exact value
Foo is not the same as the exact value
The exact value
2014 is not the same as the exact value
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” but “How 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
UKshould also return documents mentioning the
A search for
jumpshould also match
jumping, and perhaps even
johnny walkershould match
Johnnie Walker, and
johnnie deppshould match
fox news huntingshould return stories about hunting on Fox News, while
fox hunting newsshould return news stories about fox hunting.
To facilitate these types of queries on full-text fields, Elasticsearch first analyzes the text, and then uses the results to build an inverted index. We will discuss the inverted index and the analysis process in the next two sections.