# 精确值与全文 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 auser ID, but can also include exact strings like a username or an emailaddress. 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 insome 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 ofnatural languages are complex which makes them difficult for computers toparse 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 eithermatches the query, or it doesn't. This kind of query is easy to express withSQL: ~~~ 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 `Doesthis document match the query'', but`How _well_ does this document match thequery?'' 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 wantto search _within_ text fields. Not only that, but we expect search tounderstand our _intent_: - a search for `"UK"` should also return documents mentioning the `"UnitedKingdom"` - 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 buildan _inverted index_. We will discuss the inverted index and theanalysis process in the next two sections.