> For the complete documentation index, see [llms.txt](https://hezhiqiang.gitbook.io/elasticsearch/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://hezhiqiang.gitbook.io/elasticsearch/mapping_analysis/exact_vs_full_text.md).

# 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:

```javascript
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 `"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.