# Inverted\_index

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:

1. \`\`The quick brown fox jumped over the lazy dog''
2. \`\`Quick brown foxes leap over lazy dogs in summer''

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, 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* which 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:

1. `"Quick"` and `"quick"` appear as separate terms, while the user probably thinks of them as the same word.
2. `"fox"` and `"foxes"` are pretty similar, as are `"dog"` and `"dogs"` -- they share the same root word.
3. `"jumped"` and `"leap"`, while not from the same root word, are similar in meaning -- they are synonyms.

With the above 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:

1. `"Quick"` can be lowercased to become `"quick"`.
2. `"foxes"` can be *stemmed* -- reduced to its root form -- to become `"fox"`. Similarly `"dogs"` could be stemmed to `"dog"`.
3. `"jumped"` and `"leap"` are synonyms and can be indexed as just the single term `"jump"`.

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!

IMPORTANT: This is very important. You can only find terms that actually exist in your index, so: *both the indexed text and and query string must be normalized into the same form*.

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


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