If a search engine would search "live" through the documents it knows about for the occurrence of the word we're looking for it could take its time and then simply report where it found our word.
In this example our search engine has only one index: the documents itself.
However, time is something a search engine doesn't have; the query needs to be answered now.
What we need is a real index!
Boolean Index – Talk about the Matrix
The problem with a boolean index, where we put a little flag (1) or not (0) for every word for every document is that it quickly grows way and way too large.
Three documents with amongst them just four words take 12 1's or 0's — apart from the bits and bytes we need to store the word. Now imagine a matrix where one of the sides is 13,940,000,000 columns wide…
The Inverted Index
In the inverted index we record only the places (documents) where a word does occur.
It's called inverted because instead of the documents providing the occurrences of a word, the word points to which documents it occurs in.
Sorted by document pointer, the inverted index is extremely efficient in performing AND queries.
Let's reshuffle our example a little bit to make this visually clear:
If we search for documents that contain the words "search compression" and we down these rows at the same time, as soon as one row makes a jump to a higher document ID, you can jump forward in the other row as well: no use checking the intermediate ones as you now know that those won't have both words.
Knowing only about yes/no occurrences, an inverted index is horrible at phrase and proximity matching:
To be continued…
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