How do MySQL indexes work?

I am really interested in how MyS开发者_如何转开发QL indexes work, more specifically, how can they return the data requested without scanning the entire table?

It's off-topic, I know, but if there is someone who could explain this to me in detail, I would be very, very thankful.

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Basically an index on a table works like an index in a book (that's where the name came from):

Let's say you have a book about databases and you want to find some information about, say, storage. Without an index (assuming no other aid, such as a table of contents) you'd have to go through the pages one by one, until you found the topic (that's a full table scan). On the other hand, an index has a list of keywords, so you'd consult the index and see that storage is mentioned on pages 113-120,231 and 354. Then you could flip to those pages directly, without searching (that's a search with an index, somewhat faster).

Of course, how useful the index will be, depends on many things - a few examples, using the simile above:

• if you had a book on databases and indexed the word "database", you'd see that it's mentioned on pages 1-59,61-290, and 292 to 400. In such case, the index is not much help and it might be faster to go through the pages one by one (in a database, this is "poor selectivity").
• For a 10-page book, it makes no sense to make an index, as you may end up with a 10-page book prefixed by a 5-page index, which is just silly - just scan the 10 pages and be done with it.
• The index also needs to be useful - there's generally no point to index e.g. the frequency of the letter "L" per page.

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The first thing you must know is that indexes are a way to avoid scanning the full table to obtain the result that you're looking for.

There are different kinds of indexes and they're implemented in the storage layer, so there's no standard between them and they also depend on the storage engine that you're using.

InnoDB and the B+Tree index

For InnoDB, the most common index type is the B+Tree based index, that stores the elements in a sorted order. Also, you don't have to access the real table to get the indexed values, which makes your query return way faster.

The "problem" about this index type is that you have to query for the leftmost value to use the index. So, if your index has two columns, say last_name and first_name, the order that you query these fields matters a lot.

So, given the following table:

CREATE TABLE person (
    last_name VARCHAR(50) NOT NULL,
    first_name VARCHAR(50) NOT NULL,
    INDEX (last_name, first_name)
);

This query would take advantage of the index:

SELECT last_name, first_name FROM person
WHERE last_name = "John" AND first_name LIKE "J%"

But the following one would not

SELECT last_name, first_name FROM person WHERE first_name = "Constantine"

Because you're querying the first_name column first and it's not the leftmost column in the index.

This last example is even worse:

SELECT last_name, first_name FROM person WHERE first_name LIKE "%Constantine"

Because now, you're comparing the rightmost part of the rightmost field in the index.

The hash index

This is a different index type that unfortunately, only the memory backend supports. It's lightning fast but only useful for full lookups, which means that you can't use it for operations like >, < or LIKE.

Since it only works for the memory backend, you probably won't use it very often. The main case I can think of right now is the one that you create a temporary table in the memory with a set of results from another select and perform a lot of other selects in this temporary table using hash indexes.

If you have a big VARCHAR field, you can "emulate" the use of a hash index when using a B-Tree, by creating another column and saving a hash of the big value on it. Let's say you're storing a url in a field and the values are quite big. You could also create an integer field called url_hash and use a hash function like CRC32 or any other hash function to hash the url when inserting it. And then, when you need to query for this value, you can do something like this:

SELECT url FROM url_table WHERE url_hash=CRC32("http://gnu.org");

The problem with the above example is that since the CRC32 function generates a quite small hash, you'll end up with a lot of collisions in the hashed values. If you need exact values, you can fix this problem by doing the following:

SELECT url FROM url_table 
WHERE url_hash=CRC32("http://gnu.org") AND url="http://gnu.org";

It's still worth to hash things even if the collision number is high cause you'll only perform the second comparison (the string one) against the repeated hashes.

Unfortunately, using this technique, you still need to hit the table to compare the url field.

Wrap up

Some facts that you may consider every time you want to talk about optimization:

1. Integer comparison is way faster than string comparison. It can be illustrated with the example about the emulation of the hash index in InnoDB.

2. Maybe, adding additional steps in a process makes it faster, not slower. It can be illustrated by the fact that you can optimize a SELECT by splitting it into two steps, making the first one store values in a newly created in-memory table, and then execute the heavier queries on this second table.

MySQL has other indexes too, but I think the B+Tree one is the most used ever and the hash one is a good thing to know, but you can find the other ones in the MySQL documentation.

I highly recommend you to read the "High Performance MySQL" book, the answer above was definitely based on its chapter about indexes.

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Basically an index is a map of all your keys that is sorted in order. With a list in order, then instead of checking every key, it can do something like this:

1: Go to middle of list - is higher or lower than what I'm looking for?

2: If higher, go to halfway point between middle and bottom, if lower, middle and top

3: Is higher or lower? Jump to middle point again, etc.

Using that logic, you can find an element in a sorted list in about 7 steps, instead of checking every item.

Obviously there are complexities, but that gives you the basic idea.

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Take a look at this link: http://dev.mysql.com/doc/refman/5.0/en/mysql-indexes.html

How they work is too broad of a subject to cover in one SO post.

Here is one of the best explanations of indexes I have seen. Unfortunately it is for SQL Server and not MySQL. I'm not sure how similar the two are...

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In MySQL InnoDB, there are two types of index.

1. Primary key which is called clustered index. Index key words are stored with real record data in the B+Tree leaf node.

2. Secondary key which is non clustered index. These index only store primary key's key words along with their own index key words in the B+Tree leaf node. So when searching from secondary index, it will first find its primary key index key words and scan the primary key B+Tree to find the real data records. This will make secondary index slower compared to primary index search. However, if the select columns are all in the secondary index, then no need to look up primary index B+Tree again. This is called covering index.

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Take at this videos for more details about Indexing

Simple Indexing You can create a unique index on a table. A unique index means that two rows cannot have the same index value. Here is the syntax to create an Index on a table

CREATE UNIQUE INDEX index_name
ON table_name ( column1, column2,...);

You can use one or more columns to create an index. For example, we can create an index on tutorials_tbl using tutorial_author.

CREATE UNIQUE INDEX AUTHOR_INDEX
ON tutorials_tbl (tutorial_author)

You can create a simple index on a table. Just omit UNIQUE keyword from the query to create simple index. Simple index allows duplicate values in a table.

If you want to index the values in a column in descending order, you can add the reserved word DESC after the column name.

mysql> CREATE UNIQUE INDEX AUTHOR_INDEX
ON tutorials_tbl (tutorial_author DESC)

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Adding some visual representation to the list of answers.

MySQL uses an extra layer of indirection: secondary index records point to primary index records, and the primary index itself holds the on-disk row locations. If a row offset changes, only the primary index needs to be updated.

Caveat: Disk data structure looks flat in the diagram but actually is a B+ tree.

Source: link

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I want to add my 2 cents. I am far from being a database expert, but I've recently read up a bit on this topic; enough for me to try and give an ELI5. So, here's may layman's explanation.

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I understand it as such that an index is like a mini-mirror of your table, pretty much like an associative array. If you feed it with a matching key then you can just jump to that row in one "command".

But if you didn't have that index / array, the query interpreter must use a for-loop to go through all rows and check for a match (the full-table scan).

Having an index has the "downside" of extra storage (for that mini-mirror), in exchange for the "upside" of looking up content faster.

Note that (in dependence of your db engine) creating primary, foreign or unique keys automatically sets up a respective index as well. That same principle is basically why and how those keys work.

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Let's suppose you have a book, probably a novel, a thick one with lots of things to read, hence lots of words. Now, hypothetically, you brought two dictionaries, consisting of only words that are only used, at least one time in the novel. All words in that two dictionaries are stored in typical alphabetical order. In hypothetical dictionary A, words are printed only once while in hypothetical dictionary B words are printed as many numbers of times it is printed in the novel. Remember, words are sorted alphabetically in both the dictionaries. Now you got stuck at some point while reading a novel and need to find the meaning of that word from anyone of those hypothetical dictionaries. What you will do? Surely you will jump to that word in a few steps to find its meaning, rather look for the meaning of each of the words in the novel, from starting, until you reach that bugging word.

This is how the index works in SQL. Consider Dictionary A as PRIMARY INDEX, Dictionary B as KEY/SECONDARY INDEX, and your desire to get for the meaning of the word as a QUERY/SELECT STATEMENT. The index will help to fetch the data at a very fast rate. Without an index, you will have to look for the data from the starting, unnecessarily time-consuming costly task.

For more about indexes and types, look this.

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Indexes are used to find rows with specific column values quickly. Without an index, MySQL must begin with the first row and then read through the entire table to find the relevant rows. The larger the table, the more this costs. If the table has an index for the columns in question, MySQL can quickly determine the position to seek to in the middle of the data file without having to look at all the data. This is much faster than reading every row sequentially.

• Indexing adds a data structure with columns for the search conditions and a pointer

• The pointer is the address on the memory disk of the row with the
  rest of the information

• The index data structure is sorted to optimize query efficiency

• The query looks for the specific row in the index; the index refers to the pointer which will find the rest of the information.

• The index reduces the number of rows the query has to search through from 17 to 4.