How do MySQL indexes work

Question

I am really interested in how MySQL 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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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     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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In MySQL InnoDB  there are two types of index    Primary key which is called clustered index  Index key words are stored with  real record data in the B Tree leaf node  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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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  gt    lt  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    Integer comparison is way faster than string comparison  It can be illustrated with the example about the emulation of the hash index in InnoDB  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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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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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 gt  CREATE UNIQUE INDEX AUTHOR INDEX ON tutorials tbl  tutorial author DESC

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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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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

[mysql] How do MySQL indexes work?

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