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I've been using indexes on my MySQL databases for a while now but never properly learnt about them. Generally I put an index on any fields that I will be searching or selecting using a WHERE clause but sometimes it doesn't seem so black and white.
What are the best practices for MySQL indexes?
Example situations/dilemmas:
If a table has six columns and all of them are searchable, should I index all of them or none of them?
What are the negative performance impacts of indexing?
If I have a VARCHAR 2500 column which is searchable from parts of my site, should I index it?
You should definitely spend some time reading up on indexing, there's a lot written about it, and it's important to understand what's going on.
Broadly speaking, an index imposes an ordering on the rows of a table.
For simplicity's sake, imagine a table is just a big CSV file. Whenever a row is inserted, it's inserted at the end. So the "natural" ordering of the table is just the order in which rows were inserted.
Imagine you've got that CSV file loaded up in a very rudimentary spreadsheet application. All this spreadsheet does is display the data, and numbers the rows in sequential order.
Now imagine that you need to find all the rows that have some value "M" in the third column. Given what you have available, you have only one option. You scan the table checking the value of the third column for each row. If you've got a lot of rows, this method (a "table scan") can take a long time!
Now imagine that in addition to this table, you've got an index. This particular index is the index of values in the third column. The index lists all of the values from the third column, in some meaningful order (say, alphabetically) and for each of them, provides a list of row numbers where that value appears.
Now you have a good strategy for finding all the rows where the value of the third column is "M". For instance, you can perform a binary search! Whereas the table scan requires you to look N rows (where N is the number of rows), the binary search only requires that you look at log-n index entries, in the very worst case. Wow, that's sure a lot easier!
Of course, if you have this index, and you're adding rows to the table (at the end, since that's how our conceptual table works), you need to update the index each and every time. So you do a little more work while you're writing new rows, but you save a ton of time when you're searching for something.
So, in general, indexing creates a tradeoff between read efficiency and write efficiency. With no indexes, inserts can be very fast -- the database engine just adds a row to the table. As you add indexes, the engine must update each index while performing the insert.
On the other hand, reads become a lot faster.
Hopefully that covers your first two questions (as others have answered -- you need to find the right balance).
Your third scenario is a little more complicated. If you're using LIKE, indexing engines will typically help with your read speed up to the first "%". In other words, if you're SELECTing WHERE column LIKE 'foo%bar%', the database will use the index to find all the rows where column starts with "foo", and then need to scan that intermediate rowset to find the subset that contains "bar". SELECT ... WHERE column LIKE '%bar%' can't use the index. I hope you can see why.
Finally, you need to start thinking about indexes on more than one column. The concept is the same, and behaves similarly to the LIKE stuff -- essentially, if you have an index on (a,b,c), the engine will continue using the index from left to right as best it can. So a search on column a might use the (a,b,c) index, as would one on (a,b). However, the engine would need to do a full table scan if you were searching WHERE b=5 AND c=1)
Hopefully this helps shed a little light, but I must reiterate that you're best off spending a few hours digging around for good articles that explain these things in depth. It's also a good idea to read your particular database server's documentation. The way indices are implemented and used by query planners can vary pretty widely.
Check out presentations like More Mastering the Art of Indexing.
Update 12/2012: I have posted a new presentation of mine: How to Design Indexes, Really. I presented this in October 2012 at ZendCon in Santa Clara, and in December 2012 at Percona Live London.
Designing the best indexes is a process that has to match the queries you run in your app.
It's hard to recommend any general-purpose rules about which columns are best to index, or whether you should index all columns, no columns, which indexes should span multiple columns, etc. It depends on the queries you need to run.
Yes, there is some overhead so you shouldn't create indexes needlessly. But you should create the indexes that give benefit to the queries you need to run quickly. The overhead of an index is usually far outweighed by its benefit.
For a column that is VARCHAR(2500), you probably want to use a FULLTEXT index or a prefix index:
CREATE INDEX i ON SomeTable(longVarchar(100));
Note that a conventional index can't help if you're searching for words that may be in the middle of that long varchar. For that, use a fulltext index.
I won't repeat some of the good advice in other answers, but will add:
Compound Indices
You can create compound indices - an index that includes multiple columns. MySQL can use these from left to right. So if you have:
Table A
Id
Name
Category
Age
Description
if you have a compound index that includes Name/Category/Age in that order, these WHERE clauses would use the index:
WHERE Name='Eric' and Category='A'
WHERE Name='Eric' and Category='A' and Age > 18
but
WHERE Category='A' and Age > 18
would not use that index because everything has to be used from left to right.
Explain
Use Explain / Explain Extended to understand what indices are available to MySQL and which one it actually selects. MySQL will only use ONE key per query.
EXPLAIN EXTENDED SELECT * from Table WHERE Something='ABC'
Slow Query Log
Turn on the slow query log to see which queries are running slow.
Wide Columns
If you have a wide column where MOST of the distinction happens in the first several characters, you can use only the first N characters in your index. Example: We have a ReferenceNumber column defined as varchar(255) but 97% of the cases, the reference number is 10 characters or less. I changed the index to only look at the first 10 characters and improved performance quite a bit.
If a table has six columns and all of them are searchable, should i index all of them or none of them
Are you searching on a field by field basis or are some searches using multiple fields?
Which fields are most being searched on?
What are the field types? (Index works better on INTs than on VARCHARs for example)
Have you tried using EXPLAIN on the queries that are being run?
What are the negetive performance impacts of indexing
UPDATEs and INSERTs will be slower. There's also the extra storage space requirments, but that's usual unimportant these days.
If i have a VARCHAR 2500 column which is searchable from parts of my site, should i index it
No, unless it's UNIQUE (which means it's already indexed) or you only search for exact matches on that field (not using LIKE or mySQL's fulltext search).
Generally I put an index on any fields that i will be searching or selecting using a WHERE clause
I'd normally index the fields that are the most queried, and then INTs/BOOLEANs/ENUMs rather that fields that are VARCHARS. Don't forget, often you need to create an index on combined fields, rather than an index on an individual field. Use EXPLAIN, and check the slow log.
Load Data Efficiently: Indexes speed up retrievals but slow down inserts and deletes, as well as updates of values in indexed columns. That is, indexes slow down most operations that involve writing. This occurs because writing a row requires writing not only the data row, it requires changes to any indexes as well. The more indexes a table has, the more changes need to be made, and the greater the average performance degradation. Most tables receive many reads and few writes, but for a table with a high percentage of writes, the cost of index updating might be significant.
Avoid Indexes: If you don’t need a particular index to help queries perform better, don’t create it.
Disk Space: An index takes up disk space, and multiple indexes take up correspondingly more space. This might cause you to reach a table size limit more quickly than if there are no indexes. Avoid indexes wherever possible.
Takeaway: Don't over index
In general, indices help speedup database search, having the disadvantage of using extra disk space and slowing INSERT / UPDATE / DELETE queries. Use EXPLAIN and read the results to find out when MySQL uses your indices.
If a table has six columns and all of them are searchable, should i index all of them or none of them?
Indexing all six columns isn't always the best practice.
(a) Are you going to use any of those columns when searching for specific information?
(b) What is the selectivity of those columns (how many distinct values are there stored, in comparison to the total amount of records on the table)?
MySQL uses a cost-based optimizer, which tries to find the "cheapest" path when performing a query. And fields with low selectivity aren't good candidates.
What are the negetive performance impacts of indexing?
Already answered: extra disk space, lower performance during insert - update - delete.
If i have a VARCHAR 2500 column which is searchable from parts of my site, should i index it?
Try the FULLTEXT Index.
1/2) Indexes speed up certain select operations but they slow down other operations like insert, update and deletes. It can be a fine balance.
3) use a full text index or perhaps sphinx
How can I detect if an MySQL index is necessary or required?
We have the idea that some queries can be improved. And I know that I can dive in slow query logs ... but I ran across the post below for MS SQL and was wondering if there is an easy way of analyzing if an index is required (and will give immediate speed improvements) for the current MySQL database.
Help appreciated
Resource for MS SQL: https://dba.stackexchange.com/questions/56/how-to-determine-if-an-index-is-required-or-necessary
You can't.
There are ways to detect, over a period of time, whether an index is used. But there is no way to be sure that an index is not used. Let's say you have a once-a-month task that does some major maintenance on the table. And you really need a certain index to keep the task from locking the table and bringing down the application. If you checked for index usage for most of the month, but failed to include that usage, you might decide that you don't need the index. Then you would drop the index... and be sorry. (This is a real anecdote.)
Meanwhile, there are some simplistic rules about indexes...
INDEX(a) is unnecessary if you also have INDEX(a,b).
INDEX(id) is unnecessary if you also have PRIMARY KEY(id) or UNIQUE(id).
An index with 5 or more columns may be used, but is unlikely to be "useful". (Shorten it.)
INDEX(a), INDEX(b) is not the same as INDEX(a,b).
INDEX(b,a) is not the same as INDEX(a,b); you may need both.
INDEX(flag), where flag has a small number of distinct values, will probably never be used -- the optimizer will scan the table instead.
In many cases, "prefix" indexing (INDEX(foo(10))) is useless. (But there are many exceptions.)
"I indexed every column" -- a bad design pattern.
Often, but not always, having both a PRIMARY KEY and a UNIQUE key means that something is less than optimal.
InnoDB tables really should have an explicit PRIMARY KEY.
InnoDB implicitly include the PK in any secondary key. So, given PRIMARY KEY(id), INDEX(foo) is really INDEX(foo, id).
Sometimes the Optimizer will ignore the WHERE clause and use an index for the ORDER BY.
Some queries have such skewed properties that the Optimizer will use a different index depending on different constants. (I have literally see as many as 6 different explain plans for one query.)
"Index merge intersect" is almost always not as good as a composite index.
There are exceptions to most of these tips.
So, I prefer to take all the queries (SELECTs, UPDATEs, and DELETEs), decide on the optimal index for each, eliminate redundancies, etc, in order to find the "best" set of indexes. See my cookbook on creating an index, given a SELECT.
You should definitely spend some time reading up on indexing, there's a lot written about it, and it's important to understand what's going on.
Broadly speaking, and index imposes an ordering on the rows of a table.
For simplicity's sake, imagine a table is just a big CSV file. Whenever a row is inserted, it's inserted at the end. So the "natural" ordering of the table is just the order in which rows were inserted.
Imagine you've got that CSV file loaded up in a very rudimentary spreadsheet application. All this spreadsheet does is display the data, and numbers the rows in sequential order.
Now imagine that you need to find all the rows that has some value "M" in the third column. Given what you have available, you have only one option. You scan the table checking the value of the third column for each row. If you've got a lot of rows, this method (a "table scan") can take a long time!
Now imagine that in addition to this table, you've got an index. This particular index is the index of values in the third column. The index lists all of the values from the third column, in some meaningful order (say, alphabetically) and for each of them, provides a list of row numbers where that value appears.
Now you have a good strategy for finding all the rows where the value of the third column is "M". For instance, you can perform a binary search! Whereas the table scan requires you to look N rows (where N is the number of rows), the binary search only requires that you look at log-n index entries, in the very worst case. Wow, that's sure a lot easier!
Of course, if you have this index, and you're adding rows to the table (at the end, since that's how our conceptual table works), you need to update the index each and every time. So you do a little more work while you're writing new rows, but you save a ton of time when you're searching for something.
So, in general, indexing creates a tradeoff between read efficiency and write efficiency. With no indexes, inserts can be very fast -- the database engine just adds a row to the table. As you add indexes, the engine must update each index while performing the insert.
On the other hand, reads become a lot faster.
I know I need to have a primary key set, and to set anything that should be unique as a unique key, but what is an INDEX and how do I use them?
What are the benefits? Pros & Cons? I notice I can either use them or not, when should I?
Short answer:
Indexes speed up SELECT's and slow down INSERT's.
Usually it's better to have indexes, because they speed up select more than they slow down insert.
On an UPDATE the index can speed things way up if an indexed field is used in the WHERE clause and slow things down if you update one of the indexed fields.
How do you know when to use an index
Add EXPLAIN in front of your SELECT statement.
Like so:
EXPLAIN SELECT * FROM table1
WHERE unindexfield1 > unindexedfield2
ORDER BY unindexedfield3
Will show you how much work MySQL will have to do on each of the unindexed fields.
Using that info you can decide if it is worthwhile to add indexes or not.
Explain can also tell you if it is better to drop and index
EXPLAIN SELECT * FROM table1
WHERE indexedfield1 > indexedfield2
ORDER BY indexedfield3
If very little rows are selected, or MySQL decided to ignore the index (it does that from time to time) then you might as well drop the index, because it is slowing down your inserts but not speeding up your select's.
Then again it might also be that your select statement is not clever enough.
(Sorry for the complexity in the answer, I was trying to keep it simple, but failed).
Link:
MySQL indexes - what are the best practices?
Pros:
Faster lookup for results. This is all about reducing the # of Disk IO's. Instead of scanning the entire table for the results, you can reduce the number of disk IO's(page fetches) by using index structures such as B-Trees or Hash Indexes to get to your data faster.
Cons:
Slower writes(potentially). Not only do you have to write your data to your tables, but you also have to write to your indexes. This may cause the system to restructure the index structure(Hash Index, B-Tree etc), which can be very computationally expensive.
Takes up more disk space, naturally. You are storing more data.
The easiest way to think about an index is to think about a dictionary. It has words and it has definitions corresponding to those words. The dictionary has an index on "word" because when you go to a dictionary you want to look up a word quickly, then get its definition. A dictionary usually contains just one index - an index by word.
A database is analogous. When you have a bunch of data in the database, you will have certain ways that you want to get it out. Let's say you have a User table and you often look up a user by the FirstName column. Since this is an operation that you are doing often in your application, you should consider using an index on this column. That will create a structure in the database that is sorted, if you will, by that column, so that looking up something by first name is like looking up a word in a dictionary. If you didn't have this index you might need to look at ALL rows before you determine which ones have a specific FirstName. By adding an index, you have made this fast.
So why not put an index on all columns and make them all fast? Like everything, there is a trade off. Every time you insert a row into the table User, the database will need to perform its magic and sort everything on your indexed column. This can be expensive.
You don't have to have a primary key. Indexes (of any type) are used to speed up queries and, at least with the InnoDB engine, enforce foreign key constraints. Whether you use a unique or plain (non-unique) index depends on whether you want to allow duplicate values in the key.
This is a general database concept, you might use external resources to read about it, like http://beginner-sql-tutorial.com/sql-index.htm or http://en.wikipedia.org/wiki/Index_(database)
An index allows MySQL to find data quicker. You use them on columns that you'll be using in WHERE clauses. For example, if you have a column named score, and want to find everything with where score > 5, by default this means MySQL will need to scan through the WHOLE table to find those scores. However if you use a BTREE index, finding those that meet that condition will happen a LOT faster.
Indices have a price: disk and memory space. If it's a very big table, your index will grow rather large.
Think of it this way: what are the biggest benefits of having an index in a book? It's much the same thing. You have a slightly larger book, yet you're able to quickly look things up. When you create an index on a column, you're saying you want to be able to reference it in a where clause to look it up quickly.
I've been using indexes on my MySQL databases for a while now but never properly learnt about them. Generally I put an index on any fields that I will be searching or selecting using a WHERE clause but sometimes it doesn't seem so black and white.
What are the best practices for MySQL indexes?
Example situations/dilemmas:
If a table has six columns and all of them are searchable, should I index all of them or none of them?
What are the negative performance impacts of indexing?
If I have a VARCHAR 2500 column which is searchable from parts of my site, should I index it?
You should definitely spend some time reading up on indexing, there's a lot written about it, and it's important to understand what's going on.
Broadly speaking, an index imposes an ordering on the rows of a table.
For simplicity's sake, imagine a table is just a big CSV file. Whenever a row is inserted, it's inserted at the end. So the "natural" ordering of the table is just the order in which rows were inserted.
Imagine you've got that CSV file loaded up in a very rudimentary spreadsheet application. All this spreadsheet does is display the data, and numbers the rows in sequential order.
Now imagine that you need to find all the rows that have some value "M" in the third column. Given what you have available, you have only one option. You scan the table checking the value of the third column for each row. If you've got a lot of rows, this method (a "table scan") can take a long time!
Now imagine that in addition to this table, you've got an index. This particular index is the index of values in the third column. The index lists all of the values from the third column, in some meaningful order (say, alphabetically) and for each of them, provides a list of row numbers where that value appears.
Now you have a good strategy for finding all the rows where the value of the third column is "M". For instance, you can perform a binary search! Whereas the table scan requires you to look N rows (where N is the number of rows), the binary search only requires that you look at log-n index entries, in the very worst case. Wow, that's sure a lot easier!
Of course, if you have this index, and you're adding rows to the table (at the end, since that's how our conceptual table works), you need to update the index each and every time. So you do a little more work while you're writing new rows, but you save a ton of time when you're searching for something.
So, in general, indexing creates a tradeoff between read efficiency and write efficiency. With no indexes, inserts can be very fast -- the database engine just adds a row to the table. As you add indexes, the engine must update each index while performing the insert.
On the other hand, reads become a lot faster.
Hopefully that covers your first two questions (as others have answered -- you need to find the right balance).
Your third scenario is a little more complicated. If you're using LIKE, indexing engines will typically help with your read speed up to the first "%". In other words, if you're SELECTing WHERE column LIKE 'foo%bar%', the database will use the index to find all the rows where column starts with "foo", and then need to scan that intermediate rowset to find the subset that contains "bar". SELECT ... WHERE column LIKE '%bar%' can't use the index. I hope you can see why.
Finally, you need to start thinking about indexes on more than one column. The concept is the same, and behaves similarly to the LIKE stuff -- essentially, if you have an index on (a,b,c), the engine will continue using the index from left to right as best it can. So a search on column a might use the (a,b,c) index, as would one on (a,b). However, the engine would need to do a full table scan if you were searching WHERE b=5 AND c=1)
Hopefully this helps shed a little light, but I must reiterate that you're best off spending a few hours digging around for good articles that explain these things in depth. It's also a good idea to read your particular database server's documentation. The way indices are implemented and used by query planners can vary pretty widely.
Check out presentations like More Mastering the Art of Indexing.
Update 12/2012: I have posted a new presentation of mine: How to Design Indexes, Really. I presented this in October 2012 at ZendCon in Santa Clara, and in December 2012 at Percona Live London.
Designing the best indexes is a process that has to match the queries you run in your app.
It's hard to recommend any general-purpose rules about which columns are best to index, or whether you should index all columns, no columns, which indexes should span multiple columns, etc. It depends on the queries you need to run.
Yes, there is some overhead so you shouldn't create indexes needlessly. But you should create the indexes that give benefit to the queries you need to run quickly. The overhead of an index is usually far outweighed by its benefit.
For a column that is VARCHAR(2500), you probably want to use a FULLTEXT index or a prefix index:
CREATE INDEX i ON SomeTable(longVarchar(100));
Note that a conventional index can't help if you're searching for words that may be in the middle of that long varchar. For that, use a fulltext index.
I won't repeat some of the good advice in other answers, but will add:
Compound Indices
You can create compound indices - an index that includes multiple columns. MySQL can use these from left to right. So if you have:
Table A
Id
Name
Category
Age
Description
if you have a compound index that includes Name/Category/Age in that order, these WHERE clauses would use the index:
WHERE Name='Eric' and Category='A'
WHERE Name='Eric' and Category='A' and Age > 18
but
WHERE Category='A' and Age > 18
would not use that index because everything has to be used from left to right.
Explain
Use Explain / Explain Extended to understand what indices are available to MySQL and which one it actually selects. MySQL will only use ONE key per query.
EXPLAIN EXTENDED SELECT * from Table WHERE Something='ABC'
Slow Query Log
Turn on the slow query log to see which queries are running slow.
Wide Columns
If you have a wide column where MOST of the distinction happens in the first several characters, you can use only the first N characters in your index. Example: We have a ReferenceNumber column defined as varchar(255) but 97% of the cases, the reference number is 10 characters or less. I changed the index to only look at the first 10 characters and improved performance quite a bit.
If a table has six columns and all of them are searchable, should i index all of them or none of them
Are you searching on a field by field basis or are some searches using multiple fields?
Which fields are most being searched on?
What are the field types? (Index works better on INTs than on VARCHARs for example)
Have you tried using EXPLAIN on the queries that are being run?
What are the negetive performance impacts of indexing
UPDATEs and INSERTs will be slower. There's also the extra storage space requirments, but that's usual unimportant these days.
If i have a VARCHAR 2500 column which is searchable from parts of my site, should i index it
No, unless it's UNIQUE (which means it's already indexed) or you only search for exact matches on that field (not using LIKE or mySQL's fulltext search).
Generally I put an index on any fields that i will be searching or selecting using a WHERE clause
I'd normally index the fields that are the most queried, and then INTs/BOOLEANs/ENUMs rather that fields that are VARCHARS. Don't forget, often you need to create an index on combined fields, rather than an index on an individual field. Use EXPLAIN, and check the slow log.
Load Data Efficiently: Indexes speed up retrievals but slow down inserts and deletes, as well as updates of values in indexed columns. That is, indexes slow down most operations that involve writing. This occurs because writing a row requires writing not only the data row, it requires changes to any indexes as well. The more indexes a table has, the more changes need to be made, and the greater the average performance degradation. Most tables receive many reads and few writes, but for a table with a high percentage of writes, the cost of index updating might be significant.
Avoid Indexes: If you don’t need a particular index to help queries perform better, don’t create it.
Disk Space: An index takes up disk space, and multiple indexes take up correspondingly more space. This might cause you to reach a table size limit more quickly than if there are no indexes. Avoid indexes wherever possible.
Takeaway: Don't over index
In general, indices help speedup database search, having the disadvantage of using extra disk space and slowing INSERT / UPDATE / DELETE queries. Use EXPLAIN and read the results to find out when MySQL uses your indices.
If a table has six columns and all of them are searchable, should i index all of them or none of them?
Indexing all six columns isn't always the best practice.
(a) Are you going to use any of those columns when searching for specific information?
(b) What is the selectivity of those columns (how many distinct values are there stored, in comparison to the total amount of records on the table)?
MySQL uses a cost-based optimizer, which tries to find the "cheapest" path when performing a query. And fields with low selectivity aren't good candidates.
What are the negetive performance impacts of indexing?
Already answered: extra disk space, lower performance during insert - update - delete.
If i have a VARCHAR 2500 column which is searchable from parts of my site, should i index it?
Try the FULLTEXT Index.
1/2) Indexes speed up certain select operations but they slow down other operations like insert, update and deletes. It can be a fine balance.
3) use a full text index or perhaps sphinx
hey all I have tables with millions of rows in them and some of the select queries key off of 3 fields
company, user, articleid
would it be faster to create a composite index of those three fields as a key
or MD5 (company, user, articleid) together and then index the hash that's created.
?
thanks
You would have to benchmark to be sure, but I believe that you will find that there isn't going to be a significant performance difference between a composite index of three fields and a single index of a hash of those fields.
In my opinion, creating data that wouldn't otherwise exist and is only going to be used for indexing is a bad idea (except in the case of de-normalization for performance reasons, but you'd need a conclusive case to do it here). For a 32 byte field of md5 data (minus any field overhead), consider that for every one million rows you have, you have created approximately an extra 30 MB of data. Even if the index was a teensy tiny bit faster, you've just upped the disk and memory requirements for that table. Your index seek time might be offset by disk seek time. Add in the fact that you have to have application logic to support this field, and I would opine that it's not worth it.
Again, the only true way to know would be to benchmark it, but I don't think you'll find much of a difference.
for performance, you might see advantages with the composite index. if you are selecting only the fields in the index, this is a "covering index" situation. that means the data engine will not have to read the actual data page from the disk, just reading the index is enough to return the data requested by your application. this can be a big performance boost. if you store a hash, you eliminate the possibility of taking advantage of a covering index (unless you are selecting only the hash in your sql).
best regards,
don
One more consideration in favor of a composite key : having composite key on (company, user, articleid) means that it can be used when you search a record by company, or company+user, or by company+user+articleid. So you virtually have 3 indexes.
A composite index seems to be the way to go, in particular since some of the individual keys appear to be fairly selective. The only situation which may cause you to possibly avoid the composite index approach is if the length of the composite key is very long (say in excess of 64 characters, on average).
While a MD5-based index would be smaller and hence possibly slightly faster, it would let you deal with the task of filtering the false positives out of the list of records with a given MD5 value.
When building a composite index, a question arise of the order in which the keys should be listed in the index. While this speak, somewhat, to the potential efficiency of the index, the question of the ordering has more significant impact on the potential usability of the index in cases when only two (or even one...) of the keys are used in the query. One typically tries and put the most selective column(s) first, unless this (these) selective column(s) is (are) the ones most likely to not be used when a complete set of these columns is not found in the query.