# indexing – MySQL indexes – what are the best practices?

## The Question :

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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?

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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.

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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.

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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.

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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.

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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

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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.