Optimizing MySQL Query Retrieval Speed Through Table Joins

December 10, 2010

A poorly optimized query can become the bottleneck in an otherwise highly optimized process. In many cases, the source of the slow performance is improper table joining in the WHERE clause. With that in mind, this article looks at ways to optimize table joins for speedy data retrieval.

Novice query writers are quite content once they are satisfied that their query is retrieving the correct data. Beyond that, there comes the inevitable realization that it takes an unacceptable length of time for the query to come back with those results. In fact, a poorly optimized query can become the bottleneck in an otherwise highly optimized process. In many cases, the source of the slow performance is improper table joining in the WHERE clause. With that in mind, let’s take a look at ways to optimize our table joins for speedy data retrieval.

How MySQL Resolves Table Joins

We’ve already seen in the Clearing a Path through the 3NF Join Jungle article that normalized databases can require a lot of joins to get at the desired data, lots and lots of joins! As you can imagine, the mechanism through which the database engine links the tables can greatly affect retrieval speed. Let’s take a look at a simple three table query and see how MySQL scans them.

select a.au_lname,
             a.au_fname, 
             ta.royaltyper, 
             t.title, 
             t.royalty
from    authors     a,
             titleauthor ta,
             titles     t
where a.au_id = ta.au_id
and     ta.title_id = t.title_id;

The following quote from the MySQL documentation explains the single-sweep/nested-loop join method that it employs.

MySQL resolves all joins using a [single sweep] nested-loop join method. This means that MySQL reads a row from the first table, and then finds a matching row in the second table, the third table, and so on. When all tables are processed, MySQL outputs the selected columns and backtracks through the table list until a table is found for which there are more matching rows. The next row is read from this table and the process continues with the next table.

As MySQL scans each table row, values from the preceding tables are used to find rows in the current table. In a three table query like the one above, values from table A are used to find rows in table B, then values from table B are used to find values in table C. Applying that to our query, MySQL would read the authors table, then the titleauthor, then the titles. Once one full sweep is finished (found rows in table A, table B, table C), MySQL does not go back to table A, but to table B to see if there are any more rows that match the current value from table A. If there are, it gets that row and then finds matching rows in table C again.

A join can quickly become unwieldy when dealing with as few as three tables, as evidenced by the following numbers reported by Mike Papageorge in his www.fiftyfoureleven.com blog.

SELECT tableA.*, 
       locations.location 
FROM   tableA
Left Join tableA2locations on 
       tableA2locations.tableA_id = tableA.id
Left Join locations on 
       tableA2locations.location_id = locations.id
WHERE locations.location = 'sometown’;

Say that tableA in the above statement contains 40,000 rows that must be scanned before sweeping through the other tables. If there are 2000 rows retrieved from tableA2locations, that amounts to 800,000 rows of data. Adding additional tables in the join could produce result sets in the millions!

Having said that, keep in mind that this estimate leans more towards the worst case end of the scale, since other WHERE conditions will almost always dramatically reduce the real number of rows the query produces.

Now that we’ve established what can happen with haphazardly organized table joins, let’s review some strategies for improving query execution speed.

Select from the most limiting table first

The effect of selecting from a table whose result set is limited due to WHERE constraint is to minimize the number of rows that the database engine has to iterate through. This Works because the first table is the one which usually requires the most scanning. Ironically, most queries are written to select from the main table first and then join the lookups to it. We now know that this may not be the most efficient.

Use STRAIGHT_JOIN and LEFT JOIN to Specify Table Order

The table read order forced by STRAIGHT_JOIN or LEFT JOIN helps the join optimizer do its work much more quickly, because there are fewer table permutations to check. In case you’re unfamiliar with STRAIGHT_JOIN, it’s similar to JOIN, except that the left table is always read before the right table, making it perfect for those (few) cases for which the join optimizer puts the tables in the wrong order.

For other join types, the MySQL’s join optimizer calculates the order in which tables should be joined. Note that this means that if you do a query like the following one, MySQL does a full scan on b because the LEFT JOIN forces it to be read before d:

SELECT *
FROM a JOIN b LEFT JOIN c ON (c.key=a.key)
LEFT JOIN d ON (d.key=a.key)
WHERE b.key=d.key;

The fix in this case is reversing the order in which a and b are listed in the FROM clause.

SELECT *
FROM b JOIN a LEFT JOIN c ON (c.key=a.key)
LEFT JOIN d ON (d.key=a.key)
WHERE b.key=d.key;

Shortcut Joins

A shortcut join is a join that provides an alternative path between two tables. Shortcut joins improve the performance of a query by not taking into account intermediate tables, thus shortening a normally longer join path.

In the following example the column carrier_code column appears in both the tables tas_contact and TA_CASES. The carrier_code points to the same data for both tables. The normal path for a query using carrier_code from tas_contact and TA_CASES, is to pass through the intermediary table TA_CARRIER.

the column carrier_code column appears in both the tables tas_contact and TA_CASES

The shortcut join directly linking tas_contact and TA_CASES allows the query to ignore the intermediary table TA_CARRIER, optimizing the query.

The EXPLAIN Analysis Tool

You can get a good indication of how good a join is by taking the product of the values in the rows column of the EXPLAIN output. This should tell you roughly how many rows MySQL must examine to execute the query.

explain
select a.au_lname,
             a.au_fname, 
             ta.royaltyper, 
             t.title, 
             t.royalty
from    authors     a,
             titleauthor ta,
             titles     t
where a.au_id = ta.au_id
and     ta.title_id = t.title_id

id

select_type

table

type

possible_keys

key

key_len

ref

rows

Extra

1

SIMPLE

ta

ALL

(NULL)

(NULL)

(NULL)

(NULL)

800

1

SIMPLE

t

ref

NewIndex

NewIndex

23

crosstab_article.ta.title_id

100

Using where

1

SIMPLE

a

ALL

(NULL)

(NULL)

(NULL)

(NULL)

1000

Using where; Using join buffer

The fewer rows appear there, the faster the query should run.

Optimizing IN/ANY Subqueries

A very useful optimization is to “inform” the subquery that the only rows of interest are those where the inner expression inner_expr is equal to outer_expr. This is done by pushing down an appropriate equality into the subquery's WHERE clause. That is, the comparison is converted to this:

EXISTS (SELECT 1 FROM ... WHERE subquery_where AND outer_expr=inner_expr)

After the conversion, MySQL can use the pushed-down equality to limit the number of rows that it must examine when evaluating the subquery.

Conclusion

Although Join optimization should never be used as a substitute for well-thought out column indexing, it can greatly improve the performance of your queries in many instances. In a future article, we’ll look more closely at EXPLAIN as well as other analytical tools to help minimize the number of rows returned by each step of a query. In doing so, it is possible to lower result set size from tens of millions of rows to several hundred! The effect of which is impressive gains in query execution time.

» See All Articles by Columnist Rob Gravelle








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