Chapter 7. Optimization
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## Chapter 7. Optimization

Optimization is a complex task because ultimately it requires understanding of the entire system to be optimized. Although it may be possible to perform some local optimizations with little knowledge of your system or application, the more optimal you want your system to become, the more you must know about it.

This chapter tries to explain and give some examples of different ways to optimize MySQL. Remember, however, that there are always additional ways to make the system even faster, although they may require increasing effort to achieve.

## 7.1. Optimization Overview

The most important factor in making a system fast is its basic design. You must also know what kinds of processing your system is doing, and what its bottlenecks are. In most cases, system bottlenecks arise from these sources:

- Disk seeks. It takes time for the disk to find a piece of data. With modern disks, the mean time for this is usually lower than 10ms, so we can in theory do about 100 seeks a second. This time improves slowly with new disks and is very hard to optimize for a single table. The way to optimize seek time is to distribute the data onto more than one disk.

- Disk reading and writing. When the disk is at the correct position, we need to read the data. With modern disks, one disk delivers at least 10–20MB/s throughput. This is easier to optimize than seeks because you can read in parallel from multiple disks.

- CPU cycles. When we have the data in main memory, we need to process it to get our result. Having small tables compared to the amount of memory is the most common limiting factor. But with small tables, speed is usually not the problem.

- Memory bandwidth. When the CPU needs more data than can fit in the CPU cache, main memory bandwidth becomes a bottleneck. This is an uncommon bottleneck for most systems, but one to be aware of.

### 7.1.1. MySQL Design Limitations and Tradeoffs

When using the `MyISAM` storage engine, MySQL uses extremely fast table locking that allows multiple readers or a single writer. The biggest problem with this storage engine occurs when you have a steady stream of mixed updates and slow selects on a single table. If this is a problem for certain tables, you can use another storage engine for them. See Chapter 14, Storage Engines and Table Types.

MySQL can work with both transactional and non-transactional tables. To make it easier to work smoothly with non-transactional tables (which cannot roll back if something goes wrong), MySQL has the following rules. Note that these rules apply only when not running in strict SQL mode or if you use the `IGNORE` specifier for `INSERT` or `UPDATE`.

- All columns have default values.

- If you insert an inappropriate or out-of-range value into a column, MySQL sets the column to the “best possible value” instead of reporting an error. For numerical values, this is 0, the smallest possible value or the largest possible value. For strings, this is either the empty string or as much of the string as can be stored in the column.

- All calculated expressions return a value that can be used instead of signaling an error condition. For example, 1/0 returns `NULL`.

To change the preceding behaviors, you can enable stricter data handling by setting the server SQL mode appropriately. For more information about data handling, see Section 1.9.6, “How MySQL Deals with Constraints”, Section 5.2.5, “The Server SQL Mode”, and Section 13.2.4, “`INSERT` Syntax”.

### 7.1.2. Designing Applications for Portability

Because all SQL servers implement different parts of standard SQL, it takes work to write portable database applications. It is very easy to achieve portability for very simple selects and inserts, but becomes more difficult the more capabilities you require. If you want an application that is fast with many database systems, it becomes even more difficult.

All database systems have some weak points. That is, they have different design compromises that lead to different behavior.

To make a complex application portable, you need to determine which SQL servers it must work with, and then determine what features those servers support. You can use the MySQL crash-me program to find functions, types, and limits that you can use with a selection of database servers. crash-me does not check for every possible feature, but it is still reasonably comprehensive, performing about 450 tests. An example of the type of information crash-me can provide is that you should not use column names that are longer than 18 characters if you want to be able to use Informix or DB2.

The crash-me program and the MySQL benchmarks are all very database independent. By taking a look at how they are written, you can get a feeling for what you must do to make your own applications database independent. The programs can be found in the `sql-bench` directory of MySQL source distributions. They are written in Perl and use the DBI database interface. Use of DBI in itself solves part of the portability problem because it provides database-independent access methods. See Section 7.1.4, “The MySQL Benchmark Suite”.

If you strive for database independence, you need to get a good feeling for each SQL server's bottlenecks. For example, MySQL is very fast in retrieving and updating rows for `MyISAM` tables, but has a problem in mixing slow readers and writers on the same table. Oracle, on the other hand, has a big problem when you try to access rows that you have recently updated (until they are flushed to disk). Transactional database systems in general are not very good at generating summary tables from log tables, because in this case row locking is almost useless.

To make your application really database independent, you should define an easily extendable interface through which you manipulate your data. For example, C++ is available on most systems, so it makes sense to use a C++ class-based interface to the databases.

If you use some feature that is specific to a given database system (such as the `REPLACE` statement, which is specific to MySQL), you should implement the same feature for other SQL servers by coding an alternative method. Although the alternative might be slower, it enables the other servers to perform the same tasks.

With MySQL, you can use the `/*! */` syntax to add MySQL-specific keywords to a statement. The code inside `/* */` is treated as a comment (and ignored) by most other SQL servers. For information about writing comments, see Section 9.4, “Comment Syntax”.

If high performance is more important than exactness, as for some Web applications, it is possible to create an application layer that caches all results to give you even higher performance. By letting old results expire after a while, you can keep the cache reasonably fresh. This provides a method to handle high load spikes, in which case you can dynamically increase the cache size and set the expiration timeout higher until things get back to normal.

In this case, the table creation information should contain information about the initial cache size and how often the table should normally be refreshed.

An attractive alternative to implementing an application cache is to use the MySQL query cache. By enabling the query cache, the server handles the details of determining whether a query result can be reused. This simplifies your application. See Section 5.14, “The MySQL Query Cache”.

### 7.1.3. What We Have Used MySQL For

This section describes an early application for MySQL.

During MySQL initial development, the features of MySQL were made to fit our largest customer, which handled data warehousing for a couple of the largest retailers in Sweden.

From all stores, we got weekly summaries of all bonus card transactions, and were expected to provide useful information for the store owners to help them find how their advertising campaigns were affecting their own customers.

The volume of data was quite huge (about seven million summary transactions per month), and we had data for 4–10 years that we needed to present to the users. We got weekly requests from our customers, who wanted instant access to new reports from this data.

We solved this problem by storing all information per month in compressed “transaction tables.” We had a set of simple macros that generated summary tables grouped by different criteria (product group, customer id, store, and so on) from the tables in which the transactions were stored. The reports were Web pages that were dynamically generated by a small Perl script. This script parsed a Web page, executed the SQL statements in it, and inserted the results. We would have used PHP or mod_perl instead, but they were not available at the time.

For graphical data, we wrote a simple tool in C that could process SQL query results and produce GIF images based on those results. This tool also was dynamically executed from the Perl script that parses the Web pages.

In most cases, a new report could be created simply by copying an existing script and modifying the SQL query that it used. In some cases, we needed to add more columns to an existing summary table or generate a new one. This also was quite simple because we kept all transaction-storage tables on disk. (This amounted to about 50GB of transaction tables and 200GB of other customer data.)

We also let our customers access the summary tables directly with ODBC so that the advanced users could experiment with the data themselves.

This system worked well and we had no problems handling the data with quite modest Sun Ultra SPARCstation hardware (2×200MHz). Eventually the system was migrated to Linux.

### 7.1.4. The MySQL Benchmark Suite

This benchmark suite is meant to tell any user what operations a given SQL implementation performs well or poorly. You can get a good idea for how the benchmarks work by looking at the code and results in the `sql-bench` directory in any MySQL source distribution.

Note that this benchmark is single-threaded, so it measures the minimum time for the operations performed. We plan to add multi-threaded tests to the benchmark suite in the future.

To use the benchmark suite, the following requirements must be satisfied:

- The benchmark suite is provided with MySQL source distributions. You can either download a released distribution from http://dev.mysql.com/downloads/, or use the current development source tree. (See Section 2.9.3, “Installing from the Development Source Tree”.)

- The benchmark scripts are written in Perl and use the Perl DBI module to access database servers, so DBI must be installed. You also need the server-specific DBD drivers for each of the servers you want to test. For example, to test MySQL, PostgreSQL, and DB2, you must have the `DBD::mysql`, `DBD::Pg`, and `DBD::DB2` modules installed. See Section 2.14, “Perl Installation Notes”.

After you obtain a MySQL source distribution, you can find the benchmark suite located in its `sql-bench` directory. To run the benchmark tests, build MySQL, and then change location into the `sql-bench` directory and execute the `run-all-tests` script:

    shell> cd sql-bench
    shell> perl run-all-tests --server=server_name

`server_name` should be the name of one of the supported servers. To get a list of all options and supported servers, invoke this command:

    shell> perl run-all-tests --help

The crash-me script also is located in the `sql-bench` directory. crash-me tries to determine what features a database system supports and what its capabilities and limitations are by actually running queries. For example, it determines:

- What data types are supported

- How many indexes are supported

- What functions are supported

- How big a query can be

- How big a `VARCHAR` column can be

You can find the results from crash-me for many different database servers at http://dev.mysql.com/tech-resources/crash-me.php. For more information about benchmark results, visit http://dev.mysql.com/tech-resources/benchmarks/.

### 7.1.5. Using Your Own Benchmarks

You should definitely benchmark your application and database to find out where the bottlenecks are. After fixing one bottleneck (or by replacing it with a “dummy” module), you can proceed to identify the next bottleneck. Even if the overall performance for your application currently is acceptable, you should at least make a plan for each bottleneck and decide how to solve it if someday you really need the extra performance.

For examples of portable benchmark programs, look at those in the MySQL benchmark suite. See Section 7.1.4, “The MySQL Benchmark Suite”. You can take any program from this suite and modify it for your own needs. By doing this, you can try different solutions to your problem and test which really is fastest for you.

Another free benchmark suite is the Open Source Database Benchmark, available at http://osdb.sourceforge.net/.

It is very common for a problem to occur only when the system is very heavily loaded. We have had many customers who contact us when they have a (tested) system in production and have encountered load problems. In most cases, performance problems turn out to be due to issues of basic database design (for example, table scans are not good under high load) or problems with the operating system or libraries. Most of the time, these problems would be much easier to fix if the systems were not already in production.

To avoid problems like this, you should put some effort into benchmarking your whole application under the worst possible load. You can use Super Smack, available at http://jeremy.zawodny.com/mysql/super-smack/. As suggested by its name, it can bring a system to its knees, so make sure to use it only on your development systems.

## 7.2. Optimizing `SELECT` and Other Statements

First, one factor affects all statements: The more complex your permissions setup, the more overhead you have. Using simpler permissions when you issue `GRANT` statements enables MySQL to reduce permission-checking overhead when clients execute statements. For example, if you do not grant any table-level or column-level privileges, the server need not ever check the contents of the `tables_priv` and `columns_priv` tables. Similarly, if you place no resource limits on any accounts, the server does not have to perform resource counting. If you have a very high statement-processing load, it may be worth the time to use a simplified grant structure to reduce permission-checking overhead.

If your problem is with a specific MySQL expression or function, you can perform a timing test by invoking the `BENCHMARK()` function using the mysql client program. Its syntax is `BENCHMARK(`loop_count`,`expression`)`. The return value is always zero, but mysql prints a line displaying approximately how long the statement took to execute. For example:

    mysql> SELECT BENCHMARK(1000000,1+1);
    +------------------------+
    | BENCHMARK(1000000,1+1) |
    +------------------------+
    |                      0 |
    +------------------------+
    1 row in set (0.32 sec)

This result was obtained on a Pentium II 400MHz system. It shows that MySQL can execute 1,000,000 simple addition expressions in 0.32 seconds on that system.

All MySQL functions should be highly optimized, but there may be some exceptions. `BENCHMARK()` is an excellent tool for finding out if some function is a problem for your queries.

### 7.2.1. Optimizing Queries with `EXPLAIN`

    EXPLAIN tbl_name

Or:

    EXPLAIN [EXTENDED] SELECT select_options

The `EXPLAIN` statement can be used either as a synonym for `DESCRIBE` or as a way to obtain information about how MySQL executes a `SELECT` statement:

- `EXPLAIN `tbl_name`` is synonymous with `DESCRIBE `tbl_name`` or `SHOW COLUMNS FROM `tbl_name``.

- When you precede a `SELECT` statement with the keyword `EXPLAIN`, MySQL displays information from the optimizer about the query execution plan. That is, MySQL explains how it would process the `SELECT`, including information about how tables are joined and in which order.

This section describes the second use of `EXPLAIN` for obtaining query execution plan information. For a description of the `DESCRIBE` and `SHOW COLUMNS` statements, see Section 13.3.1, “`DESCRIBE` Syntax”, and Section 13.5.4.3, “`SHOW COLUMNS` Syntax”.

With the help of `EXPLAIN`, you can see where you should add indexes to tables to get a faster `SELECT` that uses indexes to find rows. You can also use `EXPLAIN` to check whether the optimizer joins the tables in an optimal order. To force the optimizer to use a join order corresponding to the order in which the tables are named in the `SELECT` statement, begin the statement with `SELECT STRAIGHT_JOIN` rather than just `SELECT`.

If you have a problem with indexes not being used when you believe that they should be, you should run `ANALYZE TABLE` to update table statistics such as cardinality of keys, that can affect the choices the optimizer makes. See Section 13.5.2.1, “`ANALYZE TABLE` Syntax”.

`EXPLAIN` returns a row of information for each table used in the `SELECT` statement. The tables are listed in the output in the order that MySQL would read them while processing the query. MySQL resolves all joins using a single-sweep multi-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.

When the `EXTENDED` keyword is used, `EXPLAIN` produces extra information that can be viewed by issuing a `SHOW WARNINGS` statement following the `EXPLAIN` statement. This information displays how the optimizer qualifies table and column names in the `SELECT` statement, what the `SELECT` looks like after the application of rewriting and optimization rules, and possibly other notes about the optimization process.

Each output row from `EXPLAIN` provides information about one table, and each row contains the following columns:

- `id`

  The `SELECT` identifier. This is the sequential number of the `SELECT` within the query.

- `select_type`

  The type of `SELECT`, which can be any of those shown in the following table:

  `SIMPLE` | Simple `SELECT` (not using `UNION` or subqueries)
  `PRIMARY` | Outermost `SELECT`
  `UNION` | Second or later `SELECT` statement in a `UNION`
  `DEPENDENT UNION` | Second or later `SELECT` statement in a `UNION`, dependent on outer query
  `UNION RESULT` | Result of a `UNION`.
  `SUBQUERY` | First `SELECT` in subquery
  `DEPENDENT SUBQUERY` | First `SELECT` in subquery, dependent on outer query
  `DERIVED` | Derived table `SELECT` (subquery in `FROM` clause)

  `DEPENDENT` typically signifies the use of a correlated subquery. See Section 13.2.8.7, “Correlated Subqueries”.

- `table`

  The table to which the row of output refers.

- `type`

  The join type. The different join types are listed here, ordered from the best type to the worst:

  - `system`

    The table has only one row (= system table). This is a special case of the `const` join type.

  - `const`

    The table has at most one matching row, which is read at the start of the query. Because there is only one row, values from the column in this row can be regarded as constants by the rest of the optimizer. `const` tables are very fast because they are read only once.

    `const` is used when you compare all parts of a `PRIMARY KEY` or `UNIQUE` index to constant values. In the following queries, `tbl_name` can be used as a `const` table:

        SELECT * FROM tbl_name WHERE primary_key=1;

        SELECT * FROM tbl_name
          WHERE primary_key_part1=1 AND primary_key_part2=2;

  - `eq_ref`

    One row is read from this table for each combination of rows from the previous tables. Other than the `system` and `const` types, this is the best possible join type. It is used when all parts of an index are used by the join and the index is a `PRIMARY KEY` or `UNIQUE` index.

    `eq_ref` can be used for indexed columns that are compared using the `=` operator. The comparison value can be a constant or an expression that uses columns from tables that are read before this table. In the following examples, MySQL can use an `eq_ref` join to process `ref_table`:

        SELECT * FROM ref_table,other_table
          WHERE ref_table.key_column=other_table.column;

        SELECT * FROM ref_table,other_table
          WHERE ref_table.key_column_part1=other_table.column
          AND ref_table.key_column_part2=1;

  - `ref`

    All rows with matching index values are read from this table for each combination of rows from the previous tables. `ref` is used if the join uses only a leftmost prefix of the key or if the key is not a `PRIMARY KEY` or `UNIQUE` index (in other words, if the join cannot select a single row based on the key value). If the key that is used matches only a few rows, this is a good join type.

    `ref` can be used for indexed columns that are compared using the `=` or `<=>` operator. In the following examples, MySQL can use a `ref` join to process `ref_table`:

        SELECT * FROM ref_table WHERE key_column=expr;

        SELECT * FROM ref_table,other_table
          WHERE ref_table.key_column=other_table.column;

        SELECT * FROM ref_table,other_table
          WHERE ref_table.key_column_part1=other_table.column
          AND ref_table.key_column_part2=1;

  - `ref_or_null`

    This join type is like `ref`, but with the addition that MySQL does an extra search for rows that contain `NULL` values. This join type optimization is used most often in resolving subqueries. In the following examples, MySQL can use a `ref_or_null` join to process `ref_table`:

        SELECT * FROM ref_table
          WHERE key_column=expr OR key_column IS NULL;

    See Section 7.2.7, “`IS NULL` Optimization”.

  - `index_merge`

    This join type indicates that the Index Merge optimization is used. In this case, the `key` column in the output row contains a list of indexes used, and `key_len` contains a list of the longest key parts for the indexes used. For more information, see Section 7.2.6, “Index Merge Optimization”.

  - `unique_subquery`

    This type replaces `ref` for some `IN` subqueries of the following form:

        value IN (SELECT primary_key FROM single_table WHERE some_expr)

    `unique_subquery` is just an index lookup function that replaces the subquery completely for better efficiency.

  - `index_subquery`

    This join type is similar to `unique_subquery`. It replaces `IN` subqueries, but it works for non-unique indexes in subqueries of the following form:

        value IN (SELECT key_column FROM single_table WHERE some_expr)

  - `range`

    Only rows that are in a given range are retrieved, using an index to select the rows. The `key` column in the output row indicates which index is used. The `key_len` contains the longest key part that was used. The `ref` column is `NULL` for this type.

    `range` can be used when a key column is compared to a constant using any of the `=`, `<>`, `>`, `>=`, `<`, `<=`, `IS NULL`, `<=>`, `BETWEEN`, or `IN` operators:

        SELECT * FROM tbl_name
          WHERE key_column = 10;

        SELECT * FROM tbl_name
          WHERE key_column BETWEEN 10 and 20;

        SELECT * FROM tbl_name
          WHERE key_column IN (10,20,30);

        SELECT * FROM tbl_name
          WHERE key_part1= 10 AND key_part2 IN (10,20,30);

  - `index`

    This join type is the same as `ALL`, except that only the index tree is scanned. This usually is faster than `ALL` because the index file usually is smaller than the data file.

    MySQL can use this join type when the query uses only columns that are part of a single index.

  - `ALL`

    A full table scan is done for each combination of rows from the previous tables. This is normally not good if the table is the first table not marked `const`, and usually very bad in all other cases. Normally, you can avoid `ALL` by adding indexes that allow row retrieval from the table based on constant values or column values from earlier tables.

- `possible_keys`

  The `possible_keys` column indicates which indexes MySQL can choose from use to find the rows in this table. Note that this column is totally independent of the order of the tables as displayed in the output from `EXPLAIN`. That means that some of the keys in `possible_keys` might not be usable in practice with the generated table order.

  If this column is `NULL`, there are no relevant indexes. In this case, you may be able to improve the performance of your query by examining the `WHERE` clause to check whether it refers to some column or columns that would be suitable for indexing. If so, create an appropriate index and check the query with `EXPLAIN` again. See Section 13.1.2, “`ALTER TABLE` Syntax”.

  To see what indexes a table has, use `SHOW INDEX FROM `tbl_name``.

- `key`

  The `key` column indicates the key (index) that MySQL actually decided to use. The key is `NULL` if no index was chosen. To force MySQL to use or ignore an index listed in the `possible_keys` column, use `FORCE INDEX`, `USE INDEX`, or `IGNORE INDEX` in your query. See Section 13.2.7, “`SELECT` Syntax”.

  For `MyISAM` and `BDB` tables, running `ANALYZE TABLE` helps the optimizer choose better indexes. For `MyISAM` tables, myisamchk --analyze does the same. See Section 13.5.2.1, “`ANALYZE TABLE` Syntax”, and Section 5.10.4, “Table Maintenance and Crash Recovery”.

- `key_len`

  The `key_len` column indicates the length of the key that MySQL decided to use. The length is `NULL` if the `key` column says `NULL`. Note that the value of `key_len` enables you to determine how many parts of a multiple-part key MySQL actually uses.

- `ref`

  The `ref` column shows which columns or constants are compared to the index named in the `key` column to select rows from the table.

- `rows`

  The `rows` column indicates the number of rows MySQL believes it must examine to execute the query.

- `Extra`

  This column contains additional information about how MySQL resolves the query. Here is an explanation of the values that can appear in this column:

  - `Distinct`

    MySQL is looking for distinct values, so it stops searching for more rows for the current row combination after it has found the first matching row.

  - `Not exists`

    MySQL was able to do a `LEFT JOIN` optimization on the query and does not examine more rows in this table for the previous row combination after it finds one row that matches the `LEFT JOIN` criteria. Here is an example of the type of query that can be optimized this way:

        SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id
          WHERE t2.id IS NULL;

    Assume that `t2.id` is defined as `NOT NULL`. In this case, MySQL scans `t1` and looks up the rows in `t2` using the values of `t1.id`. If MySQL finds a matching row in `t2`, it knows that `t2.id` can never be `NULL`, and does not scan through the rest of the rows in `t2` that have the same `id` value. In other words, for each row in `t1`, MySQL needs to do only a single lookup in `t2`, regardless of how many rows actually match in `t2`.

  - `range checked for each record (index map: `N`)`

    MySQL found no good index to use, but found that some of indexes might be used after column values from preceding tables are known. For each row combination in the preceding tables, MySQL checks whether it is possible to use a `range` or `index_merge` access method to retrieve rows. This is not very fast, but is faster than performing a join with no index at all. The applicability criteria are as described in Section 7.2.5, “Range Optimization”, and Section 7.2.6, “Index Merge Optimization”, with the exception that all column values for the preceding table are known and considered to be constants.

  - `Using filesort`

    MySQL must do an extra pass to find out how to retrieve the rows in sorted order. The sort is done by going through all rows according to the join type and storing the sort key and pointer to the row for all rows that match the `WHERE` clause. The keys then are sorted and the rows are retrieved in sorted order. See Section 7.2.12, “`ORDER BY` Optimization”.

  - `Using index`

    The column information is retrieved from the table using only information in the index tree without having to do an additional seek to read the actual row. This strategy can be used when the query uses only columns that are part of a single index.

  - `Using temporary`

    To resolve the query, MySQL needs to create a temporary table to hold the result. This typically happens if the query contains `GROUP BY` and `ORDER BY` clauses that list columns differently.

  - `Using where`

    A `WHERE` clause is used to restrict which rows to match against the next table or send to the client. Unless you specifically intend to fetch or examine all rows from the table, you may have something wrong in your query if the `Extra` value is not `Using where` and the table join type is `ALL` or `index`.

    If you want to make your queries as fast as possible, you should look out for `Extra` values of `Using filesort` and `Using temporary`.

  - `Using sort_union(...)`, `Using union(...)`, `Using intersect(...)`

    These indicate how index scans are merged for the `index_merge` join type. See Section 7.2.6, “Index Merge Optimization”, for more information.

  - `Using index for group-by`

    Similar to the `Using index` way of accessing a table, `Using index for group-by` indicates that MySQL found an index that can be used to retrieve all columns of a `GROUP BY` or `DISTINCT` query without any extra disk access to the actual table. Additionally, the index is used in the most efficient way so that for each group, only a few index entries are read. For details, see Section 7.2.13, “`GROUP BY` Optimization”.

  - `Using where with pushed condition`

    This item applies to `NDB Cluster` tables only. It means that MySQL Cluster is using condition pushdown to improve the efficiency of a direct comparison (`=`) between a non-indexed column and a constant. In such cases, the condition is “pushed down” to the cluster's data nodes where it is evaluated in all partitions simultaneously. This eliminates the need to send non-matching rows over the network, and can speed up such queries by a factor of 5 to 10 times over cases where condition pushdown could be but is not used.

    Suppose that you have a Cluster table defined as follows:

        CREATE TABLE t1 (
            a INT,
            b INT,
            KEY(a)
        ) ENGINE=NDBCLUSTER;

    In this case, condition pushdown can be used with a query such as this one:

        SELECT a,b FROM t1 WHERE b = 10;

    This can be seen in the output of `EXPLAIN SELECT`, as shown here:

        mysql> EXPLAIN SELECT a,b FROM t1 WHERE b = 10\G
        *************************** 1. row ***************************
                   id: 1
          select_type: SIMPLE
                table: t1
                 type: ALL
        possible_keys: NULL
                  key: NULL
              key_len: NULL
                  ref: NULL
                 rows: 10
                Extra: Using where with pushed condition

    Condition pushdown cannot be used with either of these two queries:

        SELECT a,b FROM t1 WHERE a = 10;
        SELECT a,b FROM t1 WHERE b + 1 = 10;

    With regard to the first of these two queries, condition pushdown is not applicable because an index exists on column `a`. In the case of the second query, a condition pushdown cannot be employed because the comparison involving the non-indexed column `b` is an indirect one. (However, it would apply if you were to reduce `b + 1 = 10` to `b = 9` in the `WHERE` clause.)

    However, a condition pushdown may also be employed when an indexed column column is compared with a constant using a `>` or `<` operator:

        mysql> EXPLAIN SELECT a,b FROM t1 WHERE a<2\G
        *************************** 1. row ***************************
                   id: 1
          select_type: SIMPLE
                table: t1
                 type: range
        possible_keys: a
                  key: a
              key_len: 5
                  ref: NULL
                 rows: 2
                Extra: Using where with pushed condition

    With regard to condition pushdown, keep in mind that:

    - Condition pushdown is relevant to MySQL Cluster only, and does not occur when executing queries against tables using any other storage engine.

    - Condition pushdown capability is not used by default. To enable it, you can start mysqld with the `--engine-condition-pushdown` option, or execute the following statement:

          SET engine_condition_pushdown=On;

      Note: Condition pushdown is not supported for columns of any of the `BLOB` or `TEXT` types.

    Condition pushdown, `Using where with pushed condition`, and `engine_condition_pushdown` were all introduced in MySQL 5.0 Cluster.

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. If you restrict queries with the `max_join_size` system variable, this row product also is used to determine which multiple-table `SELECT` statements to execute and which to abort. See Section 7.5.2, “Tuning Server Parameters”.

The following example shows how a multiple-table join can be optimized progressively based on the information provided by `EXPLAIN`.

Suppose that you have the `SELECT` statement shown here and that you plan to examine it using `EXPLAIN`:

    EXPLAIN SELECT tt.TicketNumber, tt.TimeIn,
                   tt.ProjectReference, tt.EstimatedShipDate,
                   tt.ActualShipDate, tt.ClientID,
                   tt.ServiceCodes, tt.RepetitiveID,
                   tt.CurrentProcess, tt.CurrentDPPerson,
                   tt.RecordVolume, tt.DPPrinted, et.COUNTRY,
                   et_1.COUNTRY, do.CUSTNAME
            FROM tt, et, et AS et_1, do
            WHERE tt.SubmitTime IS NULL
              AND tt.ActualPC = et.EMPLOYID
              AND tt.AssignedPC = et_1.EMPLOYID
              AND tt.ClientID = do.CUSTNMBR;

For this example, make the following assumptions:

- The columns being compared have been declared as follows:

  Table | Column | Data Type
  `tt` | `ActualPC` | `CHAR(10)`
  `tt` | `AssignedPC` | `CHAR(10)`
  `tt` | `ClientID` | `CHAR(10)`
  `et` | `EMPLOYID` | `CHAR(15)`
  `do` | `CUSTNMBR` | `CHAR(15)`

- The tables have the following indexes:

  Table | Index
  `tt` | `ActualPC`
  `tt` | `AssignedPC`
  `tt` | `ClientID`
  `et` | `EMPLOYID` (primary key)
  `do` | `CUSTNMBR` (primary key)

- The `tt.ActualPC` values are not evenly distributed.

Initially, before any optimizations have been performed, the `EXPLAIN` statement produces the following information:

    table type possible_keys key  key_len ref  rows  Extra
    et    ALL  PRIMARY       NULL NULL    NULL 74
    do    ALL  PRIMARY       NULL NULL    NULL 2135
    et_1  ALL  PRIMARY       NULL NULL    NULL 74
    tt    ALL  AssignedPC,   NULL NULL    NULL 3872
               ClientID,
               ActualPC
          range checked for each record (key map: 35)

Because `type` is `ALL` for each table, this output indicates that MySQL is generating a Cartesian product of all the tables; that is, every combination of rows. This takes quite a long time, because the product of the number of rows in each table must be examined. For the case at hand, this product is 74 × 2135 × 74 × 3872 = 45,268,558,720 rows. If the tables were bigger, you can only imagine how long it would take.

One problem here is that MySQL can use indexes on columns more efficiently if they are declared as the same type and size. In this context, `VARCHAR` and `CHAR` are considered the same if they are declared as the same size. `tt.ActualPC` is declared as `CHAR(10)` and `et.EMPLOYID` is `CHAR(15)`, so there is a length mismatch.

To fix this disparity between column lengths, use `ALTER TABLE` to lengthen `ActualPC` from 10 characters to 15 characters:

    mysql> ALTER TABLE tt MODIFY ActualPC VARCHAR(15);

Now `tt.ActualPC` and `et.EMPLOYID` are both `VARCHAR(15)`. Executing the `EXPLAIN` statement again produces this result:

    table type   possible_keys key     key_len ref         rows    Extra
    tt    ALL    AssignedPC,   NULL    NULL    NULL        3872    Using
                 ClientID,                                         where
                 ActualPC
    do    ALL    PRIMARY       NULL    NULL    NULL        2135
          range checked for each record (key map: 1)
    et_1  ALL    PRIMARY       NULL    NULL    NULL        74
          range checked for each record (key map: 1)
    et    eq_ref PRIMARY       PRIMARY 15      tt.ActualPC 1

This is not perfect, but is much better: The product of the `rows` values is less by a factor of 74. This version executes in a couple of seconds.

A second alteration can be made to eliminate the column length mismatches for the `tt.AssignedPC = et_1.EMPLOYID` and `tt.ClientID = do.CUSTNMBR` comparisons:

    mysql> ALTER TABLE tt MODIFY AssignedPC VARCHAR(15),
        ->                MODIFY ClientID   VARCHAR(15);

After that modification, `EXPLAIN` produces the output shown here:

    table type   possible_keys key      key_len ref           rows Extra
    et    ALL    PRIMARY       NULL     NULL    NULL          74
    tt    ref    AssignedPC,   ActualPC 15      et.EMPLOYID   52   Using
                 ClientID,                                         where
                 ActualPC
    et_1  eq_ref PRIMARY       PRIMARY  15      tt.AssignedPC 1
    do    eq_ref PRIMARY       PRIMARY  15      tt.ClientID   1

At this point, the query is optimized almost as well as possible. The remaining problem is that, by default, MySQL assumes that values in the `tt.ActualPC` column are evenly distributed, and that is not the case for the `tt` table. Fortunately, it is easy to tell MySQL to analyze the key distribution:

    mysql> ANALYZE TABLE tt;

With the additional index information, the join is perfect and `EXPLAIN` produces this result:

    table type   possible_keys key     key_len ref           rows Extra
    tt    ALL    AssignedPC    NULL    NULL    NULL          3872 Using
                 ClientID,                                        where
                 ActualPC
    et    eq_ref PRIMARY       PRIMARY 15      tt.ActualPC   1
    et_1  eq_ref PRIMARY       PRIMARY 15      tt.AssignedPC 1
    do    eq_ref PRIMARY       PRIMARY 15      tt.ClientID   1

Note that the `rows` column in the output from `EXPLAIN` is an educated guess from the MySQL join optimizer. You should check whether the numbers are even close to the truth by comparing the `rows` product with the actual number of rows that the query returns. If the numbers are quite different, you might get better performance by using `STRAIGHT_JOIN` in your `SELECT` statement and trying to list the tables in a different order in the `FROM` clause.

### 7.2.2. Estimating Query Performance

In most cases, you can estimate query performance by counting disk seeks. For small tables, you can usually find a row in one disk seek (because the index is probably cached). For bigger tables, you can estimate that, using B-tree indexes, you need this many seeks to find a row: `log(`row_count`) / log(`index_block_length` / 3 × 2 / (`index_length` + `data_pointer_length`)) + 1`.

In MySQL, an index block is usually 1,024 bytes and the data pointer is usually four bytes. For a 500,000-row table with an index length of three bytes (the size of `MEDIUMINT`), the formula indicates `log(500,000)/log(1024/3×2/(3+4)) + 1` = `4` seeks.

This index would require storage of about 500,000 × 7 × 3/2 = 5.2MB (assuming a typical index buffer fill ratio of 2/3), so you probably have much of the index in memory and so need only one or two calls to read data to find the row.

For writes, however, you need four seek requests to find where to place a new index value and normally two seeks to update the index and write the row.

Note that the preceding discussion does not mean that your application performance slowly degenerates by log `N`. As long as everything is cached by the OS or the MySQL server, things become only marginally slower as the table gets bigger. After the data gets too big to be cached, things start to go much slower until your applications are bound only by disk seeks (which increase by log `N`). To avoid this, increase the key cache size as the data grows. For `MyISAM` tables, the key cache size is controlled by the `key_buffer_size` system variable. See Section 7.5.2, “Tuning Server Parameters”.

### 7.2.3. Speed of `SELECT` Queries

In general, when you want to make a slow `SELECT ... WHERE` query faster, the first thing to check is whether you can add an index. All references between different tables should usually be done with indexes. You can use the `EXPLAIN` statement to determine which indexes are used for a `SELECT`. See Section 7.2.1, “Optimizing Queries with `EXPLAIN`”, and Section 7.4.5, “How MySQL Uses Indexes”.

Some general tips for speeding up queries on `MyISAM` tables:

- To help MySQL better optimize queries, use `ANALYZE TABLE` or run myisamchk --analyze on a table after it has been loaded with data. This updates a value for each index part that indicates the average number of rows that have the same value. (For unique indexes, this is always 1.) MySQL uses this to decide which index to choose when you join two tables based on a non-constant expression. You can check the result from the table analysis by using `SHOW INDEX FROM `tbl_name`` and examining the `Cardinality` value. myisamchk --description --verbose shows index distribution information.

- To sort an index and data according to an index, use myisamchk --sort-index --sort-records=1 (assuming that you want to sort on index 1). This is a good way to make queries faster if you have a unique index from which you want to read all rows in order according to the index. The first time you sort a large table this way, it may take a long time.

### 7.2.4. `WHERE` Clause Optimization

This section discusses optimizations that can be made for processing `WHERE` clauses. The examples use `SELECT` statements, but the same optimizations apply for `WHERE` clauses in `DELETE` and `UPDATE` statements.

Work on the MySQL optimizer is ongoing, so this section is incomplete. MySQL performs a great many optimizations, not all of which are documented here.

Some of the optimizations performed by MySQL follow:

- Removal of unnecessary parentheses:

         ((a AND b) AND c OR (((a AND b) AND (c AND d))))
      -> (a AND b AND c) OR (a AND b AND c AND d)

- Constant folding:

         (a<b AND b=c) AND a=5
      -> b>5 AND b=c AND a=5

- Constant condition removal (needed because of constant folding):

         (B>=5 AND B=5) OR (B=6 AND 5=5) OR (B=7 AND 5=6)
      -> B=5 OR B=6

- Constant expressions used by indexes are evaluated only once.

- `COUNT(*)` on a single table without a `WHERE` is retrieved directly from the table information for `MyISAM` and `MEMORY` tables. This is also done for any `NOT NULL` expression when used with only one table.

- Early detection of invalid constant expressions. MySQL quickly detects that some `SELECT` statements are impossible and returns no rows.

- `HAVING` is merged with `WHERE` if you do not use `GROUP BY` or aggregate functions (`COUNT()`, `MIN()`, and so on).

- For each table in a join, a simpler `WHERE` is constructed to get a fast `WHERE` evaluation for the table and also to skip rows as soon as possible.

- All constant tables are read first before any other tables in the query. A constant table is any of the following:

  - An empty table or a table with one row.

  - A table that is used with a `WHERE` clause on a `PRIMARY KEY` or a `UNIQUE` index, where all index parts are compared to constant expressions and are defined as `NOT NULL`.

  All of the following tables are used as constant tables:

      SELECT * FROM t WHERE primary_key=1;
      SELECT * FROM t1,t2
        WHERE t1.primary_key=1 AND t2.primary_key=t1.id;

- The best join combination for joining the tables is found by trying all possibilities. If all columns in `ORDER BY` and `GROUP BY` clauses come from the same table, that table is preferred first when joining.

- If there is an `ORDER BY` clause and a different `GROUP BY` clause, or if the `ORDER BY` or `GROUP BY` contains columns from tables other than the first table in the join queue, a temporary table is created.

- If you use the `SQL_SMALL_RESULT` option, MySQL uses an in-memory temporary table.

- Each table index is queried, and the best index is used unless the optimizer believes that it is more efficient to use a table scan. At one time, a scan was used based on whether the best index spanned more than 30% of the table, but a fixed percentage no longer determines the choice between using an index or a scan. The optimizer now is more complex and bases its estimate on additional factors such as table size, number of rows, and I/O block size.

- In some cases, MySQL can read rows from the index without even consulting the data file. If all columns used from the index are numeric, only the index tree is used to resolve the query.

- Before each row is output, those that do not match the `HAVING` clause are skipped.

Some examples of queries that are very fast:

    SELECT COUNT(*) FROM tbl_name;

    SELECT MIN(key_part1),MAX(key_part1) FROM tbl_name;

    SELECT MAX(key_part2) FROM tbl_name
      WHERE key_part1=constant;

    SELECT ... FROM tbl_name
      ORDER BY key_part1,key_part2,... LIMIT 10;

    SELECT ... FROM tbl_name
      ORDER BY key_part1 DESC, key_part2 DESC, ... LIMIT 10;

MySQL resolves the following queries using only the index tree, assuming that the indexed columns are numeric:

    SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;

    SELECT COUNT(*) FROM tbl_name
      WHERE key_part1=val1 AND key_part2=val2;

    SELECT key_part2 FROM tbl_name GROUP BY key_part1;

The following queries use indexing to retrieve the rows in sorted order without a separate sorting pass:

    SELECT ... FROM tbl_name
      ORDER BY key_part1,key_part2,... ;

    SELECT ... FROM tbl_name
      ORDER BY key_part1 DESC, key_part2 DESC, ... ;

### 7.2.5. Range Optimization

The `range` access method uses a single index to retrieve a subset of table rows that are contained within one or several index value intervals. It can be used for a single-part or multiple-part index. The following sections give a detailed description of how intervals are extracted from the `WHERE` clause.

#### 7.2.5.1. The Range Access Method for Single-Part Indexes

For a single-part index, index value intervals can be conveniently represented by corresponding conditions in the `WHERE` clause, so we speak of range conditions rather than “intervals.”

The definition of a range condition for a single-part index is as follows:

- For both `BTREE` and `HASH` indexes, comparison of a key part with a constant value is a range condition when using the `=`, `<=>`, `IN`, `IS NULL`, or `IS NOT NULL` operators.

- For `BTREE` indexes, comparison of a key part with a constant value is a range condition when using the `>`, `<`, `>=`, `<=`, `BETWEEN`, `!=`, or `<>` operators, or `LIKE '`pattern`'` (where `'`pattern`'` does not start with a wildcard).

- For all types of indexes, multiple range conditions combined with `OR` or `AND` form a range condition.

“Constant value” in the preceding descriptions means one of the following:

- A constant from the query string

- A column of a `const` or `system` table from the same join

- The result of an uncorrelated subquery

- Any expression composed entirely from subexpressions of the preceding types

Here are some examples of queries with range conditions in the `WHERE` clause:

    SELECT * FROM t1
      WHERE key_col > 1
      AND key_col < 10;

    SELECT * FROM t1
      WHERE key_col = 1
      OR key_col IN (15,18,20);

    SELECT * FROM t1
      WHERE key_col LIKE 'ab%'
      OR key_col BETWEEN 'bar' AND 'foo';

Note that some non-constant values may be converted to constants during the constant propagation phase.

MySQL tries to extract range conditions from the `WHERE` clause for each of the possible indexes. During the extraction process, conditions that cannot be used for constructing the range condition are dropped, conditions that produce overlapping ranges are combined, and conditions that produce empty ranges are removed.

Consider the following statement, where `key1` is an indexed column and `nonkey` is not indexed:

    SELECT * FROM t1 WHERE
      (key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR
      (key1 < 'bar' AND nonkey = 4) OR
      (key1 < 'uux' AND key1 > 'z');

The extraction process for key `key1` is as follows:

1. Start with original `WHERE` clause:

       (key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR
       (key1 < 'bar' AND nonkey = 4) OR
       (key1 < 'uux' AND key1 > 'z')

2. Remove `nonkey = 4` and `key1 LIKE '%b'` because they cannot be used for a range scan. The correct way to remove them is to replace them with `TRUE`, so that we do not miss any matching rows when doing the range scan. Having replaced them with `TRUE`, we get:

       (key1 < 'abc' AND (key1 LIKE 'abcde%' OR TRUE)) OR
       (key1 < 'bar' AND TRUE) OR
       (key1 < 'uux' AND key1 > 'z')

3. Collapse conditions that are always true or false:

   - `(key1 LIKE 'abcde%' OR TRUE)` is always true

   - `(key1 < 'uux' AND key1 > 'z')` is always false

   Replacing these conditions with constants, we get:

       (key1 < 'abc' AND TRUE) OR (key1 < 'bar' AND TRUE) OR (FALSE)

   Removing unnecessary `TRUE` and `FALSE` constants, we obtain:

       (key1 < 'abc') OR (key1 < 'bar')

4. Combining overlapping intervals into one yields the final condition to be used for the range scan:

       (key1 < 'bar')

In general (and as demonstrated by the preceding example), the condition used for a range scan is less restrictive than the `WHERE` clause. MySQL performs an additional check to filter out rows that satisfy the range condition but not the full `WHERE` clause.

The range condition extraction algorithm can handle nested `AND`/`OR` constructs of arbitrary depth, and its output does not depend on the order in which conditions appear in `WHERE` clause.

#### 7.2.5.2. The Range Access Method for Multiple-Part Indexes

Range conditions on a multiple-part index are an extension of range conditions for a single-part index. A range condition on a multiple-part index restricts index rows to lie within one or several key tuple intervals. Key tuple intervals are defined over a set of key tuples, using ordering from the index.

For example, consider a multiple-part index defined as `key1(`key_part1`, `key_part2`, `key_part3`)`, and the following set of key tuples listed in key order:

    key_part1  key_part2  key_part3
      NULL       1          'abc'
      NULL       1          'xyz'
      NULL       2          'foo'
       1         1          'abc'
       1         1          'xyz'
       1         2          'abc'
       2         1          'aaa'

The condition ``key_part1` = 1` defines this interval:

    (1,-inf,-inf) <= (key_part1,key_part2,key_part3) < (1,+inf,+inf)

The interval covers the 4th, 5th, and 6th tuples in the preceding data set and can be used by the range access method.

By contrast, the condition ``key_part3` = 'abc'` does not define a single interval and cannot be used by the range access method.

The following descriptions indicate how range conditions work for multiple-part indexes in greater detail.

- For `HASH` indexes, each interval containing identical values can be used. This means that the interval can be produced only for conditions in the following form:

          key_part1 cmp const1
      AND key_part2 cmp const2
      AND ...
      AND key_partN cmp constN;

  Here, `const1`, `const2`, … are constants, `cmp` is one of the `=`, `<=>`, or `IS NULL` comparison operators, and the conditions cover all index parts. (That is, there are `N` conditions, one for each part of an `N`-part index.) For example, the following is a range condition for a three-part `HASH` index:

      key_part1 = 1 AND key_part2 IS NULL AND key_part3 = 'foo'

  For the definition of what is considered to be a constant, see Section 7.2.5.1, “The Range Access Method for Single-Part Indexes”.

- For a `BTREE` index, an interval might be usable for conditions combined with `AND`, where each condition compares a key part with a constant value using `=`, `<=>`, `IS NULL`, `>`, `<`, `>=`, `<=`, `!=`, `<>`, `BETWEEN`, or `LIKE '`pattern`'` (where `'`pattern`'` does not start with a wildcard). An interval can be used as long as it is possible to determine a single key tuple containing all rows that match the condition (or two intervals if `<>` or `!=` is used). For example, for this condition:

      key_part1 = 'foo' AND key_part2 >= 10 AND key_part3 > 10

  The single interval is:

      ('foo',10,10) < (key_part1,key_part2,key_part3) < ('foo',+inf,+inf)

  It is possible that the created interval contains more rows than the initial condition. For example, the preceding interval includes the value `('foo', 11, 0)`, which does not satisfy the original condition.

- If conditions that cover sets of rows contained within intervals are combined with `OR`, they form a condition that covers a set of rows contained within the union of their intervals. If the conditions are combined with `AND`, they form a condition that covers a set of rows contained within the intersection of their intervals. For example, for this condition on a two-part index:

      (key_part1 = 1 AND key_part2 < 2) OR (key_part1 > 5)

  The intervals are:

      (1,-inf) < (key_part1,key_part2) < (1,2)
      (5,-inf) < (key_part1,key_part2)

  In this example, the interval on the first line uses one key part for the left bound and two key parts for the right bound. The interval on the second line uses only one key part. The `key_len` column in the `EXPLAIN` output indicates the maximum length of the key prefix used.

  In some cases, `key_len` may indicate that a key part was used, but that might be not what you would expect. Suppose that `key_part1` and `key_part2` can be `NULL`. Then the `key_len` column displays two key part lengths for the following condition:

      key_part1 >= 1 AND key_part2 < 2

  But, in fact, the condition is converted to this:

      key_part1 >= 1 AND key_part2 IS NOT NULL

Section 7.2.5.1, “The Range Access Method for Single-Part Indexes”, describes how optimizations are performed to combine or eliminate intervals for range conditions on a single-part index. Analogous steps are performed for range conditions on multiple-part indexes.

### 7.2.6. Index Merge Optimization

The Index Merge method is used to retrieve rows with several `range` scans and to merge their results into one. The merge can produce unions, intersections, or unions-of-intersections of its underlying scans.

Note: If you have upgraded from a previous version of MySQL, you should be aware that this type of join optimization is first introduced in MySQL 5.0, and represents a significant change in behavior with regard to indexes. (Formerly, MySQL was able to use at most only one index for each referenced table.)

In `EXPLAIN` output, the Index Merge method appears as `index_merge` in the `type` column. In this case, the `key` column contains a list of indexes used, and `key_len` contains a list of the longest key parts for those indexes.

Examples:

    SELECT * FROM tbl_name WHERE key_part1 = 10 OR key_part2 = 20;

    SELECT * FROM tbl_name
      WHERE (key_part1 = 10 OR key_part2 = 20) AND non_key_part=30;

    SELECT * FROM t1, t2
      WHERE (t1.key1 IN (1,2) OR t1.key2 LIKE 'value%')
      AND t2.key1=t1.some_col;

    SELECT * FROM t1, t2
      WHERE t1.key1=1
      AND (t2.key1=t1.some_col OR t2.key2=t1.some_col2);

The Index Merge method has several access algorithms (seen in the `Extra` field of `EXPLAIN` output):

- `Using intersect(...)`

- `Using union(...)`

- `Using sort_union(...)`

The following sections describe these methods in greater detail.

Note: The Index Merge optimization algorithm has the following known deficiencies:

- If a range scan is possible on some key, an Index Merge is not considered. For example, consider this query:

      SELECT * FROM t1 WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;

  For this query, two plans are possible:

  - An Index Merge scan using the `(goodkey1 < 10 OR goodkey2 < 20)` condition.

  - A range scan using the `badkey < 30` condition.

  However, the optimizer considers only the second plan. If that is not what you want, you can make the optimizer consider Index Merge by using `IGNORE INDEX` or `FORCE INDEX`. The following queries are executed using Index Merge:

      SELECT * FROM t1 FORCE INDEX(goodkey1,goodkey2)
        WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;

      SELECT * FROM t1 IGNORE INDEX(badkey)
        WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;

- If your query has a complex `WHERE` clause with deep `AND`/`OR` nesting and MySQL doesn't choose the optimal plan, try distributing terms using the following identity laws:

      (x AND y) OR z = (x OR z) AND (y OR z)
      (x OR y) AND z = (x AND z) OR (y AND z)

- Index Merge is not applicable to fulltext indexes. We plan to extend it to cover these in a future MySQL release.

The choice between different possible variants of the Index Merge access method and other access methods is based on cost estimates of various available options.

#### 7.2.6.1. The Index Merge Intersection Access Algorithm

This access algorithm can be employed when a `WHERE` clause was converted to several range conditions on different keys combined with `AND`, and each condition is one of the following:

- In this form, where the index has exactly `N` parts (that is, all index parts are covered):

      key_part1=const1 AND key_part2=const2 ... AND key_partN=constN

- Any range condition over a primary key of an `InnoDB` or `BDB` table.

Examples:

    SELECT * FROM innodb_table WHERE primary_key < 10 AND key_col1=20;

    SELECT * FROM tbl_name
      WHERE (key1_part1=1 AND key1_part2=2) AND key2=2;

The Index Merge intersection algorithm performs simultaneous scans on all used indexes and produces the intersection of row sequences that it receives from the merged index scans.

If all columns used in the query are covered by the used indexes, full table rows are not retrieved (`EXPLAIN` output contains `Using index` in `Extra` field in this case). Here is an example of such a query:

    SELECT COUNT(*) FROM t1 WHERE key1=1 AND key2=1;

If the used indexes don't cover all columns used in the query, full rows are retrieved only when the range conditions for all used keys are satisfied.

If one of the merged conditions is a condition over a primary key of an `InnoDB` or `BDB` table, it is not used for row retrieval, but is used to filter out rows retrieved using other conditions.

#### 7.2.6.2. The Index Merge Union Access Algorithm

The applicability criteria for this algorithm are similar to those for the Index Merge method intersection algorithm. The algorithm can be employed when the table's `WHERE` clause was converted to several range conditions on different keys combined with `OR`, and each condition is one of the following:

- In this form, where the index has exactly `N` parts (that is, all index parts are covered):

      key_part1=const1 AND key_part2=const2 ... AND key_partN=constN

- Any range condition over a primary key of an `InnoDB` or `BDB` table.

- A condition for which the Index Merge method intersection algorithm is applicable.

Examples:

    SELECT * FROM t1 WHERE key1=1 OR key2=2 OR key3=3;

    SELECT * FROM innodb_table WHERE (key1=1 AND key2=2) OR
      (key3='foo' AND key4='bar') AND key5=5;

#### 7.2.6.3. The Index Merge Sort-Union Access Algorithm

This access algorithm is employed when the `WHERE` clause was converted to several range conditions combined by `OR`, but for which the Index Merge method union algorithm is not applicable.

Examples:

    SELECT * FROM tbl_name WHERE key_col1 < 10 OR key_col2 < 20;

    SELECT * FROM tbl_name
      WHERE (key_col1 > 10 OR key_col2 = 20) AND nonkey_col=30;

The difference between the sort-union algorithm and the union algorithm is that the sort-union algorithm must first fetch row IDs for all rows and sort them before returning any rows.

### 7.2.7. `IS NULL` Optimization

MySQL can perform the same optimization on `col_name` `IS NULL` that it can use for `col_name` `=` `constant_value`. For example, MySQL can use indexes and ranges to search for `NULL` with `IS NULL`.

Examples:

    SELECT * FROM tbl_name WHERE key_col IS NULL;

    SELECT * FROM tbl_name WHERE key_col <=> NULL;

    SELECT * FROM tbl_name
      WHERE key_col=const1 OR key_col=const2 OR key_col IS NULL;

If a `WHERE` clause includes a `col_name` `IS NULL` condition for a column that is declared as `NOT NULL`, that expression is optimized away. This optimization does not occur in cases when the column might produce `NULL` anyway; for example, if it comes from a table on the right side of a `LEFT JOIN`.

MySQL can also optimize the combination ``col_name` = `expr` AND `col_name` IS NULL`, a form that is common in resolved subqueries. `EXPLAIN` shows `ref_or_null` when this optimization is used.

This optimization can handle one `IS NULL` for any key part.

Some examples of queries that are optimized, assuming that there is an index on columns `a` and `b` of table `t2`:

    SELECT * FROM t1 WHERE t1.a=expr OR t1.a IS NULL;

    SELECT * FROM t1, t2 WHERE t1.a=t2.a OR t2.a IS NULL;

    SELECT * FROM t1, t2
      WHERE (t1.a=t2.a OR t2.a IS NULL) AND t2.b=t1.b;

    SELECT * FROM t1, t2
      WHERE t1.a=t2.a AND (t2.b=t1.b OR t2.b IS NULL);

    SELECT * FROM t1, t2
      WHERE (t1.a=t2.a AND t2.a IS NULL AND ...)
      OR (t1.a=t2.a AND t2.a IS NULL AND ...);

`ref_or_null` works by first doing a read on the reference key, and then a separate search for rows with a `NULL` key value.

Note that the optimization can handle only one `IS NULL` level. In the following query, MySQL uses key lookups only on the expression `(t1.a=t2.a AND t2.a IS NULL)` and is not able to use the key part on `b`:

    SELECT * FROM t1, t2
      WHERE (t1.a=t2.a AND t2.a IS NULL)
      OR (t1.b=t2.b AND t2.b IS NULL);

### 7.2.8. `DISTINCT` Optimization

`DISTINCT` combined with `ORDER BY` needs a temporary table in many cases.

Because `DISTINCT` may use `GROUP BY`, you should be aware of how MySQL works with columns in `ORDER BY` or `HAVING` clauses that are not part of the selected columns. See Section 12.10.3, “`GROUP BY` with Hidden Fields”.

In most cases, a `DISTINCT` clause can be considered as a special case of `GROUP BY`. For example, the following two queries are equivalent:

    SELECT DISTINCT c1, c2, c3 FROM t1 WHERE c1 > const;

    SELECT c1, c2, c3 FROM t1 WHERE c1 > const GROUP BY c1, c2, c3;

Due to this equivalence, the optimizations applicable to `GROUP BY` queries can be also applied to queries with a `DISTINCT` clause. Thus, for more details on the optimization possibilities for `DISTINCT` queries, see Section 7.2.13, “`GROUP BY` Optimization”.

When combining `LIMIT `row_count`` with `DISTINCT`, MySQL stops as soon as it finds `row_count` unique rows.

If you do not use columns from all tables named in a query, MySQL stops scanning any unused tables as soon as it finds the first match. In the following case, assuming that `t1` is used before `t2` (which you can check with `EXPLAIN`), MySQL stops reading from `t2` (for any particular row in `t1`) when it finds the first row in `t2`:

    SELECT DISTINCT t1.a FROM t1, t2 where t1.a=t2.a;

### 7.2.9. `LEFT JOIN` and `RIGHT JOIN` Optimization

MySQL implements an ``A` LEFT JOIN `B` join_condition` as follows:

- Table `B` is set to depend on table `A` and all tables on which `A` depends.

- Table `A` is set to depend on all tables (except `B`) that are used in the `LEFT JOIN` condition.

- The `LEFT JOIN` condition is used to decide how to retrieve rows from table `B`. (In other words, any condition in the `WHERE` clause is not used.)

- All standard join optimizations are performed, with the exception that a table is always read after all tables on which it depends. If there is a circular dependence, MySQL issues an error.

- All standard `WHERE` optimizations are performed.

- If there is a row in `A` that matches the `WHERE` clause, but there is no row in `B` that matches the `ON` condition, an extra `B` row is generated with all columns set to `NULL`.

- If you use `LEFT JOIN` to find rows that do not exist in some table and you have the following test: ``col_name` IS NULL` in the `WHERE` part, where `col_name` is a column that is declared as `NOT NULL`, MySQL stops searching for more rows (for a particular key combination) after it has found one row that matches the `LEFT JOIN` condition.

The implementation of `RIGHT JOIN` is analogous to that of `LEFT JOIN` with the roles of the tables reversed.

The join optimizer calculates the order in which tables should be joined. The table read order forced by `LEFT JOIN` or `STRAIGHT_JOIN` helps the join optimizer do its work much more quickly, because there are fewer table permutations to check. Note that this means that if you do a query of the following type, 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 reverse 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;

For a `LEFT JOIN`, if the `WHERE` condition is always false for the generated `NULL` row, the `LEFT JOIN` is changed to a normal join. For example, the `WHERE` clause would be false in the following query if `t2.column1` were `NULL`:

    SELECT * FROM t1 LEFT JOIN t2 ON (column1) WHERE t2.column2=5;

Therefore, it is safe to convert the query to a normal join:

    SELECT * FROM t1, t2 WHERE t2.column2=5 AND t1.column1=t2.column1;

This can be made faster because MySQL can use table `t2` before table `t1` if doing so would result in a better query plan. To force a specific table order, use `STRAIGHT_JOIN`.

### 7.2.10. Nested Join Optimization

As of MySQL 5.0.1, the syntax for expressing joins allows nested joins. The following discussion refers to the join syntax described in Section 13.2.7.1, “`JOIN` Syntax”.

The syntax of `table_factor` is extended in comparison with the SQL Standard. The latter accepts only `table_reference`, not a list of them inside a pair of parentheses. This is a conservative extension if we consider each comma in a list of `table_reference` items as equivalent to an inner join. For example:

    SELECT * FROM t1 LEFT JOIN (t2, t3, t4)
                     ON (t2.a=t1.a AND t3.b=t1.b AND t4.c=t1.c)

is equivalent to:

    SELECT * FROM t1 LEFT JOIN (t2 CROSS JOIN t3 CROSS JOIN t4)
                     ON (t2.a=t1.a AND t3.b=t1.b AND t4.c=t1.c)

In MySQL, `CROSS JOIN` is a syntactic equivalent to `INNER JOIN` (they can replace each other). In standard SQL, they are not equivalent. `INNER JOIN` is used with an `ON` clause; `CROSS JOIN` is used otherwise.

In versions of MySQL prior to 5.0.1, parentheses in `table_references` were just omitted and all join operations were grouped to the left. In general, parentheses can be ignored in join expressions containing only inner join operations.

After removing parentheses and grouping operations to the left, the join expression:

    t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
       ON t1.a=t2.a

transforms into the expression:

    (t1 LEFT JOIN t2 ON t1.a=t2.a) LEFT JOIN t3
        ON t2.b=t3.b OR t2.b IS NULL

Yet, the two expressions are not equivalent. To see this, suppose that the tables `t1`, `t2`, and `t3` have the following state:

- Table `t1` contains rows `(1)`, `(2)`

- Table `t2` contains row `(1,101)`

- Table `t3` contains row `(101)`

In this case, the first expression returns a result set including the rows `(1,1,101,101)`, `(2,NULL,NULL,NULL)`, whereas the second expression returns the rows `(1,1,101,101)`, `(2,NULL,NULL,101)`:

    mysql> SELECT *
        -> FROM t1
        ->      LEFT JOIN
        ->      (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
        ->      ON t1.a=t2.a;
    +------+------+------+------+
    | a    | a    | b    | b    |
    +------+------+------+------+
    |    1 |    1 |  101 |  101 |
    |    2 | NULL | NULL | NULL |
    +------+------+------+------+

    mysql> SELECT *
        -> FROM (t1 LEFT JOIN t2 ON t1.a=t2.a)
        ->      LEFT JOIN t3
        ->      ON t2.b=t3.b OR t2.b IS NULL;
    +------+------+------+------+
    | a    | a    | b    | b    |
    +------+------+------+------+
    |    1 |    1 |  101 |  101 |
    |    2 | NULL | NULL |  101 |
    +------+------+------+------+

In the following example, an outer join operation is used together with an inner join operation:

    t1 LEFT JOIN (t2, t3) ON t1.a=t2.a

That expression cannot be transformed into the following expression:

    t1 LEFT JOIN t2 ON t1.a=t2.a, t3.

For the given table states, the two expressions return different sets of rows:

    mysql> SELECT *
        -> FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a;
    +------+------+------+------+
    | a    | a    | b    | b    |
    +------+------+------+------+
    |    1 |    1 |  101 |  101 |
    |    2 | NULL | NULL | NULL |
    +------+------+------+------+

    mysql> SELECT *
        -> FROM t1 LEFT JOIN t2 ON t1.a=t2.a, t3;
    +------+------+------+------+
    | a    | a    | b    | b    |
    +------+------+------+------+
    |    1 |    1 |  101 |  101 |
    |    2 | NULL | NULL |  101 |
    +------+------+------+------+

Therefore, if we omit parentheses in a join expression with outer join operators, we might change the result set for the original expression.

More exactly, we cannot ignore parentheses in the right operand of the left outer join operation and in the left operand of a right join operation. In other words, we cannot ignore parentheses for the inner table expressions of outer join operations. Parentheses for the other operand (operand for the outer table) can be ignored.

The following expression:

    (t1,t2) LEFT JOIN t3 ON P(t2.b,t3.b)

is equivalent to this expression:

    t1, t2 LEFT JOIN t3 ON P(t2.b,t3.b)

for any tables `t1,t2,t3` and any condition `P` over attributes `t2.b` and `t3.b`.

Whenever the order of execution of the join operations in a join expression (`join_table`) is not from left to right, we talk about nested joins. Consider the following queries:

    SELECT * FROM t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b) ON t1.a=t2.a
      WHERE t1.a > 1

    SELECT * FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a
      WHERE (t2.b=t3.b OR t2.b IS NULL) AND t1.a > 1

Those queries are considered to contain these nested joins:

    t2 LEFT JOIN t3 ON t2.b=t3.b
    t2, t3

The nested join is formed in the first query with a left join operation, whereas in the second query it is formed with an inner join operation.

In the first query, the parentheses can be omitted: The grammatical structure of the join expression will dictate the same order of execution for join operations. For the second query, the parentheses cannot be omitted, although the join expression here can be interpreted unambiguously without them. (In our extended syntax the parentheses in `(t2, t3)` of the second query are required, although theoretically the query could be parsed without them: We still would have unambiguous syntactical structure for the query because `LEFT JOIN` and `ON` would play the role of the left and right delimiters for the expression `(t2,t3)`.)

The preceding examples demonstrate these points:

- For join expressions involving only inner joins (and not outer joins), parentheses can be removed. You can remove parentheses and evaluate left to right (or, in fact, you can evaluate the tables in any order).

- The same is not true, in general, for outer joins or for outer joins mixed with inner joins. Removal of parentheses may change the result.

Queries with nested outer joins are executed in the same pipeline manner as queries with inner joins. More exactly, a variation of the nested-loop join algorithm is exploited. Recall by what algorithmic schema the nested-loop join executes a query. Suppose that we have a join query over 3 tables `T1,T2,T3` of the form:

    SELECT * FROM T1 INNER JOIN T2 ON P1(T1,T2)
                     INNER JOIN T3 ON P2(T2,T3)
      WHERE P(T1,T2,T3).

Here, `P1(T1,T2)` and `P2(T3,T3)` are some join conditions (on expressions), whereas `P(t1,t2,t3)` is a condition over columns of tables `T1,T2,T3`.

The nested-loop join algorithm would execute this query in the following manner:

    FOR each row t1 in T1 {
      FOR each row t2 in T2 such that P1(t1,t2) {
        FOR each row t3 in T3 such that P2(t2,t3) {
          IF P(t1,t2,t3) {
             t:=t1||t2||t3; OUTPUT t;
          }
        }
      }
    }

The notation `t1||t2||t3` means “a row constructed by concatenating the columns of rows `t1`, `t2`, and `t3`.” In some of the following examples, `NULL` where a row name appears means that `NULL` is used for each column of that row. For example, `t1||t2||NULL` means “a row constructed by concatenating the columns of rows `t1` and `t2`, and `NULL` for each column of `t3`.”

Now let's consider a query with nested outer joins:

    SELECT * FROM T1 LEFT JOIN
                  (T2 LEFT JOIN T3 ON P2(T2,T3))
                  ON P1(T1,T2)
      WHERE P(T1,T2,T3).

For this query, we modify the nested-loop pattern to get:

    FOR each row t1 in T1 {
      BOOL f1:=FALSE;
      FOR each row t2 in T2 such that P1(t1,t2) {
        BOOL f2:=FALSE;
        FOR each row t3 in T3 such that P2(t2,t3) {
          IF P(t1,t2,t3) {
            t:=t1||t2||t3; OUTPUT t;
          }
          f2=TRUE;
          f1=TRUE;
        }
        IF (!f2) {
          IF P(t1,t2,NULL) {
            t:=t1||t2||NULL; OUTPUT t;
          }
          f1=TRUE;
        }
      }
      IF (!f1) {
        IF P(t1,NULL,NULL) {
          t:=t1||NULL||NULL; OUTPUT t;
        }
      }
    }

In general, for any nested loop for the first inner table in an outer join operation, a flag is introduced that is turned off before the loop and is checked after the loop. The flag is turned on when for the current row from the outer table a match from the table representing the inner operand is found. If at the end of the loop cycle the flag is still off, no match has been found for the current row of the outer table. In this case, the row is complemented by `NULL` values for the columns of the inner tables. The result row is passed to the final check for the output or into the next nested loop, but only if the row satisfies the join condition of all embedded outer joins.

In our example, the outer join table expressed by the following expression is embedded:

    (T2 LEFT JOIN T3 ON P2(T2,T3))

Note that for the query with inner joins, the optimizer could choose a different order of nested loops, such as this one:

    FOR each row t3 in T3 {
      FOR each row t2 in T2 such that P2(t2,t3) {
        FOR each row t1 in T1 such that P1(t1,t2) {
          IF P(t1,t2,t3) {
             t:=t1||t2||t3; OUTPUT t;
          }
        }
      }
    }

For the queries with outer joins, the optimizer can choose only such an order where loops for outer tables precede loops for inner tables. Thus, for our query with outer joins, only one nesting order is possible. For the following query, the optimizer will evaluate two different nestings:

    SELECT * T1 LEFT JOIN (T2,T3) ON P1(T1,T2) AND P2(T1,T3)
      WHERE P(T1,T2,T3)

The nestings are these:

    FOR each row t1 in T1 {
      BOOL f1:=FALSE;
      FOR each row t2 in T2 such that P1(t1,t2) {
        FOR each row t3 in T3 such that P2(t1,t3) {
          IF P(t1,t2,t3) {
            t:=t1||t2||t3; OUTPUT t;
          }
          f1:=TRUE
        }
      }
      IF (!f1) {
        IF P(t1,NULL,NULL) {
          t:=t1||NULL||NULL; OUTPUT t;
        }
      }
    }

and:

    FOR each row t1 in T1 {
      BOOL f1:=FALSE;
      FOR each row t3 in T3 such that P2(t1,t3) {
        FOR each row t2 in T2 such that P1(t1,t2) {
          IF P(t1,t2,t3) {
            t:=t1||t2||t3; OUTPUT t;
          }
          f1:=TRUE
        }
      }
      IF (!f1) {
        IF P(t1,NULL,NULL) {
          t:=t1||NULL||NULL; OUTPUT t;
        }
      }
    }

In both nestings, `T1` must be processed in the outer loop because it is used in an outer join. `T2` and `T3` are used in an inner join, so that join must be processed in the inner loop. However, because the join is an inner join, `T2` and `T3` can be processed in either order.

When discussing the nested-loop algorithm for inner joins, we omitted some details whose impact on the performance of query execution may be huge. We did not mention so-called “pushed-down” conditions. Suppose that our `WHERE` condition `P(T1,T2,T3)` can be represented by a conjunctive formula:

    P(T1,T2,T2) = C1(T1) AND C2(T2) AND C3(T3).

In this case, MySQL actually uses the following nested-loop schema for the execution of the query with inner joins:

    FOR each row t1 in T1 such that C1(t1) {
      FOR each row t2 in T2 such that P1(t1,t2) AND C2(t2)  {
        FOR each row t3 in T3 such that P2(t2,t3) AND C3(t3) {
          IF P(t1,t2,t3) {
             t:=t1||t2||t3; OUTPUT t;
          }
        }
      }
    }

You see that each of the conjuncts `C1(T1)`, `C2(T2)`, `C3(T3)` are pushed out of the most inner loop to the most outer loop where it can be evaluated. If `C1(T1)` is a very restrictive condition, this condition pushdown may greatly reduce the number of rows from table `T1` passed to the inner loops. As a result, the execution time for the query may improve immensely.

For a query with outer joins, the `WHERE` condition is to be checked only after it has been found that the current row from the outer table has a match in the inner tables. Thus, the optimization of pushing conditions out of the inner nested loops cannot be applied directly to queries with outer joins. Here we have to introduce conditional pushed-down predicates guarded by the flags that are turned on when a match has been encountered.

For our example with outer joins with:

    P(T1,T2,T3)=C1(T1) AND C(T2) AND C3(T3)

the nested-loop schema using guarded pushed-down conditions looks like this:

    FOR each row t1 in T1 such that C1(t1) {
      BOOL f1:=FALSE;
      FOR each row t2 in T2
          such that P1(t1,t2) AND (f1?C2(t2):TRUE) {
        BOOL f2:=FALSE;
        FOR each row t3 in T3
            such that P2(t2,t3) AND (f1&&f2?C3(t3):TRUE) {
          IF (f1&&f2?TRUE:(C2(t2) AND C3(t3))) {
            t:=t1||t2||t3; OUTPUT t;
          }
          f2=TRUE;
          f1=TRUE;
        }
        IF (!f2) {
          IF (f1?TRUE:C2(t2) && P(t1,t2,NULL)) {
            t:=t1||t2||NULL; OUTPUT t;
          }
          f1=TRUE;
        }
      }
      IF (!f1 && P(t1,NULL,NULL)) {
          t:=t1||NULL||NULL; OUTPUT t;
      }
    }

In general, pushed-down predicates can be extracted from join conditions such as `P1(T1,T2)` and `P(T2,T3)`. In this case, a pushed-down predicate is guarded also by a flag that prevents checking the predicate for the `NULL`-complemented row generated by the corresponding outer join operation.

Note that access by key from one inner table to another in the same nested join is prohibited if it is induced by a predicate from the `WHERE` condition. (We could use conditional key access in this case, but this technique is not employed yet in MySQL 5.0.)

### 7.2.11. Outer Join Simplification

Table expressions in the `FROM` clause of a query are simplified in many cases.

At the parser stage, queries with right outer joins operations are converted to equivalent queries containing only left join operations. In the general case, the conversion is performed according to the following rule:

    (T1, ...) RIGHT JOIN (T2,...) ON P(T1,...,T2,...) =
    (T2, ...) LEFT JOIN (T1,...) ON P(T1,...,T2,...)

All inner join expressions of the form `T1 INNER JOIN T2 ON P(T1,T2)` are replaced by the list `T1,T2`, `P(T1,T2)` being joined as a conjunct to the `WHERE` condition (or to the join condition of the embedding join, if there is any).

When the optimizer evaluates plans for join queries with outer join operation, it takes into consideration only the plans where, for each such operation, the outer tables are accessed before the inner tables. The optimizer options are limited because only such plans enables us to execute queries with outer joins operations by the nested loop schema.

Suppose that we have a query of the form:

    SELECT * T1 LEFT JOIN T2 ON P1(T1,T2)
      WHERE P(T1,T2) AND R(T2)

with `R(T2)` narrowing greatly the number of matching rows from table `T2`. If we executed the query as it is, the optimizer would have no other choice besides to access table `T1` before table `T2` that may lead to a very inefficient execution plan.

Fortunately, MySQL converts such a query into a query without an outer join operation if the `WHERE` condition is null-rejected. A condition is called null-rejected for an outer join operation if it evaluates to `FALSE` or to `UNKNOWN` for any `NULL`-complemented row built for the operation.

Thus, for this outer join:

    T1 LEFT JOIN T2 ON T1.A=T2.A

Conditions such as these are null-rejected:

    T2.B IS NOT NULL,
    T2.B > 3,
    T2.C <= T1.C,
    T2.B < 2 OR T2.C > 1

Conditions such as these are not null-rejected:

    T2.B IS NULL,
    T1.B < 3 OR T2.B IS NOT NULL,
    T1.B < 3 OR T2.B > 3

The general rules for checking whether a condition is null-rejected for an outer join operation are simple. A condition is null-rejected in the following cases:

- If it is of the form `A IS NOT NULL`, where `A` is an attribute of any of the inner tables

- If it is a predicate containing a reference to an inner table that evaluates to `UNKNOWN` when one of its arguments is `NULL`

- If it is a conjunction containing a null-rejected condition as a conjunct

- If it is a disjunction of null-rejected conditions

A condition can be null-rejected for one outer join operation in a query and not null-rejected for another. In the query:

    SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                     LEFT JOIN T3 ON T3.B=T1.B
      WHERE T3.C > 0

the `WHERE` condition is null-rejected for the second outer join operation but is not null-rejected for the first one.

If the `WHERE` condition is null-rejected for an outer join operation in a query, the outer join operation is replaced by an inner join operation.

For example, the preceding query is replaced with the query:

    SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                     INNER JOIN T3 ON T3.B=T1.B
      WHERE T3.C > 0

For the original query, the optimizer would evaluate plans compatible with only one access order `T1,T2,T3`. For the replacing query, it additionally considers the access sequence `T3,T1,T2`.

A conversion of one outer join operation may trigger a conversion of another. Thus, the query:

    SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                     LEFT JOIN T3 ON T3.B=T2.B
      WHERE T3.C > 0

will be first converted to the query:

    SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
                     INNER JOIN T3 ON T3.B=T2.B
      WHERE T3.C > 0

which is equivalent to the query:

    SELECT * FROM (T1 LEFT JOIN T2 ON T2.A=T1.A), T3
      WHERE T3.C > 0 AND T3.B=T2.B

Now the remaining outer join operation can be replaced by an inner join, too, because the condition `T3.B=T2.B` is null-rejected and we get a query without outer joins at all:

    SELECT * FROM (T1 INNER JOIN T2 ON T2.A=T1.A), T3
      WHERE T3.C > 0 AND T3.B=T2.B

Sometimes we succeed in replacing an embedded outer join operation, but cannot convert the embedding outer join. The following query:

    SELECT * FROM T1 LEFT JOIN
                  (T2 LEFT JOIN T3 ON T3.B=T2.B)
                  ON T2.A=T1.A
      WHERE T3.C > 0

is converted to:

    SELECT * FROM T1 LEFT JOIN
                  (T2 INNER JOIN T3 ON T3.B=T2.B)
                  ON T2.A=T1.A
      WHERE T3.C > 0,

That can be rewritten only to the form still containing the embedding outer join operation:

    SELECT * FROM T1 LEFT JOIN
                  (T2,T3)
                  ON (T2.A=T1.A AND T3.B=T2.B)
      WHERE T3.C > 0.

When trying to convert an embedded outer join operation in a query, we must take into account the join condition for the embedding outer join together with the `WHERE` condition. In the query:

    SELECT * FROM T1 LEFT JOIN
                  (T2 LEFT JOIN T3 ON T3.B=T2.B)
                  ON T2.A=T1.A AND T3.C=T1.C
      WHERE T3.D > 0 OR T1.D > 0

the `WHERE` condition is not null-rejected for the embedded outer join, but the join condition of the embedding outer join `T2.A=T1.A AND T3.C=T1.C` is null-rejected. So the query can be converted to:

    SELECT * FROM T1 LEFT JOIN
                  (T2, T3)
                  ON T2.A=T1.A AND T3.C=T1.C AND T3.B=T2.B
      WHERE T3.D > 0 OR T1.D > 0

The algorithm that converts outer join operations into inner joins was implemented in full measure, as it has been described here, in MySQL 5.0.1. MySQL 4.1 performs only some simple conversions.

### 7.2.12. `ORDER BY` Optimization

In some cases, MySQL can use an index to satisfy an `ORDER BY` clause without doing any extra sorting.

The index can also be used even if the `ORDER BY` does not match the index exactly, as long as all of the unused portions of the index and all the extra `ORDER BY` columns are constants in the `WHERE` clause. The following queries use the index to resolve the `ORDER BY` part:

    SELECT * FROM t1
      ORDER BY key_part1,key_part2,... ;

    SELECT * FROM t1
      WHERE key_part1=constant
      ORDER BY key_part2;

    SELECT * FROM t1
      ORDER BY key_part1 DESC, key_part2 DESC;

    SELECT * FROM t1
      WHERE key_part1=1
      ORDER BY key_part1 DESC, key_part2 DESC;

In some cases, MySQL cannot use indexes to resolve the `ORDER BY`, although it still uses indexes to find the rows that match the `WHERE` clause. These cases include the following:

- You use `ORDER BY` on different keys:

      SELECT * FROM t1 ORDER BY key1, key2;

- You use `ORDER BY` on non-consecutive parts of a key:

      SELECT * FROM t1 WHERE key2=constant ORDER BY key_part2;

- You mix `ASC` and `DESC`:

      SELECT * FROM t1 ORDER BY key_part1 DESC, key_part2 ASC;

- The key used to fetch the rows is not the same as the one used in the `ORDER BY`:

      SELECT * FROM t1 WHERE key2=constant ORDER BY key1;

- You are joining many tables, and the columns in the `ORDER BY` are not all from the first non-constant table that is used to retrieve rows. (This is the first table in the `EXPLAIN` output that does not have a `const` join type.)

- You have different `ORDER BY` and `GROUP BY` expressions.

- The type of table index used does not store rows in order. For example, this is true for a `HASH` index in a `MEMORY` table.

With `EXPLAIN SELECT ... ORDER BY`, you can check whether MySQL can use indexes to resolve the query. It cannot if you see `Using filesort` in the `Extra` column. See Section 7.2.1, “Optimizing Queries with `EXPLAIN`”.

A `filesort` optimization is used that records not only the sort key value and row position, but the columns required for the query as well. This avoids reading the rows twice. The `filesort` algorithm works like this:

1. Read the rows that match the `WHERE` clause.

2. For each row, record a tuple of values consisting of the sort key value and row position, and also the columns required for the query.

3. Sort the tuples by sort key value

4. Retrieve the rows in sorted order, but read the required columns directly from the sorted tuples rather than by accessing the table a second time.

This algorithm represents a significant improvement over that used in some older versions of MySQL.

To avoid a slowdown, this optimization is used only if the total size of the extra columns in the sort tuple does not exceed the value of the `max_length_for_sort_data` system variable. (A symptom of setting the value of this variable too high is that you should see high disk activity and low CPU activity.)

If you want to increase `ORDER BY` speed, check whether you can get MySQL to use indexes rather than an extra sorting phase. If this is not possible, you can try the following strategies:

- Increase the size of the `sort_buffer_size` variable.

- Increase the size of the `read_rnd_buffer_size` variable.

- Change `tmpdir` to point to a dedicated filesystem with large amounts of empty space. This option accepts several paths that are used in round-robin fashion. Paths should be separated by colon characters (‘`:`’) on Unix and semicolon characters (‘`;`’) on Windows, NetWare, and OS/2. You can use this feature to spread the load across several directories. Note: The paths should be for directories in filesystems that are located on different physical disks, not different partitions on the same disk.

By default, MySQL sorts all `GROUP BY `col1`, `col2`, ...` queries as if you specified `ORDER BY `col1`, `col2`, ...` in the query as well. If you include an `ORDER BY` clause explicitly that contains the same column list, MySQL optimizes it away without any speed penalty, although the sorting still occurs. If a query includes `GROUP BY` but you want to avoid the overhead of sorting the result, you can suppress sorting by specifying `ORDER BY NULL`. For example:

    INSERT INTO foo
    SELECT a, COUNT(*) FROM bar GROUP BY a ORDER BY NULL;

### 7.2.13. `GROUP BY` Optimization

The most general way to satisfy a `GROUP BY` clause is to scan the whole table and create a new temporary table where all rows from each group are consecutive, and then use this temporary table to discover groups and apply aggregate functions (if any). In some cases, MySQL is able to do much better than that and to avoid creation of temporary tables by using index access.

The most important preconditions for using indexes for `GROUP BY` are that all `GROUP BY` columns reference attributes from the same index, and that the index stores its keys in order (for example, this is a `BTREE` index and not a `HASH` index). Whether use of temporary tables can be replaced by index access also depends on which parts of an index are used in a query, the conditions specified for these parts, and the selected aggregate functions.

There are two ways to execute a `GROUP BY` query via index access, as detailed in the following sections. In the first method, the grouping operation is applied together with all range predicates (if any). The second method first performs a range scan, and then groups the resulting tuples.

#### 7.2.13.1. Loose index scan

The most efficient way to process `GROUP BY` is when the index is used to directly retrieve the group fields. With this access method, MySQL uses the property of some index types that the keys are ordered (for example, `BTREE`). This property enables use of lookup groups in an index without having to consider all keys in the index that satisfy all `WHERE` conditions. This access method considers only a fraction of the keys in an index, so it is called a loose index scan. When there is no `WHERE` clause, a loose index scan reads as many keys as the number of groups, which may be a much smaller number than that of all keys. If the `WHERE` clause contains range predicates (see the discussion of the `range` join type in Section 7.2.1, “Optimizing Queries with `EXPLAIN`”), a loose index scan looks up the first key of each group that satisfies the range conditions, and again reads the least possible number of keys. This is possible under the following conditions:

- The query is over a single table.

- The `GROUP BY` includes the first consecutive parts of the index. (If, instead of `GROUP BY`, the query has a `DISTINCT` clause, all distinct attributes refer to the beginning of the index.)

- The only aggregate functions used (if any) are `MIN()` and `MAX()`, and all of them refer to the same column.

- Any other parts of the index than those from the `GROUP BY` referenced in the query must be constants (that is, they must be referenced in equalities with constants), except for the argument of `MIN()` or `MAX()` functions.

The `EXPLAIN` output for such queries shows `Using index for group-by` in the `Extra` column.

The following queries fall into this category, assuming that there is an index `idx(c1,c2,c3)` on table `t1(c1,c2,c3,c4)`:

    SELECT c1, c2 FROM t1 GROUP BY c1, c2;
    SELECT DISTINCT c1, c2 FROM t1;
    SELECT c1, MIN(c2) FROM t1 GROUP BY c1;
    SELECT c1, c2 FROM t1 WHERE c1 < const GROUP BY c1, c2;
    SELECT MAX(c3), MIN(c3), c1, c2 FROM t1 WHERE c2 > const GROUP BY c1, c2;
    SELECT c2 FROM t1 WHERE c1 < const GROUP BY c1, c2;
    SELECT c1, c2 FROM t1 WHERE c3 = const GROUP BY c1, c2;

The following queries cannot be executed with this quick select method, for the reasons given:

- There are aggregate functions other than `MIN()` or `MAX()`, for example:

      SELECT c1, SUM(c2) FROM t1 GROUP BY c1;

- The fields in the `GROUP BY` clause do not refer to the beginning of the index, as shown here:

      SELECT c1,c2 FROM t1 GROUP BY c2, c3;

- The query refers to a part of a key that comes after the `GROUP BY` part, and for which there is no equality with a constant, an example being:

      SELECT c1,c3 FROM t1 GROUP BY c1, c2;

#### 7.2.13.2. Tight index scan

A tight index scan may be either a full index scan or a range index scan, depending on the query conditions.

When the conditions for a loose index scan are not met, it is still possible to avoid creation of temporary tables for `GROUP BY` queries. If there are range conditions in the `WHERE` clause, this method reads only the keys that satisfy these conditions. Otherwise, it performs an index scan. Because this method reads all keys in each range defined by the `WHERE` clause, or scans the whole index if there are no range conditions, we term it a tight index scan. Notice that with a tight index scan, the grouping operation is performed only after all keys that satisfy the range conditions have been found.

For this method to work, it is sufficient that there is a constant equality condition for all columns in a query referring to parts of the key coming before or in between parts of the `GROUP BY` key. The constants from the equality conditions fill in any “gaps” in the search keys so that it is possible to form complete prefixes of the index. These index prefixes then can be used for index lookups. If we require sorting of the `GROUP BY` result, and it is possible to form search keys that are prefixes of the index, MySQL also avoids extra sorting operations because searching with prefixes in an ordered index already retrieves all the keys in order.

The following queries do not work with the loose index scan access method described earlier, but still work with the tight index scan access method (assuming that there is an index `idx(c1,c2,c3)` on table `t1(c1,c2,c3,c4)`).

- There is a gap in the `GROUP BY`, but it is covered by the condition `c2 = 'a'`:

      SELECT c1, c2, c3 FROM t1 WHERE c2 = 'a' GROUP BY c1, c3;

- The `GROUP BY` does not begin with the first part of the key, but there is a condition that provides a constant for that part:

      SELECT c1, c2, c3 FROM t1 WHERE c1 = 'a' GROUP BY c2, c3;

### 7.2.14. `LIMIT` Optimization

In some cases, MySQL handles a query differently when you are using `LIMIT `row_count`` and not using `HAVING`:

- If you are selecting only a few rows with `LIMIT`, MySQL uses indexes in some cases when normally it would prefer to do a full table scan.

- If you use `LIMIT `row_count`` with `ORDER BY`, MySQL ends the sorting as soon as it has found the first `row_count` rows of the sorted result, rather than sorting the entire result. If ordering is done by using an index, this is very fast. If a filesort must be done, all rows that match the query without the `LIMIT` clause must be selected, and most or all of them must be sorted, before it can be ascertained that the first `row_count` rows have been found. In either case, after the initial rows have been found, there is no need to sort any remainder of the result set, and MySQL does not do so.

- When combining `LIMIT `row_count`` with `DISTINCT`, MySQL stops as soon as it finds `row_count` unique rows.

- In some cases, a `GROUP BY` can be resolved by reading the key in order (or doing a sort on the key) and then calculating summaries until the key value changes. In this case, `LIMIT `row_count`` does not calculate any unnecessary `GROUP BY` values.

- As soon as MySQL has sent the required number of rows to the client, it aborts the query unless you are using `SQL_CALC_FOUND_ROWS`.

- `LIMIT 0` quickly returns an empty set. This can be useful for checking the validity of a query. When using one of the MySQL APIs, it can also be employed for obtaining the types of the result columns. (This trick does not work in the MySQL Monitor (the mysql program), which merely displays `Empty set` in such cases; you should instead use `SHOW COLUMNS` or `DESCRIBE` for this purpose.)

- When the server uses temporary tables to resolve the query, it uses the `LIMIT `row_count`` clause to calculate how much space is required.

### 7.2.15. How to Avoid Table Scans

The output from `EXPLAIN` shows `ALL` in the `type` column when MySQL uses a table scan to resolve a query. This usually happens under the following conditions:

- The table is so small that it is faster to perform a table scan than to bother with a key lookup. This is common for tables with fewer than 10 rows and a short row length.

- There are no usable restrictions in the `ON` or `WHERE` clause for indexed columns.

- You are comparing indexed columns with constant values and MySQL has calculated (based on the index tree) that the constants cover too large a part of the table and that a table scan would be faster. See Section 7.2.4, “`WHERE` Clause Optimization”.

- You are using a key with low cardinality (many rows match the key value) through another column. In this case, MySQL assumes that by using the key it probably will do many key lookups and that a table scan would be faster.

For small tables, a table scan often is appropriate and the performance impact is negligible. For large tables, try the following techniques to avoid having the optimizer incorrectly choose a table scan:

- Use `ANALYZE TABLE `tbl_name`` to update the key distributions for the scanned table. See Section 13.5.2.1, “`ANALYZE TABLE` Syntax”.

- Use `FORCE INDEX` for the scanned table to tell MySQL that table scans are very expensive compared to using the given index:

      SELECT * FROM t1, t2 FORCE INDEX (index_for_column)
        WHERE t1.col_name=t2.col_name;

  See Section 13.2.7, “`SELECT` Syntax”.

- Start mysqld with the `--max-seeks-for-key=1000` option or use `SET max_seeks_for_key=1000` to tell the optimizer to assume that no key scan causes more than 1,000 key seeks. See Section 5.2.2, “Server System Variables”.

### 7.2.16. Speed of `INSERT` Statements

The time required for inserting a row is determined by the following factors, where the numbers indicate approximate proportions:

- Connecting: (3)

- Sending query to server: (2)

- Parsing query: (2)

- Inserting row: (1 × size of row)

- Inserting indexes: (1 × number of indexes)

- Closing: (1)

This does not take into consideration the initial overhead to open tables, which is done once for each concurrently running query.

The size of the table slows down the insertion of indexes by log `N`, assuming B-tree indexes.

You can use the following methods to speed up inserts:

- If you are inserting many rows from the same client at the same time, use `INSERT` statements with multiple `VALUES` lists to insert several rows at a time. This is considerably faster (many times faster in some cases) than using separate single-row `INSERT` statements. If you are adding data to a non-empty table, you can tune the `bulk_insert_buffer_size` variable to make data insertion even faster. See Section 5.2.2, “Server System Variables”.

- If you are inserting a lot of rows from different clients, you can get higher speed by using the `INSERT DELAYED` statement. See Section 13.2.4.2, “`INSERT DELAYED` Syntax”.

- For a `MyISAM` table, you can use concurrent inserts to add rows at the same time that `SELECT` statements are running if there are no deleted rows in middle of the table. See Section 7.3.3, “Concurrent Inserts”.

- When loading a table from a text file, use `LOAD DATA INFILE`. This is usually 20 times faster than using `INSERT` statements. See Section 13.2.5, “`LOAD DATA INFILE` Syntax”.

- With some extra work, it is possible to make `LOAD DATA INFILE` run even faster for a `MyISAM` table when the table has many indexes. Use the following procedure:

  1. Optionally create the table with `CREATE TABLE`.

  2. Execute a `FLUSH TABLES` statement or a mysqladmin flush-tables command.

  3. Use myisamchk --keys-used=0 -rq `/path/to/db/tbl_name`. This removes all use of indexes for the table.

  4. Insert data into the table with `LOAD DATA INFILE`. This does not update any indexes and therefore is very fast.

  5. If you intend only to read from the table in the future, use myisampack to compress it. See Section 14.1.3.3, “Compressed Table Characteristics”.

  6. Re-create the indexes with myisamchk -rq `/path/to/db/tbl_name`. This creates the index tree in memory before writing it to disk, which is much faster that updating the index during `LOAD DATA INFILE` because it avoids lots of disk seeks. The resulting index tree is also perfectly balanced.

  7. Execute a `FLUSH TABLES` statement or a mysqladmin flush-tables command.

  Note that `LOAD DATA INFILE` performs the preceding optimization automatically if the `MyISAM` table into which you insert data is empty. The main difference is that you can let myisamchk allocate much more temporary memory for the index creation than you might want the server to allocate for index re-creation when it executes the `LOAD DATA INFILE` statement.

  You can also disable or enable the indexes for a `MyISAM` table by using the following statements rather than myisamchk. If you use these statements, you can skip the `FLUSH TABLE` operations:

      ALTER TABLE tbl_name DISABLE KEYS;
      ALTER TABLE tbl_name ENABLE KEYS;

- To speed up `INSERT` operations that are performed with multiple statements for non-transactional tables, lock your tables:

      LOCK TABLES a WRITE;
      INSERT INTO a VALUES (1,23),(2,34),(4,33);
      INSERT INTO a VALUES (8,26),(6,29);
      ...
      UNLOCK TABLES;

  This benefits performance because the index buffer is flushed to disk only once, after all `INSERT` statements have completed. Normally, there would be as many index buffer flushes as there are `INSERT` statements. Explicit locking statements are not needed if you can insert all rows with a single `INSERT`.

  To obtain faster insertions, for transactional tables, you should use `START TRANSACTION` and `COMMIT` instead of `LOCK TABLES`.

  Locking also lowers the total time for multiple-connection tests, although the maximum wait time for individual connections might go up because they wait for locks. For example:

  1. Connection 1 does 1000 inserts

  2. Connections 2, 3, and 4 do 1 insert

  3. Connection 5 does 1000 inserts

  If you do not use locking, connections 2, 3, and 4 finish before 1 and 5. If you use locking, connections 2, 3, and 4 probably do not finish before 1 or 5, but the total time should be about 40% faster.

  `INSERT`, `UPDATE`, and `DELETE` operations are very fast in MySQL, but you can obtain better overall performance by adding locks around everything that does more than about five inserts or updates in a row. If you do very many inserts in a row, you could do a `LOCK TABLES` followed by an `UNLOCK TABLES` once in a while (each 1,000 rows or so) to allow other threads access to the table. This would still result in a nice performance gain.

  `INSERT` is still much slower for loading data than `LOAD DATA INFILE`, even when using the strategies just outlined.

- To increase performance for `MyISAM` tables, for both `LOAD DATA INFILE` and `INSERT`, enlarge the key cache by increasing the `key_buffer_size` system variable. See Section 7.5.2, “Tuning Server Parameters”.

### 7.2.17. Speed of `UPDATE` Statements

An update statement is optimized like a `SELECT` query with the additional overhead of a write. The speed of the write depends on the amount of data being updated and the number of indexes that are updated. Indexes that are not changed do not get updated.

Another way to get fast updates is to delay updates and then do many updates in a row later. Performing multiple updates together is much quicker than doing one at a time if you lock the table.

For a `MyISAM` table that uses dynamic row format, updating a row to a longer total length may split the row. If you do this often, it is very important to use `OPTIMIZE TABLE` occasionally. See Section 13.5.2.5, “`OPTIMIZE TABLE` Syntax”.

### 7.2.18. Speed of `DELETE` Statements

The time required to delete individual rows is exactly proportional to the number of indexes. To delete rows more quickly, you can increase the size of the key cache by increasing the `key_buffer_size` system variable. See Section 7.5.2, “Tuning Server Parameters”.

To delete all rows from a table, `TRUNCATE TABLE `tbl_name`` if faster than than `DELETE FROM `tbl_name``. See Section 13.2.9, “`TRUNCATE` Syntax”.

### 7.2.19. Other Optimization Tips

This section lists a number of miscellaneous tips for improving query processing speed:

- Use persistent connections to the database to avoid connection overhead. If you cannot use persistent connections and you are initiating many new connections to the database, you may want to change the value of the `thread_cache_size` variable. See Section 7.5.2, “Tuning Server Parameters”.

- Always check whether all your queries really use the indexes that you have created in the tables. In MySQL, you can do this with the `EXPLAIN` statement. See Section 7.2.1, “Optimizing Queries with `EXPLAIN`”.

- Try to avoid complex `SELECT` queries on `MyISAM` tables that are updated frequently, to avoid problems with table locking that occur due to contention between readers and writers.

- With `MyISAM` tables that have no deleted rows in the middle, you can insert rows at the end at the same time that another query is reading from the table. If it is important to be able to do this, you should consider using the table in ways that avoid deleting rows. Another possibility is to run `OPTIMIZE TABLE` to defragment the table after you have deleted a lot of rows from it. See Section 14.1, “The `MyISAM` Storage Engine”.

- To fix any compression issues that may have occurred with `ARCHIVE` tables, you can use `OPTIMIZE TABLE`. See Section 14.8, “The `ARCHIVE` Storage Engine”.

- Use `ALTER TABLE ... ORDER BY `expr1`, `expr2`, ...` if you usually retrieve rows in ``expr1`, `expr2`, ...` order. By using this option after extensive changes to the table, you may be able to get higher performance.

- In some cases, it may make sense to introduce a column that is “hashed” based on information from other columns. If this column is short and reasonably unique, it may be much faster than a “wide” index on many columns. In MySQL, it is very easy to use this extra column:

      SELECT * FROM tbl_name
        WHERE hash_col=MD5(CONCAT(col1,col2))
        AND col1='constant' AND col2='constant';

- For `MyISAM` tables that change frequently, you should try to avoid all variable-length columns (`VARCHAR`, `BLOB`, and `TEXT`). The table uses dynamic row format if it includes even a single variable-length column. See Chapter 14, Storage Engines and Table Types.

- It is normally not useful to split a table into different tables just because the rows become large. In accessing a row, the biggest performance hit is the disk seek needed to find the first byte of the row. After finding the data, most modern disks can read the entire row fast enough for most applications. The only cases where splitting up a table makes an appreciable difference is if it is a `MyISAM` table using dynamic row format that you can change to a fixed row size, or if you very often need to scan the table but do not need most of the columns. See Chapter 14, Storage Engines and Table Types.

- If you often need to calculate results such as counts based on information from a lot of rows, it may be preferable to introduce a new table and update the counter in real time. An update of the following form is very fast:

      UPDATE tbl_name SET count_col=count_col+1 WHERE key_col=constant;

  This is very important when you use MySQL storage engines such as `MyISAM` that has only table-level locking (multiple readers with single writers). This also gives better performance with most database systems, because the row locking manager in this case has less to do.

- If you need to collect statistics from large log tables, use summary tables instead of scanning the entire log table. Maintaining the summaries should be much faster than trying to calculate statistics “live.” Regenerating new summary tables from the logs when things change (depending on business decisions) is faster than changing the running application.

- If possible, you should classify reports as “live” or as “statistical,” where data needed for statistical reports is created only from summary tables that are generated periodically from the live data.

- Take advantage of the fact that columns have default values. Insert values explicitly only when the value to be inserted differs from the default. This reduces the parsing that MySQL must do and improves the insert speed.

- In some cases, it is convenient to pack and store data into a `BLOB` column. In this case, you must provide code in your application to pack and unpack information, but this may save a lot of accesses at some stage. This is practical when you have data that does not conform well to a rows-and-columns table structure.

- Normally, you should try to keep all data non-redundant (observing what is referred to in database theory as third normal form). However, there may be situations in which it can be advantageous to duplicate information or create summary tables to gain more speed.

- Stored routines or UDFs (user-defined functions) may be a good way to gain performance for some tasks. See Chapter 17, Stored Procedures and Functions, and Section 24.2, “Adding New Functions to MySQL”, for more information.

- You can always gain something by caching queries or answers in your application and then performing many inserts or updates together. If your database system supports table locks (as do MySQL and Oracle), this should help to ensure that the index cache is only flushed once after all updates. You can also take advantage of MySQL's query cache to achieve similar results; see Section 5.14, “The MySQL Query Cache”.

- Use `INSERT DELAYED` when you do not need to know when your data is written. This reduces the overall insertion impact because many rows can be written with a single disk write.

- Use `INSERT LOW_PRIORITY` when you want to give `SELECT` statements higher priority than your inserts.

- Use `SELECT HIGH_PRIORITY` to get retrievals that jump the queue. That is, the `SELECT` is executed even if there is another client waiting to do a write.

- Use multiple-row `INSERT` statements to store many rows with one SQL statement. Many SQL servers support this, including MySQL.

- Use `LOAD DATA INFILE` to load large amounts of data. This is faster than using `INSERT` statements.

- Use `AUTO_INCREMENT` columns to generate unique values.

- Use `OPTIMIZE TABLE` once in a while to avoid fragmentation with dynamic-format `MyISAM` tables. See Section 14.1.3, “`MyISAM` Table Storage Formats”.

- Use `MEMORY` (`HEAP`) tables when possible to get more speed. See Section 14.4, “The `MEMORY` (`HEAP`) Storage Engine”. `MEMORY` tables are useful for non-critical data that is accessed often, such as information about the last displayed banner for users who don't have cookies enabled in their Web browser. User sessions are another alternative available in many Web application environments for handling volatile state data.

- With Web servers, images and other binary assets should normally be stored as files. That is, store only a reference to the file rather than the file itself in the database. Most Web servers are better at caching files than database contents, so using files is generally faster.

- Columns with identical information in different tables should be declared to have identical data types so that joins based on the corresponding columns will be faster.

- Try to keep column names simple. For example, in a table named `customer`, use a column name of `name` instead of `customer_name`. To make your names portable to other SQL servers, you should keep them shorter than 18 characters.

- If you need really high speed, you should take a look at the low-level interfaces for data storage that the different SQL servers support. For example, by accessing the MySQL `MyISAM` storage engine directly, you could get a speed increase of two to five times compared to using the SQL interface. To be able to do this, the data must be on the same server as the application, and usually it should only be accessed by one process (because external file locking is really slow). One could eliminate these problems by introducing low-level `MyISAM` commands in the MySQL server (this could be one easy way to get more performance if needed). By carefully designing the database interface, it should be quite easy to support this type of optimization.

- If you are using numerical data, it is faster in many cases to access information from a database (using a live connection) than to access a text file. Information in the database is likely to be stored in a more compact format than in the text file, so accessing it involves fewer disk accesses. You also save code in your application because you need not parse your text files to find line and column boundaries.

- Replication can provide a performance benefit for some operations. You can distribute client retrievals among replication servers to split up the load. To avoid slowing down the master while making backups, you can make backups using a slave server. See Chapter 6, Replication.

- Declaring a `MyISAM` table with the `DELAY_KEY_WRITE=1` table option makes index updates faster because they are not flushed to disk until the table is closed. The downside is that if something kills the server while such a table is open, you should ensure that the table is okay by running the server with the `--myisam-recover` option, or by running myisamchk before restarting the server. (However, even in this case, you should not lose anything by using `DELAY_KEY_WRITE`, because the key information can always be generated from the data rows.)

## 7.3. Locking Issues

### 7.3.1. Locking Methods

MySQL uses table-level locking for `MyISAM` and `MEMORY` tables, page-level locking for `BDB` tables, and row-level locking for `InnoDB` tables.

In many cases, you can make an educated guess about which locking type is best for an application, but generally it is difficult to say that a given lock type is better than another. Everything depends on the application and different parts of an application may require different lock types.

To decide whether you want to use a storage engine with row-level locking, you should look at what your application does and what mix of select and update statements it uses. For example, most Web applications perform many selects, relatively few deletes, updates based mainly on key values, and inserts into a few specific tables. The base MySQL `MyISAM` setup is very well tuned for this.

Table locking in MySQL is deadlock-free for storage engines that use table-level locking. Deadlock avoidance is managed by always requesting all needed locks at once at the beginning of a query and always locking the tables in the same order.

The table-locking method MySQL uses for `WRITE` locks works as follows:

- If there are no locks on the table, put a write lock on it.

- Otherwise, put the lock request in the write lock queue.

The table-locking method MySQL uses for `READ` locks works as follows:

- If there are no write locks on the table, put a read lock on it.

- Otherwise, put the lock request in the read lock queue.

When a lock is released, the lock is made available to the threads in the write lock queue and then to the threads in the read lock queue. This means that if you have many updates for a table, `SELECT` statements wait until there are no more updates.

You can analyze the table lock contention on your system by checking the `Table_locks_waited` and `Table_locks_immediate` status variables:

    mysql> SHOW STATUS LIKE 'Table%';
    +-----------------------+---------+
    | Variable_name         | Value   |
    +-----------------------+---------+
    | Table_locks_immediate | 1151552 |
    | Table_locks_waited    | 15324   |
    +-----------------------+---------+

If a `MyISAM` table contains no free blocks in the middle, rows always are inserted at the end of the data file. In this case, you can freely mix concurrent `INSERT` and `SELECT` statements for a `MyISAM` table without locks. That is, you can insert rows into a `MyISAM` table at the same time other clients are reading from it. (Holes can result from rows having been deleted from or updated in the middle of the table. If there are holes, concurrent inserts are disabled but are re-enabled automatically when all holes have been filled with new data.)

If you want to perform many `INSERT` and `SELECT` operations on a table when concurrent inserts are not possible, you can insert rows in a temporary table and update the real table with the rows from the temporary table once in a while. This can be done with the following code:

    mysql> LOCK TABLES real_table WRITE, insert_table WRITE;
    mysql> INSERT INTO real_table SELECT * FROM insert_table;
    mysql> TRUNCATE TABLE insert_table;
    mysql> UNLOCK TABLES;

`InnoDB` uses row locks and `BDB` uses page locks. For these two storage engines, deadlocks are possible because they automatically acquire locks during the processing of SQL statements, not at the start of the transaction.

Advantages of row-level locking:

- Fewer lock conflicts when accessing different rows in many threads.

- Fewer changes for rollbacks.

- Possible to lock a single row for a long time.

Disadvantages of row-level locking:

- Requires more memory than page-level or table-level locks.

- Slower than page-level or table-level locks when used on a large part of the table because you must acquire many more locks.

- Definitely much slower than other locks if you often do `GROUP BY` operations on a large part of the data or if you must scan the entire table frequently.

Table locks are superior to page-level or row-level locks in the following cases:

- Most statements for the table are reads.

- A mix of reads and writes, where writes are updates or deletes for a single row that can be fetched with one key read:

      UPDATE tbl_name SET column=value WHERE unique_key_col=key_value;
      DELETE FROM tbl_name WHERE unique_key_col=key_value;

- `SELECT` combined with concurrent `INSERT` statements, and very few `UPDATE` or `DELETE` statements.

- Many scans or `GROUP BY` operations on the entire table without any writers.

With higher-level locks, you can more easily tune applications by supporting locks of different types, because the lock overhead is less than for row-level locks.

Options other than row-level or page-level locking:

- Versioning (such as that used in MySQL for concurrent inserts) where it is possible to have one writer at the same time as many readers. This means that the database or table supports different views for the data depending on when access begins. Other common terms for this are “time travel,” “copy on write,” or “copy on demand.”

- Copy on demand is in many cases superior to page-level or row-level locking. However, in the worst case, it can use much more memory than using normal locks.

- Instead of using row-level locks, you can employ application-level locks, such as `GET_LOCK()` and `RELEASE_LOCK()` in MySQL. These are advisory locks, so they work only in well-behaved applications. (See Section 12.9.4, “Miscellaneous Functions”.)

### 7.3.2. Table Locking Issues

To achieve a very high lock speed, MySQL uses table locking (instead of page, row, or column locking) for all storage engines except `InnoDB` and `BDB`.

For `InnoDB` and `BDB` tables, MySQL uses only table locking if you explicitly lock the table with `LOCK TABLES`. For these storage engines, we recommend that you not use `LOCK TABLES` at all, because `InnoDB` uses automatic row-level locking and `BDB` uses page-level locking to ensure transaction isolation.

For large tables, table locking is much better than row locking for most applications, but there are some pitfalls:

- Table locking enables many threads to read from a table at the same time, but if a thread wants to write to a table, it must first get exclusive access. During the update, all other threads that want to access this particular table must wait until the update is done.

- Table updates normally are considered to be more important than table retrievals, so they are given higher priority. This should ensure that updates to a table are not “starved” even if there is heavy `SELECT` activity for the table.

- Table locking causes problems in cases such as when a thread is waiting because the disk is full and free space needs to become available before the thread can proceed. In this case, all threads that want to access the problem table are also put in a waiting state until more disk space is made available.

Table locking is also disadvantageous under the following scenario:

- A client issues a `SELECT` that takes a long time to run.

- Another client then issues an `UPDATE` on the same table. This client waits until the `SELECT` is finished.

- Another client issues another `SELECT` statement on the same table. Because `UPDATE` has higher priority than `SELECT`, this `SELECT` waits for the `UPDATE` to finish, and for the first `SELECT` to finish.

The following items describe some ways to avoid or reduce contention caused by table locking:

- Try to get the `SELECT` statements to run faster so that they lock tables for a shorter time. You might have to create some summary tables to do this.

- Start mysqld with `--low-priority-updates`. This gives all statements that update (modify) a table lower priority than `SELECT` statements. In this case, the second `SELECT` statement in the preceding scenario would execute before the `UPDATE` statement, and would not need to wait for the first `SELECT` to finish.

- You can specify that all updates issued in a specific connection should be done with low priority by using the `SET LOW_PRIORITY_UPDATES=1` statement. See Section 13.5.3, “`SET` Syntax”.

- You can give a specific `INSERT`, `UPDATE`, or `DELETE` statement lower priority with the `LOW_PRIORITY` attribute.

- You can give a specific `SELECT` statement higher priority with the `HIGH_PRIORITY` attribute. See Section 13.2.7, “`SELECT` Syntax”.

- You can start mysqld with a low value for the `max_write_lock_count` system variable to force MySQL to temporarily elevate the priority of all `SELECT` statements that are waiting for a table after a specific number of inserts to the table occur. This allows `READ` locks after a certain number of `WRITE` locks.

- If you have problems with `INSERT` combined with `SELECT`, you might want to consider switching to `MyISAM` tables, which support concurrent `SELECT` and `INSERT` statements. (See Section 7.3.3, “Concurrent Inserts”.)

- If you mix inserts and deletes on the same table, `INSERT DELAYED` may be of great help. See Section 13.2.4.2, “`INSERT DELAYED` Syntax”.

- If you have problems with mixed `SELECT` and `DELETE` statements, the `LIMIT` option to `DELETE` may help. See Section 13.2.1, “`DELETE` Syntax”.

- Using `SQL_BUFFER_RESULT` with `SELECT` statements can help to make the duration of table locks shorter. See Section 13.2.7, “`SELECT` Syntax”.

- You could change the locking code in `mysys/thr_lock.c` to use a single queue. In this case, write locks and read locks would have the same priority, which might help some applications.

Here are some tips concerning table locks in MySQL:

- Concurrent users are not a problem if you do not mix updates with selects that need to examine many rows in the same table.

- You can use `LOCK TABLES` to increase speed, because many updates within a single lock is much faster than updating without locks. Splitting table contents into separate tables may also help.

- If you encounter speed problems with table locks in MySQL, you may be able to improve performance by converting some of your tables to `InnoDB` or `BDB` tables. See Section 14.2, “The `InnoDB` Storage Engine”, and Section 14.5, “The `BDB` (`BerkeleyDB`) Storage Engine”.

### 7.3.3. Concurrent Inserts

For a `MyISAM` table, you can use concurrent inserts to add rows at the same time that `SELECT` statements are running if there are no deleted rows in middle of the table.

Under circumstances where concurrent inserts can be used, there is seldom any need to use the `DELAYED` modifier for `INSERT` statements. See Section 13.2.4.2, “`INSERT DELAYED` Syntax”.

If you are using the binary log, concurrent inserts are converted to normal inserts for `CREATE ... SELECT` or `INSERT ... SELECT` statements. This is done to ensure that you can re-create an exact copy of your tables by applying the log during a backup operation.

With `LOAD DATA INFILE`, if you specify `CONCURRENT` with a `MyISAM` table that satisfies the condition for concurrent inserts (that is, it contains no free blocks in the middle), other threads can retrieve data from the table while `LOAD DATA` is executing. Using this option affects the performance of `LOAD DATA` a bit, even if no other thread is using the table at the same time.

## 7.4. Optimizing Database Structure

### 7.4.1. Design Choices

MySQL keeps row data and index data in separate files. Many (almost all) other database systems mix row and index data in the same file. We believe that the MySQL choice is better for a very wide range of modern systems.

Another way to store the row data is to keep the information for each column in a separate area (examples are SDBM and Focus). This causes a performance hit for every query that accesses more than one column. Because this degenerates so quickly when more than one column is accessed, we believe that this model is not good for general-purpose databases.

The more common case is that the index and data are stored together (as in Oracle/Sybase, et al). In this case, you find the row information at the leaf page of the index. The good thing with this layout is that it, in many cases, depending on how well the index is cached, saves a disk read. The bad things with this layout are:

- Table scanning is much slower because you have to read through the indexes to get at the data.

- You cannot use only the index table to retrieve data for a query.

- You use more space because you must duplicate indexes from the nodes (you cannot store the row in the nodes).

- Deletes degenerate the table over time (because indexes in nodes are usually not updated on delete).

- It is more difficult to cache only the index data.

### 7.4.2. Make Your Data as Small as Possible

One of the most basic optimizations is to design your tables to take as little space on the disk as possible. This can result in huge improvements because disk reads are faster, and smaller tables normally require less main memory while their contents are being actively processed during query execution. Indexing also is a lesser resource burden if done on smaller columns.

MySQL supports many different storage engines (table types) and row formats. For each table, you can decide which storage and indexing method to use. Choosing the proper table format for your application may give you a big performance gain. See Chapter 14, Storage Engines and Table Types.

You can get better performance for a table and minimize storage space by using the techniques listed here:

- Use the most efficient (smallest) data types possible. MySQL has many specialized types that save disk space and memory. For example, use the smaller integer types if possible to get smaller tables. `MEDIUMINT` is often a better choice than `INT` because a `MEDIUMINT` column uses 25% less space.

- Declare columns to be `NOT NULL` if possible. It makes everything faster and you save one bit per column. If you really need `NULL` in your application, you should definitely use it. Just avoid having it on all columns by default.

- For `MyISAM` tables, if you do not have any variable-length columns (`VARCHAR`, `TEXT`, or `BLOB` columns), a fixed-size row format is used. This is faster but unfortunately may waste some space. See Section 14.1.3, “`MyISAM` Table Storage Formats”. You can hint that you want to have fixed length rows even if you have `VARCHAR` columns with the `CREATE TABLE` option `ROW_FORMAT=FIXED`.

- Starting with MySQL 5.0.3, `InnoDB` tables use a more compact storage format. In earlier versions of MySQL, `InnoDB` rows contain some redundant information, such as the number of columns and the length of each column, even for fixed-size columns. By default, tables are created in the compact format (`ROW_FORMAT=COMPACT`). If you wish to downgrade to older versions of MySQL, you can request the old format with `ROW_FORMAT=REDUNDANT`.

  The compact `InnoDB` format also changes how `CHAR` columns containing UTF-8 data are stored. With `ROW_FORMAT=REDUNDANT`, a UTF-8 `CHAR(`N`)` occupies 3 × `N` bytes, given that the maximum length of a UTF-8 encoded character is three bytes. Many languages can be written primarily using single-byte UTF-8 characters, so a fixed storage length often wastes space. With `ROW_FORMAT=COMPACT` format, `InnoDB` allocates a variable amount of storage in the range from `N` to 3 × `N` bytes for these columns by stripping trailing spaces if necessary. The minimum storage length is kept as `N` bytes to facilitate in-place updates in typical cases.

- The primary index of a table should be as short as possible. This makes identification of each row easy and efficient.

- Create only the indexes that you really need. Indexes are good for retrieval but bad when you need to store data quickly. If you access a table mostly by searching on a combination of columns, create an index on them. The first part of the index should be the column most used. If you always use many columns when selecting from the table, you should use the column with more duplicates first to obtain better compression of the index.

- If it is very likely that a string column has a unique prefix on the first number of characters, it's better to index only this prefix, using MySQL's support for creating an index on the leftmost part of the column (see Section 13.1.4, “`CREATE INDEX` Syntax”). Shorter indexes are faster, not only because they require less disk space, but because they give also you more hits in the index cache, and thus fewer disk seeks. See Section 7.5.2, “Tuning Server Parameters”.

- In some circumstances, it can be beneficial to split into two a table that is scanned very often. This is especially true if it is a dynamic-format table and it is possible to use a smaller static format table that can be used to find the relevant rows when scanning the table.

### 7.4.3. Column Indexes

All MySQL data types can be indexed. Use of indexes on the relevant columns is the best way to improve the performance of `SELECT` operations.

The maximum number of indexes per table and the maximum index length is defined per storage engine. See Chapter 14, Storage Engines and Table Types. All storage engines support at least 16 indexes per table and a total index length of at least 256 bytes. Most storage engines have higher limits.

With ``col_name`(`N`)` syntax in an index specification, you can create an index that uses only the first `N` characters of a string column. Indexing only a prefix of column values in this way can make the index file much smaller. When you index a `BLOB` or `TEXT` column, you must specify a prefix length for the index. For example:

    CREATE TABLE test (blob_col BLOB, INDEX(blob_col(10)));

Prefixes can be up to 1000 bytes long (767 bytes for `InnoDB` tables). Note that prefix limits are measured in bytes, whereas the prefix length in `CREATE TABLE` statements is interpreted as number of characters. Be sure to take this into account when specifying a prefix length for a column that uses a multi-byte character set.

You can also create `FULLTEXT` indexes. These are used for full-text searches. Only the `MyISAM` storage engine supports `FULLTEXT` indexes and only for `CHAR`, `VARCHAR`, and `TEXT` columns. Indexing always takes place over the entire column and partial (column prefix) indexing is not supported. For details, see Section 12.7, “Full-Text Search Functions”.

You can also create indexes on spatial data types. Currently, only `MyISAM` supports R-tree indexes on spatial types. As of MySQL 5.0.16, other storage engines use B-trees for indexing spatial types (except for `ARCHIVE` and `NDBCLUSTER`, which do not support spatial type indexing).

The `MEMORY` storage engine uses `HASH` indexes by default, but also supports `BTREE` indexes.

### 7.4.4. Multiple-Column Indexes

MySQL can create composite indexes (that is, indexes on multiple columns). An index may consist of up to 15 columns. For certain data types, you can index a prefix of the column (see Section 7.4.3, “Column Indexes”).

A multiple-column index can be considered a sorted array containing values that are created by concatenating the values of the indexed columns.

MySQL uses multiple-column indexes in such a way that queries are fast when you specify a known quantity for the first column of the index in a `WHERE` clause, even if you do not specify values for the other columns.

Suppose that a table has the following specification:

    CREATE TABLE test (
        id         INT NOT NULL,
        last_name  CHAR(30) NOT NULL,
        first_name CHAR(30) NOT NULL,
        PRIMARY KEY (id),
        INDEX name (last_name,first_name)
    );

The `name` index is an index over the `last_name` and `first_name` columns. The index can be used for queries that specify values in a known range for `last_name`, or for both `last_name` and `first_name`. Therefore, the `name` index is used in the following queries:

    SELECT * FROM test WHERE last_name='Widenius';

    SELECT * FROM test
      WHERE last_name='Widenius' AND first_name='Michael';

    SELECT * FROM test
      WHERE last_name='Widenius'
      AND (first_name='Michael' OR first_name='Monty');

    SELECT * FROM test
      WHERE last_name='Widenius'
      AND first_name >='M' AND first_name < 'N';

However, the `name` index is not used in the following queries:

    SELECT * FROM test WHERE first_name='Michael';

    SELECT * FROM test
      WHERE last_name='Widenius' OR first_name='Michael';

The manner in which MySQL uses indexes to improve query performance is discussed further in Section 7.4.5, “How MySQL Uses Indexes”.

### 7.4.5. How MySQL Uses Indexes

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. If a table has 1,000 rows, this is at least 100 times faster than reading sequentially. If you need to access most of the rows, it is faster to read sequentially, because this minimizes disk seeks.

Most MySQL indexes (`PRIMARY KEY`, `UNIQUE`, `INDEX`, and `FULLTEXT`) are stored in B-trees. Exceptions are that indexes on spatial data types use R-trees, and that `MEMORY` tables also support hash indexes.

Strings are automatically prefix- and end-space compressed. See Section 13.1.4, “`CREATE INDEX` Syntax”.

In general, indexes are used as described in the following discussion. Characteristics specific to hash indexes (as used in `MEMORY` tables) are described at the end of this section.

MySQL uses indexes for these operations:

- To find the rows matching a `WHERE` clause quickly.

- To eliminate rows from consideration. If there is a choice between multiple indexes, MySQL normally uses the index that finds the smallest number of rows.

- To retrieve rows from other tables when performing joins.

- To find the `MIN()` or `MAX()` value for a specific indexed column `key_col`. This is optimized by a preprocessor that checks whether you are using `WHERE `key_part_N` = `constant`` on all key parts that occur before `key_col` in the index. In this case, MySQL does a single key lookup for each `MIN()` or `MAX()` expression and replaces it with a constant. If all expressions are replaced with constants, the query returns at once. For example:

      SELECT MIN(key_part2),MAX(key_part2)
        FROM tbl_name WHERE key_part1=10;

- To sort or group a table if the sorting or grouping is done on a leftmost prefix of a usable key (for example, `ORDER BY `key_part1`, `key_part2``). If all key parts are followed by `DESC`, the key is read in reverse order. See Section 7.2.12, “`ORDER BY` Optimization”.

- In some cases, a query can be optimized to retrieve values without consulting the data rows. If a query uses only columns from a table that are numeric and that form a leftmost prefix for some key, the selected values may be retrieved from the index tree for greater speed:

      SELECT key_part3 FROM tbl_name
        WHERE key_part1=1

Suppose that you issue the following `SELECT` statement:

    mysql> SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

If a multiple-column index exists on `col1` and `col2`, the appropriate rows can be fetched directly. If separate single-column indexes exist on `col1` and `col2`, the optimizer tries to find the most restrictive index by deciding which index finds fewer rows and using that index to fetch the rows.

If the table has a multiple-column index, any leftmost prefix of the index can be used by the optimizer to find rows. For example, if you have a three-column index on `(col1, col2, col3)`, you have indexed search capabilities on `(col1)`, `(col1, col2)`, and `(col1, col2, col3)`.

MySQL cannot use a partial index if the columns do not form a leftmost prefix of the index. Suppose that you have the `SELECT` statements shown here:

    SELECT * FROM tbl_name WHERE col1=val1;
    SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

    SELECT * FROM tbl_name WHERE col2=val2;
    SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;

If an index exists on `(col1, col2, col3)`, only the first two queries use the index. The third and fourth queries do involve indexed columns, but `(col2)` and `(col2, col3)` are not leftmost prefixes of `(col1, col2, col3)`.

A B-tree index can be used for column comparisons in expressions that use the `=`, `>`, `>=`, `<`, `<=`, or `BETWEEN` operators. The index also can be used for `LIKE` comparisons if the argument to `LIKE` is a constant string that does not start with a wildcard character. For example, the following `SELECT` statements use indexes:

    SELECT * FROM tbl_name WHERE key_col LIKE 'Patrick%';
    SELECT * FROM tbl_name WHERE key_col LIKE 'Pat%_ck%';

In the first statement, only rows with `'Patrick' <= `key_col` < 'Patricl'` are considered. In the second statement, only rows with `'Pat' <= `key_col` < 'Pau'` are considered.

The following `SELECT` statements do not use indexes:

    SELECT * FROM tbl_name WHERE key_col LIKE '%Patrick%';
    SELECT * FROM tbl_name WHERE key_col LIKE other_col;

In the first statement, the `LIKE` value begins with a wildcard character. In the second statement, the `LIKE` value is not a constant.

If you use `... LIKE '%`string`%'` and `string` is longer than three characters, MySQL uses the Turbo Boyer-Moore algorithm to initialize the pattern for the string and then uses this pattern to perform the search more quickly.

A search using ``col_name` IS NULL` employs indexes if `col_name` is indexed.

Any index that does not span all `AND` levels in the `WHERE` clause is not used to optimize the query. In other words, to be able to use an index, a prefix of the index must be used in every `AND` group.

The following `WHERE` clauses use indexes:

    ... WHERE index_part1=1 AND index_part2=2 AND other_column=3
        /* index = 1 OR index = 2 */
    ... WHERE index=1 OR A=10 AND index=2
        /* optimized like "index_part1='hello'" */
    ... WHERE index_part1='hello' AND index_part3=5
        /* Can use index on index1 but not on index2 or index3 */
    ... WHERE index1=1 AND index2=2 OR index1=3 AND index3=3;

These `WHERE` clauses do not use indexes:

        /* index_part1 is not used */
    ... WHERE index_part2=1 AND index_part3=2

        /*  Index is not used in both parts of the WHERE clause  */
    ... WHERE index=1 OR A=10

        /* No index spans all rows  */
    ... WHERE index_part1=1 OR index_part2=10

Sometimes MySQL does not use an index, even if one is available. One circumstance under which this occurs is when the optimizer estimates that using the index would require MySQL to access a very large percentage of the rows in the table. (In this case, a table scan is likely to be much faster because it requires fewer seeks.) However, if such a query uses `LIMIT` to retrieve only some of the rows, MySQL uses an index anyway, because it can much more quickly find the few rows to return in the result.

Hash indexes have somewhat different characteristics from those just discussed:

- They are used only for equality comparisons that use the `=` or `<=>` operators (but are very fast). They are not used for comparison operators such as `<` that find a range of values.

- The optimizer cannot use a hash index to speed up `ORDER BY` operations. (This type of index cannot be used to search for the next entry in order.)

- MySQL cannot determine approximately how many rows there are between two values (this is used by the range optimizer to decide which index to use). This may affect some queries if you change a `MyISAM` table to a hash-indexed `MEMORY` table.

- Only whole keys can be used to search for a row. (With a B-tree index, any leftmost prefix of the key can be used to find rows.)

### 7.4.6. The `MyISAM` Key Cache

To minimize disk I/O, the `MyISAM` storage engine exploits a strategy that is used by many database management systems. It employs a cache mechanism to keep the most frequently accessed table blocks in memory:

- For index blocks, a special structure called the key cache (or key buffer) is maintained. The structure contains a number of block buffers where the most-used index blocks are placed.

- For data blocks, MySQL uses no special cache. Instead it relies on the native operating system filesystem cache.

This section first describes the basic operation of the `MyISAM` key cache. Then it discusses features that improve key cache performance and that enable you to better control cache operation:

- Access to the key cache no longer is serialized among threads. Multiple threads can access the cache concurrently.

- You can set up multiple key caches and assign table indexes to specific caches.

To control the size of the key cache, use the `key_buffer_size` system variable. If this variable is set equal to zero, no key cache is used. The key cache also is not used if the `key_buffer_size` value is too small to allocate the minimal number of block buffers (8).

When the key cache is not operational, index files are accessed using only the native filesystem buffering provided by the operating system. (In other words, table index blocks are accessed using the same strategy as that employed for table data blocks.)

An index block is a contiguous unit of access to the `MyISAM` index files. Usually the size of an index block is equal to the size of nodes of the index B-tree. (Indexes are represented on disk using a B-tree data structure. Nodes at the bottom of the tree are leaf nodes. Nodes above the leaf nodes are non-leaf nodes.)

All block buffers in a key cache structure are the same size. This size can be equal to, greater than, or less than the size of a table index block. Usually one these two values is a multiple of the other.

When data from any table index block must be accessed, the server first checks whether it is available in some block buffer of the key cache. If it is, the server accesses data in the key cache rather than on disk. That is, it reads from the cache or writes into it rather than reading from or writing to disk. Otherwise, the server chooses a cache block buffer containing a different table index block (or blocks) and replaces the data there by a copy of required table index block. As soon as the new index block is in the cache, the index data can be accessed.

If it happens that a block selected for replacement has been modified, the block is considered “dirty.” In this case, prior to being replaced, its contents are flushed to the table index from which it came.

Usually the server follows an LRU (Least Recently Used) strategy: When choosing a block for replacement, it selects the least recently used index block. To make this choice easier, the key cache module maintains a special queue (LRU chain) of all used blocks. When a block is accessed, it is placed at the end of the queue. When blocks need to be replaced, blocks at the beginning of the queue are the least recently used and become the first candidates for eviction.

#### 7.4.6.1. Shared Key Cache Access

Threads can access key cache buffers simultaneously, subject to the following conditions:

- A buffer that is not being updated can be accessed by multiple threads.

- A buffer that is being updated causes threads that need to use it to wait until the update is complete.

- Multiple threads can initiate requests that result in cache block replacements, as long as they do not interfere with each other (that is, as long as they need different index blocks, and thus cause different cache blocks to be replaced).

Shared access to the key cache enables the server to improve throughput significantly.

#### 7.4.6.2. Multiple Key Caches

Shared access to the key cache improves performance but does not eliminate contention among threads entirely. They still compete for control structures that manage access to the key cache buffers. To reduce key cache access contention further, MySQL also provides multiple key caches. This feature enables you to assign different table indexes to different key caches.

Where there are multiple key caches, the server must know which cache to use when processing queries for a given `MyISAM` table. By default, all `MyISAM` table indexes are cached in the default key cache. To assign table indexes to a specific key cache, use the `CACHE INDEX` statement (see Section 13.5.5.1, “`CACHE INDEX` Syntax”). For example, the following statement assigns indexes from the tables `t1`, `t2`, and `t3` to the key cache named `hot_cache`:

    mysql> CACHE INDEX t1, t2, t3 IN hot_cache;
    +---------+--------------------+----------+----------+
    | Table   | Op                 | Msg_type | Msg_text |
    +---------+--------------------+----------+----------+
    | test.t1 | assign_to_keycache | status   | OK       |
    | test.t2 | assign_to_keycache | status   | OK       |
    | test.t3 | assign_to_keycache | status   | OK       |
    +---------+--------------------+----------+----------+

The key cache referred to in a `CACHE INDEX` statement can be created by setting its size with a `SET GLOBAL` parameter setting statement or by using server startup options. For example:

    mysql> SET GLOBAL keycache1.key_buffer_size=128*1024;

To destroy a key cache, set its size to zero:

    mysql> SET GLOBAL keycache1.key_buffer_size=0;

Note that you cannot destroy the default key cache. Any attempt to do this will be ignored:

    mysql> SET GLOBAL key_buffer_size = 0;

    mysql> SHOW VARIABLES LIKE 'key_buffer_size';
    +-----------------+---------+
    | Variable_name   | Value   |
    +-----------------+---------+
    | key_buffer_size | 8384512 |
    +-----------------+---------+

Key cache variables are structured system variables that have a name and components. For `keycache1.key_buffer_size`, `keycache1` is the cache variable name and `key_buffer_size` is the cache component. See Section 5.2.3.1, “Structured System Variables”, for a description of the syntax used for referring to structured key cache system variables.

By default, table indexes are assigned to the main (default) key cache created at the server startup. When a key cache is destroyed, all indexes assigned to it are reassigned to the default key cache.

For a busy server, we recommend a strategy that uses three key caches:

- A “hot” key cache that takes up 20% of the space allocated for all key caches. Use this for tables that are heavily used for searches but that are not updated.

- A “cold” key cache that takes up 20% of the space allocated for all key caches. Use this cache for medium-sized, intensively modified tables, such as temporary tables.

- A “warm” key cache that takes up 60% of the key cache space. Employ this as the default key cache, to be used by default for all other tables.

One reason the use of three key caches is beneficial is that access to one key cache structure does not block access to the others. Statements that access tables assigned to one cache do not compete with statements that access tables assigned to another cache. Performance gains occur for other reasons as well:

- The hot cache is used only for retrieval queries, so its contents are never modified. Consequently, whenever an index block needs to be pulled in from disk, the contents of the cache block chosen for replacement need not be flushed first.

- For an index assigned to the hot cache, if there are no queries requiring an index scan, there is a high probability that the index blocks corresponding to non-leaf nodes of the index B-tree remain in the cache.

- An update operation most frequently executed for temporary tables is performed much faster when the updated node is in the cache and need not be read in from disk first. If the size of the indexes of the temporary tables are comparable with the size of cold key cache, the probability is very high that the updated node is in the cache.

`CACHE INDEX` sets up an association between a table and a key cache, but the association is lost each time the server restarts. If you want the association to take effect each time the server starts, one way to accomplish this is to use an option file: Include variable settings that configure your key caches, and an `init-file` option that names a file containing `CACHE INDEX` statements to be executed. For example:

    key_buffer_size = 4G
    hot_cache.key_buffer_size = 2G
    cold_cache.key_buffer_size = 2G
    init_file=/path/to/data-directory/mysqld_init.sql

The statements in `mysqld_init.sql` are executed each time the server starts. The file should contain one SQL statement per line. The following example assigns several tables each to `hot_cache` and `cold_cache`:

    CACHE INDEX db1.t1, db1.t2, db2.t3 IN hot_cache
    CACHE INDEX db1.t4, db2.t5, db2.t6 IN cold_cache

#### 7.4.6.3. Midpoint Insertion Strategy

By default, the key cache management system uses the LRU strategy for choosing key cache blocks to be evicted, but it also supports a more sophisticated method called the midpoint insertion strategy.

When using the midpoint insertion strategy, the LRU chain is divided into two parts: a hot sub-chain and a warm sub-chain. The division point between two parts is not fixed, but the key cache management system takes care that the warm part is not “too short,” always containing at least `key_cache_division_limit` percent of the key cache blocks. `key_cache_division_limit` is a component of structured key cache variables, so its value is a parameter that can be set per cache.

When an index block is read from a table into the key cache, it is placed at the end of the warm sub-chain. After a certain number of hits (accesses of the block), it is promoted to the hot sub-chain. At present, the number of hits required to promote a block (3) is the same for all index blocks.

A block promoted into the hot sub-chain is placed at the end of the chain. The block then circulates within this sub-chain. If the block stays at the beginning of the sub-chain for a long enough time, it is demoted to the warm chain. This time is determined by the value of the `key_cache_age_threshold` component of the key cache.

The threshold value prescribes that, for a key cache containing `N` blocks, the block at the beginning of the hot sub-chain not accessed within the last ``N` × key_cache_age_threshold / 100` hits is to be moved to the beginning of the warm sub-chain. It then becomes the first candidate for eviction, because blocks for replacement always are taken from the beginning of the warm sub-chain.

The midpoint insertion strategy allows you to keep more-valued blocks always in the cache. If you prefer to use the plain LRU strategy, leave the `key_cache_division_limit` value set to its default of 100.

The midpoint insertion strategy helps to improve performance when execution of a query that requires an index scan effectively pushes out of the cache all the index blocks corresponding to valuable high-level B-tree nodes. To avoid this, you must use a midpoint insertion strategy with the `key_cache_division_limit` set to much less than 100. Then valuable frequently hit nodes are preserved in the hot sub-chain during an index scan operation as well.

#### 7.4.6.4. Index Preloading

If there are enough blocks in a key cache to hold blocks of an entire index, or at least the blocks corresponding to its non-leaf nodes, it makes sense to preload the key cache with index blocks before starting to use it. Preloading allows you to put the table index blocks into a key cache buffer in the most efficient way: by reading the index blocks from disk sequentially.

Without preloading, the blocks are still placed into the key cache as needed by queries. Although the blocks will stay in the cache, because there are enough buffers for all of them, they are fetched from disk in random order, and not sequentially.

To preload an index into a cache, use the `LOAD INDEX INTO CACHE` statement. For example, the following statement preloads nodes (index blocks) of indexes of the tables `t1` and `t2`:

    mysql> LOAD INDEX INTO CACHE t1, t2 IGNORE LEAVES;
    +---------+--------------+----------+----------+
    | Table   | Op           | Msg_type | Msg_text |
    +---------+--------------+----------+----------+
    | test.t1 | preload_keys | status   | OK       |
    | test.t2 | preload_keys | status   | OK       |
    +---------+--------------+----------+----------+

The `IGNORE LEAVES` modifier causes only blocks for the non-leaf nodes of the index to be preloaded. Thus, the statement shown preloads all index blocks from `t1`, but only blocks for the non-leaf nodes from `t2`.

If an index has been assigned to a key cache using a `CACHE INDEX` statement, preloading places index blocks into that cache. Otherwise, the index is loaded into the default key cache.

#### 7.4.6.5. Key Cache Block Size

It is possible to specify the size of the block buffers for an individual key cache using the `key_cache_block_size` variable. This permits tuning of the performance of I/O operations for index files.

The best performance for I/O operations is achieved when the size of read buffers is equal to the size of the native operating system I/O buffers. But setting the size of key nodes equal to the size of the I/O buffer does not always ensure the best overall performance. When reading the big leaf nodes, the server pulls in a lot of unnecessary data, effectively preventing reading other leaf nodes.

Currently, you cannot control the size of the index blocks in a table. This size is set by the server when the `.MYI` index file is created, depending on the size of the keys in the indexes present in the table definition. In most cases, it is set equal to the I/O buffer size.

#### 7.4.6.6. Restructuring a Key Cache

A key cache can be restructured at any time by updating its parameter values. For example:

    mysql> SET GLOBAL cold_cache.key_buffer_size=4*1024*1024;

If you assign to either the `key_buffer_size` or `key_cache_block_size` key cache component a value that differs from the component's current value, the server destroys the cache's old structure and creates a new one based on the new values. If the cache contains any dirty blocks, the server saves them to disk before destroying and re-creating the cache. Restructuring does not occur if you change other key cache parameters.

When restructuring a key cache, the server first flushes the contents of any dirty buffers to disk. After that, the cache contents become unavailable. However, restructuring does not block queries that need to use indexes assigned to the cache. Instead, the server directly accesses the table indexes using native filesystem caching. Filesystem caching is not as efficient as using a key cache, so although queries execute, a slowdown can be anticipated. After the cache has been restructured, it becomes available again for caching indexes assigned to it, and the use of filesystem caching for the indexes ceases.

### 7.4.7. `MyISAM` Index Statistics Collection

Storage engines collect statistics about tables for use by the optimizer. Table statistics are based on value groups, where a value group is a set of rows with the same key prefix value. For optimizer purposes, an important statistic is the average value group size.

MySQL uses the average value group size in the following ways:

- To estimate how may rows must be read for each `ref` access

- To estimate how many row a partial join will produce; that is, the number of rows that an operation of this form will produce:

      (...) JOIN tbl_name ON tbl_name.key = expr

As the average value group size for an index increases, the index is less useful for those two purposes because the average number of rows per lookup increases: For the index to be good for optimization purposes, it is best that each index value target a small number of rows in the table. When a given index value yields a large number of rows, the index is less useful and MySQL is less likely to use it.

The average value group size is related to table cardinality, which is the number of value groups. The `SHOW INDEX` statement displays a cardinality value based on `N`/`S`, where `N` is the number of rows in the table and `S` is the average value group size. That ratio yields an approximate number of value groups in the table.

For a join based on the `<=>` comparison operator, `NULL` is not treated differently from any other value: `NULL <=> NULL`, just as ``N` <=> `N`` for any other `N`.

However, for a join based on the `=` operator, `NULL` is different from non-`NULL` values: ``expr1` = `expr2`` is not true when `expr1` or `expr2` (or both) are `NULL`. This affects `ref` accesses for comparisons of the form ``tbl_name.key` = `expr``: MySQL will not access the table if the current value of `expr` is `NULL`, because the comparison cannot be true.

For `=` comparisons, it does not matter how many `NULL` values are in the table. For optimization purposes, the relevant value is the average size of the non-`NULL` value groups. However, MySQL does not currently allow that average size to be collected or used.

For `MyISAM` tables, you have some control over collection of table statistics by means of the `myisam_stats_method` system variable. This variable has two possible values, which differ as follows:

- When `myisam_stats_method` is `nulls_equal`, all `NULL` values are treated as identical (that is, they all form a single value group).

  If the `NULL` value group size is much higher than the average non-`NULL` value group size, this method skews the average value group size upward. This makes index appear to the optimizer to be less useful than it really is for joins that look for non-`NULL` values. Consequently, the `nulls_equal` method may cause the optimizer not to use the index for `ref` accesses when it should.

- When `myisam_stats_method` is `nulls_unequal`, `NULL` values are not considered the same. Instead, each `NULL` value forms a separate value group of size 1.

  If you have many `NULL` values, this method skews the average value group size downward. If the average non-`NULL` value group size is large, counting `NULL` values each as a group of size 1 causes the optimizer to overestimate the value of the index for joins that look for non-`NULL` values. Consequently, the `nulls_unequal` method may cause the optimizer to use this index for `ref` lookups when other methods may be better.

If you tend to use many joins that use `<=>` rather than `=`, `NULL` values are not special in comparisons and one `NULL` is equal to another. In this case, `nulls_equal` is the appropriate statistics method.

The `myisam_stats_method` system variable has global and session values. Setting the global value affects `MyISAM` statistics collection for all `MyISAM` tables. Setting the session value affects statistics collection only for the current client connection. This means that you can force a table's statistics to be regenerated with a given method without affecting other clients by setting the session value of `myisam_stats_method`.

To regenerate table statistics, you can use any of the following methods:

- Set `myisam_stats_method`, and then issue a `CHECK TABLE` statement

- Execute myisamchk --stats_method=`method_name` --analyze

- Change the table to cause its statistics to go out of date (for example, insert a row and then delete it), and then set `myisam_stats_method` and issue an `ANALYZE TABLE` statement

Some caveats regarding the use of `myisam_stats_method`:

- You can force table statistics to be collected explicitly, as just described. However, MySQL may also collect statistics automatically. For example, if during the course of executing statements for a table, some of those statements modify the table, MySQL may collect statistics. (This may occur for bulk inserts or deletes, or some `ALTER TABLE` statements, for example.) If this happens, the statistics are collected using whatever value `myisam_stats_method` has at the time. Thus, if you collect statistics using one method, but `myisam_stats_method` is set to the other method when a table's statistics are collected automatically later, the other method will be used.

- There is no way to tell which method was used to generate statistics for a given `MyISAM` table.

- `myisam_stats_method` applies only to `MyISAM` tables. Other storage engines have only one method for collecting table statistics. Usually it is closer to the `nulls_equal` method.

### 7.4.8. How MySQL Opens and Closes Tables

When you execute a mysqladmin status command, you should see something like this:

    Uptime: 426 Running threads: 1 Questions: 11082
    Reloads: 1 Open tables: 12

The `Open tables` value of 12 can be somewhat puzzling if you have only six tables.

MySQL is multi-threaded, so there may be many clients issuing queries for a given table simultaneously. To minimize the problem with multiple client threads having different states on the same table, the table is opened independently by each concurrent thread. This uses additional memory but normally increases performance. With `MyISAM` tables, one extra file descriptor is required for the data file for each client that has the table open. (By contrast, the index file descriptor is shared between all threads.)

The `table_cache`, `max_connections`, and `max_tmp_tables` system variables affect the maximum number of files the server keeps open. If you increase one or more of these values, you may run up against a limit imposed by your operating system on the per-process number of open file descriptors. Many operating systems allow you to increase the open-files limit, although the method varies widely from system to system. Consult your operating system documentation to determine whether it is possible to increase the limit and how to do so.

`table_cache` is related to `max_connections`. For example, for 200 concurrent running connections, you should have a table cache size of at least `200 × `N``, where `N` is the maximum number of tables per join in any of the queries which you execute. You must also reserve some extra file descriptors for temporary tables and files.

Make sure that your operating system can handle the number of open file descriptors implied by the `table_cache` setting. If `table_cache` is set too high, MySQL may run out of file descriptors and refuse connections, fail to perform queries, and be very unreliable. You also have to take into account that the `MyISAM` storage engine needs two file descriptors for each unique open table. You can increase the number of file descriptors available to MySQL using the `--open-files-limit` startup option to mysqld. See Section A.2.17, “File Not Found”.

The cache of open tables is kept at a level of `table_cache` entries. The default value is 64; this can be changed with the `--table_cache` option to mysqld. Note that MySQL may temporarily open more tables than this to execute queries.

MySQL closes an unused table and removes it from the table cache under the following circumstances:

- When the cache is full and a thread tries to open a table that is not in the cache.

- When the cache contains more than `table_cache` entries and a table in the cache is no longer being used by any threads.

- When a table flushing operation occurs. This happens when someone issues a `FLUSH TABLES` statement or executes a mysqladmin flush-tables or mysqladmin refresh command.

When the table cache fills up, the server uses the following procedure to locate a cache entry to use:

- Tables that are not currently in use are released, beginning with the table least recently used.

- If a new table needs to be opened, but the cache is full and no tables can be released, the cache is temporarily extended as necessary.

When the cache is in a temporarily extended state and a table goes from a used to unused state, the table is closed and released from the cache.

A table is opened for each concurrent access. This means the table needs to be opened twice if two threads access the same table or if a thread accesses the table twice in the same query (for example, by joining the table to itself). Each concurrent open requires an entry in the table cache. The first open of any `MyISAM` table takes two file descriptors: one for the data file and one for the index file. Each additional use of the table takes only one file descriptor for the data file. The index file descriptor is shared among all threads.

If you are opening a table with the `HANDLER `tbl_name` OPEN` statement, a dedicated table object is allocated for the thread. This table object is not shared by other threads and is not closed until the thread calls `HANDLER `tbl_name` CLOSE` or the thread terminates. When this happens, the table is put back in the table cache (if the cache is not full). See Section 13.2.3, “`HANDLER` Syntax”.

You can determine whether your table cache is too small by checking the mysqld status variable `Opened_tables`:

    mysql> SHOW STATUS LIKE 'Opened_tables';
    +---------------+-------+
    | Variable_name | Value |
    +---------------+-------+
    | Opened_tables | 2741  |
    +---------------+-------+

If the value is very large, even when you have not issued many `FLUSH TABLES` statements, you should increase the table cache size. See Section 5.2.2, “Server System Variables”, and Section 5.2.4, “Server Status Variables”.

### 7.4.9. Drawbacks to Creating Many Tables in the Same Database

If you have many `MyISAM` tables in the same database directory, open, close, and create operations are slow. If you execute `SELECT` statements on many different tables, there is a little overhead when the table cache is full, because for every table that has to be opened, another must be closed. You can reduce this overhead by making the table cache larger.

## 7.5. Optimizing the MySQL Server

### 7.5.1. System Factors and Startup Parameter Tuning

We start with system-level factors, because some of these decisions must be made very early to achieve large performance gains. In other cases, a quick look at this section may suffice. However, it is always nice to have a sense of how much can be gained by changing factors that apply at this level.

The operating system to use is very important. To get the best use of multiple-CPU machines, you should use Solaris (because its threads implementation works well) or Linux (because the 2.4 and later kernels have good SMP support). Note that older Linux kernels have a 2GB filesize limit by default. If you have such a kernel and a need for files larger than 2GB, you should get the Large File Support (LFS) patch for the ext2 filesystem. Other filesystems such as ReiserFS and XFS do not have this 2GB limitation.

Before using MySQL in production, we advise you to test it on your intended platform.

Other tips:

- If you have enough RAM, you could remove all swap devices. Some operating systems use a swap device in some contexts even if you have free memory.

- Avoid external locking. Since MySQL 4.0, the default has been for external locking to be disabled on all systems. The `--external-locking` and `--skip-external-locking` options explicitly enable and disable external locking.

  Note that disabling external locking does not affect MySQL's functionality as long as you run only one server. Just remember to take down the server (or lock and flush the relevant tables) before you run myisamchk. On some systems it is mandatory to disable external locking because it does not work, anyway.

  The only case in which you cannot disable external locking is when you run multiple MySQL servers (not clients) on the same data, or if you run myisamchk to check (not repair) a table without telling the server to flush and lock the tables first. Note that using multiple MySQL servers to access the same data concurrently is generally not recommended, except when using MySQL Cluster.

  The `LOCK TABLES` and `UNLOCK TABLES` statements use internal locking, so you can use them even if external locking is disabled.

### 7.5.2. Tuning Server Parameters

You can determine the default buffer sizes used by the mysqld server using this command:

    shell> mysqld --verbose --help

This command produces a list of all mysqld options and configurable system variables. The output includes the default variable values and looks something like this:

    back_log                          50
    binlog_cache_size                 32768
    bulk_insert_buffer_size           8388608
    connect_timeout                   5
    date_format                       (No default value)
    datetime_format                   (No default value)
    default_week_format               0
    delayed_insert_limit              100
    delayed_insert_timeout            300
    delayed_queue_size                1000
    expire_logs_days                  0
    flush_time                        1800
    ft_max_word_len                   84
    ft_min_word_len                   4
    ft_query_expansion_limit          20
    ft_stopword_file                  (No default value)
    group_concat_max_len              1024
    innodb_additional_mem_pool_size   1048576
    innodb_autoextend_increment       8
    innodb_buffer_pool_awe_mem_mb     0
    innodb_buffer_pool_size           8388608
    innodb_concurrency_tickets        500
    innodb_file_io_threads            4
    innodb_force_recovery             0
    innodb_lock_wait_timeout          50
    innodb_log_buffer_size            1048576
    innodb_log_file_size              5242880
    innodb_log_files_in_group         2
    innodb_mirrored_log_groups        1
    innodb_open_files                 300
    innodb_sync_spin_loops            20
    innodb_thread_concurrency         8
    innodb_thread_sleep_delay         10000
    interactive_timeout               28800
    join_buffer_size                  131072
    key_buffer_size                   8388600
    key_cache_age_threshold           300
    key_cache_block_size              1024
    key_cache_division_limit          100
    long_query_time                   10
    lower_case_table_names            1
    max_allowed_packet                1048576
    max_binlog_cache_size             4294967295
    max_binlog_size                   1073741824
    max_connect_errors                10
    max_connections                   100
    max_delayed_threads               20
    max_error_count                   64
    max_heap_table_size               16777216
    max_join_size                     4294967295
    max_length_for_sort_data          1024
    max_relay_log_size                0
    max_seeks_for_key                 4294967295
    max_sort_length                   1024
    max_tmp_tables                    32
    max_user_connections              0
    max_write_lock_count              4294967295
    multi_range_count                 256
    myisam_block_size                 1024
    myisam_data_pointer_size          6
    myisam_max_extra_sort_file_size   2147483648
    myisam_max_sort_file_size         2147483647
    myisam_repair_threads             1
    myisam_sort_buffer_size           8388608
    net_buffer_length                 16384
    net_read_timeout                  30
    net_retry_count                   10
    net_write_timeout                 60
    open_files_limit                  0
    optimizer_prune_level             1
    optimizer_search_depth            62
    preload_buffer_size               32768
    query_alloc_block_size            8192
    query_cache_limit                 1048576
    query_cache_min_res_unit          4096
    query_cache_size                  0
    query_cache_type                  1
    query_cache_wlock_invalidate      FALSE
    query_prealloc_size               8192
    range_alloc_block_size            2048
    read_buffer_size                  131072
    read_only                         FALSE
    read_rnd_buffer_size              262144
    div_precision_increment           4
    record_buffer                     131072
    relay_log_purge                   TRUE
    relay_log_space_limit             0
    slave_compressed_protocol         FALSE
    slave_net_timeout                 3600
    slave_transaction_retries         10
    slow_launch_time                  2
    sort_buffer_size                  2097144
    sync-binlog                       0
    sync-frm                          TRUE
    sync-replication                  0
    sync-replication-slave-id         0
    sync-replication-timeout          10
    table_cache                       64
    thread_cache_size                 0
    thread_concurrency                10
    thread_stack                      196608
    time_format                       (No default value)
    tmp_table_size                    33554432
    transaction_alloc_block_size      8192
    transaction_prealloc_size         4096
    updatable_views_with_limit        1
    wait_timeout                      28800

For a mysqld server that is currently running, you can see the current values of its system variables by connecting to it and issuing this statement:

    mysql> SHOW VARIABLES;

You can also see some statistical and status indicators for a running server by issuing this statement:

    mysql> SHOW STATUS;

System variable and status information also can be obtained using mysqladmin:

    shell> mysqladmin variables
    shell> mysqladmin extended-status

For a full description of all system and status variables, see Section 5.2.2, “Server System Variables”, and Section 5.2.4, “Server Status Variables”.

MySQL uses algorithms that are very scalable, so you can usually run with very little memory. However, normally you get better performance by giving MySQL more memory.

When tuning a MySQL server, the two most important variables to configure are `key_buffer_size` and `table_cache`. You should first feel confident that you have these set appropriately before trying to change any other variables.

The following examples indicate some typical variable values for different runtime configurations.

- If you have at least 256MB of memory and many tables and want maximum performance with a moderate number of clients, you should use something like this:

      shell> mysqld_safe --key_buffer_size=64M --table_cache=256 \
                 --sort_buffer_size=4M --read_buffer_size=1M &

- If you have only 128MB of memory and only a few tables, but you still do a lot of sorting, you can use something like this:

      shell> mysqld_safe --key_buffer_size=16M --sort_buffer_size=1M

  If there are very many simultaneous connections, swapping problems may occur unless mysqld has been configured to use very little memory for each connection. mysqld performs better if you have enough memory for all connections.

- With little memory and lots of connections, use something like this:

      shell> mysqld_safe --key_buffer_size=512K --sort_buffer_size=100K \
                 --read_buffer_size=100K &

  Or even this:

      shell> mysqld_safe --key_buffer_size=512K --sort_buffer_size=16K \
                 --table_cache=32 --read_buffer_size=8K \
                 --net_buffer_length=1K &

If you are performing `GROUP BY` or `ORDER BY` operations on tables that are much larger than your available memory, you should increase the value of `read_rnd_buffer_size` to speed up the reading of rows following sorting operations.

When you have installed MySQL, the `support-files` directory contains some different `my.cnf` sample files: `my-huge.cnf`, `my-large.cnf`, `my-medium.cnf`, and `my-small.cnf`. You can use these as a basis for optimizing your system. (On Windows, look in the MySQL installation directory.)

If you specify an option on the command line for mysqld or mysqld_safe, it remains in effect only for that invocation of the server. To use the option every time the server runs, put it in an option file.

To see the effects of a parameter change, do something like this:

    shell> mysqld --key_buffer_size=32M --verbose --help

The variable values are listed near the end of the output. Make sure that the `--verbose` and `--help` options are last. Otherwise, the effect of any options listed after them on the command line are not reflected in the output.

For information on tuning the `InnoDB` storage engine, see Section 14.2.11, “`InnoDB` Performance Tuning Tips”.

### 7.5.3. Controlling Query Optimizer Performance

The task of the query optimizer is to find an optimal plan for executing an SQL query. Because the difference in performance between “good” and “bad” plans can be orders of magnitude (that is, seconds versus hours or even days), most query optimizers, including that of MySQL, perform a more or less exhaustive search for an optimal plan among all possible query evaluation plans. For join queries, the number of possible plans investigated by the MySQL optimizer grows exponentially with the number of tables referenced in a query. For small numbers of tables (typically less than 7–10) this is not a problem. However, when larger queries are submitted, the time spent in query optimization may easily become the major bottleneck in the server's performance.

MySQL 5.0.1 introduces a more flexible method for query optimization that allows the user to control how exhaustive the optimizer is in its search for an optimal query evaluation plan. The general idea is that the fewer plans that are investigated by the optimizer, the less time it spends in compiling a query. On the other hand, because the optimizer skips some plans, it may miss finding an optimal plan.

The behavior of the optimizer with respect to the number of plans it evaluates can be controlled via two system variables:

- The `optimizer_prune_level` variable tells the optimizer to skip certain plans based on estimates of the number of rows accessed for each table. Our experience shows that this kind of “educated guess” rarely misses optimal plans, and may dramatically reduce query compilation times. That is why this option is on (`optimizer_prune_level=1`) by default. However, if you believe that the optimizer missed a better query plan, this option can be switched off (`optimizer_prune_level=0`) with the risk that query compilation may take much longer. Note that, even with the use of this heuristic, the optimizer still explores a roughly exponential number of plans.

- The `optimizer_search_depth` variable tells how far into the “future” of each incomplete plan the optimizer should look to evaluate whether it should be expanded further. Smaller values of `optimizer_search_depth` may result in orders of magnitude smaller query compilation times. For example, queries with 12, 13, or more tables may easily require hours and even days to compile if `optimizer_search_depth` is close to the number of tables in the query. At the same time, if compiled with `optimizer_search_depth` equal to 3 or 4, the optimizer may compile in less than a minute for the same query. If you are unsure of what a reasonable value is for `optimizer_search_depth`, this variable can be set to 0 to tell the optimizer to determine the value automatically.

### 7.5.4. How Compiling and Linking Affects the Speed of MySQL

Most of the following tests were performed on Linux with the MySQL benchmarks, but they should give some indication for other operating systems and workloads.

You obtain the fastest executables when you link with `-static`.

On Linux, it is best to compile the server with pgcc and `-O3`. You need about 200MB memory to compile `sql_yacc.cc` with these options, because gcc or pgcc needs a great deal of memory to make all functions inline. You should also set `CXX=gcc` when configuring MySQL to avoid inclusion of the `libstdc++` library, which is not needed. Note that with some versions of pgcc, the resulting binary runs only on true Pentium processors, even if you use the compiler option indicating that you want the resulting code to work on all x586-type processors (such as AMD).

By using a better compiler and compilation options, you can obtain a 10–30% speed increase in applications. This is particularly important if you compile the MySQL server yourself.

When we tested both the Cygnus CodeFusion and Fujitsu compilers, neither was sufficiently bug-free to allow MySQL to be compiled with optimizations enabled.

The standard MySQL binary distributions are compiled with support for all character sets. When you compile MySQL yourself, you should include support only for the character sets that you are going to use. This is controlled by the `--with-charset` option to configure.

Here is a list of some measurements that we have made:

- If you use pgcc and compile everything with `-O6`, the mysqld server is 1% faster than with gcc 2.95.2.

- If you link dynamically (without `-static`), the result is 13% slower on Linux. Note that you still can use a dynamically linked MySQL library for your client applications. It is the server that is most critical for performance.

- For a connection from a client to a server running on the same host, if you connect using TCP/IP rather than a Unix socket file, performance is 7.5% slower. (On Unix, if you connect to the hostname `localhost`, MySQL uses a socket file by default.)

- For TCP/IP connections from a client to a server, connecting to a remote server on another host is 8–11% slower than connecting to a server on the same host, even for connections over 100Mb/s Ethernet.

- When running our benchmark tests using secure connections (all data encrypted with internal SSL support) performance was 55% slower than with unencrypted connections.

- If you compile with `--with-debug=full`, most queries are 20% slower. Some queries may take substantially longer; for example, the MySQL benchmarks run 35% slower. If you use `--with-debug` (without `=full`), the speed decrease is only 15%. For a version of mysqld that has been compiled with `--with-debug=full`, you can disable memory checking at runtime by starting it with the `--skip-safemalloc` option. The execution speed should then be close to that obtained when configuring with `--with-debug`.

- On a Sun UltraSPARC-IIe, a server compiled with Forte 5.0 is 4% faster than one compiled with gcc 3.2.

- On a Sun UltraSPARC-IIe, a server compiled with Forte 5.0 is 4% faster in 32-bit mode than in 64-bit mode.

- Compiling with gcc 2.95.2 for UltraSPARC with the `-mcpu=v8 -Wa,-xarch=v8plusa` options gives 4% more performance.

- On Solaris 2.5.1, MIT-pthreads is 8–12% slower than Solaris native threads on a single processor. With greater loads or more CPUs, the difference should be larger.

- Compiling on Linux-x86 using gcc without frame pointers (`-fomit-frame-pointer` or `-fomit-frame-pointer -ffixed-ebp`) makes mysqld 1–4% faster.

Binary MySQL distributions for Linux that are provided by MySQL AB used to be compiled with pgcc. We had to go back to regular gcc due to a bug in pgcc that would generate binaries that do not run on AMD. We will continue using gcc until that bug is resolved. In the meantime, if you have a non-AMD machine, you can build a faster binary by compiling with pgcc. The standard MySQL Linux binary is linked statically to make it faster and more portable.

### 7.5.5. How MySQL Uses Memory

The following list indicates some of the ways that the mysqld server uses memory. Where applicable, the name of the system variable relevant to the memory use is given:

- The key buffer (variable `key_buffer_size`) is shared by all threads; other buffers used by the server are allocated as needed. See Section 7.5.2, “Tuning Server Parameters”.

- Each connection uses some thread-specific space:

  - A stack (default 192KB, variable `thread_stack`)

  - A connection buffer (variable `net_buffer_length`)

  - A result buffer (variable `net_buffer_length`)

  The connection buffer and result buffer are dynamically enlarged up to `max_allowed_packet` when needed. While a query is running, a copy of the current query string is also allocated.

- All threads share the same base memory.

- When a thread is no longer needed, the memory allocated to it is released and returned to the system unless the thread goes back into the thread cache. In that case, the memory remains allocated.

- Only compressed `MyISAM` tables are memory mapped. This is because the 32-bit memory space of 4GB is not large enough for most big tables. When systems with a 64-bit address space become more common, we may add general support for memory mapping.

- Each request that performs a sequential scan of a table allocates a read buffer (variable `read_buffer_size`).

- When reading rows in an arbitrary sequence (for example, following a sort), a random-read buffer (variable `read_rnd_buffer_size`) may be allocated in order to avoid disk seeks.

- All joins are executed in a single pass, and most joins can be done without even using a temporary table. Most temporary tables are memory-based hash tables. Temporary tables with a large row length (calculated as the sum of all column lengths) or that contain `BLOB` columns are stored on disk.

  If an internal heap table exceeds the size of `tmp_table_size`, MySQL handles this automatically by changing the in-memory heap table to a disk-based `MyISAM` table as necessary. You can also increase the temporary table size by setting the `tmp_table_size` option to mysqld, or by setting the SQL option `SQL_BIG_TABLES` in the client program. See Section 13.5.3, “`SET` Syntax”.

- Most requests that perform a sort allocate a sort buffer and zero to two temporary files depending on the result set size. See Section A.4.4, “Where MySQL Stores Temporary Files”.

- Almost all parsing and calculating is done in a local memory store. No memory overhead is needed for small items, so the normal slow memory allocation and freeing is avoided. Memory is allocated only for unexpectedly large strings. This is done with `malloc()` and `free()`.

- For each `MyISAM` table that is opened, the index file is opened once; the data file is opened once for each concurrently running thread. For each concurrent thread, a table structure, column structures for each column, and a buffer of size `3 × `N`` are allocated (where `N` is the maximum row length, not counting `BLOB` columns). A `BLOB` column requires five to eight bytes plus the length of the `BLOB` data. The `MyISAM` storage engine maintains one extra row buffer for internal use.

- For each table having `BLOB` columns, a buffer is enlarged dynamically to read in larger `BLOB` values. If you scan a table, a buffer as large as the largest `BLOB` value is allocated.

- Handler structures for all in-use tables are saved in a cache and managed as a FIFO. By default, the cache has 64 entries. If a table has been used by two running threads at the same time, the cache contains two entries for the table. See Section 7.4.8, “How MySQL Opens and Closes Tables”.

- A `FLUSH TABLES` statement or mysqladmin flush-tables command closes all tables that are not in use at once and marks all in-use tables to be closed when the currently executing thread finishes. This effectively frees most in-use memory. `FLUSH TABLES` does not return until all tables have been closed.

ps and other system status programs may report that mysqld uses a lot of memory. This may be caused by thread stacks on different memory addresses. For example, the Solaris version of ps counts the unused memory between stacks as used memory. You can verify this by checking available swap with `swap -s`. We test mysqld with several memory-leakage detectors (both commercial and Open Source), so there should be no memory leaks.

### 7.5.6. How MySQL Uses DNS

When a new client connects to mysqld, mysqld spawns a new thread to handle the request. This thread first checks whether the hostname is in the hostname cache. If not, the thread attempts to resolve the hostname:

- If the operating system supports the thread-safe `gethostbyaddr_r()` and `gethostbyname_r()` calls, the thread uses them to perform hostname resolution.

- If the operating system does not support the thread-safe calls, the thread locks a mutex and calls `gethostbyaddr()` and `gethostbyname()` instead. In this case, no other thread can resolve hostnames that are not in the hostname cache until the first thread unlocks the mutex.

You can disable DNS hostname lookups by starting mysqld with the `--skip-name-resolve` option. However, in this case, you can use only IP numbers in the MySQL grant tables.

If you have a very slow DNS and many hosts, you can get more performance by either disabling DNS lookups with `--skip-name-resolve` or by increasing the `HOST_CACHE_SIZE` define (default value: 128) and recompiling mysqld.

You can disable the hostname cache by starting the server with the `--skip-host-cache` option. To clear the hostname cache, issue a `FLUSH HOSTS` statement or execute the mysqladmin flush-hosts command.

To disallow TCP/IP connections entirely, start mysqld with the `--skip-networking` option.

## 7.6. Disk Issues

- Disk seeks are a huge performance bottleneck. This problem becomes more apparent when the amount of data starts to grow so large that effective caching becomes impossible. For large databases where you access data more or less randomly, you can be sure that you need at least one disk seek to read and a couple of disk seeks to write things. To minimize this problem, use disks with low seek times.

- Increase the number of available disk spindles (and thereby reduce the seek overhead) by either symlinking files to different disks or striping the disks:

  - Using symbolic links

    This means that, for `MyISAM` tables, you symlink the index file and data files from their usual location in the data directory to another disk (that may also be striped). This makes both the seek and read times better, assuming that the disk is not used for other purposes as well. See Section 7.6.1, “Using Symbolic Links”.

  - Striping

    Striping means that you have many disks and put the first block on the first disk, the second block on the second disk, and the `N`-th block on the (``N` MOD `number_of_disks``) disk, and so on. This means if your normal data size is less than the stripe size (or perfectly aligned), you get much better performance. Striping is very dependent on the operating system and the stripe size, so benchmark your application with different stripe sizes. See Section 7.1.5, “Using Your Own Benchmarks”.

    The speed difference for striping is very dependent on the parameters. Depending on how you set the striping parameters and number of disks, you may get differences measured in orders of magnitude. You have to choose to optimize for random or sequential access.

- For reliability, you may want to use RAID 0+1 (striping plus mirroring), but in this case, you need 2 × `N` drives to hold `N` drives of data. This is probably the best option if you have the money for it. However, you may also have to invest in some volume-management software to handle it efficiently.

- A good option is to vary the RAID level according to how critical a type of data is. For example, store semi-important data that can be regenerated on a RAID 0 disk, but store really important data such as host information and logs on a RAID 0+1 or RAID `N` disk. RAID `N` can be a problem if you have many writes, due to the time required to update the parity bits.

- On Linux, you can get much more performance by using `hdparm` to configure your disk's interface. (Up to 100% under load is not uncommon.) The following `hdparm` options should be quite good for MySQL, and probably for many other applications:

      hdparm -m 16 -d 1

  Note that performance and reliability when using this command depend on your hardware, so we strongly suggest that you test your system thoroughly after using `hdparm`. Please consult the `hdparm` manual page for more information. If `hdparm` is not used wisely, filesystem corruption may result, so back up everything before experimenting!

- You can also set the parameters for the filesystem that the database uses:

  If you do not need to know when files were last accessed (which is not really useful on a database server), you can mount your filesystems with the `-o noatime` option. That skips updates to the last access time in inodes on the filesystem, which avoids some disk seeks.

  On many operating systems, you can set a filesystem to be updated asynchronously by mounting it with the `-o async` option. If your computer is reasonably stable, this should give you more performance without sacrificing too much reliability. (This flag is on by default on Linux.)

### 7.6.1. Using Symbolic Links

You can move tables and databases from the database directory to other locations and replace them with symbolic links to the new locations. You might want to do this, for example, to move a database to a file system with more free space or increase the speed of your system by spreading your tables to different disk.

The recommended way to do this is simply to symlink databases to a different disk. Symlink tables only as a last resort.

#### 7.6.1.1. Using Symbolic Links for Databases on Unix

On Unix, the way to symlink a database is first to create a directory on some disk where you have free space and then to create a symlink to it from the MySQL data directory.

    shell> mkdir /dr1/databases/test
    shell> ln -s /dr1/databases/test /path/to/datadir

MySQL does not support linking one directory to multiple databases. Replacing a database directory with a symbolic link works as long as you do not make a symbolic link between databases. Suppose that you have a database `db1` under the MySQL data directory, and then make a symlink `db2` that points to `db1`:

    shell> cd /path/to/datadir
    shell> ln -s db1 db2

The result is that, or any table `tbl_a` in `db1`, there also appears to be a table `tbl_a` in `db2`. If one client updates `db1.tbl_a` and another client updates `db2.tbl_a`, problems are likely to occur.

However, if you really need to do this, it is possible by altering the source file `mysys/my_symlink.c`, in which you should look for the following statement:

    if (!(MyFlags & MY_RESOLVE_LINK) ||
        (!lstat(filename,&stat_buff) && S_ISLNK(stat_buff.st_mode)))

Change the statement to this:

    if (1)

#### 7.6.1.2. Using Symbolic Links for Tables on Unix

You should not symlink tables on systems that do not have a fully operational `realpath()` call. (Linux and Solaris support `realpath()`). You can check whether your system supports symbolic links by issuing a `SHOW VARIABLES LIKE 'have_symlink'` statement.

Symlinks are fully supported only for `MyISAM` tables. For files used by tables for other storage engines, you may get strange problems if you try to use symbolic links.

The handling of symbolic links for `MyISAM` tables works as follows:

- In the data directory, you always have the table format (`.frm`) file, the data (`.MYD`) file, and the index (`.MYI`) file. The data file and index file can be moved elsewhere and replaced in the data directory by symlinks. The format file cannot.

- You can symlink the data file and the index file independently to different directories.

- You can instruct a running MySQL server to perform the symlinking by using the `DATA DIRECTORY` and `INDEX DIRECTORY` options to `CREATE TABLE`. See Section 13.1.5, “`CREATE TABLE` Syntax”. Alternatively, symlinking can be accomplished manually from the command line using `ln -s` if mysqld is not running.

- myisamchk does not replace a symlink with the data file or index file. It works directly on the file to which the symlink points. Any temporary files are created in the directory where the data file or index file is located.

- Note: When you drop a table that is using symlinks, both the symlink and the file to which the symlink points are dropped. This is an extremely good reason why you should not run mysqld as the system `root` or allow system users to have write access to MySQL database directories.

- If you rename a table with `ALTER TABLE ... RENAME` and you do not move the table to another database, the symlinks in the database directory are renamed to the new names and the data file and index file are renamed accordingly.

- If you use `ALTER TABLE ... RENAME` to move a table to another database, the table is moved to the other database directory. The old symlinks and the files to which they pointed are deleted. In other words, the new table is not symlinked.

- If you are not using symlinks, you should use the `--skip-symbolic-links` option to mysqld to ensure that no one can use mysqld to drop or rename a file outside of the data directory.

Table symlink operations that are not yet supported:

- `ALTER TABLE` ignores the `DATA DIRECTORY` and `INDEX DIRECTORY` table options.

- `BACKUP TABLE` and `RESTORE TABLE` do not respect symbolic links.

- The `.frm` file must never be a symbolic link (as indicated previously, only the data and index files can be symbolic links). Attempting to do this (for example, to make synonyms) produces incorrect results. Suppose that you have a database `db1` under the MySQL data directory, a table `tbl1` in this database, and in the `db1` directory you make a symlink `tbl2` that points to `tbl1`:

      shell> cd /path/to/datadir/db1
      shell> ln -s tbl1.frm tbl2.frm
      shell> ln -s tbl1.MYD tbl2.MYD
      shell> ln -s tbl1.MYI tbl2.MYI

  Problems result if one thread reads `db1.tbl1` and another thread updates `db1.tbl2`:

  - The query cache is “fooled” (it has no way of knowing that `tbl1` has not been updated, so it returns outdated results).

  - `ALTER` statements on `tbl2` fail.

#### 7.6.1.3. Using Symbolic Links for Databases on Windows

Symbolic links are enabled by default for all Windows servers. This enables you to put a database directory on a different disk by setting up a symbolic link to it. This is similar to the way that database symbolic links work on Unix, although the procedure for setting up the link is different. If you do not need symbolic links, you can disable them using the `--skip-symbolic-links` option.

On Windows, create a symbolic link to a MySQL database by creating a file in the data directory that contains the path to the destination directory. The file should be named ``db_name`.sym`, where `db_name` is the database name.

Suppose that the MySQL data directory is `C:\mysql\data` and you want to have database `foo` located at `D:\data\foo`. Set up a symlink using this procedure

1. Make sure that the `D:\data\foo` directory exists by creating it if necessary. If you already have a database directory named `foo` in the data directory, you should move it to `D:\data`. Otherwise, the symbolic link will be ineffective. To avoid problems, make sure that the server is not running when you move the database directory.

2. Create a text file `C:\mysql\data\foo.sym` that contains the pathname `D:\data\foo\`.

After this, all tables created in the database `foo` are created in `D:\data\foo`. Note that the symbolic link is not used if a directory with the same name as the database exists in the MySQL data directory.

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