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.

MySQL Enterprise For instant notification of system bottlenecks subscribe to the MySQL Enterprise Monitor. For more information see http://www.mysql.com/products/enterprise/advisors.html.

### 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 13, Storage Engines.

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 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 1.8.6, “How MySQL Deals with Constraints”, Section 5.1.6, “SQL Modes”, and Section 12.2.4, “`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. 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.

MySQL Enterprise For expert advice on choosing the database engine suitable to your circumstances subscribe to the MySQL Enterprise Monitor. For more information see http://www.mysql.com/products/enterprise/advisors.html.

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 8.5, “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 7.5.3, “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 2.15, “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 12.3.1, “`DESCRIBE` Syntax”, and Section 12.5.4.3, “`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 12.5.2.1, “`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.

In MySQL version 4.1, the `EXPLAIN` output format was changed to work better with constructs such as `UNION` statements, subqueries, and derived tables. Most notable is the addition of two new columns: `id` and `select_type`. You do not see these columns when using servers older than MySQL 4.1. `EXPLAIN` syntax also was augmented to allow the `EXTENDED` keyword. When this 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)
  `UNCACHEABLE SUBQUERY` | A subquery for which the result cannot be cached and must be re-evaluated for each row of the outer query

  `DEPENDENT` typically signifies the use of a correlated subquery. See Section 12.2.8.7, “Correlated Subqueries”.

  “DEPENDENT SUBQUERY” evaluation differs from `UNCACHEABLE SUBQUERY` evaluation. For “DEPENDENT SUBQUERY”, the subquery is re-evaluated only once for each set of different values of the variables from its outer context. For `UNCACHEABLE SUBQUERY`, the subquery is re-evaluated for each row of the outer context. Cacheability of subqueries is subject to the restrictions detailed in Section 7.5.3.1, “How the Query Cache Operates”. For example, referring to user variables makes a subquery uncacheable.

- `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;

  - `fulltext`

    The join is performed using a `FULLTEXT` index.

  - `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 was added for MySQL 4.1.1 and is used mostly when 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 7.2.6, “`IS NULL` 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 12.1.2, “`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. If MySQL decides to use one of the `possible_keys` indexes to look up rows, that index is listed as the key value.

  It is possible that `key` will name an index that is not present in the `possible_keys` value. This can happen if none of the `possible_keys` indexes are suitable for looking up rows, but all the columns selected by the query are columns of some other index. That is, the named index covers the selected columns, so although it is not used to determine which rows to retrieve, an index scan is more efficient than a data row scan.

  For `InnoDB`, a secondary index might cover the selected columns even if the query also selects the primary key because `InnoDB` stores the primary key value with each secondary index. If `key` is `NULL`, MySQL found no index to use for executing the query more efficiently.

  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 12.2.7.2, “Index Hint 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 12.5.2.1, “`ANALYZE TABLE` Syntax”, and Section 5.7.4, “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. The following list explains the values that can appear in this column. If you want to make your queries as fast as possible, you should look out for `Extra` values of `Using filesort` and `Using temporary`.

  - `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.

  - `Impossible WHERE noticed after reading const tables`

    MySQL has read all `const` (and `system`) tables and notice that the `WHERE` clause is always false.

  - `No tables`

    The query has no `FROM` clause, or has a `FROM DUAL` clause.

  - `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` access method to retrieve rows. The applicability criteria are as described in Section 7.2.5, “Range Optimization”, with the exception that all column values for the preceding table are known and considered to be constants.

    This is not very fast, but is faster than performing a join with no index at all.

  - `Select tables optimized away`

    The query contained only aggregate functions (`MIN()`, `MAX()`) that were all resolved using an index, or `COUNT(*)` for `MyISAM`, and no `GROUP BY` clause. The optimizer determined that only one row should be returned.

  - `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 7.2.8, “`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`.

  - `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 7.2.9, “`GROUP BY` Optimization”.

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. (For `ISAM` tables, indexes may not be used at all unless the columns are declared the same.) 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.

MySQL Enterprise Subscribers to the MySQL Enterprise Monitor regularly receive expert advice on optimization. For more information see http://www.mysql.com/products/enterprise/advisors.html.

### 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 only bound 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”.

MySQL Enterprise The MySQL Enterprise Monitor provides a number of advisors specifically designed to improve query performance. For more information see http://www.mysql.com/products/enterprise/advisors.html.

### 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` (`HASH`) 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` comparisons if the argument to `LIKE` is a constant string that does not start with a wildcard character.

- 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. `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 4.1.1 and up can also optimize the combination ``col_name` = `expr` OR `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);

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

MySQL implements a ``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;

Starting from 4.0.14, 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`.

### 7.2.8. `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 use `ORDER BY` with an expression that includes terms other than the key column name:

      SELECT * FROM t1 ORDER BY ABS(key);
      SELECT * FROM t1 ORDER BY -key;

- 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.

Availability of an index for sorting may be affected by the use of column aliases. Suppose that the column `t1.a` is indexed. In this statement, the name of the column in the select list is `a`. It refers to `t1.a`, so for the reference to `a` in the `ORDER BY`, the index can be used:

    SELECT a FROM t1 ORDER BY a;

In this statement, the name of the column in the select list is also `a`, but it is the alias name. It refers to `ABS(a)`, so for the reference to `a` in the `ORDER BY`, the index cannot be used:

    SELECT ABS(a) AS a FROM t1 ORDER BY a;

In the following statement, the `ORDER BY` refers to a name that is not the name of a column in the select list. But there is a column in `t1` named `a`, so the `ORDER BY` uses that, and the index can be used. (The resulting sort order may be completely different from the order for `ABS(a)`, of course.)

    SELECT ABS(a) AS b FROM t1 ORDER BY a;

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;

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`”.

MySQL has two `filesort` algorithms for sorting and retrieving results. The original method uses only the `ORDER BY` columns. The modified method uses not just the `ORDER BY` columns, but all the columns used in the query.

The optimizer selects which `filesort` algorithm to use. Prior to MySQL 4.1, it uses the original algorithm. As of MySQL 4.1, it normally uses the modified algorithm except when `BLOB` or `TEXT` columns are involved, in which case it uses the original algorithm.

The original `filesort` algorithm works as follows:

1. Read all rows according to key or by table scanning. Rows that do not match the `WHERE` clause are skipped.

2. For each row, store a pair of values in a buffer (the sort key and the row pointer). The size of the buffer is the value of the `sort_buffer_size` system variable.

3. When the buffer gets full, run a qsort (quicksort) on it and store the result in a temporary file. Save a pointer to the sorted block. (If all pairs fit into the sort buffer, no temporary file is created.)

4. Repeat the preceding steps until all rows have been read.

5. Do a multi-merge of up to `MERGEBUFF` (7) regions to one block in another temporary file. Repeat until all blocks from the first file are in the second file.

6. Repeat the following until there are fewer than `MERGEBUFF2` (15) blocks left.

7. On the last multi-merge, only the pointer to the row (the last part of the sort key) is written to a result file.

8. Read the rows in sorted order by using the row pointers in the result file. To optimize this, we read in a big block of row pointers, sort them, and use them to read the rows in sorted order into a row buffer. The size of the buffer is the value of the `read_rnd_buffer_size` system variable. The code for this step is in the `sql/records.cc` source file.

One problem with this approach is that it reads rows twice: One time when evaluating the `WHERE` clause, and again after sorting the pair values. And even if the rows were accessed successively the first time (for example, if a table scan is done), the second time they are accessed randomly. (The sort keys are ordered, but the row positions are not.)

The modified `filesort` algorithm incorporates an optimization such that it records not only the sort key value and row position, but also the columns required for the query. This avoids reading the rows twice. The modified `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.

Using the modified `filesort` algorithm, the tuples are longer than the pairs used in the original method, and fewer of them fit in the sort buffer (the size of which is given by `sort_buffer_size`). As a result, it is possible for the extra I/O to make the modified approach slower, not faster. To avoid a slowdown, the 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.)

For slow queries for which `filesort` is not used, you might try lowering `max_length_for_sort_data` to a value that is appropriate to trigger a `filesort`.

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.

- Use less RAM per row by declaring columns only as large as they need to be to hold the values stored in them. For example, `CHAR(16)` is better than `CHAR(200)` if values never exceed 16 characters.

- Change `tmpdir` to point to a dedicated filesystem with large amounts of free space. Also, if you use MySQL 4.1 or later, this option accepts several paths that are used in round-robin fashion, so you you can use this feature to spread the load across several directories. Paths should be separated by colon characters (“`:`”) on Unix and semicolon characters (“`;`”) on Windows, NetWare, and OS/2. The paths should be for directories in filesystems that are located on different physical disks, not different partitions on the same disk.

### 7.2.9. `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.

In MySQL, `GROUP BY` is used for sorting, so the server may also apply `ORDER BY` optimizations to grouping. See Section 7.2.8, “`ORDER BY` Optimization”.

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.

#### 7.2.9.1. 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;

### 7.2.10. `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 11.11.3, “`GROUP BY` and `HAVING` 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 7.2.9, “`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;

### 7.2.11. `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.

### 7.2.12. 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.

MySQL Enterprise For expert advice on avoiding excessive table scans subscribe to the MySQL Enterprise Monitor. For more information see http://www.mysql.com/products/enterprise/advisors.html.

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 12.5.2.1, “`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 12.2.7.2, “Index Hint 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 5.1.3, “System Variables”.

### 7.2.13. 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 5.1.3, “System Variables”.

- If multiple clients are inserting a lot of rows, you can get higher speed by using the `INSERT DELAYED` statement. See Section 12.2.4.2, “`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 data file. 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 12.2.5, “`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 13.1.3.3, “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.

  `LOAD DATA INFILE` performs the preceding optimization automatically if the `MyISAM` table into which you insert data is empty. The main difference between automatic optimization and using the procedure explicitly 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.

  As of MySQL 4.0, 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. Suppose that five clients attempt to perform inserts simultaneously as follows:

  - Connection 1 does 1000 inserts

  - Connections 2, 3, and 4 do 1 insert

  - 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 successive inserts or updates. If you do very many successive inserts, 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”.

MySQL Enterprise For more advice on optimizing the performance of your server, subscribe to the MySQL Enterprise Monitor. Numerous advisors are dedicated to monitoring performance. For more information see, http://www.mysql.com/products/enterprise/advisors.html.

### 7.2.14. 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 12.5.2.5, “`OPTIMIZE TABLE` Syntax”.

### 7.2.15. 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`` is faster than than `DELETE FROM Truncate operations are not transaction-safe; an error occurs when attempting one in the course of an active transaction or active table lock. `tbl_name``. See Section 12.2.9, “`TRUNCATE` Syntax”.

### 7.2.16. 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.

- `MyISAM` supports concurrent inserts: If a table has no free blocks in the middle of the data file, you can `INSERT` new rows into it at the same time that other threads are 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 7.3.3, “Concurrent Inserts”.

  MySQL Enterprise For optimization tips geared to your specific circumstances subscribe to the MySQL Enterprise Monitor. For more information see http://www.mysql.com/products/enterprise/advisors.html.

- To fix any compression issues that may have occurred with `ARCHIVE` tables, you can use `OPTIMIZE TABLE`. See Section 13.7, “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, reasonably unique, and indexed, 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 13, Storage Engines.

- 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 13, Storage Engines.

- 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` and `ISAM` that have 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.

- UDFs (user-defined functions) may be a good way to get more performance for some tasks. See Section 19.2, “Adding New Functions to MySQL”, for more information.

- You can increase performance by caching queries or answers in your application and then executing many inserts or updates together. If your database system supports table locks, 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 7.5.3, “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.

  `LOW_PRIORITY` and `HIGH_PRIORITY` have an effect only for storage engines that use only table-level locking (`MyISAM`, `MEMORY`, `MERGE`).

- 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 so that each row in a table can be identified by a single unique value. unique values.

- Use `OPTIMIZE TABLE` once in a while to avoid fragmentation with dynamic-format `MyISAM` tables. See Section 13.1.3, “`MyISAM` Table Storage Formats”.

- Use `MEMORY` (`HEAP`) tables when possible to get more speed. See Section 13.4, “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. Before MySQL 3.23, you get slow joins otherwise.

  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

MySQL manages contention for table contents using locking:

- Internal locking is performed within the MySQL server itself to manage contention for table contents by multiple threads. This type of locking is internal because it is performed entirely by the server and involves no other programs. See Section 7.3.1, “Internal Locking Methods”.

- External locking occurs when the server and other programs lock table files to coordinate among themselves which program can access the tables at which time. See Section 7.3.4, “External Locking”. See Section 7.3.4, “External Locking”.

### 7.3.1. Internal Locking Methods

This section discusses internal locking; that is, locking performed within the MySQL server itself to manage contention for table contents by multiple threads. This type of locking is internal because it is performed entirely by the server and involves no other programs. External locking occurs when the server and other programs lock table files to coordinate among themselves which program can access the tables at which time. See Section 7.3.4, “External Locking”.

MySQL uses table-level locking for `ISAM`, `MyISAM`, and `MEMORY` (`HEAP`) 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.

MySQL Enterprise The MySQL Enterprise Monitor provides expert advice on when to use table-level locking and when to use row-level locking. To subscribe see http://www.mysql.com/products/enterprise/advisors.html.

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.

MySQL grants table `WRITE` locks as follows:

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

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

MySQL grants table `READ` locks as follows:

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

2. 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.

Starting in MySQL 3.23.33, 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   |
    +-----------------------+---------+

As of MySQL 3.23.7 (3.23.25 for Windows), the `MyISAM` storage engine supports concurrent inserts to reduce contention between readers and writers for a given table: If a `MyISAM` table has no free blocks in the middle of the data file, rows are always 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 `real_table` when concurrent inserts are not possible, you can insert rows into a temporary table `temp_table` and update the real table with the rows from the temporary table periodically. This can be done with the following code:

    mysql> LOCK TABLES real_table WRITE, temp_table WRITE;
    mysql> INSERT INTO real_table SELECT * FROM temp_table;
    mysql> DELETE FROM temp_table;
    mysql> UNLOCK TABLES;

`InnoDB` uses row locks and `BDB` uses page locks. Deadlocks are possible for these storage engines 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

- Statements for the table are 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 those provided by `GET_LOCK()` and `RELEASE_LOCK()` in MySQL. These are advisory locks, so they work only in well-behaved applications. See Section 11.10.4, “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`, `BDB`, and `NDBCLUSTER`.

For `InnoDB` and `BDB` tables, MySQL only uses 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`. For storage engines that use only table-level locking (`MyISAM`, `MEMORY`, `MERGE`), 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 12.5.3, “`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 12.2.7, “`SELECT` Syntax”.

- Starting from MySQL 3.23.7, 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 12.2.4.2, “`INSERT DELAYED` Syntax”.

- If you have problems with mixed `SELECT` and `DELETE` statements, the `LIMIT` option to `DELETE` may help. See Section 12.2.1, “`DELETE` Syntax”.

- Using `SQL_BUFFER_RESULT` with `SELECT` statements can help to make the duration of table locks shorter. See Section 12.2.7, “`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 13.2, “The `InnoDB` Storage Engine”, and Section 13.5, “The `BDB` (`BerkeleyDB`) Storage Engine”.

  MySQL Enterprise Lock contention can seriously degrade performance. The MySQL Enterprise Monitor provides expert advice on avoiding this problem. To subscribe see http://www.mysql.com/products/enterprise/advisors.html.

### 7.3.3. Concurrent Inserts

As of MySQL 3.23.7 (3.23.25 for Windows), the `MyISAM` storage engine supports concurrent inserts to reduce contention between readers and writers for a given table: If a `MyISAM` table has no holes in the data file (deleted rows in the middle), inserts can be performed to add rows to the end of the table at the same time that `SELECT` statements are reading rows from the table. Concurrent inserts are enabled by default, but can be disabled by setting the `concurrent_insert` system variable to 0.

Under circumstances where concurrent inserts can be used, there is seldom any need to use the `DELAYED` modifier for `INSERT` statements. See Section 12.2.4.2, “`INSERT DELAYED` Syntax”.

If you are using the update log or 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. See Section 5.3.4, “The Binary Log”.

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. Use of the `CONCURRENT` option affects the performance of `LOAD DATA` a bit, even if no other thread is using the table at the same time.

If you specify `HIGH_PRIORITY`, it overrides the effect of the `--low-priority-updates` option if the server was started with that option. It also causes concurrent inserts not to be used.

For `LOCK TABLE`, the difference between `READ LOCAL` and `READ` is that `READ LOCAL` allows non-conflicting `INSERT` statements (concurrent inserts) to execute while the lock is held. However, this cannot be used if you are going to manipulate the database using processes external to the server while you hold the lock.

### 7.3.4. External Locking

External locking is the use of filesystem locking to manage contention for database tables by multiple processes. External locking is used in situations where a single process such as the MySQL server cannot be assumed to be the only process that requires access to tables. Here are some examples:

- If you run multiple servers that use the same database directory (not recommended), each server must have external locking enabled.

- If you use myisamchk to perform table maintenance operations on `MyISAM` tables, you must either ensure that the server is not running, or that the server has external locking enabled so that it locks table files as necessary to coordinate with myisamchk for access to the tables. The same is true for use of myisampack to pack `MyISAM` tables.

With external locking in effect, each process that requires access to a table acquires a filesystem lock for the table files before proceeding to access the table. If all necessary locks cannot be acquired, the process is blocked from accessing the table until the locks can be obtained (after the process that currently holds the locks releases them).

External locking affects server performance because the server must sometimes wait for other processes before it can access tables.

External locking is unnecessary if you run a single server to access a given data directory (which is the usual case) and if no other programs such as myisamchk need to modify tables while the server is running. If you only read tables with other programs, external locking is not required, although myisamchk might report warnings if the server changes tables while myisamchk is reading them.

With external locking disabled, to use myisamchk, you must either stop the server while myisamchk executes or else lock and flush the tables before running myisamchk. (See Section 7.5.1, “System Factors and Startup Parameter Tuning”.) To avoid this requirement as of MySQL 3.23, use the `CHECK TABLE` and `REPAIR TABLE` statements to check and repair `MyISAM` tables.

For mysqld, external locking is controlled by the value of the `skip_external_locking` system variable. (Before MySQL 4.0.3, this variable is named `skip_locking`.) When this variable is enabled, external locking is disabled, and vice versa. From MySQL 4.0 on, external locking is disabled by default. Before MySQL 4.0, external locking is enabled by default on Linux or when MySQL is configured to use MIT-pthreads.

Use of external locking can be controlled at server startup by using the `--external-locking` or `--skip-external-locking` option. (Before MySQL 4.0.3, these options are named `--enable-locking` and `--skip-locking`.)

If you do use external locking option to enable updates to `MyISAM` tables from many MySQL processes, you must ensure that the following conditions are satisfied:

- You should not use the query cache for queries that use tables that are updated by another process.

- You should not start the server with the `--delay-key-write=ALL` option or use the `DELAY_KEY_WRITE=1` table option for any shared tables. Otherise, index corruption can occur.

The easiest way to satisfy these conditions is to always use `--external-locking` together with `--delay-key-write=OFF` and `--query-cache-size=0`. (This is not done by default because in many setups it is useful to have a mixture of the preceding options.)

## 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 13, Storage Engines.

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 13.1.3, “`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`.

- 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 12.1.4, “`CREATE INDEX` Syntax”). Shorter indexes are faster, not only because they require less disk space, but because they also give 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 13, Storage Engines. 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.

The `MyISAM` and (as of MySQL 4.0.14) `InnoDB` storage engines also support indexing on `BLOB` and `TEXT` columns. When indexing 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). (Before MySQL 4.1.2, the limit is 255 bytes for all 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.

As of MySQL 3.23.23, you can also create `FULLTEXT` indexes. They 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 column prefix indexing is not supported. For details, see Section 11.8, “Full-Text Search Functions”.

As of MySQL 4.1.0, you can create indexes on spatial data types. Spatial indexes use R-trees. Currently, only `MyISAM` supports indexes on spatial types.

The `MEMORY` (`HEAP`) storage engine uses `HASH` indexes by default. It also supports `BTREE` indexes as of MySQL 4.1.0.

### 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` (`HEAP`) tables support hash indexes.

Strings are automatically prefix- and end-space compressed. See Section 12.1.4, “`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. 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. For example, `VARCHAR(10)` and `CHAR(10)` are the same size, but `VARCHAR(10)` and `CHAR(15)` are not.

  Comparison of dissimilar columns may prevent use of indexes if values cannot be compared directly without conversion. Suppose that a numeric column is compared to a string column. For a given value such as `1` in the numeric column, it might compare equal to any number of values in the string column such as `'1'`, `' 1'`, `'00001'`, or `'01.e1'`. This rules out use of any indexes for the string column.

- 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 7.2.8, “`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 an 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.

MySQL 4.0 and later versions perform an additional `LIKE` optimization. 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.)

MySQL Enterprise Often, it is not possible to predict exactly what indexes will be required or will be most efficient — actual table usage is the best indicator. The MySQL Enterprise Monitor provides expert advice on this topic. For more information see http://www.mysql.com/products/enterprise/advisors.html.

### 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 changes made in MySQL 4.1 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.

The key cache mechanism also is used for `ISAM` tables. However, the significance of this fact is on the wane. `ISAM` table use has been decreasing since MySQL 3.23 when `MyISAM` was introduced. MySQL 4.1 carries this trend further; the `ISAM` storage engine is disabled by default. (Subsequent MySQL release series have no support at all for `ISAM`.)

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).

MySQL Enterprise For expert advice on identifying the optimum size for `key_buffer_size`, subscribe to the MySQL Enterprise Monitor. See http://www.mysql.com/products/enterprise/advisors.html.

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

Prior to MySQL 4.1, access to the key cache is serialized: No two threads can access key cache buffers simultaneously. The server processes a request for an index block only after it has finished processing the previous request. As a result, a request for an index block not present in any key cache buffer blocks access by other threads while a buffer is being updated to contain the requested index block.

Starting from version 4.1.0, the server supports shared access to the key cache:

- 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 4.1.1 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 12.5.5.1, “`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       |
    +---------+--------------------+----------+----------+

### Note

If the server has been built with the `ISAM` storage engine enabled, `ISAM` tables use the key cache mechanism. However, `ISAM` indexes use only the default key cache and cannot be reassigned to a different cache.

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 5.1.4.1, “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

MySQL Enterprise For advice on how best to configure your `my.cnf/my.ini` option file subscribe to MySQL Enterprise Monitor. Recommendations are based on actual table usage. For more information see http://www.mysql.com/products/enterprise/advisors.html.

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 of MySQL 4.1 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

MySQL 4.1 introduces a new `key_cache_block_size` variable on a per-key cache basis. This variable specifies the size of the block buffers for a key cache. It is intended to allow 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.1.2.18, “'`File`' Not Found and Similar Errors”.

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 Enterprise Performance may suffer if `table_cache` is set too low. For expert advice on the optimum value for this variable, subscribe to the MySQL Enterprise Monitor. For more information see http://www.mysql.com/products/enterprise/advisors.html.

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 12.2.3, “`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 5.1.3, “System Variables”, and Section 5.1.5, “Status Variables”.

### 7.4.9. Drawbacks to Creating Many Tables in the Same Database

If you have many `MyISAM` or `ISAM` tables in a 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 increasing the number of entries allowed in the table cache.

## 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.

- Use the `--skip-external-locking` MySQL option to avoid external locking. This option is turned on by default as of MySQL 4.0. Before that, it is on by default when compiling with MIT-pthreads, because `flock()` is not fully supported by MIT-pthreads on all platforms. It is also on by default for Linux because Linux file locking is not yet safe.

  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 with this command (prior to MySQL 4.1, omit `--verbose`):

    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                 current value: 5
    bdb_cache_size           current value: 1048540
    binlog_cache_size        current value: 32768
    connect_timeout          current value: 5
    delayed_insert_limit     current value: 100
    delayed_insert_timeout   current value: 300
    delayed_queue_size       current value: 1000
    flush_time               current value: 0
    interactive_timeout      current value: 28800
    join_buffer_size         current value: 131072
    key_buffer_size          current value: 1048540
    long_query_time          current value: 10
    lower_case_table_names   current value: 0
    max_allowed_packet       current value: 1048576
    max_binlog_cache_size    current value: 4294967295
    max_connect_errors       current value: 10
    max_connections          current value: 100
    max_delayed_threads      current value: 20
    max_heap_table_size      current value: 16777216
    max_join_size            current value: 4294967295
    max_sort_length          current value: 1024
    max_tmp_tables           current value: 32
    max_write_lock_count     current value: 4294967295
    myisam_sort_buffer_size  current value: 8388608
    net_buffer_length        current value: 16384
    net_read_timeout         current value: 30
    net_retry_count          current value: 10
    net_write_timeout        current value: 60
    read_buffer_size         current value: 131072
    read_rnd_buffer_size     current value: 262144
    slow_launch_time         current value: 2
    sort_buffer              current value: 2097116
    table_cache              current value: 64
    thread_concurrency       current value: 10
    thread_stack             current value: 131072
    tmp_table_size           current value: 1048576
    wait_timeout             current value: 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 5.1.3, “System Variables”, and Section 5.1.5, “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. The examples use the mysqld_safe script and use `--`var_name`=`value`` syntax to set the variable `var_name` to the value `value`. This syntax is available as of MySQL 4.0. For older versions of MySQL, take the following differences into account:

- Use safe_mysqld rather than mysqld_safe.

- Set variables using `--set-variable=`var_name`=`value`` or `-O `var_name`=`value`` syntax.

- For variable names that end in `_size`, you may need to specify them without `_size`. For example, the old name for `sort_buffer_size` is `sort_buffer`. The old name for `read_buffer_size` is `record_buffer`. To see which variables your version of the server recognizes, use mysqld --help.

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.

You can make use of the example option files included with your MySQL distribution; see Section 4.2.2.2.2, “Preconfigured Option Files”.

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 (prior to MySQL 4.1, omit `--verbose`):

    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 13.2.12, “`InnoDB` Performance Tuning Tips”.

MySQL Enterprise For expert advice on tuning system parameters subscribe to the MySQL Enterprise Monitor. For more information see http://www.mysql.com/products/enterprise/advisors.html.

### 7.5.3. The MySQL Query Cache

From version 4.0.1 on, MySQL Server features a query cache. When in use, the query cache stores the text of a `SELECT` statement together with the corresponding result that was sent to the client. If an identical statement is received later, the server retrieves the results from the query cache rather than parsing and executing the statement again.

The query cache is extremely useful in an environment where you have tables that do not change very often and for which the server receives many identical queries. This is a typical situation for many Web servers that generate many dynamic pages based on database content.

### Note

The query cache does not return stale data. When tables are modified, any relevant entries in the query cache are flushed.

### Note

The query cache does not work in an environment where you have multiple mysqld servers updating the same `MyISAM` tables.

### Note

The query cache is not used for server-side prepared statements. If you are using server-side prepared statements, consider that these statements will not be satisfied by the query cache. See Section 16.2.4, “C API Prepared Statements”.

Some performance data for the query cache follows. These results were generated by running the MySQL benchmark suite on a Linux Alpha 2×500MHz system with 2GB RAM and a 64MB query cache.

- If all the queries you are performing are simple (such as selecting a row from a table with one row), but still differ so that the queries cannot be cached, the overhead for having the query cache active is 13%. This could be regarded as the worst case scenario. In real life, queries tend to be much more complicated, so the overhead normally is significantly lower.

- Searches for a single row in a single-row table are 238% faster with the query cache than without it. This can be regarded as close to the minimum speedup to be expected for a query that is cached.

To disable the query cache at server startup, set the `query_cache_size` system variable to 0. By disabling the query cache code, there is no noticeable overhead. If you build MySQL from source, query cache capabilities can be excluded from the server entirely by invoking configure with the `--without-query-cache` option.

#### 7.5.3.1. How the Query Cache Operates

This section describes how the query cache works when it is operational. Section 7.5.3.3, “Query Cache Configuration”, describes how to control whether it is operational.

Incoming queries are compared to those in the query cache before parsing, so the following two queries are regarded as different by the query cache:

    SELECT * FROM tbl_name
    Select * from tbl_name

Queries must be exactly the same (byte for byte) to be seen as identical. In addition, query strings that are identical may be treated as different for other reasons. Queries that use different databases, different protocol versions, or different default character sets are considered different queries and are cached separately.

Because comparison of a query against those in the cache occurs before parsing, the cache is not used for queries of the following types:

- Prepared statements

- Queries that are a subquery of an outer query

Before a query result is fetched from the query cache, MySQL checks that the user has `SELECT` privilege for all databases and tables involved. If this is not the case, the cached result is not used.

If a query result is returned from query cache, the server increments the `Qcache_hits` status variable, not `Com_select`. See Section 7.5.3.4, “Query Cache Status and Maintenance”.

If a table changes, all cached queries that use the table become invalid and are removed from the cache. This includes queries that use `MERGE` tables that map to the changed table. A table can be changed by many types of statements, such as `INSERT`, `UPDATE`, `DELETE`, `TRUNCATE`, `ALTER TABLE`, `DROP TABLE`, or `DROP DATABASE`.

In MySQL 4.0, the query cache is disabled within transactions (it does not return results). Beginning with MySQL 4.1.1, the query cache also works within transactions when using `InnoDB` tables.

A query that begins with a leading comment may be cached, but cannot be fetched from the cache.

The query cache works for `SELECT SQL_CALC_FOUND_ROWS ...` queries and stores a value that is returned by a following `SELECT FOUND_ROWS()` query. `FOUND_ROWS()` returns the correct value even if the preceding query was fetched from the cache because the number of found rows is also stored in the cache. The `SELECT FOUND_ROWS()` query itself cannot be cached.

A query cannot be cached if it contains any of the functions shown in the following table:

`BENCHMARK()` | `CONNECTION_ID()` | `CONVERT_TZ()`
`CURDATE()` | `CURRENT_DATE()` | `CURRENT_TIME()`
`CURRENT_TIMESTAMP()` | `CURTIME()` | `DATABASE()`
`ENCRYPT()` with one parameter | `FOUND_ROWS()` | `GET_LOCK()`
`LAST_INSERT_ID()` | `LOAD_FILE()` | `MASTER_POS_WAIT()`
`NOW()` | `RAND()` | `RELEASE_LOCK()`
`SYSDATE()` | `UNIX_TIMESTAMP()` with no parameters | `USER()`

A query also is not cached under these conditions:

- It refers to user-defined functions (UDFs).

- It refers to user variables.

- It refers to tables in the `mysql` system database.

- It is of any of the following forms:

      SELECT ... IN SHARE MODE
      SELECT ... FOR UPDATE
      SELECT ... INTO OUTFILE ...
      SELECT ... INTO DUMPFILE ...
      SELECT * FROM ... WHERE autoincrement_col IS NULL

  The last form is not cached because it is used as the ODBC workaround for obtaining the last insert ID value. See the MyODBC section of Chapter 17, Connectors.

- It was issued as a prepared statement, even if no placeholders were employed. For example, the query used here is not cached:

      char *my_sql_stmt = "SELECT a, b FROM table_c";
      /* ... */
      mysql_stmt_prepare(stmt, my_sql_stmt, strlen(my_sql_stmt));

  See Section 16.2.4, “C API Prepared Statements”.

- It uses `TEMPORARY` tables.

- It does not use any tables.

- The user has a column-level privilege for any of the involved tables.

#### 7.5.3.2. Query Cache `SELECT` Options

Two query cache-related options may be specified in `SELECT` statements:

- `SQL_CACHE`

  The query result is cached if it is cacheable and the value of the `query_cache_type` system variable is `ON` or `DEMAND`.

- `SQL_NO_CACHE`

  The query result is not cached.

Examples:

    SELECT SQL_CACHE id, name FROM customer;
    SELECT SQL_NO_CACHE id, name FROM customer;

#### 7.5.3.3. Query Cache Configuration

The `have_query_cache` server system variable indicates whether the query cache is available:

    mysql> SHOW VARIABLES LIKE 'have_query_cache';
    +------------------+-------+
    | Variable_name    | Value |
    +------------------+-------+
    | have_query_cache | YES   |
    +------------------+-------+

Several other system variables control query cache operation. These can be set in an option file or on the command line when starting mysqld. The query cache system variables all have names that begin with `query_cache_`. They are described briefly in Section 5.1.3, “System Variables”, with additional configuration information given here.

To set the size of the query cache, set the `query_cache_size` system variable. Setting it to 0 disables the query cache. The default size is 0, so the query cache is disabled by default.

MySQL Enterprise For expert advice on configuring the query cache subscribe to the MySQL Enterprise Monitor. For more information see, http://www.mysql.com/products/enterprise/advisors.html.

### Note

When using the Windows Configuration Wizard to install or configure MySQL, the default value for `query_cache_size` will be configured automatically for you based on the different configuration types available. When using the Windows Configuration Wizard, the query cache may be enabled (i.e. set to a non-zero value) due to the selected configuration. The query cache is also controlled by the setting of the `query_cache_type` variable. You should check the values of these variables as set in your `my.ini` file after configuration has taken place.

When you set `query_cache_size` to a non-zero value, keep in mind that the query cache needs a minimum size of about 40KB to allocate its structures. (The exact size depends on system architecture.) If you set the value too small, you'll get a warning, as in this example:

    mysql> SET GLOBAL query_cache_size = 40000;
    Query OK, 0 rows affected, 1 warning (0.00 sec)

    mysql> SHOW WARNINGS\G
    *************************** 1. row ***************************
      Level: Warning
       Code: 1282
    Message: Query cache failed to set size 39936; new query cache size is 0

    mysql> SET GLOBAL query_cache_size = 41984;
    Query OK, 0 rows affected (0.00 sec)

    mysql> SHOW VARIABLES LIKE 'query_cache_size';
    +------------------+-------+
    | Variable_name    | Value |
    +------------------+-------+
    | query_cache_size | 41984 |
    +------------------+-------+

For the query cache to actually be able to hold any query results, its size must be set larger:

    mysql> SET GLOBAL query_cache_size = 1000000;
    Query OK, 0 rows affected (0.04 sec)

    mysql> SHOW VARIABLES LIKE 'query_cache_size';
    +------------------+--------+
    | Variable_name    | Value  |
    +------------------+--------+
    | query_cache_size | 999424 |
    +------------------+--------+
    1 row in set (0.00 sec)

The `query_cache_size` will be aligned to the nearest 1024 byte block. The value reported may therefore be different from the value that you set.

If the query cache size is greater than 0, the `query_cache_type` variable influences how it works. This variable can be set to the following values:

- A value of `0` or `OFF` prevents caching or retrieval of cached results.

- A value of `1` or `ON` allows caching except of those statements that begin with `SELECT SQL_NO_CACHE`.

- A value of `2` or `DEMAND` causes caching of only those statements that begin with `SELECT SQL_CACHE`.

Setting the `GLOBAL` `query_cache_type` value determines query cache behavior for all clients that connect after the change is made. Individual clients can control cache behavior for their own connection by setting the `SESSION` `query_cache_type` value. For example, a client can disable use of the query cache for its own queries like this:

    mysql> SET SESSION query_cache_type = OFF;

To control the maximum size of individual query results that can be cached, set the `query_cache_limit` system variable. The default value is 1MB.

When a query is to be cached, its result (the data sent to the client) is stored in the query cache during result retrieval. Therefore the data usually is not handled in one big chunk. The query cache allocates blocks for storing this data on demand, so when one block is filled, a new block is allocated. Because memory allocation operation is costly (timewise), the query cache allocates blocks with a minimum size given by the `query_cache_min_res_unit` system variable. When a query is executed, the last result block is trimmed to the actual data size so that unused memory is freed. Depending on the types of queries your server executes, you might find it helpful to tune the value of `query_cache_min_res_unit`:

- The default value of `query_cache_min_res_unit` is 4KB. This should be adequate for most cases.

- If you have a lot of queries with small results, the default block size may lead to memory fragmentation, as indicated by a large number of free blocks. Fragmentation can force the query cache to prune (delete) queries from the cache due to lack of memory. In this case, you should decrease the value of `query_cache_min_res_unit`. The number of free blocks and queries removed due to pruning are given by the values of the `Qcache_free_blocks` and `Qcache_lowmem_prunes` status variables.

- If most of your queries have large results (check the `Qcache_total_blocks` and `Qcache_queries_in_cache` status variables), you can increase performance by increasing `query_cache_min_res_unit`. However, be careful to not make it too large (see the previous item).

`query_cache_min_res_unit` is present from MySQL 4.1.

MySQL Enterprise If the query cache is under-utilized, performance will suffer. Advice on avoiding this problem is provided to subscribers to the MySQL Enterprise Monitor. For more information see, http://www.mysql.com/products/enterprise/advisors.html.

#### 7.5.3.4. Query Cache Status and Maintenance

You can check whether the query cache is present in your MySQL server using the following statement:

    mysql> SHOW VARIABLES LIKE 'have_query_cache';
    +------------------+-------+
    | Variable_name    | Value |
    +------------------+-------+
    | have_query_cache | YES   |
    +------------------+-------+

You can defragment the query cache to better utilize its memory with the `FLUSH QUERY CACHE` statement. The statement does not remove any queries from the cache.

The `RESET QUERY CACHE` statement removes all query results from the query cache. The `FLUSH TABLES` statement also does this.

To monitor query cache performance, use `SHOW STATUS` to view the cache status variables:

    mysql> SHOW STATUS LIKE 'Qcache%';
    +-------------------------+--------+
    | Variable_name           | Value  |
    +-------------------------+--------+
    | Qcache_free_blocks      | 36     |
    | Qcache_free_memory      | 138488 |
    | Qcache_hits             | 79570  |
    | Qcache_inserts          | 27087  |
    | Qcache_lowmem_prunes    | 3114   |
    | Qcache_not_cached       | 22989  |
    | Qcache_queries_in_cache | 415    |
    | Qcache_total_blocks     | 912    |
    +-------------------------+--------+

Descriptions of each of these variables are given in Section 5.1.5, “Status Variables”. Some uses for them are described here.

The total number of `SELECT` queries is given by this formula:

      Com_select
    + Qcache_hits
    + queries with errors found by parser

The `Com_select` value is given by this formula:

      Qcache_inserts
    + Qcache_not_cached
    + queries with errors found during the column-privileges check

The query cache uses variable-length blocks, so `Qcache_total_blocks` and `Qcache_free_blocks` may indicate query cache memory fragmentation. After `FLUSH QUERY CACHE`, only a single free block remains.

Every cached query requires a minimum of two blocks (one for the query text and one or more for the query results). Also, every table that is used by a query requires one block. However, if two or more queries use the same table, only one table block needs to be allocated.

The information provided by the `Qcache_lowmem_prunes` status variable can help you tune the query cache size. It counts the number of queries that have been removed from the cache to free up memory for caching new queries. The query cache uses a least recently used (LRU) strategy to decide which queries to remove from the cache. Tuning information is given in Section 7.5.3.3, “Query Cache Configuration”.

### 7.5.4. Examining Thread Information

When you are attempting to ascertain what your MySQL server is doing, it can be helpful to examine the process list, which is the set of threads currently executing within the server. Process list information is available from these sources:

- The `SHOW [FULL] PROCESSLIST` statement (Section 12.5.4.16, “`SHOW PROCESSLIST` Syntax”)

- The myqladmin processlist command (Section 4.5.2, “mysqladmin — Client for Administering a MySQL Server”)

You can always view information about your own threads. To view information about threads being executed for other accounts, you must have the `PROCESS` privilege.

Each process list entry contains several pieces of information:

- `Id` is the connection identifier for the client associated with the thread.

- `User` and `Host` indicate the account associated with the thread.

- `db` is the default database for the thread, or `NULL` if none is selected.

- `Command` and `State` indicate what the thread is doing.

  Most states correspond to very quick operations. If a thread stays in a given state for many seconds, there might be a problem that needs to be investigated.

- `Time` indicates how long the thread has been in its current state.

- `Info` contains the text of the statement being executed by the thread, or `NULL` if it is not executing one. By default, this value contains only the first 100 characters of the statement. To see the complete statements, use `SHOW FULL PROCESSLIST`.

The following sections list the possible `Command` values, and `State` values grouped by category. The meaning for some of these values is self-evident. For others, additional description is provided.

#### 7.5.4.1. Thread Command Values

A thread can have any of the following `Command` values:

- `Binlog Dump`

  This is a thread on a master server for sending binary log contents to a slave server.

- `Change user`

  The thread is executing a change-user operation.

- `Close stmt`

  The thread is closing a prepared statement.

- `Connect`

  A replication slave is connected to its master.

- `Connect Out`

  A replication slave is connecting to its master.

- `Create DB`

  The thread is executing a create-database operation.

- `Daemon`

  This thread is internal to the server, not a thread that services a client connection.

- `Debug`

  The thread is generating debugging information.

- `Delayed insert`

  The thread is a delayed-insert handler.

- `Drop DB`

  The thread is executing a drop-database operation.

- `Error`

- `Execute`

  The thread is executing a prepared statement.

- `Fetch`

  The thread is fetching the results from executing a prepared statement.

- `Field List`

  The thread is retrieving information for table columns.

- `Init DB`

  The thread is selecting a default database.

- `Kill`

  The thread is killing another thread.

- `Long Data`

  The thread is retrieving long data in the result of executing a prepared statement.

- `Ping`

  The thread is handling a server-ping request.

- `Prepare`

  The thread is preparing a prepared statement.

- `Processlist`

  The thread is producing information about server threads.

- `Query`

  The thread is executing a statement.

- `Quit`

  The thread is terminating.

- `Refresh`

  The thread is flushing table, logs, or caches, or resetting status variable or replication server information.

- `Register Slave`

  The thread is registering a slave server.

- `Reset stmt`

  The thread is resetting a prepared statement.

- `Set option`

  The thread is setting or resetting a client statement-execution option.

- `Shutdown`

  The thread is shutting down the server.

- `Sleep`

  The thread is waiting for the client to send a new statement to it.

- `Statistics`

  The thread is producing server-status information.

- `Table Dump`

  The thread is sending table contents to a slave server.

- `Time`

  Unused.

#### 7.5.4.2. General Thread States

The following list describes thread `State` values that are associated with general query processing and not more specialized activities such as replication. Many of these are useful only for finding bugs in the server.

- `After create`

  Occurs when the thread creates a table (including internal temporary tables), at the end of the function that creates the table. This state is used even if the table could not be created due to some error.

- `Analyzing`

  The thread is calculating a `MyISAM` table key distributions (for example, for `ANALYZE TABLE`).

- `Checking table`

  The thread is performing a table check operation.

- `cleaning up`

  The thread has processed one command and is preparing to free memory and reset certain state variables.

- `closing tables`

  Means that the thread is flushing the changed table data to disk and closing the used tables. This should be a fast operation. If not, you should verify that you do not have a full disk and that the disk is not in very heavy use.

- `converting HEAP to MyISAM`

  The thread is converting an internal temporary table from a `MEMORY` table to an on-disk `MyISAM` table.

- `copy to tmp table`

  The thread is processing an `ALTER TABLE` statement. This state occurs after the table with the new structure has been created but before rows are copied into it.

- `Copying to group table`

  If a statement has different `ORDER BY` and `GROUP BY` criteria, the rows are sorted by group and copied to a temporary table.

- `Copying to tmp table`

  The server is copying to a temporary table in memory.

- `Copying to tmp table on disk`

  The server is copying to a temporary table on disk. The temporary result set was larger than `tmp_table_size` and the thread is changing the temporary table from in-memory to disk-based format to save memory.

- `Creating index`

  The thread is processing `ALTER TABLE ... ENABLE KEYS` for a `MyISAM` table.

- `Creating sort index`

  The thread is processing a `SELECT` that is resolved using an internal temporary table.

- `creating table`

  The thread is creating a table. This includes creation of temporary tables.

- `Creating tmp table`

  The thread is creating a temporary table in memory or on disk. If the table is created in memory but later is converted to an on-disk table, the state during that operation will be `Copying to tmp table on disk`.

- `deleting from main table`

  The server is executing the first part of a multiple-table delete. It is deleting only from the first table, and saving fields and offsets to be used for deleting from the other (reference) tables.

- `deleting from reference tables`

  The server is executing the second part of a multiple-table delete and deleting the matched rows from the other tables.

- `discard_or_import_tablespace`

  The thread is processing an `ALTER TABLE ... DISCARD TABLESPACE` or `ALTER TABLE ... IMPORT TABLESPACE` statement.

- `end`

  This occurs at the end but before the cleanup of `ALTER TABLE`, `DELETE`, `INSERT`, `SELECT`, or `UPDATE` statements.

- `Execution of init_command`

  The thread is executing statements in the value of the `init_command` system variable.

- `freeing items`

  The thread has executed a command. This state is usually followed by `cleaning up`.

- `Flushing tables`

  The thread is executing `FLUSH TABLES` and is waiting for all threads to close their tables.

- `FULLTEXT initialization`

  The server is preparing to perform a natural-language full-text search.

- `init`

  This occurs before the initialization of `ALTER TABLE`, `DELETE`, `INSERT`, `SELECT`, or `UPDATE` statements.

- `Killed`

  Someone has sent a `KILL` statement to the thread and it should abort next time it checks the kill flag. The flag is checked in each major loop in MySQL, but in some cases it might still take a short time for the thread to die. If the thread is locked by some other thread, the kill takes effect as soon as the other thread releases its lock.

- `Locked`

  The query is locked by another query.

- `logging slow query`

  The thread is writing a statement to the slow-query log.

- `login`

  The initial state for a connection thread until the client has been authenticated successfully.

- `Opening tables`, `Opening table`

  The thread is trying to open a table. This is should be very fast procedure, unless something prevents opening. For example, an `ALTER TABLE` or a `LOCK TABLE` statement can prevent opening a table until the statement is finished.

- `preparing`

  This state occurs during query optimization.

- `Purging old relay logs`

  The thread is removing unneeded relay log files.

- `query end`

  This state occurs after processing a query but before the `freeing items` state.

- `Reading from net`

  The server is reading a packet from the network.

- `Removing duplicates`

  The query was using `SELECT DISTINCT` in such a way that MySQL could not optimize away the distinct operation at an early stage. Because of this, MySQL requires an extra stage to remove all duplicated rows before sending the result to the client.

- `removing tmp table`

  The thread is removing an internal temporary table after processing a `SELECT` statement. This state is not used if no temporary table was created.

- `rename`

  The thread is renaming a table.

- `rename result table`

  The thread is processing an `ALTER TABLE` statement, has created the new table, and is renaming it to replace the original table.

- `Reopen tables`

  The thread got a lock for the table, but noticed after getting the lock that the underlying table structure changed. It has freed the lock, closed the table, and is trying to reopen it.

- `Repair by sorting`

  The repair code is using a sort to create indexes.

- `Repair done`

  The thread has completed a multi-threaded repair for a `MyISAM` table.

- `Repair with keycache`

  The repair code is using creating keys one by one through the key cache. This is much slower than `Repair by sorting`.

- `Rolling back`

  The thread is rolling back a transaction.

- `Saving state`

  For `MyISAM` table operations such as repair or analysis, the thread is saving the new table state to the `.MYI` file header. State includes information such as number of rows, the `AUTO_INCREMENT` counter, and key distributions.

- `Searching rows for update`

  The thread is doing a first phase to find all matching rows before updating them. This has to be done if the `UPDATE` is changing the index that is used to find the involved rows.

- `Sending data`

  The thread is processing rows for a `SELECT` statement and also is sending data to the client.

- `setup`

  The thread is beginning an `ALTER TABLE` operation.

- `Sorting for group`

  The thread is doing a sort to satisfy a `GROUP BY`.

- `Sorting for order`

  The thread is doing a sort to satisfy a `ORDER BY`.

- `Sorting index`

  The thread is sorting index pages for more efficient access during a `MyISAM` table optimization operation.

- `Sorting result`

  For a `SELECT` statement, this is similar to `Creating sort index`, but for non-temporary tables.

- `statistics`

  The server is calculating statistics to develop a query execution plan.

- `System lock`

  The thread is going to request or is waiting for an external system lock for the table. If you are not using multiple mysqld servers that are accessing the same tables, you can disable system locks with the `--skip-external-locking` option.

- `Table lock`

  The next thread state after `System lock`. The thread has acquired an external lock and is going to request an internal table lock.

- `Updating`

  The thread is searching for rows to update and is updating them.

- `updating main table`

  The server is executing the first part of a multiple-table update. It is updating only the first table, and saving fields and offsets to be used for updating the other (reference) tables.

- `updating reference tables`

  The server is executing the second part of a multiple-table update and updating the matched rows from the other tables.

- `User lock`

  The thread is going to request or is waiting for an advisory lock requested with a `GET_LOCK()` call.

- `Waiting for tables`, `Waiting for table`

  The thread got a notification that the underlying structure for a table has changed and it needs to reopen the table to get the new structure. However, to reopen the table, it must wait until all other threads have closed the table in question.

  This notification takes place if another thread has used `FLUSH TABLES` or one of the following statements on the table in question: `FLUSH TABLES `tbl_name``, `ALTER TABLE`, `RENAME TABLE`, `REPAIR TABLE`, `ANALYZE TABLE`, or `OPTIMIZE TABLE`.

- `Waiting on cond`

  A generic state in which the the thread is waiting for a condition to become true. No specific state information is available.

- `Writing to net`

  The server is writing a packet to the network.

#### 7.5.4.3. Delayed-Insert Thread States

These thread states are associated with processing for `DELAYED` inserts (see Section 12.2.4.2, “`INSERT DELAYED` Syntax”). Some states are associated with connection threads that process `INSERT DELAYED` statements from clients. Other states are associated with delayed-insert handler threads that insert the rows. There is a delayed-insert handler thread for each table for which `INSERT DELAYED` statements are issued.

States associated with a connection thread that processes an `INSERT DELAYED` statement from the client:

- `allocating local table`

  The thread is preparing to feed rows to the delayed-insert handler thread.

- `Creating delayed handler`

  The thread is creating a handler for `DELAYED` inserts.

- `got handler lock`

  This occurs before the `allocating local table` state and after the `waiting for handler lock` state, when the connection thread gets access to the delayed-insert handler thread.

- `got old table`

  This occurs after the `waiting for handler open` state. The delayed-insert handler thread has signaled that it has ended its initialization phase, which includes opening the table for delayed inserts.

- `storing row into queue`

  The thread is adding a new row to the list of rows that the delayed-insert handler thread must insert.

- `update`

- `waiting for delay_list`

  This occurs during the initialization phase when the thread is trying to find the delayed-insert handler thread for the table, and before attempting to gain access to the list of delayed-insert threads.

- `waiting for handler insert`

  An `INSERT DELAYED` handler has processed all pending inserts and is waiting for new ones.

- `waiting for handler lock`

  This occurs before the `allocating local table` state when the connection thread waits for access to the delayed-insert handler thread.

- `waiting for handler open`

  This occurs after the `Creating delayed handler` state and before the `got old table` state. The delayed-insert handler thread has just been started, and the connection thread is waiting for it to initialize.

States associated with a delayed-insert handler thread that inserts the rows:

- `insert`

  The state that occurs just before inserting rows into the table.

- `reschedule`

  After inserting a number of rows, the delayed-insert thread sleeps to let other threads do work.

- `upgrading lock`

  A delayed-insert handler is trying to get a lock for the table to insert rows.

- `Waiting for INSERT`

  A delayed-insert handler is waiting for a connection thread to add rows to the queue (see `storing row into queue`).

#### 7.5.4.4. Replication Master Thread States

The following list shows the most common states you may see in the `State` column for the master's `Binlog Dump` thread. If you see no `Binlog Dump` threads on a master server, this means that replication is not running — that is, that no slaves are currently connected.

- `Sending binlog event to slave`

  Binary logs consist of events, where an event is usually an update plus some other information. The thread has read an event from the binary log and is now sending it to the slave.

- `Finished reading one binlog; switching to next binlog`

  The thread has finished reading a binary log file and is opening the next one to send to the slave.

- `Has sent all binlog to slave; waiting for binlog to be updated`

  The thread has read all outstanding updates from the binary logs and sent them to the slave. The thread is now idle, waiting for new events to appear in the binary log resulting from new updates occurring on the master.

- `Waiting to finalize termination`

  A very brief state that occurs as the thread is stopping.

#### 7.5.4.5. Replication Slave I/O Thread States

The following list shows the most common states you see in the `State` column for a slave server I/O thread. Beginning with MySQL 4.1.1, this state also appears in the `Slave_IO_State` column displayed by `SHOW SLAVE STATUS`, so you can get a good view of what is happening by using that statement.

- `Waiting for master update`

  The initial state before `Connecting to master`.

- `Connecting to master`

  The thread is attempting to connect to the master.

- `Checking master version`

  A state that occurs very briefly, after the connection to the master is established.

- `Registering slave on master`

  A state that occurs very briefly after the connection to the master is established.

- `Requesting binlog dump`

  A state that occurs very briefly, after the connection to the master is established. The thread sends to the master a request for the contents of its binary logs, starting from the requested binary log filename and position.

- `Waiting to reconnect after a failed binlog dump request`

  If the binary log dump request failed (due to disconnection), the thread goes into this state while it sleeps, then tries to reconnect periodically. The interval between retries can be specified using the `--master-connect-retry` option.

- `Reconnecting after a failed binlog dump request`

  The thread is trying to reconnect to the master.

- `Waiting for master to send event`

  The thread has connected to the master and is waiting for binary log events to arrive. This can last for a long time if the master is idle. If the wait lasts for `slave_net_timeout` seconds, a timeout occurs. At that point, the thread considers the connection to be broken and makes an attempt to reconnect.

- `Queueing master event to the relay log`

  The thread has read an event and is copying it to the relay log so that the SQL thread can process it.

- `Waiting to reconnect after a failed master event read`

  An error occurred while reading (due to disconnection). The thread is sleeping for `master-connect-retry` seconds before attempting to reconnect.

- `Reconnecting after a failed master event read`

  The thread is trying to reconnect to the master. When connection is established again, the state becomes `Waiting for master to send event`.

- `Waiting for the slave SQL thread to free enough relay log space`

  You are using a non-zero `relay_log_space_limit` value, and the relay logs have grown large enough that their combined size exceeds this value. The I/O thread is waiting until the SQL thread frees enough space by processing relay log contents so that it can delete some relay log files.

- `Waiting for slave mutex on exit`

  A state that occurs briefly as the thread is stopping.

#### 7.5.4.6. Replication Slave SQL Thread States

The following list shows the most common states you may see in the `State` column for a slave server SQL thread:

- `Waiting for the next event in relay log`

  The initial state before `Reading event from the relay log`.

- `Reading event from the relay log`

  The thread has read an event from the relay log so that the event can be processed.

- `Has read all relay log; waiting for the slave I/O thread to update it`

  The thread has processed all events in the relay log files, and is now waiting for the I/O thread to write new events to the relay log.

- `Making temp file`

  The thread is executing a `LOAD DATA INFILE` statement and is creating a temporary file containing the data from which the slave will read rows.

- `Waiting for slave mutex on exit`

  A very brief state that occurs as the thread is stopping.

The `State` column for the I/O thread may also show the text of a statement. This indicates that the thread has read an event from the relay log, extracted the statement from it, and is executing it.

#### 7.5.4.7. Replication Slave Connection Thread States

These thread states occur on a replication slave but are associated with connection threads, not with the I/O or SQL threads.

- `Changing master`

  The thread is processing a `CHANGE MASTER` statement.

- `Creating table from master dump`

  The slave is creating a table using the `CREATE TABLE` statement contained in the dump from the master. Used for `LOAD TABLE FROM MASTER` and `LOAD DATA FROM MASTER`.

- `Killing slave`

  The thread is processing a `SLAVE STOP` statement.

- `Opening master dump table`

  This state occurs after `Creating table from master dump`.

- `Reading master dump table data`

  This state occurs after `Opening master dump table`.

- `Rebuilding the index on master dump table`

  This state occurs after `Reading master dump table data`.

- `starting slave`

  The thread is starting the slave threads after processing a successful `LOAD DATA FROM MASTER` load operation.

#### 7.5.4.8. MySQL Cluster Thread States

- `Committing events to binlog`

- `Opening mysql.ndb_apply_status`

- `Processing events`

  The thread is processing events for binary logging.

- `Processing events from schema table`

  The thread is doing the work of schema replication.

- `Shutting down`

- `Syncing ndb table schema operation and binlog`

  This is used to have a correct binary log of schema operations for NDB.

- `Waiting for event from ndbcluster`

  The server is acting as an SQL node in a MySQL Cluster, and is connected to a cluster management node.

- `Waiting for first event from ndbcluster`

- `Waiting for ndbcluster binlog update to reach current position`

- `Waiting for ndbcluster to start`

- `Waiting for schema epoch`

  The thread is waiting for a schema epoch (that is, a global checkpoint).

### 7.5.5. 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.

### 7.5.6. 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 is shared by all threads; its size is determined by the `key_buffer_size` variable. 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. The following list indicates these and which variables control their size:

  - A stack (default 64KB before MySQL 4.0.10 and 192KB thereafter, variable `thread_stack`)

  - A connection buffer (variable `net_buffer_length`)

  - A result buffer (variable `net_buffer_length`)

  The connection buffer and result buffer both begin with a size given by `net_buffer_length` but are dynamically enlarged up to `max_allowed_packet` bytes as needed. The result buffer shrinks to `net_buffer_length` after each SQL statement. While a statement is running, a copy of the current statement 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 `ISAM` and `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.

  One problem before MySQL 3.23.2 is that if an internal in-memory heap table exceeds the size of `tmp_table_size`, the error `The table `tbl_name` is full` occurs. From 3.23.2 on, this is handled automatically by changing the in-memory heap table to a disk-based `MyISAM` table as necessary. To work around this problem for older servers, you can 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 12.5.3, “`SET` Syntax”.

  In MySQL 3.20, the maximum size of the temporary table is `record_buffer*16`; if you are using this version, you have to increase the value of `record_buffer`. You can also start mysqld with the `--big-tables` option to always store temporary tables on disk. However, this affects the speed of many complicated queries.

- 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.1.4.4, “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` or `ISAM` table that is opened, the index file is opened once and 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` and `ISAM` storage engines maintain 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.

### 7.5.7. How MySQL Uses Internal Temporary Tables

In some cases, the server creates internal temporary tables while processing queries. A temporary table can be held in memory and processed by the `MEMORY` storage engine, or stored on disk and processed by the `MyISAM` storage engine. Temporary tables can be created under conditions such as these:

- 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.

- `DISTINCT` combined with `ORDER BY` may require a temporary table.

You can tell whether a query requires a temporary table by using `EXPLAIN` and checking the `Extra` column to see whether it says `Using temporary`. See Section 7.2.1, “Optimizing Queries with `EXPLAIN`”.

Some conditions prevent the use of a `MEMORY` temporary table, in which case the server uses a `MyISAM` table instead:

- Presence of a `TEXT` or `BLOB` column in the table

- Presence of any column in a `GROUP BY` or `DISTINCT` clause larger than 512 bytes

- Presence of any column larger than 512 bytes in the `SELECT` list, if `UNION` or `UNION ALL` is used.

A temporary table that is created initially as a `MEMORY` table might be converted to a `MyISAM` table and stored on disk if it becomes too large. The `max_heap_table_size` system variable determines how large `MEMORY` tables are allowed to grow. It applies to all `MEMORY` tables, including those created with `CREATE TABLE`. However, for internal `MEMORY` tables, the actual maximum size is determined by `max_heap_table_size` in combination with `tmp_table_size`: Whichever value is smaller is the one that applies. If the size of an internal `MEMORY` table exceeds the limit, MySQL automatically converts it to an on-disk `MyISAM` table.

### 7.5.8. 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.

If you really need to do this, you can change one of the source files. The file to modify depends on your version of MySQL. For MySQL 4.0 and up, look for the following statement in the `mysys/my_symlink.c` file:

    if (!(MyFlags & MY_RESOLVE_LINK) ||
        (!lstat(filename,&stat_buff) && S_ISLNK(stat_buff.st_mode)))

Before MySQL 4.0, look for this statement in the `mysys/mf_format.c` file:

    if (flag & 32 || (!lstat(to,&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

Before MySQL 4.0, you should not symlink tables unless you are very careful with them. The problem is that if you run `ALTER TABLE`, `REPAIR TABLE`, or `OPTIMIZE TABLE` on a symlinked table, the symlinks are removed and replaced by the original files. This happens because these statements work by creating a temporary file in the database directory and replacing the original file with the temporary file when the statement operation is complete.

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.

In MySQL 4.0, 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 in MySQL 4.0 works the following way:

- 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 12.1.5, “`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. The same is true for the `ALTER TABLE`, `OPTIMIZE TABLE`, and `REPAIR TABLE` statements.

- ### 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` or `RENAME TABLE` 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` or `RENAME TABLE` to move a table to another database, the table is moved to the other database directory. If the table name changed, the symlinks in the new database directory are renamed to the new names and the data file and index file are renamed accordingly.

- 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.

`SHOW CREATE TABLE` does not report if a table has symbolic links prior to MySQL 4.0.15. This is also true for mysqldump, which uses `SHOW CREATE TABLE` to generate `CREATE TABLE` statements.

Table symlink operations that are not supported up through MySQL 4.1:

- `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

Beginning with MySQL 3.23.16, the mysqld-max and `mysql-max-nt` servers for Windows are compiled with the `-DUSE_SYMDIR` option. 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 symbolic links work on Unix, although the procedure for setting up the link is different.

It is necessary to define `USE_SYMDIR` explicitly only before MySQL 4.0; for mysqld-max and `mysql-max-nt`, you can enable symbolic links by using the `--symbolic-links` option. As of MySQL 4.0, symbolic links are enabled by default for all Windows servers. If you do not need them, you can disable them with 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`.

The following limitations apply to the use of `.sym` files for database symbolic linking on Windows:

- The symbolic link is not used if a directory with the same name as the database exists in the MySQL data directory.

- The `--innodb_file_per_table` option cannot be used.

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