MySQL keeps row data and index data in separate files. Many (almost all) other databases 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 will cause 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 will 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 can't use only the index table to retrieve data for a query.
You use more space because you must duplicate indexes from the nodes (you can't store the row in the nodes).
Deletes will degenerate the table over time (because indexes in nodes are usually not updated on delete).
It's harder to cache only the index data.
One of the most basic optimizations is to design your tables to take as little space on the disk as possible. This can give 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 a lot of different table types and row formats. For each table, you can decide which storage/index method to use. Choosing the right table format for your application may give you a big performance gain. See Chapter 15, MySQL Storage Engines and Table Types.
You can get better performance on a table and minimize storage space using the techniques listed here:
Use the most efficient (smallest) data types possible. MySQL has many specialized types that save disk space and memory.
Use the smaller integer types if possible to get smaller tables. For example, MEDIUMINT is often better than INT.
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 don't have any variable-length columns (VARCHAR, TEXT, or BLOB columns), a fixed-size record format is used. This is faster but unfortunately may waste some space. See MyISAM table formats.
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 things fast. If you mostly access a table by searching on a combination of columns, make an index on them. The first index part should be the most used column. If you are always using many columns, you should use the column with more duplicates first to get better compression of the index.
If it's very likely that a column has a unique prefix on the first number of characters, it's better to index only this prefix. MySQL supports an index on the leftmost part of a character column. Shorter indexes are faster not only because they take less disk space, but also because they will give you more hits in the index cache and thus fewer disk seeks. See the section called “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.
All MySQL column 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 15, MySQL Storage Engines and Table Types. All storage engines support at least 16 indexes per table and a total index length of at least 256 bytes. Most storage engines have higher limits.
With col_name(length) syntax in an index specification, you can create an index that uses only the first length characters of a CHAR or VARCHAR column. Indexing only a prefix of column values like this can make the index file much smaller. See the section called “Column Indexes”.
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 255 bytes long (or 1000 bytes for MyISAM and InnoDB tables as of MySQL 4.1.2). Note that prefix limits are measured in bytes, whereas the prefix length in CREATE TABLE statements is interpreted as number of characters. 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 table type supports FULLTEXT indexes and only for CHAR, VARCHAR, and TEXT columns. Indexing always happens over the entire column and partial (prefix) indexing is not supported. See the section called “Full-Text Search Functions” for details.
As of MySQL 4.1.0, you can create indexes on spatial column types. Currently, spatial types are supported only by the MyISAM storage engine. Spatial indexes use R-trees.
The MEMORY (HEAP) storage engine supports hash indexes. As of MySQL 4.1.0, the engine also supports B-tree indexes.
MySQL can create indexes on multiple columns. An index may consist of up to 15 columns. For certain column types, you can index a prefix of the column (see the section called “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 don't 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 last_name and first_name. 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 will be 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 will not be 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 the next section.
Indexes are used to find rows with specific column values fast. Without an index, MySQL has to start with the first record and then read through the whole 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. Note that if you need to access almost all 1,000 rows, it is faster to read sequentially, because that minimizes disk seeks.
Most MySQL indexes (PRIMARY KEY, UNIQUE, INDEX, and FULLTEXT) are stored in B-trees. Exceptions are that indexes on spatial column types use R-trees, and MEMORY (HEAP) tables support hash indexes.
Strings are automatically prefix- and end-space compressed. See CREATE INDEX.
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.
To quickly find the rows that match a WHERE clause.
To eliminate rows from consideration. If there is a choice between multiple indexes, MySQL normally uses the index that finds the smallest number of rows.
To retrieve rows from other tables when performing joins.
To find the MIN() or MAX() value for a specific indexed column key_col. This is optimized by a preprocessor that checks whether you are using WHERE key_part_# = constant on all key parts that occur before key_col in the index. In this case, MySQL will do a single key lookup for each MIN() or MAX() expression and replace it with a constant. If all expressions are replaced with constants, the query will return 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 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 will find 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 can't use a partial index if the columns don't 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 col2=val2; SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;
If an index exists on (col1, col2, col3), only the first of the preceding queries uses the index. The second and third queries do involve indexed columns, but (col2) and (col2, col3) are not leftmost prefixes of (col1, col2, col3).
An index is used for columns that you compare with the =, >, >=, <, <=, or BETWEEN operators.
MySQL also uses indexes for LIKE comparisons if the argument to LIKE is a constant string that doesn't 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 will 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 up performs an additional LIKE optimization. If you use ... LIKE '%string%' and string is longer than three characters, MySQL will use the Turbo Boyer-Moore algorithm to initialize the pattern for the string and then use this pattern to perform the search quicker.
Searching using col_name IS NULL will use indexes if col_name is indexed.
Any index that doesn't 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 AND parts */
... WHERE index=1 OR A=10
/* No index spans all rows */
... WHERE index_part1=1 OR index_part2=10
Sometimes MySQL will not use an index, even if one is available. One way this occurs is when the optimizer estimates that using the index would require MySQL to access a large percentage of the rows in the table. (In this case, a table scan is probably much faster, because it will require many fewer seeks.) However, if such a query uses LIMIT to only retrieve part of the rows, MySQL will use an index anyway, because it can much more quickly find the few rows to return in the result.
Hash indexes have somewhat different characteristics than those just discussed:
They are used only for = or <=> comparisons (but are very fast).
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.)
To minimize disk I/O, the MyISAM storage engine employs a strategy that is used by many database management systems. It exploits a cache mechanism to keep the most frequently accessed table blocks in memory:
For index blocks, a special structure called the key cache (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.
You can control the size of the key cache by means of the key_buffer_size system variable. If this variable is set equal to zero, no key cache is used. The key cache also is not used if the key_buffer_size value is too small to allocate the minimal number of block buffers (8).
When the key cache is not operational, index files are accessed using only the native filesystem buffering provided by the operating system. (In other words, table index blocks are accessed using the same strategy as that employed for table data blocks.)
An index block is a contiguous unit of access to the MyISAM index files. Usually the size of an index block is equal to the size of nodes of the index B-tree. (Indexes are represented on disk using a B-tree data structure. Nodes at the bottom of the tree are leaf nodes. Nodes above the leaf nodes are non-leaf nodes.)
All block buffers in a key cache structure are the same size. This size can be equal to, greater than, or less than the size of a table index block. Usually one these two values is a multiple of the other.
When data from any table index block must be accessed, the server first checks whether it is available in some block buffer of the key cache. If it is, the server accesses data in the key cache rather than on disk. That is, it reads from the cache or writes into it rather than reading from or writing to disk. Otherwise, the server chooses a cache block buffer containing a different table index block (or blocks) and replaces the data there by a copy of required table index block. As soon as the new index block is in the cache, the index data can be accessed.
If it happens that a block selected for replacement has been modified, the block is considered “dirty.” In this case, before 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 be able to make such a choice easy, 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.
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 allows the server to improve throughput significantly.
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 offers the feature of multiple key caches. This allows you to assign different table indexes to different key caches.
When there can be 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.
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;
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 the section called “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. This is used 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. This is used for medium-sized intensively modified tables, such as temporary tables.
A warm key cache that takes up 60% of the key cache space. This is 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. Queries that access tables assigned to one cache do not compete with queries 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 will remain in the cache.
An update operation most frequently executed for temporary tables is performed much faster when the updated node already 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 already will be in the cache.
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. In the future, we will allow the hit count to depend on the B-tree level of the node corresponding to an index block: Fewer hits will be required for promotion of an index block if it contains a non-leaf node from the upper levels of the index B-tree than if it contains a leaf node.
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 will be preserved in the hot sub-chain during an index scan operation as well.
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, then 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 still will be 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 will be fetched from disk in a random order, 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.
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. In the future, this will be changed and then key_cache_block_size variable will be fully employed.
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 set 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 will execute, a slowdown can be anticipated. Once 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.
When you execute a mysqladmin status command, you'll 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 takes some 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 ISAM storage engine shares this behavior.
You can read more about this topic in the next section. See the section called “How MySQL Opens and Closes Tables”.
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 in a join. You also need to 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 for MySQL with the --open-files-limit startup option to mysqld_safe. See the section called “File Not Found”.
The cache of open tables will be 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 even more tables to be able to execute queries.
An unused table is closed and removed 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 thread is no longer using a table.
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, in least recently used order.
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 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 isn't full). See HANDLER.
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 quite big, even when you haven't issued a lot of FLUSH TABLES statements, you should increase your table cache size. See the section called “Server System Variables” and the section called “Server Status Variables”.
If you have many MyISAM or ISAM tables in a database directory, open, close, and create operations will be slow. If you execute SELECT statements on many different tables, there will be a little overhead when the table cache is full, because for every table that has to be opened, another must be closed. You can reduce this overhead by making the table cache larger.