Hosting guebs

Capítulo 7. Optimización de MySQL

Tabla de contenidos

7.1. Panorámica sobre optimización
7.1.1. Limitaciones y soluciones de compromiso en el diseño de MySQL
7.1.2. Diseñar aplicaciones pensando en la portabilidad
7.1.3. Para qué hemos usado MySQL
7.1.4. El paquete de pruebas de rendimiento (benchmarks) de MySQL
7.1.5. Usar pruebas de rendimiento (benchmarks) propios
7.2. Optimizar sentencias SELECT y otras consultas
7.2.1. Sintaxis de EXPLAIN (Obtener información acerca de un SELECT)
7.2.2. Estimar el renidimiento de una consulta
7.2.3. Velocidad de las consultas SELECT
7.2.4. Optimización de las cláusulas WHERE por parte de MySQL
7.2.5. Optimización de rango
7.2.6. Index Merge Optimization
7.2.7. Cómo optimiza MySQL IS NULL
7.2.8. Cómo MySQL optimiza DISTINCT
7.2.9. Cómo optimiza MySQL los LEFT JOIN y RIGHT JOIN
7.2.10. Cómo optimiza MySQL ORDER BY
7.2.11. Cómo optimiza MySQL los GROUP BY
7.2.12. Cómo optimiza MySQL las cláusulas LIMIT
7.2.13. Cómo evitar lecturas completas de tablas
7.2.14. Velocidad de la sentencia INSERT
7.2.15. Velocidad de las sentencias UPDATE
7.2.16. Velocidad de sentencias DELETE
7.2.17. Otros consejos sobre optimización
7.3. Temas relacionados con el bloqueo
7.3.1. Métodos de bloqueo
7.3.2. Cuestiones relacionadas con el bloqueo (locking) de tablas
7.4. Optimizar la estructura de una base de datos
7.4.1. Elecciones de diseño
7.4.2. Haga sus datos lo más pequeños posibles
7.4.3. Índices de columna
7.4.4. Índices de múltiples columnas
7.4.5. Cómo utiliza MySQL los índices
7.4.6. La caché de claves de MyISAM
7.4.7. Cómo cuenta MySQL las tablas abiertas
7.4.8. Cómo abre y cierra tablas MySQL
7.4.9. Desventajas de crear muchas tablas en la misma base de datos
7.5. Optimización del servidor MySQL
7.5.1. Factores de sistema y afinamientos de parámetros de arranque
7.5.2. Afinar parámetros del servidor
7.5.3. Vigilar el rendimiento del optimizador de consultas
7.5.4. Efectos de la compilación y del enlace en la velocidad de MySQL
7.5.5. Cómo utiliza MySQL la memoria
7.5.6. Cómo usa MySQL las DNS
7.6. Cuestiones relacionadas con el disco
7.6.1. Utilizar enlaces simbólicos

La optimización es una tarea compleja, porque requiere un conocimiento de todo el sistema a optimizar. Se podría optimizar sólo algunos aspectos teniendo poco conocimiento del sistema o aplicación, pero cuanto más óptimo se quiera el sistema, más se tiene que conocer acerca del mismo.

Este capítulo intenta explicar y da algunos ejemplos de las diferentes maneras de optimizar MySQL. Sin embargo se debe recordar que siempre existen vías de hacer todavía más rápido el sistema, aunque pudieran requerir un notable incremento de esfuerzos.

7.1. Panorámica sobre optimización

El factor más importante para hacer un sistema rápido es su diseño básico. Ademas, es necesario conocer los procesos que cumple el sistema y cuáles son sus cuellos de botella. En la mayoría de casos los cuellos de botella nacen de los siguientes factores:

  • Búsqueda en Disco. El disco necesita cierto tiempo para encontrar un paquete de datos. Con discos modernos, el tiempo medio para esto es usualmente menor a 10ms, así que en teoría se pueden hacer 100 búsquedas por segundo. Este tiempo mejora lentamente con los discos nuevos y es muy difícil optimizarlo para una sola tabla. La manera de optimizar el tiempo de búsqueda es distribuir los datos dentro de más de un disco.

  • Lectura y escritura en disco. Cuando el disco se encuentra en la posición correcta, necesitamos leer los datos. Los discos modernos transfieren al menos 10-20MB/s. Esto es mucho más fácil de optimizar que las búsquedas, puesto que podemos leer en paralelo desde múltiples discos.

  • Ciclos de CPU. Cuando tenemos datos en la memoria principal, necesitamos procesarlos para obtener algún resultado. Tener tablas pequeñas en comparación con la cantidad de memoria es el factor mas común de limitación. Pero con tablas pequeñas, la rapidez no es usualmente el problema.

  • Ancho de banda de Memoria. Cuando el CPU necesita más datos de los que puede almacenar en la cache de la CPU, el ancho principal de la memoria se convierte en un cuello de botella. Es poco común en la mayoría de casos, pero debe tenerse en cuenta.

7.1.1. Limitaciones y soluciones de compromiso en el diseño de MySQL

Al utilizar el motor de almacenamiento MyISAM, MySQL usa un bloqueo (lock) extremadamente rápido de tablas, que permite múltiples lecturas o una sola escritura. El mayor problema con este motor de almacenamiento ocurre cuando se tiene un flujo constante de actualizaciones y selecciones lentas de una sola tabla. Si éste es el problema para algunas tablas, puede usar otro motor de almacenamiento para ellas. Ver Capítulo 14, Motores de almacenamiento de MySQL y tipos de tablas.

MySQL puede trabajar con tablas transaccionales y no transaccionales. Para hacer el trabajo más facil con tablas no transaccionales (donde no se puede deshacer una transacción si algo va mal), MySQL tiene las siguientes reglas. Obsérvese que estas reglas sólo se aplican cuando no se está corriendo en modo SQL estricto o si se usa el especificador IGNORE para un INSERT o un UPDATE.

  • Todas las columnas tienen valores por defecto. Obsérvese que cuando se ejecuta en modo SQL estricto (incluyendo modo SQL TRADITIONAL), se debe especificar cualquier valor para una columna NOT NULL.

  • Si se inserta un valor inapropiado o fuera de rango dentro de una columna, MySQL atribuye a la columna el “ mejor valor posible ” en vez de reportar un error. Para valores numéricos, éste es el 0, el valor más pequeño posible o el valor más grande posible. Para cadenas de texto, puede ser una cadena vacía o el trozo de la cadena más grande que quepa en la columna. Este comportamiento no se aplica cuando se ejecuta en modo SQL estricto TRADITIONAL.

  • Todas las expresiones calculadas retornan un valor que puede ser usado en vez de señalar una condición de error. Por ejemplo, 1/0 devuelve NULL. (Este comportamiento puede ser cambiado usando el modo SQL ERROR_FOR_DIVISION_BY_ZERO).

Si se usa una tabla no transaccional, no debería usar MySQL para comprobar el contenido de la columna. En general, la manera más segura (y generalmente más rápida) es usar la aplicacion para asegurarse que se están pasando sólo valores legales a la base de datos.

Para más informacion acerca de esto, ver Sección 1.7.6, “Cómo trata MySQL las restricciones (Constraints)” y Sección 13.2.4, “Sintaxis de INSERT o Sección 5.3.2, “El modo SQL del servidor”.

7.1.2. Diseñar aplicaciones pensando en la portabilidad

Debido a que todos los servidores SQL implementan diferentes partes del estándar SQL, toma trabajo escribir aplicaciones SQL portables. Es muy fácil obtener portabilidad para selects muy simples y para inserts, pero es más difícil cuantas más funcionalidades se requieran. Obtener una aplicación que sea rápida en varios sistemas de bases de datos, es una tarea muy difícil.

Para hacer aplicaciones complejas portables, necesita determinar para cuáles servidores SQL debe trabajar, después determinar qué características soportan esos servidores.

Todos los sistemas de bases de datos tienen algunos puntos débiles. Esto es, tienen diferentes concesiones de diseño que conducen a comportamientos diferentes.

Puede usar el programa de MySQL crash-me para encontrar funciones, tipos y límites que puede usar con una selección de servidores de bases de datos. crash-me no comprueba cada posible característica, pero es razonablemente completo, puesto que hace cerca de 450 pruebas.

Un ejemplo de un tipo de información de la que el programa crash-me puede proveer es que no debería usar nombres de columnas que sean mayores de 18 caracteres si quiere que la aplicación funcione en Informix o DB2.

El programa crash-me y MySQL benchmarks son independientes de la base de datos. Mirando por encima cómo están escritos, puede hacerse una idea de qué debe hacer que sus propias aplicaciones sean independientes de bases de datos. Los programas se encuentran en el directorio sql-bench dentro del código fuente de la distribución de MySQL. Estan escritos en Perl y usan la interfaz para bases de datos DBI. Usar DBI ya soluciona una parte de los problemas de portabilidad puesto que provee de métodos de acceso independientes a las bases de datos.

Ver http://dev.mysql.com/tech-resources/benchmarks/ para los resultados de las mediciones.

Esforzarse para ser independiente del motor de bases de datos, implica prestar atención a los cuellos de botella de cada servidor SQL. Por ejemplo, MySQL es muy rápido obteniendo y actualizando registros para tablas MyISAM, pero tiene un problema mezclando lecturas y escrituras lentas sobre la misma tabla. Por otra parte, Oracle tiene un enorme problema cuando intenta acceder a registros que se han actualizado recientemente (hasta que se escriben en el disco). Las bases de datos transaccionales en general no son muy buenas generando resúmenes de tablas desde las tablas de registro (log), puesto que en este caso el bloqueo (lock) de registros es casi inútil.

Para hacer una aplicación realmente independiente de la base de datos, se necesita definir una interfaz fácilmente extendible a través de la cual se manipularán los datos. Puesto que C++ está disponible en la mayoría de los sistemas, tiene sentido usar una interfaz basada en clases de C++ hacia la base de datos.

Si usa alguna característica que es específica de algún sistema de base de datos (por ejemplo la sentencia REPLACE, que es específica en MySQL), debería implementar la misma característica para otros servidores SQL codificando un método alternativo. Aunque la alternativa sea más lenta, permite que la misma tarea se haga en otros servidores.

Con MySQL, puede usar la sintaxis /*! */ para agregar algunas palabras claves de MySQL a una consulta. El código dentro de /**/ es tratado como un comentario (e ignorado) por la mayoría de los otros servidores SQL.

Si el alto rendimiento es más importante que la exactitud, como en algunas aplicaciones Web, es posible crear una capa de la aplicación que almacene en una cache todos los resultados para obtener un rendimiento mejor. Dejando que los resultados viejos expiren en determinado tiempo, puede mantener la cache razonablemente actualizado. Éste es un método para soportar picos de carga, al que se añade la posibilidad de implementar un incremento dinámico de la cache y un aumento del tiempo de expiración, hasta que las cosas regresen a la normalidad.

En este caso, la información de creación de la tabla debe contener información del tamaño inicial de la cache y de la frecuencia de refresco de la tabla.

Una alternativa para implementar una aplicación en cache es usar la cache para consultas de MySQL. Habilitando la cache para consultas, el servidor toma los detalles de las consultas determinando qué resultados pueden ser reutilizados. Esto simplifica la aplicación. Ver Sección 5.12, “La caché de consultas de MySQL”.

7.1.3. Para qué hemos usado MySQL

Esta sección describe un uso inicial de MySQL.

Durante el inicio del desarrollo de MySQL, las características de MySQL fueron pensadas para ajustarse a nuestro cliente más grande, que hacía data-warehouse para un par de los mayoristas más grandes de Suecia.

Desde todas las tiendas obteníamos resúmenes semanales de todas las transacciones con tarjetas de bonos, y se esperaba de nosotros información útil para que los dueños de las tiendas entendieran cómo sus campañas publicitarias afectaban a sus propios clientes.

El volumen de datos era enorme (cerca de siete millones de transacciones resumidas por mes), y teníamos datos de 4-10 años que debíamos presentar a los usuarios. Teníamos peticiones semanales de nuestros clientes, quienes querían acceso instantáneo a los nuevos reportes de esta información.

Solucionamos este problema almacenando toda la información por mes en unas “tablas de transacciones” comprimidas. Teníamos una serie de sencillas macros que generaban resúmenes de tablas agrupados por diferentes criterios (grupo de productos, id del cliente, tienda, etc) a partir de las tablas en las que fueron almacenadas las transacciones. Los reportes eran páginas Web que eran generadas dinámicamente por un pequeño script escrito en Perl. Este script analizaba la página Web, ejecutaba una sentencia SQL y escribía los resultados. Debimos usar PHP o mod_perl en vez de eso, pero no estaban disponibles en aquel entonces.

Para datos gráficos, escribimos una sencilla herramienta en C que podía procesar los resultados de una consulta SQL y generar una imagen GIF basada en los resultados. Esta herramienta también era dinámicamente invocada desde el script en Perl que analizaba las páginas Web.

En la mayoría de casos, se podía crear un nuevo reporte simplemente copiando un script existente y modificando la consulta SQL que utilizaba. En algunos casos, necesitábamos agregar más columnas a una tabla de resumen existente o generar una nueva. Esto también fue sencillo puesto que guardábamos todas las tablas con las transacciones en disco. (El tamaño era de alrededor de 50GB de tablas de transacciones y 200GB de otros datos del cliente.)

También permitimos a nuestros clientes acceder a las tablas de resumen directamente con ODBC, para que los usuarios avanzados pudieran experimentar con los datos por sí mismos.

Este sistema trabajó bien y no tuvimos problemas elaborando los datos en un modesto servidor Sun Ultra SPARCstation (2x200Mhx). Finalmente el sistema fue migrado a Linux.

7.1.4. El paquete de pruebas de rendimiento (benchmarks) de MySQL

Esta sección debería contener una descripción técnica del paquete de pruebas de rendimiento de MySQL (así como del crash-me), pero esa descripción aún no ha sido escrita. Sin embargo, puede hacerse una buena idea de cómo hacer pruebas de rendimiento viendo el código y los resultados dentro del directorio sql-bench en el código fuente de la distribución de MySQL.

La finalidad de este paquete de pruebas de rendimiento es visualizar qué operaciones se realizan bien y cuáles lo hacen pobremente en cada implementación de SQL.

Estas pruebas de rendimiento no son multi hilo, así que miden el tiempo mínimo para las operaciones realizadas. Se planea agregar en un futuro pruebas multi hilo al paquete.

Para usar el paquete, deben satisfacerse los siguientes requisitos:

Una vez obtenido el código fuente de la distribución de MySQL, el paquete de pruebas de rendimiento se encuentra en el directorio sql-bench. Para ejecutar las pruebas de rendimiento, compílese MySQL, váyase al directorio sql-bench y ejecútese el script run-all-tests:

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

nombre_servidor debe ser uno de los servidores soportados. Para obtener la lista completa de opciones y servidores soportados, invóquese el comando:

shell> perl run-all-tests --help

El script crash-me también está situado dentro del directorio sql-bench. crash-me intenta determinar qué características soporta una base de datos y cuáles son sus capacidades y limitaciones. Esto lo consigue ejecutando consultas. Determina por ejemplo:

  • Cuáles tipos de columnas se soportan

  • Cuántos índicies se soportan

  • Qué funciones se soportan

  • Qué tamaño puede alcanzar una consulta

  • Que tamaño puede alcanzar una columna VARCHAR

Para más información acerca de resultados de pruebas de rendimiento, visítese http://dev.mysql.com/tech-resources/benchmarks/.

7.1.5. Usar pruebas de rendimiento (benchmarks) propios

Decididamente debería realizar pruebas de rendimiento sobre su aplicación y su base de datos para determinar dónde se encuentran los cuellos de botella. Eliminando un cuello de botella (o reemplazándolo con un módulo “tonto”) puede fácilmente identificar el siguiente cuello de botella. Aunque el rendimiento global de su aplicación sea aceptable, debería al menos hacer un plan para cada cuello de botella, y decidir cómo eliminarlo si algún día realmente necesita un rendimiento extra.

Para ejemplos de programas de pruebas de rendimiento portables, mire el paquete de pruebas de rendimiento MySQL. Ver Sección 7.1.4, “El paquete de pruebas de rendimiento (benchmarks) de MySQL”. Puede tomar cualquier programa de este paquete y modificarlo en base a sus necesidades. Así puede probar diferentes soluciones para su problema y probar cuál realmente es más rápida para usted.

Otro paquete de pruebas de rendimiento es Open Source Database Benchmark, disponible en http://osdb.sourceforge.net/.

Es muy común que un problema ocurra sólo cuando el sistema está bajo mucha carga. Muchos de nuestros clientes nos contactan cuando tienen un sistema (probado) en producción y encuentran problemas de carga. En la mayoría de casos, los problemas de rendimiento se deben a cuestiones relacionadas con el diseño básico de la base de datos (por ejemplo, lecturas completas de las tablas no son buenas bajo alta carga) o problemas con el sistema operativo o las bibliotecas. En la mayoría de casos, esos problemas se solucionarían mucho más fácilmente si los sistemas no estuvieran en producción.

Para evitar este tipo de problemas, es conveniente hacer pruebas de rendimiento a las aplicaciones enteras bajo la mayor carga de trabajo posible. Para ello, puede utilizarse Super Smack, disponible en http://jeremy.zawodny.com/mysql/super-smack/. Como su nombre indica, puede poner de rodillas a un sistema, así que asegúrese de usarlo sólo en sistemas de desarrollo.

7.2. Optimizar sentencias SELECT y otras consultas

Ante todo, un factor afecta a todas las sentencias: cuanto más compleja es la configuración de los permisos, mayor es la carga.

Usar permisos simples cuando se ejecuta una sentencia GRANT permite a MySQL reducir la carga en la verificación de permisos cuando los clientes ejecutan sentencias. Por ejemplo, si no se concede (grant) ningún privilegio a nivel de tabla o de columna, el servidor no necesita verificar nunca el contenido de las tablas tables_priv y columns_priv. Similarmente, si no se establece límites de recursos sobre ninguna cuenta, el servidor no tiene que realizar conteos de recursos. Cuando se tiene un alto volumen de consultas, puede valer la pena utilizar una estructura de permisos simplificada para reducir la carga de verificación de los mismos.

Si el problema es con una expresión o función específica de MySQL, se puede utilizar la función BENCHMARK() en el cliente mysql para realizar una prueba de velocidad. Su sintaxis es BENCHMARK(número_iteraciones,expresion). El valor devuelto siempre es cero, pero mysql muestra cuánto tardó en ejecutarse la sentencia. Por ejemplo:

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

Este resultado se obtuvo en un sistema Pentium II 400MHz. Muestra que MySQL puede ejecutar 1,000,000 de expresiones simples de suma en 0.32 segundo sobre ese sistema.

Todas las funciones de MySQL deberían estar altamente optimizadas, pero pueden existir algunas excepciones. BENCHMARK() es una excelente herramienta para determinar si alguna función es un problema en una consulta.

7.2.1. Sintaxis de EXPLAIN (Obtener información acerca de un SELECT)

EXPLAIN nombre_de_tabla

O:

EXPLAIN SELECT opciones_de_select

La sentencia EXPLAIN puede utilizarse como un sinónimo de DESCRIBE o también como una manera para obtener información acerca de cómo MySQL ejecuta una sentencia SELECT:

  • EXPLAIN nombre_de_tabla es sinónimo de DESCRIBE nombre_de_tabla o SHOW COLUMNS FROM nombre_de_tabla.

  • Cuando se precede una sentencia SELECT con la palabra EXPLAIN, MySQL muestra información del optimizador sobre el plan de ejecución de la sentencia. Es decir, MySQL explica cómo procesaría el SELECT, proporcionando también información acerca de cómo y en qué orden están unidas (join) las tablas.

Esta sección trata sobre el segundo uso de EXPLAIN.

EXPLAIN es una ayuda para decidir qué índices agregar a las tablas, con el fin de que las sentencias SELECT encuentren registros más rápidamente. EXPLAIN puede utilizarse también para verificar si el optimizador une (join) las tablas en el orden óptimo. Si no fuera así, se puede forzar al optimizador a unir las tablas en el orden en el que se especifican en la sentencia SELECT empezando la sentencia con SELECT STRAIGHT_JOIN en vez de simplemente SELECT.

Si un índice no está siendo utilizado por las sentencias SELECT cuando debiera, debe ejecutarse el comando ANALYZE TABLE, a fin de actualizar las estadísticas de la tabla como la cardinalidad de sus claves, que pueden afectar a la decisiones que el optimizador toma. Ver Sección 13.5.2.1, “Sintaxis de ANALYZE TABLE.

EXPLAIN muestra una línea de información para cada tabla utilizada en la sentencia SELECT. Las tablas se muestran en el mismo orden en el que MySQL las leería al procesar la consulta. MySQL resuelve todas las uniones (joins) usando un método de single-sweep multi-join. Esto significa que MySQL lee un registro de la primera tabla; encuentra su correspondiente en la segunda tabla, en la tercera, y así sucesivamente. Cuando todas las tablas han sido procesadas, MySQL muestra las columnas seleccionadas y recorre a la inversa la lista de tablas hasta que encuentra aquélla para la que la sentencia devuelve más registros. Se lee entonces el siguiente registro de esta tabla y el proceso continúa con la siguiente tabla.

EXPLAIN retorna una tabla; cada línea de esta tabla muestra información acerca de una tabla, y tiene las siguientes columnas:

  • 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 the following:

    • 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)

  • 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 with 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 const types, this is the best possible join type. It is used when all parts of an index are used by the join and the index is a PRIMARY KEY or UNIQUE index.

      eq_ref can be used for indexed columns that are compared using the = operator. The comparison value can be a constant or an expression that uses columns from tables that are read before this table.

      In the following examples, MySQL can use an eq_ref join to process ref_table:

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

    • ref

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

      ref can be used for indexed columns that are compared using the = or <=> operator.

      In the following examples, MySQL can use a ref join to process ref_table:

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

    • ref_or_null

      This join type is like ref, but with the addition that MySQL does an extra search for rows that contain NULL values. This join type optimization is used most often in resolving subqueries.

      In the following examples, MySQL can use a ref_or_null join to process ref_table:

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

      See Sección 7.2.7, “Cómo optimiza MySQL IS NULL.

    • index_merge

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

    • unique_subquery

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

      value IN (SELECT primary_key FROM single_table WHERE some_expr)
      

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

    • index_subquery

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

      value IN (SELECT key_column FROM single_table WHERE some_expr)
      

    • range

      Only rows that are in a given range are retrieved, using an index to select the rows. The key column 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 for 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 could 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 see 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 Sección 13.1.2, “Sintaxis de ALTER TABLE.

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

  • key

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

    For MyISAM and BDB tables, running ANALYZE TABLE helps the optimizer choose better indexes. For MyISAM tables, myisamchk --analyze does the same. See Sección 13.5.2.1, “Sintaxis de ANALYZE TABLE and Sección 5.8.3, “Mantenimiento de tablas y recuperación de un fallo catastrófico (crash)”.

  • 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 allows you to determine how many parts of a multiple-part key MySQL actually uses.

  • ref

    The ref column shows which columns or constants are used with the key to select rows from the table.

  • rows

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

  • Extra

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

    • Distinct

      MySQL stops searching for more rows for the current row combination after it has found the first matching row.

    • Not exists

      MySQL was able to do a LEFT JOIN optimization on the query and does not examine more rows in this table for the previous row combination after it finds one row that matches the LEFT JOIN criteria.

      Here is an example of the type of query that can be optimized this way:

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

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

    • range checked for each record (index map: #)

      MySQL found no good index to use, but found that some of indexes might be used once column values from preceding tables are known. For each row combination in the preceding tables, MySQL checks whether it is possible to use a range or index_merge access method to retrieve rows. The applicability criteria are as described in Sección 7.2.5, “Optimización de rango” and Sección 7.2.6, “Index Merge 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.

    • Using filesort

      MySQL needs to 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 Sección 7.2.10, “Cómo optimiza MySQL ORDER BY.

    • Using index

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

    • Using temporary

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

    • Using where

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

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

    • Using sort_union(...), Using union(...), Using intersect(...)

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

    • Using index for group-by

      Similar to the Using index way of accessing a table, Using index for group-by indicates that MySQL found an index that can be used to retrieve all columns of a GROUP BY or DISTINCT query without any extra disk access to the actual table. Additionally, the index is used in the most efficient way so that for each group, only a few index entries are read. For details, see Sección 7.2.11, “Cómo optimiza MySQL los GROUP BY.

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 product also is used to determine which multiple-table SELECT statements to execute. See Sección 7.5.2, “Afinar parámetros del servidor”.

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 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:

    TableColumnColumn Type
    ttActualPCCHAR(10)
    ttAssignedPCCHAR(10)
    ttClientIDCHAR(10)
    etEMPLOYIDCHAR(15)
    doCUSTNMBRCHAR(15)
  • The tables have the following indexes:

    TableIndex
    ttActualPC
    ttAssignedPC
    ttClientID
    etEMPLOYID (primary key)
    doCUSTNMBR (primary key)
  • The tt.ActualPC values are not evenly distributed.

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

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

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

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

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 is executed 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);

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

This is almost as good as it can get.

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;

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. If not, you may get better performance by using STRAIGHT_JOIN in your SELECT statement and trying to list the tables in a different order in the FROM clause.

7.2.2. Estimar el renidimiento de una consulta

In most cases, you can estimate the 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 1024 bytes and the data pointer is usually 4 bytes. For a 500,000-row table with an index length of 3 bytes (medium integer), 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 (as above) to find where to place the new index and normally two seeks to update the index and write the row.

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

7.2.3. Velocidad de las consultas SELECT

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 Sección 7.4.5, “Cómo utiliza MySQL los índices” and Sección 7.2.1, “Sintaxis de EXPLAIN (Obtener información acerca de un SELECT)”.

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 (if 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 records in order according to the index. Note that the first time you sort a large table this way, it may take a long time.

7.2.4. Optimización de las cláusulas WHERE por parte de MySQL

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.

Note that work on the MySQL optimizer is ongoing, so this section is incomplete. MySQL does many optimizations, not all of which are documented here.

Some of the optimizations performed by MySQL are listed here:

  • 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 HEAP 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 don't use GROUP BY or group 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 records 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 SQL_SMALL_RESULT, 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. The optimizer is more complex and bases its estimate on additional factors such as table size, number of rows, and I/O block size, so a fixed percentage no longer determines the choice between using an index or a scan.

  • 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 record 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;

The following queries are resolved 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. Optimización de rango

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

7.2.5.1. Método de acceso a rango para índices simples

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

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

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

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

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

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

  • A constant from the query string

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

  • The result of an uncorrelated subquery

  • Any expression composed entirely from subexpressions of the preceding types

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

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

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

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

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

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

For example, 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 right way to remove them is to replace them with TRUE, so that we don't miss any matching records 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 in the 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 doesn't depend on the order in which conditions appear in WHERE clause.

7.2.5.2. 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 records 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.)

    See Sección 7.2.5.1, “Método de acceso a rango para índices simples” for the definition of what is considered to be a constant.

    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 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 records 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 records 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 records contained within intervals are combined with OR, they form a condition that covers a set of records contained within the union of their intervals. If the conditions are combined with AND, they form a condition that covers a set of records 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 is:

    (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
    

Sección 7.2.5.1, “Método de acceso a rango para índices simples” describes how optimizations are performed to combine or eliminate intervals for range conditions on single-part index. Analogous steps are performed for range conditions on multiple-part keys.

7.2.6. Index Merge Optimization

The Index Merge (index_merge) method is used to retrieve rows with several ref, ref_or_null, or range scans and merge the results into one. This method is employed when the table condition is a disjunction of conditions for which ref, ref_or_null, or range could be used with different keys.

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

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

Examples:

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

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

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

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

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

  • intersection

  • union

  • sort-union

The following sections describe these methods in greater detail.

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

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

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

    For this query, two plans are possible:

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

    2. A range scan using the badkey < 30 condition.

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

    SELECT * FROM t1 FORCE INDEX(goodkey1,goodkey2)
    WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;
    
    SELECT * FROM t1 IGNORE INDEX(badkey)
    WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;
    
  • If your query has a complex WHERE clause with deep AND/OR nesting and MySQL doesn't choose the optimal plan, try distributing terms using the following identity laws:

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

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

7.2.6.1. Index Merge Intersection Access Algorithm

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

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

    key_part1=const1 AND key_part2=const2 ... AND key_partN=constN
    
  • Any range condition over a primary key of an InnoDB or BDB table.

Here are some examples:

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

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

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

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

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

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

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

7.2.6.2. Algoritmo de acceso a Index Merge Union Access Algorithm

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

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

    key_part1=const1 AND key_part2=const2 ... AND key_partN=constN
    
  • Any range condition over a primary key of an InnoDB or BDB table.

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

Here are some examples:

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

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

7.2.6.3. Index Merge Sort-Union Access Algorithm

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

Here are some examples:

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

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

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

7.2.7. Cómo optimiza MySQL IS NULL

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

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

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

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

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

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

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

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

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

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

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

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

7.2.8. Cómo MySQL optimiza DISTINCT

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

Note that 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 Sección 12.10.3, “GROUP BY con campos escondidos”.

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 Sección 7.2.11, “Cómo optimiza MySQL los GROUP BY.

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

If you don't use columns from all tables named in a query, MySQL stops scanning the not-used 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 the first row in t2 is found:

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

7.2.9. Cómo optimiza MySQL los LEFT JOIN y RIGHT JOIN

An A LEFT JOIN B join_condition is implemented in MySQL 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 done, 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 done.

  • 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 don't 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.

RIGHT JOIN is implemented analogously to 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 and 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,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 a and b are listed in the FROM clause:

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

MySQL 5.0 performs the following LEFT JOIN optimization: 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 this would result in a better query plan. To force a specific table order, use STRAIGHT_JOIN.

7.2.10. Cómo optimiza MySQL ORDER BY

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

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

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

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

  • You use ORDER BY on different keys:

    SELECT * FROM t1 ORDER BY key1, key2;
    
  • You use ORDER BY on non-consecutive parts of a key:

    SELECT * FROM t1 WHERE key2=constant ORDER BY key_part2;
    
  • You mix ASC and DESC:

    SELECT * FROM t1 ORDER BY key_part1 DESC, key_part2 ASC;
    
  • The key used to fetch the rows is not the same as the one used in the ORDER BY:

    SELECT * FROM t1 WHERE key2=constant ORDER BY key1;
    
  • You are joining many tables, and the columns in the ORDER BY are not all from the first non-constant table that is used to retrieve rows. (This is the first table in the EXPLAIN output that doesn't have a const join type.)

  • You have different ORDER BY and GROUP BY expressions.

  • The type of table index used doesn't store rows in order. For example, this is true for a HASH index in a HEAP table.

With EXPLAIN SELECT ... ORDER BY, you can check whether MySQL can use indexes to resolve the query. It cannot if you see Using filesort in the Extra column. See Sección 7.2.1, “Sintaxis de EXPLAIN (Obtener información acerca de un SELECT)”.

In MySQL 5.0, a filesort optimization is used that records not only the sort key value and row position, but the columns required for the query as well. This avoids reading the rows twice. The filesort algorithm works like this:

  1. Read the rows that match the WHERE clause, as before.

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

  3. Sort the tuples by sort key value

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

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

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

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

  • Increase the size of the sort_buffer_size variable.

  • Increase the size of the read_rnd_buffer_size variable.

  • Change tmpdir to point to a dedicated filesystem with large amounts of empty space. In MySQL 5.0, this option accepts several paths that are used in round-robin fashion. Paths should be separated by colon characters (':') on Unix and semicolon characters (';') on Windows, NetWare, and OS/2. You can use this feature to spread the load across several directories. Note: The paths should be for directories in filesystems that are located on different physical disks, not different partitions on the same disk.

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

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

7.2.11. Cómo optimiza MySQL los GROUP BY

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 the index stores its keys in order (for example, this is a B-Tree index, and not a HASH index). Whether usage of temporary tables can be replaced by index access also depends on which parts of an index are used in a query, the conditions specified for these parts, and the selected aggregate functions.

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

7.2.11.1. Loose index scan

The most efficient way is when the index is used to directly retrieve the group fields. With this access method, MySQL uses the property of some index types (for example, B-Trees) that the keys are ordered. This property allows use of lookup groups in an index without having to consider all keys in the index that satisfy all WHERE conditions. Since this access method considers only a fraction of the keys in an index, it is called a loose index scan . When there is no WHERE clause, a loose index scan reads as many keys as the number of groups, which may be a much smaller number than that of all keys. If the WHERE clause contains range predicates (see the discussion in Sección 7.2.1, “Sintaxis de EXPLAIN (Obtener información acerca de un SELECT)” of the range join type), a loose index scan looks up the first key of each group that satisfies the range conditions, and again reads the least possible number of keys. This is possible under the following conditions:

  • The query is over a single table.

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

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

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

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

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

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

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

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

    SELECT c1, SUM(c2) FROM t1 GROUP BY c1;
    
  • The fields in the GROUP BY clause do not refer to the beginning of the index, as shown here:

    SELECT c1,c2 FROM t1 GROUP BY c2, c3;
    
  • The query refers to a part of a key that comes after the GROUP BY part, and for which there is no equality with a constant, an example being:

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

7.2.11.2. Tight index scan

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

When the conditions for a loose index scan are not met, it is still possible to avoid creation of temporary tables for GROUP BY queries. If there are range conditions in the WHERE clause, this method reads only the keys that satisfy these conditions. Otherwise, it performs an index scan. Since 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, for all columns in a query referring to parts of the key coming before or in between parts of the GROUP BY key, there is a constant equality condition. 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 can be then 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 first method above, but still work with the second index access method (assuming we have the aforementioned index idx on table t1):

  • 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;
    

7.2.12. Cómo optimiza MySQL las cláusulas LIMIT

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, once the 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, 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, the LIMIT row_count clause is used to calculate how much space is required.

7.2.13. Cómo evitar lecturas completas de tablas

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 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 Sección 7.2.4, “Optimización de las cláusulas WHERE por parte de MySQL”.

  • 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 does a lot of key lookups and that a table scan would be faster.

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

7.2.14. Velocidad de la sentencia INSERT

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

  • Connecting: (3)

  • Sending query to server: (2)

  • Parsing query: (2)

  • Inserting record: (1 x size of record)

  • Inserting indexes: (1 x 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 may tune the bulk_insert_buffer_size variable to make data insertion even faster. See Sección 5.3.3, “Variables de sistema del servidor”.

  • If you are inserting a lot of rows from different clients, you can get higher speed by using the INSERT DELAYED statement. See Sección 13.2.4, “Sintaxis de INSERT.

  • With MyISAM tables you can insert rows at the same time that SELECT statements are running if there are no deleted rows in the tables.

  • When loading a table from a text file, use LOAD DATA INFILE. This is usually 20 times faster than using a lot of INSERT statements. See Sección 13.2.5, “Sintaxis de LOAD DATA INFILE.

  • With some extra work, it is possible to make LOAD DATA INFILE run even faster 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 Sección 14.1.3.3, “Características de las tablas comprimidas”.

    6. Re-create the indexes with myisamchk -r -q /path/to/db/tbl_name. This creates the index tree in memory before writing it to disk, which is much faster because it avoids lots of disk seeks. The resulting index tree is also perfectly balanced.

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

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

    In MySQL 5.0, you can also use ALTER TABLE tbl_name DISABLE KEYS instead of myisamchk --keys-used=0 -rq /path/to/db/tbl_name and ALTER TABLE tbl_name ENABLE KEYS instead of myisamchk -r -q /path/to/db/tbl_name. In this way, you can also skip the FLUSH TABLES steps.

  • You can speed up INSERT operations that are done with multiple statements by locking 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 statement.

    For transactional tables, you should use BEGIN and COMMIT instead of LOCK TABLES to obtain faster insertions.

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

    Connection 1 does 1000 inserts
    Connections 2, 3, and 4 do 1 insert
    Connection 5 does 1000 inserts
    

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

    INSERT, UPDATE, and DELETE operations are very fast in MySQL, but you can obtain better overall performance by adding locks around everything that does more than about five inserts or updates in a row. If you do very many inserts in a row, you could do a LOCK TABLES followed by an UNLOCK TABLES once in a while (about each 1,000 rows) 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 get some more speed for MyISAM tables, for both LOAD DATA INFILE and INSERT, enlarge the key cache by increasing the key_buffer_size system variable. See Sección 7.5.2, “Afinar parámetros del servidor”.

7.2.15. Velocidad de las sentencias UPDATE

Update statements are optimized as 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 does not get updated.

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

Note that for a MyISAM table that uses dynamic record format, updating a record to a longer total length may split the record. If you do this often, it is very important to use OPTIMIZE TABLE occasionally. See Sección 13.5.2.5, “Sintaxis de OPTIMIZE TABLE.

7.2.16. Velocidad de sentencias DELETE

The time to delete individual records is exactly proportional to the number of indexes. To delete records more quickly, you can increase the size of the key cache. See Sección 7.5.2, “Afinar parámetros del servidor”.

If you want to delete all rows from a table, use TRUNCATE TABLE tbl_name rather than DELETE FROM tbl_name. See Sección 13.2.9, “Sintaxis de TRUNCATE.

7.2.17. Otros consejos sobre optimización

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 can't 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 Sección 7.5.2, “Afinar parámetros del servidor”.

  • Always check whether all your queries really use the indexes you have created in the tables. In MySQL, you can do this with the EXPLAIN statement. See Sección 7.2.1, “Sintaxis de EXPLAIN (Obtener información acerca de un SELECT)”.

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

  • With MyISAM tables that have no deleted rows, you can insert rows at the end at the same time that another query is reading from the table. If this is important for you, you should consider using the table in ways that avoid deleting rows. Another possibility is to run OPTIMIZE TABLE after you have deleted a lot of rows.

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

  • In some cases, it may make sense to introduce a column that is “hashed” based on information from other columns. If this column is short and reasonably unique, it may be much faster than a big 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 a lot, you should try to avoid all variable-length columns (VARCHAR, BLOB, and TEXT). The table uses dynamic record format if it includes even a single variable-length column. See Capítulo 14, Motores de almacenamiento de MySQL y tipos de tablas.

  • It is normally not useful to split a table into different tables just because rows become large. To access a row, the biggest performance hit is the disk seek to find the first byte of the row. After finding the data, most modern disks can read the entire row quickly enough for most applications. The only case in which it is advantageous to split up a table is if the table is a MyISAM table with dynamic record format (see above) that you can change to a fixed record size, or if you very often need to scan the table but do not need most of the columns. See Capítulo 14, Motores de almacenamiento de MySQL y tipos de tablas.

  • 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 important when you use a MySQL storage engine such as MyISAM that has only table-level locking (multiple readers / single writers). This gives better performance with most databases, 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.

  • When possible, take advantage columns' default values to insert values explicitly only when the value to be inserted differs from the default. This reduces the amount parsing required of MySQL and so improves the speed of insert operations.

  • 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 in order to gain more speed.

  • Stored procedures or UDFs (user-defined functions) may be a good way to gain performance for some tasks. See Capítulo 19, Procedimientos almacenados y funciones and Sección 27.2, “Añadir nuevas funciones a MySQL” for more information about these.

  • You can always gain something by caching queries or answers in your application and then performing many inserts or updates together. If your database supports table locks (like MySQL and Oracle), this should help to ensure that the index cache is only flushed once after all updates. You can also take advantage of MySQL's query cache to achieve similar results; see Sección 5.12, “La caché de consultas de MySQL”.

  • Use INSERT DELAYED when you do not need to know when your data is written. This speeds things up because many records can be written with a single disk write.

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

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

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

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

  • Use AUTO_INCREMENT columns to generate unique values.

  • Use OPTIMIZE TABLE once in a while to avoid fragmentation with MyISAM tables when using a dynamic table format. See Sección 14.1.3, “Formatos de almacenamiento de tablas MyISAM.

  • Use MEMORY tables when possible to get more speed. See Sección 14.3, “El motor de almacenamiento MEMORY (HEAP)”.

  • 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.

  • Use in-memory tables 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.

  • Columns with identical information in different tables should be declared to have identical data types.

    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 types 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 don't have to parse your text files to find line and column boundaries.

  • Replication can provide performance benefits 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 Capítulo 6, Replicación en MySQL.

  • 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 they are 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. Temas relacionados con el bloqueo

7.3.1. Métodos de bloqueo

MySQL 5.0 supports table-level locking for MyISAM and MEMORY tables, page-level locking for BDB tables, and row-level locking for InnoDB tables.

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

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

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

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

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

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

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

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

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

When a lock is released, the lock is made available to the threads in the write lock queue, then to the threads in the read lock queue.

This means that if you have many updates for a table, SELECT statements wait until there are no more updates.

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

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

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

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

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

InnoDB uses row locks and BDB uses page locks. For these two storage engines, deadlocks are possible. This is because InnoDB automatically acquires row locks and BDB acquires page 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.

  • Makes it possible to lock a single row a long time.

Disadvantages of row-level locking:

  • Takes more memory than page-level or table-level locks.

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

  • Is definitely much worse than other locks if you often do GROUP BY operations on a large part of the data or if you often must scan the entire table.

  • With higher-level locks, you can also more easily support locks of different types to tune the application, because the lock overhead is less than for row-level locks.

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

  • Most statements for the table are reads.

  • Reads and updates on strict keys, where you update or delete a row that can be fetched with a single 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.

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

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

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

  • Instead of using row-level locks, you can employ application-level locks, such as GET_LOCK() and RELEASE_LOCK() in MySQL. These are advisory locks, so they work only in well-behaved applications.

7.3.2. Cuestiones relacionadas con el bloqueo (locking) de tablas

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

For InnoDB and BDB tables, MySQL uses only table locking if you explicitly lock the table with LOCK TABLES. For these table types, 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 list describes some ways to avoid or reduce contention caused by table locking:

  • Try to get the SELECT statements to run faster. You might have to create some summary tables to do this.

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

  • You can specify that all updates issued in a specific connection should be done with low priority by using the SET LOW_PRIORITY_UPDATES=1 statement. See Sección 13.5.3, “Sintaxis de SET.

  • 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 Sección 13.2.7, “Sintaxis de SELECT.

  • 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 MyISAM tables, which support concurrent SELECT and INSERT statements.

  • If you mix inserts and deletes on the same table, INSERT DELAYED may be of great help. See Sección 13.2.4.2, “Sintaxis de INSERT DELAYED.

  • If you have problems with mixed SELECT and DELETE statements, the LIMIT option to DELETE may help. See Sección 13.2.1, “Sintaxis de DELETE.

  • Using SQL_BUFFER_RESULT with SELECT statements can help to make the duration of table locks shorter. See Sección 13.2.7, “Sintaxis de SELECT.

  • 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 don't mix updates with selects that need to examine many rows in the same table.

  • You can use LOCK TABLES to increase speed, as many updating 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 Capítulo 15, El motor de almacenamiento InnoDB. See Sección 14.4, “El motor de almacenamiento BDB (BerkeleyDB)”.

7.4. Optimizar la estructura de una base de datos

7.4.1. Elecciones de diseño

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

  • It's harder to cache only the index data.

7.4.2. Haga sus datos lo más pequeños posibles

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 Capítulo 14, Motores de almacenamiento de MySQL y tipos de tablas.

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 a better choice than INT since 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 record format is used. This is faster but unfortunately may waste some space. See Sección 14.1.3, “Formatos de almacenamiento de tablas MyISAM. You can hint that you want to have fixed length rows even if you have VARCHAR columns with the CREATE option ROW_FORMAT=fixed.

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

    The compact InnoDB format also changes the way how CHAR columns containing UTF-8 data are stored. In the ROW_FORMAT=REDUNDANT format, a UTF-8 CHAR(n) occupies 3*n bytes, given that the maximum length of a UTF-8 encoded character is 3 bytes. Since many languages can be written mostly with single-byte UTF-8 characters, a fixed storage length often wastes space. The ROW_FORMAT=COMPACT format allocates a variable amount of n..3*n bytes for these columns by stripping trailing spaces if necessary. The minimum storage length is kept as n bytes in order to facilitate in-place updates in typical cases.

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

  • Create only the indexes that you really need. Indexes are good for retrieval but bad when you need to store data quickly. If you access a table mostly by searching on a combination of columns, make an index on them. The first index part should be the most used column. 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 column has a unique prefix on the first number of characters, it is 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 give you more hits in the index cache and thus fewer disk seeks. See Sección 7.5.2, “Afinar parámetros del servidor”.

  • 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. Índices de columna

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 Capítulo 14, Motores de almacenamiento de MySQL y tipos de tablas. 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.

The MyISAM and 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)));

Im MySQL 5.0, prefixes can be up to 1000 bytes long for MyISAM and InnoDB tables. Note that prefix limits are measured in bytes, whereas the prefix length in CREATE TABLE statements is interpreted as number of characters. Be sure to take this into account when specifying a prefix length for a column that uses a multi-byte character set.

You can also create FULLTEXT indexes. These are used for full-text searches. In MySQL 5.0, only the MyISAM storage engine supports FULLTEXT indexes and only for CHAR, VARCHAR, and TEXT columns. Indexing always takes place over the entire column and partial (prefix) indexing is not supported. See Sección 12.7, “Funciones de búsqueda de texto completo (Full-Text)” for details.

In MySQL 5.0, you can also create indexes on spatial column types. Spatial types are supported only by the MyISAM storage engine. Spatial indexes use R-trees.

The MEMORY (HEAP) storage engine uses hash indexes by default, but also supports B-tree indexes in MySQL 5.0.

7.4.4. Índices de múltiples columnas

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 Sección 7.4.3, “Índices de columna”).

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 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 the next section.

7.4.5. Cómo utiliza MySQL los índices

Indexes are used to find rows with specific column values quickly. Without an index, MySQL must begin with the first record 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, then this is at least 100 times faster than reading sequentially. Note that 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 column types use R-trees, and that MEMORY tables also support hash indexes.

Strings are automatically prefix- and end-space compressed. See Sección 13.1.4, “Sintaxis de 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.

Indexes are used for these operations:

  • To find the rows matching a WHERE clause quickly.

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

  • To retrieve rows from other tables when performing joins.

  • To find the MIN() or MAX() value for a specific indexed column key_col. This is optimized by a preprocessor that checks whether you are using WHERE key_part_# = 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 replace 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 Sección 7.2.10, “Cómo optimiza MySQL ORDER BY.

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

    SELECT key_part3 FROM tbl_name 
      WHERE key_part1=1
    

Suppose that you issue the following SELECT statement:

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

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

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

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

SELECT * FROM tbl_name WHERE col1=val1;
SELECT * FROM tbl_name WHERE 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).

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 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 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 5.0 performs 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 employs 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 AND parts */
... 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, since it requires fewer seeks.) However, if such a query uses LIMIT to only retrieve 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 than those just discussed:

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

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

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

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

7.4.6. La caché de claves de MyISAM

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 recent 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.

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, 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, the least recently used index block is selected. To make such a choice easier, the key cache module maintains a special queue (known as an 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. Acceso compartido a la caché de claves

In older versions of MySQL, access to the key cache was serialized, and no two threads could access key cache buffers simultaneously. However, in MySQL 5.0, 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.

7.4.6.2. Múltiples cachés de claves

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 5.0 also provides multiple key caches, which allow 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 Sección 13.5.5.1, “Sintaxis de CACHE INDEX).

For example, the following statement assigns indexes from the tables t1, t2, and t3 to the key cache named hot_cache:

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

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

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

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

mysql> SET GLOBAL keycache1.key_buffer_size=0;

Note that you cannot destroy the default key cache. An 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 Sección 9.4.1, “Variables estructuradas de sistema” 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. 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 remain in the cache.

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

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

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

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

CACHE INDEX a.t1, a.t2, b.t3 IN hot_cache
CACHE INDEX a.t4, b.t5, b.t6 IN cold_cache

7.4.6.3. Midpoint Insertion Strategy

By default, the key cache management system in MySQL 5.0 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. Precarga de índices

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 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 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.

7.4.6.5. Tamaño de bloque de la caché de claves

In MySQL 5.0, it is possible to specify the size of the block buffers for an individual key cache using the key_cache_block_size variable. This permits tuning of the performance of I/O operations for index files.

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

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

7.4.6.6. Reestructurar la caché de claves

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 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.

7.4.7. Cómo cuenta MySQL las tablas abiertas

When you execute a mysqladmin status command, you 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.)

You can read more about this topic in the next section, Sección 7.4.8, “Cómo abre y cierra tablas MySQL”.

7.4.8. Cómo abre y cierra tablas MySQL

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 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 to MySQL using the --open-files-limit startup option to mysqld_safe. See Sección A.2.17, “No se encontró el fichero”.

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 in order 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 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 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 with other threads and is not closed until the thread calls HANDLER tbl_name CLOSE or until the thread terminates. When this happens, the table is returned to the table cache (if the cache isn't full). See Sección 13.2.3, “Sintaxis de 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 large, even when you have not issued a lot of FLUSH TABLES statements, you should increase the table cache size. See Sección 5.3.3, “Variables de sistema del servidor” and Sección 5.3.4, “Variables de estado del servidor”.

7.4.9. Desventajas de crear muchas tablas en la misma base de datos

If you have many MyISAM tables in the same database directory, open, close, and create operations are slow. If you execute SELECT statements on many different tables, there is a little overhead when the table cache is full, because for every table that has to be opened, another must be closed. You can reduce this overhead by making the table cache larger.

7.5. Optimización del servidor MySQL

7.5.1. Factores de sistema y afinamientos de parámetros de arranque

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 things at this level.

The default 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 on by default in MySQL 5.0.

    Note that the --skip-external-locking option 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 this option is mandatory, because the external locking does not work in any case.

    The only case where you cannot use --skip-external-locking is if 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.

    You can still use LOCK TABLES and UNLOCK TABLES even if you are using --skip-external-locking.

7.5.2. Afinar parámetros del servidor

You can determine the default buffer sizes used by the mysqld server using this command:

shell> mysqld --verbose --help

This command produces a list of all mysqld options and configurable system variables. The output includes the default variable values and looks something like this:

back_log                          50
binlog_cache_size                 32768
bulk_insert_buffer_size           8388608
connect_timeout                   5
date_format                       (No default value)
datetime_format                   (No default value)
default_week_format               0
delayed_insert_limit              100
delayed_insert_timeout            300
delayed_queue_size                1000
expire_logs_days                  0
flush_time                        1800
ft_max_word_len                   84
ft_min_word_len                   4
ft_query_expansion_limit          20
ft_stopword_file                  (No default value)
group_concat_max_len              1024
innodb_additional_mem_pool_size   1048576
innodb_autoextend_increment       8
innodb_buffer_pool_awe_mem_mb     0
innodb_buffer_pool_size           8388608
innodb_concurrency_tickets        500
innodb_file_io_threads            4
innodb_force_recovery             0
innodb_lock_wait_timeout          50
innodb_log_buffer_size            1048576
innodb_log_file_size              5242880
innodb_log_files_in_group         2
innodb_mirrored_log_groups        1
innodb_open_files                 300
innodb_sync_spin_loops            20
innodb_thread_concurrency         8
innodb_thread_sleep_delay         10000
interactive_timeout               28800
join_buffer_size                  131072
key_buffer_size                   8388600
key_cache_age_threshold           300
key_cache_block_size              1024
key_cache_division_limit          100
long_query_time                   10
lower_case_table_names            1
max_allowed_packet                1048576
max_binlog_cache_size             4294967295
max_binlog_size                   1073741824
max_connect_errors                10
max_connections                   100
max_delayed_threads               20
max_error_count                   64
max_heap_table_size               16777216
max_join_size                     4294967295
max_length_for_sort_data          1024
max_relay_log_size                0
max_seeks_for_key                 4294967295
max_sort_length                   1024
max_tmp_tables                    32
max_user_connections              0
max_write_lock_count              4294967295
multi_range_count                 256
myisam_block_size                 1024
myisam_data_pointer_size          6
myisam_max_extra_sort_file_size   2147483648
myisam_max_sort_file_size         2147483647
myisam_repair_threads             1
myisam_sort_buffer_size           8388608
net_buffer_length                 16384
net_read_timeout                  30
net_retry_count                   10
net_write_timeout                 60
open_files_limit                  0
optimizer_prune_level             1
optimizer_search_depth            62
preload_buffer_size               32768
query_alloc_block_size            8192
query_cache_limit                 1048576
query_cache_min_res_unit          4096
query_cache_size                  0
query_cache_type                  1
query_cache_wlock_invalidate      FALSE
query_prealloc_size               8192
range_alloc_block_size            2048
read_buffer_size                  131072
read_only                         FALSE
read_rnd_buffer_size              262144
div_precision_increment           4
record_buffer                     131072
relay_log_purge                   TRUE
relay_log_space_limit             0
slave_compressed_protocol         FALSE
slave_net_timeout                 3600
slave_transaction_retries         10
slow_launch_time                  2
sort_buffer_size                  2097144
sync-binlog                       0
sync-frm                          TRUE
sync-replication                  0
sync-replication-slave-id         0
sync-replication-timeout          10
table_cache                       64
thread_cache_size                 0
thread_concurrency                10
thread_stack                      196608
time_format                       (No default value)
tmp_table_size                    33554432
transaction_alloc_block_size      8192
transaction_prealloc_size         4096
updatable_views_with_limit        1
wait_timeout                      28800

If there is a mysqld server currently running, you can see what values it actually is using for the 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

You can find a full description for all system and status variables in Sección 5.3.3, “Variables de sistema del servidor” and Sección 5.3.4, “Variables de estado del servidor”.

MySQL uses algorithms that are very scalable, so you can usually run with very little memory. However, normally you get better performance by giving MySQL more memory.

When tuning a MySQL server, the two most important variables to configure are key_buffer_size and table_cache. You should first feel confident that you have these set appropriately before trying to change any other variables.

The following examples indicate some typical variable values for different runtime configurations.

  • If you have at least 256MB of memory and many tables and want maximum performance with a moderate number of clients, you should use something like this:

    shell> mysqld_safe --key_buffer_size=64M --table_cache=256 \
               --sort_buffer_size=4M --read_buffer_size=1M &
    
  • If you have only 128MB of memory and only a few tables, but you still do a lot of sorting, you can use something like this:

    shell> mysqld_safe --key_buffer_size=16M --sort_buffer_size=1M
    

    If there are very many simultaneous connections, swapping problems may occur unless mysqld has been configured to use very little memory for each connection. mysqld performs better if you have enough memory for all connections.

  • With little memory and lots of connections, use something like this:

    shell> mysqld_safe --key_buffer_size=512K --sort_buffer_size=100K \
               --read_buffer_size=100K &
    

    Or even this:

    shell> mysqld_safe --key_buffer_size=512K --sort_buffer_size=16K \
               --table_cache=32 --read_buffer_size=8K \
               --net_buffer_length=1K &
    

If you are doing 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 after sorting operations.

When you have installed MySQL, the support-files directory contains some different my.cnf sample files: my-huge.cnf, my-large.cnf, my-medium.cnf, and my-small.cnf. You can use these as a basis for optimizing your system.

Note that if you specify an option on the command line for mysqld or mysqld_safe, it remains in effect only for that invocation of the server. To use the option every time the server runs, put it in an option file.

To see the effects of a parameter change, do something like this:

shell> mysqld --key_buffer_size=32M --verbose --help

The variable values are listed near the end of the output. Make sure that the --verbose and --help options are last. Otherwise, the effect of any options listed after them on the command line are not reflected in the output.

For information on tuning the InnoDB storage engine, see Sección 15.11, “Consejos de afinamiento del rendimiento de InnoDB.

7.5.3. Vigilar el rendimiento del optimizador de consultas

The task of the query optimizer is to find an optimal plan for executing an SQL query. Because the difference in performance between “good” and “bad” plans can be orders of magnitude (that is, seconds versus hours or even days), most query optimizers, including that of MySQL, perform a more or less exhaustive search for an optimal plan among all possible query evaluation plans. For join queries, the number of possible plans investigated by the MySQL optimizer grows exponentially with the number of tables referenced in a query. For small numbers of tables (typically less than 7-10) this is not a problem. However, when bigger queries are submitted, the time spent in query optimization may easily become the major bottleneck in the server's performance.

MySQL 5.0.1 introduces a more flexible method for query optimization that allows the user to control how exhaustive the optimizer is in its search for an optimal query evaluation plan. The general idea is that the fewer plans that are investigated by the optimizer, the less time it spends in compiling a query. On the other hand, because the optimizer skips some plans, it may miss finding an optimal plan.

The behavior of the optimizer with respect to the number of plans it evaluates can be controlled via two system variables:

  • The optimizer_prune_level variable tells the optimizer to skip certain plans based on estimates of the number of rows accessed for each table. Our experience shows that this kind of “educated guess” rarely misses optimal plans, and may dramatically reduce query compilation times. That is why this option is on (optimizer_prune_level=1) by default. However, if you believe that the optimizer missed a better query plan, then this option can be switched off (optimizer_prune_level=0) with the risk that query compilation may take much longer. Notice that even with the use of this heuristic, the optimizer still explores a roughly exponential number of plans.

  • The optimizer_search_depth variable tells how far into the “future” of each incomplete plan the optimizer should look in order to evaluate whether it should be expanded further. Smaller values of optimizer_search_depth may result in orders of magnitude smaller query compilation times. For example, queries with 12, 13, or more tables may easily require hours and even days to compile if optimizer_search_depth is close to the number of tables in the query. At the same time, if compiled with optimizer_search_depth equal to 3 or 4, the compiler may compile in less than a minute for the same query. If you are unsure of what a reasonable value is for optimizer_search_depth, this variable can be set to 0 to tell the optimizer to determine the value automatically.

7.5.4. Efectos de la compilación y del enlace en la velocidad de 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.

  • If you strip your mysqld binary with strip mysqld, the resulting binary can be up to 4% faster.

  • 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 options -mcpu=v8 -Wa,-xarch=v8plusa gives 4% better performance.

  • On Solaris 2.5.1, MIT-pthreads is 8-12% slower than Solaris native threads on a single processor. With more load or CPUs, the difference should be larger.

  • Compiling on Linux-x86 using gcc without frame pointers (-fomit-frame-pointer or -fomit-frame-pointer -ffixed-ebp) makes mysqld 1-4% faster.

Binary MySQL distributions for Linux that are provided by MySQL AB used to be compiled with pgcc. We had to go back to regular gcc due to a bug in pgcc that would generate binaries that do not run on AMD. We will continue using gcc until that bug is resolved. In the meantime, if you have a non-AMD machine, you can build a faster binary by compiling with pgcc. The standard MySQL Linux binary is linked statically to make it faster and more portable.

7.5.5. Cómo utiliza MySQL la memoria

The following list indicates some of the ways that the mysqld server uses memory. Where applicable, the name of the system variable relevant to the memory use is given:

  • The key buffer (variable key_buffer_size) is shared by all threads; other buffers used by the server are allocated as needed. See Sección 7.5.2, “Afinar parámetros del servidor”.

  • Each connection uses some thread-specific space:

    • A stack (default 64KB, variable thread_stack)

    • A connection buffer (variable net_buffer_length)

    • A result buffer (variable net_buffer_length)

    The connection buffer and result buffer are dynamically enlarged up to max_allowed_packet when needed. While a query is running, a copy of the current query string is also allocated.

  • All threads share the same base memory.

  • Only compressed MyISAM tables are memory mapped. This is because the 32-bit memory space of 4GB is not large enough for most big tables. When systems with a 64-bit address space become more common, we may add general support for memory mapping.

  • Each request that performs a sequential scan of a table allocates a read buffer (variable read_buffer_size).

  • When reading rows in an arbitrary sequence (for example, following a sort), a random-read buffer (variable read_rnd_buffer_size) may be allocated in order to avoid disk seeks.

  • All joins are executed in a single pass, and most joins can be done without even using a temporary table. Most temporary tables are memory-based (HEAP) tables. Temporary tables with a large record length (calculated as the sum of all column lengths) or that contain BLOB columns are stored on disk.

    If an internal heap table exceeds the size of tmp_table_size, MySQL 5.0 handles this automatically by changing the in-memory heap table to a disk-based MyISAM table as necessary. You can also increase the temporary table size by setting the tmp_table_size option to mysqld, or by setting the SQL option SQL_BIG_TABLES in the client program. See Sección 13.5.3, “Sintaxis de SET.

  • Most requests that perform a sort allocate a sort buffer and zero to two temporary files depending on the result set size. See Sección A.4.4, “Dónde almacena MySQL los archivos temporales”.

  • Almost all parsing and calculating is done in a local memory store. No memory overhead is needed for small items, so the normal slow memory allocation and freeing is avoided. Memory is allocated only for unexpectedly large strings; this is done with malloc() and free().

  • For each MyISAM table that is opened, the index file is opened once; the data file is opened once for each concurrently running thread. For each concurrent thread, a table structure, column structures for each column, and a buffer of size 3 * N are allocated (where N is the maximum row length, not counting BLOB columns). A BLOB column requires five to eight bytes plus the length of the BLOB data. The MyISAM storage engine maintains one extra row buffer for internal use.

  • For each table having BLOB columns, a buffer is enlarged dynamically to read in larger BLOB values. If you scan a table, a buffer as large as the largest BLOB value is allocated.

  • Handler structures for all in-use tables are saved in a cache and managed as a FIFO. By default, the cache has 64 entries. If a table has been used by two running threads at the same time, the cache contains two entries for the table. See Sección 7.4.8, “Cómo abre y cierra tablas MySQL”.

  • A FLUSH TABLES statement or mysqladmin flush-tables command closes all tables that are not in use at once and marks all in-use tables to be closed when the currently executing thread finishes. This effectively frees most in-use memory. FLUSH TABLES does not return until all tables have been closed.

ps and other system status programs may report that mysqld uses a lot of memory. This may be caused by thread stacks on different memory addresses. For example, the Solaris version of ps counts the unused memory between stacks as used memory. You can verify this by checking available swap with swap -s. We test mysqld with several memory-leakage detectors (both commercial and open source), so there should be no memory leaks.

7.5.6. Cómo usa MySQL las 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 doesn't 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.

If you want to disallow TCP/IP connections entirely, start mysqld with the --skip-networking option.

7.6. Cuestiones relacionadas con el disco

  • Disk seeks are a major 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/or data file 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 Sección 7.6.1, “Utilizar enlaces simbólicos”.

    • 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 Nth 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 Sección 7.1.5, “Usar pruebas de rendimiento (benchmarks) propios”.

      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 don't 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. Utilizar enlaces simbólicos

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. Utilización de enlaces simbólicos para bases de datos en 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

For 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 result.

However, if you really need to do this, it is possible by altering the source file mysys/my_symlink.c, in which you should look for the following statement:

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

Change the statement to this:

if (1)

Note that, in MySQL 5.0, symbolic link support is enabled by default for all Windows servers.

7.6.1.2. Utilización de enlaces simbólicos para tablas en Unix

You should not symlink tables on systems that do not have a fully operational realpath() call. (Linux and Solaris support realpath()). You can check whether your system supports symbolic links by issuing a SHOW VARIABLES LIKE 'have_symlink' statement.

In MySQL 5.0, symlinks are fully supported only for MyISAM tables. For other table types, you may get strange problems if you try to use symbolic links on files in the operating system with any of the preceding statements.

The handling of symbolic links for MyISAM tables in MySQL 5.0 works the following way:

  • In the data directory, you always have the table definition file, the data file, and the index file. The data file and index file can be moved elsewhere and replaced in the data directory by symlinks. The definition file cannot.

  • You can symlink the data file and the index file independently to different directories.

  • Symlinking can be accomplished manually from the command line using ln -s if mysqld is not running. Aternativly, you can instruct a running MySQL server to perform the symlinking by using the DATA DIRECTORY and INDEX DIRECTORY options to CREATE TABLE. See Sección 13.1.5, “Sintaxis de CREATE TABLE.

  • myisamchk does not replace a symlink with the data file or index file. It works directly on the file to which the symlink points. Any temporary files are created in the directory where the data file or index file is located.

  • Note: When you drop a table that is using symlinks, both the symlink and the file to which the symlink points are dropped. This is an extremely good reason why you should not run mysqld as the system root or allow system users to have write access to MySQL database directories.

  • If you rename a table with ALTER TABLE ... RENAME and you do not move the table to another database, the symlinks in the database directory are renamed to the new names and the data file and index file are renamed accordingly.

  • If you use ALTER TABLE ... RENAME to move a table to another database, the table is moved to the other database directory. The old symlinks and the files to which they pointed are deleted. In other words, the new table is not symlinked.

  • If you are not using symlinks, you should use the --skip-symbolic-links option to mysqld to ensure that no one can use mysqld to drop or rename a file outside of the data directory.

Table symlink operations that are not yet supported:

  • ALTER TABLE ignores the DATA DIRECTORY and INDEX DIRECTORY table options.

  • BACKUP TABLE and RESTORE TABLE don't 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 also fail.

7.6.1.3. Usar enlaces simbólicos para bases de datos en Windows

The mysqld-max and mysql-max-nt servers for Windows are compiled with the -DUSE_SYMDIR option. This allows 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.

In MySQL 5.0, symbolic links are enabled by default. If you do not need them, you can disable them with the skip-symbolic-links option:

[mysqld]
skip-symbolic-links

On Windows, you 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 as shown here:

  1. Make sure that the D:\data\foo directory exists by creating it if necessary. If you have a database directory named foo in the data directory, you should move it to D:\data. Otherwise, the symbolic link is ineffective. To avoid problems, the server should not be running when you move the database directory.

  2. Create a text file C:\mysql\data\foo.sym that contains the pathname D:\data\foo\.

After this, all tables created in the database foo are created in D:\data\foo. Note that the symbolic link is not used if a directory with the same name as the database exists in the MySQL data directory.


Ésta es una traducción del manual de referencia de MySQL, que puede encontrarse en dev.mysql.com. El manual de referencia original de MySQL está escrito en inglés, y esta traducción no necesariamente está tan actualizada como la versión original. Para cualquier sugerencia sobre la traducción y para señalar errores de cualquier tipo, no dude en dirigirse a mysql-es@vespito.com.