{"id":7745,"date":"2019-08-23T16:06:45","date_gmt":"2019-08-23T15:06:45","guid":{"rendered":"https:\/\/blog.capdata.fr\/?p=7745"},"modified":"2019-09-13T10:33:46","modified_gmt":"2019-09-13T09:33:46","slug":"faciliter-la-lecture-des-mesures-de-temps-dans-performance_schema","status":"publish","type":"post","link":"https:\/\/blog.capdata.fr\/index.php\/faciliter-la-lecture-des-mesures-de-temps-dans-performance_schema\/","title":{"rendered":"Faciliter la lecture des mesures de temps dans performance_schema"},"content":{"rendered":"\"twitter\"<\/a>\"linkedin\"<\/a>\"mail\"<\/a>

\"\"<\/p>\n

Admettons que nous devions remonter pour une base mysql donn\u00e9e le top 10 des tables les plus acc\u00e9d\u00e9es en lecture par temps d’acc\u00e8s. On pourrait utiliser P_S.table_io_waits_summary_by_table<\/i> par exemple :<\/p>\n

\r\nmysql> select OBJECT_NAME, INDEX_NAME, COUNT_READ, AVG_TIMER_READ\r\n    -> from performance_schema.table_io_waits_summary_by_index_usage\r\n    -> where OBJECT_SCHEMA='mabase'\r\n    -> order by AVG_TIMER_READ desc limit 10 ;\r\n+---------------+------------+------------+----------------+\r\n| OBJECT_NAME   | INDEX_NAME | COUNT_READ | AVG_TIMER_READ |\r\n+---------------+------------+------------+----------------+\r\n| t_1           | idx3       |          1 |       70377507 |\r\n| t_1           | PRIMARY    |          6 |       20655678 |\r\n| t_2           | PRIMARY    |         10 |        3984852 |\r\n| t_2           | idx3       |         76 |        2099178 |\r\n| t_3           | NULL       |        156 |         894048 |\r\n| t_4           | PRIMARY    |      92726 |         525003 |\r\n| t_3           | idx2       |          0 |              0 |\r\n| t_3           | idx4       |          0 |              0 |\r\n| t_3           | idx5       |          0 |              0 |\r\n| t_1           | idx2       |          0 |              0 |\r\n+---------------+------------+------------+----------------+\r\n10 rows in set (1.07 sec)\r\n<\/pre>\n
Bon c’est bien gentil mais on ne sait pas \u00e0 priori quelle sera l’unit\u00e9 utilis\u00e9e pour exprimer cette valeur de AVG_TIMER_READ. Sont-ce des microsecondes, des nanosecondes … La documentation <\/a>sur la vue ne le dit pas. <\/p>\n
La gestion des timers dans performance_schema<\/h2>\n
Ce pourra faire l’objet d’un autre post, mais pour simplifier on peut dire que les vues dans performance_schema ob\u00e9issent \u00e0 une hi\u00e9rarchie bas\u00e9e sur des instruments<\/i> et des consumers<\/i>. <\/p>\n
Globalement, les instruments<\/i> sont les \u00e9v\u00e8nements que l’on veut tracer, et les consumers<\/i> les destinations de trace. Si on veut faire l’analogie avec les Extended Events de SQL Server, on peut dire que les instruments<\/i> sont les events et les consumers<\/i> les targets. Sauf que pour MySQL, les destinations sont toutes des vues. <\/p>\n
Les classes principales ci-dessous transaction, statement, stage, wait et idle sont associ\u00e9es \u00e0 de la mesure de temps :
\n\"\"
\n– Statement<\/i> est une requ\u00eate unitaire, faisant partie d’une transaction.
\n– Stage<\/i> repr\u00e9sente chaque \u00e9tape lors de l’ex\u00e9cution d’un statement<\/i>: parsing, ouverture de la table, etc… Tout le monde conna\u00eet l’infame Copying to tmp table<\/i>, et bien c’est un stage<\/i>.
\n– Wait<\/i> regroupe tous les postes d’attente : les IO sur les fichiers de table IBD, les fichiers MyISAM, les redologs, les datafiles InnoDB, etc…
\nSi bien que ces 3 classes sont imbriqu\u00e9es les unes dans les autres, avec la classe transaction qui est la classe la plus haute dans la hi\u00e9rarchie:
\n\"\"
\nTout \u00e7a pour dire que nos classes principales ont un besoin de mesurer le temps avec une granularit\u00e9 diff\u00e9rente selon l’ordre de grandeur de ce que l’on mesure: l’ordre de mesure de l’ex\u00e9cution d’une requ\u00eate peut se trouver entre la milliseconde et la seconde, alors que celui pour mesurer le temps d’attente sur un mutex, serait plut\u00f4t de l’ordre de la nanoseconde, voire du cycle. <\/p>\n
Ca tombe bien, tout \u00e7a est tr\u00e8s bien organis\u00e9, et on dispose dans P_S de tables de configuration pour nous indiquer quelle unit\u00e9 de temps est utilis\u00e9e pour quelle classe:<\/p>\n
\r\nmysql> select * from performance_schema.setup_timers ;\r\n+-------------+-------------+\r\n| NAME        | TIMER_NAME  |\r\n+-------------+-------------+\r\n| idle        | MICROSECOND |\r\n| wait        | CYCLE       |\r\n| stage       | NANOSECOND  |\r\n| statement   | NANOSECOND  |\r\n| transaction | NANOSECOND  |\r\n+-------------+-------------+\r\n<\/pre>\n
Sys pour se simplifier la vie<\/h2>\n
Le probl\u00e8me c’est que la vue que l’on utilise (table_io_waits_summary_by_index_usage<\/i>) ne fait pas partie de la hi\u00e9rarchie classique stage \/ statement \/ wait, donc on n’est pas plus avanc\u00e9. <\/p>\n
Or depuis la version 5.7.7, le tr\u00e8s bon Mark Leith <\/a> d’Oracle nous a gratifi\u00e9 du sch\u00e9ma sys <\/a>qui doit simplifier les arcanes compliqu\u00e9es de P_S et en acc\u00e9l\u00e9rer l’adoption. <\/p>\n
En fouillant un peu dans le code des vues, on retrouve une invocation \u00e0 une fonction format_time()<\/i> appliqu\u00e9e aux colonnes de temps de table_io_waits_summary_by_table<\/i>, par exemple dans la d\u00e9finition de sys.schema_table_statistics<\/i> : <\/p>\n
\r\nmysql> show create table schema_table_statistics \\G\r\n*************************** 1. row ***************************\r\n                View: schema_table_statistics\r\n         Create View: CREATE algorithm=temptable definer=`mysql.sys`@`localhost` SQL security invoker view `schema_table_statistics`\r\nAS \r\n  SELECT    `pst`.`object_schema`                                    AS `table_schema`, \r\n            `pst`.`object_name`                                      AS `table_name`, \r\n            `sys`.`format_time`(`pst`.`sum_timer_wait`)              AS `total_latency`, \r\n            `pst`.`count_fetch`                                      AS `rows_fetched`, \r\n            `sys`.`format_time`(`pst`.`sum_timer_fetch`)             AS `fetch_latency`, \r\n            `pst`.`count_insert`                                     AS `rows_inserted`, \r\n            `sys`.`format_time`(`pst`.`sum_timer_insert`)            AS `insert_latency`, \r\n            `pst`.`count_update`                                     AS `rows_updated`, \r\n            `sys`.`format_time`(`pst`.`sum_timer_update`)            AS `update_latency`, \r\n            `pst`.`count_delete`                                     AS `rows_deleted`, \r\n            `sys`.`format_time`(`pst`.`sum_timer_delete`)            AS `delete_latency`, \r\n            `fsbi`.`count_read`                                      AS `io_read_requests`, \r\n            `sys`.`format_bytes`(`fsbi`.`sum_number_of_bytes_read`)  AS `io_read`, \r\n            `sys`.`format_time`(`fsbi`.`sum_timer_read`)             AS `io_read_latency`, \r\n            `fsbi`.`count_write`                                     AS `io_write_requests`, \r\n            `sys`.`format_bytes`(`fsbi`.`sum_number_of_bytes_write`) AS `io_write`, \r\n            `sys`.`format_time`(`fsbi`.`sum_timer_write`)            AS `io_write_latency`, \r\n            `fsbi`.`count_misc`                                      AS `io_misc_requests`, \r\n            `sys`.`format_time`(`fsbi`.`sum_timer_misc`)             AS `io_misc_latency` \r\n  FROM      (`performance_schema`.`table_io_waits_summary_by_table` `pst` \r\n            LEFT JOIN `sys`.`x$ps_schema_table_statistics_io` `fsbi` \r\n            ON       ((( `pst`.`object_schema` = `fsbi`.`table_schema`) \r\n            AND       ( `pst`.`object_name` = `fsbi`.`table_name`)))) \r\n  ORDER BY  `pst`.`sum_timer_wait` DESC\r\ncharacter_set_client: utf8\r\ncollation_connection: utf8_general_ci\r\n1 row in set (0.00 sec)\r\n<\/pre>\n
Voyons un peu la d\u00e9finition de cette fonction format_time()<\/i> :<\/p>\n
\r\nmysql> select ROUTINE_NAME, ROUTINE_BODY, ROUTINE_DEFINITION\r\n    -> from information_schema.routines\r\n    -> where routine_schema='sys' and ROUTINE_NAME='format_time' \\G\r\n*************************** 1. row ***************************\r\n      ROUTINE_NAME: format_time\r\n      ROUTINE_BODY: SQL\r\nROUTINE_DEFINITION: \r\nBEGIN \r\n  IF picoseconds IS NULL THEN \r\n    RETURN NULL; \r\n  ELSEIF picoseconds >= 604800000000000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 604800000000000000, 2), ' w'); \r\n  ELSEIF picoseconds >= 86400000000000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 86400000000000000, 2), ' d'); \r\n  ELSEIF picoseconds >= 3600000000000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 3600000000000000, 2), ' h'); \r\n  ELSEIF picoseconds >= 60000000000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 60000000000000, 2), ' m'); \r\n  ELSEIF picoseconds >= 1000000000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 1000000000000, 2), ' s'); \r\n  ELSEIF picoseconds >= 1000000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 1000000000, 2), ' ms'); \r\n  ELSEIF picoseconds >= 1000000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 1000000, 2), ' us'); \r\n  ELSEIF picoseconds >= 1000 THEN \r\n    RETURN CONCAT(Round(picoseconds \/ 1000, 2), ' ns'); \r\n  ELSE \r\n    RETURN CONCAT(picoseconds, ' ps'); \r\n  END IF; \r\nEND \r\n1 row in set (0.03 sec)\r\n<\/pre>\n
la fonction part du principe qu’on lui passe des picosecondes. On en d\u00e9duit donc que le temps hors ‘timers’ sera exprim\u00e9 dans la mesure la plus petite, soit des picosecondes. <\/p>\n
D’ailleurs, si vous \u00eates en 5.7.7 minimum, et que vous souhaitez r\u00e9soudre simplement cette question de mesure, vous pouvez utiliser la fonction directement sur les vues de P_S:<\/p>\n
\r\nmysql> select OBJECT_NAME, INDEX_NAME, COUNT_READ, sys.format_time(AVG_TIMER_READ)\r\n    -> from performance_schema.table_io_waits_summary_by_index_usage\r\n    ->  where OBJECT_SCHEMA='mabase'\r\n    -> order by AVG_TIMER_READ desc limit 10 ;\r\n+-------------------+-------------------+------------+---------------------------------+\r\n| OBJECT_NAME       | INDEX_NAME        | COUNT_READ | sys.format_time(AVG_TIMER_READ) |\r\n+-------------------+-------------------+------------+---------------------------------+\r\n| t_1               | idx3              |         88 | 33.52 us                        |\r\n| t_1               | PRIMARY           |        180 | 26.55 us                        |\r\n| t_2               | PRIMARY           |         49 | 25.95 us                        |\r\n| t_2               | idx3              |         26 | 23.71 us                        |\r\n| t_3               | NULL              |         94 | 21.76 us                        |\r\n| t_4               | PRIMARY           |          1 | 21.21 us                        |\r\n| t_3               | idx2              |          1 | 19.58 us                        |\r\n| t_3               | idx4              |          1 | 18.83 us                        |\r\n| t_3               | idx5              |         18 | 15.86 us                        |\r\n| t_1               | idx2              |          1 | 14.91 us                        |\r\n+-------------------+-------------------+------------+---------------------------------+\r\n10 rows in set (1.34 sec)\r\n<\/pre>\n
Et c’est tout pour aujourd’hui, bon weekend \u00e0 toutes et tous et \u00e0 bient\u00f4t !
\n~David.<\/p>\n\"twitter\"<\/a>\"linkedin\"<\/a>\"mail\"<\/a>","protected":false},"excerpt":{"rendered":"
Admettons que nous devions remonter pour une base mysql donn\u00e9e le top 10 des tables les plus acc\u00e9d\u00e9es en lecture par temps d’acc\u00e8s. On pourrait utiliser P_S.table_io_waits_summary_by_table par exemple : Bon c’est bien gentil mais on ne sait pas \u00e0… Continuer la lecture →<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":7747,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[324,325],"class_list":["post-7745","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mysql","tag-monitoring","tag-performance_schema"],"yoast_head":"\nFaciliter la lecture des mesures de temps dans performance_schema - Capdata TECH BLOG<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blog.capdata.fr\/index.php\/faciliter-la-lecture-des-mesures-de-temps-dans-performance_schema\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Faciliter la lecture des mesures de temps dans performance_schema - Capdata TECH BLOG\" \/>\n<meta property=\"og:description\" content=\"Admettons que nous devions remonter pour une base mysql donn\u00e9e le top 10 des tables les plus acc\u00e9d\u00e9es en lecture par temps d’acc\u00e8s. 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