上次我们说到mysql的一些sql查询方面的优化，包括查看explain执行计划，分析索引等等。 今天我们分享一些 分析mysql表读写、索引等等操作的sql语句。
闲话不多说，直接上代码：
-- 反映表的读写压力
SELECT file_name AS file, count_read, sum_number_of_bytes_read AS total_read, count_write, sum_number_of_bytes_write AS total_written, (sum_number_of_bytes_read + sum_number_of_bytes_write) AS total FROM performance_schema.file_summary_by_instance ORDER BY sum_number_of_bytes_read+ sum_number_of_bytes_write DESC;
-- 反映文件的延迟 SELECT (file_name) AS file, count_star AS total, CONCAT(ROUND(sum_timer_wait / 3600000000000000, 2), 'h') AS total_latency, count_read, CONCAT(ROUND(sum_timer_read / 1000000000000, 2), 's') AS read_latency, count_write, CONCAT(ROUND(sum_timer_write / 3600000000000000, 2), 'h')AS write_latency FROM performance_schema.file_summary_by_instance ORDER BY sum_timer_wait DESC;
-- table 的读写延迟 SELECT object_schema AS table_schema, object_name AS table_name, count_star AS total, CONCAT(ROUND(sum_timer_wait / 3600000000000000, 2), 'h') as total_latency, CONCAT(ROUND((sum_timer_wait / count_star) / 1000000, 2), 'us') AS avg_latency, CONCAT(ROUND(max_timer_wait / 1000000000, 2), 'ms') AS max_latency FROM performance_schema.objects_summary_global_by_type ORDER BY sum_timer_wait DESC;
-- 查看表操作频度 SELECT object_schema AS table_schema, object_name AS table_name, count_star AS rows_io_total, count_read AS rows_read, count_write AS rows_write, count_fetch AS rows_fetchs, count_insert AS rows_inserts, count_update AS rows_updates, count_delete AS rows_deletes, CONCAT(ROUND(sum_timer_fetch / 3600000000000000, 2), 'h') AS fetch_latency, CONCAT(ROUND(sum_timer_insert / 3600000000000000, 2), 'h') AS insert_latency, CONCAT(ROUND(sum_timer_update / 3600000000000000, 2), 'h') AS update_latency, CONCAT(ROUND(sum_timer_delete / 3600000000000000, 2), 'h') AS delete_latency FROM performance_schema.table_io_waits_summary_by_table ORDER BY sum_timer_wait DESC ;
-- 索引状况 SELECT OBJECT_SCHEMA AS table_schema, OBJECT_NAME AS table_name, INDEX_NAME as index_name, COUNT_FETCH AS rows_fetched, CONCAT(ROUND(SUM_TIMER_FETCH / 3600000000000000, 2), 'h') AS select_latency, COUNT_INSERT AS rows_inserted, CONCAT(ROUND(SUM_TIMER_INSERT / 3600000000000000, 2), 'h') AS insert_latency, COUNT_UPDATE AS rows_updated, CONCAT(ROUND(SUM_TIMER_UPDATE / 3600000000000000, 2), 'h') AS update_latency, COUNT_DELETE AS rows_deleted, CONCAT(ROUND(SUM_TIMER_DELETE / 3600000000000000, 2), 'h')AS delete_latency FROM performance_schema.table_io_waits_summary_by_index_usage WHERE index_name IS NOT NULL ORDER BY sum_timer_wait DESC;
-- 全表扫描情况
SELECT object_schema, object_name, count_read AS rows_full_scanned FROM performance_schema.table_io_waits_summary_by_index_usage WHERE index_name IS NULL AND count_read > 0 ORDER BY count_read DESC;
-- 没有使用的index SELECT object_schema, object_name, index_name FROM performance_schema.table_io_waits_summary_by_index_usage WHERE index_name IS NOT NULL AND count_star = 0 AND object_schema not in ('mysql','v_monitor') AND index_name <> 'PRIMARY' ORDER BY object_schema, object_name;
-- 糟糕的sql问题摘要
SELECT (DIGEST_TEXT) AS query, SCHEMA_NAME AS db, IF(SUM_NO_GOOD_INDEX_USED > 0 OR SUM_NO_INDEX_USED > 0, '*', '') AS full_scan, COUNT_STAR AS exec_count, SUM_ERRORS AS err_count, SUM_WARNINGS AS warn_count, (SUM_TIMER_WAIT) AS total_latency, (MAX_TIMER_WAIT) AS max_latency, (AVG_TIMER_WAIT) AS avg_latency, (SUM_LOCK_TIME) AS lock_latency, format(SUM_ROWS_SENT,0) AS rows_sent, ROUND(IFNULL(SUM_ROWS_SENT / NULLIF(COUNT_STAR, 0), 0)) AS rows_sent_avg, SUM_ROWS_EXAMINED AS rows_examined, ROUND(IFNULL(SUM_ROWS_EXAMINED / NULLIF(COUNT_STAR, 0), 0)) AS rows_examined_avg, SUM_CREATED_TMP_TABLES AS tmp_tables, SUM_CREATED_TMP_DISK_TABLES AS tmp_disk_tables, SUM_SORT_ROWS AS rows_sorted, SUM_SORT_MERGE_PASSES AS sort_merge_passes, DIGEST AS digest, FIRST_SEEN AS first_seen, LAST_SEEN as last_seen FROM performance_schema.events_statements_summary_by_digest d where d ORDER BY SUM_TIMER_WAIT DESC limit 20;
掌握这些sql，你能轻松知道你的库那些表存在问题，然后考虑怎么去优化。
另外，有些博友问我为何每次博客不写全面，比如为何优化什么的，我想说的是，大部分人只关心如何用，至于为什么，其实可以自己去找答案，而且我也没太多时间去写。至于优不优质博客我不在乎，这些算是我的自己的日常积累吧
