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。   

总结