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Mysql 优化——分析表读写和sql效率问题

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发布2020-12-25 14:46:57
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发布2020-12-25 14:46:57
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文章被收录于专栏:Java日常Java日常

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

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原始发表:2017-06-01 ,如有侵权请联系 cloudcommunity@tencent.com 删除

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