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    分析Mysql表读写、索引等操作的sql语句效率优化问题

    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

    以上就是这篇文章的全部内容了,希望本文的内容对大家的学习或者工作具有一定的参考学习价值,谢谢大家对脚本之家的支持。如果你想了解更多相关内容请查看下面相关链接

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