上次我們說到mysql的一些sql查詢方面的優(yōu)化，包括查看explain執(zhí)行計劃，分析索引等等。今天我們分享一些 分析mysql表讀寫、索引等等操作的sql語句。
閑話不多說，直接上代碼：
反映表的讀寫壓力
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;
反映文件的延遲
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 的讀寫延遲
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;
查看表操作頻度
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 ;
索引狀況
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;
全表掃描情況
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;
沒有使用的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問題摘要
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;
掌握這些sql，你能輕松知道你的庫那些表存在問題，然后考慮怎么去優(yōu)化。
總結
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