Answers:
另一个商业选择是MySQL查询分析器，它是MySQL Enterprise Monitor的一部分。我发现它在帮助剖析奇数球查询以找出提高其性能的方法方面适度有用。
您还可以签出MySQLTuner
我用这个小脚本。尽管对我没有任何帮助，但它一直对我很有用。
我使用了许多很棒的脚本和其他工具，但我发现Jet Profiler确实擅长于实时监视和可视化正在发生的事情以及事物的变化情况。完整版要花钱，但受限的免费版也很有用，使您对完整版可以做什么有个良好的感觉。
我强烈建议以下
从旧的MAATKIT文档中
Column Meaning
============ ==========================================================
Rank The query's rank within the entire set of queries analyzed
Query ID The query's fingerprint
Response time The total response time, and percentage of overall total
Calls The number of times this query was executed
R/Call The mean response time per execution
Apdx The Apdex score; see --apdex-threshold for details
V/M The Variance-to-mean ratio of response time
EXPLAIN If --explain was specified, a sparkline; see --explain
Item The distilled query
在DBA StackExchange中，我回答了MySQL一般查询日志的性能影响。在我的旧帖子中，我建议使用mk-query-digest代替常规日志或慢日志。从那篇文章中，这是mk-query-digest完成的查询分析的示例输出：
# Rank Query ID Response time Calls R/Call Item
# ==== ================== ================ ======= ========== ====
# 1 0x812D15015AD29D33 336.3867 68.5% 910 0.369656 SELECT mt_entry mt_placement mt_category
# 2 0x99E13015BFF1E75E 25.3594 5.2% 210 0.120759 SELECT mt_entry mt_objecttag
# 3 0x5E994008E9543B29 16.1608 3.3% 46 0.351321 SELECT schedule_occurrence schedule_eventschedule schedule_event schedule_eventtype schedule_event schedule_eventtype schedule_occurrence.start
# 4 0x84DD09F0FC444677 13.3070 2.7% 23 0.578567 SELECT mt_entry
# 5 0x377E0D0898266FDD 12.0870 2.5% 116 0.104199 SELECT polls_pollquestion mt_category
# 6 0x440EBDBCEDB88725 11.5159 2.3% 21 0.548376 SELECT mt_entry
# 7 0x1DC2DFD6B658021F 10.3653 2.1% 54 0.191949 SELECT mt_entry mt_placement mt_category
# 8 0x6C6318E56E149036 8.8294 1.8% 44 0.200667 SELECT schedule_occurrence schedule_eventschedule schedule_event schedule_eventtype schedule_event schedule_eventtype schedule_occurrence.start
# 9 0x392F6DA628C7FEBD 8.5243 1.7% 9 0.947143 SELECT mt_entry mt_objecttag
# 10 0x7DD2B294CFF96961 7.3753 1.5% 70 0.105362 SELECT polls_pollresponse
# 11 0x9B9092194D3910E6 5.8124 1.2% 57 0.101973 SELECT content_specialitem content_basecontentitem advertising_product organizations_neworg content_basecontentitem_item_attributes
# 12 0xA909BF76E7051792 5.6005 1.1% 55 0.101828 SELECT mt_entry mt_objecttag mt_tag
# 13 0xEBE07AC48DB8923E 5.5195 1.1% 54 0.102213 SELECT rssfeeds_contentfeeditem
# 14 0x3E52CF0261A7C3FF 4.4676 0.9% 44 0.101536 SELECT schedule_occurrence schedule_occurrence.start
# 15 0x9D0BCD3F6731195B 4.2804 0.9% 41 0.104401 SELECT mt_entry mt_placement mt_category
# 16 0x7961BD4C76277EB7 4.0143 0.8% 18 0.223014 INSERT UNION UPDATE UNION mt_session
# 17 0xD2F486BA41E7A623 3.1448 0.6% 21 0.149754 SELECT mt_entry mt_placement mt_category mt_objecttag mt_tag
# 18 0x3B9686D98BB8E054 2.9577 0.6% 11 0.268885 SELECT mt_entry mt_objecttag mt_tag
# 19 0xBB2443BF48638319 2.7239 0.6% 9 0.302660 SELECT rssfeeds_contentfeeditem
# 20 0x3D533D57D8B466CC 2.4209 0.5% 15 0.161391 SELECT mt_entry mt_placement mt_category
在此输出上方是这20个表现最差的查询的直方图
第一个条目的直方图示例
# Query 1: 0.77 QPS, 0.28x concurrency, ID 0x812D15015AD29D33 at byte 0 __
# This item is included in the report because it matches --limit.
# pct total min max avg 95% stddev median
# Count 36 910
# Exec time 58 336s 101ms 2s 370ms 992ms 230ms 393ms
# Lock time 0 0 0 0 0 0 0 0
# Users 1 mt
# Hosts 905 10.64.95.74:54707 (2), 10.64.95.74:56133 (2), 10.64.95.80:33862 (2)... 901 more
# Databases 1 mt1
# Time range 1321642802 to 1321643988
# bytes 1 1.11M 1.22k 1.41k 1.25k 1.26k 25.66 1.20k
# id 36 9.87G 11.10M 11.11M 11.11M 10.76M 0.12 10.76M
# Query_time distribution
# 1us
# 10us
# 100us
# 1ms
# 10ms
# 100ms ################################################################
# 1s ###
# 10s+
# Tables
# SHOW TABLE STATUS FROM `mt1` LIKE 'mt_entry'\G
# SHOW CREATE TABLE `mt1`.`mt_entry`\G
# SHOW TABLE STATUS FROM `mt1` LIKE 'mt_placement'\G
# SHOW CREATE TABLE `mt1`.`mt_placement`\G
# SHOW TABLE STATUS FROM `mt1` LIKE 'mt_category'\G
# SHOW CREATE TABLE `mt1`.`mt_category`\G
# EXPLAIN
SELECT `mt_entry`.`entry_id`, `mt_entry`.`entry_allow_comments`, `mt_entry`.`entry_allow_pings`, `mt_entry`.`entry_atom_id`, `mt_entry`.`entry_author_id`, `mt_entry`.`entry_authored_on`, `mt_entry`.`entry_basename`, `mt_entry`.`entry_blog_id`, `mt_entry`.`entry_category_id`, `mt_entry`.`entry_class`, `mt_entry`.`entry_comment_count`, `mt_entry`.`entry_convert_breaks`, `mt_entry`.`entry_created_by`, `mt_entry`.`entry_created_on`, `mt_entry`.`entry_excerpt`, `mt_entry`.`entry_keywords`, `mt_entry`.`entry_modified_by`, `mt_entry`.`entry_modified_on`, `mt_entry`.`entry_ping_count`, `mt_entry`.`entry_pinged_urls`, `mt_entry`.`entry_status`, `mt_entry`.`entry_tangent_cache`, `mt_entry`.`entry_template_id`, `mt_entry`.`entry_text`, `mt_entry`.`entry_text_more`, `mt_entry`.`entry_title`, `mt_entry`.`entry_to_ping_urls`, `mt_entry`.`entry_week_number` FROM `mt_entry` INNER JOIN `mt_placement` ON (`mt_entry`.`entry_id` = `mt_placement`.`placement_entry_id`) INNER JOIN `mt_category` ON (`mt_placement`.`placement_category_id` = `mt_category`.`category_id`) WHERE (`mt_entry`.`entry_status` = 2 AND `mt_category`.`category_basename` IN ('business_review' /*... omitted 3 items ...*/ ) AND NOT (`mt_entry`.`entry_id` IN (53441))) ORDER BY `mt_entry`.`entry_authored_on` DESC LIMIT 4\G