IIS 6.0 (Windows Server 2003) 上的 HTTP 压缩

 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 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 而不是一般日志或慢日志。从那篇文章中，这里是 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

此输出上方是这 20 个性能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

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