Ton*_*ony 6 postgresql indexing sql-execution-plan postgresql-performance
我有一个问题:
EXPLAIN ANALYZE
SELECT CAST(DATE(associationtime) AS text) AS date ,
cast(SUM(extract(epoch
FROM disassociationtime) - extract(epoch
FROM associationtime)) AS bigint) AS sessionduration,
cast(SUM(tx) AS bigint)AS tx,
cast(SUM(rx) AS bigint) AS rx,
cast(SUM(dataRetries) AS bigint) AS DATA,
cast(SUM(rtsRetries) AS bigint) AS rts,
count(*)
FROM SESSION
WHERE ssid_id=42
AND ap_id=1731
AND DATE(associationtime)>=DATE('Tue Nov 04 00:00:00 MSK 2014')
AND DATE(associationtime)<=DATE('Thu Nov 20 00:00:00 MSK 2014')
GROUP BY(DATE(associationtime))
ORDER BY DATE(associationtime);
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输出是:
GroupAggregate (cost=0.44..17710.66 rows=1 width=32) (actual time=4.501..78.880 rows=17 loops=1)
-> Index Scan using session_lim_values_idx on session (cost=0.44..17538.94 rows=6868 width=32) (actual time=0.074..73.266 rows=7869 loops=1)
Index Cond: ((date(associationtime) >= '2014-11-04'::date) AND (date(associationtime) <= '2014-11-20'::date))
Filter: ((ssid_id = 42) AND (ap_id = 1731))
Rows Removed by Filter: 297425
Total runtime: 78.932 ms
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看看这一行:
Index Scan using session_lim_values_idx
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如您所见,查询使用三个字段进行扫描:ssid_id,ap_id和associationtime.我有一个索引:
ssid_pkey | btree | {id}
ap_pkey | btree | {id}
testingshit_pkey | btree | {one,two,three}
session_date_ssid_idx | btree | {ssid_id,date(associationtime),"date_trunc('hour'::text, associationtime)"}
session_pkey | btree | {associationtime,disassociationtime,sessionduration,clientip,clientmac,devicename,tx,rx,protocol,snr,rssi,dataretries,rtsretries }
session_main_idx | btree | {ssid_id,ap_id,associationtime,disassociationtime,sessionduration,clientip,clientmac,devicename,tx,rx,protocol,snr,rssi,dataretres,rtsretries}
session_date_idx | btree | {date(associationtime),"date_trunc('hour'::text, associationtime)"}
session_date_apid_idx | btree | {ap_id,date(associationtime),"date_trunc('hour'::text, associationtime)"}
session_date_ssid_apid_idx | btree | {ssid_id,ap_id,date(associationtime),"date_trunc('hour'::text, associationtime)"}
ap_apname_idx | btree | {apname}
users_pkey | btree | {username}
user_roles_pkey | btree | {user_role_id}
session_lim_values_idx | btree | {date(associationtime)}
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它被称为session_date_ssid_apid_idx.但为什么查询使用错误的索引?
session_date_ssid_apid_idx:
------------+-----------------------------+-------------------------------------------
ssid_id | integer | ssid_id
ap_id | integer | ap_id
date | date | date(associationtime)
date_trunc | timestamp without time zone | date_trunc('hour'::text, associationtime)
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session_lim_values_idx:
date | date | date(associationtime)
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你会创建什么指数?
UPD: \d session
--------------------+-----------------------------+------------------------------------------------------
id | integer | NOT NULL DEFAULT nextval('session_id_seq'::regclass)
ssid_id | integer | NOT NULL
ap_id | integer | NOT NULL
associationtime | timestamp without time zone | NOT NULL
disassociationtime | timestamp without time zone | NOT NULL
sessionduration | character varying(100) | NOT NULL
clientip | character varying(100) | NOT NULL
clientmac | character varying(100) | NOT NULL
devicename | character varying(100) | NOT NULL
tx | integer | NOT NULL
rx | integer | NOT NULL
protocol | character varying(100) | NOT NULL
snr | integer | NOT NULL
rssi | integer | NOT NULL
dataretries | integer | NOT NULL
rtsretries | integer | NOT NULL
???????:
"session_pkey" PRIMARY KEY, btree (associationtime, disassociationtime, sessionduration, clientip, clientmac, devicename, tx, rx, protocol, snr, rssi, dataretries, rtsretries)
"session_date_ap_ssid_idx" btree (ssid_id, ap_id, associationtime)
"session_date_apid_idx" btree (ap_id, date(associationtime), date_trunc('hour'::text, associationtime))
"session_date_idx" btree (date(associationtime), date_trunc('hour'::text, associationtime))
"session_date_ssid_apid_idx" btree (ssid_id, ap_id, associationtime)
"session_date_ssid_idx" btree (ssid_id, date(associationtime), date_trunc('hour'::text, associationtime))
"session_lim_values_idx" btree (date(associationtime))
"session_main_idx" btree (ssid_id, ap_id, associationtime, disassociationtime, sessionduration, clientip, clientmac, devicename, tx, rx, protocol, snr, rssi, dataretries, rtsretries)
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在谓词中非常常见的值,ssid_id并且ap_id可以使Postgres选择较小的索引session_lim_values_idx(仅1 date列)比看似更好的拟合更便宜,但更大的索引session_date_ssid_apid_idx(4列)并过滤其余的.
在你的情况下,大约4%的行有ssid_id=42 AND ap_id=1731.这通常不应该保证切换到较小的索引.但是其他一些因素正在发挥作用,可能会使规模倾斜,主要是成本设置和统计数据.细节:
如果您没有按照建议链接上面的答案,请调整您的费用设置.
增加所涉及列的统计目标ssid_id,ap_id并运行ANALYZE:
这里有一个特殊因素:Postgres 为索引中的表达式收集单独的统计信息.检查:
SELECT * FROM pg_statistic
WHERE starelid = 'session_date_ssid_apid_idx'::regclass;
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您将找到表达式的专用行date(associationtime).更多细节:
session_date_ssid_apid_idx通过删除第4列使索引更具吸引力(更小)"date_trunc('hour'::text, associationtime).查看以后添加的表定义,您已经这样做了.
我宁愿使用强制转换的标准语法:cast(associationtime AS date)而不是函数语法date(associationtime).不是说这很重要,我只知道正常工作的标准方法.您可以associationtime::date在查询中使用与表达式索引兼容的简写语法,但在索引定义中使用详细形式.
此外,通过仅删除/重新创建要测试的索引来测试EXPLAIN ANALYZE查询计划实际上更快.然后你会看到Postgres是否选择了最好的计划.
你有很多索引,我会检查是否所有索引都是实际使用的并且除去其余的索引.索引具有维护成本,如果可能的话,专注于更少的索引通常是有益的(更容易适应缓存并且可以在需要时缓存).权衡成本与收益.
我用的是:
SUM(extract(epoch FROM disassociationtime
- associationtime)::int) AS sessionduration
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