[英]Slow Postgres 9.3 queries
我試圖找出是否可以加快對存儲電子郵件消息的數據庫的兩個查詢。 這是桌子:
\d messages;
Table "public.messages"
Column | Type | Modifiers
----------------+---------+-------------------------------------------------------
id | bigint | not null default nextval('messages_id_seq'::regclass)
created | bigint |
updated | bigint |
version | bigint |
threadid | bigint |
userid | bigint |
groupid | bigint |
messageid | text |
date | bigint |
num | bigint |
hasattachments | boolean |
placeholder | boolean |
compressedmsg | bytea |
revcount | bigint |
subject | text |
isreply | boolean |
likes | bytea |
isspecial | boolean |
pollid | bigint |
username | text |
fullname | text |
Indexes:
"messages_pkey" PRIMARY KEY, btree (id)
"idx_unique_message_messageid" UNIQUE, btree (groupid, messageid)
"idx_unique_message_num" UNIQUE, btree (groupid, num)
"idx_group_id" btree (groupid)
"idx_message_id" btree (messageid)
"idx_thread_id" btree (threadid)
"idx_user_id" btree (userid)
SELECT relname, relpages, reltuples::numeric, pg_size_pretty(pg_table_size(oid)) FROM pg_class WHERE oid='messages'::regclass;
是
relname | relpages | reltuples | pg_size_pretty
----------+----------+-----------+----------------
messages | 1584913 | 7337880 | 32 GB
一些可能相關的postgres配置值:
shared_buffers = 1536MB
effective_cache_size = 4608MB
work_mem = 7864kB
maintenance_work_mem = 384MB
這是解釋分析輸出:
explain analyze SELECT * FROM messages WHERE groupid=1886 ORDER BY id ASC LIMIT 20 offset 4440;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=479243.63..481402.39 rows=20 width=747) (actual time=14167.374..14167.408 rows=20 loops=1)
-> Index Scan using messages_pkey on messages (cost=0.43..19589605.98 rows=181490 width=747) (actual time=14105.172..14167.188 rows=4460 loops=1)
Filter: (groupid = 1886)
Rows Removed by Filter: 2364949
Total runtime: 14167.455 ms
(5 rows)
第二個查詢:
explain analyze SELECT * FROM messages WHERE groupid=1886 ORDER BY created ASC LIMIT 20 offset 4440;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=538650.72..538650.77 rows=20 width=747) (actual time=671.983..671.992 rows=20 loops=1)
-> Sort (cost=538639.62..539093.34 rows=181490 width=747) (actual time=670.680..671.829 rows=4460 loops=1)
Sort Key: created
Sort Method: top-N heapsort Memory: 7078kB
-> Bitmap Heap Scan on messages (cost=7299.11..526731.31 rows=181490 width=747) (actual time=84.975..512.969 rows=200561 loops=1)
Recheck Cond: (groupid = 1886)
-> Bitmap Index Scan on idx_unique_message_num (cost=0.00..7253.73 rows=181490 width=0) (actual time=57.239..57.239 rows=203423 loops=1)
Index Cond: (groupid = 1886)
Total runtime: 672.787 ms
(9 rows)
這是在8GB Ram實例的SSD上,平均負載通常為0.15左右。
我絕對不是專家。 這是否只是數據散布在整個磁盤上的情況? 我是使用CLUSTER的唯一解決方案嗎?
我不明白的一件事是為什么它使用idx_unique_message_num
作為第二個查詢的索引。 為什么按ID訂購的速度這么慢?
如果有許多記錄的組groupid=1886
(從注釋:有200,563),要在行的排序子集的OFFSET處獲取記錄,將需要進行排序(或等效的堆算法),這很慢。
這可以通過添加索引來解決。 在這種情況下,一個在(groupid,id)
,另一個在(groupid,created)
。
摘自評論:這確實有所幫助,將運行時間減少到5ms-10ms。
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