Question

我试图弄清楚我是否可以加快对存储电子邮件的数据库的两次查询。这是表格：

\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;

的输出

是

 relname  | relpages | reltuples | pg_size_pretty
----------+----------+-----------+----------------
 messages |  1584913 |   7337880 | 32 GB

一些可能相关的postgres配置值：

shared_buffers = 1536MB
effective_cache_size = 4608MB
work_mem = 7864kB
maintenance_work_mem = 384MB

以下是解释分析输出：

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)

第二个问题：

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)

这是在SSD，8GB Ram实例上，平均负载通常在0.15左右。

我绝对不是专家。这是数据刚刚遍布整个磁盘的情况吗？我唯一的解决方案是使用CLUSTER吗？

我不明白的一件事是为什么使用idx_unique_message_num作为第二个查询的索引。为什么按ID排序要慢得多？

Answer 1

如果有许多记录groupid=1886（来自评论：有200,563），要获取已排序的行子集的OFFSET记录，需要排序（或等效的堆算法），这很慢

这可以通过添加索引来解决。在这种情况下，(groupid,id)上的一个和(groupid,created)上的另一个。

来自评论：这确实有帮助，将运行时间缩短到5ms-10ms。

慢Postgres 9.3查询

1 个答案: