我目前正在研究数据跟踪系统。该系统是一个用 Python 编写的多进程应用程序，工作方式如下：
- 每 S 秒，它从数据库（当前为 Postgres）中选择 N 个最合适的任务并为其查找数据
- 如果没有任务，则创建 N 个新任务并返回 (1)。
问题如下 - 目前我有大约。80GB 的数据和 36M 的任务以及对任务表的查询开始变得越来越慢（它是填充最多和最常用的表）。
性能的主要瓶颈是任务跟踪查询：
LOCK TABLE task IN ACCESS EXCLUSIVE MODE;
SELECT * FROM task WHERE line = 1 AND action = ANY(ARRAY['Find', 'Get']) AND (stat IN ('', 'CR1') OR stat = 'ERROR' AND (actiondate <= NOW() OR actiondate IS NULL)) ORDER BY taskid, actiondate, action DESC, idtype, date ASC LIMIT 36;
Table "public.task"
Column | Type | Modifiers
------------+-----------------------------+-------------------------------------------------
number | character varying(16) | not null
date | timestamp without time zone | default now()
stat | character varying(16) | not null default ''::character varying
idtype | character varying(16) | not null default 'container'::character varying
uri | character varying(1024) |
action | character varying(16) | not null default 'Find'::character varying
reason | character varying(4096) | not null default ''::character varying
rev | integer | not null default 0
actiondate | timestamp without time zone |
modifydate | timestamp without time zone |
line | integer |
datasource | character varying(512) |
taskid | character varying(32) |
found | integer | not null default 0
Indexes:
"task_pkey" PRIMARY KEY, btree (idtype, number)
"action_index" btree (action)
"actiondate_index" btree (actiondate)
"date_index" btree (date)
"line_index" btree (line)
"modifydate_index" btree (modifydate)
"stat_index" btree (stat)
"taskid_index" btree (taskid)
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=312638.87..312638.96 rows=36 width=668) (actual time=1838.193..1838.197 rows=36 loops=1)
-> Sort (cost=312638.87..313149.54 rows=204267 width=668) (actual time=1838.192..1838.194 rows=36 loops=1)
Sort Key: taskid, actiondate, action, idtype, date
Sort Method: top-N heapsort Memory: 43kB
-> Bitmap Heap Scan on task (cost=107497.61..306337.31 rows=204267 width=668) (actual time=1013.491..1343.751 rows=914586 loops=1)
Recheck Cond: ((((stat)::text = ANY ('{"",CR1}'::text[])) OR ((stat)::text = 'ERROR'::text)) AND (line = 1))
Filter: (((action)::text = ANY ('{Find,Get}'::text[])) AND (((stat)::text = ANY ('{"",CR1}'::text[])) OR (((stat)::text = 'ERROR'::text) AND ((actiondate <= now()) OR (actiondate IS NULL)))))
Rows Removed by Filter: 133
Heap Blocks: exact=76064
-> BitmapAnd (cost=107497.61..107497.61 rows=237348 width=0) (actual time=999.457..999.457 rows=0 loops=1)
-> BitmapOr (cost=9949.15..9949.15 rows=964044 width=0) (actual time=121.936..121.936 rows=0 loops=1)
-> Bitmap Index Scan on stat_index (cost=0.00..9449.46 rows=925379 width=0) (actual time=117.791..117.791 rows=920900 loops=1)
Index Cond: ((stat)::text = ANY ('{"",CR1}'::text[]))
-> Bitmap Index Scan on stat_index (cost=0.00..397.55 rows=38665 width=0) (actual time=4.144..4.144 rows=30262 loops=1)
Index Cond: ((stat)::text = 'ERROR'::text)
-> Bitmap Index Scan on line_index (cost=0.00..97497.14 rows=9519277 width=0) (actual time=853.033..853.033 rows=9605462 loops=1)
Index Cond: (line = 1)
Planning time: 0.284 ms
Execution time: 1838.882 ms
(19 rows)
当然，所有涉及的字段都被索引。我目前在考虑两个方向：
- 如何优化查询以及它实际上是否会给我带来透视性能改进（目前每个查询大约需要 10 秒，这在动态任务跟踪中是不可接受的）
- 在哪里以及如何更有效地存储任务数据 - 可能我应该为此目的使用另一个数据库 - Cassandra、VoltDB 或另一个大数据存储？
我认为应该以某种方式预先订购数据以尽快获得实际任务。
另外请记住，我目前的 80G 容量很可能是此类任务的最小值而不是最大值。
提前致谢！