如何提高Postgres select语句的速度？

我有以下表格:

CREATE TABLE views (
    view_id bigint NOT NULL,
    usr_id bigint,
    ip inet,
    referer_id bigint,
    country_id integer,
    validated smallint,
    completed smallint,
    value numeric
);

ALTER TABLE ONLY views
    ADD CONSTRAINT "Views_pkey" PRIMARY KEY (view_id);

CREATE TABLE country (
    country_id integer NOT NULL,
    country character varying(2)
);

ALTER TABLE ONLY country
    ADD CONSTRAINT country_pkey PRIMARY KEY (country_id);

CREATE TABLE file_id_view_id (
    file_id bigint,
    view_id bigint,
    created_ts timestamp without time zone
);

CREATE TABLE file_owner (
    file_id bigint NOT NULL,
    owner_id bigint
);

ALTER TABLE ONLY file_owner
        ADD CONSTRAINT owner_table_pkey PRIMARY KEY (file_id);

CREATE TABLE referer (
    referer_id bigint NOT NULL,
    referer character varying(255)
);

ALTER TABLE ONLY referer
    ADD CONSTRAINT referer_pkey PRIMARY KEY (referer_id);

在views与file_id_view_id表有大约340M行各.每小时它们都会增加600K行.

该file_owner表有75K行,每小时增加100行.

该country表有233行,很少更改.

该referer表有6494行,很少更改.

我的目标是能够执行如下查询:

SELECT Count(ft.*)                     AS total_views,
       ( Count(ft.*) - SUM(ft.valid) ) AS invalid_views,
       SUM(ft.valid)                   AS valid_views,
       SUM(ft.values)                  AS VALUES,
       ft.day                          AS day,
       ( CASE
           WHEN r.referer IS NULL THEN 'Unknown'
           ELSE r.referer
         END )                         AS referer,
       ( CASE
           WHEN c.country IS NULL THEN 'Unknown'
           ELSE c.country
         END )                         AS country
FROM   country c
       right join (referer r
                   right join (SELECT v.validated  AS valid,
                                      v.value      AS VALUES,
                                      vf.day       AS day,
                                      vf.view_id   AS view_id,
                                      v.referer_id AS referer_id,
                                      v.country_id AS country_id
                               FROM   VIEWS v,
                                      (SELECT view_id,
fivi.created_ts :: timestamp :: DATE AS
day
FROM   file_id_view_id fivi
join (SELECT file_id
      FROM   file_owner
      WHERE  owner_id = 75
      GROUP  BY file_id) fo
  ON ( fo.file_id = fivi.file_id )
WHERE  ( fivi.created_ts BETWEEN
  '2015-11-01' AND '2015-12-01' )
GROUP  BY view_id,
   day) vf
WHERE  v.view_id = vf.view_id) ft
ON ( ft.referer_id = r.referer_id ))
ON ( ft.country_id = c.country_id )
GROUP  BY day,
          referer,
          country;

生产:

total_views | invalid_views | valid_views | values |    day     |     referer     | country 
------------+---------------+-------------+--------+------------+-----------------+---------

生成此类查询时EXPLAIN ANALYZE会生成以下内容:

GroupAggregate  (cost=38893491.99..40443007.61 rows=182295955 width=52) (actual time=183725.696..205882.889 rows=172 loops=1)
  Group Key: ((fivi.created_ts)::date), r.referer, c.country
  ->  Sort  (cost=38893491.99..38984639.97 rows=182295955 width=52) (actual time=183725.655..200899.098 rows=8390217 loops=1)
        Sort Key: ((fivi.created_ts)::date), r.referer, c.country
        Sort Method: external merge  Disk: 420192kB
        ->  Hash Left Join  (cost=16340128.88..24989809.75 rows=182295955 width=52) (actual time=23399.900..104337.332 rows=8390217 loops=1)
              Hash Cond: (v.country_id = c.country_id)
              ->  Hash Left Join  (cost=16340125.36..24800637.72 rows=182295955 width=49) (actual time=23399.782..102534.655 rows=8390217 loops=1)
                    Hash Cond: (v.referer_id = r.referer_id)
                    ->  Merge Join  (cost=16340033.52..24051874.62 rows=182295955 width=29) (actual time=23397.410..99955.000 rows=8390217 loops=1)
                          Merge Cond: (fivi.view_id = v.view_id)
                          ->  Group  (cost=16340033.41..16716038.36 rows=182295955 width=16) (actual time=23397.298..30454.444 rows=8390217 loops=1)
                                Group Key: fivi.view_id, ((fivi.created_ts)::date)
                                ->  Sort  (cost=16340033.41..16434985.73 rows=189904653 width=16) (actual time=23397.294..28165.729 rows=8390217 loops=1)
                                      Sort Key: fivi.view_id, ((fivi.created_ts)::date)
                                      Sort Method: external merge  Disk: 180392kB
                                      ->  Nested Loop  (cost=6530.43..8799350.01 rows=189904653 width=16) (actual time=63.123..15131.956 rows=8390217 loops=1)
                                            ->  HashAggregate  (cost=6530.31..6659.62 rows=43104 width=8) (actual time=62.983..90.331 rows=43887 loops=1)
                                                  Group Key: file_owner.file_id
                                                  ->  Bitmap Heap Scan on file_owner  (cost=342.90..6508.76 rows=43104 width=8) (actual time=5.407..50.779 rows=43887 loops=1)
                                                        Recheck Cond: (owner_id = 75)
                                                        Heap Blocks: exact=5904
                                                        ->  Bitmap Index Scan on owner_id_index  (cost=0.00..340.74 rows=43104 width=0) (actual time=4.327..4.327 rows=45576 loops=1)
                                                              Index Cond: (owner_id = 75)
                                            ->  Index Scan using file_id_view_id_indexing on file_id_view_id fivi  (cost=0.11..188.56 rows=4406 width=24) (actual time=0.122..0.306 rows=191 loops=43887)
                                                  Index Cond: (file_id = file_owner.file_id)
                                                  Filter: ((created_ts >= '2015-11-01 00:00:00'::timestamp without time zone) AND (created_ts <= '2015-12-01 00:00:00'::timestamp without time zone))
                                                  Rows Removed by Filter: 184
                          ->  Index Scan using "Views_pkey" on views v  (cost=0.11..5981433.17 rows=338958763 width=25) (actual time=0.088..46804.757 rows=213018702 loops=1)
                    ->  Hash  (cost=68.77..68.77 rows=6591 width=28) (actual time=2.344..2.344 rows=6495 loops=1)
                          Buckets: 1024  Batches: 1  Memory Usage: 410kB
                          ->  Seq Scan on referer r  (cost=0.00..68.77 rows=6591 width=28) (actual time=0.006..1.156 rows=6495 loops=1)
              ->  Hash  (cost=2.70..2.70 rows=233 width=7) (actual time=0.078..0.078 rows=233 loops=1)
                    Buckets: 1024  Batches: 1  Memory Usage: 10kB
                    ->  Seq Scan on country c  (cost=0.00..2.70 rows=233 width=7) (actual time=0.005..0.042 rows=233 loops=1)
Planning time: 1.015 ms
Execution time: 206034.660 ms
(37 rows)

计划在explain.depesz.com上:http://explain.depesz.com/s/OiN

206s运行时间.

有些事情需要注意,

Postgresql版本9.4

我调整了配置如下:

shared_buffers = 30GB

work_mem = 32MB

random_page_cost = 2.0

cpu_tuple_cost = 0.0030

cpu_index_tuple_cost = 0.0010

cpu_operator_cost = 0.0005

effective_cache_size = 52GB

目前存在以下索引:

CREATE INDEX country_index使用btree(国家)的国家/地区;

CREATE INDEX created_ts_index ON file_id_view_id使用btree(created_ts);

CREATE INDEX file_id_created_ts_index ON file_id_view_id使用btree(created_ts,file_id);

CREATE INDEX file_id_view_id_indexing ON file_id_view_id使用btree(file_id);

CREATE INDEX owner_id_file_id_index ON file_owner使用btree(file_id,owner_id);

CREATE INDEX owner_id_index ON file_owner使用btree(owner_id);

CREATE INDEX referer_index ON referer使用btree(referer);

前一次查询使用的所有者ID将其拾取保守,某些查询可能导致1/3的的file_id_view_id表被与接合视图.

改变数据结构是最后的手段.在这个阶段,这种变化必须引起严重关切.

如果需要,db可以被认为是只读的,正在写入的数据是每小时完成的,并且在每次写入后给Postgres充足的喘息空间.在600K每小时写入期间的当前时刻,数据库将在1100s内返回(这是由于插入成本旁边的其他原因).如果它会增加读取速度,则有足够的空间来添加附加索引,读取速度是优先级.

硬件规格如下:

CPU:http://ark.intel.com/products/83356/Intel-Xeon-Processor-E5-2630-v3-20M-Cache-2_40-GHz

内存:128GB

存储:1.5TB PCIE SSD

如何优化我的数据库或查询,以便我可以在合理的时间范围内从数据库中检索出我需要的信息？

我该怎么做才能优化我目前的设计？

我相信Postgres及其运行的硬件具有比目前更好的性能.

UPDATE

我试过了:

分析表,不影响性能.

增加work_mem,这导致速度增加到116s.

通过避免子选择来依赖Postgres的查询规划器,这会对性能产生负面影响.

事先单独进行数据库查找,这似乎没有正面/负面影响.

有没有人有重建这么大的经验？这可行吗？需要几天,几小时(当然估计)？

我正在考虑对数据库进行反规范化,因为它实际上只会在此方法中引用.我唯一担心的是 - 如果要从带有索引owner_id的表中调用100M行,它会足够快还是我仍然面临相同的性能问题？不愿意走一条路然后不得不回溯.

我正在研究的另一个解决方案是@ ivan.panasuik建议,将所有日期数据分组到另一个表中,因为一旦过了一天,该信息是不变的,不需要更改或更新.但是我不确定如何顺利地实现这一点 - 我是否应该在插入处于暂停状态时查询数据并尽可能快地捕获日期？从那时起有一个触发器设置？