PostgreSQL的并行化包含三个重要组件：进程本身(leader进程)、gather、workers。没有开启并行化的时候，进程自身处理所有的数据；一旦计划器决定某个查询或查询中部分可以使用并行的时候，就会在查询的并行化部分添加一个gather节点，将gather节点作为子查询树的根节点。
查询执行是从leader进程开始。一旦开启了并行或查询中部分支持并行，就会分配一个gather节点和多个worker线程。relation的blocks在各个workers线程之间划分。workers的数量受postgresql的配置参数控制。workers之间使用共享内存相互协调和通信，一旦worker完成了自己的工作，结果就被传给了leader进程。
workers和leader进程之间使用消息队列(依赖共享内存)进行通信。每个进程有两个队列：一个是error队列；一个是tuples队列。
并行顺序扫描(Parallel sequential scan)
在PostgreSQL 9.6中，增加了对并行顺序扫描的支持。顺序扫描是在表上进行的扫描，在该表中一个接一个的块顺序地被评估。就其本质而言，顺序扫描允许并行化。这样，整个表将在多个workers线程之间顺序扫描。
并行顺序扫描快并不是因为可以并行地读，而是将数据分散到了多个cpu。
abce=# explain analyze select work_hour from hh_adds_static_day where create_time <= date '20201010'-interval '10' day;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------
Seq Scan on hh_adds_static_day (cost=0.00..261864.36 rows=4241981 width=4) (actual time=0.012..1407.214 rows=4228109 loops=1)
Filter: (create_time <= '2020-09-30 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 735600
Planning Time: 0.108 ms
Execution Time: 1585.835 ms
(5 rows)
abce=#顺序扫描产生了大量的行，但是没有使用聚合函数。因此，查询使用的是单个cpu核心。
增加一个sum函数后，很明显使用了两个工作线程，从而使得查询加速：
abce=# explain analyze select sum(work_hour) from hh_adds_static_day where create_time <= date '20201010'-interval '10' day;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=231089.60..231089.61 rows=1 width=4) (actual time=749.998..751.529 rows=1 loops=1)
-> Gather (cost=231089.38..231089.59 rows=2 width=4) (actual time=749.867..751.515 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=230089.38..230089.39 rows=1 width=4) (actual time=746.463..746.464 rows=1 loops=3)
-> Parallel Seq Scan on hh_adds_static_day (cost=0.00..225670.65 rows=1767492 width=4) (actual time=0.032..489.501 rows=1409370 loops=3)
Filter: (create_time <= '2020-09-30 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 245200
Planning Time: 0.112 ms
Execution Time: 751.611 ms
(10 rows)
abce=#
并行聚合(Parallel Aggregation)
在数据库中，计算聚合是非常昂贵的操作。如果以单个进程进行执行，则这将花费相当长的时间。在PostgreSQL 9.6中，通过简单地将它们分成多个块（分而治之策略）来增加了并行计算的能力。多个worker线程执行聚合的部分，然后leader再根据它们的结果计算最终结果。
从技术上讲，将Partial Aggregate节点添加到计划树中，并且每个Partial Aggregate节点包含一个worker线程的输出。然后将这些输出发送到Finalize Aggregate节点，该节点合并来自多个（所有）Partial Aggregate节点的聚合。如此有效的并行部分计划在根部包括一个Finalize Aggregate节点，以及一个将Partial Aggregate节点作为子节点的Gather节点。
''Parallel Seq Scan''节点生成用于部分聚合(''Partial Aggregate'')的行。
''Partial Aggregate''节点使用SUM()函数减少这些行。最后，由''Gather''节点收集每个worker的总和计数。
''Finalize Aggregate''节点计算最后的总和。如果你使用的自己的聚合函数，别忘了将其标记为''parallel safe''的。
worker数量
可以动态调整worker的数量：
abce=# show max_parallel_workers_per_gather;
max_parallel_workers_per_gather
---------------------------------
2
(1 row)
abce=# alter system set max_parallel_workers_per_gather=4;
ALTER SYSTEM
abce=# select * from pg_reload_conf();
pg_reload_conf
----------------
t
(1 row)
abce=# explain analyze select sum(work_hour) from hh_adds_static_day where create_time <= date '20201010'-interval '10' day;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=218981.25..218981.26 rows=1 width=4) (actual time=473.424..475.156 rows=1 loops=1)
-> Gather (cost=218980.83..218981.24 rows=4 width=4) (actual time=473.314..475.144 rows=5 loops=1)
Workers Planned: 4
Workers Launched: 4
-> Partial Aggregate (cost=217980.83..217980.84 rows=1 width=4) (actual time=468.769..468.770 rows=1 loops=5)
-> Parallel Seq Scan on hh_adds_static_day (cost=0.00..215329.59 rows=1060495 width=4) (actual time=0.036..306.854 rows=845622 loops=5)
Filter: (create_time <= '2020-09-30 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 147120
Planning Time: 0.150 ms
Execution Time: 475.218 ms
(10 rows)
abce=#我们将数量从2变成了4。
使用多少个worker进程
首先，max_parallel_workers_per_gather参数定义了worker的最小数量。
其次，查询执行器从池中获取worker的数量受限于max_parallel_workers的值。
最后，最顶层的限制是max_worker_processes的值，该参数定义了后台worker进程的总数量。
如果分配worker进程失败，就会切换成单进程执行。
查询计划器会根据表或索引的大小，考虑减少worker进程的数量。受参数min_parallel_table_scan_size、min_parallel_index_scan_size影响。
参数的默认设置：
abce=# show min_parallel_table_scan_size;
min_parallel_table_scan_size
------------------------------
8MB
(1 row)
abce=# show min_parallel_index_scan_size ;
min_parallel_index_scan_size
------------------------------
512kB
(1 row)
abce=#影响关系：
set min_parallel_table_scan_size='8MB'
8MB table => 1 worker
24MB table => 2 workers
72MB table => 3 workers
x => log(x / min_parallel_table_scan_size) / log(3) + 1 worker每当表的大小是min_parallel(index|table)scan_size的三倍，postgresql就会增加一个worker进程。worker进程的数量不是基于cost的。
在实践中，这些规则并不总是被遵守的，可以对特定的表执行alter table ... set (parallel_workers=N)进行设置。
为什么并行执行没有被使用？
除了并行执行的一些限制，postgresql也检查成本(cost)：
·parallel_setup_cost避免了对小的查询采用并行执行。它对用于内存设置、进程启动和初始通信的时间进行建模
·parallel_tuple_cost：leader进程和worker进程之间的通信会花费很长的时间。时间与worker发送的元组数量成比例。该参数对通信成本进行建模。
嵌套循环连接(Nested loop joins)
从9.6版本开始，postgresql支持对“Nested loop”执行并行操作：
explain (costs off) select c_custkey, count(o_orderkey)
from customer left outer join orders on
c_custkey = o_custkey and o_comment not like '%special%deposits%'
group by c_custkey;
QUERY PLAN
--------------------------------------------------------------------------------------
Finalize GroupAggregate
Group Key: customer.c_custkey
-> Gather Merge
Workers Planned: 4
-> Partial GroupAggregate
Group Key: customer.c_custkey
-> Nested Loop Left Join
-> Parallel Index Only Scan using customer_pkey on customer
-> Index Scan using idx_orders_custkey on orders
Index Cond: (customer.c_custkey = o_custkey)
Filter: ((o_comment)::text !~~ '%special%deposits%'::text)gather发生在最后阶段，因此“Nested Loop Left Join”是并行操作。从版本10开始提供“Parallel Index Only Scan”。其行为与并行顺序扫描类似。条件c_custkey = o_custkey为每个客户行读取一个订单。 因此，它不是并行的。
哈希连接(Hash Join)
在PostgreSQL 11之前，每个worker都构建自己的哈希表。因此，4个以上的workers进程无法提高性能。新的实现使用一个共享哈希表。每个worker都可以利用WORK_MEM来构建哈希表。
select
l_shipmode,
sum(case
when o_orderpriority = '1-URGENT'
or o_orderpriority = '2-HIGH'
then 1
else 0
end) as high_line_count,
sum(case
when o_orderpriority <> '1-URGENT'
and o_orderpriority <> '2-HIGH'
then 1
else 0
end) as low_line_count
from
orders,
lineitem
where
o_orderkey = l_orderkey
and l_shipmode in ('MAIL', 'AIR')
and l_commitdate < l_receiptdate
and l_shipdate < l_commitdate
and l_receiptdate >= date '1996-01-01'
and l_receiptdate < date '1996-01-01' + interval '1' year
group by
l_shipmode
order by
l_shipmode
LIMIT 1;QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=1964755.66..1964961.44 rows=1 width=27) (actual time=7579.592..7922.997 rows=1 loops=1)
-> Finalize GroupAggregate (cost=1964755.66..1966196.11 rows=7 width=27) (actual time=7579.590..7579.591 rows=1 loops=1)
Group Key: lineitem.l_shipmode
-> Gather Merge (cost=1964755.66..1966195.83 rows=28 width=27) (actual time=7559.593..7922.319 rows=6 loops=1)
Workers Planned: 4
Workers Launched: 4
-> Partial GroupAggregate (cost=1963755.61..1965192.44 rows=7 width=27) (actual time=7548.103..7564.592 rows=2 loops=5)
Group Key: lineitem.l_shipmode
-> Sort (cost=1963755.61..1963935.20 rows=71838 width=27) (actual time=7530.280..7539.688 rows=62519 loops=5)
Sort Key: lineitem.l_shipmode
Sort Method: external merge Disk: 2304kB
Worker 0: Sort Method: external merge Disk: 2064kB
Worker 1: Sort Method: external merge Disk: 2384kB
Worker 2: Sort Method: external merge Disk: 2264kB
Worker 3: Sort Method: external merge Disk: 2336kB
-> Parallel Hash Join (cost=382571.01..1957960.99 rows=71838 width=27) (actual time=7036.917..7499.692 rows=62519 loops=5)
Hash Cond: (lineitem.l_orderkey = orders.o_orderkey)
-> Parallel Seq Scan on lineitem (cost=0.00..1552386.40 rows=71838 width=19) (actual time=0.583..4901.063 rows=62519 loops=5)
Filter: ((l_shipmode = ANY ('{MAIL,AIR}'::bpchar[])) AND (l_commitdate < l_receiptdate) AND (l_shipdate < l_commitdate) AND (l_receiptdate >= '1996-01-01'::date) AND (l_receiptdate < '1997-01-01 00:00:00'::timestamp without time zone))
Rows Removed by Filter: 11934691
-> Parallel Hash (cost=313722.45..313722.45 rows=3750045 width=20) (actual time=2011.518..2011.518 rows=3000000 loops=5)
Buckets: 65536 Batches: 256 Memory Usage: 3840kB
-> Parallel Seq Scan on orders (cost=0.00..313722.45 rows=3750045 width=20) (actual time=0.029..995.948 rows=3000000 loops=5)
Planning Time: 0.977 ms
Execution Time: 7923.770 ms这里每个worker帮助构建一个共享的hash表。
合并连接(Merge Join)
鉴于合并连接的自身特性，使其不支持并行查询。如果合并连接是查询执行的最后一个阶段-你仍可以看到并行执行。
-- Query 2 from TPC-H
explain (costs off) select s_acctbal, s_name, n_name, p_partkey, p_mfgr, s_address, s_phone, s_comment
from part, supplier, partsupp, nation, region
where
p_partkey = ps_partkey
and s_suppkey = ps_suppkey
and p_size = 36
and p_type like '%BRASS'
and s_nationkey = n_nationkey
and n_regionkey = r_regionkey
and r_name = 'AMERICA'
and ps_supplycost = (
select
min(ps_supplycost)
from partsupp, supplier, nation, region
where
p_partkey = ps_partkey
and s_suppkey = ps_suppkey
and s_nationkey = n_nationkey
and n_regionkey = r_regionkey
and r_name = 'AMERICA'
)
order by s_acctbal desc, n_name, s_name, p_partkey
LIMIT 100;
QUERY PLAN
----------------------------------------------------------------------------------------------------------
Limit
-> Sort
Sort Key: supplier.s_acctbal DESC, nation.n_name, supplier.s_name, part.p_partkey
-> Merge Join
Merge Cond: (part.p_partkey = partsupp.ps_partkey)
Join Filter: (partsupp.ps_supplycost = (SubPlan 1))
-> Gather Merge
Workers Planned: 4
-> Parallel Index Scan using <strong>part_pkey</strong> on part
Filter: (((p_type)::text ~~ '%BRASS'::text) AND (p_size = 36))
-> Materialize
-> Sort
Sort Key: partsupp.ps_partkey
-> Nested Loop
-> Nested Loop
Join Filter: (nation.n_regionkey = region.r_regionkey)
-> Seq Scan on region
Filter: (r_name = 'AMERICA'::bpchar)
-> Hash Join
Hash Cond: (supplier.s_nationkey = nation.n_nationkey)
-> Seq Scan on supplier
-> Hash
-> Seq Scan on nation
-> Index Scan using idx_partsupp_suppkey on partsupp
Index Cond: (ps_suppkey = supplier.s_suppkey)
SubPlan 1
-> Aggregate
-> Nested Loop
Join Filter: (nation_1.n_regionkey = region_1.r_regionkey)
-> Seq Scan on region region_1
Filter: (r_name = 'AMERICA'::bpchar)
-> Nested Loop
-> Nested Loop
-> Index Scan using idx_partsupp_partkey on partsupp partsupp_1
Index Cond: (part.p_partkey = ps_partkey)
-> Index Scan using supplier_pkey on supplier supplier_1
Index Cond: (s_suppkey = partsupp_1.ps_suppkey)
-> Index Scan using nation_pkey on nation nation_1
Index Cond: (n_nationkey = supplier_1.s_nationkey)“Merge Join”节点在“Gather Merge”上方。 因此，合并不使用并行执行。但是“Parallel Index Scan”节点仍然有助于part_pkey。
分区智能连接(Partition-wise join)
PostgreSQL 11默认禁用分区智能连接(partition-wise join)功能。分区智能联接的计划成本很高。分区相似的表的联接可以逐分区进行。这允许postgres使用较小的哈希表。每个分区联接操作都可以并行执行。
tpch=# set enable_partitionwise_join=t;
tpch=# explain (costs off) select * from prt1 t1, prt2 t2
where t1.a = t2.b and t1.b = 0 and t2.b between 0 and 10000;
QUERY PLAN
---------------------------------------------------
Append
-> Hash Join
Hash Cond: (t2.b = t1.a)
-> Seq Scan on prt2_p1 t2
Filter: ((b >= 0) AND (b <= 10000))
-> Hash
-> Seq Scan on prt1_p1 t1
Filter: (b = 0)
-> Hash Join
Hash Cond: (t2_1.b = t1_1.a)
-> Seq Scan on prt2_p2 t2_1
Filter: ((b >= 0) AND (b <= 10000))
-> Hash
-> Seq Scan on prt1_p2 t1_1
Filter: (b = 0)
tpch=# set parallel_setup_cost = 1;
tpch=# set parallel_tuple_cost = 0.01;
tpch=# explain (costs off) select * from prt1 t1, prt2 t2
where t1.a = t2.b and t1.b = 0 and t2.b between 0 and 10000;
QUERY PLAN
-----------------------------------------------------------
Gather
Workers Planned: 4
-> Parallel Append
-> Parallel Hash Join
Hash Cond: (t2_1.b = t1_1.a)
-> Parallel Seq Scan on prt2_p2 t2_1
Filter: ((b >= 0) AND (b <= 10000))
-> Parallel Hash
-> Parallel Seq Scan on prt1_p2 t1_1
Filter: (b = 0)
-> Parallel Hash Join
Hash Cond: (t2.b = t1.a)
-> Parallel Seq Scan on prt2_p1 t2
Filter: ((b >= 0) AND (b <= 10000))
-> Parallel Hash
-> Parallel Seq Scan on prt1_p1 t1
Filter: (b = 0)最重要的是，仅在分区足够大的情况下，分区智能联接才能使用并行执行。
并行追加(Parallel Append)
通常可以在UNION ALL查询中看到这一点。缺点是并行性差，因为每个work最终都是为单个查询工作。
即使启用了四个worker，也只有两个被启动。
tpch=# explain (costs off) select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day union all select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '2000-12-01' - interval '105' day;
QUERY PLAN
------------------------------------------------------------------------------------------------
Gather
Workers Planned: 2
-> Parallel Append
-> Aggregate
-> Seq Scan on lineitem
Filter: (l_shipdate <= '2000-08-18 00:00:00'::timestamp without time zone)
-> Aggregate
-> Seq Scan on lineitem lineitem_1
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
重要的变量
·work_mem
·max_parallel_workers_per_gather：执行器为每个并行执行的计划节点分配的worker数量
·max_worker_processes
·max_parallel_workers
在9.6版本中，并行查询执行被引入；
在10版本中，默认开启并行执行；
在负载重的oltp系统上，建议关闭并行执行。
来源：oschina
链接：https://my.oschina.net/u/4296574/blog/4722165