PgSQL-并行查询系列-介绍
现代CPU模型拥有大量的CPU核心。多年来，数据库应用程序都是并发向数据库发送查询的。查询处理多个表的行时，若可以使用多核，则可以客观地提升性能。PgSQL 9.6引入了并行查询的新特性，开启并行查询后可以大幅提升性能。
1、局限性
1）若所有CPU核心已经饱和，则不要启动并行查询。并行执行会从其他查询中窃取CPU时间，并增加响应时间
2）进一步需要注意：并行处理会显著增加内存使用（需要注意work_mem的值）。因为，每个hash join或者排序操作都会使用work_mem大小的内存。
3）低延迟的OLTP查询并不能通过并行显著提升性能。特别是仅返回1行的查询，若启用并行，性能会变得特烂。
4）并行执行仅支持没有锁谓词的SELECT查询
5）不支持cursor和会挂起的查询
6）windowed 函数和ordered-set聚合函数都不是并行的
7）对于负载已达IO瓶颈的，并没有啥好处
8）没有并行排序算法。然而，排序查询在某些方面仍然可以并行
9）将CTE（WITH...）替换为sub-select以支持并行执行
10）FDW还不支持并行（后面版本可以，注意哪个版本支持）
11）full outer join不支持
12）客户端设置了max_rows，禁止并行执行
13）如果查询中使用了没有标记为PARALLEL SAFE的函数，那他就是单线程执行
14）SERIALIZABLE事务隔离级别禁用并行执行
2、并行顺序扫描
并行顺序扫描很快，原因可能不是并行读，而是将数据访问分散到多个CPU上。现代操作系统给PgSQL的数据文件提供了很好的缓冲机制。预取允许从存储中获取一个块，而不仅是PgSQL请求的块。因此查询性能限制往往不在IO上，它消耗CPU周期：从表数据页中逐行读取；比较行值和WHERE条件
我们执行一个简单查询：
tpch=# explain analyze select l_quantity as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------
Seq Scan on lineitem (cost=0.00..1964772.00 rows=58856235 width=5) (actual time=0.014..16951.669 rows=58839715 loops=1)
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 1146337
Planning Time: 0.203 ms
Execution Time: 19035.100 ms
一个顺序扫描，没有聚合，需要产生大量行。因此该查询被一个CPU核心执行。添加聚合SUM()后，可以清晰的看到有2个进程帮助查询：
explain analyze select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=1589702.14..1589702.15 rows=1 width=32) (actual time=8553.365..8553.365 rows=1 loops=1)
-> Gather (cost=1589701.91..1589702.12 rows=2 width=32) (actual time=8553.241..8555.067 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
-> Partial Aggregate (cost=1588701.91..1588701.92 rows=1 width=32) (actual time=8547.546..8547.546 rows=1 loops=3)
-> Parallel Seq Scan on lineitem (cost=0.00..1527393.33 rows=24523431 width=5) (actual time=0.038..5998.417 rows=19613238 loops=3)
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 382112
Planning Time: 0.241 ms
Execution Time: 8555.131 ms
性能提升2.2倍。
3、并行聚合
“Parallel Seq Scan”节点为partial aggregation提供行。“Partial Aggregate”节点先对SUM()进行一次操作。最后“Gather”节点汇总每个进程的SUM值。“Finalize Aggregate”节点进行最后计算。如果你使用了聚合函数，不要忘记标记他们为“parallel safe”。
4、进程个数
可以不重启服务，增加并行进程个数：
alter system set max_parallel_workers_per_gather=4;
select * from pg_reload_conf();
Now, there are 4 workers in explain output:
tpch=# explain analyze select sum(l_quantity) as sum_qty from lineitem where l_shipdate <= date '1998-12-01' - interval '105' day;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate (cost=1440213.58..1440213.59 rows=1 width=32) (actual time=5152.072..5152.072 rows=1 loops=1)
-> Gather (cost=1440213.15..1440213.56 rows=4 width=32) (actual time=5151.807..5153.900 rows=5 loops=1)
Workers Planned: 4
Workers Launched: 4
-> Partial Aggregate (cost=1439213.15..1439213.16 rows=1 width=32) (actual time=5147.238..5147.239 rows=1 loops=5)
-> Parallel Seq Scan on lineitem (cost=0.00..1402428.00 rows=14714059 width=5) (actual time=0.037..3601.882 rows=11767943 loops=5)
Filter: (l_shipdate <= '1998-08-18 00:00:00'::timestamp without time zone)
Rows Removed by Filter: 229267
Planning Time: 0.218 ms
Execution Time: 5153.967 ms
我们将并发进程由2改成了4，但是查询仅快1.6599倍。实际上，我们有2个进程+一个leader，配置改好成为4+1。并行最大提升可以：5/3=1.66倍。
5、如何工作？
查询执行总是从“leader”进程开始。Leader进程执行所有非并行动作。其他进程执行相同查询，称为“worker”进程。并行利用Dynamic Backgroud workers基础架构（9.4引入）执行。因此创建3个工作进程的查询可能比传统执行快4倍。
Worker进程使用消息队列（基于共享内存）和leader进行通信。每个进程有2个队列：一个为errors，另一个是tuples。
5、进程使用个数
1）max_parallel_workers_per_gather是workers进程数的最小限制
2）查询执行使用的workers限制为max_parallel_workes
3）最上层的限制是max_worker_processes：后台进程的总数
分配进程失败，会导致使用单进程执行。查询规划器会根据表或索引大小来增加worker个数。min_parallel_table_scan_size和min_parallel_index_scan_size控制该行为。
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
表比min_parallel_(index|table)_scan_size值每大3倍，PG增加一个worker进程。Workers进程个数不是基于成本的。循环依赖使得复杂的实现变得困难。相反,规划者使用简单的规则。
可以通过ALTER TABLE … SET (parallel_workers = N)来对某个表指定并行进程数。
6、为什么不使用并行
除了并行限制外，PG还会检查代价：
parallel_setup_cost:避免短查询的并行执行。模拟用于内存设置、流程启动和初始通信的时间
parallel_tuple_cost:leader和worker之间通信可能花费很长时间。时间和worker发送的记录数成正比。参数对通信成本进行建模。
7、Nested Loop Join
PgSQL9.6+可以以并行形式执行“Nested loop”。
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。“Parallel Index Only Scan”在版本10才可以使用，和并行顺序扫描类似。c_custkey = o_custkey条件读取每个customer行的order列，因此不是并行。
8、Hash Join
PgSQL11中每个worker构建自己的hash table。因此，4+ workers不能提升性能。新的实现方式：使用一个共享hash table。每个worker可以利用WORK_MEM来构建hash table>
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
TPC-H中的SQL12是并行hash join的一个很好的哪里，每个进程都帮助构建共享hash table。
9、Merge Join
由于merge join的特性，使得不能并行。如果merge join是查询执行的最后阶段，那么不用担心，仍可以使用并行。
-- 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。因此merge不使用并行。但是“Parallel Index Scan”仍旧有助于part_pkey。
10、Partition-wise join
PgSQL11默认禁止partition-wise join特性。它有一个很高的规划代价。分区表可以一个分区一个分区的进行join。允许使用更小的hash table。每个per-partition join操作可以并行：
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)
分区连接只有在分区足够大的情况下才能使用并行执行
11、Parallel Append
Parallel Append通常在UNION ALL中。缺点：较小的并行度，因为每个worker进程最终都为一个查询服务。即使启用了4个进程，也会仍旧发起2个：
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)
12、更重要的变量
WORKER_MEM：限制每个进程的使用内存。每个查询：work_mem*processes*joins-->会导致内存使用很大
max_parallel_workers_per_gather:执行器使用多少进程并发执行该节点
max_worker_processes：根据服务器上CPU核数调整进程数
max_parallel_workers:和并发进程数一样
13、总结
从9.6并行查询执行开始,可以显著提高扫描许多行或索引记录的复杂查询的性能。不要忘记在高oltp工作负载的服务器上禁止并行执行。顺序扫描或索引扫描仍然耗费大量资源。如果您没有针对整个数据集运行报表,那么只需添加缺失的索引或使用适当的分区就可以提高查询性能。
原文
https://www.percona.com/blog/parallel-queries-in-postgresql/