並行順序掃描(Parallel sequential scan)
在PostgreSQL 9.6中，增加了對並行順序掃描的支持。順序掃描是在表上進行的掃描，在該表中一個接一個的塊順序地被評估。就其本質而言，順序掃描允許並行化。這樣，整個表將在多個workers線程之間順序掃描。
並行順序掃描快並不是因為可以並行地讀，而是將數據分散到了多個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=#
順序掃描產生了大量的行，但是沒有使用聚合函數。因此，查詢使用的是單個cpu核心。
增加一個sum函數后，很明顯使用了兩個工作線程，從而使得查詢加速：
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=#
並行聚合(Parallel Aggregation)
在數據庫中，計算聚合是非常昂貴的操作。如果以單個進程進行執行，則這將花費相當長的時間。在PostgreSQL 9.6中，通過簡單地將它們分成多個塊（分而治之策略）來增加了並行計算的能力。多個worker線程執行聚合的部分，然后leader再根據它們的結果計算最終結果。
從技術上講，將Partial Aggregate節點添加到計划樹中，並且每個Partial Aggregate節點包含一個worker線程的輸出。然后將這些輸出發送到Finalize Aggregate節點，該節點合並來自多個（所有）Partial Aggregate節點的聚合。如此有效的並行部分計划在根部包括一個Finalize Aggregate節點，以及一個將Partial Aggregate節點作為子節點的Gather節點。
''Parallel Seq Scan''節點生成用於部分聚合(''Partial Aggregate'')的行。
''Partial Aggregate''節點使用SUM()函數減少這些行。最后，由''Gather''節點收集每個worker的總和計數。
''Finalize Aggregate''節點計算最后的總和。如果你使用的自己的聚合函數，別忘了將其標記為''parallel safe''的。
worker數量
可以動態調整worker的數量：
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=#
我們將數量從2變成了4。
使用多少個worker進程
首先，max_parallel_workers_per_gather參數定義了worker的最小數量。
其次，查詢執行器從池中獲取worker的數量受限於max_parallel_workers的值。
最后，最頂層的限制是max_worker_processes的值，該參數定義了后台worker進程的總數量。
如果分配worker進程失敗，就會切換成單進程執行。
查詢計划器會根據表或索引的大小，考慮減少worker進程的數量。受參數min_parallel_table_scan_size、min_parallel_index_scan_size影響。
參數的默認設置：
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=#
影響關系：
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
每當表的大小是min_parallel(index|table)scan_size的三倍，postgresql就會增加一個worker進程。worker進程的數量不是基於cost的。
在實踐中，這些規則並不總是被遵守的，可以對特定的表執行alter table ... set (parallel_workers=N)進行設置。
為什么並行執行沒有被使用？
除了並行執行的一些限制，postgresql也檢查成本(cost)：
·parallel_setup_cost避免了對小的查詢采用並行執行。它對用於內存設置、進程啟動和初始通信的時間進行建模
·parallel_tuple_cost：leader進程和worker進程之間的通信會花費很長的時間。時間與worker發送的元組數量成比例。該參數對通信成本進行建模。
嵌套循環連接(Nested loop joins)
從9.6版本開始，postgresql支持對“Nested loop”執行並行操作：
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)
哈希連接(Hash Join)
在PostgreSQL 11之前，每個worker都構建自己的哈希表。因此，4個以上的workers進程無法提高性能。新的實現使用一個共享哈希表。每個worker都可以利用WORK_MEM來構建哈希表。
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
這里每個worker幫助構建一個共享的hash表。
合並連接(Merge Join)
鑒於合並連接的自身特性，使其不支持並行查詢。如果合並連接是查詢執行的最后一個階段-你仍可以看到並行執行。
-- 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”節點在“Gather Merge”上方。 因此，合並不使用並行執行。但是“Parallel Index Scan”節點仍然有助於part_pkey。
分區智能連接(Partition-wise join)
PostgreSQL 11默認禁用分區智能連接(partition-wise join)功能。分區智能聯接的計划成本很高。分區相似的表的聯接可以逐分區進行。這允許postgres使用較小的哈希表。每個分區聯接操作都可以並行執行。
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，僅在分區足夠大的情況下，分區智能聯接才能使用並行執行。
通常可以在UNION ALL查詢中看到這一點。缺點是並行性差，因為每個work最終都是為單個查詢工作。
即使啟用了四個worker，也只有兩個被啟動。
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)
重要的變量
·work_mem
·max_parallel_workers_per_gather：執行器為每個並行執行的計划節點分配的worker數量
·max_worker_processes
·max_parallel_workers
在9.6版本中，並行查詢執行被引入；
在10版本中，默認開啟並行執行；
在負載重的oltp系統上，建議關閉並行執行。