Greenplum聚合函数的两种实现：HashAggregate与
GroupAggregate
在Postgresql/Greenplum中，聚合函数有两种实现⽅式：HashAggregate与GroupAggregate。
我们现在通过⼀个最简单的sql来分析这两种聚合的区别以及其应⽤场景。
lect count(1) from pg_class group by oid;
⼀、两种实现算法的⽐较：
HashAggregate
对于hash聚合来说，数据库会根据group by字段后⾯的值算出hash值，并根据前⾯使⽤的聚合函数在内存中维护对应的列表。如果lect后⾯有两个聚合函数，那么在内存中就会维护两个对应的数据。同样的，有n个聚合函数就会维护n个同样的数组。对于hash来说，数组的长度肯定是⼤于group by的字段的distinct值的个数的，且跟这个值应该呈线性关系，group by后⾯的值越唯⼀，使⽤的内存也就越⼤。
执⾏计划如下：
[sql]
1. aligputf8=# explain lect count(1) from pg_class group by oid;
2. QUERY PLAN
3. ----------------------------------------------------------------------
4. HashAggregate (cost=1721.40..2020.28 rows=23910 width=4)
5. Group By: oid
6. -> Seq Scan on pg_class (cost=0.00..1004.10 rows=143460 width=4)
7. Settings: enable_qscan=on
8. (4 rows)
GroupAggregate
对于普通聚合函数，使⽤group聚合，其原理是先将表中的数据按照group by的字段排序，这样⼦同⼀
个group by的值就在⼀起，这样就只需要对排好序的数据进⾏⼀次全扫描，就可以得到聚合的结果了。
执⾏计划如下：
[sql]
1. aligputf8=# t enable_hashagg = off;
2. SET
3. aligputf8=# explain lect count(1) from pg_class group by oid;
4. QUERY PLAN
5. ----------------------------------------------------------------------------
6. GroupAggregate (cost=13291.66..14666.48 rows=23910 width=4)
7. Group By: oid
8. -> Sort (cost=13291.66..13650.31 rows=143460 width=4)
9. Sort Key: oid
10. -> Seq Scan on pg_class (cost=0.00..1004.10 rows=143460 width=4)
11. Settings: enable_hashagg=off; enable_qscan=on
12. (6 rows)
从上⾯的两个执⾏计划的cost来说，GroupAggregate 由于需要排序，效率很差，消耗是HashAggregate的7倍。所以在GP⾥⾯，对于这种聚合函数的使⽤，采⽤的都是HashAggregate。
⼆、两种实现的内存消耗：
先建⽴⼀张表，并且往⾥⾯inrt数据，通过每个字段的数据唯⼀性不⼀致，还有聚合函数的个数来观察HashAggregate与GroupAggregate在内存的消耗情况以及实际的计算时间的⽐较。
1.表结构如下：
[html]
1. create table test_group(
2. id integer
课型分为哪几类
3. ,col1 numeric
4. ,col2 numeric
5. ,col3 numeric
最终流放河汉6. ,col4 numeric
7. ,col5 numeric
8. ,col6 numeric
9. ,col7 numeric
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10. ,col8 numeric
11. ,col9 numeric
12. ,col11 varchar(100)
13. ,col12 varchar(100)
14. ,col13 varchar(100)
15. ,col14 varchar(100)
16. )distributed by(id);
2.插⼊数据，通过random函数，实现每个字段数据的唯⼀性不⼀样
[sql]
1. aligputf8=# inrt into test_group
2. aligputf8-# lect generate_ries(1,100000),
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3. aligputf8-# (random()*200)::int,
4. aligputf8-# (random()*800)::int,
5. aligputf8-# (random()*1600)::int,
6. aligputf8-# (random()*3200)::int,
7. aligputf8-# (random()*6400)::int,
8. aligputf8-# (random()*12800)::int,
9. aligputf8-# (random()*40000)::int,
10. aligputf8-# (random()*100000)::int,
11. aligputf8-# (random()*1000000)::int,
12. aligputf8-# 'hello',
13. aligputf8-# 'welcome',
14. aligputf8-# 'haha',
15. aligputf8-# 'chen';
16. INSERT 0 100000
表⼤⼩为：
[sql]
1. aligputf8=# lect pg_size_pretty(pg_relation_size('test_group'));
2. pg_size_pretty
3. ----------------
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4. 12 MB
5. (1 row)
3.使⽤explain analyze来观察实际数据库消耗的内存差异：
以下是底层单个节点来计算的，避免了⼴播的时间跟内存消耗
HashAggregate
[sql]
1. aligputf8=# explain analyze lect sum(col1),sum(col2),sum(col3),sum(col4),sum(col5),sum(col6),sum(col7),sum(col8),sum(col9) from test_group group by col5;
2. QUERY PLAN
3. ----------------------------------------------------------------------------------------------------
4. HashAggregate (cost=4186.96..5432.88 rows=38336 width=62)
5. Group By: col5
6. Rows out: 6401 rows with 289 ms to first row, 295 ms to end, start offt by 0.143 ms.
7. Executor memory: 2818K bytes.
8. -> Seq Scan on test_group (cost=0.00..1480.56 rows=108256 width=62)
9. Rows out: 100000 rows with 0.023 ms to first row, 48 ms to end, start offt by 0.218 ms.
10. Slice statistics:
11. (slice0) Executor memory: 2996K bytes.
12. Settings: enable_qscan=off
13. Total runtime: 296.283 ms
14. (10 rows)
GroupAggregate
[sql]
1. aligputf8=# explain analyze lect sum(col1),sum(col2),sum(col3),sum(col4),sum(col5),sum(col6),sum(col7),sum(col8),sum(col9) from test_group group by col5;
2. QUERY PLAN
3. ----------------------------------------------------------------------------------------------------------
4. GroupAggregate (cost=10532.97..1475分数和小数的互化
5.93 rows=38336 width=62)
5. Group By: col5
6. Rows out: 6401 rows with 306 ms to first row, 585 ms to end, start offt by 0.092 ms.
7. Executor memory: 8K bytes.
8. -> Sort (cost=10532.97..10803.61 rows=108256 width=62)
9. Sort Key: col5
10. Rows out: 100000 rows with 306 ms to first row, 342 ms to end, start offt by 0.093 ms.
11. Executor memory: 19449K bytes.
12. Work_mem ud: 19449K bytes.
13. -> Seq Scan on test_group (cost=0.00..1480.56 rows=108256 width=62)
14. Rows out: 100000 rows with 0.021 ms to first row, 46 ms to end, start offt by 0.116 ms.
15. Slice statistics:
16. (slice0) Executor memory: 19623K bytes. Work_mem: 19449K bytes max.
17. Settings: enable_hashagg=off; enable_qscan=off
18. Total runtime: 586.114 ms
19. (15 rows)
通过这种⽅法，可以看出，消耗的内存跟实际执⾏时间的⽐例：
SQL:
explain analyze lect sum(col1),sum(col2),sum(col3),sum(col4),sum(col5),sum(col6),sum(col7),sum(col8),sum(col9) from test_group group by id;
9个聚合函数
group by字段col1col2col3col4col5col6col7col8col9id HashAggregate Executor memory554K786K10
74K1715K2996K5469K13691K21312K29428K29476K 时间（ms）266272275281296323357359352340
GroupAggregate Executor memory19623K19623K19623K19623K19623K19623K19623K19623K19623K19615K 时间（ms）500533547568589609636652649387
SQL:
27个聚合函数
group by字段col1col2col3col4col5col6col7col8col9id HashAggregate Executor memory514K1299K2340K4405K8504K19687K69947K93859K106419K106876K 时间（ms）504.91511.03523.36559.85616.94937.731179.051395.561391.271391.14
GroupAggregate Executor memory19687K19687K19687K19687K19687K19687K19687K19687K19687K19687K 时间（ms）759.58782.56802.4838.07880.38939.521104.751256.921365.611142
explain analyze lect sum(col1),sum(col2),sum(col3),sum(col4),sum(col5),sum(col6),sum(col7),su
m(col8),sum(col9),
max(col1),max(col2),max(col3),max(col4),max(col5),max(col6),max(col7),max(col8),max(col9),
avg(col1),avg(col2),avg(col3),avg(col4),avg(col5),avg(col6),avg(col7),avg(col8),avg(col9) from test_group group by id;
可以看出，对于GroupAggregate来说，消耗的内存基本上是恒定的，⽆论group by哪个字段。当聚合函数较少的时候，速度也相对较慢，但是相对稳定。
HashAggregate在少数聚合函数是表现优异，但是很多聚合函数，性能跟消耗的内存差异很明显。尤其是受group by字段的唯⼀性很明显，字段
count（district）值越⼤，hash聚合消耗的内存越多，性能下降剧烈。
所以在sql中有⼤量聚合函数，group by 的字段由相对⽐较唯⼀的时候，应该⽤GroupAggregate，⽽不能⽤HashAggregate。
三、在GP4.1出现的SQL报错：
在GP4.1中，之前出现过有⼤量聚合函数，并且group by 的字段由相对⽐较唯⼀的SQL报错如下：
ERROR: Unexpected internal error: Segment process received signal SIGSEGV
这个sql其实应该就是占⽤内存太多，进程被发出信号⼲掉导致的报错。
查看执⾏计划，发现是HashAggregate搞得⿁。⼀般来说，数据库会根据统计信息来选择HashAggregate或者是GroupAggregate，但是有可能统计信息不够详细或者sql太复杂⽽选错执⾏计划。
⼀般遇到这种问题，有两张办法：
1.拆分成多个sql来跑，减少HashAggregate使⽤的内存.
2.在跑sql之前，先执⾏enable_hashagg = off；将hash聚合参数关掉。强制不⾛HashAggregate，建议⽤这种。
下次如果再遇到这种sql报错，建议采⽤这种⽅法改⼀下脚本试⼀下。
注：当work_mem不够内存使⽤时：
[sql]
1. aligputf8=# explain analyze lect sum(col1),sum(col2),sum(col3),sum(col4),sum(col5),sum(col6),sum(col7),sum(col8),sum(col9),
2. max(col1),max(col2),max(col3),max(col4),max(col5),max(col6),max(col7),max(col8),max(col9),
3. avg(col1),avg(col2),avg(col3),avg(col4),avg(col5),avg(col6),avg(col7),avg(col8),avg(col9) from test_group group by id;
4. QUERY PLAN
5. ----------------------------------------------------------------------------------------------------
6. HashAggregate (cost=15225.85..29783.06 rows=108256 width=66)
7. Group By: id
8. Rows out: 100000 rows with 722 ms to first row, 1367 ms to end, start offt by 0.125 ms.
9. Executor memory: 32536K bytes.屑读音
10. Work_mem ud: 32001K bytes.
11. Work_mem wanted: 106876K bytes to lesn workfile I/O.
12. 100000 groups total in 32 batches; 1 overflows; 100000 spill groups.
13. Hash chain length 1.8 avg, 20 max, using 74100 of 135168 buckets.
14. -> Seq Scan on test_group (cost=0.00..1480.56 rows=108256 width=66)
15. Rows out: 100000 rows with 0.016 ms to first row, 51 ms to end, start offt by 0.142 ms.
16. Slice statistics:
湖州长兴
17. (slice0) * Executor memory: 32697K bytes. Work_mem: 32001K bytes max, 106876K bytes wanted.
18. Settings: enable_groupagg=off; enable_hashagg=on; enable_qscan=off; work_mem=32000kB
19. Total runtime: 1391.138 ms
20. (14 rows)
当work_mem⾜够时：
[sql]
1. QUERY PLAN
2. ----------------------------------------------------------------------------------------------------
3. HashAggregate (cost=9058.48..17448.32 rows=108256 width=66)
4. Group By: id
5. Rows out: 100000 rows with 460 ms to first row, 1014 ms to end, start offt by 0.120 ms.
6. Executor memory: 110093K bytes.
7. -> Seq Scan on test_group (cost=0.00..1480.56 rows=108256 width=66)
8. Rows out: 100000 rows with 0.019 ms to first row, 52 ms to end, start offt by 0.554 ms.
9. Slice statistics:
10. (slice0) Executor memory: 110271K bytes.
11. Settings: enable_groupagg=off; enable_hashagg=on; enable_qscan=off; work_mem=320000kB
12. Total runtime: 1038.209 ms
13.
14. (10 rows)
15.
