Greenplum 计算能力估算 - 暨多大表需要分区，单个分区多大适宜
背景
在数据仓库业务中，单表的数据量通常是非常巨大的。Greenplum在这方面做了很多的优化
1、支持列存储
2、支持向量计算
3、支持分区表
4、支持btree, bitmap, gist索引接口
5、执行并行计算
6、支持HASH JOIN
等
提高数据筛选的效率是一个较为低廉有效的优化手段，比如表分区。
但是分区是不是越多越好呢？
实际上分区过多也会引入导致优化器生成执行计划较慢，元数据过多，SYSCACHE过大等问题。
设置多大分区应该权衡影响，同时又要考虑计算能力。
单个SEGMENT多大数据量合适
GPDB是一个分布式数据库，执行一条复杂QUERY时，所有的SEGMENT可能并行参与计算。
那么最慢的SEGMENT就成为了整个SQL的瓶颈，单个SEGMENT多少记录合适呢？
可以做一个简单的测试，生成一份测试报告，以供参考。
创建3种常用字段类型，分别测试这几种类型的聚合统计能力，JOIN能力。
1、int8类型
postgres=> create temp table t1 (id int8) with (APPENDONLY=true, ORIENTATION=column);
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
CREATE TABLE
postgres=> create temp table t2 (id int8) with (APPENDONLY=true, ORIENTATION=column);
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
CREATE TABLE
postgres=> insert into t1 select generate_series(1,100000000);
INSERT 0 100000000
postgres=> insert into t2 select * from t1;
INSERT 0 100000000
2、text类型
postgres=> create temp table tt1 (id text) with (APPENDONLY=true, ORIENTATION=column);
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
CREATE TABLE
postgres=> create temp table tt2 (id text) with (APPENDONLY=true, ORIENTATION=column);
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
CREATE TABLE
postgres=> insert into tt1 select id from t1;
INSERT 0 100000000
postgres=> insert into tt2 select id from tt1;
INSERT 0 100000000
3、numeric类型
postgres=> create temp table ttt1 (id numeric) with (APPENDONLY=true, ORIENTATION=column);
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
CREATE TABLE
postgres=> create temp table ttt2 (id numeric) with (APPENDONLY=true, ORIENTATION=column);
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' as the Greenplum Database data distribution key for this table.
HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
CREATE TABLE
postgres=> insert into ttt1 select id from t1;
INSERT 0 100000000
postgres=> insert into ttt2 select id from t1;
INSERT 0 100000000
测试环境为单物理机（64线程机器），48个SEGMENT。1亿记录。
1 聚合
1、int8类型
postgres=> explain analyze select count(*),sum(id),avg(id),min(id),max(id),stddev(id) from t1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=2524480.55..2524480.56 rows=1 width=120)
Rows out: 1 rows with 0.002 ms to first row, 159 ms to end, start offset by 1.624 ms.
-> Gather Motion 48:1 (slice1; segments: 48) (cost=2524480.02..2524480.52 rows=1 width=120)
Rows out: 48 rows at destination with 221 ms to end, start offset by 1.626 ms.
-> Aggregate (cost=2524480.02..2524480.02 rows=1 width=120)
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.001 ms to first row, 134 ms to end, start offset by 10 ms.
-> Append-only Columnar Scan on t1 (cost=0.00..1024480.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 10 ms to end, start offset by 37 ms.
Slice statistics:
(slice0) Executor memory: 315K bytes.
(slice1) Executor memory: 378K bytes avg x 48 workers, 378K bytes max (seg0).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 382.093 ms
(16 rows)
2、text类型
postgres=> explain analyze select count(*),sum(t1.id::int8),avg(t1.id::int8),min(t1.id::int8),max(t1.id::int8),stddev(t1.id::int8) from tt1 t1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=2527178.57..2527178.58 rows=1 width=120)
Rows out: 1 rows with 0.003 ms to first row, 798 ms to end, start offset by 1.382 ms.
-> Gather Motion 48:1 (slice1; segments: 48) (cost=2527178.02..2527178.53 rows=1 width=120)
Rows out: 48 rows at destination with 1006 ms to end, start offset by 1.385 ms.
-> Aggregate (cost=2527178.02..2527178.04 rows=1 width=120)
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.003 ms to first row, 926 ms to end, start offset by 14 ms.
-> Append-only Columnar Scan on tt1 t1 (cost=0.00..1027178.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 16 ms to end, start offset by 36 ms.
Slice statistics:
(slice0) Executor memory: 315K bytes.
(slice1) Executor memory: 378K bytes avg x 48 workers, 378K bytes max (seg0).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 1805.789 ms
(16 rows)
3、numeric类型
postgres=> explain analyze select count(*),sum(id),avg(id),min(id),max(id),stddev(id) from ttt1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=2527512.55..2527512.56 rows=1 width=168)
Rows out: 1 rows with 0.001 ms to first row, 1712 ms to end, start offset by 1.292 ms.
-> Gather Motion 48:1 (slice1; segments: 48) (cost=2527512.02..2527512.52 rows=1 width=168)
Rows out: 48 rows at destination with 1926 ms to end, start offset by 1.293 ms.
-> Aggregate (cost=2527512.02..2527512.02 rows=1 width=168)
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0 ms to first row, 1849 ms to end, start offset by 4.436 ms.
-> Append-only Columnar Scan on ttt1 (cost=0.00..1027512.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 7.385 ms to end, start offset by 53 ms.
Slice statistics:
(slice0) Executor memory: 315K bytes.
(slice1) Executor memory: 378K bytes avg x 48 workers, 378K bytes max (seg0).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 3640.108 ms
(16 rows)
2 JOIN 聚合
1、int8类型
postgres=> explain analyze select count(*),sum(t1.id),avg(t1.id),min(t1.id),max(t1.id),stddev(t1.id) from t1 join t2 using (id);
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=6689588.56..6689588.57 rows=1 width=120)
Rows out: 1 rows with 0.003 ms to first row, 908 ms to end, start offset by 1.505 ms.
-> Gather Motion 48:1 (slice1; segments: 48) (cost=6689588.02..6689588.52 rows=1 width=120)
Rows out: 48 rows at destination with 1517 ms to end, start offset by 1.508 ms.
-> Aggregate (cost=6689588.02..6689588.03 rows=1 width=120)
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.002 ms to first row, 81 ms to end, start offset by 11 ms.
-> Hash Join (cost=2372137.00..5189588.00 rows=2083334 width=8)
Hash Cond: t1.id = t2.id
Rows out: Avg 2083333.3 rows x 48 workers. Max 2083479 rows (seg42) with 0.013 ms to first row, 1359 ms to end, start offset by 38 ms.
Executor memory: 65105K bytes avg, 65109K bytes max (seg42).
Work_mem used: 65105K bytes avg, 65109K bytes max (seg42). Workfile: (0 spilling, 0 reused)
-> Append-only Columnar Scan on t1 (cost=0.00..1024480.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 0.003 ms to end, start offset by 38 ms.
-> Hash (cost=1024480.00..1024480.00 rows=2083334 width=8)
Rows in: (No row requested) 0 rows (seg0) with 0 ms to end.
-> Append-only Columnar Scan on t2 (cost=0.00..1024480.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 30 ms to end, start offset by 54 ms.
Slice statistics:
(slice0) Executor memory: 315K bytes.
(slice1) Executor memory: 378K bytes avg x 48 workers, 378K bytes max (seg0). Work_mem: 65109K bytes max.
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 2426.790 ms
(25 rows)
2、text类型
postgres=> explain analyze select count(*),sum(t1.id::int8),avg(t1.id::int8),min(t1.id::int8),max(t1.id::int8),stddev(t1.id::int8) from tt1 t1 join tt2 using (id);
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=6694984.57..6694984.58 rows=1 width=120)
Rows out: 1 rows with 0.001 ms to first row, 2068 ms to end, start offset by 1.423 ms.
-> Gather Motion 48:1 (slice1; segments: 48) (cost=6694984.02..6694984.53 rows=1 width=120)
Rows out: 48 rows at destination with 3169 ms to end, start offset by 1.425 ms.
-> Aggregate (cost=6694984.02..6694984.04 rows=1 width=120)
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.004 ms to first row, 1049 ms to end, start offset by 11 ms.
-> Hash Join (cost=2374835.00..5194984.00 rows=2083334 width=8)
Hash Cond: t1.id = tt2.id
Rows out: Avg 2083333.3 rows x 48 workers. Max 2084068 rows (seg4) with 0.012 ms to first row, 2240 ms to end, start offset by 60 ms.
Executor memory: 65105K bytes avg, 65128K bytes max (seg4).
Work_mem used: 65105K bytes avg, 65128K bytes max (seg4). Workfile: (0 spilling, 0 reused)
-> Append-only Columnar Scan on tt1 t1 (cost=0.00..1027178.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 0.003 ms to end, start offset by 11 ms.
-> Hash (cost=1027178.00..1027178.00 rows=2083334 width=8)
Rows in: (No row requested) 0 rows (seg0) with 0 ms to end.
-> Append-only Columnar Scan on tt2 (cost=0.00..1027178.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 37 ms to end, start offset by 43 ms.
Slice statistics:
(slice0) Executor memory: 315K bytes.
(slice1) Executor memory: 378K bytes avg x 48 workers, 378K bytes max (seg0). Work_mem: 65128K bytes max.
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 5249.571 ms
(25 rows)
3、numeric类型
postgres=> explain analyze select count(*),sum(t1.id),avg(t1.id),min(t1.id),max(t1.id),stddev(t1.id) from ttt1 t1 join ttt2 using (id);
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=6695652.56..6695652.57 rows=1 width=168)
Rows out: 1 rows with 0.003 ms to first row, 2661 ms to end, start offset by 1.406 ms.
-> Gather Motion 48:1 (slice1; segments: 48) (cost=6695652.02..6695652.52 rows=1 width=168)
Rows out: 48 rows at destination with 4696 ms to end, start offset by 1.409 ms.
-> Aggregate (cost=6695652.02..6695652.03 rows=1 width=168)
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.004 ms to first row, 2770 ms to end, start offset by 4.078 ms.
-> Hash Join (cost=2375169.00..5195652.00 rows=2083334 width=8)
Hash Cond: t1.id = ttt2.id
Rows out: Avg 2083333.3 rows x 48 workers. Max 2083627 rows (seg10) with 0.015 ms to first row, 3745 ms to end, start offset by 35 ms.
Executor memory: 65105K bytes avg, 65114K bytes max (seg10).
Work_mem used: 65105K bytes avg, 65114K bytes max (seg10). Workfile: (0 spilling, 0 reused)
-> Append-only Columnar Scan on ttt1 t1 (cost=0.00..1027512.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 0.012 ms to end, start offset by 45 ms.
-> Hash (cost=1027512.00..1027512.00 rows=2083334 width=8)
Rows in: (No row requested) 0 rows (seg0) with 0 ms to end.
-> Append-only Columnar Scan on ttt2 (cost=0.00..1027512.00 rows=2083334 width=8)
Rows out: 0 rows (seg0) with 30 ms to end, start offset by 46 ms.
Slice statistics:
(slice0) Executor memory: 315K bytes.
(slice1) Executor memory: 378K bytes avg x 48 workers, 378K bytes max (seg0). Work_mem: 65114K bytes max.
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 7369.522 ms
(25 rows)
性能指标
类型 | 总记录数 | segment数 | 单segment记录数 | 单表聚合耗时 | 多表JOIN+聚合耗时 | 每segment每秒聚合记录数 | 每segment每秒JOIN+聚合记录数
—|—|—|—|—|—|—|—
INT8 | 1亿行 | 48 | 208万行 | 0.38秒 | 2.4秒 | 547万行 | 86万行 * 2
TEXT | 1亿行 | 48 | 208万行 | 1.8秒 | 5.2秒 | 115万行 | 40万行 * 2
NUMERIC | 1亿行 | 48 | 208万行 | 3.6秒 | 7.37秒 | 57万行 | 28万行 * 2
小结
设置多少个分区，除了业务逻辑层面的因素（比如按日、月、年，或者按LIST等），另外还应该考虑两方面的因素：
1、分区过多也会引入导致优化器生成执行计划较慢，元数据过多，SYSCACHE过大等问题。
2、单个SEGMENT的计算能力。（将分区后单个SEGMENT的单个分区内的记录数压缩到可以接受的范围。）例如:
- 100亿条记录，1000个SEGMENT，不分区的情况下，一个SEGMENT有1000万条记录。如果要满足在输入WHERE条件过滤数据后(假设过滤后要计算的记录数小于50亿条)INT8类型字段聚合1秒响应，根据以上性能测试数据，建议至少分成2个区。
数值类型的选择，除非精度要求，建议不要使用numeric。 建议使用int, int8, float, float8等类型。从以上测试可以看出性能差异巨大。
参考
《PostgreSQL 11 preview - 分区表智能并行JOIN (已类似MPP架构，性能暴增)》
《PostgreSQL 11 preview - 分区表智能并行聚合、分组计算(已类似MPP架构，性能暴增)》