Greenplum 行存、列存，堆表、AO表的原理和选择
背景
Greenplum支持行存和列存，支持堆表和AO表，那么他们有什么不同，如何选择呢？
行存和列存的原理
1、行存，以行为形式组织存储，一行是一个tuple，存在一起。当需要读取某列时，需要将这列前面的所有列都进行deform，所以访问第一列和访问最后一列的成本实际上是不一样的。
在这篇文档中，有deform的详细介绍。《PostgreSQL 向量化执行插件(瓦片式实现) 10x提速OLAP》
行存小结：
全表扫描要扫描更多的数据块。
压缩比较低。
读取任意列的成本不一样，越靠后的列，成本越高。
不适合向量计算、JIT架构。（简单来说，就是不适合批处理形式的计算）
需要REWRITE表时，需要对全表进行REWRITE，例如加字段有默认值。
2、列存，以列为形式组织存储，每列对应一个或一批文件。读取任一列的成本是一样的，但是如果要读取多列，需要访问多个文件，访问的列越多，开销越大。
列存小结：
压缩比高。
仅仅支持AO存储（后面会将）。
读取任意列的成本是一样的。
非常适合向量计算、JIT架构。对大批量数据的访问和统计，效率更高。
读取很多列时，由于需要访问更多的文件，成本更高。例如查询明细。
需要REWRITE表时，不需要对全表操作，例如加字段有默认值，只是添加字段对应的那个文件。
什么时候选择行存
如果OLTP的需求偏多，例如经常需要查询表的明细（输出很多列），需要更多的更新和删除操作时。可以考虑行存。
什么时候选择列存
如果OLAP的需求偏多，经常需要对数据进行统计时，选择列存。
需要比较高的压缩比时，选择列存。
如果用户有混合需求，可以采用分区表，例如按时间维度的需求分区，近期的数据明细查询多，那就使用行存，对历史的数据统计需求多那就使用列存。
堆表和AO表的原理
1、堆表，实际上就是PG的堆存储，堆表的所有变更都会产生REDO，可以实现时间点恢复。但是堆表不能实现逻辑增量备份（因为表的任意一个数据块都有可能变更，不方便通过堆存储来记录位点。）。
一个事务结束时，通过clog以及REDO来实现它的可靠性。同时支持通过REDO来构建MIRROR节点实现数据冗余。
2、AO表，看名字就知道，只追加的存储，删除更新数据时，通过另一个BITMAP文件来标记被删除的行，通过bit以及偏移对齐来判定AO表上的某一行是否被删除。
事务结束时，需要调用FSYNC，记录最后一次写入对应的数据块的偏移。（并且这个数据块即使只有一条记录，下次再发起事务又会重新追加一个数据块）同时发送对应的数据块给MIRROR实现数据冗余。
因此AO表不适合小事务，因为每次事务结束都会FSYNC，同时事务结束后这个数据块即使有空余也不会被复用。（你可以测试一下，AO表单条提交的IO放大很严重）。
虽然如此，AO表非常适合OLAP场景，批量的数据写入，高压缩比，逻辑备份支持增量备份，因此每次记录备份到的偏移量即可。加上每次备份全量的BITMAP删除标记（很小）。
什么时候选择堆表
当数据写入时，小事务偏多时选择堆表。
当需要时间点恢复时，选择堆表。
什么时候选择AO表
当需要列存时，选择AO表。
当数据批量写入时，选择AO表。
测试对比行存deform和列存的性能差别
1、创建一个函数，用于创建400列的表（行存堆表、AO行存表、AO列存表）。
create or replace function f(name, int, text) returns void as $$
declare
res text := '';
begin
for i in 1..$2 loop
res := res||'c'||i||' int8,';
end loop;
res := rtrim(res, ',');
if $3 = 'ao_col' then
res := 'create table '||$1||'('||res||') with (appendonly=true, blocksize=8192, compresstype=none, orientation=column)';
elsif $3 = 'ao_row' then
res := 'create table '||$1||'('||res||') with (appendonly=true, blocksize=8192, orientation=row)';
elsif $3 = 'heap_row' then
res := 'create table '||$1||'('||res||') with (appendonly=false)';
else
raise notice 'use ao_col, ao_row, heap_row as $3';
return;
end if;
execute res;
end;
$$ language plpgsql;
2、创建表如下
postgres=# select f('tbl_ao_col', 400, 'ao_col');
postgres=# select f('tbl_ao_row', 400, 'ao_row');
postgres=# select f('tbl_heap_row', 400, 'heap_row');
3、创建1个函数，用于填充数据，其中第一个和最后3个字段为测试数据的字段，其他都填充1。
create or replace function f_ins1(name, int, int8) returns void as $$
declare
res text := '';
begin
for i in 1..($2-4) loop
res := res||'1,';
end loop;
res := 'id,'||res;
res := rtrim(res, ',');
res := 'insert into '||$1||' select '||res||'id,random()*10000,random()*100000 from generate_series(1,'||$3||') t(id)';
execute res;
end;
$$ language plpgsql;
4、填充数据
postgres=# select f_ins1('tbl_ao_col',400,1000000);
5、创建1个函数，用于填充数据，其中前4个字段为测试数据的字段，其他都填充1。
create or replace function f_ins2(name, int, int8) returns void as $$
declare
res text := '';
begin
for i in 1..($2-4) loop
res := res||'1,';
end loop;
res := 'id,id,random()*10000,random()*100000,'||res;
res := rtrim(res, ',');
res := 'insert into '||$1||' select '||res||' from generate_series(1,'||$3||') t(id)';
execute res;
end;
$$ language plpgsql;
6、填充数据
postgres=# select f_ins1('tbl_ao_col',400,1000000);
f_ins1
--------
(1 row)
postgres=# insert into tbl_ao_row select * from tbl_ao_col;
INSERT 0 1000000
postgres=# insert into tbl_heap_row select * from tbl_ao_col;
INSERT 0 1000000
7、表分析
postgres=# analyze tbl_ao_col ;
ANALYZE
postgres=# analyze tbl_ao_row;
ANALYZE
postgres=# analyze tbl_heap_row;
ANALYZE
8、表大小
postgres=# select pg_size_pretty(pg_relation_size('tbl_ao_col'));
pg_size_pretty
----------------
3060 MB
(1 row)
postgres=# select pg_size_pretty(pg_relation_size('tbl_ao_row'));
pg_size_pretty
----------------
3117 MB
(1 row)
postgres=# select pg_size_pretty(pg_relation_size('tbl_heap_row'));
pg_size_pretty
----------------
3473 MB
(1 row)
9、行存堆表，前面几个字段的统计
postgres=# explain analyze select c2,count(*),sum(c3),avg(c3),min(c3),max(c3) from tbl_heap_row group by c2;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 48:1 (slice2; segments: 48) (cost=136132.40..136132.42 rows=1 width=96)
Rows out: 1 rows at destination with 135 ms to end, start offset by 1.922 ms.
-> HashAggregate (cost=136132.40..136132.42 rows=1 width=96)
Group By: tbl_heap_row.c2
Rows out: 1 rows (seg42) with 0.002 ms to first row, 36 ms to end, start offset by 48 ms.
-> Redistribute Motion 48:48 (slice1; segments: 48) (cost=136132.35..136132.37 rows=1 width=96)
Hash Key: tbl_heap_row.c2
Rows out: 48 rows at destination (seg42) with 53 ms to end, start offset by 48 ms.
-> HashAggregate (cost=136132.35..136132.35 rows=1 width=96)
Group By: tbl_heap_row.c2
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.008 ms to first row, 1.993 ms to end, start offset by 48 ms.
-> Seq Scan on tbl_heap_row (cost=0.00..121134.54 rows=20831 width=16)
Rows out: Avg 20833.3 rows x 48 workers. Max 20854 rows (seg42) with 40 ms to first row, 73 ms to end, start offset by 50 ms.
Slice statistics:
(slice0) Executor memory: 345K bytes.
(slice1) Executor memory: 751K bytes avg x 48 workers, 751K bytes max (seg0).
(slice2) Executor memory: 359K bytes avg x 48 workers, 374K bytes max (seg42).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 138.524 ms
(22 rows)
10、行存堆表，末尾几个字段的统计
postgres=# explain analyze select c398,count(*),sum(c399),avg(c399),min(c399),max(c399) from tbl_heap_row group by c398;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 48:1 (slice2; segments: 48) (cost=136576.82..136799.05 rows=9877 width=96)
Rows out: 10001 rows at destination with 212 ms to end, start offset by 1.917 ms.
-> HashAggregate (cost=136576.82..136799.05 rows=206 width=96)
Group By: tbl_heap_row.c398
Rows out: Avg 208.4 rows x 48 workers. Max 223 rows (seg17) with 0.001 ms to first row, 70 ms to end, start offset by 14 ms.
-> Redistribute Motion 48:48 (slice1; segments: 48) (cost=136132.35..136329.89 rows=206 width=96)
Hash Key: tbl_heap_row.c398
Rows out: Avg 8762.2 rows x 48 workers at destination. Max 9422 rows (seg46) with 93 ms to end, start offset by 48 ms.
-> HashAggregate (cost=136132.35..136132.35 rows=206 width=96)
Group By: tbl_heap_row.c398
Rows out: Avg 8762.2 rows x 48 workers. Max 8835 rows (seg2) with 0.003 ms to first row, 12 ms to end, start offset by 49 ms.
-> Seq Scan on tbl_heap_row (cost=0.00..121134.54 rows=20831 width=16)
Rows out: Avg 20833.3 rows x 48 workers. Max 20854 rows (seg42) with 40 ms to first row, 133 ms to end, start offset by 51 ms.
Slice statistics:
(slice0) Executor memory: 377K bytes.
(slice1) Executor memory: 1156K bytes avg x 48 workers, 1156K bytes max (seg0).
(slice2) Executor memory: 414K bytes avg x 48 workers, 414K bytes max (seg1).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 214.024 ms
(22 rows)
11、行存AO表，前面几个字段的统计
postgres=# explain analyze select c2,count(*),sum(c3),avg(c3),min(c3),max(c3) from tbl_ao_row group by c2;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 48:1 (slice2; segments: 48) (cost=124755.04..124755.07 rows=1 width=96)
Rows out: 1 rows at destination with 149 ms to end, start offset by 1.890 ms.
-> HashAggregate (cost=124755.04..124755.07 rows=1 width=96)
Group By: tbl_ao_row.c2
Rows out: 1 rows (seg42) with 0.004 ms to first row, 55 ms to end, start offset by 64 ms.
-> Redistribute Motion 48:48 (slice1; segments: 48) (cost=124755.00..124755.02 rows=1 width=96)
Hash Key: tbl_ao_row.c2
Rows out: 48 rows at destination (seg42) with 32 ms to end, start offset by 64 ms.
-> HashAggregate (cost=124755.00..124755.00 rows=1 width=96)
Group By: tbl_ao_row.c2
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.001 ms to first row, 46 ms to end, start offset by 59 ms.
-> Append-only Scan on tbl_ao_row (cost=0.00..109755.00 rows=20834 width=16)
Rows out: Avg 20833.3 rows x 48 workers. Max 20854 rows (seg42) with 24 ms to end, start offset by 59 ms.
Slice statistics:
(slice0) Executor memory: 345K bytes.
(slice1) Executor memory: 770K bytes avg x 48 workers, 770K bytes max (seg0).
(slice2) Executor memory: 359K bytes avg x 48 workers, 374K bytes max (seg42).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 152.386 ms
(22 rows)
12、行存AO表，末尾几个字段的统计
postgres=# explain analyze select c398,count(*),sum(c399),avg(c399),min(c399),max(c399) from tbl_ao_row group by c398;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 48:1 (slice2; segments: 48) (cost=125186.01..125401.52 rows=9578 width=96)
Rows out: 10001 rows at destination with 183 ms to end, start offset by 1.846 ms.
-> HashAggregate (cost=125186.01..125401.52 rows=200 width=96)
Group By: tbl_ao_row.c398
Rows out: Avg 208.4 rows x 48 workers. Max 223 rows (seg17) with 0.003 ms to first row, 97 ms to end, start offset by 22 ms.
-> Redistribute Motion 48:48 (slice1; segments: 48) (cost=124755.00..124946.56 rows=200 width=96)
Hash Key: tbl_ao_row.c398
Rows out: Avg 8762.2 rows x 48 workers at destination. Max 9422 rows (seg46) with 32 ms to end, start offset by 68 ms.
-> HashAggregate (cost=124755.00..124755.00 rows=200 width=96)
Group By: tbl_ao_row.c398
Rows out: Avg 8762.2 rows x 48 workers. Max 8835 rows (seg2) with 0.013 ms to first row, 48 ms to end, start offset by 22 ms.
-> Append-only Scan on tbl_ao_row (cost=0.00..109755.00 rows=20834 width=16)
Rows out: Avg 20833.3 rows x 48 workers. Max 20854 rows (seg42) with 22 ms to end, start offset by 71 ms.
Slice statistics:
(slice0) Executor memory: 377K bytes.
(slice1) Executor memory: 1144K bytes avg x 48 workers, 1144K bytes max (seg0).
(slice2) Executor memory: 414K bytes avg x 48 workers, 414K bytes max (seg0).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 184.723 ms
(22 rows)
13、列存AO表，前面几个字段的统计
postgres=# explain analyze select c2,count(*),sum(c3),avg(c3),min(c3),max(c3) from tbl_ao_col group by c2;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 48:1 (slice2; segments: 48) (cost=122928.04..122928.07 rows=1 width=96)
Rows out: 1 rows at destination with 104 ms to end, start offset by 1.878 ms.
-> HashAggregate (cost=122928.04..122928.07 rows=1 width=96)
Group By: tbl_ao_col.c2
Rows out: 1 rows (seg42) with 0.003 ms to first row, 18 ms to end, start offset by 55 ms.
-> Redistribute Motion 48:48 (slice1; segments: 48) (cost=122928.00..122928.02 rows=1 width=96)
Hash Key: tbl_ao_col.c2
Rows out: 48 rows at destination (seg42) with 30 ms to end, start offset by 55 ms.
-> HashAggregate (cost=122928.00..122928.00 rows=1 width=96)
Group By: tbl_ao_col.c2
Rows out: Avg 1.0 rows x 48 workers. Max 1 rows (seg0) with 0.007 ms to first row, 3.991 ms to end, start offset by 54 ms.
-> Append-only Columnar Scan on tbl_ao_col (cost=0.00..107928.00 rows=20834 width=16)
Rows out: 0 rows (seg0) with 40 ms to end, start offset by 56 ms.
Slice statistics:
(slice0) Executor memory: 345K bytes.
(slice1) Executor memory: 903K bytes avg x 48 workers, 903K bytes max (seg0).
(slice2) Executor memory: 359K bytes avg x 48 workers, 374K bytes max (seg42).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 106.859 ms
(22 rows)
14、列存AO表，末尾几个字段的统计
postgres=# explain analyze select c398,count(*),sum(c399),avg(c399),min(c399),max(c399) from tbl_ao_col group by c398;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------
Gather Motion 48:1 (slice2; segments: 48) (cost=123364.18..123582.28 rows=9693 width=96)
Rows out: 10001 rows at destination with 120 ms to end, start offset by 1.921 ms.
-> HashAggregate (cost=123364.18..123582.28 rows=202 width=96)
Group By: tbl_ao_col.c398
Rows out: Avg 208.4 rows x 48 workers. Max 223 rows (seg17) with 0.001 ms to first row, 54 ms to end, start offset by 35 ms.
-> Redistribute Motion 48:48 (slice1; segments: 48) (cost=122928.00..123121.86 rows=202 width=96)
Hash Key: tbl_ao_col.c398
Rows out: Avg 8762.2 rows x 48 workers at destination. Max 9422 rows (seg46) with 31 ms to end, start offset by 63 ms.
-> HashAggregate (cost=122928.00..122928.00 rows=202 width=96)
Group By: tbl_ao_col.c398
Rows out: Avg 8762.2 rows x 48 workers. Max 8835 rows (seg2) with 0.004 ms to first row, 8.004 ms to end, start offset by 82 ms.
-> Append-only Columnar Scan on tbl_ao_col (cost=0.00..107928.00 rows=20834 width=16)
Rows out: 0 rows (seg0) with 28 ms to end, start offset by 64 ms.
Slice statistics:
(slice0) Executor memory: 377K bytes.
(slice1) Executor memory: 1272K bytes avg x 48 workers, 1272K bytes max (seg0).
(slice2) Executor memory: 414K bytes avg x 48 workers, 414K bytes max (seg0).
Statement statistics:
Memory used: 128000K bytes
Settings: optimizer=off
Optimizer status: legacy query optimizer
Total runtime: 122.173 ms
(22 rows)
对于非分布键的分组聚合请求，Greenplum采用了多阶段聚合如下：
第一阶段，在SEGMENT本地聚合。（需要扫描所有数据，这里不同存储，前面的列和后面的列的差别就体现出来了，行存储的deform开销，在对后面的列进行统计时性能影响很明显。） Greenplum会根据字段的distinct值的比例，考虑是直接重分布数据，还是先在本地聚合后再重分布数据（减少重分布的数据量）。
第二阶段，根据分组字段，将结果数据重分布。（重分布需要用到的字段，此时结果很小。）
第三阶段，再次在SEGMENT本地聚合。（需要对重分布后的数据进行聚合。）
第四阶段，返回结果给master，有必要的话master节点调用聚合函数的final func（已经是很少的记录数和运算量）。
如何查看表的存储结构
pg_class.relstorage表示这个对象是什么存储：
postgres=# select distinct relstorage from pg_class ;
relstorage
------------
a -- 行存储AO表
h -- heap堆表、索引
x -- 外部表(external table)
v -- 视图
c -- 列存储AO表
(5 rows)
查询当前数据库有哪些AO表：
postgres=# select t2.nspname, t1.relname from pg_class t1, pg_namespace t2 where t1.relnamespace=t2.oid and relstorage in ('c', 'a');
nspname | relname
----------+-------------------
postgres | tbl_tag
postgres | tbl_pos_1_prt_p1
postgres | tbl_pos_1_prt_p2
postgres | tbl_pos_1_prt_p3
postgres | tbl_pos_1_prt_p4
postgres | tbl_pos_1_prt_p5
postgres | tbl_pos_1_prt_p6
postgres | tbl_pos_1_prt_p7
postgres | tbl_pos_1_prt_p8
postgres | tbl_pos_1_prt_p9
postgres | tbl_pos_1_prt_p10
postgres | tbl_pos
postgres | xx_czrk_qm_col
postgres | ao1
(14 rows)
查询当前数据库有哪些堆表：
select t2.nspname, t1.relname from pg_class t1, pg_namespace t2 where t1.relnamespace=t2.oid and relstorage in ('h') and relkind='r';
参考
《PostgreSQL 向量化执行插件(瓦片式实现) 10x提速OLAP》
http://greenplum.org/docs/best_practices/schema.html