行存和列存的原理

什么时候选择行存

什么时候选择列存

堆表和AO表的原理

什么时候选择堆表

什么时候选择AO表

测试对比行存deform和列存的性能差别

如何查看表的存储结构

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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(后面会讲)。

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。

GreenPlum6 的gpperfmon中监控系统的表很多都是列存

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(append only)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)。

note : WX 注意两个mirror冗余的方式不一样，考虑一下这个问题对role切换有什么影响？如何考量？

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。

note:checkPoint https://blog.csdn.net/MyySophia/article/details/103227277

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')