Greenplum 函数 gp_dist_random

转载自：https://yq.aliyun.com/articles/7593

函数作用：

gp_dist_random('gp_id')本质上就是在所有节点查询gp_id，
gp_dist_random('pg_authid')就是在所有节点查询pg_authid，

使用greenplum时，如果需要调用一个函数，这个函数很可能就在master执行，而不会跑到segment上去执行。
例如 random()函数。
通过select random()来调用的话，不需要将这条SQL发送到segment节点，所以执行计划如下，没有gather motion的过程。

postgres=# explain analyze select random();
                                       QUERY PLAN
----------------------------------------------------------------------------------------
 Result  (cost=0.01..0.02 rows=1 width=0)
   Rows out:  1 rows with 0.017 ms to end, start offset by 0.056 ms.
   InitPlan
     ->  Result  (cost=0.00..0.01 rows=1 width=0)
           Rows out:  1 rows with 0.004 ms to end of 2 scans, start offset by 0.059 ms.
 Slice statistics:
   (slice0)    Executor memory: 29K bytes.
   (slice1)    Executor memory: 29K bytes.
 Statement statistics:
   Memory used: 128000K bytes
 Total runtime: 0.074 ms
(11 rows)  

如果要让这个函数在segment执行，怎么办呢？
通过gp_dist_random('gp_id')来调用，gp_dist_random的参数是一个可查询的视图，或表。

postgres=# explain analyze select random() from gp_dist_random('gp_id');
                                                               QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 240:1  (slice1; segments: 240)  (cost=0.00..4.00 rows=240 width=0)
   Rows out:  240 rows at destination with 6.336 ms to first row, 59 ms to end, start offset by 4195 ms.
   ->  Seq Scan on gp_id  (cost=0.00..4.00 rows=1 width=0)
         Rows out:  Avg 1.0 rows x 240 workers.  Max 1 rows (seg0) with 0.073 ms to first row, 0.075 ms to end, start offset by 4207 ms.
 Slice statistics:
   (slice0)    Executor memory: 471K bytes.
   (slice1)    Executor memory: 163K bytes avg x 240 workers, 163K bytes max (seg0).
 Statement statistics:
   Memory used: 128000K bytes
 Total runtime: 4279.445 ms
(10 rows)  

gp_id在每个segment中都有一条记录，所以以上SQL会在每个SEGMENT中调用一次random()并返回所有结果，例如我的测试环境中有240个segment, 那么以上SQL将返回240条记录。

在gp_id的定义中，介绍了gp_dist_random用它可以做一些管理的工作：
譬如查询数据库的大小，查询表的大小，其实都是这样统计的。
src/backend/catalog/postgres_bki_srcs

/*-------------------------------------------------------------------------
 *
 * gp_id.h
 *        definition of the system "database identifier" relation (gp_dbid)
 *        along with the relation's initial contents.
 *
 * Copyright (c) 2009-2010, Greenplum inc
 *
 * NOTES
 *    Historically this table was used to supply every segment with its
 * identification information.  However in the 4.0 release when the file
 * replication feature was added it could no longer serve this purpose
 * because it became a requirement for all tables to have the same physical
 * contents on both the primary and mirror segments.  To resolve this the
 * information is now passed to each segment on startup based on the
 * gp_segment_configuration (stored on the master only), and each segment
 * has a file in its datadirectory (gp_dbid) that uniquely identifies the
 * segment.
 *
 *   The contents of the table are now irrelevant, with the exception that
 * several tools began relying on this table for use as a method of remote
 * function invocation via gp_dist_random('gp_id') due to the fact that this
 * table was guaranteed of having exactly one row on every segment.  The
 * contents of the row have no defined meaning, but this property is still
 * relied upon.
 */
#ifndef _GP_ID_H_
#define _GP_ID_H_  

#include "catalog/genbki.h"
/*
 * Defines for gp_id table
 */
#define GpIdRelationName                        "gp_id"  

/* TIDYCAT_BEGINFAKEDEF  

   CREATE TABLE gp_id
   with (shared=true, oid=false, relid=5001, content=SEGMENT_LOCAL)
   (
   gpname       name     ,
   numsegments  smallint ,
   dbid         smallint ,
   content      smallint
   );  

   TIDYCAT_ENDFAKEDEF
*/  

查询数据库大小的GP函数

postgres=# \df+ pg_database_size
                                                                                                     List of functions
   Schema   |       Name       | Result data type | Argument data types |  Type  |  Data access   | Volatility |  Owner   | Language |      Source code      |                         Description
------------+------------------+------------------+---------------------+--------+----------------+------------+----------+----------+-----------------------+-------------------------------------------------------------
 pg_catalog | pg_database_size | bigint           | name                | normal | reads sql data | volatile   | dege.zzz | internal | pg_database_size_name | Calculate total disk space usage for the specified database
 pg_catalog | pg_database_size | bigint           | oid                 | normal | reads sql data | volatile   | dege.zzz | internal | pg_database_size_oid  | Calculate total disk space usage for the specified database
(2 rows)  

其中pg_database_size_name 的源码如下：
很明显，在统计数据库大小时也用到了select sum(pg_database_size('%s'))::int8 from gp_dist_random('gp_id');

Datum
pg_database_size_name(PG_FUNCTION_ARGS)
{
        int64           size = 0;
        Name            dbName = PG_GETARG_NAME(0);
        Oid                     dbOid = get_database_oid(NameStr(*dbName));  

        if (!OidIsValid(dbOid))
                ereport(ERROR,
                                (errcode(ERRCODE_UNDEFINED_DATABASE),
                                 errmsg("database \"%s\" does not exist",
                                                NameStr(*dbName))));  

        size = calculate_database_size(dbOid);  

        if (Gp_role == GP_ROLE_DISPATCH)
        {
                StringInfoData buffer;  

                initStringInfo(&buffer);  

                appendStringInfo(&buffer, "select sum(pg_database_size('%s'))::int8 from gp_dist_random('gp_id');", NameStr(*dbName));  

                size += get_size_from_segDBs(buffer.data);
        }  

        PG_RETURN_INT64(size);
}  

不信我们可以直接查询这个SQL，和使用pg_database_size函数得到的结果几乎是一样的，只差了calculate_database_size的部分。

postgres=# select sum(pg_database_size('postgres'))::int8 from gp_dist_random('gp_id');
      sum
----------------
 16006753522624
(1 row)  

postgres=# select pg_database_size('postgres');
 pg_database_size
------------------
   16006763924106
(1 row)  

gp_dist_random('gp_id')本质上就是在所有节点查询gp_id，
gp_dist_random('pg_authid')就是在所有节点查询pg_authid，
例如：

postgres=# select * from gp_dist_random('gp_id');
  gpname   | numsegments | dbid | content
-----------+-------------+------+---------
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
 Greenplum |          -1 |   -1 |      -1
。。。。。。  

如果不想返回太多记录，可以使用limit 来过滤，但是执行还是会在所有的segment都执行，如下：

postgres=# explain analyze select random() from gp_dist_random('gp_id') limit 1;
                                                                  QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=0.00..0.04 rows=1 width=0)
   Rows out:  1 rows with 5.865 ms to first row, 5.884 ms to end, start offset by 4212 ms.
   ->  Gather Motion 240:1  (slice1; segments: 240)  (cost=0.00..0.04 rows=1 width=0)
         Rows out:  1 rows at destination with 5.857 ms to end, start offset by 4212 ms.
         ->  Limit  (cost=0.00..0.02 rows=1 width=0)
               Rows out:  Avg 1.0 rows x 240 workers.  Max 1 rows (seg0) with 0.062 ms to first row, 0.063 ms to end, start offset by 4228 ms.
               ->  Seq Scan on gp_id  (cost=0.00..4.00 rows=1 width=0)
                     Rows out:  Avg 1.0 rows x 240 workers.  Max 1 rows (seg0) with 0.060 ms to end, start offset by 4228 ms.
 Slice statistics:
   (slice0)    Executor memory: 463K bytes.
   (slice1)    Executor memory: 163K bytes avg x 240 workers, 163K bytes max (seg0).
 Statement statistics:
   Memory used: 128000K bytes
 Total runtime: 4288.007 ms
(14 rows)