Greenplum创建表--分布键
Greenplum是分布式系统，创建表时需要指定分布键（创建表需要CREATEDBA权限），目的在于将数据平均分布到各个segment。选择分布键非常重要，选择错了会导致数据不唯一，更严重的是会造成SQL性能急剧下降。
Greenplum有两种分布策略：
1、hash分布。
Greenplum默认使用hash分布策略。该策略可选一个或者多个列作为分布键（distribution key，简称DK）。分布键做hash算法来确认数据存放到对应的segment上。相同分布键值会hash到相同的segment上。表上最好有唯一键或者主键，这样能保证数据均衡分不到各个segment上。语法，distributed by。
如果没有主键或者唯一键，默认选择第一列作为分布键。增加主键
2、随机（randomly）分布。
数据会被随机分不到segment上，相同记录可能会存放在不同的segment上。随机分布可以保证数据平均，但是Greenplum没有跨节点的唯一键约束数据，所以无法保证数据唯一。基于唯一性和性能考虑，推荐使用hash分布，性能部分会另开一篇文档详细介绍。语法，distributed randomly。
一、hash分布键
创建表，未指定分布列、分布类型，默认创建hash分布表，把第一列ID字段作为了分布键。
testDB=# create table t_hash(id int,name varchar(50)) distributed by (id);
CREATE TABLE
testDB=#
testDB=# \d t_hash
Table "public.t_hash"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) |
Distributed by: (id)添加主键后，主键升级为分布键替代了id列。
testDB=# alter table t_hash add primary key (name);
NOTICE: updating distribution policy to match new primary key
NOTICE: ALTER TABLE / ADD PRIMARY KEY will create implicit index "t_hash_pkey" for table "t_hash"
ALTER TABLE
testDB=# \d t_hash
Table "public.t_hash"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Indexes:
"t_hash_pkey" PRIMARY KEY, btree (name)
Distributed by: (name)验证hash分布表可实现主键或者唯一键值的唯一性
testDB=# insert into t_hash values(1,'szlsd1'); INSERT 0 1 testDB=# testDB=# insert into t_hash values(2,'szlsd1'); ERROR: duplicate key violates unique constraint "t_hash_pkey"(seg2 gp-s3:40000 pid=3855)
另外，主键列上依然能够创建唯一键
testDB=# create unique index u_id on t_hash(name);
CREATE INDEX
testDB=#
testDB=#
testDB=# \d t_hash
Table "public.t_hash"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Indexes:
"t_hash_pkey" PRIMARY KEY, btree (name)
"u_id" UNIQUE, btree (name)
Distributed by: (name)但是，非主键列无法单独创建唯一索引，想创建的话必须包含多有分布键列
testDB=# create unique index uk_id on t_hash(id);
ERROR: UNIQUE index must contain all columns in the distribution key of relation "t_hash"
testDB=# create unique index uk_id on t_hash(id,name);
CREATE INDEX
testDB=# \d t_hash
Table "public.t_hash"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Indexes:
"t_hash_pkey" PRIMARY KEY, btree (name)
"uk_id" UNIQUE, btree (id, name)
Distributed by: (name)删除主键后，原hash分布键依然不变。
testDB=# alter table t_hash drop constraint t_hash_pkey;
ALTER TABLE
testDB=# \d t_hash
Table "public.t_hash"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Distributed by: (name)当分布键不是主键或者唯一键时，我们来验证分布键的相同值落在一个segment的结论。
下面的实验，name列是分布键，我们插入相同的name值，可以看到7条记录都落在了2号segment节点中。
testDB=# insert into t_hash values(1,'szlsd');
INSERT 0 1
testDB=# insert into t_hash values(2,'szlsd');
INSERT 0 1
testDB=# insert into t_hash values(3,'szlsd');
INSERT 0 1
testDB=# insert into t_hash values(4,'szlsd');
INSERT 0 1
testDB=# insert into t_hash values(5,'szlsd');
INSERT 0 1
testDB=# insert into t_hash values(6,'szlsd');
INSERT 0 1
testDB=#
testDB=#
testDB=# select gp_segment_id,count(*) from t_hash group by gp_segment_id;
gp_segment_id | count
---------------+-------
2 | 7
(1 row)二、随机分布键
创建随机分布表需加distributed randomly关键字，具体使用哪列作为分布键不得而知。
testDB=# create table t_random(id int ,name varchar(100)) distributed randomly;
CREATE TABLE
testDB=#
testDB=#
testDB=# \d t_random
Table "public.t_random"
Column | Type | Modifiers
--------+------------------------+-----------
id | integer |
name | character varying(100) |
Distributed randomly验证主键/唯一键的唯一性，可以看到随机分布表不能创建主键和唯一键
testDB=# alter table t_random add primary key (id,name); ERROR: PRIMARY KEY and DISTRIBUTED RANDOMLY are incompatible testDB=# testDB=# create unique index uk_r_id on t_random(id); ERROR: UNIQUE and DISTRIBUTED RANDOMLY are incompatible testDB=#
从实验中可以看出无法实现数据的唯一性。并且，数据插入随机分布表，并不是轮询插入，实验中共有3个segment，但是在1号插入3条记录，在2号segment节点插入2条记录后，才在0号segment中插入数据。随机分布表如何实现数据平均分配不得而知。这个实验也验证了随机分布表的相同值分布在不同segment的结论。
testDB=# insert into t_random values(1,'szlsd3');
INSERT 0 1
testDB=# select gp_segment_id,count(*) from t_random group by gp_segment_id;
gp_segment_id | count
---------------+-------
1 | 1
(1 row)
testDB=#
testDB=# insert into t_random values(1,'szlsd3');
INSERT 0 1
testDB=# select gp_segment_id,count(*) from t_random group by gp_segment_id;
gp_segment_id | count
---------------+-------
2 | 1
1 | 1
(2 rows)
testDB=# insert into t_random values(1,'szlsd3');
INSERT 0 1
testDB=# select gp_segment_id,count(*) from t_random group by gp_segment_id;
gp_segment_id | count
---------------+-------
2 | 1
1 | 2
(2 rows)
testDB=# insert into t_random values(1,'szlsd3');
INSERT 0 1
testDB=# select gp_segment_id,count(*) from t_random group by gp_segment_id;
gp_segment_id | count
---------------+-------
2 | 2
1 | 2
(2 rows)
testDB=# insert into t_random values(1,'szlsd3');
INSERT 0 1
testDB=# select gp_segment_id,count(*) from t_random group by gp_segment_id;
gp_segment_id | count
---------------+-------
2 | 2
1 | 3
(2 rows)
testDB=# insert into t_random values(1,'szlsd3');
INSERT 0 1
testDB=# select gp_segment_id,count(*) from t_random group by gp_segment_id;
gp_segment_id | count
---------------+-------
2 | 2
1 | 3
0 | 1
(3 rows)三、CTAS继承原表分布键
Greenplum中有两种CTAS语法，无论哪种语法，都默认继承原表的分布键。但是，不会继承表的一些特殊属性，如主键、唯一键、APPENDONLY、COMPRESSTYPE（压缩）等。
testDB=# \d t_hash;
Table "public.t_hash"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Indexes:
"t_hash_pkey" PRIMARY KEY, btree (name)
"uk_id" UNIQUE, btree (id, name)
Distributed by: (name)
testDB=#
testDB=#
testDB=# create table t_hash_1 as select * from t_hash;
NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'name' 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.
SELECT 0
testDB=# \d t_hash_1
Table "public.t_hash_1"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) |
Distributed by: (name)
testDB=#
testDB=# create table t_hash_2 (like t_hash);
NOTICE: Table doesn't have 'distributed by' clause, defaulting to distribution columns from LIKE table
CREATE TABLE
testDB=# \d t_hash_2
Table "public.t_hash_2"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Distributed by: (name)如果CTAS创建表改变分布键，加上distributed by即可。
testDB=# create table t_hash_3 as select * from t_hash distributed by (id);
SELECT 0
testDB=#
testDB=# \d t_hash_3
Table "public.t_hash_3"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) |
Distributed by: (id)
testDB=#
testDB=#
testDB=# create table t_hash_4 (like t_hash) distributed by (id);
CREATE TABLE
testDB=#
testDB=# \d t_hash4
Did not find any relation named "t_hash4".
testDB=# \d t_hash_4
Table "public.t_hash_4"
Column | Type | Modifiers
--------+-----------------------+-----------
id | integer |
name | character varying(50) | not null
Distributed by: (id)CTAS时，randomly随机分布键要特别注意，一定要加上distributed randomly，不然原表是hash分布键，CTAS新表则是随机分布键。
testDB=# \d t_random
Table "public.t_random"
Column | Type | Modifiers
--------+------------------------+-----------
id | integer |
name | character varying(100) |
Distributed randomly
testDB=#
testDB=# \d t_random_1
Table "public.t_random_1"
Column | Type | Modifiers
--------+------------------------+-----------
id | integer |
name | character varying(100) |
Distributed by: (id)testDB=# create table t_random_2 as select * from t_random distributed randomly;
SELECT 7
testDB=#
testDB=# \d t_random_2
Table "public.t_random_2"
Column | Type | Modifiers
--------+------------------------+-----------
id | integer |
name | character varying(100) |
Distributed randomly参考：
《Greenplum企业应用实战》
《Greenplum4.2.2管理员指南》
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