hive语句执行顺序
msyql语句执行顺序
代码写的顺序：
select ... from... where.... group by... having... order by..
或者
from ... select ...
代码的执行顺序：
from... where...group by... having.... select ... order by...
hive 语句执行顺序
大致顺序
from... where.... select...group by... having ... order by...
explain查看执行计划
hive语句和mysql都可以通过explain查看执行计划，这样就可以查看执行顺序，比如代码
explain
select city,ad_type,device,sum(cnt) as cnt
from tb_pmp_raw_log_basic_analysis
where day = '2016-05-28' and type = 0 and media = 'sohu' and (deal_id = '' or deal_id = '-' or deal_id is NULL)
group by city,ad_type,device显示执行计划如下
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 is a root stage
STAGE PLANS:
Stage: Stage-1
Map Reduce
Map Operator Tree:
TableScan
alias: tb_pmp_raw_log_basic_analysis
Statistics: Num rows: 8195357 Data size: 580058024 Basic stats: COMPLETE Column stats: NONE
Filter Operator
predicate: (((deal_id = '') or (deal_id = '-')) or deal_id is null) (type: boolean)
Statistics: Num rows: 8195357 Data size: 580058024 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: city (type: string), ad_type (type: string), device (type: string), cnt (type: bigint)
outputColumnNames: city, ad_type, device, cnt
Statistics: Num rows: 8195357 Data size: 580058024 Basic stats: COMPLETE Column stats: NONE
Group By Operator
aggregations: sum(cnt)
keys: city (type: string), ad_type (type: string), device (type: string)
mode: hash
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 8195357 Data size: 580058024 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string)
sort order: +++
Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: string)
Statistics: Num rows: 8195357 Data size: 580058024 Basic stats: COMPLETE Column stats: NONE
value expressions: _col3 (type: bigint)
Reduce Operator Tree:
Group By Operator
aggregations: sum(VALUE._col0)
keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 4097678 Data size: 290028976 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: bigint)
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 4097678 Data size: 290028976 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 4097678 Data size: 290028976 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Stage: Stage-0
Fetch Operator
limit: -1具体介绍如下
**stage1的map阶段**
TableScan：from加载表，描述中有行数和大小等
Filter Operator：where过滤条件筛选数据，描述有具体筛选条件和行数、大小等
Select Operator：筛选列，描述中有列名、类型，输出类型、大小等。
Group By Operator：分组，描述了分组后需要计算的函数，keys描述用于分组的列，outputColumnNames为输出的列名，可以看出列默认使用固定的别名_col0，以及其他信息
Reduce Output Operator：map端本地的reduce，进行本地的计算，然后按列映射到对应的reduce
**stage1的reduce阶段Reduce Operator Tree**
Group By Operator：总体分组，并按函数计算。map计算后的结果在reduce端的合并。描述类似。mode: mergepartial是说合并map的计算结果。map端是hash映射分组
Select Operator：最后过滤列用于输出结果
File Output Operator：输出结果到临时文件中，描述介绍了压缩格式、输出文件格式。
stage0第二阶段没有，这里可以实现limit 100的操作。
总结
1，每个stage都是一个独立的MR，复杂的hql语句可以产生多个stage，可以通过执行计划的描述，看看具体步骤是什么。
2，执行计划有时预测数据量，不是真实运行，可能不准确
group by的MR
hive语句最好写子查询嵌套，这样分阶段的导入数据，可以逐步减少数据量。但可能会浪费时间。所以需要设计好。
group by本身也是一种数据筛选，可以大量减少数据，尤其用于去重等方面，功效显著。但group by产生MR有时不可控，不知道在哪个阶段更好。尤其，map端本地的reduce减少数据有很大作用。
尤其，hadoop的MR不患寡而患不均。数据倾斜将是MR计算的最大瓶颈。hive中可以设置分区、桶、distribute by等来控制分配数据给Reduce。
那么，group by生成MR是否可以优化呢?
下面两端代码，可以对比一下，
代码1
explain
select advertiser_id,crt_id,ad_place_id,channel,ad_type,rtb_type,media,count(1) as cnt
from (
select
split(all,'\\\\|~\\\\|')[41] as advertiser_id,
split(all,'\\\\|~\\\\|')[11] as crt_id,
split(all,'\\\\|~\\\\|')[8] as ad_place_id,
split(all,'\\\\|~\\\\|')[34] as channel,
split(all,'\\\\|~\\\\|')[42] as ad_type,
split(all,'\\\\|~\\\\|')[43] as rtb_type,
split(split(all,'\\\\|~\\\\|')[5],'/')[1] as media
from tb_pmp_raw_log_bid_tmp tb
) a
group by advertiser_id,crt_id,ad_place_id,channel,ad_type,rtb_type,media;代码2
explain
select
split(all,'\\\\|~\\\\|')[41] as advertiser_id,
split(all,'\\\\|~\\\\|')[11] as crt_id,
split(all,'\\\\|~\\\\|')[8] as ad_place_id,
split(all,'\\\\|~\\\\|')[34] as channel,
split(all,'\\\\|~\\\\|')[42] as ad_type,
split(all,'\\\\|~\\\\|')[43] as rtb_type,
split(split(all,'\\\\|~\\\\|')[5],'/')[1] as media
from tb_pmp_raw_log_bid_tmp tb
group by split(all,'\\\\|~\\\\|')[41],split(all,'\\\\|~\\\\|')[11],split(all,'\\\\|~\\\\|')[8],split(all,'\\\\|~\\\\|')[34],split(all,'\\\\|~\\\\|')[42],split(all,'\\\\|~\\\\|')[43],split(split(all,'\\\\|~\\\\|')[5],'/')[1]先进行子查询，然后group by，还是直接group by，两种那个好一点，
我个人测试后认为，数据量小，第一种会好一点，如果数据量大，可能第二种会好。至于数据量多大。TB级以下的都是小数据。
两个执行计划对比如下，可以看出基本执行的步骤的数据分析量差不多。
group by一定要用，但内外，先后执行顺序效果差不多。
代码1
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 is a root stage
STAGE PLANS:
Stage: Stage-1
Map Reduce
Map Operator Tree:
TableScan
alias: tb
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: split(all, '\\|~\\|')[41] (type: string), split(all, '\\|~\\|')[11] (type: string), split(all, '\\|~\\|')[8] (type: string), split(all, '\\|~\\|')[34] (type: string), split(all, '\\|~\\|')[42] (type: string), split(all, '\\|~\\|')[43] (type: string), split(split(all, '\\|~\\|')[5], '/')[1] (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Group By Operator
aggregations: count(1)
keys: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
mode: hash
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
sort order: +++++++
Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
value expressions: _col7 (type: bigint)
Reduce Operator Tree:
Group By Operator
aggregations: count(VALUE._col0)
keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7
Statistics: Num rows: 563288391 Data size: 56328839118 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: bigint)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7
Statistics: Num rows: 563288391 Data size: 56328839118 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 563288391 Data size: 56328839118 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Stage: Stage-0
Fetch Operator
limit: -1代码2
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 is a root stage
STAGE PLANS:
Stage: Stage-1
Map Reduce
Map Operator Tree:
TableScan
alias: tb
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: all (type: string)
outputColumnNames: all
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Group By Operator
keys: split(all, '\\|~\\|')[41] (type: string), split(all, '\\|~\\|')[11] (type: string), split(all, '\\|~\\|')[8] (type: string), split(all, '\\|~\\|')[34] (type: string), split(all, '\\|~\\|')[42] (type: string), split(all, '\\|~\\|')[43] (type: string), split(split(all, '\\|~\\|')[5], '/')[1] (type: string)
mode: hash
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
sort order: +++++++
Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
Statistics: Num rows: 1126576783 Data size: 112657678336 Basic stats: COMPLETE Column stats: NONE
Reduce Operator Tree:
Group By Operator
keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
Statistics: Num rows: 563288391 Data size: 56328839118 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
Statistics: Num rows: 563288391 Data size: 56328839118 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 563288391 Data size: 56328839118 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Stage: Stage-0
Fetch Operator
limit: -1