hive explain

hive 语句执行顺序

大体顺序
from... where.... select...group by... having ... order by...

explain查看执行计划

hive语句和mysql均可以经过explain查看执行计划，这样就能够查看执行顺序，好比

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

显示执行计划以下

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

具体介绍以下 

**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，描述了分组后须要计算的函数，keys描述用于分组的列，outputColumnNames为输出的列名，能够看出列默认使用固定的别名_col0，以及其余信息
        Reduce Output Operator：map端本地的reduce，进行本地的计算，而后按列映射到对应的reduce
**stage1的reduce阶段Reduce Operator Tree**
        Group By Operator：整体分组，并按函数计算。map计算后的结果在reduce端的合并。描述相似。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，这里能够实现limit 100的操做。

总结

1，每一个stage都是一个独立的MR，复杂的hql语句能够产生多个stage，能够经过执行计划的描述，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，可能不许确