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Group By Syntax

groupByClause: GROUP BY groupByExpression (, groupByExpression)*

groupByExpression: expression

groupByQuery: SELECT expression (, expression)* FROM src groupByClause?

In groupByExpression columns are specified by name, not by position number. However in Hive 0.11.0 and later, columns can be specified by position when configured as follows:

• For Hive 0.11.0 through 2.1.x, set hive.groupby.orderby.position.alias to true (the default is false).
• For Hive 2.2.0 and later, set hive.groupby.position.alias to true (the default is false).
  

Simple Examples

In order to count the number of rows in a table:

  SELECT COUNT(*) FROM table2;

Note that for versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

In order to count the number of distinct users by gender one could write the following query:

  INSERT OVERWRITE TABLE pv_gender_sum
  SELECT pv_users.gender, count (DISTINCT pv_users.userid)
  FROM pv_users
  GROUP BY pv_users.gender;

Multiple aggregations can be done at the same time, however, no two aggregations can have different DISTINCT columns. e.g while For example, the following is possible because count(DISTINCT) and sum(DISTINCT) specify the same column:

  INSERT OVERWRITE TABLE pv_gender_agg
  SELECT pv_users.gender, count(DISTINCT pv_users.userid), count(*), sum(DISTINCT pv_users.userid)
  FROM pv_users
  GROUP BY pv_users.gender;

Note that for versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

However, the following query is not allowed. We don't allow multiple DISTINCT expressions in the same query.

  INSERT OVERWRITE TABLE pv_gender_agg
  SELECT pv_users.gender, count(DISTINCT pv_users.userid), count(DISTINCT pv_users.ip)
  FROM pv_users
  GROUP BY pv_users.gender;

...

When using group by clause, the select statement can only include columns included in the group by clause. Of course, you can have as many aggregation functions (e.g. count) in the select statement as well.
Let's take a simple example

CREATE TABLE t1(a INTEGER, b INTGER);

A group by query on the above table could look like:

SELECT
   a,
   sum(b)
FROM
   t1
GROUP BY
   a;

...

However, the query below DOES NOT work:

SELECT
   a,
   b
FROM
   t1
GROUP BY
   a;

This is because the select clause has an additional column (b) that is not included in the group by clause (and it's not an aggregation function either). This is because, if the table t1 looked like:

a    b
------
100  1
100  2
100  3

...

The output of the aggregations or simple selects can be further sent into multiple tables or even to hadoop dfs files (which can then be manipulated using hdfs utilitites). e.g. if along with the gender breakdown, one needed to find the breakdown of unique page views by age, one could accomplish that with the following query:

  FROM pv_users 
  INSERT OVERWRITE TABLE pv_gender_sum
    SELECT pv_users.gender, count(DISTINCT pv_users.userid) 
    GROUP BY pv_users.gender 
  INSERT OVERWRITE DIRECTORY '/user/facebook/tmp/pv_age_sum'
    SELECT pv_users.age, count(DISTINCT pv_users.userid) 
    GROUP BY pv_users.age; 

...

hive.map.aggr controls how we do aggregations. The default is false. If it is set to true, Hive will do the first-level aggregation directly in the map task.
This usually provides better efficiency, but may require more memory to run successfully.

  set hive.map.aggr=true;
  SELECT COUNT(*) FROM table2;

Note that for versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

Grouping Sets, Cubes, Rollups, and the GROUPING__ID Function

See Enhanced Aggregation, Cube, Grouping and Rollup for information about these aggregation operators.

Also see the JIRAs:

• HIVE-2397 Support with rollup option for group by
• HIVE-3433 Implement CUBE and ROLLUP operators in Hive
• HIVE-3471 Implement grouping sets in Hive
• HIVE-3613 Implement grouping_id function

New in Hive release 0.11.0:

• HIVE-3552 HIVE-3552 performant manner for performing cubes/rollups/grouping sets for a high number of grouping set keys