GROUP BY

Group the SELECT results by the values of the specified columns or expressions. The aggregate functions (COUNT, MAX, MIN, SUM, AVG) are often used together with GROUP BY to perform calculations in each group.

Syntax

SELECT                             -- In SELECT, you can use:
    column1,                       -- key columns specified in GROUP BY
    key_n,                         -- named expressions specified in GROUP BY
    column1 + key_n,               -- random non-aggregate derived functions
    Aggr_Func1( column2 ),         -- aggregate functions containing any columns in arguments,
    Aggr_Func2( key_n + column2 ), --   including named expressions specified in GROUP BY
    ...
FROM table
GROUP BY
    column1, column2, ...,
    <expr> AS key_n           -- When grouping by expression, it can be assigned a name via AS,
                              -- that may be used in SELECT

Querying the SELECT * FROM table GROUP BY k1, k2, ... type returns all the columns listed in GROUP BY, which is equivalent to the SELECT DISTINCT k1, k2, ... query FROM table.

An asterisk can also be used as an argument for the COUNT aggregate function. COUNT(*) means "the count of rows in the group".

YQL also supports factories of aggregate functions implemented using the AGGREGATION_FACTORY and AGGREGATE_BY functions.

Examples

SELECT key, COUNT(*) FROM my_table
GROUP BY key;

SELECT double_key, COUNT(*) FROM my_table
GROUP BY key + key AS double_key;

SELECT
   double_key,                           -- OK: key column
   COUNT(*) AS group_size,               -- OK: COUNT(*)
   SUM(key + subkey) AS sum1,            -- OK: aggregate function
   CAST(SUM(1 + 2) AS String) AS sum2,   -- OK: aggregate function with constant argument
   SUM(SUM(1) + key) AS sum3,            -- ERROR: nested aggregations are not allowed
   key AS k1,                            -- ERROR: use of non-key Key column without aggregation
   key * 2 AS dk1,                       -- ERROR in YQL: use of non-key Key column without aggregation
FROM my_table
GROUP BY
  key * 2 AS double_key,
  subkey as sk,

GROUP BY ... SessionWindow()

YQL supports grouping by session. To standard expressions in GROUP BY, you can add a special SessionWindow function:

SELECT
  user,
  session_start,
  SessionStart() AS same_session_start, -- the same as session_start
  COUNT(*) AS session_size,
  SUM(value) AS sum_over_session,
FROM my_table
GROUP BY user, SessionWindow(<time_expr>, <timeout_expr>) AS session_start

The following happens in this case:

1. The input table is partitioned by the grouping keys specified in GROUP BY, ignoring SessionWindow (in this case, it's based on user).
   If GROUP BY includes nothing more than SessionWindow, then the input table gets into one partition
2. Each partition is split into disjoint subsets of rows (sessions).
   For this, the partition is sorted in the ascending order of the time_expr expression.
   The session limits are drawn between neighboring items of the partition, that differ in their time_expr values by more than timeout_expr
3. The sessions obtained in this way are the final partitions on which aggregate functions are calculated.

The SessionWindow() key column (in the example, it's session_start) has the value "the minimum time_expr in the session".
Also, if SessionWindow() is present in GROUP BY, you can use the special
SessionStart aggregate function.

An extended version of SessionWindow with four arguments is also supported:

SessionWindow(<order_expr>, <init_lambda>, <update_lambda>, <calculate_lambda>)

Where:

• <order_expr> is an expression based on which the original partition is sorted
• <init_lambda> is a lambda function for initializing the session calculation status. It has the signature (TableRow())->State. It's called once for the first (following the sorting order) element of the source partition
• <update_lambda> is a lambda function for refreshing the session calculation state and defining session limits. It has the signature (TableRow(), State)->Tuple<Bool, State>. It's called for every item of the source partition, except the first one. The new value of state is calculated based on the current row of the table and the previous state. If the first item in the return tuple is True, then a new session starts from the current row. The new session key is obtained by applying <calculate_lambda> to the second tuple element.
• <calculate_lambda>: A lambda function for calculating the session key (the "value" of SessionWindow() that is also accessible via SessionStart()). The function has the signature (TableRow(), State)->SessionKey. It's called in the first partition element (after <init_lambda>) and on those elements for which <update_lambda> returnedTrue as the first tuple element. It's worth noting that to start a new session, <calculate_lambda> must return a value that is different from the previous session key. Sessions having the same keys are not merged. For example, if <calculate_lambda> sequentially returns 0, 1, 0, 1, they will be four different sessions.

Using the extended version of SessionWindow, you can, for example, do the following: divide a partition into sessions, as in the SessionWindow use case with two arguments, but with the maximum session length limited by a certain constant:

Example

$max_len = 1000; -- maximum session length
$timeout = 100; -- timeout (timeout_expr in a simplified variant of SessionWindow)

$init = ($row) -> (AsTuple($row.ts, $row.ts)); -- session state - tuple of 1) values of temporary ts column in the first session row and 2) in the current row
$update = ($row, $state) -> {
  $is_end_session = $row.ts - $state.0 > $max_len OR $row.ts - $state.1 > $timeout;
  $new_state = AsTuple(IF($is_end_session, $row.ts, $state.0), $row.ts);
  return AsTuple($is_end_session, $new_state);
};
$calculate = ($row, $state) -> ($row.ts);
SELECT
  user,
  session_start,
  SessionStart() AS same_session_start, -- the same as session_start
  COUNT(*) AS session_size,
  SUM(value) AS sum_over_session,
FROM my_table
GROUP BY user, SessionWindow(ts, $init, $update, $calculate) AS session_start

You can use SessionWindow in GROUP BY only once.

ROLLUP, CUBE, and GROUPING SETS

The results of calculating the aggregate function as subtotals for the groups and overall totals over individual columns or whole table.

Syntax

SELECT
    c1, c2,                          -- columns based on which grouping is performed

AGGREGATE_FUNCTION(c3) AS outcome_c  -- (SUM, AVG, MIN, MAX, COUNT) aggregate function

FROM table_name

GROUP BY
    GROUP_BY_EXTENSION(c1, c2)       -- GROUP BY: ROLLUP, CUBE, or GROUPING SETS extension

• ROLLUP groups the column values in the order they are listed in the arguments (strictly from left to right), generates subtotals for each group and the overall total.
• CUBE groups the values for every possible combination of columns, generates the subtotals for each group and the overall total.
• GROUPING SETS sets the groups for subtotals.

You can combine ROLLUP, CUBE and GROUPING SETS, separating them by commas.

GROUPING

The values of columns not used in calculations are replaced with NULL in the subtotal. In the overall total, the values of all columns are replaced by NULL. GROUPING: A function that allows you to distinguish the source NULL values from the NULL values added while calculating subtotals and overall totals.

GROUPING returns a bit mask:

• 0: If NULL is used for the original empty value.
• 1: If NULL is added for a subtotal or overall total.

Example

SELECT
    column1,
    column2,
    column3,

    CASE GROUPING(
        column1,
        column2,
        column3,
    )
        WHEN 1  THEN "Subtotal: column1 and column2"
        WHEN 3  THEN "Subtotal: column1"
        WHEN 4  THEN "Subtotal: column2 and column3"
        WHEN 6  THEN "Subtotal: column3"
        WHEN 7  THEN "Grand total"
        ELSE         "Individual group"
    END AS subtotal,

    COUNT(*) AS rows_count

FROM my_table

GROUP BY
    ROLLUP(
        column1,
        column2,
        column3
    ),
    GROUPING SETS(
        (column2, column3),
        (column3)
        -- if you added (column2) as well, then
        -- summing up these ROLLUP and GROUPING SETS would give the result
        -- similar to CUBE
    )
;

DISTINCT

Applying aggregate functions only to distinct values of the column.

Example

SELECT
  key,
  COUNT(DISTINCT value) AS count -- top 3 keys based on the number of unique values
FROM my_table
GROUP BY key
ORDER BY count DESC
LIMIT 3.

You can also use DISTINCT to fetch unique rows using SELECT DISTINCT.

COMPACT

The presence of SQL hint COMPACT right after the GROUP keyword allows for more effective aggregation in cases where the query author knows beforehand that no aggregation key produces large amounts of data (more than a gigabyte or millions of rows). If this assumption fails to materialize, then the operation may fail with Out of Memory error or start running much slower compared to the non-COMPACT version.

Unlike regular GROUP BY, Map-side combiner and additional Reduce for each field with DISTINCT aggregation are disabled.

Example:

SELECT
  key,
  COUNT(DISTINCT value) AS count -- top 3 keys based on the number of unique values
FROM my_table
GROUP /*+ COMPACT() */ BY key
ORDER BY count DESC
LIMIT 3;

HAVING

Filtering a SELECT based on the aggregate function calculation results. The syntax is similar to the WHERE clause.

Example

SELECT
    key
FROM my_table
GROUP BY key
HAVING COUNT(value) > 100;