SUM Function

An aggregate function that returns the sum of a set of numbers. Its single argument can be numeric column, or the numeric result of a function or expression applied to the column value. Rows with a NULL value for the specified column are ignored. If the table is empty, or all the values supplied to MIN are NULL, SUM returns NULL.

Syntax:

SUM([DISTINCT | ALL] expression) [OVER (analytic_clause)]

When the query contains a GROUP BY clause, returns one value for each combination of grouping values.

Return type: BIGINT for integer arguments, DOUBLE for floating-point arguments

Complex type considerations:

To access a column with a complex type (ARRAY, STRUCT, or MAP) in an aggregation function, you unpack the individual elements using join notation in the query, and then apply the function to the final scalar item, field, key, or value at the bottom of any nested type hierarchy in the column. See Complex Types (Impala 2.3 or higher only) for details about using complex types in Impala.

The following example demonstrates calls to several aggregation functions using values from a column containing nested complex types (an ARRAY of STRUCT items). The array is unpacked inside the query using join notation. The array elements are referenced using the ITEM pseudocolumn, and the structure fields inside the array elements are referenced using dot notation. Numeric values such as SUM() and AVG() are computed using the numeric R_NATIONKEY field, and the general-purpose MAX() and MIN() values are computed from the string N_NAME field.

describe region;
+-------------+-------------------------+---------+
| name        | type                    | comment |
+-------------+-------------------------+---------+
| r_regionkey | smallint                |         |
| r_name      | string                  |         |
| r_comment   | string                  |         |
| r_nations   | array<struct<           |         |
|             |   n_nationkey:smallint, |         |
|             |   n_name:string,        |         |
|             |   n_comment:string      |         |
|             | >>                      |         |
+-------------+-------------------------+---------+

select r_name, r_nations.item.n_nationkey
  from region, region.r_nations as r_nations
order by r_name, r_nations.item.n_nationkey;
+-------------+------------------+
| r_name      | item.n_nationkey |
+-------------+------------------+
| AFRICA      | 0                |
| AFRICA      | 5                |
| AFRICA      | 14               |
| AFRICA      | 15               |
| AFRICA      | 16               |
| AMERICA     | 1                |
| AMERICA     | 2                |
| AMERICA     | 3                |
| AMERICA     | 17               |
| AMERICA     | 24               |
| ASIA        | 8                |
| ASIA        | 9                |
| ASIA        | 12               |
| ASIA        | 18               |
| ASIA        | 21               |
| EUROPE      | 6                |
| EUROPE      | 7                |
| EUROPE      | 19               |
| EUROPE      | 22               |
| EUROPE      | 23               |
| MIDDLE EAST | 4                |
| MIDDLE EAST | 10               |
| MIDDLE EAST | 11               |
| MIDDLE EAST | 13               |
| MIDDLE EAST | 20               |
+-------------+------------------+

select
  r_name,
  count(r_nations.item.n_nationkey) as count,
  sum(r_nations.item.n_nationkey) as sum,
  avg(r_nations.item.n_nationkey) as avg,
  min(r_nations.item.n_name) as minimum,
  max(r_nations.item.n_name) as maximum,
  ndv(r_nations.item.n_nationkey) as distinct_vals
from
  region, region.r_nations as r_nations
group by r_name
order by r_name;
+-------------+-------+-----+------+-----------+----------------+---------------+
| r_name      | count | sum | avg  | minimum   | maximum        | distinct_vals |
+-------------+-------+-----+------+-----------+----------------+---------------+
| AFRICA      | 5     | 50  | 10   | ALGERIA   | MOZAMBIQUE     | 5             |
| AMERICA     | 5     | 47  | 9.4  | ARGENTINA | UNITED STATES  | 5             |
| ASIA        | 5     | 68  | 13.6 | CHINA     | VIETNAM        | 5             |
| EUROPE      | 5     | 77  | 15.4 | FRANCE    | UNITED KINGDOM | 5             |
| MIDDLE EAST | 5     | 58  | 11.6 | EGYPT     | SAUDI ARABIA   | 5             |
+-------------+-------+-----+------+-----------+----------------+---------------+

Examples:

The following example shows how to use SUM() to compute the total for all the values in the table, a subset of values, or the sum for each combination of values in the GROUP BY clause:

-- Total all the values for this column in the table.
select sum(c1) from t1;
-- Find the total for this column from a subset of the table.
select sum(c1) from t1 where month = 'January' and year = '2013';
-- Find the total from a set of numeric function results.
select sum(length(s)) from t1;
-- Often used with functions that return predefined values to compute a score.
select sum(case when grade = 'A' then 1.0 when grade = 'B' then 0.75 else 0) as class_honors from test_scores;
-- Can also be used in combination with DISTINCT and/or GROUP BY.
-- Return more than one result.
select month, year, sum(purchase_price) from store_stats group by month, year;
-- Filter the input to eliminate duplicates before performing the calculation.
select sum(distinct x) from t1;

The following examples show how to use SUM() in an analytic context. They use a table containing integers from 1 to 10. Notice how the SUM() is reported for each input value, as opposed to the GROUP BY clause which condenses the result set.

select x, property, sum(x) over (partition by property) as sum from int_t where property in ('odd','even');
+----+----------+-----+
| x  | property | sum |
+----+----------+-----+
| 2  | even     | 30  |
| 4  | even     | 30  |
| 6  | even     | 30  |
| 8  | even     | 30  |
| 10 | even     | 30  |
| 1  | odd      | 25  |
| 3  | odd      | 25  |
| 5  | odd      | 25  |
| 7  | odd      | 25  |
| 9  | odd      | 25  |
+----+----------+-----+

Adding an ORDER BY clause lets you experiment with results that are cumulative or apply to a moving set of rows (the "window"). The following examples use SUM() in an analytic context (that is, with an OVER() clause) to produce a running total of all the even values, then a running total of all the odd values. The basic ORDER BY x clause implicitly activates a window clause of RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which is effectively the same as ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, therefore all of these examples produce the same results:

select x, property,
  sum(x) over (partition by property order by x) as 'cumulative total'
  from int_t where property in ('odd','even');
+----+----------+------------------+
| x  | property | cumulative total |
+----+----------+------------------+
| 2  | even     | 2                |
| 4  | even     | 6                |
| 6  | even     | 12               |
| 8  | even     | 20               |
| 10 | even     | 30               |
| 1  | odd      | 1                |
| 3  | odd      | 4                |
| 5  | odd      | 9                |
| 7  | odd      | 16               |
| 9  | odd      | 25               |
+----+----------+------------------+

select x, property,
  sum(x) over
  (
    partition by property
    order by x
    range between unbounded preceding and current row
  ) as 'cumulative total'
from int_t where property in ('odd','even');
+----+----------+------------------+
| x  | property | cumulative total |
+----+----------+------------------+
| 2  | even     | 2                |
| 4  | even     | 6                |
| 6  | even     | 12               |
| 8  | even     | 20               |
| 10 | even     | 30               |
| 1  | odd      | 1                |
| 3  | odd      | 4                |
| 5  | odd      | 9                |
| 7  | odd      | 16               |
| 9  | odd      | 25               |
+----+----------+------------------+

select x, property,
  sum(x) over
  (
    partition by property
    order by x
    rows between unbounded preceding and current row
  ) as 'cumulative total'
  from int_t where property in ('odd','even');
+----+----------+------------------+
| x  | property | cumulative total |
+----+----------+------------------+
| 2  | even     | 2                |
| 4  | even     | 6                |
| 6  | even     | 12               |
| 8  | even     | 20               |
| 10 | even     | 30               |
| 1  | odd      | 1                |
| 3  | odd      | 4                |
| 5  | odd      | 9                |
| 7  | odd      | 16               |
| 9  | odd      | 25               |
+----+----------+------------------+

Changing the direction of the ORDER BY clause causes the intermediate results of the cumulative total to be calculated in a different order:

select sum(x) over (partition by property order by x desc) as 'cumulative total'
  from int_t where property in ('odd','even');
+----+----------+------------------+
| x  | property | cumulative total |
+----+----------+------------------+
| 10 | even     | 10               |
| 8  | even     | 18               |
| 6  | even     | 24               |
| 4  | even     | 28               |
| 2  | even     | 30               |
| 9  | odd      | 9                |
| 7  | odd      | 16               |
| 5  | odd      | 21               |
| 3  | odd      | 24               |
| 1  | odd      | 25               |
+----+----------+------------------+

The following examples show how to construct a moving window, with a running total taking into account 1 row before and 1 row after the current row, within the same partition (all the even values or all the odd values). Because of a restriction in the Impala RANGE syntax, this type of moving window is possible with the ROWS BETWEEN clause but not the RANGE BETWEEN clause:

select x, property,
  sum(x) over
  (
    partition by property
    order by x
    rows between 1 preceding and 1 following
  ) as 'moving total'
  from int_t where property in ('odd','even');
+----+----------+--------------+
| x  | property | moving total |
+----+----------+--------------+
| 2  | even     | 6            |
| 4  | even     | 12           |
| 6  | even     | 18           |
| 8  | even     | 24           |
| 10 | even     | 18           |
| 1  | odd      | 4            |
| 3  | odd      | 9            |
| 5  | odd      | 15           |
| 7  | odd      | 21           |
| 9  | odd      | 16           |
+----+----------+--------------+

-- Doesn't work because of syntax restriction on RANGE clause.
select x, property,
  sum(x) over
  (
    partition by property
    order by x
    range between 1 preceding and 1 following
  ) as 'moving total'
from int_t where property in ('odd','even');
ERROR: AnalysisException: RANGE is only supported with both the lower and upper bounds UNBOUNDED or one UNBOUNDED and the other CURRENT ROW.

Restrictions:

Due to the way arithmetic on FLOAT and DOUBLE columns uses high-performance hardware instructions, and distributed queries can perform these operations in different order for each query, results can vary slightly for aggregate function calls such as SUM() and AVG() for FLOAT and DOUBLE columns, particularly on large data sets where millions or billions of values are summed or averaged. For perfect consistency and repeatability, use the DECIMAL data type for such operations instead of FLOAT or DOUBLE.

Related information:

Impala Analytic Functions