COUNT Function

An aggregate function that returns the number of rows, or the number of non-NULL rows.

Syntax:

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

Depending on the argument, COUNT() considers rows that meet certain conditions:

The notation COUNT(*) includes NULL values in the total.
The notation COUNT(column_name) only considers rows where the column contains a non-NULL value.
You can also combine COUNT with the DISTINCT operator to eliminate duplicates before counting, and to count the combinations of values across multiple columns.

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

Return type: BIGINT

Usage notes:

If you frequently run aggregate functions such as MIN(), MAX(), and COUNT(DISTINCT) on partition key columns, consider enabling the OPTIMIZE_PARTITION_KEY_SCANS query option, which optimizes such queries. This feature is available in Impala 2.5 and higher. See OPTIMIZE_PARTITION_KEY_SCANS Query Option (Impala 2.5 or higher only) for the kinds of queries that this option applies to, and slight differences in how partitions are evaluated when this query option is enabled.

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:

-- How many rows total are in the table, regardless of NULL values?
select count(*) from t1;
-- How many rows are in the table with non-NULL values for a column?
select count(c1) from t1;
-- Count the rows that meet certain conditions.
-- Again, * includes NULLs, so COUNT(*) might be greater than COUNT(col).
select count(*) from t1 where x > 10;
select count(c1) from t1 where x > 10;
-- Can also be used in combination with DISTINCT and/or GROUP BY.
-- Combine COUNT and DISTINCT to find the number of unique values.
-- Must use column names rather than * with COUNT(DISTINCT ...) syntax.
-- Rows with NULL values are not counted.
select count(distinct c1) from t1;
-- Rows with a NULL value in _either_ column are not counted.
select count(distinct c1, c2) from t1;
-- Return more than one result.
select month, year, count(distinct visitor_id) from web_stats group by month, year;

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

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

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 COUNT() in an analytic context (that is, with an OVER() clause) to produce a running count of all the even values, then a running count 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,
  count(x) over (partition by property order by x) as 'cumulative count'
  from int_t where property in ('odd','even');
+----+----------+------------------+
| x  | property | cumulative count |
+----+----------+------------------+
| 2  | even     | 1                |
| 4  | even     | 2                |
| 6  | even     | 3                |
| 8  | even     | 4                |
| 10 | even     | 5                |
| 1  | odd      | 1                |
| 3  | odd      | 2                |
| 5  | odd      | 3                |
| 7  | odd      | 4                |
| 9  | odd      | 5                |
+----+----------+------------------+

select x, property,
  count(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 count |
+----+----------+------------------+
| 2  | even     | 1                |
| 4  | even     | 2                |
| 6  | even     | 3                |
| 8  | even     | 4                |
| 10 | even     | 5                |
| 1  | odd      | 1                |
| 3  | odd      | 2                |
| 5  | odd      | 3                |
| 7  | odd      | 4                |
| 9  | odd      | 5                |
+----+----------+------------------+

select x, property,
  count(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 count |
+----+----------+------------------+
| 2  | even     | 1                |
| 4  | even     | 2                |
| 6  | even     | 3                |
| 8  | even     | 4                |
| 10 | even     | 5                |
| 1  | odd      | 1                |
| 3  | odd      | 2                |
| 5  | odd      | 3                |
| 7  | odd      | 4                |
| 9  | odd      | 5                |
+----+----------+------------------+

The following examples show how to construct a moving window, with a running count 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). Therefore, the count is consistently 3 for rows in the middle of the window, and 2 for rows near the ends of the window, where there is no preceding or no following row in the partition. 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,
  count(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     | 2            |
| 4  | even     | 3            |
| 6  | even     | 3            |
| 8  | even     | 3            |
| 10 | even     | 2            |
| 1  | odd      | 2            |
| 3  | odd      | 3            |
| 5  | odd      | 3            |
| 7  | odd      | 3            |
| 9  | odd      | 2            |
+----+----------+--------------+

-- Doesn't work because of syntax restriction on RANGE clause.
select x, property,
  count(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.

Note:

By default, Impala only allows a single COUNT(DISTINCT columns) expression in each query.

If you do not need precise accuracy, you can produce an estimate of the distinct values for a column by specifying NDV(column); a query can contain multiple instances of NDV(column). To make Impala automatically rewrite COUNT(DISTINCT) expressions to NDV(), enable the APPX_COUNT_DISTINCT query option.

To produce the same result as multiple COUNT(DISTINCT) expressions, you can use the following technique for queries involving a single table:

select v1.c1 result1, v2.c1 result2 from
  (select count(distinct col1) as c1 from t1) v1
    cross join
  (select count(distinct col2) as c1 from t1) v2;

Because CROSS JOIN is an expensive operation, prefer to use the NDV() technique wherever practical.

Related information:

Impala Analytic Functions