COUNT function
An aggregate function that returns the number of rows, or the number of
nonNULL
rows.
Syntax:
COUNT([DISTINCT  ALL] expression) [OVER (analytic_clause)]
Depending on the argument, COUNT()
considers rows that meet certain conditions:

The notation
COUNT(*)
includesNULL
values in the total. 
The notation
COUNT(column_name)
only considers rows where the column contains a nonNULL
value. 
You can also combine
COUNT
with theDISTINCT
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. See the OPTIMIZE_PARTITION_KEY_SCANS query
option topic 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 for details about using complex types in Impala.
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 generalpurpose 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 nonNULL 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;
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.