Aggregate functions (General) , Window functions
Returns either the number of non-NULL records for the specified columns, or the total number of records.
Syntax¶Aggregate function
COUNT( [ DISTINCT ] <expr1> [ , <expr2> ... ] ) COUNT(*) COUNT(<alias>.*)
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Window function
COUNT( [ DISTINCT ] <expr1> [ , <expr2> ... ] ) OVER (
[ PARTITION BY <expr3> ]
[ ORDER BY <expr4> [ ASC | DESC ] [ <window_frame> ] ]
)
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For detailed window_frame syntax, see Window function syntax and usage.
expr1
A column name, which can be a qualified name (for example, database.schema.table.column_name).
expr2
You can include additional column name(s) if you wish. For example, you could count the number of distinct combinations of last name and first name.
expr3
The column to partition on, if you want the result to be split into multiple windows.
expr4
The column to order each window on. Note that this is separate from any ORDER BY clause to order the final result set.
*
Returns the total number of records.
When you pass a wildcard to the function, you can qualify the wildcard with the name or alias for the table. For example, to pass in all of the columns from the table named mytable, specify the following:
You can also use the ILIKE and EXCLUDE keywords for filtering:
ILIKE filters for column names that match the specified pattern. Only one pattern is allowed. For example:
EXCLUDE filters out column names that don’t match the specified column or columns. For example:
(* EXCLUDE col1) (* EXCLUDE (col1, col2))
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Qualifiers are valid when you use these keywords. The following example uses the ILIKE keyword to filter for all of the columns that match the pattern col1% in the table mytable:
(mytable.* ILIKE 'col1%')
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The ILIKE and EXCLUDE keywords can’t be combined in a single function call.
If you specify an unqualified and unfiltered wildcard (*), the function returns the total number of records, including records with NULL values.
If you specify a wildcard with the ILIKE or EXCLUDE keyword for filtering, the function excludes records with NULL values.
For this function, the ILIKE and EXCLUDE keywords are valid only in a SELECT list or GROUP BY clause.
For more information about the ILIKE and EXCLUDE keywords, see the “Parameters” section in SELECT.
alias.*
Returns the number of records that don’t contain any NULL values. For an example, see Examples.
Returns a value of type NUMBER.
Usage notes¶This function treats JSON null (VARIANT NULL) as SQL NULL.
For more information about NULL values and aggregate functions, see Aggregate functions and NULL values.
When this function is called as an aggregate function:
If the DISTINCT keyword is used, it applies to all columns. For example, DISTINCT col1, col2, col3 means to return the number of different combinations of columns col1, col2, and col3. For example, assume the data is:
1, 1, 1 1, 1, 1 1, 1, 1 1, 1, 2
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In this case, the function returns 2, because that’s the number of distinct combinations of values in the three columns.
When this function is called as a window function with an OVER clause that contains an ORDER BY clause:
A window frame is required. If no window frame is specified explicitly, the following implied window frame is used:
RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
For more information about window frames, including syntax, usage notes, and examples, see Window function syntax and usage.
Using the keyword DISTINCT inside the window function is prohibited and results in a compile-time error.
To return the number of rows that match a condition, use COUNT_IF.
When possible, use the COUNT function on tables and views without a row access policy. The query with this function is faster and more accurate on tables or views without a row access policy. The reasons for the performance difference include:
Snowflake maintains statistics on tables and views, and this optimization allows simple queries to run faster.
When a row access policy is set on a table or view and the COUNT function is used in a query, Snowflake must scan each row and determine whether the user is allowed to view the row.
The following examples use the COUNT function on data with NULL values.
Create a table and insert values:
CREATE TABLE basic_example (i_col INTEGER, j_col INTEGER);
INSERT INTO basic_example VALUES
(11,101), (11,102), (11,NULL), (12,101), (NULL,101), (NULL,102);
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Query the table:
SELECT *
FROM basic_example
ORDER BY i_col;
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+-------+-------+ | I_COL | J_COL | |-------+-------| | 11 | 101 | | 11 | 102 | | 11 | NULL | | 12 | 101 | | NULL | 101 | | NULL | 102 | +-------+-------+
SELECT COUNT(*) AS "All",
COUNT(* ILIKE 'i_c%') AS "ILIKE",
COUNT(* EXCLUDE i_col) AS "EXCLUDE",
COUNT(i_col) AS "i_col",
COUNT(DISTINCT i_col) AS "DISTINCT i_col",
COUNT(j_col) AS "j_col",
COUNT(DISTINCT j_col) AS "DISTINCT j_col"
FROM basic_example;
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+-----+-------+---------+-------+----------------+-------+----------------+ | All | ILIKE | EXCLUDE | i_col | DISTINCT i_col | j_col | DISTINCT j_col | |-----+-------+---------+-------+----------------+-------+----------------| | 6 | 4 | 5 | 4 | 2 | 5 | 2 | +-----+-------+---------+-------+----------------+-------+----------------+
The All column in this output shows that when an unqualified and unfiltered wildcard is specified for COUNT, the function returns the total number of rows in the table, including rows with NULL values. The other columns in the output show that when a column or a wildcard with filtering is specified, the function excludes rows with NULL values.
The next query uses the COUNT function with the GROUP BY clause:
SELECT i_col, COUNT(*), COUNT(j_col)
FROM basic_example
GROUP BY i_col
ORDER BY i_col;
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+-------+----------+--------------+ | I_COL | COUNT(*) | COUNT(J_COL) | |-------+----------+--------------| | 11 | 3 | 2 | | 12 | 1 | 1 | | NULL | 2 | 2 | +-------+----------+--------------+
The following example shows that COUNT(alias.*) returns the number of rows that don’t contain any NULL values. The basic_example table has a total of six rows, but three rows have at least one NULL value, and the other three rows have no NULL values.
SELECT COUNT(n.*) FROM basic_example AS n;
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+------------+ | COUNT(N.*) | |------------| | 3 | +------------+
The following example shows that JSON null (VARIANT NULL) is treated as SQL NULL by the COUNT function.
Create the table and insert data that contains both SQL NULL and JSON null values:
CREATE OR REPLACE TABLE count_example_with_variant_column ( i_col INTEGER, j_col INTEGER, v VARIANT);
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BEGIN WORK;
INSERT INTO count_example_with_variant_column (i_col, j_col, v)
VALUES (NULL, 10, NULL);
INSERT INTO count_example_with_variant_column (i_col, j_col, v)
SELECT 1, 11, PARSE_JSON('{"Title": null}');
INSERT INTO count_example_with_variant_column (i_col, j_col, v)
SELECT 2, 12, PARSE_JSON('{"Title": "O"}');
INSERT INTO count_example_with_variant_column (i_col, j_col, v)
SELECT 3, 12, PARSE_JSON('{"Title": "I"}');
COMMIT WORK;
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In this SQL code, note the following:
The first INSERT INTO statement inserts a SQL NULL for both a VARIANT column and a non-VARIANT column.
The second INSERT INTO statement inserts a JSON null (VARIANT NULL).
The last two INSERT INTO statements insert non-NULL VARIANT values.
Show the data:
SELECT i_col, j_col, v, v:Title
FROM count_example_with_variant_column
ORDER BY i_col;
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+-------+-------+-----------------+---------+
| I_COL | J_COL | V | V:TITLE |
|-------+-------+-----------------+---------|
| 1 | 11 | { | null |
| | | "Title": null | |
| | | } | |
| 2 | 12 | { | "O" |
| | | "Title": "O" | |
| | | } | |
| 3 | 12 | { | "I" |
| | | "Title": "I" | |
| | | } | |
| NULL | 10 | NULL | NULL |
+-------+-------+-----------------+---------+
Show that the COUNT function treats both the NULL and the JSON null (VARIANT NULL) values as NULLs. There are four rows in the table. One has a SQL NULL and the other has a JSON null. Both those rows are excluded from the count, so the count is 2.
SELECT COUNT(v:Title)
FROM count_example_with_variant_column;
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+----------------+ | COUNT(V:TITLE) | |----------------| | 2 | +----------------+
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