COUNT

Returns the number of qualifying rows of a group of values from a windowed query. A windowed query is defined in terms of time or rows. For information about windowed queries, see Windowed Queries.

When you use COUNT, be aware of the following:

• If you don't use the OVER clause, COUNT is calculated as an aggregate function. In this case, the aggregate query must contain a GROUP BY clause on a monotonic expression based on ROWTIME that groups the stream into finite rows. Otherwise, the group is the infinite stream, and the query will never complete and no rows will be emitted. For more information, see Aggregate Functions.

• A windowed query that uses a GROUP BY clause processes rows in a tumbling window. For more information, see Tumbling Windows (Aggregations Using GROUP BY).

• If you use the OVER clause, COUNT is calculated as an analytic function. For more information, see Analytic Functions.

• A windowed query that uses an OVER clause processes rows in a sliding window. For more information, see Sliding Windows

Syntax

Tumbling Windowed Query

COUNT(number-expression) ... GROUP BY monotonic-expression | time-based-expression
     

Sliding Windowed Query

COUNT(* | ALL number-expression) OVER window-specification
     

Parameters

*

Counts all rows.

ALL

Counts all rows. ALL is the default.

number-expression

Specifies the value expressions evaluated for each row in the aggregation.

OVER window-specification

Divides records in a stream partitioned by the time range interval or the number of rows. A window specification defines how records in the stream are partitioned by the time range interval or the number of rows.

GROUP BY monotonic-expression | time-based-expression

Groups records based on the value of the grouping expression returning a single summary row for each group of rows that has identical values in all columns.

Examples

Example Dataset

The examples following are based on the sample stock dataset that is part of Getting Started in the Amazon Kinesis Analytics Developer Guide. To run each example, you need an Amazon Kinesis Analytics application that has the sample stock ticker input stream. To learn how to create an Analytics application and configure the sample stock ticker input stream, see Getting Started in the Amazon Kinesis Analytics Developer Guide.

The sample stock dataset has the schema following.

(ticker_symbol  VARCHAR(4),
sector          VARCHAR(16),
change          REAL,
price           REAL)
      

Example 1: Return the Number of Values Using the GROUP BY Clause

In this example, the aggregate query has a GROUP BY clause on ROWTIME that groups the stream into finite rows. The COUNT function is then calculated from the rows returned by the GROUP BY clause.

Using STEP (Recommended)

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
    ticker_symbol VARCHAR(4), 
    count_price     DOUBLE);

CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
  INSERT INTO "DESTINATION_SQL_STREAM" 
    SELECT STREAM 
        ticker_symbol,
        COUNT(Price) AS count_price
    FROM "SOURCE_SQL_STREAM_001"
    GROUP BY ticker_symbol, STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '60' SECOND);
      

Using FLOOR

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
    ticker_symbol VARCHAR(4), 
    count_price     DOUBLE);
-- CREATE OR REPLACE PUMP to insert into output
CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
  INSERT INTO "DESTINATION_SQL_STREAM" 
    SELECT STREAM 
        ticker_symbol,
        COUNT(Price) AS count_price
    FROM "SOURCE_SQL_STREAM_001"
    GROUP BY ticker_symbol, FLOOR("SOURCE_SQL_STREAM_001".ROWTIME TO MINUTE);
      

Results

The preceding examples output a stream similar to the following.

Example 2: Return the Number of Values Using the OVER Clause

In this example, the OVER clause divides records in a stream partitioned by the time range interval of '1' hour preceding. The COUNT function is then calculated from the rows returned by the OVER clause.

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
    ticker_symbol VARCHAR(4),
    count_price     DOUBLE);
CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
    INSERT INTO "DESTINATION_SQL_STREAM"
    SELECT STREAM ticker_symbol, 
        COUNT(price) OVER (
            PARTITION BY ticker_symbol
            RANGE INTERVAL '1' HOUR PRECEDING) AS count_price
    FROM "SOURCE_SQL_STREAM_001"
      

The preceding example outputs a stream similar to the following.

Usage Notes

Amazon Kinesis Analytics doesn't support the FILTER clause of the COUNT function or the use of COUNT DISTINCT in either aggregate functions or analytic functions. For more information on aggregate and analytic functions, see Aggregate Functions and Analytic Functions. This functionality is a departure from the SQL:2008 standard.

When used as an analytic function, COUNT returns zero if the window being evaluated contains no rows. For more information, see Analytic Functions. COUNT also returns zero for a PARTITION BY clause for which the partition within the window matching the input row contains no rows. For more information about PARTITION BY, see WINDOW Clause (Sliding Windows).

COUNT ignores null values from the set of values or a numeric expression. For example, each of the following return the value of 3:

• COUNT(1, 2, 3) = 3

• COUNT(1,null, 2, null, 3, null) = 3

Related Topics

• Windowed Queries

• Aggregate Functions

• GROUP BY clause

• Analytic Functions

• Getting Started Exercise

• WINDOW Clause (Sliding Windows)