COUNT_DISTINCT_ITEMS_TUMBLING Function

Returns a count of the number of distinct items in the specified in-application stream column over a tumbling window. The resulting count is approximate; the function uses the HyperLogLog algorithm.

For more information, see HyperLogLog.

When you use COUNT_DISTINCT_ITEMS_TUMBLING, be aware of the following:

• When there are less than or equal to 10,000 items in the window, the function returns an exact count.

• Getting an exact count of the number of distinct items can be inefficient and costly. Therefore, this function approximates the count. For example, if there are 100,000 distinct items, the algorithm may return 99,700. If cost and efficiency is not a consideration, you can write your own SELECT statement to get the exact count.

  The following example demonstrates how to get an exact count of distinct rows for each ticker symbol in a five second tumbling window. The SELECT statement uses all of the columns (except ROWTIME) in determining the uniqueness.

  CREATE OR REPLACE STREAM output_stream (ticker_symbol VARCHAR(4), unique_count BIGINT); 
  
  
  CREATE OR REPLACE PUMP stream_pump AS 
  INSERT INTO output_stream 
  SELECT STREAM TICKER_SYMBOL, COUNT(distinct_stream.price) AS unique_count
    FROM (
      SELECT STREAM DISTINCT rowtime as window_time, 
  
        TICKER_SYMBOL,   
        CHANGE, 
        PRICE, 
        STEP((SOURCE_SQL_STREAM_001.rowtime) BY INTERVAL '5' SECOND) 
  
        FROM SOURCE_SQL_STREAM_001) as distinct_stream 
    GROUP BY TICKER_SYMBOL, 
        STEP((distinct_stream.window_time) BY INTERVAL '5' SECOND);
        
          

The function operates on a tumbling window. You specify the size of the tumbling window as a parameter.

Syntax

COUNT_DISTINCT_ITEMS_TUMBLING (

      in-application-streamPointer,
      'columnName',     
      windowSize
   )  

Parameters

The following sections describe the parameters.

in-application-streamPointer

Using this parameter, you provide a pointer to an in-application stream. You can set a pointer using the CURSOR function. For example, the following statement sets a pointer to InputStream.

CURSOR(SELECT STREAM * FROM InputStream)

columnName

Column name in your in-application stream that you want the function to use to count distinct values. Note the following about the column name:

• Must appear in single quotation marks ('). For example, 'column1'.

windowSize

Size of the tumbling window in seconds. The size should be at least 1 second and should not exceed 1 hour = 3600 seconds.

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: Approximate the number of distinct values in a column

The following example demonstrates how to use the COUNT_DISTINCT_ITEMS_TUMBLING function to approximate the number of distinct TICKER_SYMBOL values in the current tumbling window of the in-application stream. For more information about tumbling windows, see Tumbling Windows.

CREATE OR REPLACE STREAM DESTINATION_SQL_STREAM (
    NUMBER_OF_DISTINCT_ITEMS BIGINT);

CREATE OR REPLACE PUMP "STREAM_PUMP" AS 
   INSERT INTO "DESTINATION_SQL_STREAM"
      SELECT STREAM * 
      FROM TABLE(COUNT_DISTINCT_ITEMS_TUMBLING(
          CURSOR(SELECT STREAM * FROM "SOURCE_SQL_STREAM_001"),  -- pointer to the data stream
            'TICKER_SYMBOL',                                     -- name of column in single quotes
            60                                                   -- tumbling window size in seconds
      )
);        
      

The preceding example outputs a stream similar to the following: