Rolling Time Window with Distinct Count in Big SQL

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In this article, we will explore how to achieve a rolling time window with distinct count in Big SQL for Infosphere BigInsights v3.0. The problem statement involves counting the number of distinct catalog numbers that have appeared within the last X minutes.

Background and Problem Statement

The question provides a sample dataset with columns row, starttime, orderNumber, and catalogNumb. The goal is to calculate the distinct count of catalogNumb for each row, but only considering the rows from the last 5 minutes. This means that we need to apply a rolling time window to the data.

The Big SQL query provided by the user includes various aggregate functions (count, avg, sum) over the catalogNumb column and other attributes, but it does not support the COUNT(DISTINCT) function, which is necessary for this problem.

Solution Overview

To solve this problem, we will employ a workaround using DB2’s DENSE_RANK() function. This function assigns a unique rank to each row based on a specific column (in this case, catalogNumb). We can then use the maximum value from these ranks to obtain the count of distinct values.

Step 1: Understanding DENSE_RANK()

DENSE_RANK() is an OLAP function that assigns a unique rank to each row based on the order of the values in the specified column. Unlike RANK(), which skips duplicate values, DENSE_RANK() includes duplicates and assigns consecutive ranks.

For example, given the following data:

The output of DENSE_RANK() would be:

Step 2: Using DENSE_RANK() to Count Distinct Values

To count the distinct values, we can take the maximum value from the dense_rank column. This is because each rank corresponds to a unique value.

Here’s an example query:

SELECT 
    catalogNumb,
    COUNT(*) as countCatalog,
    MAX(dense_rank) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS max_dense_rank
FROM 
    your_table_name
WHERE 
    starttime >= NOW() - INTERVAL 5 MINUTE;

In this query, we use MAX(dense_rank) to get the maximum rank for each unique value of catalogNumb. We also filter the data to only include rows from the last 5 minutes.

Step 3: Handling Duplicate Values

Since we’re using dense_rank(), duplicate values will not be included in the count. However, if you want to count the number of occurrences of each distinct value, you can modify the query as follows:

SELECT 
    catalogNumb,
    COUNT(*) as countCatalog,
    MAX(dense_rank) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS max_dense_rank
FROM 
    your_table_name
WHERE 
    starttime >= NOW() - INTERVAL 5 MINUTE;

This will give you the correct count of distinct values.

Step 4: Applying Aggregate Functions

Once you have obtained the countCatalog value, you can apply other aggregate functions (avg, sum) over the catalogNumb column and other attributes. For example:

SELECT 
    catalogNumb,
    COUNT(*) as countCatalog,
    AVG(orderNumber) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS avg_order_number,
    SUM(catalogNumb) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS sum_catalog_numb
FROM 
    your_table_name
WHERE 
    starttime >= NOW() - INTERVAL 5 MINUTE;

This will give you the average and sum of orderNumber and catalogNumb for each distinct value.

The final query can be combined into a single statement using UNION ALL or INTERSECT operators, depending on your specific requirements:

SELECT 
    catalogNumb,
    COUNT(*) as countCatalog,
    MAX(dense_rank) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS max_dense_rank
FROM 
    your_table_name
WHERE 
    starttime >= NOW() - INTERVAL 5 MINUTE

UNION ALL

SELECT 
    catalogNumb,
    AVG(orderNumber) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS avg_order_number,
    SUM(catalogNumb) OVER (PARTITION BY catalogNumb ORDER BY starttime DESC) AS sum_catalog_numb
FROM 
    your_table_name
WHERE 
    starttime >= NOW() - INTERVAL 5 MINUTE;

This will give you both the count of distinct values and the average and sum of other columns.

Conclusion

In conclusion, using DENSE_RANK() as a workaround for counting distinct values in Big SQL allows us to apply aggregate functions over the catalogNumb column and other attributes. This solution is particularly useful when dealing with duplicate values or requiring more complex calculations.

Last modified on 2024-06-20