How Can I Aggregate Consecutive Duplicate Rows in SQL with Start and End Times?

When working with log data in SQL, particularly when it includes consecutive duplicate entries, it’s common to face the challenge of efficiently summarizing this data. Recently, I came across this task where I needed to compress consecutive duplicates into single rows that show the start and end time of the duplicate sequence, as well as the count of occurrences. Here’s a step-by-step guide on how I approached this problem, why the initial attempts were not successful, and the solution that finally worked.

Initial Attempts and Issues

Firstly, let’s discuss the initial SQL queries I tried and identify why they didn’t yield the desired results.

1. Using GROUP BY Alone:

SELECT MIN(TimeStamp) AS StartTime, MIN(TimeStamp) AS EndTime, ColumnA, ColumnB, ColumnC, ColumnD, ColumnE, COUNT(*)
FROM table
GROUP BY ColumnA, ColumnB, ColumnC, ColumnD, ColumnE
HAVING COUNT(*) > 1

This query attempts to group the table by all columns other than TimeStamp and then select the minimum TimeStamp as both the start and end time. The issue here is that GROUP BY combines all identical rows across the whole table, disregarding whether they are consecutive or not. Thus, non-consecutive duplicates also get merged, resulting in inaccurate start and end times.

1. Using PARTITION BY Incorrectly:

SELECT * FROM (
  SELECT MIN(TimeStamp) AS StartTime, MAX(TimeStamp) AS EndTime, ColumnA, ColumnB, ColumnC, ColumnD, ColumnE,
  ROW_NUMBER() OVER (PARTITION BY ColumnA, ColumnB, ColumnC, ColumnD, ColumnE ORDER BY TimeStamp) AS RowNum
  FROM table
) d

The intention here was to order and rank the rows partitioned by the specified columns. However, this approach didn’t help in identifying consecutive rows or in determining the correct number of occurrences. The ROW_NUMBER() function assigns a unique row number within each partition of the result set, but doesn’t help in aggregating or summarizing data as needed.

Working Solution: Using the Tabibitosan Method

The key challenge here is to identify and group only consecutive rows. This can be achieved using the “tabibitosan” method, which involves creating a helper column to detect changes in consecutive duplicate rows. Here’s how it’s done:

WITH MarkedRows AS (
  SELECT
    TimeStamp,
    ColumnA,
    ColumnB,
    ColumnC,
    ColumnD,
    ColumnE,
    ROW_NUMBER() OVER (ORDER BY TimeStamp) - 
    ROW_NUMBER() OVER (PARTITION BY ColumnA, ColumnB, ColumnC, ColumnD, ColumnE ORDER BY TimeStamp) AS GroupID
  FROM
    table
),
GroupedData AS (
  SELECT
    MIN(TimeStamp) AS StartTime,
    MAX(TimeStamp) AS EndTime,
    ColumnA,
    ColumnB,
    ColumnC,
    ColumnD,
    ColumnE,
    COUNT(*) AS NumOccur
  FROM
    MarkedRows
  GROUP BY
    ColumnA, ColumnB, ColumnC, ColumnD, ColumnE, GroupID
)
SELECT * FROM GroupedData;

Explanation:

1. MarkedRows CTE:

• We calculate two ROW_NUMBER() values: one for all rows ordered by TimeStamp (i.e., giving each row a unique identifier) and another for rows partitioned by the content columns (ColumnA to ColumnE), also ordered by TimeStamp.

• Subtracting these two values gives a unique GroupID for each set of consecutive duplicates because any change in the sequence of duplicates results in different row numbers.

1. GroupedData CTE:

• We then group by the GroupID (along with the content columns), capturing the minimum and maximum TimeStamp and counting occurrences for each group.

This method allows us to compress consecutive duplicates and calculate both their duration (from start time to end time) and count of occurrences effectively. Through this detailed and structured approach, my SQL query provided a clear and concise summary of log data, respecting the boundaries of consecutive sequences.