Finding Assign Group ID Based on Time Overlapping and IDs: A Step-by-Step Solution Using SQL

Understanding the Query: Finding Assign Group ID Based on Time Overlapping and ID

In this article, we’ll delve into a Stack Overflow question that involves finding assign group IDs based on time overlapping and IDs. We’ll break down the problem, understand the solution, and explore its underlying concepts.

Introduction to the Problem

The problem statement presents a scenario where we have data representing various activities with their start and end times. The task is to identify groups of activities that overlap in time and assign them a unique group ID. The group ID should be assigned based on the time overlapping pattern between consecutive activities.

Understanding the Solution

To tackle this problem, the provided solution employs several SQL techniques, including window functions, cumulative sums, and aggregations. We’ll break down each step of the solution and explain its purpose in detail.

Step 1: Identifying “Islands” using Cumulative Max of End Date

The first step involves identifying the “islands” of non-overlapping activities by taking a cumulative max of the end date (ignoring the current row). This is done using the following SQL:

WITH cte_StepOne as (
  SELECT id,
         START_TIME,
         END_TIME,
         LAG(Start_TIME, 1) OVER (ORDER BY id) AS LagStart_TIMEValue,
         LAG(END_TIME, 1) OVER (ORDER BY id) AS LagEND_TIMEValue
  FROM [ACTIVITY]
)

This CTE calculates the lagged start and end times for each activity. The cumulative max of the end date is then used to identify non-overlapping activities.

Step 2: Assigning Group IDs using Cumulative Sum

The second step involves assigning group IDs to all islands by using a cumulative sum. This is achieved through the following SQL:

, cte_result as (
  SELECT id,
         START_TIME,
         END_TIME,
         LagStart_TIMEValue,LagEND_TIMEValue,
         CASE 
             WHEN START_TIME between LagStart_TIMEValue AND LagEND_TIMEValue  or 
               END_TIME between LagStart_TIMEValue AND LagEND_TIMEValue 
               and   ID=LAG(ID, 1) OVER (partition by id ORDER BY id)
                THEN ID 

              WHEN LagStart_TIMEValue IS NULL and LagEND_TIMEValue is null THEN ID
            ELSE  id+1 
             END AS OverLapID
  FROM cte_StepOne)

This CTE assigns group IDs to activities based on the time overlapping pattern. If a row overlaps with the previous row, it uses the previous row’s ID; otherwise, it increments the ID by 1.

Step 3: Assigning Group IDs using Row Number

The final step involves assigning group IDs to all islands using row numbers. This is achieved through the following SQL:

, cte_result1 as (
  SELECT id,
         START_TIME,
         END_TIME,
         LagStart_TIMEValue,LagEND_TIMEValue,
         CASE 
            WHEN LagStart_TIMEValue IS NULL and LagEND_TIMEValue is null THEN OverLapID

             WHEN START_TIME between LagStart_TimeValue AND LagEnd_TimeValue  or 
               END_TIME between LagStart_TimeValue AND LagEnd_TimeValue 
           and   OverLapID=LAG(OverLapID, 1) OVER (partition by OverLapID ORDER BY id)
            THEN OverLapID 

         WHEN START_TIME &gt; LagStart_TimeValue AND END_TIME &gt;LagEND_TimeValue  
           and   OverLapID=LAG(OverLapID, 1) OVER (partition by OverLapID ORDER BY id)
            THEN OverLapID +1 

                 WHEN START_TIME &gt; LagStart_TimeValue AND END_TIME &lt;LagEND_TimeValue  
           and   OverLapID=LAG(OverLapID, 1) OVER (partition by OverLapID ORDER BY id)
            THEN OverLapID +1 


             WHEN LagStart_TimeValue &gt; START_TIME and  LagEND_TimeValue &gt;END_TIME  
           and   OverLapID=LAG(OverLapID, 1) OVER (partition by OverLapID ORDER BY id)
            THEN OverLapID+1


             WHEN LagStart_TimeValue &lt; START_TIME and  LagEND_TimeValue &lt;END_TIME  
           and   OverLapID=LAG(OverLapID, 1) OVER (partition by OverLapID ORDER BY id)
            THEN OverLapID


        ELSE  OverLapID
         END AS OverLapID
  FROM cte_result)

This CTE assigns group IDs to activities based on the time overlapping pattern. It uses row numbers to ensure that each group ID is unique.

Step 4: Aggregating Results

The final step involves aggregating the results by grouping the activities with the same start and end times. This is achieved through the following SQL:

SELECT id, min(start), max(end),
       row_number() over (order by id, min(start)) as group_id
FROM (
  select t.*,
         sum(case when max_end >= start then 0 else 1 end) over (partition by id order by start) as grp
      from (
        select t.*
             max(end) over (partition by id order by start rows between unbounded preceding and 1 preceding) as max_end
          from t
         ) t
     ) t
group by id, grp
order by id, min(start);

This CTE aggregates the results by grouping activities with the same start and end times. It uses row numbers to ensure that each group ID is unique.

Conclusion

In this article, we explored a Stack Overflow question that involves finding assign group IDs based on time overlapping and IDs. We broke down the solution into four steps: identifying “islands” using cumulative max of end date, assigning group IDs using cumulative sum, assigning group IDs using row numbers, and aggregating results.

The final answer uses a combination of window functions, cumulative sums, and aggregations to assign unique group IDs to activities based on their time overlapping patterns. The solution is efficient and effective in identifying groups of non-overlapping activities.

Note that the provided SQL code assumes that the id, start_time, and end_time columns exist in the t table. Additionally, the max_end column is used as a placeholder for the maximum end time of each group. You may need to modify the SQL code to suit your specific requirements.

Additional Notes

The provided solution uses several SQL techniques, including:

Window functions (LAG, SUM, and ROW_NUMBER)
Cumulative sums
Aggregations (GROUP BY)
Row numbers

These techniques are commonly used in data analysis and data science applications to solve complex problems.

In addition to the provided solution, there are other ways to approach this problem. Some alternative solutions may involve using different SQL techniques or technologies, such as:

Using a different grouping strategy
Using a different aggregation function (e.g., MAX, MIN, or AVG)
Using a different data structure (e.g., a graph database)

However, the provided solution is efficient and effective in identifying groups of non-overlapping activities.

Next Steps

If you’re interested in exploring more SQL techniques or solving similar problems, here are some next steps:

Practice solving other SQL problems on platforms like LeetCode, HackerRank, or CodeWars
Explore different SQL libraries and frameworks (e.g., Pandas, NumPy, or SQLAlchemy)
Delve deeper into data analysis and machine learning topics

Remember to always practice regularly and keep improving your SQL skills. Happy coding!

Last modified on 2024-02-22