Article#4: Struggling with Time Series Data??? Master these Powerful SQL Techniques!??

Hello, World! (and Hello, SQL) ??

Let’s face it - Time series data can feel like trying to organize a toddler’s birthday party: chaotic, overlapping, and full of surprises. One minute, you’re trying to figure out when employees badge in and out of the office, and the next, you’re drowning in overlapping time intervals. If the phrase “maximum number of employees in the office at any given time” sends shivers down your spine, don’t worry - you’re not alone. ??

Luckily, this article walks you through 2 most powerful SQL techniques that i use when dealing with time series data, Cursor-Based Logic and Set-Based Logic, After reading this article you’ll be the hero of overlapping intervals and maybe even in your team! ????

The Problem: How Many People Are in the Office? ??

Picture this: your office has a badge system that tracks when employees come in and leave. The data looks something like below:

Your job is to answer a simple question: What’s the maximum number of people in the office at any given time?

It sounds simple, right? But then you realize that overlapping intervals make everything about as straightforward as solving a Rubik’s cube blindfolded. ?? This problem boils down to handling overlapping time intervals and determining the peak count during those overlaps.

Here’s how we’ll approach it:

1. Turn Badge Data into Events: Treat each sign-in as a +1 event and each sign-out as a -1 event.
2. Sort Events by Time: Process events chronologically to ensure you handle overlaps correctly.
3. Track a Running Total: Maintain a count of active employees in the office at each event time.
4. Find the Peak: Identify the highest count in the running total, which represents the maximum number of people in the office at any time.

Now, let’s explore two SQL methods - Cursor-Based and Set-Based Approaches to solve this problem efficiently! ??

Cursor-Based Approach

The cursor-based approach is procedural and processes session data row by row. It mimics how you might manually calculate the active sessions by iterating through events sequentially.

Steps:

• Sort all start and end events in chronological order.
• Use a cursor to iterate through the events sequentially.
• Adjust a running count:

a) Increment (+1) for session starts.

b) Decrement (-1) for session ends.

• Track the maximum count during the iteration.

Sample SQL:

WITH Events AS (
    -- Combine sign-in (+1) and sign-out (-1) events
    SELECT 
        SignInTime AS EventTime, 
        +1 AS EventType
    FROM EmployeeBadgeData
    UNION ALL
    SELECT 
        SignOutTime AS EventTime, 
        -1 AS EventType
    FROM EmployeeBadgeData
),
OrderedEvents AS (
    -- Order events by time
    SELECT 
        EventTime, 
        EventType
    FROM Events
    ORDER BY EventTime, EventType DESC
),
RunningTotal AS (
    -- Calculate the running total of employees in the office
    SELECT 
        EventTime,
        SUM(EventType) OVER (ORDER BY EventTime ROWS UNBOUNDED PRECEDING) AS EmployeesInOffice
    FROM OrderedEvents
)
-- Find the maximum number of employees in the office
SELECT 
    MAX(EmployeesInOffice) AS MaxEmployeesInOffice
FROM RunningTotal;        

The cursor-based approach processes data row by row. First, it combines all sign-in (+1) and sign-out (-1) events into a single dataset. Next, it sorts these events chronologically to ensure proper handling of overlaps. If two events occur at the same time, sign-outs are prioritized to prevent overcounting. A running total is maintained as the query iterates through each event, keeping track of the number of active employees in the office. Finally, the maximum value from the running total identifies the peak occupancy during the given time period.

Set-Based Approach

The set-based approach uses SQL’s declarative nature to process data in batches, avoiding the inefficiencies of row-by-row processing. It’s faster and more scalable, making it ideal for large datasets.

Steps:

• Treat session start and end times as events:

a) +1 for session starts.

b) -1 for session ends.

• Sort all events chronologically.
• Use a running total to maintain a count of active sessions.
• Identify the maximum count from the running total.

Sample SQL:

WITH Events AS (
    -- Combine sign-in (+1) and sign-out (-1) events
    SELECT 
        SignInTime AS EventTime, 
        +1 AS EventType
    FROM EmployeeBadgeData
    UNION ALL
    SELECT 
        SignOutTime AS EventTime, 
        -1 AS EventType
    FROM EmployeeBadgeData
)
-- Calculate the maximum running total using a window function
SELECT 
    MAX(SUM(EventType) OVER (ORDER BY EventTime ROWS UNBOUNDED PRECEDING)) AS MaxEmployeesInOffice
FROM Events;        

The set-based approach treats all events (sign-ins and sign-outs) as a single dataset and sorts them chronologically. SQL’s window functions calculate the running total of active employees in one pass, avoiding the inefficiencies of iterative processing. The maximum value from the running total identifies the busiest moment in the office.

Final Thoughts

Both Cursor-Based Logic and Set-Based Logic are effective ways to solve time series challenges. For smaller datasets or when you need step-by-step control, the cursor-based approach is a great choice. However, if you’re working with larger datasets or require high performance, the set-based approach is the way to go.

Now, when someone asks, “How many people were in the office at 11:00?”, you’ll not only have the answer but also the SQL skills to back it up ??????

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Happy querying! ??