1. Introduction

A Fixed Window (sometimes called a tumbling window) is a windowing strategy that partitions a data stream into non-overlapping, equal-sized time intervals (such as every 5 minutes, 1 hour, or 24 hours).

2. Why This Concept Exists

In streaming pipelines, data arrives continuously forever. If you want to compute statistics like "total purchases per hour," you need to segment the infinite timeline into distinct buckets. Fixed Windows are the simplest and most common way to group streaming events into clean, predictable time boxes.

3. Key Terminology

Window Size: The duration of each time interval (e.g. 5 minutes).
Tumbling / Non-overlapping: An event belongs to exactly one window. There is no overlap between adjacent windows.
Window Boundary: The start and end time of a window (e.g., 12:00:00 to 12:04:59).

4. How It Works

Define size: Set the window duration in seconds.
Assign: As events arrive, Beam inspects their Event timestamp.
Map: Beam assigns the element to the corresponding interval block.
Aggregate: Subsequent aggregations (like sum or average) are computed independently inside each time block.

5. Visual Diagram

Each incoming event is assigned to a single, static time slice:

Window 1 (12:00 - 12:05)

Event 12:01Event 12:03

Window 2 (12:05 - 12:10)

Event 12:06Event 12:09

Events are partitioned into discrete, non-overlapping intervals.

6. Code Example

Calculating transactions totals over 10-minute fixed intervals:

python
import apache_beam as beam
from apache_beam.transforms.window import FixedWindows

with beam.Pipeline() as p:
    (p
     | "ReadTransactions" >> beam.io.ReadFromPubSub(subscription="projects/my-gcp/subs/transactions")
     | "ParseAmounts" >> beam.Map(lambda msg: float(msg.attributes.get("amount", 0)))
     | "ApplyFixedWindows" >> beam.WindowInto(FixedWindows(10 * 60)) # 10 minutes
     | "SumTransactions" >> beam.CombineGlobally(sum).without_defaults()
     | "LogTotals" >> beam.Map(print))

7. Code Explanation

beam.WindowInto(FixedWindows(10 * 60)) divides the stream into 600-second (10-minute) windows.
CombineGlobally(sum) aggregates the parsed values per window.
without_defaults() prevents empty window firings if no elements arrive during the interval.

8. Real Production Example

In network traffic monitoring, a pipeline windows cellular event feeds into 1-minute fixed windows to compute the rate of failed call attempts per tower, alerting operators if failures spike in any single minute.

9. Common Mistakes

Forgetting to set timestamp attributes: If your data source does not inject event timestamps, Beam defaults to the ingestion time (processing time), which makes it impossible to run accurate backfills.
Assuming ordering across windows: Output results from windows are processed as separate independent collections and can arrive downstream out of sequence.

10. Best Practices

Use Fixed Windows for standard dashboard metrics (e.g. active users per day, server hits per hour).
Always configure an Allowed Lateness policy if your network experiences latencies to avoid discarding delayed events.

11. Summary

Fixed windows segment timelines into equal, non-overlapping intervals.
Assigned based on Event timestamps.
Outputs are calculated independently per time slice.

12. Interactive Challenges

Challenge 1: Daily Log Rotator (Beginner)

Write a windowing transform statement that partitions an incoming stream of server logs into 24-hour fixed window buckets.

Challenge 2: Hourly Transaction Counter (Intermediate)

Write a pipeline segment inside a context block that reads purchase events from Pub/Sub, maps them to key-value tuples of (store_id, amount), windows them into 1-hour fixed windows, and sums the transactions per store per hour.

Challenge 3: Fixed Windows with Allowed Lateness (Advanced)

Write a windowing segment that applies a 15-minute fixed window to input elements, but allows late-arriving data for up to 3 minutes after the watermark passes.

Fixed Windows