Welcome to Day 25 of the Spark Mastery Series. Today we move from “reading streams” to real-time analytics.
This is where most streaming pipelines fail - not because of code, but because of state mismanagement.
Let’s fix that.
🌟 Why Streaming Aggregations Are Hard
Streaming data never ends.
If you aggregate without limits, Spark keeps data forever.
Result:
- Growing state
- Memory pressure
- Job crashes
🌟 Event Time Is Mandatory
Always use event time, not processing time.
Why?
- Processing time depends on delays
- Event time reflects real business time Correct analytics depend on event time.
🌟 Windows - Turning Infinite into Finite
Windows slice infinite streams into manageable chunks.
Example:
- Sales every 10 minutes
- Clicks per hour
- Orders per day
🌟 Watermarking — Cleaning Old State
Watermark tells Spark:
'You can forget data older than X minutes.'
This:
- Bounds memory usage
- Allows append mode
- Handles late data safely
🌟 Real-World Example
E-commerce
- Window: 5 minutes
- Watermark: 10 minutes
Meaning:
- Accept data late by 10 minutes
- Drop anything older This balances accuracy and performance.
🚀 Summary
We learned:
- Streaming aggregations
- Event time vs processing time
- Windowed analytics
- Tumbling & sliding windows
- Late data handling
- Watermarking
- Output modes
Follow for more such content. Let me know if I missed anything. Thank you
Top comments (0)