Welcome to Day 28 of the Spark Mastery Series.
Today we tackle the biggest fear in streaming systems:
Jobs that work fine initially… then crash after hours or days.
This happens because of state mismanagement.
Let’s fix it.
🌟 Why Streaming Is Harder Than Batch
Batch jobs:
- Start
- Finish
- Release memory
Streaming jobs:
- Never stop
- Accumulate state
- Must self-clean
Without cleanup → failure is guaranteed.
🌟 Watermark Is Your Lifeline
Watermark controls:
- How late data is accepted
- When old state is removed
No watermark = infinite memory usage.
🌟 Choosing the Right Trigger
Triggers define:
- Latency
- Cost
- Stability
Too fast → expensive
Too slow → delayed insights
Most production jobs use 10–30 seconds.
🌟 Output Mode Matters More Than You Think
Complete mode rewrites entire result every batch.
This:
- Increases state
- Increases CPU
- Increases cost
Use append/update wherever possible.
🌟 Monitoring Is Mandatory
A streaming job without monitoring is a ticking bomb.
Always monitor:
- State size
- Batch duration
- Input rate
- Processing rate
🚀 Summary
We learned:
- What streaming state is
- Why state grows
- How watermark bounds state
- Trigger tuning
- Output mode impact
- Checkpoint best practices
- Monitoring strategies
Follow for more such content. Let me know if I missed anything. Thank you!!
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