Stream Processing
Master real-time data processing patterns for continuous, low-latency data analytics
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🔥 Stream Processing Performance Calculator
📊 Performance Metrics
Actual Throughput: 66,666.667 msg/sec
End-to-End Latency: 3550.0 ms
Messages in Window: 500,000
Estimated Memory: 488.3 MB
System Availability: 99.9%
🚨 System Health
⚠️ Backpressure detected! Consider:
- Adding more consumers
- Increasing partitions
- Optimizing processing logic
Stream Processing Fundamentals
📊 Batch vs Stream
Batch Processing:
- Processes data in chunks
- Higher latency, higher throughput
- Scheduled execution
- Complete data view
Stream Processing:
- Processes data continuously
- Low latency, real-time
- Event-driven execution
- Partial data view
⚡ Key Characteristics
- Low Latency: Sub-second processing times
- High Throughput: Millions of events per second
- Fault Tolerance: Handles failures gracefully
- Scalability: Horizontal scaling capabilities
- Exactly-Once: Guarantees processing semantics
- State Management: Maintains processing state
Stream Processing Architecture
🔄 Processing Pipeline
Data Sources
IoT, Logs, APIs
→
Stream Ingestion
Kafka, Pulsar
→
Stream Processing
Flink, Storm, Spark
→
Data Sinks
DB, Cache, Analytics
🎯 Windowing
Time-Based Windows:
- Tumbling: Fixed, non-overlapping
- Sliding: Fixed size, overlapping
- Session: Variable, gap-based
Count-Based Windows:
- Fixed number of events
- Sliding count windows
🔄 Processing Guarantees
At-Most-Once:
May lose messages, no duplicates
At-Least-Once:
No message loss, may duplicate
Exactly-Once:
No loss, no duplicates (hardest)
⚖️ Backpressure Handling
Buffering: Temporary storage
Dropping: Discard excess data
Blocking: Slow down producers
Load Shedding: Selective dropping
Auto-scaling: Dynamic resources
Popular Stream Processing Frameworks
🌊 Apache Flink
True stream processing with low latency and exactly-once semantics
Key Features:
- Event time processing
- Savepoints and checkpoints
- Complex event processing
- SQL interface
- State management
Use Cases:
- Real-time fraud detection
- Live recommendations
- IoT data processing
⚡ Apache Kafka Streams
Library for building stream processing applications on Kafka
Key Features:
- Native Kafka integration
- Exactly-once semantics
- Windowing operations
- Stream-table joins
- Interactive queries
Use Cases:
- Event-driven microservices
- Real-time analytics
- Stream transformations
🔥 Apache Spark Streaming
Micro-batch processing with unified batch and stream API
Key Features:
- Unified API (batch + stream)
- Fault tolerance
- Integration with Spark ecosystem
- Structured streaming
- Machine learning support
Use Cases:
- ETL pipelines
- Real-time ML inference
- Data lake processing
🌪️ Apache Storm
Distributed real-time computation system
Key Features:
- Real-time processing
- Fault-tolerant
- Scalable
- Language agnostic
- Guaranteed message processing
Use Cases:
- Real-time analytics
- Online machine learning
- Continuous computation
Real-World Applications
🏦 Financial Services
Fraud Detection:
Real-time transaction monitoring and anomaly detection
Risk Management:
Live risk calculations and position monitoring
Algorithmic Trading:
High-frequency trading with microsecond latency
🛒 E-commerce
Real-time Recommendations:
Personalized product suggestions based on user behavior
Inventory Management:
Live stock updates and demand forecasting
Price Optimization:
Dynamic pricing based on market conditions
🏭 IoT & Manufacturing
Predictive Maintenance:
Equipment health monitoring and failure prediction
Quality Control:
Real-time defect detection and process optimization
Supply Chain:
Live tracking and logistics optimization
📱 Social Media & Gaming
Live Feeds:
Real-time content updates and notifications
Content Moderation:
Automated detection of inappropriate content
Live Analytics:
Real-time engagement metrics and trending topics
Stream Processing Best Practices
✅ Design Principles
- • Design for failure and recovery
- • Use idempotent operations
- • Implement proper monitoring
- • Plan for backpressure handling
- • Choose appropriate windowing
- • Optimize serialization formats
- • Consider event ordering requirements
⚠️ Common Pitfalls
- • Not handling late-arriving data
- • Ignoring memory management
- • Over-complicated windowing logic
- • Insufficient error handling
- • Poor partitioning strategy
- • Not testing at scale
- • Inadequate security measures
Performance Optimization
🚀 Throughput Optimization
- • Increase parallelism
- • Batch processing where possible
- • Optimize serialization
- • Use appropriate data structures
- • Minimize network calls
⏱️ Latency Reduction
- • Reduce batch sizes
- • Optimize data locality
- • Use faster serialization
- • Minimize checkpointing
- • Optimize JVM settings
💾 Memory Management
- • Use off-heap storage
- • Implement proper cleanup
- • Monitor memory usage
- • Optimize state size
- • Use compression
📝 Stream Processing Knowledge Quiz
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