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

1 of 5Current: 0/5

What is the key difference between batch and stream processing?