What is Apache Kafka?
Apache Kafka is a distributed event streaming platform originally developed by LinkedIn, now maintained by the Apache Software Foundation. It's designed to handle high-throughput, real-time data feeds with fault tolerance and horizontal scalability.
2M+
Messages/sec capacity
<10ms
End-to-end latency
99.9%
Availability
PB
Data scale
Key Features & Use Cases
Core Capabilities
Distributed by Design
Horizontal scaling across multiple brokers
Fault Tolerant
Automatic failover and data replication
Persistent Storage
Configurable retention and replay capability
High Throughput
Millions of messages per second
Common Use Cases
Real-time data pipelines
Event-driven microservices
Stream processing and analytics
Log aggregation and monitoring
Change data capture (CDC)
Core Concepts
Topic
A category or feed name to which events are written
Key Features
- Ordered within partitions
- Immutable log
- Configurable retention
- Multi-producer/consumer
Example
user-events, order-updates, payment-notifications🧮 Kafka Cluster Calculator
Cluster Configuration
Performance Metrics
Throughput54,000 msg/sec
Storage/Day8,899 GB
Fault Tolerance1 failures
Can survive 1 broker failure(s)
Parallelism6x
Maximum consumer parallelism
Event Streaming Patterns
Event Sourcing
Store all changes as a sequence of events instead of current state
Implementation
topic: account-events, events: AccountCreated, MoneyDeposited, MoneyWithdrawn✅ Advantages
- Complete audit trail
- Temporal queries
- System replay capability
- Natural fit for Kafka
⚠️ Challenges
- Storage overhead
- Query complexity
- Snapshot overhead
- Learning curve
Performance & Best Practices
Producer Optimization
Batching
Group messages together to improve throughput
batch.size=16384, linger.ms=5Compression
Reduce network bandwidth usage
compression.type=snappyPartitioning
Distribute load evenly across partitions
partitioner.class=hash(key)Consumer Optimization
Fetch Size
Balance between latency and throughput
fetch.min.bytes=1, max.poll.records=500Parallel Processing
Scale consumers to match partitions
consumers = partitionsOffset Management
Handle failures gracefully
enable.auto.commit=false🏢 Real-world Implementations
LinkedIn: Activity Streams
• 1+ trillion messages per day
• 2000+ Kafka clusters
• User activity, feed updates, recommendations
• 7 million messages/second peak
Pattern: Event sourcing for user activity, CQRS for feed generation
Netflix: Stream Processing
• 700+ billion events per day
• Real-time recommendations
• A/B testing data pipeline
• 8+ petabytes of data daily
Pattern: Stream processing for real-time analytics and recommendations
Uber: Real-time Updates
• Ride tracking and ETAs
• Driver location updates
• Surge pricing calculations
• 100+ microservices coordination
Pattern: Event-driven microservices with real-time location streaming
Shopify: E-commerce Events
• Order processing pipeline
• Inventory updates
• Payment processing events
• 1+ million merchants supported
Pattern: Saga pattern for distributed transactions, CDC for data sync
💡 Key Takeaways
- • Start Simple: Begin with basic pub/sub, evolve to complex patterns as needed
- • Plan Partitions: More partitions = more parallelism, but also more complexity
- • Monitor Everything: Lag, throughput, error rates are critical metrics
- • Schema Evolution: Plan for backwards-compatible message formats
- • Operational Excellence: Kafka requires dedicated expertise to run at scale
📝 Apache Kafka Mastery Quiz
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