Apache Flink
What it is: Distributed stream processing framework. True real-time (not micro-batching). Stateful event-driven applications with exactly-once guarantees.
What It Does Best
True streaming. Processes events as they arrive. No micro-batching delays. Sub-second latency at scale.
Stateful processing. Maintain state across billions of events. Exactly-once semantics even with failures.
Event time processing. Handle out-of-order events correctly. Watermarks, late data, complex time windows.
Key Features
Exactly-once semantics: Guaranteed correctness even with failures
Event time processing: Handle out-of-order and late events correctly
Stateful operations: Windows, joins, aggregations with managed state
Savepoints: Versioned snapshots for upgrades and rollbacks
SQL support: Query streams with ANSI SQL via Flink SQL
Pricing
Open Source: Free, Apache 2.0 license (self-hosted)
AWS Kinesis Data Analytics: $0.11/hour per KPU (Kinesis Processing Unit)
Confluent Cloud: Pay-per-use streaming compute
Alibaba Cloud: Usage-based pricing for Realtime Compute
When to Use It
✅ Real-time event processing pipelines
✅ Complex event pattern detection
✅ Stateful stream transformations
✅ Continuous ETL and data enrichment
✅ Low latency requirements (milliseconds)
When NOT to Use It
❌ Batch processing (use Spark)
❌ Simple streaming (Kafka Streams simpler)
❌ Small team without stream expertise
❌ Infrequent processing (batch better)
❌ No ops team (complex to operate)
Common Use Cases
Real-time analytics: Continuous aggregations and dashboards
Fraud detection: Pattern matching on transaction streams
CEP (Complex Event Processing): Detecting patterns across event streams
Stream enrichment: Join streams with reference data in real-time
Alerting systems: Real-time anomaly detection and notifications
Flink vs Alternatives
vs Spark Streaming: Flink true streaming, Spark micro-batching (Flink lower latency)
vs Kafka Streams: Flink more powerful, Kafka Streams simpler and lighter
vs Storm: Flink has exactly-once and better state management
Unique Strengths
True streaming model: Not micro-batching, processes events immediately
Advanced state management: Large state with efficient checkpointing
Savepoints: Upgrade and rollback without data loss
Unified batch and streaming: Same API for both paradigms
Bottom line: Most advanced stream processing framework. More complex than Spark Streaming but truly real-time. Choose Flink for mission-critical streaming where latency matters. Steep learning curve, powerful results.