Flink is for true 'sub-second' streaming with complex state. Spark (Structured Streaming) is the choice for unified batch/stream processing with existing Spark talent.
Introduction
### The Streaming Purity Test
In the world of real-time data, there is a fundamental split: **Native Streaming** vs. **Micro-batching**.
**Apache Flink** is a native streaming engine. It processes every single event as it arrives. This allows for ultra-low latency (milliseconds) and sophisticated "stateful" processing (e.g., tracking a user's session over hours in real-time).
**Apache Spark (Structured Streaming)** uses a micro-batch model. It collects data for a few seconds and then processes it like a tiny batch job. While this is slightly slower (latencies of 1s+), it benefits from the massive Spark ecosystem and a unified API that works for both batch and streaming.
Feature Comparison
Feature
Apache Flink
Spark Streaming
Winner
Core Model
Native Streaming (Event-by-event)
Micro-batching
Apache Flink
Latency
Milliseconds (Ultra-low)
Seconds (Usually 1s+)
Apache Flink
State Management
Excellent (Native state backend)
Good (but more complex for long-lived state)
Apache Flink
Ease of Use
Lower (Steep learning curve)
High (Unified Spark API)
Spark Streaming
Community Support
Growing (Strong in China/US Enterprise)
Massive (De-facto standard)
Spark Streaming
✅ Apache Flink Pros
Truly sub-second latency for critical apps
Superior windowing (Session, Sliding, Tumbling)
Exact-once processing guarantees are robust
Excellent handling of 'out-of-order' data
⚠️ Apache Flink Cons
Harder to manage and monitor in production
Less integration with the broader data ecosystem
Requires dedicated streaming expertise
✅ Spark Streaming Pros
Unifies batch and streaming logic into one codebase
Massive library support (MLlib, GraphX)
Easier to hire and find documentation for
Better integration with Data Lakes (Delta Lake)
⚠️ Spark Streaming Cons
Micro-batching can lead to higher infra costs
Latency is fundamentally limited
State management can become a bottleneck at scale
Final Verdict
### Verdict
**Choose Apache Flink if:**
* Your use case is truly 'real-time' (fraud detection, ad-bidding, sensors).
* You have complex stateful logic spanning long time windows.
* Latency requirements are consistently under 500ms.
**Choose Spark Streaming if:**
* You already use Spark for batch processing and want to reuse logic.
* Human-scale latency (1-10 seconds) is acceptable.
* You want a unified platform for Analytics and Streaming.