Here’s what happened when Netflix decided to remove Kafka from Tudum’s architecture—and what we can all learn from their decision:

Background: Tudum’s Original Architecture (CQRS with Kafka)

Tudum (Netflix’s fan site) initially implemented a CQRS (Command Query Responsibility Segregation) pattern.
Workflow:
1. Content writes (coming from CMS) were ingested and sent to Kafka.
2. A consumer transformed and stored it in Cassandra.
3. A caching layer served data to the UI for performance.
  This approach supported scalability and decoupling but came with latency issues—especially for real-time content previews.

The Problem: Latency & Inconsistent Previews

Despite caching, editors faced delays from several seconds to minutes for content previews.
Cache refresh intervals were sluggish and couldn't scale effectively as data grew.
Ultimately, the architecture’s reliance on multiple sequential I/O layers became a bottleneck.

Netflix’s Solution: RAW Hollow (In-Memory Object Store)

To eliminate latency and simplify architecture, Netflix developed RAW Hollow, a compact, compressed in-memory object store embedded within the microservices.

Key Benefits:

Low latency reads: Responses dropped from ~1.4s to ~0.4s.
Fast write visibility: Editors saw edits almost instantly.
Minimal memory footprint: Storage of three years’ content in about 130 MB.
Simplified stack: Removed Kafka, Cassandra, and caching layers—drastically reducing complexity and I/O operations.

Lessons Learned: When “Less is More”

Takeaway	Explanation
Match the tool to the problem	Kafka is powerful, but for a small, read-heavy, and highly dynamic dataset, an in-memory solution is more efficient.
Simplify for performance	Removing layers—especially I/O-dependent ones—can dramatically reduce latency.
Prefer consistency over complexity	RAW Hollow provides strong read-after-write guarantees without eventual consistency delays.
Domain-specific architectures win	A custom, lean solution tailored to the use case (Tudum’s editorial flow) works better than generic pipelines.

Summary

Netflix’s decision to remove Kafka from Tudum wasn’t about Kafka itself—it was about rethinking the stack to prioritize editor experience, speed, and simplicity. When every second counts during content previews, the right optimization isn't more complexity—it’s the right architecture.

Let me know if you'd like a visual architecture diagram comparing the old vs new design or a deeper deep dive into RAW Hollow!

Why Netflix Removed Kafka from Tudum’s Architecture ?