Kafka vs Redpanda Architecture🔗

🔹 What is Apache Kafka?🔗

Kafka is the most popular distributed event streaming platform (open source, from LinkedIn originally, now under Apache).
It stores and streams records (events) in topics.
It requires a cluster of brokers, ZooKeeper (legacy) or KRaft (newer) for metadata management, and usually has separate dependencies like JVM, OS tuning.
Kafka is known for high throughput, fault tolerance, and ecosystem support (Connectors, Streams, ksqlDB).

🔹 What is Redpanda?🔗

Redpanda is a Kafka API-compatible event streaming platform (drop-in replacement for Kafka).
You can use the same Kafka clients, but under the hood it has a different architecture:
Written in C++ (Kafka is Java/Scala).
No ZooKeeper (built-in Raft consensus).
Optimized for modern hardware (NVMe SSDs, fast CPUs).
Lower operational overhead (single binary, no JVM tuning).

🔹 High-level Similarities🔗

Feature	Kafka	Redpanda
API	Kafka API	Kafka API (100% compatible)
Concepts	Topics, Partitions, Producers, Consumers	Same
Use cases	Event streaming, ETL, real-time analytics	Same
Ecosystem	Kafka Connect, ksqlDB, Kafka Streams	Works with same tools

👉 From an application developer’s perspective, Redpanda is Kafka. But architecturally, they diverge a lot.

🔹 Detailed Architectural Differences🔗

1. Language & Runtime🔗

Kafka: Written in Java/Scala, runs on the JVM. Needs tuning (GC, heap sizes, etc.).
Redpanda: Written in C++, runs natively. No JVM/GC overhead. Lower latency, more predictable performance.

2. Metadata Management🔗

Kafka (Legacy): Uses ZooKeeper for metadata (cluster state, topics, partitions, configs).
Kafka (New): Moving to KRaft mode (Kafka Raft), but still evolving.
Redpanda: Always used Raft consensus internally (no ZooKeeper ever). Metadata is embedded → simpler operations.

3. Storage Engine🔗

Kafka: Uses log segments stored on disk. Relies on Linux page cache for performance. Requires tuning of log cleaner, retention policies.
Redpanda: Custom storage engine built with Seastar framework. Direct I/O to NVMe SSDs, bypasses page cache. Optimized for zero-copy reads/writes.

4. Threading Model🔗

Kafka: Traditional thread-per-connection model. Needs locks → more context switching, harder scaling under high concurrency.
Redpanda: Uses Seastar’s shard-per-core model (each CPU core runs independently, event-driven, no locks). Extremely efficient on modern multicore CPUs.

5. Deployment & Operations🔗

Kafka:
Needs multiple services (brokers + ZooKeeper).
Requires JVM tuning, OS tuning, storage tuning.
Typically runs with Confluent or other management platforms.
Redpanda:
Single binary, no external dependencies.
Lower ops overhead, easier for small teams.
Cloud-native (Kubernetes-friendly).

6. Performance & Latency🔗

Kafka: Good throughput, but higher latency (ms → tens of ms) under load due to JVM GC and thread model.
Redpanda: Lower latency (sub-ms to few ms), especially with SSD/NVMe, because of C++ + Seastar.

7. Ecosystem Features🔗

Kafka: Has mature ecosystem → Kafka Connect, Kafka Streams, ksqlDB.
Redpanda: Compatible with most Kafka tools, plus extras like Redpanda Console (UI for debugging streams), WASM engine for in-line stream transforms.

🔹 Visual Difference🔗

Kafka (Classic):

Producer → Broker (JVM, log segments) → ZooKeeper → Consumer

Redpanda:

Producer → Redpanda Broker (C++, Raft, Seastar, native storage) → Consumer

👉 Kafka has more moving parts; Redpanda is simpler, leaner, faster.

🔹 When to Choose What?🔗

✅ Kafka (Apache/Confluent)

If you need the mature ecosystem (Kafka Streams, Connectors, ksqlDB).
If your org already has Kafka expertise.
Large-scale legacy deployments.

✅ Redpanda

If you want Kafka API without Kafka ops pain.
If low latency and simplicity are critical (e.g., trading, fraud detection, IoT).
If you’re cloud-native, Kubernetes-heavy, or want to avoid JVM tuning.

👉 In short:

Kafka = battle-tested, ecosystem-rich, but heavier to run.
Redpanda = Kafka reimagined for modern hardware: faster, simpler, no ZooKeeper, no JVM.

Great follow-up 🙌 — Seastar is at the heart of why Redpanda performs so differently from Kafka. Let’s break it down carefully.

🔹 What is Seastar?🔗

Seastar is a high-performance, open-source C++ framework for building asynchronous, event-driven applications.
It was originally created by ScyllaDB (a high-performance NoSQL database that replaced Cassandra) and is now used by Redpanda for streaming.
Seastar’s design principle:
Exploit modern hardware (multi-core CPUs, NVMe SSDs, high-throughput networks).
Avoid traditional multithreading pitfalls (locks, context switching, kernel overhead).

🔹 The Thread-per-Core Model🔗

In traditional multithreaded apps (like Kafka’s JVM model):

Many threads run across CPU cores.
Threads often compete for resources (memory, I/O).
Coordination requires locks and synchronization, which create contention.
The OS scheduler context-switches threads → overhead increases as concurrency rises.

👉 This becomes a bottleneck for very high-throughput systems.

Seastar’s Approach (Thread-per-Core):

Each CPU core runs its own independent shard of the application.
A shard handles its own memory, I/O, and data — no shared state, no locks.
If data needs to move between shards, Seastar uses explicit message passing (like an internal network).
Each shard runs in a single-threaded event loop, processing tasks asynchronously (like Node.js, but per-core and highly optimized).

So instead of threads competing, each core is fully utilized and works independently.

🔹 Storage with Seastar🔗

Seastar integrates with modern storage (NVMe SSDs, high-bandwidth disks) using:

Direct I/O (bypassing OS page cache):
Kafka relies on the Linux page cache to buffer disk I/O.
Redpanda (via Seastar) uses direct disk access with async I/O, avoiding double-buffering.
This reduces kernel overhead and improves predictability of latency.

Got it — let’s break this down step by step.

1. How Kafka handles disk I/O🔗

Kafka writes its logs to disk, but it doesn’t directly manage the disk I/O.
Instead, it uses the Linux OS page cache:
When Kafka writes data, the OS writes it into memory first (the page cache).
Later, the OS flushes it to disk.
Similarly, when Kafka reads, the OS might serve it from the cache instead of the physical disk.
✅ Advantage: simple, no need for Kafka to manage buffers.
❌ Downside:
Data gets double buffered (once in Kafka’s memory, again in the OS cache).
Extra kernel/user-space context switches.
Latency can be unpredictable because flushing is controlled by the OS, not Kafka.

2. How Redpanda handles disk I/O🔗

Redpanda uses Seastar, a high-performance C++ framework designed for low-latency workloads.
Instead of relying on the OS page cache, it uses Direct I/O:
Data is written directly to disk (or read directly), bypassing the page cache.
Seastar manages its own memory for buffering and caching.
✅ Advantages:
No double-buffering (only one copy in Redpanda’s memory).
Lower kernel overhead because fewer syscalls and context switches.
Predictable latency — Redpanda decides when data is flushed, not the OS.

3. The key tradeoff🔗

Kafka: simpler, leverages the OS, but introduces variability.
Redpanda: more complex (self-manages I/O), but yields higher throughput and consistent low latency because it avoids surprises from the kernel’s page cache flush policy.

🔑 In short:

Kafka → “I trust Linux to handle disk I/O efficiently.” (But OS adds overhead + jitter).
Redpanda → “I’ll handle my own I/O, bypass the OS page cache.” (More control, more predictability).

Do you want me to also show this with a diagram of the data path (Kafka vs Redpanda I/O flow) so it’s crystal clear?

Zero-Copy Data Path:
Events are read/written directly from disk/network buffers without unnecessary copies in user space.
Example: A message can move from disk → network without extra CPU copy overhead.
Shard-local Storage:
Each shard (CPU core) manages its own chunk of logs.
Parallelism is achieved by letting each core handle its own partitions.
This is why Redpanda can fully saturate NVMe SSD throughput.

🔹 Why It’s Powerful🔗

No Locks → Removes contention bottlenecks.
No Context Switching → No wasted cycles by the OS.
NUMA Awareness → Each shard/core uses memory local to it (faster access).
Full Hardware Utilization → Each core is “its own Kafka broker,” so scaling cores = scaling throughput.
Predictable Latency → No GC pauses (like Kafka’s JVM) and no kernel page cache unpredictability.

🔹 Analogy🔗

Kafka (JVM + thread pools): Imagine a busy restaurant where all waiters share one notebook for orders. They constantly fight for access, scribble, and waste time.
Redpanda (Seastar): Each waiter (CPU core) has their own notebook, serves their own tables (partitions), and only passes notes to others when absolutely necessary. Way faster and smoother.

🔹 Example in Redpanda🔗

When Redpanda ingests events:

Producer sends data → assigned to a partition.
That partition is “owned” by one shard (CPU core).
The shard writes it directly to its NVMe segment via async I/O.
Replication across nodes is handled via Raft consensus, also shard-local.
Consumer reads → served by the same shard → zero-copy stream out.

Let’s break that line down carefully:

1. What a shard is🔗

In Redpanda (via Seastar), the application is built around shards, which are like lightweight execution units.
Each shard is pinned to a CPU core, so it handles all work (networking, scheduling, disk I/O) without locking or context switches.
This is different from Kafka, where threads can float across cores and share resources.

2. NVMe segment🔗

Data in Redpanda is stored in segments (just like Kafka log segments).
But Redpanda is designed to map each shard to a slice of the log → so a shard writes only its own portion of the log to disk.
These segments are placed on NVMe SSDs (very fast storage designed for parallelism).
This allows the shard to take advantage of NVMe’s native capability: lots of independent parallel I/O queues.

3. Async I/O (Direct I/O)🔗

Instead of using the OS page cache and blocking syscalls (write(), fsync()), Redpanda does async direct I/O:
The shard issues a non-blocking write request straight to NVMe.
The kernel does not buffer the data in the page cache.
Completion is signaled via an event (polling or completion queue).

✅ Benefits:

No double buffering.
No thread blocking → the shard keeps processing other requests.
Full use of NVMe parallelism (each shard can queue operations independently).
Latency predictability: flush happens when Redpanda decides, not when the OS decides.

Putting it together🔗

👉 “The shard writes it directly to its NVMe segment via async I/O” means: A CPU-core–pinned shard in Redpanda takes incoming data, and instead of passing it through the OS page cache, it sends it straight to its assigned log segment on an NVMe SSD using non-blocking (asynchronous) disk operations. This ensures low latency, avoids kernel overhead, and fully exploits NVMe hardware parallelism.

👉 The result: low-latency streaming with minimal CPU overhead.

✅ In short:

Seastar gives Redpanda its lock-free, thread-per-core architecture.
This enables direct, async NVMe I/O and full CPU utilization.
Compared to Kafka’s JVM/thread model, Redpanda achieves lower latency, higher throughput, and simpler ops.

Kafka `fysnc` model🔗

Perfect — let’s contrast Kafka’s fsync model vs Redpanda’s async direct I/O model:

🔹 1. Kafka (fsync + page cache)🔗

Write path:
Producer sends message → Kafka broker writes it into a user-space buffer.
Kafka calls write() → data goes to the Linux page cache, not directly to disk.
Data sits in the cache until:
- The kernel decides to flush it, or
- Kafka explicitly calls fsync() to force persistence.
Characteristics:
Simpler, because Linux handles buffering, flushing, batching.
Double buffering: message exists in Kafka’s memory buffer and page cache.
Flush timing is partially up to the OS → latency jitter.
Works fine for HDDs/SSDs, but can’t fully exploit NVMe’s parallel queues.

🔹 2. Redpanda (async direct I/O via Seastar)🔗

Write path:
Producer sends message → shard (CPU-core–pinned task) buffers it in its own memory.
Shard issues async direct I/O request straight to its NVMe segment.
NVMe handles writes via parallel I/O queues, completion events notify the shard.
Characteristics:
No OS page cache involvement → no double buffering.
No fsync() blocking calls → everything is non-blocking, shard never stalls.
Each shard gets its own log segment, mapped neatly onto NVMe queues → parallelism scales linearly with cores.
Much more predictable latency, since flush happens when Redpanda wants it, not when the OS decides.

🔹 3. Comparison at a glance🔗

Aspect	Kafka (fsync + page cache)	Redpanda (async direct I/O)
I/O Path	User buffer → Page cache → Disk	Shard buffer → Direct NVMe write
Buffering	Double (app + page cache)	Single (app buffer only)
Flush Control	Kernel + fsync	Application (shard)
Latency	Variable (depends on OS flush)	Predictable (app-controlled)
Parallelism	Limited (threads + OS queues)	High (per-core shards + NVMe queues)
Overhead	Syscalls, kernel memory mgmt	User-space async I/O, no blocking