Aiven's Diskless 2.0: A New Paradigm for Kafka Durability

Kafka just solved a problem that’s been hiding in plain sight. I was reading Kafka’s 80% Problem by Aiven, and halfway through it, I literally thought — why didn’t we do this earlier? Most Kafka setups today are overbuilt. We treat even small workloads like they’re enterprise-scale — multi-AZ clusters, local disks, 3x replicas — just to keep things “safe.” It works, but it’s far from efficient. Aiven’s new Diskless Kafka (Inkless) challenges that whole model. It finally decouples compute from storage, moving data to object storage and making brokers stateless. No more heavy local disks. No overprovisioning just to handle scale. What clicked for me is how simple the idea actually is — make Kafka cloud-native in spirit, not just in hosting. Stateless brokers that scale instantly, topics that can choose between classic or diskless modes, and cost savings that actually make sense. It’s one of those architectural shifts that feels obvious in hindsight — like, this is how Kafka should’ve evolved all along. If you’ve ever run Kafka and felt the pain of managing storage, scaling brokers, or balancing cost vs. reliability — you’ll relate to this. The future of Kafka really might be diskless. https://lnkd.in/grT2ze8a

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Explore how Redis evolved from a simple cache to a real-time data powerhouse—covering replication, sharding, streams, and consumer groups. Review the detailed technical analysis outlined in the PDF and examine the Redis integration with FastAPI on GitHub, which incorporates sophisticated caching techniques and real-time data handling patterns. Github repo - https://lnkd.in/gHnvD9TS

Creating #ApacheIceberg tables from #ApacheKafka is harder than most realize. Many teams try using #ApacheSpark jobs, micro-batching, or even #ZeroCopy approaches via tiered storage in their Kafka clusters. But each of these solutions comes with hidden costs: - High latency - Complex Spark infrastructure - Single-writer conflicts - Small file explosion - Manual compaction - …and more background jobs than anyone wants to manage The dream of a “zero-copy” #DataLake sounds good on paper. But in reality, coupling Iceberg table generation to your Kafka tiered storage usually means: - Compromised query performance - Operational headaches - Vendor lock-in - And sad Iceberg tables WarpStream just announced a purpose-built solution that finally makes Kafka-to-Iceberg ingestion fast, clean, and cloud-native. It works with ANY Kafka: #OpenSource, Confluent, #Amazon #MSK, WarpStream itself, and others. Think of Tableflow as a #MagicBox. Kafka goes in, Iceberg comes out. No more Spark pipelines, no more rube-goldberg jobs for compaction and cleanup. Just scalable, auto-managed streaming lakehouse pipelines that you can deploy in your own cloud account. And yes, it even keeps your tables compacted and fresh, without hurting your Kafka brokers or real-time performance. If you’re building #DataStreaming architectures or data platforms with #ApacheKafka and #ApacheIceberg, this is a big deal. Is Kafka-to-Iceberg table creation a challenge in your environment? How are you solving it today?

Confluent's Warpstream's Tableflow implementation for automating #Kafka streams to #Iceberg tables has just been released. Scalable, fast, and efficient, it's a great way to Tableflow functionality working wherever you need it. BYOC or on-prem with Confluent Platform.

Explore EloqDoc, a MongoDB-compatible document database, engineered for high-performance and elastic scalability. Unlike traditional setups, it leverages object storage as a primary component, significantly reducing infrastructure costs. Use Cases: - Web applications needing scalable document storage. - Content platforms requiring high throughput. Real-world applications: - Achieves 60% higher throughput than MongoDB Atlas in mixed workloads. - Maintains performance with local NVMe cache and object storage, avoiding IO bottlenecks. Learn more: https://lnkd.in/gpzYeubM #AI #Database #MongoDB #CloudComputing #DataManagement

Week 2 of my series "System Design in Action", where I build meaningful demos to explain real-world distributed systems concepts. 🔹 Consistent Hashing Ever wondered what really happens when a server is added to a distributed system like a cache or database? I built a tiny Go demo to see exactly that, comparing Consistent Hashing against the basic Naive Modulo Hashing. It’s a hands-on look at why modern distributed systems like DynamoDB, Cassandra, and Redis Cluster all rely on hash rings. What I Built: I created a command-line application in Go that implements two hashing algorithms: - A Consistent Hashing ring with support for virtual nodes to ensure balanced distribution. - A Naive Modulo Hasher to serve as a baseline for comparison. How I Tested It: I ran a script that simulated common real-world scenarios: 📈 Scaling Up: Adding a new server to an existing 3-node cluster. 💥 Node Failure: Removing a server to simulate an outage or decommissioning. For each scenario, the tool mapped 50,000 keys and calculated exactly how many had to be moved for each hashing method. My Findings: When scaling the cluster from 3 nodes to 4: ✅ Consistent Hashing: Only ~25% of keys were remapped. The disruption was minimal and localized, affecting only the keys that needed to move to the new node. ❌ Naive Hashing: A staggering ~75% of keys were remapped, triggering a catastrophic, system-wide data shuffle. This is why consistent hashing is critical for stability. In a real system, that massive data movement from naive hashing would mean widespread cache misses, a flooded network, and a huge performance hit. Consistent hashing keeps the system calm and predictable during changes. The code is on GitHub if you want to run the demo yourself! https://lnkd.in/enEeidj5 #SystemDesign #DistributedSystems #Go #Golang #Backend #ConsistentHashing #Scalability

Built an automation tool for a client that ended up growing to 15.2M+ records in MongoDB. At one point, even a simple findOne() took 2 minutes Then I discovered indexing - added the right indexes, the query time dropped to 0.1 seconds Later learned that indexing is actually powered by hash tables under the hood. Mind blown. Databases are pure magic when you start understanding how they work.

🧠 The Essence of Performance — Beyond Databases “Performance isn’t about one technology — it’s about where you put the bottleneck, and how you control it.” Over the past few days, I revisited a deceptively simple database problem — a “reservation system” that caps bookings at 3 slots. But what looked like a database question actually opened a deeper topic: the essence of performance and concurrency. Here are my core guiding principles when designing high-performance systems 👇 ⚙️ 1. Database is not your concurrency layer If your logic depends on SELECT … FOR UPDATE, you’ve already lost scalability. The DB should guarantee durability, not throughput. Concurrency must move outward — to memory, queues, and distributed coordination. ⚡ 2. Performance is about ownership of serialization You can’t avoid serialization — you just decide where to put it: • Inside the DB (slow, global) • In Redis (fast, but local) • Or at the Actor / partition level (isolated, distributed) The last one — actor-based single writers — is how modern high-concurrency systems scale without losing consistency. 🧩 3. Design for read-parallelism and write-isolation Let writes be serialized and predictable. Let reads fan out, cached, eventually consistent. Performance = control of contention, not elimination of it. 🧠 4. Observe first, optimize second Before tuning anything, trace where serialization naturally occurs — CPU queues, Redis locks, Kafka partitions, DB commits. Only after observing contention flow can you place the right boundaries. 🚀 5. State is asynchronous by nature Every distributed system is a delayed reflection of state. Once you accept that, you can design with confidence — with message queues, cache snapshots, and idempotency instead of fear of “race conditions.” I’m currently building a teaching-grade distributed reservation system on GitHub (FastAPI + Kafka + Redis + PostgreSQL). It demonstrates exactly these ideas — from Redis atomic ops to actor-based message processing. Stay tuned. I’ll share the repo soon. 🔥 💬 What’s your view on where serialization should live — DB, cache, or message layer? Would love to hear from engineers who’ve fought real-world concurrency dragons. #SystemDesign #DistributedSystems #PerformanceEngineering #FastAPI #Kafka #Redis #PostgreSQL #Architecture #Concurrency #Scalability