Old Dragonfly replication design: one big Set or Hash held a single lock for its entire flush. Even while waiting on the network. Every other write on that core queued up behind it. New design: tagged chunks. Large entries split into tagged pieces. Other keys keep flushing while a big one is still mid-transfer. The replica reassembles by stream ID. Benchmark: 200K-entry sets, live writes the whole time. Throughput: 2x P99 latency: down up to 87% Writes completed during sync: up about 9x One blocked fiber used to stall a whole core. Now it doesn't. Full writeup below. dragonflydb.io/blog/how-drag…

Three years ago we made a bet. Our co-founder Roman had been a principal engineer on the ElastiCache team at AWS, and he'd reached a conclusion: no amount of tuning would get Redis past its wall on large workloads. The limit was architectural, not a matter of effort.  The bet was to start over. Do multi-threaded from the ground up, fully Redis-compatible, with an architecture that makes massive scale effortless. For that bet to be worth anything, workloads had to keep growing past what a single-threaded engine could hold.  Three years later, this is truer than we expected. AI is only accelerating that trend. When we started, a large workload was 100 GB. Then a terabyte. Now we're supporting teams using 100’s of TB, and they're no longer the exception.  On September 16 we're putting our whole story together in one place, at Dragonfly Ascent. Register now to hear to the latest: - What three years of hardening led to: 60% more throughput, 30% less memory, and the end of OOM-on-snapshot - See how our customers are deploying Dragonfly in production, and what they’ve learned along the way - Where Dragonfly is headed as feature stores, embeddings, and agent traffic push workloads into next phase of data consumption Register today: hubs.la/Q04rJGGY0 See you September 16th.

We’re breaking the memory barrier! 🚀 Introducing data tiering in Dragonfly, extending RAM with SSDs to handle massive datasets at a fraction of the cost. Read the announcement, deep dive, and benchmarks vs. ElastiCache. #DataInfrastructure #DataTiering hubs.la/Q03VQXLD0

🚀 One week until #P99CONF! Our developer advocate @joezhou_df is joining the stage as well. Huge thanks to @ScyllaDB for hosting! If you’re into high-performance, low-latency, AI-native data systems, this is where you need to be. #AI #Databases #ML #RealTimeData #Scalability

The full Modern Data Infrastructure Summit playlist is live 🎥 Talks from the organizations leading the way - exploring what “modern” really means for #RealTime, #AI-driven, #CloudNative systems. #MDISummit #DataInfra Watch the full playlist! hubs.la/Q03MzQh80

Every dollar counts, but not equally. With Dragonfly Cloud, you can buy 42% more capacity and 193% higher write throughput than #ElastiCache on a 100GB instance. See the full breakdown and maximize your cloud spend today: hubs.la/Q03tS5HC0 #AWS #CloudComputing #Valkey

Network security leading provider (and our happy customer) @abusix recently doubled their data capacity with Dragonfly Cloud to keep pace with rapid growth. No downtime, no headaches. Just reliable performance and seamless scaling. hubs.la/Q03qVZk-0 #DataMigration

Our CTO @romanger joined @seanfalconer and @alexbdebrie for an episode of @SoftwareHuddle! Learn how Dragonfly's multi-threaded architecture delivers significantly higher throughput than #Redis and the challenges of high-scale caching. youtube.com/watch?v=gw453p6E… #DragonflyDB youtube.com/watch?v=gw453p6E…

In this video, @BetterStackHQ Developer Advocate James Shopland sits down with @romanger, co-founder and CTO of #DragonflyDB, to discuss why he left #AWS to build a high-performance #Redis alternative. Check it out! youtu.be/tXdDddCgGnE?si=MEOT…

Announcing #Dragonfly Swarm — the first in-memory data solution that seamlessly scales both vertically and horizontally. Say goodbye to #Redis Cluster headaches and hello to limitless scale. Read our blog to learn more! dragonflydb.io/blog/announci…