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VeltrixDB: High-Performance KV Storage We built VeltrixDB for bare-metal NVMe. By ditching LSM-trees for an In-memory Adaptive Radix Tree (ART) and append-only Value Log, we’ve eliminated compaction spikes. Stack: Linux io_uring + Direct I/O. Performance: Flat, microsecond latencies at billion-key scale. Trade-off: Optimized for pure IOPS & lock-free point reads (No range queries). Perfect for HFT, AdTech & extreme caching. Benchmarks: https://github.com/VeltrixDB/veltrixdb-benchmark
Hi Product Hunt! I’m Shubham, Co-founder of VeltrixDB.
The Problem: Managing huge key-value stores at scale is frustrating. You provision expensive, blazing-fast NVMe drives, but legacy LSM-tree databases (like RocksDB or Pebble) choke your hardware with heavy background compactions and massive write amplification. Write-stalls and latency spikes become inevitable.
The Solution: We wanted a storage engine that stops fighting the hardware. So, we threw out the LSM-tree entirely. VeltrixDB uses an in-memory Adaptive Radix Tree (ART) index paired with an append-only disk log. By utilising io_uring and O_DIRECT, we write straight to the NVMe at its physical limits—achieving zero background sorting, zero compaction stutter, and a strict ~1.0x write amplification.
The Trade-offs: I want to be brutally honest with developers—this isn't a drop-in SQL replacement. It is memory-bound: The entire index lives in RAM (~80 bytes/key). No range queries: It is built strictly for high-speed point lookups. Bare-metal only: It bypasses OS caches, meaning it will crawl on standard cloud EBS volumes.
It is a hyper-specialised scalpel built for AdTech (RTB), High-Frequency Trading, and extreme session caching where microsecond point-lookup speed is everything.
I’d love to answer any deep architectural questions or hear about the database scaling bottlenecks you are currently dealing with! Fire away in the comments.
VeltrixDB was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #15 on the daily leaderboard. VeltrixDB: High-Performance KV Storage We built VeltrixDB for bare-metal NVMe. By ditching LSM-trees for an In-memory Adaptive Radix Tree (ART) and append-only Value Log, we’ve eliminated compaction spikes. Stack: Linux io_uring + Direct I/O. Performance: Flat, microsecond latencies at billion-key scale. Trade-off: Optimized for pure IOPS & lock-free point reads (No range queries). Perfect for HFT, AdTech & extreme caching. Benchmarks: https://github.com/VeltrixDB/veltrixdb-benchmark
VeltrixDB was featured in Developer Tools (514k followers), Tech (625.6k followers) and Database (2.1k followers) on Product Hunt. Together, these topics include over 237.9k products, making this a competitive space to launch in.
Who hunted VeltrixDB?
VeltrixDB was hunted by Shubham sharma. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Shubham sharma
Hi Product Hunt! I’m Shubham, Co-founder of VeltrixDB.
The Problem: Managing huge key-value stores at scale is frustrating. You provision expensive, blazing-fast NVMe drives, but legacy LSM-tree databases (like RocksDB or Pebble) choke your hardware with heavy background compactions and massive write amplification. Write-stalls and latency spikes become inevitable.
The Solution: We wanted a storage engine that stops fighting the hardware. So, we threw out the LSM-tree entirely. VeltrixDB uses an in-memory Adaptive Radix Tree (ART) index paired with an append-only disk log. By utilising io_uring and O_DIRECT, we write straight to the NVMe at its physical limits—achieving zero background sorting, zero compaction stutter, and a strict ~1.0x write amplification.
The Trade-offs: I want to be brutally honest with developers—this isn't a drop-in SQL replacement.
It is memory-bound: The entire index lives in RAM (~80 bytes/key).
No range queries: It is built strictly for high-speed point lookups.
Bare-metal only: It bypasses OS caches, meaning it will crawl on standard cloud EBS volumes.
It is a hyper-specialised scalpel built for AdTech (RTB), High-Frequency Trading, and extreme session caching where microsecond point-lookup speed is everything.
For the tech-savvy folks, we've open-sourced our benchmarks here: https://github.com/VeltrixDB/veltrixdb-benchmark
I’d love to answer any deep architectural questions or hear about the database scaling bottlenecks you are currently dealing with! Fire away in the comments.