Switching tile formats on a LiDAR ingestion pipeline cut output object count by roughly 25x at the same data volume, independent of compression. Here is why object count is its own cost and performance axis on object storage, and what to check before you scale a pipeline that writes many small files.
MinIO site replication has no cross-site quorum, and its replication queue exists only inside the source cluster: lose that site and the un-replicated backlog is gone and un-enumerable. Sync mode costs latency without fixing that. The precise model, the RTO/RPO table, and the DR patterns that match real SLAs.
MinIO has no NameNode. It puts the namespace on XFS as real directories and xl.meta files. On Lots of Small Files that means you can exhaust inodes while df -h still looks fine, and a flat leaf can stall PUT, LIST, scanner, and ILM together. Here is the on-disk model, the inode math, and a prefix recipe that keeps XFS inside a regime you can operate.
MinIO fixed the HDFS NameNode limit, but it has no index: it writes one xl.meta per object on every drive of the erasure set. On 5 servers of 12 NVMe, MinIO picks a 15-wide set by default, so each small object is stored 15 times over. A worked sizing that looks fine for two years and dies in days.
Sail is an open-source Apache Spark replacement written in Rust. It drops the JVM, speaks Spark Connect, and runs 4 to 6 times faster than Spark with native accelerators on ClickBench. A deep dive into its architecture, benchmarks, and production readiness.
Most AI infrastructure teams spend 35-60% more on GPU compute than they need to. The cause isn’t cloud pricing. It is architecture, and it is fixable.
Spark sets executor CPU requests equal to limits, so a Kubernetes cluster reserves twice the CPU it uses and refuses to schedule pending pods. Kubernetes has no native overcommit. Here is the mutating-webhook operator I use to fix it.
Two MinIO platforms, same root cause: used as a NoSQL store. Field notes on LIST IOPS, XFS directory limits, scanner & heal SLAs, the erasure-coding storage-efficiency inversion on small objects, and why Apache Cassandra (or Ceph) is the right answer on-prem in 2026.
When Tesla published patent WO2024073080 describing a new file format internally called “.smol”, the headline was simple: 4x reduction in IOPS for AI training. Most people read this as a hardware story. It isn’t. It’s a data architecture story. And it exposes a structural weakness in how most enterprise data Read more
GPUs are no longer the bottleneck. Data movement is. I recently attended an online talk that stayed with me longer than most.Not because of a new GPU announcement, but because it clearly articulated something I have seen repeatedly over the years, across very different systems. That message, strongly emphasized by Read more