
Using Salting to Lower Latency for Large Blobs in ScyllaDB
A modified salting technique that cuts P99 write latency 22x for large blobs
https://www.scylladb.com/2026/06/25/using-salting-to-lower-latency-for-large-blobs-in-scylladb/

A modified salting technique that cuts P99 write latency 22x for large blobs
https://www.scylladb.com/2026/06/25/using-salting-to-lower-latency-for-large-blobs-in-scylladb/
How ScyllaDB is using per-tablet Raft groups to bring strong consistency to data, without sacrificing the parallelism that makes it fast
https://www.scylladb.com/2026/06/24/raft-strong-consistency/
Many performance metrics and system parameters are inherently volatile or fluctuate rapidly. When using a monitoring system that periodically “scrapes” (polls) a target for its current metric value, the collected data point is merely a snapshot of the system’s state at that precise moment. It doesn’t reveal much about what’s actually happening in that area. Sometimes it’s possible to overcome this problem by accumulating those values somehow – for example, by using histograms or exporting a derived monotonically increasing counter. This article suggests yet another way to extend this approach for a broader set of frequently changing parameters.