This article explains the treap data structure—its BST and heap properties, random priority balancing, and implementation details—then dives into Go's runtime semaRoot treap, showing step‑by‑step enqueue and dequeue algorithms with code, rotations, and performance reasoning.
This article investigates why an InfluxDB 1.8 instance on a 32‑core, 64 GB server consumes over 58 GB of resident memory and generates heavy disk I/O, examines Go runtime memory accounting, uses system tools such as top, pmap, pprof and iostat for diagnosis, and presents configuration and runtime tweaks that reduce memory pressure and I/O load.
The article investigates why an InfluxDB 1.8 instance on a 32‑core, 64 GB production server consumes over 95% memory and generates heavy disk I/O, analyzes runtime statistics, pprof data, and Go memory‑release behavior, and presents configuration and runtime tweaks that reduce memory usage to ~55% and I/O load to acceptable levels.
This article explores the design principles, evolution, and implementation details of schedulers across operating systems, the Go programming language runtime, and Kubernetes, covering concepts such as task and resource allocation, cooperative vs. preemptive scheduling, work‑stealing, priority algorithms, and the latest scheduling framework extensions.
