This article explains how Linux memory watermarks, kswapd, direct reclaim, and the OOM Killer interact, provides detailed code examples, shows real‑world case studies, and offers practical tuning steps—including kernel parameters, cgroup limits, and monitoring tools—to prevent system stalls and crashes.
This article explains Linux swap fundamentals, detailing how the kernel uses swap partitions or files, the role of the swappiness parameter in balancing cache reclamation versus anonymous page swapping, the operation of kswapd, and the impact of memory watermarks on reclamation decisions.
This article provides a comprehensive overview of real-time stream processing, covering data streams, window types, event and processing time, Flink's operator model, watermark mechanisms, and strategies for handling out-of-order and late data to ensure accurate, timely analytics.
This article explains how to define time characteristics, assign timestamps and watermarks, use Flink's window API, implement custom process functions, side outputs, and triggers, and handle late events, providing both Scala and Java code examples for real‑time stream processing.
This article extends the concepts introduced in Streaming 101 by deeply exploring data processing patterns for unbounded data, covering windowing, watermarks, triggers, accumulation modes, and their practical implications for building robust low‑latency streaming pipelines.
This article explains Linux swap fundamentals, the role of swappiness, kswapd behavior, memory watermarks, and related kernel parameters, providing practical guidance on configuring swap priority, zone reclaim, and other settings to optimize system performance under various workloads.
