This article guides ops engineers and development leads through three essential questions—architecture suitability, team capability, and cost‑benefit analysis—to determine whether migrating to Kubernetes adds real value or just extra complexity.
This article details Alibaba's decade‑long evolution of its real‑time computing platform, the massive operational challenges of managing Flink clusters at million‑core scale, and the comprehensive strategies—including SLA metrics, self‑healing services, cloud‑native redesign, and job‑level advisory tools—used to ensure stability, cost efficiency, and performance during peak events like Double‑11.
This article introduces AZ Mover, a lightweight HDFS client‑side tool that intelligently scans, schedules, and migrates block replicas across multiple availability zones, detailing its design goals, core workflow, command‑line options, concurrency controls, and future enhancements for robust big‑data disaster recovery.
This guide explains how to optimize Kubernetes API Server performance by configuring the max-mutating-requests-inflight limit and watch-cache-size, offering recommended values for different cluster sizes, monitoring metrics, and step‑by‑step adjustment strategies for stable, high‑throughput clusters.
This article explains how K3s's new embedded container image feature lets Kubernetes nodes share OCI images peer‑to‑peer, eliminating external registry dependence and speeding up distribution in offline, low‑bandwidth, or geographically dispersed environments.
The article explains that the kube‑apiserver’s built‑in LoopbackClient certificate expires after one year, causing API server failures in long‑running clusters, and examines the community’s support policies, recent Alibaba Cloud changes, and ongoing discussions about introducing a true Kubernetes LTS.
This article analyzes Elasticsearch shard imbalance incidents, explains the built‑in shard balancing algorithm and its configuration parameters, demonstrates weight calculations with source code, and proposes practical improvements—including shard count adjustments and a custom load‑aware balancing tool—to achieve more effective cluster load distribution.
This guide compiles over thirty practical Kubernetes troubleshooting steps, covering pod startup failures, networking issues, resource bottlenecks, node abnormalities, cluster‑wide service problems, and detailed explanations of common container exit codes to help operators quickly diagnose and resolve issues.
The article examines a 7‑node Elasticsearch cluster with 500 million documents, uncovering excessive heap usage, high OS memory pressure, numerous deleted documents, large translog, low query‑cache hit rate, and an over‑sharded design, then offers concrete tuning and redesign recommendations to restore performance.
This article walks through a real‑world Kubernetes incident where a node ran out of space due to Ceph storage inconsistencies and cgroup leaks, detailing step‑by‑step diagnostics, Ceph repair commands, pod eviction, node reboot, and post‑mortem recommendations for cluster operations.
Facing severe IO-wait and read bottlenecks as product data grew from tens of millions to billions, this article analyzes root causes in Elasticsearch clusters and presents a comprehensive solution involving index parameter tuning, merge settings, translog async writes, query optimizations, and hardware upgrades to restore performance and stability.
This article walks through essential Elasticsearch node‑level APIs—covering how to retrieve basic node info, detailed statistics, thread‑pool usage, and hot‑thread diagnostics—complete with request examples, response samples, and practical tips for diagnosing common cluster issues.
This article explains the Kubernetes pod lifecycle, the meaning of the Terminating state, detailed pod creation and deletion processes, and the eviction mechanisms of both kube‑controller‑manager and kubelet, offering troubleshooting steps and best practices to resolve pods that remain stuck in Terminating.
This article examines the technical challenges of scaling Kafka clusters to handle millions of partitions—including Zookeeper node explosion, replication overhead, controller recovery latency, and broker restart delays—and proposes solutions such as parallel ZK fetching, metadata synchronization via internal topics, logical cluster composition, and physical cluster splitting.
This article explains the metadata, controller, and Zookeeper challenges of supporting a million‑plus Kafka partitions and presents practical solutions such as parallel ZK fetching, metadata‑via‑topic redesign, logical cluster assembly, and physical cluster splitting to achieve large‑scale, stable Kafka deployments.
During a rolling upgrade of an Elasticsearch cluster, stopping nodes—especially the master—can block write requests, cause client connection failures, trigger master re‑election, and lead to temporary data duplication, making it essential to understand the shutdown sequence and its impact on read/write operations.
The article explains why operating massive Kubernetes clusters is as challenging as building large systems, outlines three critical stability questions, shares real‑world data collection, visualization, and tooling practices, and provides concrete recommendations for high‑availability, monitoring, and performance optimization.
The article shares practical experience on operating massive Kubernetes clusters, focusing on three stability questions, data collection and visualization, and a suite of operational tools to achieve reliable, high‑availability services in production environments.
This article shares practical experience and best‑practice guidelines for operating large‑scale Kubernetes clusters, covering stability checks, component failure impact, recovery strategies, alerting mechanisms, data collection, visualization, and the suite of operational tools that help ensure reliable, high‑performance cloud‑native infrastructure.
