This guide walks operators through the most frequent reasons Docker containers fail to start—such as image issues, misconfigurations, resource limits, permission problems, network errors, and volume mishaps—and provides a step‑by‑step command checklist to diagnose and fix each scenario.
The article explains that observability goes beyond simple monitoring by combining metrics, logs, and traces to pinpoint where and why a system issue occurs, especially in microservice and cloud‑native environments, and stresses the importance of correlating data rather than merely collecting more.
This guide shows how to extend an existing Prometheus‑Grafana‑Loki stack with SkyWalking to capture full request traces in Spring Boot, explaining trace fundamentals, automatic instrumentation, manual spans, log‑trace correlation, cross‑service topology, and production considerations.
This guide walks operators through a systematic, step‑by‑step process for diagnosing Docker container startup failures, covering status checks, log inspection, detailed use of docker inspect, and categorized troubleshooting of image, configuration, resource, permission, network, and volume issues with concrete commands and examples.
This article reviews the 2026 observability landscape, outlines selection principles, compares open‑source and commercial solutions for data collection, storage, alerting and event management, and discusses how AI is reshaping monitoring and AIOps practices.
This guide shows Linux administrators step‑by‑step how to locate reboot timestamps, retrieve full reboot histories, examine log files, analyze kernel and crash logs, check service and resource issues, and investigate human or scheduled actions, enabling fast root‑cause diagnosis of unplanned server restarts.
This article provides a comprehensive guide to MySQL’s three essential logs—redo, undo, and binlog—detailing their hierarchy, purposes, write mechanisms, configuration parameters, and how they cooperate during transaction processing and replication, while also offering troubleshooting tips for common issues.
Logs, tracing, and metrics each serve distinct monitoring goals—logs capture discrete events for debugging and audit, traces map request flows to pinpoint performance bottlenecks, and metrics provide time‑series health data; understanding their differences and integrating tools like ELK, OpenTelemetry, Prometheus, and Grafana enables robust observability.
This article provides a deep dive into MySQL’s architecture, covering the InnoDB storage engine’s layered structure, index design and usage, query execution plans, deep pagination challenges, slow‑query diagnostics, transaction processing, MVCC isolation, and the roles of undo, redo and binary logs.
This article introduces SigNoz, a self‑hosted open‑source observability platform that unifies metrics, logs, and traces, outlines its core capabilities, shows how to install it with Docker, and compares its resource efficiency to commercial solutions like DataDog and Elastic.
This article explains Docker's logging mechanisms, lists supported log drivers, shows how to inspect and truncate large json-file logs with shell scripts, and demonstrates using max-size and max-file options to prevent uncontrolled log growth and free disk space.
This article explains MySQL’s internal architecture, covering how data pages, B+‑tree and hash indexes, the Buffer Pool, Adaptive Hash Index, Change Buffer, Undo/Redo logs, the InnoDB storage engine, and the server layer work together to provide fast, reliable CRUD operations and support replication.
This article explains how to build a robust observability system for cloud‑native microservice architectures, detailing the three core signals—metrics, traces, and logs—common challenges such as complexity and data silos, and presents Yanhuang Data’s integrated platform with unified data collection, storage, analysis, and visualization solutions.
This article explains how YHP enables cloud‑native systems to achieve comprehensive observability by defining the three core signals—metrics, traces, and logs—addressing common enterprise pain points, and presenting an integrated platform that unifies data collection, storage, analysis, and visualization for efficient fault diagnosis and performance monitoring.
This guide walks you through ten practical steps for diagnosing Kubernetes problems, from POD startup failures and resource limits to network connectivity, storage configuration, container logs, DNS resolution, and final troubleshooting tips, helping you keep your clusters stable and reliable.
When a Kubernetes pod repeatedly crashes and the container keeps restarting, the standard kubectl logs command may miss the previous instance's output, but using the --previous flag lets you fetch logs from the last terminated container by reading the symlinked files under /var/log/pods.
When a pod crashes and continuously restarts, using kubelet logs may fail to capture output, but the kubectl --previous flag lets you access the logs of the previous container instance by reading the linked log files stored under /var/log/pods.
When a pod crashes and continuously restarts, kubelet logs may miss the output, but using the kubectl --previous flag lets you access the logs of the prior container instance by reading the symlinked files under /var/log/pods, enabling effective troubleshooting of Kubernetes crashes.
This article explains how to assess hardware resources, calculate required Elasticsearch cluster size based on data volume, and perform indexing and search benchmark tests to ensure stable performance and optimal throughput for log and metric workloads in production environments.
This article explains how to streamline TiDB 6.0 fault diagnosis by replacing manual screenshot and log collection with PingCAP's Clinic service and the TiUP Diag tool, covering data collection, upload procedures, security considerations, and additional diagnostic capabilities.
In this interview series, three industry experts explain how monitoring differs from observability, the shifts required for ops, developers, and architects, the core methodologies and technologies behind metrics, traces, and logs, and practical guidance for selecting and integrating observability tools in cloud‑native environments.
This article explains the challenges of long request chains in micro‑service architectures, reviews Google’s Dapper tracing requirements, introduces OpenTracing and OpenCensus standards, compares their strengths, and details how OpenTelemetry unifies tracing, metrics and logs with practical integration steps and best‑practice guidance.
Learn a systematic approach to diagnosing Kubernetes core component issues by identifying faulty nodes, analyzing logs via systemd or static pods, and leveraging Go's pprof tool for performance profiling, including step‑by‑step commands and UI visualizations for components like kube‑apiserver, scheduler, controller‑manager, and kubelet.
This guide walks you through the essential daily maintenance tasks for GaussDB T clusters, covering ETCD startup, cluster health checks, host resource monitoring, tablespace usage, abnormal wait events, log inspection, and common error troubleshooting with concrete commands and SQL examples.
This guide presents essential Docker log techniques—including displaying all logs, real‑time streaming, tailing, time‑based filtering, combining options, and redirecting output to files—to help developers quickly troubleshoot containerized applications.
This note distills Jay Kreps' extensive blog on logs, explaining their core role in distributed databases, real‑time data pipelines, replication, and state‑machine consistency, and showing how logs unify concepts from version control to streaming architectures.
This article provides a comprehensive overview of MySQL's architecture, detailing its database and instance components, various file types, configuration parameters, and the roles of different log files such as error, slow query, binary, and redo logs.
This comprehensive guide explains MySQL's various log types, how to view and configure error, general, slow, and binary logs, and details essential server variables for effective database administration and troubleshooting.
This article explains how logs—simple, append‑only, time‑ordered records—serve as the core abstraction behind databases, distributed systems, data integration pipelines, and modern stream‑processing platforms such as Kafka and Hadoop, illustrating their design, scalability, and practical challenges.
This article explains how logs—simple, append‑only, time‑ordered records—serve as the fundamental abstraction behind databases, distributed systems, data integration pipelines, and stream‑processing platforms like Kafka and Hadoop, illustrating their role in ordering, replication, scalability, and real‑time analytics.
