0 views collected around this technical thread.
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 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 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.
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.