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This article explains how to generate a unique traceId for each request, propagate it through Rest, RocketMQ, and Dubbo RPC modules, and configure Log4j2 (including custom Message, MessageFactory, and PatternConverter) to output the traceId so that logs across services can be correlated and read sequentially.
This guide explains how to set up distributed logging for microservices using Spring Cloud Sleuth for tracing, Zipkin as a trace UI, and the ELK stack (Elasticsearch, Logstash, Kibana) for log collection, storage, and visualization, including detailed configuration and code examples.
This article demonstrates how to set up distributed logging for Spring Cloud microservices using Sleuth for tracing and the ELK stack (Elasticsearch, Logstash, Kibana), covering dependency configuration, service registration, Logback setup, Logstash pipelines, and log querying in Kibana.
This guide explains how to set up distributed logging for microservices using Spring Cloud Sleuth, Zipkin, and the ELK stack, covering dependency configuration, service registration, Logstash and Kibana setup, logback-spring.xml customization, and log querying to trace inter‑service calls.
Tencent’s Eagle Eye distributed real‑time monitoring and log analysis platform was migrated to the cloud by rebuilding its LogSender and Kafka‑to‑ES components, switching to cloud CKafka and Elasticsearch, which boosted throughput fourfold, cut resource usage by about half, saved roughly 20 million RMB annually, and set the stage for further enhancements such as comprehensive monitoring and exactly‑once delivery.
This article details Bilibili's Billions log platform—from its fragmented origins and design goals to the elastic‑stack‑based architecture, shard management, log sampling, custom Go splitters, and monitoring enhancements—highlighting the challenges faced and the roadmap for future improvements.
This article describes the background, requirements, architecture choices, performance testing, and lessons learned from building a large‑scale, distributed log collection and analysis platform at Hujiang using Elasticsearch, Logstash, Kibana, Filebeat, and Kafka to handle billions of log entries daily.
The article chronicles Zhihu's growth from a two‑engineer startup using Python and Tornado on a single Linode server to a large‑scale backend employing high‑availability MySQL, Redis sharding, a custom distributed logging system (Kids), event‑driven processing with Sink and Beanstalkd, component‑based page rendering via ZhihuNode, and a multi‑layer SOA built on evolving RPC frameworks.