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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 article explains how to generate a unique traceId at request entry and propagate it through Rest, MQ, and RPC modules using Log4j2, MDC, and custom converters, enabling clear, linked log output across the entire request chain.
This article explains how to introduce a unique traceId for request tracing across Rest, MQ, and RPC modules using Log4j2, custom message factories, converters, and AOP, enabling clear, linked logs throughout the service chain.
This article explains how to generate a unique TraceId, propagate it via HTTP headers and SLF4J MDC, integrate the mechanism with Logback, Feign clients, thread pools, and SkyWalking, and details the underlying MDC and Logback placeholder implementations for reliable log tracing in Java backend applications.
By instrumenting each node of ZuanZuan’s push system with a Prometheus counter labeled by node name and traceId, and visualizing these metrics in a Grafana Flowcharting dashboard that dynamically highlights the trace path, developers can instantly pinpoint failures, cutting troubleshooting time from minutes to near‑zero.
This article describes how to use Grafana's Flowcharting plugin and Prometheus metrics to create a dynamic, interactive dashboard that visualizes each logical node of a push notification pipeline, enabling rapid trace‑ID based troubleshooting and reducing manual investigation effort.
This article explains how to use SLF4J MDC together with a TraceId header to correlate logs across threads, services, and distributed tracing tools like SkyWalking, providing code examples for filters, Feign interceptors, thread‑pool adapters, and Logback configuration.
This article explains how to use Log4j's Mapped Diagnostic Context (MDC) to generate, propagate, and record a traceId across micro‑service calls, providing a low‑cost, non‑intrusive solution for full‑stack log tracing and debugging.
This article explains what MDC (Mapped Diagnostic Context) is, outlines its API, demonstrates how to configure logging and interceptors, and provides concrete solutions—including a custom thread‑pool wrapper and HTTP client interceptors—to ensure TraceId is correctly propagated across threads and remote calls in Java backend systems.
This article explains the types and formats of OceanBase logs, how to identify relevant log files, interpret log fields such as trace_id, lt, and dc, and provides step-by-step methods for using error codes and trace IDs to pinpoint the root cause of errors.
This article explains how to use MDC to generate and propagate a traceId across Spring Cloud microservices, configure Logback to output the traceId, and leverage ELK Stack for log aggregation, enabling comprehensive request‑level log tracing both within and across services.
This article demonstrates how to create a Spring AOP request‑logging aspect that captures IP, URL, HTTP method, class method, parameters, results and execution time, and extends it with high‑concurrency handling, error logging, and TraceId propagation for better traceability.
This article explains how to create a Spring AOP request‑logging aspect, covering basic pointcut and advice definitions, parameter extraction, high‑concurrency optimizations with unified JSON logs, exception handling, and adding traceId support for end‑to‑end request tracing.
This article explains what MDC (Mapped Diagnostic Context) is, shows its API and advantages, demonstrates how to add interceptors and log patterns to include a traceId, and provides solutions for traceId loss in child threads and HTTP calls by wrapping thread pools and implementing HTTP client interceptors.
This article explains what MDC (Mapped Diagnostic Context) is, its API and advantages, demonstrates how to integrate it into Spring MVC interceptors and log patterns, and provides solutions for traceId loss in child threads and HTTP calls using custom thread‑pool wrappers and HTTP client interceptors.
The article explains why unified log structures and traceId identifiers are essential for quickly locating bugs in high‑traffic backend services, provides a Java LogObject example, demonstrates chain‑style setters, shows how to combine request and response in one log entry, and compares manual logging with AOP‑based approaches for performance and maintainability.
This article explains how to use Mapped Diagnostic Context (MDC) in Java logging frameworks to propagate traceId across threads and HTTP calls, covering API usage, advantages, common pitfalls, and practical solutions including interceptor implementation, thread‑pool wrappers, and client‑side interceptors for HttpClient, OkHttp, and RestTemplate.
This article introduces MDC, explains its API and benefits, and provides concrete solutions—including a thread‑pool wrapper and HTTP client interceptors—to reliably propagate traceId across threads and remote calls in Java backend applications.
This article explains what MDC is, its API, advantages, common issues like traceId loss in child threads and HTTP calls, and provides practical solutions using custom thread‑pool wrappers and HTTP interceptors to ensure reliable logging in Spring Boot applications.
This article explains the concept of MDC (Mapped Diagnostic Context) in Java logging, details its API, demonstrates how to propagate traceId across threads and HTTP calls using interceptors and custom thread‑pool wrappers, and shows how to configure log patterns to include the traceId.