This practical guide walks you through Linux kernel stack backtracing, covering GDB installation, core dump analysis, handling corrupted stacks, and advanced tracing techniques using ftrace and eBPF, with step‑by‑step commands, code examples, and troubleshooting tips to pinpoint the root cause of crashes.
Learn how to monitor and analyze crashes in HarmonyOS Next by handling native C/C++ signals and ArkTS/JavaScript errors, using hiAppEvent watchers, simulating crash scenarios, and leveraging build artifacts such as source maps, debug .so files, and nameCache for precise stack trace reconstruction and improved app stability.
This article analyzes why micro‑frontend frameworks like qiankun and micro‑app cause error stack column and line offsets, demonstrates the root cause of code injection, and provides both framework‑specific and universal solutions to adjust stack traces for accurate debugging and monitoring.
This article explains how to address common on‑call debugging pain points by analyzing error screenshots, extracting key information, and developing a lightweight Java tool that leverages StackTraceElement to generate a clear, filtered method‑call chain, dramatically speeding up code‑origin identification and issue resolution.
This article explains common causes of unexpected Linux program termination and demonstrates how to employ the backtrace utility, along with signal handling and related functions, to capture and analyze stack traces, enabling precise identification and resolution of issues such as memory overflows, null pointer dereferences, and other runtime errors.
This article analyzes why asynchronous Log4j2 logging becomes a performance bottleneck when handling massive mapping data, tracing the issue through Disruptor internals, stack‑trace location retrieval, and JNI calls, and proposes practical configuration and code adjustments to eliminate the slowdown.
retsnoop is an eBPF‑based tracing utility that uses wildcard patterns to hook kernel functions, automatically captures full stack traces whenever a function returns an error, and offers three complementary modes—stack trace, function‑call trace, and LBR—to quickly pinpoint the source of kernel failures, with practical examples and source‑code insights.
This article explains Go's error‑handling shortcomings, introduces the popular third‑party pkg/errors library, demonstrates how to wrap errors with additional context and stack traces, and provides a detailed line‑by‑line walkthrough of its source files (errors.go, go113.go, stack.go) to reveal the underlying design and implementation.
Facing uncontrolled log proliferation, the author shares a real-world case of log governance that reduces costs and boosts efficiency by categorizing logs, simplifying formats, consolidating tools, introducing a three‑layer log architecture, and applying stack‑trace reversal techniques with concrete code examples.
This article examines why a single Thread.currentThread().getStackTrace() call is insufficient for modern Android performance debugging, compares instrumentation and native stack‑capture approaches, and provides detailed step‑by‑step implementations, code snippets, and optimization tips for reliable stack tracing.
The article explains how to monitor, locate, and remediate Android ANRs by installing a LooperPrinter‑based timeout detector, extending coverage to IdleHandler and touch events, dumping and aggregating main‑thread stacks via Firebase Crashlytics, and showcases real‑world fixes that cut online ANRs by 73.8%.
A low‑overhead monitoring solution for iOS apps resets the global malloc_logger to capture every allocation, filters large allocations exceeding a configurable threshold, records lightweight stack traces using dyld image maps, and sends address‑only data for offline symbolication, enabling developers to identify and fix memory‑heavy code, reducing OOM crashes.
MDAP is a multi‑dimensional real‑time monitoring platform for mobile apps that gathers metrics, logs, and traces via lightweight SDKs, processes data through micro‑service back‑ends using Flink, Spark, and ClickHouse, applies intelligent analysis for smoothness scoring, memory‑snapshot optimization, stack de‑obfuscation, crash clustering, and URL templating, and aims to extend end‑to‑end observability and predictive issue detection.
The article explains how to analyze core‑dump files with GDB by examining the function call stack, key registers, and memory contents, classifying crash causes, using GDB commands to inspect variables and addresses, reconstructing corrupted stacks, handling optimized‑away data and vtable errors, and following a systematic debugging workflow.
The article offers a thorough guide to Android ANR analysis, detailing generation mechanisms, timeout thresholds, log examination techniques, and case studies of common causes such as idle main threads, long operations, lock contention, CPU preemption, memory pressure, and system‑service timeouts, providing practical diagnostic examples.
The article investigates why a MySQL instance experiences frequent "Opening tables" states, analyzing stack traces, identifying a data dictionary lock held by the dict_stats_thread, and concluding that slow disk writes trigger InnoDB's synchronous dirty‑page flush, blocking many operations.
The article outlines a Flutter jank monitoring system that detects frame stalls by measuring UI callback intervals and captures the offending Dart call stack, evaluating SDK modification, AOT native unwinding, and ultimately selecting a low‑overhead signal‑based stack‑collection method for production use.
This article shares practical experiences from developing the Shenghai crash monitoring SDK for Android, covering Java exception capture, stack trace collection, processing, real‑time reporting, crash metrics, and ANR detection using FileObserver and WatchDog mechanisms.
By instrumenting every C++ function with GCC’s ‑finstrument‑functions and recording call addresses in per‑thread TLS, the team built a fast, lock‑free stack‑trace mechanism that outperforms libunwind by up to 60×, integrates with Android’s malloc_debug, and powers an automated native‑memory‑leak detection framework with web‑based analysis.
