This article explains why tasks submitted with submit() in a Java thread pool silently swallow exceptions, contrasts it with execute(), and presents three practical solutions—including try‑catch, a default UncaughtExceptionHandler, and overriding afterExecute—to reliably capture and handle those errors.
This article outlines how to correctly create, monitor, and configure Java ThreadPoolExecutor instances, explains why using the Executors factory can cause OOM, recommends separate named pools per business, provides formulas for sizing CPU‑bound and I/O‑bound workloads, and highlights real‑world pitfalls and dynamic‑configuration solutions.
This article summarizes the typical causes of slow backend interfaces—such as MySQL slow queries, complex business logic, thread‑pool misconfiguration, lock contention and machine‑level problems—and provides concrete optimization techniques, code examples, and best‑practice recommendations for Java services.
This tutorial demonstrates how to configure a SpringBoot application to repeatedly call a third‑party HTTP API, parse the returned JSON with FastJSON, and process each data item concurrently using a fixed thread pool initialized in a scheduled task.
This article explains the default Tomcat configuration bundled with Spring Boot 2.7.10, details key parameters such as accept‑count, max‑connections, thread pool sizes and timeouts, shows how Tomcat’s internal threads work, provides sample YAML configurations and testing results, and offers practical tuning guidance for Java backend services.
The article explains how a Java ThreadPoolExecutor can retain hundreds of waiting threads when shutdown is omitted, analyzes the GC‑root relationship of running threads, and demonstrates through code and JVisualVM screenshots that invoking shutdown or shutdownNow properly interrupts idle workers, removes them from the pool, and allows both the worker threads and the thread‑pool object to be garbage‑collected.
After a recent deployment a backend update operation became extremely slow, revealing RPC retry, thread‑pool misconfiguration, and row‑lock contention between services, which were analyzed and resolved through thread‑pool tuning and transaction separation.
The article systematically analyzes why backend APIs become slow—covering database slow queries, complex business logic, thread‑pool misconfiguration, lock design flaws, and machine problems—and presents practical Java‑based optimization techniques such as pagination fixes, index tuning, multithreading, proper thread‑pool sizing, lock refinement, caching, and asynchronous callbacks.
This article explores the design and implementation of MemoryLimitedLBQ and MemorySafeLBQ, two custom LinkedBlockingQueue variants that limit memory usage to avoid Out‑Of‑Memory errors in Java thread pools, comparing their mechanisms, instrumentation dependencies, and practical usage in open‑source projects.
This article explains how to prevent loss of tracing information in Spring Cloud by using traceable thread pools, detailing three approaches: wrapping an ExecutorService with TraceableExecutorService, using Tracer.currentTraceContext().wrap, and employing TraceCallable/TraceRunnable for context propagation.
This article explains how to use Mapped Diagnostic Context (MDC) in SpringBoot to store and propagate traceId across threads and HTTP requests, covering MDC basics, API, advantages, common pitfalls, and practical solutions with interceptors, thread‑pool wrappers, and log pattern configuration.
This article summarizes typical causes of backend interface latency—such as slow MySQL queries, complex business logic, thread‑pool and lock misconfigurations, and machine constraints—and presents concrete optimization techniques including pagination fixes, indexing strategies, multithreading, proper thread‑pool tuning, lock refinement, caching, and asynchronous callbacks.
This article explains how Java thread pools work, clarifies common misconceptions about core thread creation, details the role of BlockingQueue, and provides practical guidelines for sizing core, max threads, and queue capacity based on concurrency and GC considerations.
The article explains Alibaba's Java Development Manual rules—covering logging APIs, collection initialization, foreach loop modifications, static SimpleDateFormat, string concatenation in loops, serialVersionUID handling, subList usage, inheritance, isSuccess naming, COUNT(*) versus COUNT(col), and thread‑pool creation—providing the rationale behind each prohibition for safer, more efficient backend Java code.
This article details a real‑world Elasticsearch cluster recovery issue where setting the shard recovery concurrency too high saturated the generic thread pool, causing the entire cluster to hang, and explains the underlying concepts, reproduction steps, analysis, and mitigation measures.
This article explains how misuse of ThreadLocal in Java thread pools can cause memory‑leak‑induced Out‑Of‑Memory errors, illustrates the problem with a reproducible example, analyzes the underlying ThreadLocalMap mechanics, and provides best‑practice guidelines such as invoking remove() to avoid leaks.
An in‑depth case study of a no‑card payment system reveals common backend pitfalls—database deadlocks, long‑running transactions, thread‑pool misuse, excessive logging, and missing monitoring—and demonstrates practical fixes using Redis locks, refined transaction scopes, controlled thread pools, and optimized logging to dramatically improve scalability and reliability.
This article walks through a real‑world payment service’s performance evolution, detailing the server environment, a dozen common bottlenecks such as database deadlocks, long‑running transactions, CPU saturation, thread‑pool misuse, logging overload, cache issues, and provides concrete code‑level optimizations, architectural changes, and monitoring tips that raised throughput and stability.
This article explores the fundamentals of concurrency versus parallelism, the challenges of writing correct concurrent programs, the evolution from OS threads to green threads, Goroutine scheduling, actor‑model concepts, and modern strategies such as thread pools, async callbacks, and Rust's ownership model.
