A friend who landed a 16K job in a first‑tier city shares more than 30 interview questions he encountered across ten+ companies, covering JVM internals, performance tuning, concurrency, design patterns, database locking, distributed transactions, and system reliability.
The article explains how CPU pipelines, cache misses, branch‑prediction failures and lock contention cause non‑linear usage spikes, illustrates common pitfalls such as infinite loops, lock‑heavy spinning and catastrophic regex backtracking, and offers practical detection with perf and three rules—avoid busy‑waiting, use cache‑friendly layouts, and limit thread contention.
The article explains how Spring AOP can cause object escape leading to concurrency safety problems, describes the impact of asynchronous threads and caching, and provides practical guidance on ordering AOP advices such as distributed lock, @Retryable, and dynamic datasource to avoid business anomalies.
This article explains the core concepts of Java's AbstractQueuedSynchronizer (AQS), covering semaphore vs. monitor, lock acquisition and release mechanisms, the internal FIFO queue, code examples of ReentrantLock and a custom mutex, and how AQS underpins many concurrency utilities.
This article explains the concepts of processes and threads, describes Python's Global Interpreter Lock (GIL) and its impact on concurrency, compares the low‑level _thread module with the higher‑level threading module, and provides example code illustrating thread creation, synchronization with locks, and common pitfalls.
This curated collection gathers eleven in‑depth articles covering the most frequent Go programming mistakes—from optimization and unit testing to concurrency, error handling, and code organization—providing practical examples, dates, and direct links for developers seeking to improve code quality and avoid common pitfalls.
This article examines nine frequent errors developers make when writing Go unit tests—such as improper test classification, neglecting the race detector, ignoring parallel and shuffle flags, avoiding table‑driven tests, using sleep, mishandling time APIs, overlooking httptest/iotest, misusing benchmarks, and skipping fuzz testing—providing analysis and concrete code‑based solutions to improve test reliability and efficiency.
In high‑concurrency Java applications, LongAdder—introduced in JDK 8 and using partitioned cells to reduce contention—generally outperforms the single‑value AtomicLong, which relies on CAS and can cause CPU waste under heavy load, so Alibaba advises LongAdder for scalable distributed counters, though memory usage and workload specifics must be considered.
The article explains how to correctly combine Java's CompletableFuture with Stream API to achieve true asynchronous parallelism, highlights common pitfalls that lead to sequential execution, and provides the proper pattern of creating a CompletableFuture stream followed by a terminal operation.
Netty 4 employs a global multithreaded, locally single‑threaded (event‑loop) architecture where a boss thread pool accepts connections and delegates them to worker thread pools, each containing NioEventLoop instances with selectors, task queues, and pipelines, ensuring lock‑free, ordered processing while avoiding thread blocking.
This article explains the internal workings of Java's ThreadLocal and Netty's FastThreadLocal, compares their memory management and lookup mechanisms, provides code examples, and offers guidance on when to choose each for high‑concurrency backend applications.
This article explains MySQL's three concurrency types—read‑read, read‑write, and write‑write—describes how InnoDB uses MVCC, shared and exclusive locks, and provides practical examples and code for handling update‑loss and write‑write conflicts with optimistic and pessimistic locking.
This article explains the Fork/Join model and Java's ForkJoinPool, covering divide‑and‑conquer theory, task types, pool construction, core methods, performance testing, and practical recommendations such as avoiding the commonPool for blocking tasks.
This article explains Java virtual threads introduced by Project Loom, compares them with platform threads in terms of memory usage and creation time, provides code examples for creating and managing virtual threads, and outlines practical guidelines and use‑cases for high‑concurrency backend applications.
This article explains why simple GET‑modify‑SET inventory updates cause overselling in flash‑sale spikes and presents several Redis‑based solutions—including Lua scripts, WATCH‑based optimistic locks, distributed SETNX locks, and asynchronous queue processing—detailing their implementation, advantages, and trade‑offs.
While Python dominates the machine learning ecosystem, Go offers compelling performance, concurrency, and static typing advantages, making it a strong contender for high‑throughput prediction services, large data pipelines, resource‑constrained environments, and custom ML components, especially when teams already leverage Go in production.
The article describes a backend solution that uses a fixed‑size FIFO queue to throttle concurrent MySQL export operations, provides Java implementations of the ExportQueue, an abstract EasyExcel‑based exporter, a concrete ExportImpl service, and a test controller, and discusses remaining challenges and alternative approaches.
The article traces Java’s concurrency evolution from heavyweight thread‑per‑request and complex reactive models to JDK 21’s virtual threads, which act as stack‑ful coroutines offering lightweight, heap‑allocated threads, full stack traces, and blocking‑I/O compatibility while preserving the familiar thread API.
This article demonstrates how to simulate a high‑concurrency flash‑sale scenario with SpringBoot and MySQL, analyzes why naive lock‑and‑transaction code causes overselling, and presents six refined solutions—including early locking, AOP, pessimistic and optimistic database locks, blocking queues, and Disruptor queues—along with performance observations and a concise summary.
This article explains what deadlocks are, outlines the four classic conditions that cause them, provides Java thread and MySQL transaction examples, and offers practical solutions such as consistent lock ordering, timeout settings, batch updates, and shortening transaction duration to prevent deadlocks.
An in‑depth analysis reveals how misuse of InheritableThreadLocal with shared thread pools can sporadically produce NullPointerExceptions, detailing the root cause, code examples, debugging steps, and best‑practice recommendations to avoid thread‑local contamination in Java backend services.
This article explains how to improve slow PostgreSQL IN queries caused by large parameter lists by defining a custom Spring AOP annotation that automatically splits the parameter set, runs the sub‑queries concurrently in multiple threads, and merges the results back together, complete with code examples and usage guidelines.
This article examines frequent mistakes developers make when writing concurrent Go programs—such as misusing context, leaking goroutines, mishandling channels, and causing data races—and provides concrete code examples, impact analyses, and best‑practice recommendations to write safer, more efficient Go concurrency code.
This article explains the low‑level implementation of Java's synchronized keyword, covering monitorenter/monitorexit bytecode, object header structures, Mark Word layouts, and the evolution from heavyweight locks to biased and lightweight locks with lock‑upgrade mechanisms.
This article explains the inner workings of Caffeine's getIfPresent method, the design of its MPSC ReadBuffer and WriteBuffer, the maintenance cycle, eviction strategies, and the TinyLFU algorithm, providing Java code examples and diagrams to illustrate how caching decisions are made in high‑concurrency environments.
This article explains why modern multi‑core CPUs need memory barriers, describes the different kinds of barriers (full, read, write), shows how they are implemented in the Linux kernel and hardware, and illustrates their use in multithreaded and cache‑coherent programming.
This article explains how the Java Memory Model defines the interaction between threads, thread stacks, and the heap, illustrates these concepts with diagrams and example code, and discusses how modern hardware memory architecture, caches, and CPU registers affect visibility and race conditions in concurrent Java programs.
This article announces the updated Spring Boot 3 case‑study e‑book with 108 examples and then dives into Java 24’s latest preview features—enhanced pattern matching, flexible constructors, module import, single‑file source, custom stream collectors, Vector API, scoped values, and structured concurrency—complete with code snippets and links.
This article explains why Java performance matters, outlines fundamental principles, and provides concrete code, memory, concurrency, I/O, and database optimization techniques, along with profiling tools, common pitfalls, real‑world case studies, and emerging trends to help developers build faster, more stable applications.
This article explains how to merge multiple user‑detail requests into a single database query using a blocking queue, scheduled thread pool, and CompletableFuture in Spring Boot, providing code examples, a high‑concurrency test, and discussion of trade‑offs such as added latency and timeout handling.
This article introduces Go language fundamentals for developers familiar with other object‑oriented languages, covering variable and constant declarations, zero values, methods, structs, interfaces, slices, maps, goroutines, channels, mutexes, and error handling with clear code examples and explanations.
After being laid off, the author interviewed only four companies, detailing each interview’s technical questions—from Spring Cloud and MySQL to concurrency and design patterns—and reflecting on the interviewers’ professionalism, the relevance of his skill set, and the importance of matching resumes to job requirements.
This article explains the concept of memory pools, how they reduce allocation overhead and fragmentation compared to traditional malloc/new, describes various pool designs and Linux kernel APIs, provides multiple C and C++ implementations, and discusses performance benefits and typical application scenarios such as servers, real‑time and embedded systems.
The article reviews key Java backend interview topics—including thread‑safe collections, JVM memory layout and heap tuning, Full GC troubleshooting, Java 8 features, optimistic locking, stock‑over‑sell handling with Redis, MySQL scaling, cache‑consistency patterns, anti‑bot safeguards, and Bloom filters—while noting JD’s recent 30% salary hike for algorithm engineers.
This article explains the differences between ExecutorService and CompletionService, demonstrates how to replace a simple Future‑based approach with CompletionService for faster result retrieval, analyzes the underlying source code, and outlines typical use‑cases such as load‑balancing and early‑return patterns in concurrent Java applications.
This article explains the principles of CAS, compares Java's AtomicLong and LongAdder counters, highlights the ABA problem, and shows why Alibaba recommends LongAdder for high‑concurrency scenarios due to its segmented lock design, better performance, scalability, and ease of use.
This article explains the motivation behind Netty's FastThreadLocal, details its internal design using an indexed array to avoid hash collisions, analyzes the core source code of InternalThreadLocalMap, FastThreadLocalThread, and FastThreadLocal, and discusses performance implications and reclamation strategies.
The article compares Java and Go across syntax, variable declaration, object‑oriented features, error handling, concurrency models, garbage collection, resource usage, and ecosystem maturity, concluding that Go offers simpler code, lightweight concurrency and lower overhead, while Java provides a richer library ecosystem and more mature tooling, making the choice dependent on project requirements.
The article uses everyday analogies to explain why multithreaded programming is intrinsically difficult, especially when threads share data, leading to complex debugging, nondeterministic behavior, and performance trade‑offs such as lock granularity and memory ordering.
This article explains the key performance metrics TPS (transactions per second) and QPS (queries per second), provides formulas for calculating them, and demonstrates practical calculations for multi-node deployments, illustrating how request latency, thread pools, and instance count affect overall system concurrency and throughput.
This article compares Go, Python, and Rust concurrency implementations—covering CSP‑based goroutines, GIL constraints, and ownership‑driven thread safety—to help developers select the most suitable model for high‑throughput, CPU‑bound, or safety‑critical applications.
This article explains the async def syntax introduced in Python 3.5, its role in defining coroutine functions, when to use it, and provides a detailed example with code walkthrough to illustrate asynchronous I/O handling with asyncio.
The article revises a supermarket checkout performance test by incorporating elderly customers who take twice as long to pay, demonstrates using Java's ThreadLocalRandom to randomly assign customer age groups, modifies the pay() method to double payment time for seniors, and explains why realistic scenarios improve testing accuracy.
This article explains how Linux memory barriers work, why they are needed on SMP systems, the different barrier types, their kernel implementations, and practical examples showing their impact on multithreaded performance and driver development.
This article explains Java thread run states, analyzes why threads appear in WAITING or TIMED_WAITING during concurrent tasks, shows flame‑graph diagnostics, and offers concrete code‑level optimizations to reduce CPU pressure and improve overall backend performance.
This article explains Rust's closure system, detailing how the three traits Fn, FnMut, and FnOnce map to different capture modes and ownership semantics, and provides practical examples, common pitfalls, and performance tips for writing safe and efficient Rust code.
This article explains Go closures—what they are, how they capture surrounding variables, key characteristics, practical code examples, common pitfalls like memory leaks and concurrency issues, and typical use‑cases such as function factories, state management, callbacks, and interface implementation.
The article examines the architectural concepts behind Java's Striped64 and ConcurrentHashMap, explains how they achieve high‑performance concurrency, and discusses the memory, complexity, and scalability drawbacks that developers should consider when choosing these data structures for backend systems.
This article explains the fundamentals of concurrency, how Nginx and HAProxy handle connections and requests, provides configuration examples, monitoring metrics, system resource limits, and performance testing techniques to optimize high‑availability load‑balancing deployments.
This article explains the concepts, usage conditions, execution flow, internal mechanisms, and best‑practice guidelines for Java's wait() and notify() methods, illustrated with a producer‑consumer example and code snippets for safe thread coordination.
This article explains why backend developers are moving from PHP to Go, outlines the concurrency challenges of high‑traffic live streaming services, and demonstrates two practical Go patterns—sync.WaitGroup and errgroup.Group—with code examples, while also warning about common closure pitfalls in loops.
Promise.allSettled overcomes the fatal flaw of Promise.all by waiting for all promises to settle—whether fulfilled or rejected—returning a uniform result array that lets developers identify successful outcomes and handle failures without aborting the entire operation, enabling more resilient asynchronous code.
This article examines frequent errors when using Go's select statement—such as omitting a default case, misunderstanding case order, handling nil channels, missing timeout logic, and duplicate cases—explaining their impact, offering best‑practice guidance, and providing corrected code examples for each scenario.
This article introduces Java 21's virtual threads, explains their lightweight and high‑concurrency advantages, demonstrates basic and delayed usage with code examples, shows how to enable them in Spring Boot, compares performance against traditional threads, and offers additional Java performance‑tuning techniques.
This article examines the new free‑threading feature introduced in Python 3.13 via PEP‑703, demonstrates how asyncio.to_thread can be used for CPU‑bound tasks, and compares execution times with and without the GIL on an M3 MacBook Pro, showing a clear performance gain.
Java 21’s virtual threads provide lightweight, high‑concurrency execution managed by the JVM, enabling developers to create hundreds of thousands of threads with minimal overhead; this article explains their basics, demonstrates Spring Boot integration, compares performance against traditional threads, and shares additional Java optimization techniques.
This article explains how to combine multiple simultaneous user requests on the server side using a queue, scheduled thread pool and CompletableFuture in Spring Boot, reducing database connections while handling high concurrency, and discusses implementation details, testing, and potential pitfalls.
This article explains the evolution of Java virtual threads in AJDK, details performance improvements, examines pinning problems caused by synchronization and native code, provides migration steps, configuration tips, and code examples, and introduces diagnostic tools for troubleshooting virtual thread issues.
This article explains what the Python Global Interpreter Lock (GIL) is, why it was introduced, how it affects multithreading and multiprocessing, and discusses recent efforts to make the GIL optional and eventually remove it from CPython.
This article explains Java 21 virtual threads, demonstrates how to create and use them in plain Java and Spring Boot, compares their performance against traditional threads in large‑scale and HTTP workloads, and offers additional Java performance optimization tips.
An in‑depth comparison of Java’s Hashtable and HashMap covers their thread‑safety mechanisms, null‑key/value policies, inheritance hierarchy, default capacities, resizing strategies, and iterator behavior, highlighting performance trade‑offs and best‑practice alternatives for concurrent applications.
This guide explains why graceful shutdown matters in Go programs, outlines the steps to capture termination signals, use context for coordinated goroutine exit, and properly close HTTP servers without data loss or resource leaks.
This article provides a comprehensive guide to Java thread pools, explaining their purpose, construction parameters, execution flow, worker reuse, task retrieval with timeout, lifecycle states, shutdown mechanisms, monitoring methods, and best practices for customizing pools in real-world projects.
This article examines the PHP True Async RFC, outlining its goals to provide a standard C‑API for non‑blocking I/O, describing implicit versus explicit async models, presenting a scheduler‑reactor design, and showing example code that runs concurrent fibers without altering existing PHP code.
This article explains the main concurrency scheduling models—including user‑level, kernel‑level, two‑level (M:N), and coroutine approaches—covers common scheduling algorithms, and details Go’s unique M:P:G scheduler, highlighting its principles, workflow, and advantages for high‑performance parallel execution.
The article explains why Redis needs asynchronous IO threading, describes the shortcomings of previous IO‑thread models, details the design of the new async IO thread architecture with event‑notified client queues and thread‑safety mechanisms, and presents performance test results showing up to double the QPS and significantly lower latency.
This article examines the challenges of using JPA optimistic locking for inventory deduction under high concurrency, demonstrates why naive retry logic can cause deadlocks and stale data, and walks through five progressively refined implementations that resolve transaction, caching, and isolation issues.
This comprehensive guide explores why locks are essential in concurrent programming, explains the principles behind mutexes, semaphores, read‑write locks, and re‑entrant locks, provides Python code examples, discusses common pitfalls like deadlocks, and offers best‑practice strategies for production environments.
This article explains how Java's ThreadPoolExecutor ensures core threads stay alive by analyzing the execute, addWorker, runWorker, and getTask methods, detailing the dead‑loop logic, timeout handling, and the conditions under which core threads can be reclaimed.
This article walks through designing and implementing a Java multithreaded execution class for performance testing, covering thread‑pool creation, task coordination with CountDownLatch, start/stop control, and a complete demo that runs concurrent tasks with timed logging.
This article provides a comprehensive walkthrough of Caffeine cache's architecture, explaining its fixed-size eviction policy, underlying data structures such as ConcurrentHashMap, MPSC buffers, Count‑Min Sketch frequency tracking, and the dynamic window‑probation‑protected zones, while detailing key methods like put, getIfPresent, and maintenance.
The article explains how Java's synchronized keyword works only within a single JVM, why it fails to provide mutual exclusion across multiple processes in distributed systems, and presents alternative distributed locking mechanisms such as database locks and Redis-based locks with code examples.
This article explains what a mutex is, why it is needed in concurrent programming, shows basic lock/unlock operations with Go code examples, compares mutexes with atomic operations, and provides best‑practice guidelines to avoid deadlocks and improve performance.
Ben Dicken conducted a massive benchmark running 10 billion nested loops across many languages—Zig, Julia, Perl, Elixir, Fortran, C#, Lua, and more—while the community contributed optimizations such as goroutine‑based Go improvements, sparking discussions on fair measurement, startup overhead, and concurrency advantages.
The program that loops on a non‑volatile boolean flag sometimes terminates because changing the counter to a volatile int or to an Integer causes the JVM to emit hidden memory barriers on reference writes, making the flag visible, but this behavior is JVM‑specific and not a reliable substitute for declaring the flag volatile.
This article explains why backend developers are moving from PHP to Go, demonstrates how Go's built-in concurrency primitives like sync.WaitGroup and errgroup simplify high‑traffic live‑streaming services, and warns about common closure pitfalls when launching goroutines in loops.
This article explains why Python’s Global Interpreter Lock limits CPU core usage, introduces the multiprocessing module for parallel execution of CPU‑intensive tasks, and provides step‑by‑step code examples, key concepts, synchronization tools, a real‑world image‑processing case, and best practices to dramatically speed up your programs.
The article explains the Helper class’s built‑in thread coordination operations—including Waiters, Rendezvous, Joiners, and execution‑aborting methods—detailing their APIs, usage scenarios, parameters, and behavior such as signaling, timeouts, rejoinable points, and how they differ from standard Java synchronization primitives.
The article explains the background of the Go mutex performance proposal, details the new spinning flag added to the mutex state, walks through fast‑path, spinning, and sleep phases of lock acquisition, presents benchmark results showing up to 70% speed‑up, and provides references for further reading.
An in‑depth exploration of Go’s sync.Map reveals its concurrent map implementation, covering core structures, read‑only and dirty maps, entry states, and detailed walkthroughs of Store, Load, Delete, Range, Clear, and advanced operations like LoadOrStore, CompareAndSwap, and CompareAndDelete with code examples.
This comprehensive guide explores ten critical backend optimization strategies—from defensive validation and batch N+1 query elimination to asynchronous processing, parallel execution, caching, connection pooling, compression, message queuing, and design patterns—providing practical examples, Go code snippets, and best‑practice insights to boost performance and reliability.
This article explains the concept of distributed locks and compares three common implementation approaches—using a database, Redis, and ZooKeeper—detailing their mechanisms, advantages, drawbacks, and suitable scenarios for ensuring consistent access to shared resources in distributed systems.
This article explains why asynchronous programming boosts Java application performance and walks through seven practical methods—Thread, FutureTask, CompletableFuture, Guava ListenableFuture, EA‑Async, Cactoos, and Jcabi‑Aspects—each illustrated with complete, runnable code examples.
This article explains how to manage Promise concurrency in JavaScript, covering built‑in methods like Promise.all, allSettled, race, any, custom throttling functions, third‑party libraries, generator‑based solutions, and message‑queue approaches to improve performance and user experience.
This article reviews Java Spring code for a coupon claim and approval workflow, identifies concurrency and transaction issues such as missing locks and inconsistent update order, and proposes optimizations including user‑level locking, simplifying lock management, and aligning transactional update sequences to prevent deadlocks.
This article compares three Java concurrency approaches—Future with Callable, CompletableFuture, and FutureTask—explaining their underlying principles, step‑by‑step usage, code examples, and trade‑offs so you can decide which asynchronous tool best fits your reporting workload.
Java 21’s virtual threads provide a lightweight, JVM‑managed alternative to OS threads that enables hundreds of thousands of concurrent tasks, simplifies scheduling, integrates easily into Spring Boot via a preview flag and Tomcat executor, and delivers up to five‑fold speed‑ups and lower latency in high‑load I/O‑intensive applications.
This article explains the concept of deadlocks in concurrent programming, outlines the four necessary conditions, examines common causes in Java, provides a runnable code example, and presents practical prevention, avoidance, detection, and recovery techniques to keep multithreaded applications running smoothly.
The article analyzes why using the auto‑instrumentation command `otel go build -race` causes a crash, tracing the failure to the injected runtime code, the Go‑C calling conventions, and a zeroed race context, and then presents practical fixes to prevent the crash.
This guide explains how to build high‑performance lock‑free data structures in Rust by using atomic operations, memory ordering semantics, and practical examples such as a thread‑safe counter and a lock‑free stack, followed by best‑practice recommendations.
This comprehensive guide introduces Java's CompletableFuture, explains its advantages over the traditional Future API, demonstrates how to create and compose asynchronous tasks, manage thread pools, handle timeouts and cancellations, and apply robust exception handling techniques for reliable concurrent programming.
This article explains how to improve backend performance by fetching independent report data concurrently using Java's Future, Callable, CompletableFuture, and FutureTask classes, provides code examples for each approach, and compares their principles, usage patterns, and cancellation capabilities.
This article compares PHP and Go across history, ecosystem, performance, concurrency, memory management, and typical use‑cases, providing guidance on when to select PHP for rapid web development or Go for high‑performance, cloud‑native and distributed systems.
This article explores common overselling problems in high‑concurrency flash‑sale scenarios and presents seven practical solutions—including lock timing adjustments, AOP locking, pessimistic and optimistic database locks, and queue‑based approaches—each illustrated with SpringBoot code and performance test results.
This article explains what unfair locks are, why they can improve throughput compared to fair locks, provides a Java ReentrantLock example with code, describes the internal acquisition process, and outlines typical high‑concurrency scenarios where unfair locks are advantageous.
The article details how moving a high‑throughput observability compute layer from Java to Rust—leveraging Rust’s ownership, zero‑cost async, and static binary deployment—cut memory usage by roughly 68%, CPU consumption by 40%, while outlining monitoring setup, concurrency model, and the steep learning‑curve challenges.
The article examines inventory pre‑reservation in e‑commerce, detailing performance challenges under high concurrency, and evaluates solutions such as asynchronous rate limiting, horizontal stock splitting, Redis caching, database transaction handling, deadlock avoidance, and data consistency mechanisms, highlighting a 24‑fold throughput increase.
This article clarifies the differences among Java’s thread interruption methods—interrupt(), Thread.interrupted(), and isInterrupted()—explaining how each affects a thread’s interrupt status, when exceptions are thrown, and how to properly monitor and handle interruptions in concurrent applications.
ThreadLocal provides a simple yet powerful way to give each thread its own independent variable, eliminating shared-state conflicts; this guide explains its underlying mechanism, creation methods, basic get/set operations, a complete example with console output, and important usage considerations such as potential memory leaks.
This article explains how to correctly declare Java thread pools using ThreadPoolExecutor, monitor their runtime status, configure parameters for CPU‑bound and I/O‑bound workloads, assign meaningful names, avoid common pitfalls such as unbounded queues and thread‑local leakage, and leverage dynamic pool frameworks.
