This article walks Python developers through designing high‑concurrency back‑end systems, covering architectural planning, modular project layout, async I/O with asyncio and FastAPI, load balancing with Gunicorn, database scaling, Celery task queues, caching, rate limiting, monitoring, and graceful shutdown techniques.
This article walks through practical techniques for designing a highly concurrent Python backend that stays stable under load, covering architecture planning, async programming, load balancing, database scaling, distributed tasks, caching, rate limiting, monitoring, and graceful shutdown.
This article explains why synchronous Python code blocks on I/O, introduces asyncio’s event loop and coroutine model, and walks through creating and managing tasks, using TaskGroup, handling timeouts, avoiding common pitfalls, and applying best‑practice patterns for high‑performance I/O‑bound programs.
Python 3.14 introduces a collection of subtle yet useful enhancements—such as TypedDict's NotRequired, improved static analysis, lazy imports, clearer error messages, a new contextlib.chdir manager, refined async task cancellation, better subprocess isolation, richer pattern‑matching errors, and import‑time profiling—that together make scripts run smoother, easier to debug, and more maintainable.
This article breaks down Python’s Global Interpreter Lock, explains when to use multiprocessing, multithreading or asyncio, provides concrete performance benchmarks and a hybrid process‑coroutine pattern, and guides you on choosing the right concurrency model for interview questions.
This article reviews the most popular Python web frameworks of 2024—FastAPI, Django, Flask, Requests, Asyncio, and Django REST Framework—detailing their usage trends, strengths, weaknesses, and ideal use cases, while also highlighting emerging tools and how to choose the right one for your project.
This guide introduces the PHP AsyncIO extension built on PHP Fiber and Workerman, explains its high‑performance features, shows how to install and quickly start with code examples for basic, concurrent, timeout‑controlled, and managed tasks, and provides a complete API reference and advanced usage tips.
Although Python’s async/await syntax lets you pause and resume functions, it alone doesn’t run tasks concurrently; you must explicitly schedule coroutines with tools like asyncio.create_task(), gather(), or TaskGroup, and understand when to use sequential versus concurrent execution to avoid common pitfalls.
Learn how to efficiently debug asynchronous Python code by leveraging three powerful tools—pdb for simple bugs, aiomonitor for real‑time event‑loop inspection, and asynctest for preventing race‑condition errors—complete with practical examples and step‑by‑step guidance.
This comprehensive guide explores Python concurrency and parallelism, covering threading, multiprocessing, async/await, the GIL, thread safety, thread pools, process pools, producer‑consumer patterns, performance testing, and practical best‑practice recommendations for choosing the right concurrency model.
This comprehensive guide walks you through Python's evolution from simple generators using yield, through two‑way communication with yield expressions and generator delegation with yield from, to modern asynchronous programming with asyncio and async/await, including practical examples, performance comparisons, best practices, and common pitfalls.
Explore the evolution of Python asynchronous programming—from basic generators and yield expressions to advanced async/await syntax, including practical examples like network requests, file I/O, producer-consumer patterns, performance comparisons, error handling, and best-practice recommendations for efficient, scalable code.
This article explains the differences between concurrency and parallelism, the impact of Python's Global Interpreter Lock, and provides a detailed comparison of threading, multiprocessing, and asyncio with code examples, performance tests, decision flowcharts, mixed‑usage patterns, common pitfalls, and best‑practice recommendations for choosing the right approach.
Python’s async features, introduced in 3.5, excel at network I/O but face limitations from the GIL, lack of async file I/O, and fragmented library support, while upcoming 3.14 enhancements like free threads and sub‑interpreters promise broader concurrency, yet raise new integration challenges.
This article presents a comprehensive guide to advanced Python techniques, covering sophisticated syntax, powerful data structures, functional programming, concurrency with threading and asyncio, performance optimization using NumPy, Cython, Numba, and practical debugging and visualization tools, illustrated with clear code examples.
This article reveals nine advanced, battle‑tested asyncio performance techniques—including uvloop, memory‑aware task patterns, semaphore limits, executor offloading, graceful cancellation, debugging helpers, connection pooling, loop saturation monitoring, and leak‑free background tasks—that can transform a modest 200‑request‑per‑second service into a 3000+‑request‑per‑second production‑grade system.
This article explains how to decide between asyncio, threading, and multiprocessing for Python programs by examining I/O‑bound versus CPU‑bound workloads, showing real code examples, trade‑offs, and common pitfalls to help you pick the right concurrency tool for your task.
This article explains the fundamentals of Python coroutines and the asyncio library, covering basic concepts, key components, practical examples, advanced techniques such as mixing with threads, cancellation handling, performance optimizations, and common troubleshooting tips for asynchronous programming.
This tutorial walks through multiple Python techniques for downloading regular files, web pages, Amazon S3 objects, and other resources, covering basic requests, wget, handling redirects, chunked large‑file downloads, parallel downloads, progress bars, urllib, urllib3, proxy usage, boto3 for S3, and asynchronous downloads with asyncio.
This guide introduces asynchronous Python, explaining why async improves speed, how coroutines and the event loop work, and provides practical examples and common pitfalls to help developers write faster, more efficient I/O‑bound code.
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.
This article introduces three core tools—Python's built‑in debugger (pdb), the real‑time event‑loop monitor aiomonitor, and the testing library asynctest—to help developers track, debug, and prevent bugs in asynchronous Python programs, complete with practical code examples and usage instructions.
Python 3.13 introduces experimental free‑threading, a JIT compiler, enhanced type‑system utilities, asyncio improvements, and standard‑library updates that together aim to reduce the Global Interpreter Lock bottleneck, accelerate compute‑intensive workloads, and simplify deployment of AI and ML applications across diverse platforms.
This guide explains Python asynchronous programming using the asyncio library, covering async functions, coroutine objects, the event loop, await syntax, task creation, and concurrent execution with asyncio.run, create_task, and gather to improve I/O‑bound performance.
Debugging asynchronous Python code can be challenging, but by using the built-in pdb debugger, the aiomonitor tool for real-time event-loop inspection, and the asynctest library for testing race conditions, developers can efficiently identify and prevent bugs in async applications.
This tutorial teaches how to download files in Python using various modules such as requests, wget, urllib, urllib3, boto3, and asyncio, covering basic downloads, handling redirects, chunked large-file downloads, parallel batch downloads, progress bars, proxy usage, and asynchronous techniques.
This article explains how to cancel asyncio tasks, invoke sub‑coroutines, cancel sub‑coroutines, handle multiple task cancellations, and manage errors in Python's asynchronous programming, providing clear code examples for each scenario and demonstrating best practices for robust async code.
This article explains how to use Python's asyncio library to synchronize and communicate between coroutines, covering Event, Lock, Semaphore, Condition, and Queue primitives with clear code examples demonstrating each mechanism in practice.
This article explains how Python's asyncio library schedules callbacks with call_soon, call_at, call_later, and call_soon_threadsafe, providing detailed usage descriptions and multiple example code snippets that demonstrate immediate, absolute‑time, relative‑time, and thread‑safe task scheduling within an event loop.
This article presents a comprehensive guide to Python exception handling, covering basic try‑except usage, multiple exception capture, else/finally clauses, custom exceptions, exception chaining, logging, context managers, assertions, traceback, concurrent and asynchronous error handling, and testing techniques.
This tutorial teaches how to download files in Python using various modules such as requests, wget, urllib, urllib3, boto3 for S3, and asyncio, covering basic downloads, redirects, chunked large‑file handling, parallel downloads, progress bars, proxy usage, and asynchronous techniques.
This article demonstrates how to use Python's asyncio library together with the aiohttp package to perform asynchronous HTTP requests, covering installation, concurrent request execution, GET and POST examples, error handling, timeouts, SSL verification, and proxy usage, with complete code snippets.
This article introduces Python's async and await keywords, explains how they enable coroutine programming, and provides multiple practical examples including simple coroutine definitions, awaiting tasks, concurrent execution with asyncio.gather, HTTP requests using aiohttp, and resource management with custom coroutine-based managers.
Instagram grew from a simple photo‑sharing app to over 2.5 billion users, prompting engineers to adopt horizontal scaling, replace Python code with Cython, use region‑specific Cassandra clusters, employ the Akkio data‑placement service, and optimize PostgreSQL and Memcache handling to improve resource utilization, data consistency, and latency.
This guide walks through creating a Telegram bot with Python's telebot library, covering data architecture, obtaining a BotFather token, configuring bot details, installing the package, writing message handlers, linking to a local LLM server, handling audio responses, and running the bot.
This article introduces nine powerful Python standard library modules that enhance code efficiency and readability, covering context management, iterators, concurrency, file operations, functional programming, AST parsing, type hints, data classes, and async programming.
This comprehensive guide explains Python’s multiprocessing module, covering process creation, inter‑process communication, pools, synchronization primitives, error handling, and real‑world examples such as web crawlers, data analysis, and game servers, helping developers harness multiple CPU cores to boost performance and avoid GIL limitations.
This article introduces the pytest-asyncio plugin, explains how to install it, and provides multiple code examples demonstrating how to test asynchronous functions, fixtures, marked tests, parameterized tests, and exception handling with pytest in Python projects.
This article presents ten advanced Python techniques—including list comprehensions, lambda functions, decorators, context managers, generators, metaclasses, asyncio coroutines, enums, slicing, and exception handling—each illustrated with detailed code examples to help readers deepen their mastery of Python programming.
This tutorial explains how to download regular files, web pages, Amazon S3 objects, and other resources in Python by covering multiple modules such as requests, wget, urllib, urllib3, Boto3, and asyncio, and demonstrates handling redirects, large files, parallel downloads, progress bars, proxies, and asynchronous execution.
This article explains the concept of IO, the role of the operating system, the complete IO workflow, and compares five common IO models—blocking, non‑blocking, select/poll/epoll multiplexing, signal‑driven, and asynchronous—highlighting their mechanisms, advantages, and typical use cases.
This article introduces LangChain's callback mechanism, demonstrates how to implement custom synchronous and asynchronous callbacks in Python, compares them with JavaScript async patterns, and shows how to monitor token usage and control costs using OpenAI callbacks.
This article introduces Python concurrency programming, explaining how to use multithreading, multiprocessing, thread and process pools, async/await, coroutines, and producer‑consumer models with code examples, helping developers improve performance and responsiveness for time‑consuming tasks and concurrent network requests.
This article introduces Python coroutines and asynchronous programming using the asyncio module, explains key concepts such as event loops and non‑blocking I/O, and provides ten detailed code examples that demonstrate creating, managing, and synchronizing async tasks for improved performance.
This article introduces Python coroutines as a lightweight concurrency model and provides ten practical code examples demonstrating how to use the asyncio, gevent, and trio libraries for asynchronous I/O, concurrent network requests, file operations, task scheduling, and more.
This article explores three Python coroutine libraries - asyncio, gevent, and trio - providing 10 practical code examples demonstrating their use in asynchronous programming for tasks like IO operations, network requests, file handling, and more.
This guide explains how to add, manage, and persist asynchronous scheduled jobs in a FastAPI application using APScheduler's AsyncIOScheduler with SQLite storage, covering task types, code examples, limitations, and alternative solutions such as arq, fastapi‑utils, and Celery.
This guide introduces Swoole, a high‑performance asynchronous PHP framework for IoT, and walks through installing the extension, building TCP and WebSocket servers, performing asynchronous file I/O, and leveraging coroutines with complete code examples.
A recent Redis‑py library vulnerability caused ChatGPT to expose personal data of about 1.2% of Plus users, prompting an OpenAI apology, a detailed post‑mortem, and a series of backend and security fixes to prevent similar incidents.
A recent bug in the open‑source redis‑py library caused ChatGPT to leak personal data of about 1.2 % of Plus users, allowing some users to see others' names, emails, and partial credit‑card details; OpenAI issued an apology, published a post‑mortem, and deployed a patch to fix the Redis Cluster async client issue.
This tutorial demonstrates multiple Python techniques for downloading files—including simple requests.get calls, wget and urllib modules, handling redirects and large files with chunked streaming, parallel batch downloads, progress bars, proxy support, S3 retrieval via boto3, and asynchronous downloads with asyncio—providing a comprehensive guide for developers.
This tutorial walks through multiple Python approaches for downloading files—including simple requests.get calls, the wget module, handling redirects, chunked large‑file downloads, parallel batch downloads, proxy usage with urllib, S3 retrieval via Boto3, and asynchronous fetching with asyncio—providing code examples and best‑practice tips.
This guide demonstrates how to perform asynchronous CRUD operations on MySQL, PostgreSQL, and Redis using Python libraries such as aiomysql, asyncpg, and SQLAlchemy, covering connection setup, querying, inserting, updating, deleting, and handling result objects with practical code examples.
This article demonstrates a complete Python workflow—using aiohttp and asyncio to crawl nationwide university information, processing the JSON data into CSV files with pandas, and visualizing the results in Tableau—providing a practical guide for large‑scale data collection and analysis.
This article explains how to use Async I/O in Flink, describes the ordered and unordered output modes, details the internal AsyncWaitOperator implementation with its producer‑consumer model, and discusses fault‑tolerance mechanisms including state snapshot and recovery.
This tutorial demonstrates how to build a simple aiohttp web server in Python, install required dependencies, retrieve random Mars photos from the NASA API, validate image size and color mode using Pillow, and serve the selected image directly to the browser.
This tutorial walks you through building a Python scraper that extracts video titles and URLs from Pear Video, then downloads the videos to your computer using single‑threaded requests, a thread‑pool, and asynchronous asyncio techniques, complete with full code examples.
This article explains what coroutines are, compares Python's coroutine implementation with Go's goroutine model, discusses their execution characteristics, and provides code examples illustrating producer‑consumer pipelines and async/await usage in both languages.
This article introduces a high‑performance Python microservice framework built on Sanic, detailing its architecture, asynchronous design principles, integration of uvloop, asyncpg, aiohttp, Peewee ORM, OpenTracing, Swagger API documentation, middleware, testing, and deployment considerations for scalable backend services.
This article explains Python's asyncio asynchronous I/O model, how to define and run coroutines, use callbacks, manage multiple concurrent tasks with gather, compare run_until_complete with run_forever, and properly close the event loop, providing clear code examples throughout.
This article explains what coroutines are, compares them with threads, outlines when to use them, introduces the async‑capable httpx library, shows installation and sample code, and demonstrates a performance test where asynchronous requests cut execution time by about 73% compared to synchronous requests.
This article provides a detailed English overview of Netty's complete server lifecycle, covering service startup, connection establishment, data read/write handling, business logic processing, and the procedures for closing connections and shutting down the service.
This article explains how Python implements coroutines for high‑performance network and web programming by combining OS‑level IO multiplexing, generator‑based control flow, callback elimination, stack‑driven call‑chain traversal, Future objects, and the evolution toward async/await syntax.
This article explains the concepts of synchronous and asynchronous programming in Python, compares their architectures, discusses how async frameworks and greenlet‑based libraries work, and clarifies when asynchronous code can outperform synchronous code, especially under high I/O‑bound load.
This tutorial explains how to download files, webpages, and Amazon S3 objects using Python, covering the requests and wget modules, handling redirects, chunked and parallel downloads, progress bars, proxy support, urllib/urllib3 usage, and asynchronous downloading with asyncio.
This article explains Python coroutines, their advantages over threads, demonstrates producer‑consumer patterns, introduces the asyncio event loop, shows how to use async/await syntax, and provides examples of asynchronous network requests and a simple aiohttp server for high‑concurrency I/O.
This tutorial teaches you how to download files in Python using various modules—including requests, wget, urllib, urllib3, boto3, and asyncio—covering simple downloads, handling redirects, chunked large-file downloads, multithreaded batch downloads, progress bars, proxy usage, and Amazon S3 retrieval.
This tutorial explains how to perform asynchronous MySQL operations in Python using aiomysql, covering basic connections, CRUD actions, safe parameter handling to prevent SQL injection, connection pooling, transaction management, integration with SQLAlchemy for ORM queries, and a custom reconnection wrapper for resilient database access.
This article explains how to control request frequency in Python using the ratelimiter library for synchronous and asynchronous code, and introduces SlowAPI as a flexible solution for FastAPI and Flask, helping backend developers reduce server load and protect APIs from abuse.
This article explains how crawler engineers can fetch real‑time data such as sports scores, stock quotes, or cryptocurrency prices by comparing polling and WebSocket approaches, introducing the aiowebsocket library, and providing complete Python code to perform handshake, subscription, and continuous data streaming.
This tutorial walks you through using various Python modules—including requests, wget, urllib, urllib3, boto3, and asyncio—to download regular files, web pages, Amazon S3 objects, handle redirects, large files, parallel downloads, proxies, and progress bars, all with clear code examples.
This article demonstrates how to configure Python's logging module for console and file output, extend it with HTTPHandler for remote logging, and improve performance by using custom handlers, threading, thread pools, and asynchronous aiohttp calls to avoid blocking the main program.
This article explains the limitations of Python's GIL, compares synchronous and asynchronous execution models, introduces asyncio and aiohttp, provides concrete code examples for single and multiple concurrent HTTP requests, and offers solutions for common concurrency errors such as too many file descriptors.
This tutorial walks through multiple Python approaches for downloading files—including simple requests and wget calls, handling redirects, large and multi‑file downloads, proxy usage, urllib/urllib3 methods, and asynchronous techniques—providing complete code snippets and practical tips for each scenario.
A comprehensive guide that explains Python concurrency concepts—including processes, threads, synchronization primitives, thread pools, asyncio, greenlet, and gevent—while showing how to choose the right model for CPU‑bound or I/O‑bound tasks.
This article walks through the evolution of Python's coroutine support—from early generator‑based yield/send patterns, through the introduction of @asyncio.coroutine and yield from, to the modern async and await syntax—explaining concepts, execution flow, and providing clear code examples.
This article explains how Python's asyncio library enables asynchronous programming by defining coroutine functions with async/await, managing execution with an event loop, coordinating multiple tasks using gather, wait, and ensure_future, and integrating synchronous code via executors for concurrent execution.
This tutorial demonstrates how to use asynchronous Python (aiohttp, motor, asyncio) to scrape all comments from a popular Bilibili video, store them in MongoDB, and generate a Chinese word‑cloud analysis to reveal why the video went viral.
This article explains the fundamentals of asynchronous programming, contrasting blocking, non‑blocking, synchronous and asynchronous execution, and demonstrates how to implement async behavior in Python using Tornado, asyncio, and aiohttp, including coroutine definitions, yield usage, and practical code examples.
This article explains Python's asyncio module, presents a practical HTTP keep-alive benchmark tool built with async I/O, details test setup and performance results, and summarizes key asyncio concepts such as event loops, futures, coroutines, and generators.
This article introduces Python's asyncio library, explains how coroutines are defined and run, demonstrates event‑loop management with run_until_complete and run_forever, covers callbacks, gathering multiple coroutines, and best practices for closing loops, all illustrated with clear code screenshots.
The article introduces the Requests‑HTML library—a powerful Python tool that merges HTTP downloading and HTML parsing, outlines its key features such as JavaScript support and built‑in selectors, and provides step‑by‑step code examples for quick, efficient web scraping.
This article explains the concept of asynchronous I/O, introduces Python’s provisional asyncio module, and walks through a complete example that creates an HTTP keep‑alive benchmark tool, including code, test environment, performance results, and key asyncio concepts such as event loops, futures, coroutines, and generators.
Learn how Python's asyncio library enables asynchronous I/O by defining coroutines with async/await, managing event loops, handling futures, using gather and wait, and properly closing loops, with clear code examples illustrating each concept and practical tips for concurrent programming.
This article compares JMeter, LoadRunner, Locust, asyncio (Python) and Go-based load testing tools by measuring CPU, memory, Nginx response and TPS under varying concurrency levels, concluding that Go delivers the highest throughput while Python coroutines offer significant improvements over older Python versions.
This article walks through the design and implementation of a lightweight asynchronous Python web framework, covering its core components such as HTTPServer, HTTPConnection, request parsing, routing, timeout handling, and the App class, while explaining design choices, limitations, and testing strategies.
This tutorial explains Python's asynchronous programming model—including coroutines, the yield‑from syntax, async/await keywords, and event‑loop management—while providing a complete, runnable example that fetches Reddit JSON data concurrently using aiohttp.
