This article explains how to build a high‑throughput like and read counter that can handle 100,000 requests per second by layering Redis caching, optional AOF or MySQL persistence, cleaning invalid client requests, and separating business, statistics, and reporting logic.
This article presents a complete, step‑by‑step analysis of a high‑concurrency Redis counter component that supports up to 100 000 QPS, covering business pain points, architectural design, two core counting strategies, sharding, local batch optimization, code walkthroughs, and performance benchmark results.
Prometheus supports four core metric types—gauge, counter, summary, and histogram—each with distinct semantics and usage patterns; this guide explains their definitions, how to update them via client libraries, and how they appear in the Prometheus text exposition format, including example code and query tips.
This article explains Redis's atomic counter commands (INCR, INCRBY, DECR, DECRBY), highlights their pitfalls such as missing TTL and concurrency issues, and presents practical design solutions—including SET NX+EX initialization, double‑TTL compensation, retry queues, fallback strategies, and performance optimizations like Lua scripting and pipelining.)
This article tackles a common Python question—how to count the number of 1s in a list—by presenting four concise solutions using filter with a lambda, list comprehensions, the Counter class, and the built‑in count method, each illustrated with clear code examples.
This article introduces several useful classes from Python's collections module—including Counter, defaultdict, deque, namedtuple, ChainMap, UserList/UserDict/UserString, as well as heapq and an OrderedDict‑based LRU cache—explaining their purposes and providing ready‑to‑run code examples for each.
This article explains how to design ranking systems for games and social apps, compares database sorting and client‑side approaches, and provides step‑by‑step Java code for implementing Redis‑based counters, leaderboards, performance optimizations, and real‑world use cases.
This article introduces Python’s collections module as a versatile alternative to built‑in containers, detailing the purpose, key methods, and practical code examples for Counter, defaultdict, OrderedDict, namedtuple, deque, and ChainMap, helping developers choose the right specialized container for their tasks.
This article introduces Python's collections module, explaining the purpose and usage of its specialized container types—including Counter, defaultdict, OrderedDict, namedtuple, deque, and ChainMap—along with their most common methods and practical code examples.
This article presents sixteen practical Redis use‑cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, tags, filtering, follow models, and ranking—explaining their data types, commands, and sample code.
This article explores how to leverage Redis for a wide range of backend functionalities—including caching, distributed data sharing, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow relationships, and leaderboards—providing practical code examples and usage patterns.
This article provides a comprehensive overview of Python’s collections module, detailing its specialized container types such as Counter, defaultdict, OrderedDict, namedtuple, deque, and ChainMap, along with usage examples and key methods for each.
This tutorial explains how to count word frequencies using Python's collections.Counter from static, random, or file‑based text sources and then visualize the results with the WordCloud library, covering code examples, parameter settings, and available colormaps.
Explore essential Python tricks—from dictionary and set comprehensions to using Counter for counting, pretty‑printing JSON, and building a quick XML‑RPC file service—showcasing concise code snippets that illustrate the language’s clean syntax and powerful standard library.
This article explains Prometheus data collection by describing metric syntax, label usage, time‑series concepts, the four logical metric types (Counter, Gauge, Histogram, Summary), and provides practical naming, labeling, and selection guidelines for effective monitoring.
This tutorial demonstrates how to acquire, clean, and analyze online restaurant review data using Python, then create customized bar and pie charts with pyecharts, while also illustrating the use of Python's Counter for rating distribution analysis.
This article shares five practical Python tips—dictionary and set comprehensions, the Counter class, pretty‑printing JSON, a quick XML‑RPC web service, and criteria for choosing solid open‑source libraries—each illustrated with clear code examples to help developers write cleaner, more efficient code.
This article explains the four core Prometheus metric types—Counter, Gauge, Histogram, and Summary—detailing their characteristics, appropriate use cases, PromQL functions, and how they differ, while providing language-specific client library references and visual examples.
