The article explains how to quickly determine whether an integer exists among ten million numbers by comparing hash tables, boolean arrays, bitmaps, and Bloom filters, and shows practical Java and Redis implementations with code examples and memory‑usage analysis.
This article explains how to use Redis Bitmap to efficiently store and query massive binary‑state data such as user login status and daily sign‑in records, covering underlying memory structures, command usage, and practical examples for counting, checking, and aggregating large‑scale user activity.
This article explains the principles of BitMap, BitSet, and Bloom Filter data structures, illustrating how they use bits to efficiently store, add, delete, and query large sets of integers, and discusses their practical applications such as fast sorting, deduplication, and membership testing.
The article analyzes the memory inefficiency of storing per‑message read/unread user lists in large group chats and proposes a bitmap‑based solution that maps users to sequential IDs, uses compact bitmaps for read status and handles member exits, achieving over 95% storage reduction.
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.
The article presents a high‑efficiency Android bitmap‑reuse strategy that combines the inBitmap API with atomic reference counting and a three‑layer LRU cache (active, LruCache, InBitmapPool), dramatically lowering garbage‑collection pauses and achieving over 80 % reuse on pre‑Android 8 devices.
This article outlines a variety of practical Redis use cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow/recommendation models, and ranking—demonstrating how Redis can support diverse backend functionalities.
This article explains how to use Redis bitmap operations to efficiently implement like, sign‑in, and other binary state features, covering bitmap fundamentals, common use cases, essential commands, Java code examples with Jedis, and range query techniques.
This article explains how to choose the right Redis data structures—Bitmap, HyperLogLog, Set, List, and Sorted Set—to efficiently handle massive statistical scenarios such as UV counting, ranking, and aggregation in high‑traffic mobile applications.
This article explains how to implement a user check‑in feature using two approaches—direct MySQL storage and Redis bitmap—detailing database schema, sign‑in logic, code examples, and a comparison of their advantages and disadvantages for backend development.
This article explains the bitmap concept, demonstrates how to store and query billions of integers using bit-level operations, introduces BitSet for dynamic bit vectors, and describes Bloom filters as probabilistic structures for fast, memory‑efficient membership testing in large‑scale data scenarios.
The article details a thorough investigation of native‑memory crashes in the K歌 Android app, describing how memory usage was profiled, comparing three analysis tools, building a custom loli_profiler‑based hook to track malloc/free and bitmap allocations, exposing leaks, and outlining fixes and future optimization plans.
This article explains how bitmap and Bloom filter data structures reduce memory usage for large integer sets, demonstrates a Java bitmap implementation, and explores high‑concurrency cache design, consistency, penetration, and avalanche problems with practical mitigation techniques.
The article explains how to perform efficient user retention, funnel, and session analysis on large ClickHouse datasets by replacing costly multi‑table joins with bitmap compression, the built‑in retention function, windowFunnel, and high‑order array functions, providing practical SQL examples and performance insights.
This article explains the bitmap concept, demonstrates how to store and query billions of integers using bitwise operations, introduces BitSet and Bloom Filter implementations, and provides practical Java code snippets for adding, removing, and checking elements while highlighting their memory‑saving advantages.
This article explains how to use Redis sets, sorted sets, bitmaps, and HyperLogLog for various aggregation, sorting, binary state, and cardinality statistics, providing concrete command examples, design patterns, and performance considerations for large‑scale applications.
This article explains how Suning built a high‑performance user‑tag platform on ClickHouse, replacing Elasticsearch with bitmap‑based storage and a new architecture that delivers sub‑second profiling queries for over 600 million users, detailing the design, implementation, and future enhancements.
This article explains how Suning built a massive user‑tag platform using ClickHouse and RoaringBitmap to enable second‑level user profiling for over 600 million users, covering tag generation, storage, query architecture, and performance optimizations for marketing campaigns.
This article examines common techniques for implementing a user check‑in function in web and mobile applications, comparing a straightforward MySQL table solution with a Redis bitmap approach, detailing the data structures, code examples, workflow, and the advantages and disadvantages of each method.
The article explains how to sort a massive 4.6 GB file containing 500 million random integers by first attempting in‑memory quicksort and merge sort, then using a bitmap approach, and finally applying an external sort that splits the data into manageable chunks and merges them efficiently.
This article walks through nine practical Redis use cases—including basic KV caching, distributed locking, delayed queues, rate limiting, service discovery, bitmap storage, HyperLogLog counting, roaring bitmaps, and Bloom filters—explaining the underlying concepts, configuration tips, and code examples for robust backend development.
The article details how adding risk‑control measures slowed a new‑user group order API, how profiling identified the costly "determine new user" step, and how replacing a database query with a Redis BitMap reduced response time by 31% while exposing a production‑scale BitMap offset limitation.
This article details how a backend team diagnosed and resolved a 5‑10% performance drop in a new‑user group‑buying order API by profiling with StopWatch, replacing costly database checks with Redis bitmap flags, and addressing bitmap size limits that caused production failures.
This article explains how different Android versions handle image memory, clarifies screen size, density, and resolution concepts, shows how folder placement and bitmap formats affect RAM usage, and provides practical Glide configuration to prevent out‑of‑memory crashes.
This article describes how a data management platform (DMP) at Beike leverages ClickHouse bitmap structures and Spark pipelines to generate global numeric user IDs, design tag-specific bitmap rules for enum, continuous, and date attributes, handle boundary cases, and produce high‑performance bitmap SQL for real‑time user group estimation and complex segment logic.
This article explains GrowingIO's event analysis data model, the challenges of metric‑dimension calculations on massive datasets, and how a BitMap‑based vertical storage and dimension‑combination numbering dramatically improve query efficiency and scalability.
This tutorial explains Redis bitmap operations, covering SETBIT, GETBIT, BITCOUNT, and BITOP commands, their syntax, underlying SDS structure, time‑complexity analysis, memory‑size calculations, and practical scenarios such as user sign‑in tracking and online‑user statistics.
This article demonstrates how to capture a video progress bar in an Android app with UiAutomator, extract its bitmap, analyze the red channel values, and compute the playback percentage for automated test assertions.
This article explores Redis beyond its traditional caching role, covering distributed locks, message queues, bitmaps, Bloom filters, geolocation, and rate limiting, while highlighting typical implementation patterns, code examples, and the inherent limitations of each scenario.
ResourceCanary, a Matrix sub‑module, automatically detects Activity memory leaks and redundant Bitmap objects in large‑scale Android apps by monitoring lifecycle callbacks, leveraging WeakReference queues, parsing Hprof dumps, and providing concise reference chains for fast debugging and performance optimization.
This article shares WeChat's engineering experience in detecting and preventing various Android memory leaks—including Activity, Bitmap, native, and thread leaks—by building custom tools, refining monitoring pipelines, and applying fallback protection to keep the app stable at scale.
An interviewee faces the challenge of determining whether a newly given integer exists within a set of 4 billion numbers, and the article explores efficient solutions—from naive disk‑I/O approaches to distributed processing and the memory‑saving bitmap technique—highlighting their performance trade‑offs and implementation details.
This article explores practical algorithms for sorting, de‑duplicating, and extracting top‑K records from massive data sets, covering bitmap techniques, external sorting, hash‑based counting, min‑heap selection, divide‑and‑conquer, Bloom filters, and distributed processing strategies.
An interview question about checking whether a new integer belongs to a set of 4 billion numbers leads to a discussion of distributed loading across eight machines, bitmap representation using 500 MB of memory, and interval‑based external sorting, illustrating practical big‑data algorithm design.
Meitu’s Naix distributed bitmap system accelerates massive user‑data analytics by using a three‑layer architecture, sharded RoaringBitmap storage, and PalDB, delivering over 600× faster queries than Hive, supporting fast generation plugins, fault‑tolerant replication, and millisecond‑level RPC query responses while reducing storage by 67%.
This article details a big‑data precise‑marketing solution that leverages HBase storage and Roaring BitMap indexing to efficiently handle billions of user records, describing project background, technology selection, architecture, partitioning strategy, and coprocessor implementation for fast multidimensional queries.
This guide explains how to convert video stream data into individual bitmap images on Android by using SurfaceTexture to receive decoded frames, OpenGL to render them as external textures, and glReadPixels to capture the RGBA data for saving as PNG files.
This guide outlines practical principles for creating Oracle indexes, compares B‑Tree and bitmap index types, explains their creation syntax, highlights performance characteristics, and lists common situations where indexes may be ignored or unsuitable.
This article explains the definition and 4V characteristics of big data and presents practical algorithms—including hash partitioning, min‑heap top‑K selection, bitmap extensions, and Bloom filter techniques—to efficiently handle ultra‑large log files, integer sets, and keyword searches within strict memory limits.
This article explains Android's memory allocation and reclamation mechanisms, enumerates common memory problems such as leaks, OOM, and bitmap handling, and demonstrates a comprehensive optimization case study on the JOOX music app that reduced average memory consumption by over 40 MB.
This article explains how to use Redis bit operations and bitmap keys to record daily user logins and efficiently compute statistics such as today’s active users, users active over the past three days, and users active within a week.
This article explains Android's DisplayMetrics density values, shows how to calculate a Bitmap's runtime memory footprint using getByteCount, explores the impact of screen density, pixel format, and scaling, and provides practical techniques such as inSampleSize, matrix scaling, and pixel format selection to minimize memory consumption.
This article explains how bitmap data structures, combined with compression and in‑memory techniques, enable fast, flexible, and scalable multi‑dimensional analytics for large‑scale data platforms, addressing historical marketing inefficiencies and outlining future directions such as memory‑mapped files and distributed bitmap computation.
