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141 articles · Page 1 of 2
IT Services Circle
IT Services Circle
Aug 9, 2026 · Backend Development

How to Detect a 30‑Day Continuous Sign‑In for 1 B Users with 1 GB Memory

The article breaks down a large‑scale interview question, showing why storing each sign‑in as a database row is infeasible, how a bitmap compresses a year of data to 46 bytes per user, the pitfalls of BITCOUNT, the importance of key dimension design, and the exact Redis commands and local‑scan algorithms—including a five‑step bit‑wise trick—to reliably determine a 30‑day continuous sign‑in.

AlgorithmBitmapInterview
0 likes · 9 min read
How to Detect a 30‑Day Continuous Sign‑In for 1 B Users with 1 GB Memory
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Jul 13, 2026 · Backend Development

Beyond Caching: 10 Advanced Redis Use Cases You’re Probably Missing

This article walks through ten advanced Redis features—including Bloom filters, Redisson distributed locks, delayed queues, token‑bucket rate limiting, bitmaps, HyperLogLog, GEO, Streams, Lua scripts, and RedisJSON—explaining their principles, pros and cons, typical scenarios, and providing complete Spring Boot code examples.

BitmapBloom filterGEO
0 likes · 23 min read
Beyond Caching: 10 Advanced Redis Use Cases You’re Probably Missing
IT Services Circle
IT Services Circle
Jul 4, 2026 · Interview Experience

Finding the Poisoned Bottle Among 1000: Why 10 Mice Are Enough

The article explains how a binary‑encoding strategy lets you identify a single poisoned bottle out of 1000 using only ten mice by assigning each mouse to a bit position, sampling wines accordingly, and interpreting the death pattern as a 10‑bit address.

BitmapBloom filterbinary
0 likes · 7 min read
Finding the Poisoned Bottle Among 1000: Why 10 Mice Are Enough
Architect's Guide
Architect's Guide
Jun 24, 2026 · Databases

Nine Essential Aspects of Redis: A Comprehensive Technical Guide

This article provides an in‑depth technical overview of Redis, covering its single‑threaded model, core data structures, persistence options, master‑slave replication, Sentinel and cluster architectures, eviction policies, progressive rehash, skiplist implementation, bitmap usage for massive active‑user counting, and strategies for handling MySQL‑Redis write‑through consistency.

BitmapClusterData Structures
0 likes · 30 min read
Nine Essential Aspects of Redis: A Comprehensive Technical Guide
Lobster Programming
Lobster Programming
Jun 22, 2026 · Databases

Common Redis Use Cases in Real-World Projects

This article outlines nine practical Redis scenarios—including hot‑data caching, distributed locks with Redisson, Bloom filters for cache‑penetration protection, delayed queues using ZSet, token‑bucket rate limiting, bitmap boolean statistics, UV deduplication via Set/HyperLogLog/Bitmap, geospatial indexing, and lightweight Stream queues—explaining their motivations, implementation steps, and trade‑offs.

BitmapBloom filterDelayed Queue
0 likes · 7 min read
Common Redis Use Cases in Real-World Projects
IoT Full-Stack Technology
IoT Full-Stack Technology
Apr 29, 2026 · Databases

10+ Practical Redis Use Cases You Can Implement Today

This article walks through more than ten common Redis scenarios—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow/fan relationships, and ranking—showing concrete command examples and code snippets for each.

BitmapFollow SystemMessage Queue
0 likes · 9 min read
10+ Practical Redis Use Cases You Can Implement Today
java1234
java1234
Apr 18, 2026 · Backend Development

Beyond Simple Caching: 8 Essential Redis Use Cases for Java Backend Engineers

This guide walks Java backend developers through Redis’s eight core scenarios—caching, distributed locks, rate limiting, session sharing, leaderboards, counters, message and delay queues, bitmap statistics, and geolocation—providing complete code, diagrams, and production‑grade best practices.

BitmapGEOLeaderboard
0 likes · 21 min read
Beyond Simple Caching: 8 Essential Redis Use Cases for Java Backend Engineers
Cloud Architecture
Cloud Architecture
Apr 8, 2026 · Backend Development

How to Build a High‑Performance Sign‑In System with Spring Boot & Redis BitMap

This article walks through the design and production‑grade implementation of a high‑throughput sign‑in service using Spring Boot, Redis BitMap, Lua scripts, and asynchronous reward processing, covering why traditional relational tables fall short, key generation, offset calculation, continuous‑sign‑in logic, architecture layering, scaling, monitoring, and testing strategies.

BitmapRedisSpring Boot
0 likes · 35 min read
How to Build a High‑Performance Sign‑In System with Spring Boot & Redis BitMap
Java Architect Handbook
Java Architect Handbook
Apr 4, 2026 · Backend Development

16 Powerful Redis Use Cases Every Backend Engineer Should Know

This article presents a comprehensive guide to 16 practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, queues, lotteries, likes, product tagging, filtering, social graphs, and leaderboards—complete with command examples, code snippets, and visual illustrations to help developers implement these patterns efficiently.

BitmapLeaderboardRedis
0 likes · 11 min read
16 Powerful Redis Use Cases Every Backend Engineer Should Know
Tech Freedom Circle
Tech Freedom Circle
Mar 17, 2026 · Databases

Why HyperLogLog Misses 100M Daily Active Users and How Bitmap Solves It

The article dissects an Alibaba interview question on counting 100 million daily active users, showing why HyperLogLog’s error and lack of per‑user state make it unsuitable, and presents a detailed Bitmap‑based architecture—including sharding, pre‑computation, and ClickHouse integration—to achieve precise, high‑performance analytics.

BitmapClickHouseDailyActiveUsers
0 likes · 16 min read
Why HyperLogLog Misses 100M Daily Active Users and How Bitmap Solves It
ITPUB
ITPUB
Dec 27, 2025 · Fundamentals

How to Test Membership in 4 Billion Numbers Using Only 1 GB Memory

This article explains how to determine whether a given unsigned integer belongs to a set of 4 billion distinct numbers within a 1 GB memory limit, comparing a bitmap approach with a Bloom filter, providing detailed implementation steps and C++ code examples for both methods.

BitmapBloom filterC++
0 likes · 8 min read
How to Test Membership in 4 Billion Numbers Using Only 1 GB Memory
Huolala Tech
Huolala Tech
Oct 17, 2025 · Big Data

How HuoLala Accelerated User Profiling 30× Faster with Apache Doris

This article details how HuoLala built a high‑performance user profiling platform on Apache Doris, redesigning data models, leveraging bitmap storage, and applying query‑level optimizations to achieve up to 30‑fold speed gains, lower memory usage, and scalable real‑time analytics.

Apache DorisBig DataBitmap
0 likes · 17 min read
How HuoLala Accelerated User Profiling 30× Faster with Apache Doris
NiuNiu MaTe
NiuNiu MaTe
Sep 22, 2025 · Big Data

How to De‑duplicate 4 Billion QQ Numbers with Only 1 GB RAM

Learn four practical techniques—simple sorting, hashmap deduplication, external merge sort, and bitmap bit‑set optimization—to efficiently remove duplicate QQ numbers from a 40‑billion‑record file while staying within a strict 1 GB memory limit, even handling tighter 100 MB constraints.

AlgorithmBig DataBitmap
0 likes · 9 min read
How to De‑duplicate 4 Billion QQ Numbers with Only 1 GB RAM
macrozheng
macrozheng
Sep 18, 2025 · Fundamentals

How Bitmaps and Bloom Filters Slash Memory Usage for Massive Datasets

This article explains how using a bitmap can reduce the memory needed to store billions of integers from about 15 GB to under 500 MB, describes the bitmap concept, introduces Bloom filters, outlines their principles, advantages, common use cases, and provides Java and Redis code examples for implementation.

BitmapBloom filterData Structures
0 likes · 11 min read
How Bitmaps and Bloom Filters Slash Memory Usage for Massive Datasets
Architect
Architect
Aug 28, 2025 · Backend Development

Mastering Redis BitMap for Efficient Sign‑In and Statistics in Spring Boot

This article explains how to implement a memory‑efficient sign‑in feature and continuous‑sign‑in statistics using Redis BitMap, covering basic commands, Spring Boot integration, key design, code examples, and advanced use cases such as cache‑penetration mitigation, providing step‑by‑step guidance for backend developers.

BitmapCache PenetrationRedis
0 likes · 12 min read
Mastering Redis BitMap for Efficient Sign‑In and Statistics in Spring Boot
Java Architect Essentials
Java Architect Essentials
Aug 22, 2025 · Fundamentals

How Bitmap and Bloom Filter Slash Memory Usage for Billions of IDs

This article explains how using a bitmap reduces the storage of 4 billion unsigned integers from 14.9 GB to about 476 MB, introduces the concept and benefits of bitmaps, and then details Bloom filter principles, advantages, limitations, common use cases, and Java/Redis implementation examples.

BitmapBloom filterspace optimization
0 likes · 12 min read
How Bitmap and Bloom Filter Slash Memory Usage for Billions of IDs
ITPUB
ITPUB
Jul 29, 2025 · Big Data

How to Deduplicate 4 Billion QQ IDs Using a Bitmap Within 1 GB Memory

Learn how to efficiently remove duplicates from 4 billion QQ numbers using a memory‑friendly Bitmap approach that fits within a 1 GB limit, including calculations, step‑by‑step implementation, Java code, and a discussion of its advantages and drawbacks.

Big DataBitmapData Structures
0 likes · 9 min read
How to Deduplicate 4 Billion QQ IDs Using a Bitmap Within 1 GB Memory
Su San Talks Tech
Su San Talks Tech
Jul 17, 2025 · Big Data

How to De‑Duplicate 1 Billion QQ Numbers Using Under 1 GB of Memory

This article explores multiple techniques—including bitmap indexing, Bloom filters, external sorting, Spark, and layered bitmap structures—to efficiently remove duplicate QQ numbers from a dataset of up to one billion entries while keeping memory usage below a gigabyte and maintaining high accuracy.

BitmapBloom filterSpark
0 likes · 12 min read
How to De‑Duplicate 1 Billion QQ Numbers Using Under 1 GB of Memory
ITPUB
ITPUB
Jul 14, 2025 · Databases

How to Compute Mutual Friends for 100M Users in Seconds with Redis

This article breaks down the massive‑scale challenge of calculating mutual friends for hundreds of millions of users, analyzes data‑size, latency, and memory constraints, and presents three Redis‑based solutions—basic Set, bitmap, and a sharded architecture—complete with code snippets, performance numbers, and practical trade‑offs.

BitmapMutual FriendsRedis
0 likes · 10 min read
How to Compute Mutual Friends for 100M Users in Seconds with Redis
Ubiquitous Tech
Ubiquitous Tech
Jun 8, 2025 · Big Data

Inside ByteDance’s Trillion‑Scale Universal Tag Management Platform

ByteDance’s internally built “塔阁平台” (Tag Platform) powers Douyin e‑commerce’s trillion‑scale tag management and audience selection, handling over a thousand tag types, millions of audience combinations, and ten‑million QPS through a hybrid offline‑online architecture that combines Hive, ClickHouse, Bitmap compression, rule‑engine and materialized views, while ensuring high availability, low latency, and robust monitoring.

Audience SegmentationBig DataBitmap
0 likes · 10 min read
Inside ByteDance’s Trillion‑Scale Universal Tag Management Platform
macrozheng
macrozheng
Apr 7, 2025 · Big Data

How to Deduplicate 4 Billion QQ Numbers Using a Bitmap Under 1 GB

This article explains how to efficiently remove duplicates from 4 billion QQ numbers within a 1 GB memory limit by replacing the naïve HashSet approach with a memory‑saving Bitmap data structure, complete with calculations, algorithm steps, Java code, and a discussion of its pros and cons.

BitmapMemory Optimizationdeduplication
0 likes · 9 min read
How to Deduplicate 4 Billion QQ Numbers Using a Bitmap Under 1 GB
Architect's Guide
Architect's Guide
Mar 18, 2025 · Databases

Strategies for Splitting Large Keys and Values in Redis

The article explains why large keys, oversized values, massive key counts, and huge Bitmaps or Bloom filters degrade Redis performance and provides practical splitting techniques—using multiple keys, hashes, bucketed hashing, and bitmap partitioning—to reduce memory pressure and improve response times.

BitmapBloom filterHash
0 likes · 8 min read
Strategies for Splitting Large Keys and Values in Redis
Bilibili Tech
Bilibili Tech
Feb 21, 2025 · Databases

Applying ClickHouse Bitmap and BSI Techniques for Real-Time Audience Selection in a Data Management Platform

By integrating ClickHouse bitmap structures, a dictionary service for dense ID mapping, and Bit‑Slice Indexes, Bilibili’s Data Management Platform now supports flexible, multi‑dimensional audience selection and profiling over petabyte‑scale data with minute‑level latency, cutting storage by over twenty‑fold and query times from hours to seconds.

Audience SegmentationBSIBig Data
0 likes · 23 min read
Applying ClickHouse Bitmap and BSI Techniques for Real-Time Audience Selection in a Data Management Platform
JD Tech Talk
JD Tech Talk
Dec 26, 2024 · Databases

Using ClickHouse for Efficient Tag Bitmap Storage and Group Computation in a CDP

This article explains how ClickHouse’s columnar storage, bitmap functions, and distributed architecture can be leveraged to store billions of tag bitmaps, combine them efficiently, and support fast group calculations for customer data platforms, while addressing data‑warehouse integration, storage format, and performance challenges.

Bitmapcolumnar storagedata-warehouse
0 likes · 10 min read
Using ClickHouse for Efficient Tag Bitmap Storage and Group Computation in a CDP
Deepin Linux
Deepin Linux
Dec 21, 2024 · Fundamentals

Understanding Linux Kernel Bitmap Data Structure and Its APIs

This article explains the Linux kernel bitmap data structure, its definition, macro-based declarations, related assembly instructions, core bitmap APIs such as set_bit and test_bit, various bit‑operation functions, and practical applications ranging from PID allocation to device management and file‑system indexing.

BitmapData StructureLinux
0 likes · 27 min read
Understanding Linux Kernel Bitmap Data Structure and Its APIs
Lobster Programming
Lobster Programming
Dec 12, 2024 · Backend Development

How to Use Redis Bitmap for Efficient User Sign‑In Tracking

This article explains why storing daily sign‑in data in a traditional database can cause performance issues at scale and demonstrates how Redis bitmap, a memory‑efficient bit‑level structure, can record and compute continuous user sign‑ins both by date and by user.

BitmapData StructuresRedis
0 likes · 5 min read
How to Use Redis Bitmap for Efficient User Sign‑In Tracking
php Courses
php Courses
Nov 26, 2024 · Databases

Using Redis Bitmap Operations for Precise Statistics with PHP

This article explains Redis bitmap data structures, introduces the main bitmap commands (BITSET, BITGET, BITCOUNT), and provides a complete PHP example that demonstrates how to record user sign‑ins and count them efficiently using Redis bit operations.

BitmapNoSQLPHP
0 likes · 5 min read
Using Redis Bitmap Operations for Precise Statistics with PHP
Java Architect Essentials
Java Architect Essentials
Nov 24, 2024 · Big Data

Using Bitmap and Bloom Filter for Large‑Scale Data Deduplication in Java

The article explains how to store and deduplicate billions of identifiers by using a bitmap to represent presence with a single bit per value, calculates memory requirements, shows Redis bitmap commands, and introduces Bloom filters as an extension with multiple hash functions for efficient large‑scale data handling.

BitmapBloom filterData Structures
0 likes · 5 min read
Using Bitmap and Bloom Filter for Large‑Scale Data Deduplication in Java
Programmer XiaoFu
Programmer XiaoFu
Oct 21, 2024 · Databases

Understanding the Differences Between Redis Bitmap and Bloom Filter

This article explains how Redis Bitmap stores binary states using bit arrays, details its commands and use cases, then introduces Bloom Filter's probabilistic set membership, compares their advantages, limitations, and typical scenarios such as cache penetration protection and blacklist filtering.

BitmapBloom filterData Structures
0 likes · 12 min read
Understanding the Differences Between Redis Bitmap and Bloom Filter
IT Services Circle
IT Services Circle
Oct 15, 2024 · Fundamentals

Designing a Simple File System with Blocks, Bitmaps, Inodes, and Directories

This article walks through the step‑by‑step design of a simple file system on a 1 TB disk, introducing logical blocks, block bitmaps, inode structures, directory handling, indirect indexing, superblocks, and hierarchical file organization, while comparing the design to the classic ext2 filesystem.

BitmapExt2block allocation
0 likes · 10 min read
Designing a Simple File System with Blocks, Bitmaps, Inodes, and Directories
Bilibili Tech
Bilibili Tech
Aug 23, 2024 · Big Data

Accelerating Multi‑Dimensional OLAP Queries in ClickHouse with Grouping Sets, RBM, and Dense Dictionary Encoding

To achieve sub‑second, multi‑dimensional analytics on Bilibili’s hundred‑million‑row datasets, the team built a ClickHouse‑based acceleration layer that combines grouping‑set pre‑aggregation, bitmap (RBM) distinct handling, and a dense dictionary encoding service, dramatically cutting CPU, memory and query latency versus traditional OLAP pipelines.

Big DataBitmapClickHouse
0 likes · 28 min read
Accelerating Multi‑Dimensional OLAP Queries in ClickHouse with Grouping Sets, RBM, and Dense Dictionary Encoding
DataFunTalk
DataFunTalk
Jun 9, 2024 · Big Data

Optimizing ClickHouse Performance in WeChat: Observation Tools, Lakehouse Reading, Bitmap Acceleration, and AI Integration

This article details how the WeChat team leverages ClickHouse at massive scale, introduces a suite of performance observation tools, describes lakehouse reading and bitmap optimizations, and explains the integration of AI workloads, demonstrating overall query speedups of up to tenfold across diverse scenarios.

Big DataBitmapClickHouse
0 likes · 10 min read
Optimizing ClickHouse Performance in WeChat: Observation Tools, Lakehouse Reading, Bitmap Acceleration, and AI Integration
JD Cloud Developers
JD Cloud Developers
Jun 3, 2024 · Big Data

Speed Up Blacklist Filtering to Milliseconds with Bitmaps and Multithreading

The article examines performance bottlenecks in marketing systems when filtering blacklisted accounts, proposes multithreaded processing and bitmap-based set operations—including Java BitSet and RoaringBitmap—to dramatically reduce processing time from tens of minutes to milliseconds, and discusses implementation details, memory efficiency, and broader applications such as indexing and Redis.

Bitmapmultithreadingperformance optimization
0 likes · 13 min read
Speed Up Blacklist Filtering to Milliseconds with Bitmaps and Multithreading
JD Tech
JD Tech
Mar 1, 2024 · Fundamentals

Optimizing Marketing System Blacklist Filtering with Bitmaps

This article examines how bitmap data structures and multithreading can dramatically accelerate blacklist filtering in large‑scale marketing systems, reducing processing time from tens of minutes to milliseconds while saving memory and improving overall system performance.

BitmapBlacklistRoaringBitmap
0 likes · 13 min read
Optimizing Marketing System Blacklist Filtering with Bitmaps
macrozheng
macrozheng
Feb 20, 2024 · Databases

10 Real-World Redis Use Cases Every Backend Engineer Should Know

Discover ten practical Redis scenarios—from counting page visits and caching category trees to implementing distributed locks, leaderboards, rate limiting, bitmap analytics, message queues, and global ID generation—each illustrated with concise code snippets and best‑practice tips for robust backend development.

BitmapLeaderboardMessage Queue
0 likes · 11 min read
10 Real-World Redis Use Cases Every Backend Engineer Should Know
DataFunSummit
DataFunSummit
Feb 10, 2024 · Big Data

User Profile Platform: Architecture, Core Functions, and Engineering Optimization Strategies

This article presents a comprehensive overview of a user profile platform, covering its value, typical functionalities, layered architecture with open‑source implementations, and detailed engineering optimization techniques such as wide‑table generation, crowd selection, bitmap usage, and task‑based processing to improve performance and scalability.

Bitmaparchitecturereal-time
0 likes · 19 min read
User Profile Platform: Architecture, Core Functions, and Engineering Optimization Strategies
JD Tech
JD Tech
Jan 22, 2024 · Big Data

Efficient High‑Concurrency Data Retrieval Using Inverted Index and Bitmap Techniques

This article explores how to achieve fast, scalable data retrieval in million‑level high‑concurrency scenarios by replacing naïve full‑combination rule matching with column‑wise inverted indexes and bitmap operations, dramatically reducing time complexity and improving stability while leveraging RoaringBitmap compression for space efficiency.

BitmapRoaringBitmaphigh concurrency
0 likes · 12 min read
Efficient High‑Concurrency Data Retrieval Using Inverted Index and Bitmap Techniques
Su San Talks Tech
Su San Talks Tech
Jan 18, 2024 · Databases

10 Real-World Redis Use Cases Every Backend Engineer Should Know

This article explores ten practical Redis scenarios—from counting page visits and caching category trees to implementing distributed locks, leaderboards, rate limiting, bitmap statistics, cache acceleration, message queues, and global ID generation—providing code snippets and best‑practice tips for each use case.

BitmapLeaderboarddistributed lock
0 likes · 10 min read
10 Real-World Redis Use Cases Every Backend Engineer Should Know
DataFunSummit
DataFunSummit
Dec 16, 2023 · Databases

Optimizing Precise Deduplication with Doris Bitmap: Architecture, Performance Enhancements, and Practical Practices

This article presents a comprehensive overview of precise deduplication in Meituan's Doris database, detailing the underlying bitmap data structures, aggregation bottlenecks, and a series of optimizations—including memory management, fast union, orthogonal encoding, and vectorized engine integration—that together achieve significant performance gains in high‑cardinality scenarios.

BitmapDatabaseDoris
0 likes · 20 min read
Optimizing Precise Deduplication with Doris Bitmap: Architecture, Performance Enhancements, and Practical Practices
Top Architect
Top Architect
Nov 28, 2023 · Backend Development

Implementing Sign‑In and Statistics with Redis BitMap in Spring Boot

This article explains how to use Redis BitMap to build an efficient sign‑in feature and monthly statistics in a Spring Boot microservice, covering basic BitMap commands, key design, code implementation, testing procedures, and a cache‑penetration mitigation strategy.

BitmapCache PenetrationRedis
0 likes · 12 min read
Implementing Sign‑In and Statistics with Redis BitMap in Spring Boot
dbaplus Community
dbaplus Community
Nov 15, 2023 · Databases

Scaling Bloom Filter for 800 Million OpenIDs in Redis

This article explains how to use a Bloom filter backed by Redis bitmap and Roaring Bitmap sharding to efficiently filter 800 million OpenID queries, covering memory planning, hash function selection, code implementation, and performance‑tuned batch write strategies.

BitmapBloom filterLarge-Scale Data
0 likes · 13 min read
Scaling Bloom Filter for 800 Million OpenIDs in Redis
Code Ape Tech Column
Code Ape Tech Column
Oct 14, 2023 · Backend Development

Implementing Sign‑In and Statistics with Redis BitMap in Spring Boot

This article explains how to use Redis BitMap to build a memory‑efficient sign‑in feature and continuous‑sign‑in statistics in a Spring Boot microservice, covering basic BitMap commands, key design, core Java code, testing steps, and a bitmap‑based solution for cache‑penetration protection.

BitmapSpring Bootbackend
0 likes · 10 min read
Implementing Sign‑In and Statistics with Redis BitMap in Spring Boot
Top Architect
Top Architect
Aug 31, 2023 · Backend Development

Implementing Sign‑in and Statistics with Redis BitMap in Spring Boot

This article explains how to use Redis BitMap to build an efficient sign‑in feature and continuous‑sign‑in statistics in a Spring Boot microservice, covering basic BitMap commands, integration steps, core source code, and a cache‑penetration solution using BitMap hashing.

BitmapRedisSpringBoot
0 likes · 12 min read
Implementing Sign‑in and Statistics with Redis BitMap in Spring Boot
Code Ape Tech Column
Code Ape Tech Column
Aug 31, 2023 · Backend Development

Implementing Sign‑in and Statistics with Redis BitMap in Spring Boot

This article explains how to implement a user sign‑in feature and its statistical analysis using Redis BitMap within a Spring Boot backend, covering basic BitMap commands, integration steps, core source code, continuous sign‑in calculation, and a bitmap‑based solution for cache penetration.

BitmapCache PenetrationSpring Boot
0 likes · 11 min read
Implementing Sign‑in and Statistics with Redis BitMap in Spring Boot
ITPUB
ITPUB
Aug 27, 2023 · Databases

How to Use Redis Sets for Powerful Intersection, Union, and Difference Statistics

This article explains how to leverage Redis Set, Sorted Set, Bitmap, and HyperLogLog data structures for various statistical scenarios such as user sign‑in tracking, friend recommendations, comment ranking, and UV counting, while addressing performance and memory considerations.

BitmapDifferenceHyperLogLog
0 likes · 11 min read
How to Use Redis Sets for Powerful Intersection, Union, and Difference Statistics
Architect's Guide
Architect's Guide
Jul 24, 2023 · Big Data

Using Bitmap and Bloom Filter to De‑duplicate 4 Billion IDs Within 1 GB Memory

The article explains how to store and de‑duplicate 4 billion unsigned integers using a bitmap to reduce memory from 14.9 GB to under 500 MB, introduces the concept and benefits of bitmaps, describes Bloom filters, their principles, advantages, limitations, typical use cases, and provides Java and Redis implementation examples.

Big DataBitmapBloom filter
0 likes · 10 min read
Using Bitmap and Bloom Filter to De‑duplicate 4 Billion IDs Within 1 GB Memory
Su San Talks Tech
Su San Talks Tech
Jun 29, 2023 · Backend Development

Master Redis Set Operations for Scalable Statistics and Analytics

This article explains how to use Redis sets, sorted sets, lists, bitmaps, and HyperLogLog to perform aggregation, ordered queries, binary state tracking, and cardinality estimation for large‑scale applications such as sign‑in systems, e‑commerce comments, and social‑network friend lists.

AggregationBitmapHyperLogLog
0 likes · 10 min read
Master Redis Set Operations for Scalable Statistics and Analytics
WeChat Backend Team
WeChat Backend Team
May 24, 2023 · Databases

Boost ClickHouse Bitmap Queries 10x with BitBooster: Techniques & Results

This article explains how the BitBooster suite accelerates ClickHouse bitmap (BitMap) queries by up to tenfold, covering background, performance bottlenecks, single‑node and read optimizations, layout and instruction‑set enhancements, encoding dictionaries, multi‑node scaling, and real‑world benchmark results.

BitmapClickHouseOptimization
0 likes · 23 min read
Boost ClickHouse Bitmap Queries 10x with BitBooster: Techniques & Results
Sohu Tech Products
Sohu Tech Products
Mar 8, 2023 · Mobile Development

Android Image Compression Techniques and Luban Algorithm Analysis

This article provides a comprehensive overview of Android image compression, covering bitmap fundamentals, ARGB color model, size calculations, quality and dimension compression methods, the Luban algorithm, its implementation details, performance considerations, and related JPEG Huffman coding techniques.

AndroidBitmapJPEG
0 likes · 27 min read
Android Image Compression Techniques and Luban Algorithm Analysis
DataFunTalk
DataFunTalk
Dec 6, 2022 · Databases

Performance Optimization of Apache Doris for A/B Experiment Queries at Xiaomi

This article analyzes the performance bottlenecks of A/B experiment report queries on Apache Doris at Xiaomi, presents data-driven insights on query latency, field usage, and experiment ID matching, and details a series of optimizations—including pre‑aggregation, materialized views, bitmap deduplication, and schema redesign—that reduced query times by up to 60× and lowered cluster load.

A/B testingApache DorisBitmap
0 likes · 17 min read
Performance Optimization of Apache Doris for A/B Experiment Queries at Xiaomi
vivo Internet Technology
vivo Internet Technology
Oct 26, 2022 · Big Data

Cardinality Counting in Presto: Algorithms, Implementation, and Best Practices

The article explains cardinality counting in Presto, comparing exact set‑based methods with memory‑efficient bitmap, Linear Count, and HyperLogLog approximations, detailing their algorithms, implementation in Presto’s query engine, and offering best‑practice recommendations for choosing the appropriate technique in business workloads.

BitmapHyperLogLogSQL
0 likes · 16 min read
Cardinality Counting in Presto: Algorithms, Implementation, and Best Practices
Sohu Tech Products
Sohu Tech Products
Sep 28, 2022 · Mobile Development

Understanding Android Image Compression: Fundamentals, Algorithms, and Luban Implementation

This article explains Android image compression fundamentals—including ARGB, bitmap memory, bit‑depth vs. colour‑depth, quality and sampling compression methods—then details the Luban algorithm, its shortcomings, proposed improvements, and the underlying Skia/Huffman/JPEG native implementation with code examples.

AndroidBitmapHuffman
0 likes · 27 min read
Understanding Android Image Compression: Fundamentals, Algorithms, and Luban Implementation
ITPUB
ITPUB
Aug 14, 2022 · Backend Development

Mastering Redis Bitmap for Efficient Binary State Statistics in Mobile Apps

This guide explains how to use Redis Bitmap to efficiently store and query massive binary state data—such as user login status and daily sign‑ins—by leveraging GETBIT, SETBIT, BITCOUNT, BITPOS, and BITOP commands, dramatically reducing memory usage for millions of users.

Binary StatisticsBitmapData Structures
0 likes · 11 min read
Mastering Redis Bitmap for Efficient Binary State Statistics in Mobile Apps
Shepherd Advanced Notes
Shepherd Advanced Notes
Aug 12, 2022 · Databases

Understanding Redis: Core Data Structures and Essential Commands

This article explains why Redis’s single‑threaded design achieves high performance, details its five fundamental data types—string, list, set, hash, and sorted set—their internal implementations, and introduces advanced structures such as Geo, Bitmap, and HyperLogLog with practical command examples.

BitmapData StructuresGeospatial
0 likes · 23 min read
Understanding Redis: Core Data Structures and Essential Commands
ITPUB
ITPUB
Jul 20, 2022 · Databases

Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM

This article explains how Redis stores key‑value pairs, dives into the underlying dict, dictEntry and redisObject structures, and presents seven practical memory‑saving tricks—including key shortening, encoding tweaks, object sharing, bitmap usage, hash consolidation, fragmentation cleanup, and 32‑bit deployment—to dramatically reduce RAM consumption while preserving performance.

BitmapData StructuresHash
0 likes · 19 min read
Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM
DeWu Technology
DeWu Technology
Jul 8, 2022 · Big Data

Optimizing Large-Scale Product Set Refresh with RoaringBitmap

By representing pre‑and post‑refresh SPU sets as RoaringBitmaps and diffing them, the system avoids full‑insert writes, cuts memory usage by orders of magnitude, speeds refreshes by over 50 % and reduces write volume nearly 87 %, solving large‑scale tag‑based product refresh challenges.

BitmapDataStructureHBase
0 likes · 14 min read
Optimizing Large-Scale Product Set Refresh with RoaringBitmap
php Courses
php Courses
Jun 8, 2022 · Backend Development

Using Redis Bitmap for Online User Statistics in Laravel

This article explains how to efficiently count daily visits and online users in a Laravel application by leveraging Redis bitmap, detailing its memory‑saving advantages, providing complete PHP code for setting, counting, and checking online status, and comparing alternative methods such as tables, sorted sets, and HyperLogLog.

BitmapRedislaravel
0 likes · 4 min read
Using Redis Bitmap for Online User Statistics in Laravel
Top Architect
Top Architect
May 18, 2022 · Backend Development

Designing Efficient Read/Unread Tracking for Group Chat Messages Using Bitmaps

The article analyzes the memory inefficiency of storing per‑user read/unread lists for group chat messages, proposes a bitmap‑based solution with user‑to‑map ID mapping, discusses handling of member exits, and evaluates the storage savings and scalability of the new design.

BitmapData Structurebackend design
0 likes · 7 min read
Designing Efficient Read/Unread Tracking for Group Chat Messages Using Bitmaps
IT Services Circle
IT Services Circle
Apr 12, 2022 · Big Data

Finding Missing Unsigned Integers in a 4‑Billion‑Element File Using Interval Counting and Bitmap Technique

The article explains how to locate all missing 32‑bit unsigned integers in a 4 billion‑entry file by first partitioning the range into intervals, counting entries per interval with a tiny int[64] array, and then applying a bitmap method only to under‑filled intervals, achieving a memory footprint of just a few hundred bytes.

AlgorithmBig DataBitmap
0 likes · 5 min read
Finding Missing Unsigned Integers in a 4‑Billion‑Element File Using Interval Counting and Bitmap Technique
Selected Java Interview Questions
Selected Java Interview Questions
Apr 2, 2022 · Databases

Advanced Redis Bit Operations: Commands, Underlying Data Structures, Complexity, Storage Calculation, and Use Cases

This article explains Redis bitmap (bit) operations, covering command syntax for SETBIT, GETBIT, BITCOUNT, and BITOP, analyzing the underlying SDS data structure, detailing time‑complexity and storage calculations, and presenting practical application scenarios such as user sign‑in tracking and online status monitoring.

BITCOUNTBITOPBitmap
0 likes · 8 min read
Advanced Redis Bit Operations: Commands, Underlying Data Structures, Complexity, Storage Calculation, and Use Cases
Code Ape Tech Column
Code Ape Tech Column
Jan 19, 2022 · Databases

Choosing Appropriate Redis Data Structures for Large‑Scale Statistics: Cardinality, Sorting, and Aggregation

This article explains how to select Redis data structures such as Bitmap, HyperLogLog, Set, List, Sorted Set, and Hash to efficiently handle massive statistical scenarios like user login status, UV counting, ranking, and set aggregation, while providing concrete command examples and best‑practice recommendations.

AggregationBitmapCardinality
0 likes · 11 min read
Choosing Appropriate Redis Data Structures for Large‑Scale Statistics: Cardinality, Sorting, and Aggregation
Architect
Architect
Jan 2, 2022 · Backend Development

Efficient Read/Unread Tracking for Group Chat Messages Using Bitmaps

The article examines how to efficiently store read/unread status for group chat messages by replacing per‑user lists with compact bitmap structures, discusses handling member exits, presents C++‑style struct definitions, and quantifies storage savings compared to the naïve 8‑byte per‑user approach.

BitmapData StorageOptimization
0 likes · 7 min read
Efficient Read/Unread Tracking for Group Chat Messages Using Bitmaps
Programmer DD
Programmer DD
Dec 22, 2021 · Backend Development

Designing a Space‑Efficient Read/Unread Store for Large Group Chats

This article examines how to model read/unread status for group chat messages using bitmap techniques, evaluates memory costs of naive approaches, and presents a compact storage schema that handles member joins, exits, and scalability while drastically reducing per‑message overhead.

Bitmapbackend designgroup chat
0 likes · 7 min read
Designing a Space‑Efficient Read/Unread Store for Large Group Chats
Python Crawling & Data Mining
Python Crawling & Data Mining
Dec 13, 2021 · Big Data

How to De‑duplicate 4 Billion QQ Numbers with Only 1 GB RAM

This article explains several algorithmic strategies—including sorting, hash maps, file splitting, and bitmap techniques—to remove duplicates from a file containing 4 billion QQ numbers while staying within a 1 GB memory limit, and it provides extension exercises for sorting, median, top‑K, and duplicate detection.

AlgorithmBig DataBitmap
0 likes · 8 min read
How to De‑duplicate 4 Billion QQ Numbers with Only 1 GB RAM
NiuNiu MaTe
NiuNiu MaTe
Nov 26, 2021 · Big Data

How to Deduplicate 4 Billion QQ Numbers Using Only 1 GB of Memory

This article walks through four practical techniques—sorting, hashmap, file splitting, and bitmap—to remove duplicate QQ numbers from a 4‑billion‑record file within a 1 GB memory limit, and provides extended exercises for sorting, median, top‑K, and duplicate detection.

AlgorithmBig DataBitmap
0 likes · 8 min read
How to Deduplicate 4 Billion QQ Numbers Using Only 1 GB of Memory
JavaEdge
JavaEdge
Nov 24, 2021 · Fundamentals

How 12306 Ticket‑Booking Uses Bitmap & Bitwise Operations to Prevent Overbooking

This article explains the 12306 train ticket reservation algorithm, showing how bitmaps combined with bitwise OR operations can model seat availability across multiple stations, using a simplified example of four seats and three segments to illustrate ticket allocation, conflict detection, and remaining‑ticket calculation.

12306Bitmapbitwise operations
0 likes · 5 min read
How 12306 Ticket‑Booking Uses Bitmap & Bitwise Operations to Prevent Overbooking
Laravel Tech Community
Laravel Tech Community
Nov 16, 2021 · Backend Development

Git 2.34 Release Highlights and New Features

Git 2.34 introduces a sparse index for large monorepo repositories, multi‑package reachability bitmaps, the default "ort" merge strategy, OpenSSH‑based tag signing, interactive autocorrect prompts, performance improvements for fetch and push, and various submodule enhancements.

BitmapSparse Indexort merge
0 likes · 2 min read
Git 2.34 Release Highlights and New Features