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24 articles

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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

DeWu Technology

Apr 28, 2025 · Databases

GreptimeDB Distributed Architecture, Transparent Caching, and Flow‑Based Real‑Time Analytics

GreptimeDB solves front‑end observability challenges with a distributed architecture (frontend, datanode, flownode, metasrv), transparent two‑level caching, elastic scaling, and an SQL‑based flow engine for real‑time multi‑granularity aggregation and approximate counting, delivering millisecond query latency and cost‑effective storage.

GreptimeDBHyperLogLogReal-time analytics

0 likes · 12 min read

GreptimeDB Distributed Architecture, Transparent Caching, and Flow‑Based Real‑Time Analytics

Lobster Programming

Mar 21, 2025 · Databases

How to Count Website Visits Efficiently with Redis: Hash, Bitmap, and HyperLogLog

This article explains three Redis-based methods—Hash, Bitmap, and HyperLogLog—for tracking website user visits, detailing how each structure works, their implementation steps, memory and accuracy trade‑offs, and guidance on choosing the best approach for different traffic scenarios.

BitmapDatabase PerformanceHash

0 likes · 6 min read

How to Count Website Visits Efficiently with Redis: Hash, Bitmap, and HyperLogLog

Full-Stack Internet Architecture

Mar 4, 2024 · Databases

Redis Extended Data Types: Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog

This article introduces Redis's five extended data types—Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog—explaining their concepts, common commands, and practical code examples, and highlights how they address specific internet‑scale scenarios more efficiently than traditional relational solutions.

BitfieldBitmapData Types

0 likes · 6 min read

Redis Extended Data Types: Stream, Geospatial, Bitmap, Bitfield, and HyperLogLog

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

dbaplus Community

Aug 17, 2023 · Databases

Mastering Redis Set Operations for Scalable Statistics and Aggregations

This article explains how to leverage Redis data structures such as Set, Sorted Set, Bitmap, and HyperLogLog to perform aggregation, sorted, binary‑state, and cardinality statistics efficiently in large‑scale applications, while addressing performance considerations and practical implementation details.

BackendHyperLogLogSet

0 likes · 10 min read

Mastering Redis Set Operations for Scalable Statistics and Aggregations

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.

BackendBitmapHyperLogLog

0 likes · 10 min read

Master Redis Set Operations for Scalable Statistics and Analytics

Architecture & Thinking

Nov 8, 2022 · Databases

Mastering Redis HyperLogLog: Efficient Cardinality Estimation for Big Data

This article explains Redis HyperLogLog, its underlying principles, memory efficiency, typical use cases like UV/PV counting, and provides practical command examples (PFADD, PFCOUNT, PFMERGE) to perform high‑performance cardinality estimation on massive datasets.

Big DataCardinalityHyperLogLog

0 likes · 9 min read

Mastering Redis HyperLogLog: Efficient Cardinality Estimation for Big Data

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.

BitmapHyperLogLogPresto

0 likes · 16 min read

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

Sohu Tech Products

Apr 13, 2022 · Backend Development

Implementing Page UV Counting with Redis Set, Hash, Bitmap, and HyperLogLog Using Redisson

This article explains how to use Redis data structures—Set, Hash, Bitmap, and HyperLogLog—along with Redisson in Java to efficiently count unique page visitors (UV) in high‑traffic mobile internet scenarios, comparing memory usage, precision, and scalability.

HashHyperLogLogSet

0 likes · 12 min read

Implementing Page UV Counting with Redis Set, Hash, Bitmap, and HyperLogLog Using Redisson

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.

BitmapCardinalityData Structures

0 likes · 11 min read

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

Beike Product & Technology

Jul 8, 2021 · Fundamentals

Understanding HyperLogLog: Algorithm Principles, Redis Implementation, and Experimental Analysis

This article explores the HyperLogLog algorithm for cardinality estimation, tracing its development from Linear and LogLog counting, detailing its Redis implementation with sparse and dense encodings and command workflows, and presenting experiments that demonstrate its memory efficiency and analyze observed error rates versus the theoretical 0.81% standard deviation.

HyperLogLogalgorithmcardinality estimation

0 likes · 13 min read

Understanding HyperLogLog: Algorithm Principles, Redis Implementation, and Experimental Analysis

Sohu Tech Products

Jun 23, 2021 · Backend Development

Using Redis Data Structures for Efficient Large‑Scale Statistics: Cardinality, Sorting, and Aggregation

The article explains how to choose appropriate Redis data structures—such as Bitmap, HyperLogLog, Set, List, Hash, and Sorted Set—to efficiently handle massive statistical scenarios like UV counting, ranking, and set‑based aggregation, while providing concrete command examples and performance considerations.

Data StructuresHyperLogLogSorted Set

0 likes · 13 min read

Using Redis Data Structures for Efficient Large‑Scale Statistics: Cardinality, Sorting, and Aggregation

Su San Talks Tech

Jun 18, 2021 · Backend Development

Mastering Redis for Massive Data Statistics: Bitmap, HyperLogLog, Sets, and Sorted Sets

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.

BitmapData StatisticsHyperLogLog

0 likes · 14 min read

Mastering Redis for Massive Data Statistics: Bitmap, HyperLogLog, Sets, and Sorted Sets

Programmer DD

May 20, 2021 · Backend Development

Counting Web Page Visits with Redis: Hash, Bitset, and HyperLogLog Techniques

This article explains three Redis-based techniques—Hash, Bitset, and HyperLogLog—for efficiently counting daily page visits, detailing command usage, memory trade‑offs, and accuracy considerations, helping backend engineers implement scalable visitor statistics in high‑traffic environments like large e‑commerce platforms.

Backend DevelopmentBitsetHash

0 likes · 6 min read

Counting Web Page Visits with Redis: Hash, Bitset, and HyperLogLog Techniques

WeChat Backend Team

Mar 6, 2021 · Backend Development

How We Scaled a Live Chatroom to 15 Million Concurrent Users

This article details the evolution of a WeChat live‑room chat component from its 1.0 high‑performance design to a 2.0 architecture that overcomes scalability, reliability, and traffic‑isolation challenges, enabling a single room to support up to 15 million simultaneous online users.

ChatroomDistributed SystemsHyperLogLog

0 likes · 13 min read

How We Scaled a Live Chatroom to 15 Million Concurrent Users

vivo Internet Technology

Jan 6, 2021 · Big Data

How HyperLogLog Estimates Cardinality in Massive Data Sets

This article explains the cardinality‑counting problem behind DAU/MAU and unique visitor metrics, compares naïve solutions like Set, Bitmap and Bloom filter, introduces big‑data algorithms such as Linear Counting, LogLog and HyperLogLog, and shows how Redis implements HyperLogLog with dense and sparse storage optimizations.

Big DataCardinalityHyperLogLog

0 likes · 17 min read

How HyperLogLog Estimates Cardinality in Massive Data Sets

Code Ape Tech Column

Dec 2, 2020 · Databases

Mastering Redis Set Operations: Intersection, Union, Difference, and More

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.

BackendBitmapHyperLogLog

0 likes · 11 min read

Mastering Redis Set Operations: Intersection, Union, Difference, and More

Java Backend Technology

Oct 8, 2020 · Backend Development

Unlocking Redis: 9 Real‑World Patterns from KV Cache to Bloom Filters

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.

BitmapHyperLogLogbloom-filter

0 likes · 20 min read

Unlocking Redis: 9 Real‑World Patterns from KV Cache to Bloom Filters

Java Architect Essentials

Oct 6, 2020 · Backend Development

Using Redis to Count Website Visits: Hash, Bitset, and Probabilistic Algorithms

The article explains three Redis-based techniques—Hash, Bitset, and HyperLogLog—for counting daily page visits on high‑traffic sites, detailing command usage, memory trade‑offs, and the pros and cons of each method.

BitsetHashHyperLogLog

0 likes · 6 min read

Using Redis to Count Website Visits: Hash, Bitset, and Probabilistic Algorithms

Selected Java Interview Questions

May 16, 2020 · Big Data

How Reddit Counts Page Views at Scale Using HyperLogLog and Kafka

The article explains Reddit's large‑scale page‑view counting system, detailing its real‑time requirements, the challenges of naive hash‑set storage, and how a hybrid approach using linear probability and HyperLogLog algorithms together with Kafka, Redis, and Cassandra achieves accurate, low‑memory, near‑real‑time analytics.

Big DataHyperLogLogKafka

0 likes · 7 min read

How Reddit Counts Page Views at Scale Using HyperLogLog and Kafka

Big Data Technology & Architecture

Jan 7, 2020 · Big Data

Using HyperLogLog for High-Performance Pre-Aggregation in Big Data with Spark-Alchemy

The article explains how pre‑aggregation combined with the HyperLogLog algorithm and Spark‑Alchemy's native HLL functions can dramatically accelerate distinct‑count calculations in big‑data workloads while maintaining low error rates and cross‑system compatibility.

Approximate Distinct CountBig DataHyperLogLog

0 likes · 7 min read

Using HyperLogLog for High-Performance Pre-Aggregation in Big Data with Spark-Alchemy

ITPUB

Dec 21, 2019 · Databases

How to Count Website Visits with Redis: Hash, Bitset, and HyperLogLog

This article explains three Redis‑based techniques—using hash tables, bitsets, and the HyperLogLog probabilistic algorithm—to accurately count daily page views, detailing the required commands, implementation steps, advantages, and limitations for high‑traffic sites.

BitsetHashHyperLogLog

0 likes · 6 min read

How to Count Website Visits with Redis: Hash, Bitset, and HyperLogLog

Efficient Ops

Mar 15, 2016 · Operations

How to Use Redis for Efficient Deduplication in Operations Data Analysis

This article explains practical methods for deduplicating and counting data in operational analytics using Redis, covering SET, ZSET, BITSET, HyperLogLog, and Bloom filter structures, their advantages, limitations, and suitable scenarios for real‑time and large‑scale metric calculations.

HyperLogLogdeduplicationredis

0 likes · 10 min read

How to Use Redis for Efficient Deduplication in Operations Data Analysis