HyperLogLog | BestHub

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.

GreptimeDBHyperLogLogSQL

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.

BitMapHyperLogLogMemory Optimization

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.

BitMapBitfieldData Types

0 likes · 6 min read

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

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 DataCardinalityDatabase

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.

Big DataBitMapHyperLogLog

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.

BitMapHyperLogLogRedis

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.

AggregationBitMapCardinality

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.

Data StructuresHyperLogLogRedis

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.

BackendData StructuresHyperLogLog

0 likes · 13 min read

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

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.

BackendBitSetHyperLogLog

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

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.

Big DataHyperLogLogRedis

0 likes · 10 min read

How to Use Redis for Efficient Deduplication in Operations Data Analysis