This article explains how Uber solves the "nearby driver" problem by employing the open‑source H3 hexagonal spatial index, hierarchical grids, Cassandra for persistent storage, and Redis caching to deliver fast, accurate, and scalable real‑time location services.
Facing the challenge of moving 15 billion image files from Cassandra to S3, we iteratively redesigned the migration pipeline—from a single‑process approach to multi‑process, queue‑driven, and multi‑cluster deployments—reducing the projected 120‑day effort to just 40 days while ensuring reliability and performance.
This article explains what wide tables are, why they are controversial, outlines three common design pitfalls with practical avoidance tips, and introduces three key technologies—ClickHouse, Cassandra, and Hudi/Iceberg—to help engineers build performant, maintainable wide‑table solutions in data warehouses.
Instagram grew from a simple photo‑sharing app to over 2.5 billion users, prompting engineers to adopt horizontal scaling, replace Python code with Cython, use region‑specific Cassandra clusters, employ the Akkio data‑placement service, and optimize PostgreSQL and Memcache handling to improve resource utilization, data consistency, and latency.
This article analyzes feed‑timeline architectures, compares pull‑based and push‑based models, proposes an online‑push/offline‑pull hybrid, and details practical implementations using Redis SortedSets, multi‑level caching with Cassandra, cursor‑based pagination, and large‑scale push task sharding.
Discord’s rapid growth forced a series of massive database migrations—from MongoDB to Cassandra in 2017, then to ScyllaDB in 2023—detailing the motivations, requirements, data‑model design, performance challenges, migration tooling, and the resulting operational improvements.
This article explains how Cassandra’s newly added vector data type and ANN search capabilities empower AI developers to store, index, and query high‑dimensional embeddings at scale, enabling use cases such as image retrieval, recommendation, and large‑language‑model integration.
This article explains how to model massive time‑series data in Cassandra by using bucketing techniques to control partition size, avoid hotspots, and improve write and read performance, including practical CQL schema examples and Python code for concurrent queries.
This article presents a step‑by‑step experiment on a Cassandra cluster spanning two data centers, demonstrating how token ownership, data distribution, and fault‑tolerance behave when nodes fail or are removed, and explains the observed owns percentages and replication effects.
The article traces the evolution from Codd's relational model to modern RDBMS scaling limits, explains why centralized Hadoop/HBase architectures struggle with high‑concurrency workloads, and shows how Cassandra’s decentralized design—using consistent hashing, gossip, and virtual nodes—overcomes these bottlenecks while offering flexible consistency guarantees.
This article explains how Cassandra clusters organize nodes, racks, and data centers, describes the gossip and snitch protocols, token ring architecture, replication strategies, and provides step‑by‑step commands to create, list, and delete users and roles while highlighting cross‑data‑center visibility constraints and common errors.
This article explains the concept of feed streams, compares push and pull implementation models, discusses storage options such as MySQL, Redis SortedSet and Cassandra, and presents optimization techniques including online‑push/offline‑pull strategies, pagination methods, and deep‑paging solutions for large‑scale systems.
The article explains how K8ssandra, an Apache Cassandra distribution for Kubernetes, evolved from using Helm charts to developing a dedicated Operator to overcome Helm's limitations, improve multi‑cluster support, and align more closely with Kubernetes best practices.
This document details the motivation, architecture, components, query language, and deployment of a Loki‑based log collection and analysis platform for JD Daojia, comparing it with ELK, describing ingestion, real‑time and historical log handling, technical challenges, configuration examples, and future scaling plans.
Apache Cassandra 4.0 has been officially released after a long development cycle, offering higher speed, better scalability, improved consistency, stronger security, reduced latency, and more efficient compression, while supporting enterprise compliance and a new annual release cadence for future stability.
In this interview, JanusGraph PMC members Florian Hockmann and Jason Plurad discuss the project's origins, compare JanusGraph with Neo4j, share advice for production deployments, outline future expectations for JanusGraph and TinkerPop, and provide practical tips for graph modeling and community contribution.
Monzo, the UK digital bank, shares how it built a Kubernetes‑based, cloud‑native platform to run over 1,600 Go‑written microservices backed by Cassandra, implements fine‑grained service isolation with network policies, and creates internal tooling to automate security and deployment at massive scale.
This article explains Cassandra’s row‑oriented storage model, the multi‑step write and read processes, how tombstones and compaction manage data growth, and the impact of its distributed architecture on high availability, fault tolerance, and configurable consistency levels.
This article presents a comprehensive overview of handling spatiotemporal data using Cassandra, covering data types, space‑filling curves, GeoHash encoding, the GeoMesa and GeoTrellis ecosystems, Cassandra storage schemas, and practical Spark integration for large‑scale geospatial analytics.
This article summarizes a Cassandra meetup presentation that traces the database's origins from BigTable and Dynamo, outlines its key milestones, explains its peer‑to‑peer and LSM architecture, highlights current features, real‑world deployments, performance advantages, and previews upcoming 4.0 releases and community projects.
This article details how 360 adopted Cassandra for its cloud drive, describing Cassandra’s decentralized architecture, the reasons for its selection over HBase, large‑scale deployment challenges, performance optimizations, reliability improvements, disk utilization techniques, and the evolution of the system from 2010 to present.
ScyllaDB, a high‑performance C++ implementation of Apache Cassandra, introduces row‑level repair to replace the traditional partition‑level repair, reducing data transfer and I/O by operating on individual rows; the presentation details its architecture, multi‑stage process, experimental results, and the resulting six‑fold speedup.
This tutorial explains how to create a highly scalable, fault‑tolerant real‑time data processing platform by configuring a Kafka topic, a Cassandra keyspace, adding Spark and connector dependencies, developing a Java‑based Spark Streaming pipeline, enabling checkpoints, and deploying the application with spark‑submit.
This article explains the fundamentals of consistency in distributed storage systems, contrasts it with database transaction consistency, and details the various consistency levels offered by Azure Cosmos DB, Apache Cassandra, and OceanBase, highlighting their guarantees, configurations, and the performance‑availability trade‑offs involved.
Learn how to construct a scalable data processing platform using the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka—covering storage design, processing workflows, resource management, deployment options, and fault‑tolerant task execution for both batch and streaming workloads.
The article uses everyday observations from a McDonald’s restaurant to illustrate core distributed system concepts such as master‑slave architecture, two‑phase commit, microservice decomposition, task queues, and container orchestration, showing how these principles apply to backend engineering.
This article details how Discord scaled from tens of millions to over a hundred million daily messages by replacing MongoDB with Cassandra, covering the motivations, data model design, handling of tombstones, performance results, unexpected issues, and future scalability plans.
This article traces Facebook's evolution from a small social site to a global platform, explains how its massive data‑storage challenges led to the adoption of NoSQL solutions like Cassandra and HBase, and breaks down the core patterns, consistency models, and scaling techniques that power such large‑scale systems.
This guide compares common relational databases and NoSQL solutions, outlining ideal use‑cases for MySQL, PostgreSQL, MongoDB, key‑value stores, Cassandra, Neo4j, and maps specific business scenarios to the most suitable database technology.
While Hadoop enjoys a strong reputation in big‑data applications, the article argues that NoSQL databases—specifically MongoDB, Cassandra, and HBase—are more widely deployed, comparing their strengths, use cases, and market popularity to help developers decide which technology best fits their needs.
The article examines why enterprises should consider NoSQL over Hadoop for big data storage, compares the three leading NoSQL databases—MongoDB, Cassandra, and HBase—based on market popularity, technical strengths, scalability, and use‑case suitability, and concludes with guidance on selecting the most appropriate solution.
During a lively “Sit and Discuss” session, experts compared Spark and Hadoop, evaluated Flink against Spark, contrasted HBase with Cassandra, explained why Kafka (and sometimes Flink) is preferred for distributed messaging, and shared insights on Tachyon’s role in modern big‑data ecosystems.
An in‑depth overview compares eight leading NoSQL databases—CouchDB, Redis, MongoDB, Riak, Membase, Neo4j, Cassandra, and HBase—detailing their languages, licensing, protocols, features, replication models, and ideal application scenarios to help architects choose the most suitable solution.
An extensive overview of fifteen popular NoSQL databases—including MongoDB, CouchDB, HBase, Cassandra, Redis, and more—detailing their architectures, key features, performance characteristics, and typical applications, helping readers choose the right solution for high‑scale, high‑concurrency data storage needs.
