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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.
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.
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 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.
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.
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.