This article explains how JD.com designs a multi‑layer data storage architecture—using MySQL for core metadata, HBase and MongoDB for comment text, Solr/Elasticsearch for indexing, and Redis for caching—to handle billions of daily review requests with high performance and availability.
This article compiles the most frequently used configuration parameters for MapReduce, HBase, and Spark, explaining their purposes and recommended settings to improve job performance, reliability, and resource utilization in big‑data environments.
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 article explains HBase’s internal architecture, covering the roles of HRegionServer, Client, Zookeeper, Master, RegionServer, the physical storage layout, StoreFile and HFile structures, and the Write-Ahead Log mechanism that ensures data durability and fault tolerance.
This article provides a comprehensive overview of HBase, covering its distributed architecture, component roles, data organization, read/write mechanisms, and best practices for schema and region design to ensure efficient big‑data storage and retrieval.
This article provides a detailed comparison of five popular NoSQL databases—HBase, Redis, MongoDB, Couchbase, and LevelDB—covering their data models, performance characteristics, CAP classification, Node.js client options, advantages, drawbacks, and ideal use‑cases to help developers choose the right storage solution for a new Node project.
This article describes how the Youzan data team combined Apache Kylin, Mondrian, and Saiku into a three‑layer OLAP system, covering background, component overviews, technical architecture, schema integration challenges, count‑distinct handling, Kylin‑specific SQL quirks, and practical solutions.
This article analyzes the characteristics of log data, explains why HBase is chosen for log storage, discusses the shortcomings of self‑built indexes, and presents optimization strategies such as rowKey design, filter usage, coprocessor integration, and third‑party indexing to improve query performance.
This article examines the challenges of storing and querying log data in HBase, outlines the drawbacks of custom indexing, and presents practical rowKey design, filter usage, and integration with external search engines to improve query performance.
This article explains how to use the open‑source Kiji framework together with HBase, Avro, and MapReduce to build a scalable, entity‑centric real‑time recommendation system that can instantly refresh suggestions based on user context and recent interactions.
An overview of HBase’s 2015 milestones—including the stable 1.0 release, clearer API with BufferedMutator, multi‑region replicas for high availability, family‑level flush improvements, RPC call‑queue separation, online configuration changes, and various Q&A on performance, replication, and best‑practice design considerations.
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.
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.
This article explains how to migrate live service data between systems without downtime, covering migration types, a four‑stage process, practical examples with MySQL and HBase, and key techniques for ensuring consistency and smooth cut‑over.
This article explains HBase's column‑oriented data model, demonstrates Java API examples for creating, reading, and deleting tables, and provides practical optimization techniques—including pre‑splitting, Rowkey design, ColumnFamily reduction, caching, and compaction settings—to improve read/write performance in large‑scale deployments.
The article examines various distributed storage design approaches—decentralized (GlusterFS), centralized (Hadoop), database‑based (GridFS and HBase), and metadata‑bypassing (FastDFS)—detailing their advantages, drawbacks, and practical considerations for cloud storage systems.
This article explains HBase's column‑oriented architecture, key concepts such as Rowkey, ColumnFamily, and Region, provides Java API examples for table operations, and offers practical optimization techniques—including pre‑splitting, Rowkey design, caching, and compaction settings—to improve read/write performance.
This article presents a non‑intrusive, high‑performance engine that adds secondary multi‑column indexes to Apache HBase, enabling efficient complex condition queries while preserving HBase's scalability, and details its principles, architecture, query API, index configuration, and practical trade‑offs.
This article explains the four‑step online data migration process—including dual‑write, historical data transfer, read cut‑over, and cleanup—using a real‑world MySQL‑to‑HBase fan‑list case study, and provides practical tips for ensuring zero‑downtime and high data consistency.
