HBase stores huge tables by splitting them into regions, distributing these across region servers managed by a master, and further dividing each region into column-family stores, memstores, and StoreFiles, forming a layered architecture built on Hadoop’s distributed storage.
This article compares traditional relational table design with HBase's column‑family model, showing how tables, rows, column families, and versioned cells are defined and how data is inserted using HBase's put commands.
Qihoo 360 shares how it grew from a 2011 MongoDB adopter to running over 1500 instances handling 200 billion queries per day, detailing its architecture, key applications, migration decisions, backup strategies, scaling best practices, and lessons for future MongoDB projects.
This article provides a comprehensive overview of Elasticsearch 2.x, covering its distributed architecture, core concepts such as clusters, nodes, indices, shards and replicas, the ZenDiscovery master‑election process, scaling mechanisms, recovery, query features, and the underlying system components like Guice, Netty, and thread‑pool designs.
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 demonstrates how MySQL 5.7.12’s new JavaScript‑based shell lets you query, insert, update, and delete both traditional tables and JSON document collections using intuitive method‑chaining syntax, eliminating the need for SQL knowledge and simplifying migration from MongoDB‑style workflows.
The article explains how a company is retiring Oracle due to cost and scalability, outlines a staged move to NoSQL and cloud storage, argues that SQL remains vital, and shares practical examples of Spark SQL rewrites and JDBC‑based Hive integration.
An overview of NoSQL database types—including key‑value stores like Redis, column‑oriented systems such as BigTable and HBase, and document databases like MongoDB—covers their architectures, strengths, typical use cases, and key factors to consider when selecting a NoSQL solution for web applications.
This article explains how MySQL can serve as a fast, reliable key‑value store, presents Wix's multi‑region deployment data showing sub‑millisecond latency and high throughput, and offers practical modeling and query guidelines to avoid typical relational bottlenecks.
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 curated collection of online MongoDB learning resources, including the official website, Chinese community, issue tracker, blogs, and various tutorial sites such as Shiyanlou, imooc, Yiibai, Runoob, and others for developers seeking comprehensive database education.
MySQL 5.7 adds native JSON document storage, merging relational database strengths with NoSQL flexibility, enabling developers to use a single system for both structured and document‑oriented data while preserving ACID transactions and indexing capabilities.
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.
The article compares relational databases like MySQL—highlighting their consistency, transaction support, and join capabilities—with NoSQL alternatives, explaining each type’s strengths and limitations in handling massive writes, schema changes, variable fields, and simple queries, and guides developers on when to choose each.
This article compares relational SQL statements with MongoDB’s document‑oriented commands, presenting a side‑by‑side mapping of concepts and CRUD operations, illustrated with example documents and images to help developers quickly grasp MongoDB’s approach.
This article compares NoSQL and SQL databases, highlighting NoSQL’s strengths in scalability, flexible schemas, and performance, while also discussing its lack of unified standards, operational complexity, and why SQL remains essential for strong consistency and transactional integrity in critical systems.
This article explains how Uber engineered Schemaless, a highly available, horizontally scalable datastore built on MySQL that stores immutable JSON cells without a fixed schema, detailing its design goals, architecture, data model, trigger system, indexing strategy, and fault‑tolerant read/write mechanisms.
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 provides a comprehensive overview of Hadoop and its surrounding ecosystem, detailing core components, storage principles, key algorithms, and a wide range of modern big‑data technologies such as Spark, Flink, Kafka, NoSQL databases, and cloud‑based processing platforms.
SSDB is a high‑performance NoSQL database compatible with Redis, offering rich data structures, LevelDB storage for terabyte‑scale data, multi‑language client support, and features like replication and load balancing, positioning it as a viable Redis replacement for large‑scale applications.
This talk explores how Meipai designed a scalable, reliable database architecture that balances SQL and NoSQL technologies to support rapid product iteration and high‑peak traffic, highlighting key considerations for storage service reliability and performance optimization.
The article reviews eight expert presentations from the 2015 SDCC Database Practice Forum, covering distributed database architectures, performance tuning, high‑availability solutions, and practical case studies from leading Chinese internet companies.
This article explains how LinkedIn manages real‑time profile updates, news feeds, and social graph data through a three‑phase architecture that combines RDBMS, NoSQL stores, caching, and Lucene indexes to achieve high consistency, availability, and partition tolerance at massive scale.
This article compares MongoDB to traditional relational databases, highlighting advantages such as eventual consistency, document storage, built‑in GridFS and sharding, rich third‑party support, and performance, while also discussing drawbacks like lack of transactions, high disk usage, and limited maintenance tools.
This guide explains how to leverage MongoDB's flexible document model, introduces the open‑source Variety tool for schema analysis, demonstrates practical commands for sampling, depth control, filtering, sorting and result persistence, and covers MongoDB 3.2+ document validation features and their limitations.
The 2015 database landscape saw cloud integration, open‑source momentum, and major product releases—from SequoiaDB and MySQL Cluster to OceanBase and Greenplum—highlighting shifting market dynamics, emerging Chinese solutions, and the continued relevance of relational databases amid NoSQL growth.
This article introduces NoSQL concepts, compares common storage models, and details MongoDB's replication architectures, special node roles, synchronization processes, and the variety of index types available for efficient data handling.
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.
This article explores the 4V characteristics of big data, real‑world data growth examples, historical analogies, Google’s GFS‑MapReduce‑BigTable model, Hadoop’s architecture and HDFS processes, HBase components, NoSQL alternatives, and practical big‑data applications at Tencent and beyond.
This article surveys the evolution of Hadoop and its ecosystem, explains core storage and processing concepts, and introduces contemporary big‑data technologies such as Spark, Flink, Kafka, Lambda architecture, NoSQL databases, and cloud‑native solutions, highlighting their roles and trade‑offs.
This article explains how to translate a classic SQL many‑to‑many schema for articles and tags into a MongoDB design, showing document structures and practical queries for listing tags, retrieving articles, and filtering by specific tags.
This tutorial walks through MongoDB query basics, showing how to perform simple retrievals, conditional filters, and nested‑document searches with clear code examples and step‑by‑step explanations for inserting and querying data.
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.
This article systematically explores the distributed characteristics of NoSQL databases, covering data consistency, placement, peer systems, anti‑entropy protocols, eventual consistency data types, sharding, fault detection, and coordinator election, illustrating how these strategies balance scalability, availability, latency, and fault tolerance.
This article compares SQL and NoSQL databases, outlining their core characteristics, advantages, and trade‑offs, and demonstrates practical schema designs for contact lists, social networks, and warehouse management, helping developers decide which technology best fits their project requirements.
This article compares traditional relational tables with document‑oriented NoSQL, showing how JSON‑based storage simplifies handling multiple phone numbers, emails, addresses, and evolving contact data without schema changes or complex joins.
Tencent’s operations team explains how its SET‑based NoSQL clusters deliver ultra‑low latency, high availability, and seamless disaster recovery for billions of users, detailing deployment models, synchronization mechanisms, cost‑saving techniques, and the Data‑as‑Service approach that underpins its massive social platforms.
This article chronicles the historical shift from early hierarchical data models to relational SQL, the rise of NoSQL, and the emergence of NewSQL solutions like Alibaba's DRDS, illustrating key concepts with diagrams, code examples, and a detailed Q&A.
This article surveys major NoSQL data models—including document, graph, relational, object, key‑value, BigTable‑type, data‑structure, and grid databases—and provides practical guidance on selecting the most suitable database for various application requirements such as scalability, consistency, transaction support, and data complexity.
This article systematically explores the distributed characteristics of NoSQL databases, covering consistency trade‑offs, replication techniques, anti‑entropy protocols, data placement strategies, failure detection, and coordinator election, while illustrating each concept with diagrams and practical examples.
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.
This article compares relational databases with distributed document-oriented NoSQL databases, explaining their scaling models, data modeling differences, and offering practical advice on when and how to adopt document databases such as SequoiaDB for flexible, high‑performance applications in the big‑data era.
This article explains how Google App Engine Datastore works from a programmer's perspective, covering its entity‑based data model, hierarchical structure, query capabilities, comparison with relational databases, and the underlying implementation built on BigTable including entities, indexes, transactions, and backup mechanisms.
This article explains the challenges of location‑based services, why a two‑dimensional index is needed, and how Redis Geo provides precise, high‑performance geospatial operations that handle millions of requests per second for mobile applications.
The article explains MySQL Cluster as a real‑time, ACID‑compliant, in‑memory distributed database, describes its node architecture, transparent sharding for linear scalability, various SQL/NoSQL access methods, and presents a benchmark achieving 200 million queries per second on 32 data nodes.
This article provides a comprehensive overview of major big‑data platforms such as Hadoop, Spark, Flink, Kafka, and related ecosystem components, explaining their core concepts, storage models, processing frameworks, and architectural patterns for handling massive, distributed datasets.
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.
The article examines the evolution of database technologies—from traditional relational databases and their ACID guarantees to NoSQL, NewSQL, and Hadoop—illustrating how a gaming company can combine these solutions to handle massive online traffic, ensure data integrity, and enable advanced analytics.
RethinkDB 2.0, released after five years of development, brings over 2,000 improvements, live query support, distributed scaling, new driver integrations, and enterprise services, and is already powering real‑time applications for companies ranging from startups to Fortune 500 firms.
The article shares five practical insights—balancing hardware cost and capability, testing NoSQL yourself, monitoring memory and CPU metrics, accounting for network latency, and avoiding virtualization—to help engineers achieve cost‑effective, high‑performance scaling of distributed databases.
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.
In an in‑depth interview, 360’s web platform architect Wang Chao explains the open‑source and proprietary database technologies, multi‑data‑center NoSQL system Bada, and layered security mechanisms that power 360’s search, cloud storage and other high‑traffic services.
This interview with JD’s distributed cache and NoSQL lead reveals how JimDB evolved from a Redis‑based engine to a two‑tier SSD‑optimized key‑value store, detailing fault‑tolerant design, online migration, scaling strategies, and the shifting role of DBAs in massive e‑commerce traffic.
This article explains why NoSQL emerged as an alternative to relational databases, outlines the four main NoSQL categories—key‑value, document, column‑family, and graph—describes their characteristics, typical use cases, and lists notable products and adopters.
The interview with Fourinone creator Peng Yuan reveals the framework's evolution from a parallel computing library to a lightweight 220KB distributed system with its own NoSQL database engine, compares it to Hadoop, discusses the CoolHash design, and outlines Huawei's FusionInsight big‑data platform, while providing open‑source repository links.
The interview with Fourinone founder Peng Yuan explores the framework's evolution from a parallel computing project to a 220 KB distributed system with its own NoSQL database engine CoolHash, compares it to Hadoop, and discusses its open‑source release, technical design choices, and real‑world deployments in finance and enterprise environments.
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.
This article highlights several often‑overlooked MongoDB limitations—including excessive disk‑space preallocation, a 12‑node replica‑set cap, lack of high‑availability in master‑slave mode, 32‑bit version constraints, high consulting fees, and weak management tools—helping developers avoid costly surprises.
This article examines the ongoing debate between SQL and NoSQL databases for big‑data projects, presenting expert arguments on performance, scalability, standardization, and flexibility to help enterprises decide the optimal 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.
The article warns that mastering Free Open Source Software—from cloud and big‑data tools like OpenStack, Hadoop and NoSQL to web technologies such as Drupal, PHP, HTML5 and jQuery, mobile app development, popular programming languages, and security certifications—will become essential for IT job seekers, prompting universities to embed FOSS curricula.
In this interview a veteran full‑stack engineer shares his career path, explains Java 8’s key features such as lambda expressions and JavaFX enhancements, offers practical advice for programmers, and discusses the complementary roles of NoSQL and relational databases, highlighting MongoDB’s strengths.
