This article introduces MongoDB as an open-source document database, explains NoSQL categories, details MongoDB's data model (fields, documents, collections, databases) with relational database analogies, and provides step-by-step installation instructions for MongoDB 7.0 on CentOS 7 via yum and manual RPM.
The article explains how to translate domain‑driven design into concrete database schemas by first shifting focus from tables to domain models, then mapping the four relationship types, choosing an inheritance strategy, and finally adapting the approach for NoSQL databases.
Enterprise data platforms face common questions—why add a data warehouse when MySQL or Oracle exist, why introduce a data lake after a warehouse, and what problems NoSQL, ClickHouse, or Doris solve—this article breaks down six storage technologies, their core use‑cases, and how to choose among them.
This article demystifies databases by contrasting them with Excel, explains core concepts such as tables, rows, columns, primary and foreign keys, compares relational and NoSQL systems, introduces SQL CRUD operations, ACID properties, and provides a quick MySQL hands‑on guide, helping readers decide when to adopt a database.
This article explains the four major NoSQL data models—key‑value, document, column‑family, and graph—detailing their structures, strengths, weaknesses, CAP trade‑offs, consistency options, and how to match each model to specific business queries and requirements, including polyglot persistence strategies.
The fourth national security and reliability test released on May 26, 2026 lists 23 certified database products, highlighting a surge in Level II certifications, the entry of time‑series NoSQL databases, and a diversification of vendors—including telecom and fintech players—signaling a major shift in China's domestic database market.
This article introduces SleekDB, a dependency‑free PHP flat‑file NoSQL database that stores data as JSON, outlines its installation via Composer, highlights key features such as powerful queries, full‑text search, pagination, joins and caching, and provides a practical code example for a blog system.
This article explains how to integrate MongoDB into a Spring Boot application, covering suitable use cases, environment setup, core dependencies, configuration, entity mapping, CRUD operations via MongoRepository and MongoTemplate, indexing, performance considerations, and a concise comparison with MySQL.
As data volumes surge, the article compares six mainstream storage technologies—relational databases, data warehouses, NoSQL databases, search storage, object storage, and data lakes—explaining their core principles, ideal use cases, and trade‑offs to help you select the most suitable solution for your workload.
Jakarta NoSQL 1.0 introduces a standardized, annotation‑driven API that lets Java developers map objects to document, key‑value, column‑family, or graph databases with type‑safe CRUD and fluent queries, while supporting CDI, annotation processors, and IDE integration for higher productivity and portability.
This comprehensive guide walks you through fundamental database design goals, a step‑by‑step lifecycle, nine essential strategies—including normalization, indexing, and security—plus modern distributed and NoSQL considerations, performance tuning, high‑availability tactics, and practical tools for robust data governance.
This article explains why NoSQL emerged to address the scalability and schema rigidity limits of relational databases, clarifies that MySQL data may reside in memory or on disk depending on the storage engine, and provides a practical guide to selecting the right database for various workloads.
This article explains how Xiaohongshu built the REDgraph graph database to tackle ultra‑large social network queries, compares graph databases with traditional relational databases, showcases a Gremlin example, and highlights the scalability and efficiency benefits of storing relationships as first‑class citizens.
This comprehensive guide compares MySQL, PostgreSQL, and MongoDB across core features, transaction models, indexing, scalability, and real‑world use cases, offering a step‑by‑step selection method, detailed pros and cons, and a practical decision flowchart to help engineers pick the right database for their projects.
This comprehensive MongoDB tutorial covers its core concepts, suitable scenarios, basic CRUD operations, document structure, query syntax, indexing, aggregation framework, advanced features like MapReduce and transactions, Python integration, real‑world case studies, and provides command summaries with practice exercises for beginners to intermediate users.
JSON has become the de‑facto format for data exchange, and modern relational databases like PostgreSQL and MySQL now support native JSON types alongside NoSQL solutions such as MongoDB, offering developers flexible storage, indexing, and query capabilities while balancing schema rigidity, performance, and scalability.
Microsoft announced that its PostgreSQL‑based DocumentDB project will become a Linux‑Foundation‑led open‑source effort, detailing its origins, recent MongoDB compatibility, community support, and the strategic reasons behind the donation.
This guide walks you through the core MongoDB commands—including creating and switching databases, listing databases, inserting, updating, querying (with filters, pagination, distinct, and fuzzy search), counting records, and deleting documents—providing concise examples for each operation.
The article presents a step‑by‑step analysis of how to shrink a 11‑second API response to 170 ms by applying batch database writes, Redis pipeline, asynchronous processing, thread‑pool design, local‑memory buffering, MQ integration, and other performance‑tuning patterns, backed by real‑world benchmarks and code samples.
The article examines the June 2025 TIOBE ranking where SQL fell to its lowest position, recounts its historical highs and removal from the list, highlights everyday reliance on SQL, and analyzes the growing prominence of NoSQL and other programming languages in the era of AI and big data.
The latest TIOBE index shows SQL falling to 12th place as NoSQL gains traction, driven by AI’s demand for unstructured data, while languages like Python surge, and major vendors such as IBM acquire NoSQL specialists to accelerate large‑scale AI production.
The article examines SQL's recent drop to 12th place in the TIOBE June 2025 ranking, recounts its historical highs and removal from the list, highlights its pervasive use in everyday systems, and discusses how the rise of AI and NoSQL databases may reshape its future role.
This article traces MySQL’s architectural evolution from single‑node to sharded clusters, explains the fundamentals and classifications of NoSQL databases, and provides a comprehensive guide to Redis—including installation, memory management, and detailed usage of its five core data types with best‑practice considerations.
This tutorial introduces NoSQL concepts and provides step‑by‑step Python code examples for installing, connecting, inserting, querying, updating, and deleting data in both MongoDB and Redis, highlighting their suitable use cases and key differences.
Facing massive query latency, deadlocks and costly vertical scaling, our team abandoned a textbook‑perfect PostgreSQL setup, tried extensive SQL optimizations, added Redis caching and read replicas, and finally migrated critical order services to MongoDB, achieving five‑fold capacity, zero downtime and significant cost savings.
Redis, a non‑relational database, implements transaction-like behavior using commands such as MULTI, EXEC, DISCARD, and WATCH, allowing queued execution, optimistic locking, and various rollback granularities, though its semantics differ from traditional relational database transactions.
This guide provides a comprehensive overview of Redis data structures—including strings, hashes, lists, sets, and sorted sets—along with essential commands for creating, reading, updating, and deleting keys, managing expirations, and performing set operations, enabling developers to effectively leverage Redis as a high‑performance NoSQL database.
The paper proposes a multi‑level storage architecture for Bilibili’s comment service that replaces TiDB with a custom KV store (Taishan) and Redis caching, introduces unstructured indexes, CAS‑based consistency, real‑time and offline reconciliation, and a hedged degradation strategy to boost reliability, read throughput, and scalability during traffic spikes.
This article explores how modern data technologies—from traditional relational databases through NoSQL to vector‑based retrieval and RAG—demystify data, address the 4V challenges, and enable applications to adopt flexible, scalable, and AI‑driven data paradigms.
This article explores how modern data technologies—from relational databases and NoSQL to vector databases and AI‑driven retrieval—address the 4V challenges of volume, velocity, variety, and value, enabling polyglot persistence, semantic embeddings, and retrieval‑augmented generation for next‑generation applications.
This article explains Redis bitmap data structures, introduces the main bitmap commands (BITSET, BITGET, BITCOUNT), and provides a complete PHP example that demonstrates how to record user sign‑ins and count them efficiently using Redis bit operations.
This article surveys several lightweight Python databases—including PickleDB, TinyDB, ZODB, Durus, Buzhug, Gadfly, and PyTables—detailing their main features, typical use cases, cautions, and providing basic code examples to help developers choose and apply the right storage solution for small‑scale or prototype projects.
This article surveys the evolution of modern data technologies—from the 4V characteristics of big data and the limitations of traditional relational databases, through the rise of NoSQL and polyglot persistence, to embedding‑driven vector search, hybrid retrieval and RAG, illustrating how each paradigm frees applications from data constraints.
This article walks through the design and implementation of a decoupled, high‑performance business operation log solution that captures MySQL binlog changes via Canal, streams them through Kafka, and stores and queries them in Elasticsearch, addressing challenges such as batch operations, multi‑table transactions, and non‑business data filtering.
Tencent’s X‑Stor is a cloud‑native, multi‑model NoSQL database that consolidates heterogeneous data stores, improves resource utilization, and scales to petabytes and billions of requests, as detailed in a VLDB paper describing its architecture, workload management, and performance advantages over MongoDB, Redis, and ArangoDB.
This guide explains the MariaDB MaxScale NoSQL protocol module, its key features, typical use cases, step‑by‑step installation, configuration, and how to perform MongoDB‑compatible operations that are transparently stored in MySQL/MariaDB.
The article introduces X‑Stor, a cloud‑native NoSQL database service that supports multiple data models and process‑level multi‑tenant isolation, describes its plugin‑based architecture and Request Unit metric, and presents experimental results showing superior performance, effective tenant isolation, and load‑balancing benefits compared with traditional single‑model NoSQL systems.
MongoDB reported a 13% revenue rise to $478.1 million in Q2 2025, highlighted a major win over PostgreSQL at Fanatics Betting & Gaming, and emphasized its flexible JSON‑like architecture, horizontal scaling, and sharding advantages while noting ongoing competition and a revised growth outlook.
This comprehensive guide walks you through MongoDB fundamentals, installation checks, service management, user authentication, database and collection creation, and essential CRUD commands, providing clear examples and code snippets to help you efficiently manage and query your NoSQL data.
This article introduces X‑Stor, a cloud‑native NoSQL service that supports multiple data models and process‑level multi‑tenant isolation, explains its architecture and design choices, and presents extensive experimental results that compare its performance and resource management against single‑model databases.
This article examines why the unified content platform switched from MongoDB to HBase, outlines HBase’s high‑performance, scalability, and consistency features, and details four optimization techniques—including cluster upgrade, connection pooling, column‑read strategy, and compaction tuning—that significantly improved read/write latency and operational stability.
Despite the hype around NoSQL, this article argues that SQL and relational databases remain the superior choice for transactional data, tracing two decades of research, highlighting the limitations of NoSQL, and showing how modern databases now incorporate the best features of both worlds.
This article reviews two decades of database research, analyzing the rise and decline of various data models—from hierarchical and relational to NoSQL, vector, and graph databases—while highlighting how AI, cloud, and hardware advances are reshaping DBMS architecture and predicting which approaches will dominate tomorrow’s data landscape.
This article explains the concept of high concurrency, its key metrics, and presents four practical backend solutions—static/dynamic separation with CDN, read‑write separation using NoSQL and CQRS, various caching techniques, and lightweight read‑optimisation—to improve performance and scalability of distributed systems.
Vivo's unified content platform replaced its unwieldy 60 TB MongoDB store with HBase, then upgraded the cluster, introduced table‑specific connection pools, column‑only reads, tuned compaction, and leveraged multi‑version cells, cutting response times from seconds to under ten milliseconds and dramatically lowering operational costs while boosting read/write performance.
This article introduces graph databases, explains their advantages over relational databases, and provides concise overviews of popular open‑source options such as Neo4j, ArangoDB, Dgraph, and many others, including key features, performance traits, and download links.
Donald Chamberlin, co‑author of SQL, explains why he now supports the NoSQL movement, describing how modern web applications demand scalability that traditional relational databases struggle to provide, and discusses emerging query languages like SQL++ (PartiQL) that bridge the gap between relational and document models.
This article provides an in‑depth overview of Redis, covering its core concepts, data structures, performance characteristics, persistence mechanisms, high‑availability solutions, clustering, cache design patterns, operational best practices, and underlying implementation details for developers and architects.
This article provides a comprehensive overview of Redis' eight common data structures—including String, List, Hash, Set, Sorted Set, Bitmap, HyperLogLog, and Geospatial—detailing their internal implementations, core commands, practical code examples, and typical application scenarios for developers.
This tutorial walks you through installing MongoDB on three CentOS 7.9 servers, configuring a replica set with a master and two slaves, creating admin and test users, securing communication with key files, and verifying failover by stopping the primary node, all illustrated with commands and screenshots.
The article presents a cloud‑native distributed Data Warebase that unifies relational, NoSQL, search and analytical capabilities into a single horizontally‑scalable system, reducing data‑access barriers and improving developer productivity while addressing consistency, latency, cost and operational complexity.
BTS (Baidu Table Storage) is Baidu Intelligent Cloud’s high‑performance, low‑cost semi‑structured NoSQL service that evolved from single‑table to multi‑model (wide tables, time‑series, soon documents), featuring a three‑layer compute‑storage separation architecture, multi‑level caching, hot‑backup HA, and supporting massive IoT, AI, autonomous‑driving and monitoring workloads.
This comprehensive guide walks through Redis's core data structures—including strings, lists, sets, sorted sets, hashes, HyperLogLog, bitmaps, streams, and geospatial indexes—explaining their characteristics and providing concrete command‑line examples to help developers select and use the right structure for their applications.
The global database market is accelerating beyond $1 trillion, driven by cloud adoption, AI workloads, and the rise of vector and NoSQL databases, while traditional relational vendors slow, creating a unique growth dynamic for enterprises seeking the right data solutions.
This article details Baidu Cloud's BTS semi‑structured storage system, covering its three‑generation evolution, three‑layer architecture, performance optimizations, high‑availability mechanisms, and real‑world use cases such as autonomous driving and large‑scale system monitoring.
This article outlines why databases become slow, introduces a four‑layer optimization framework, and presents eight practical solutions—including data reduction, archiving, sharding, caching, and read‑replica strategies—to help engineers systematically diagnose and resolve the majority of performance bottlenecks.
This article introduces Redis Stack’s RedisJSON module, explains its JSONPath syntax, details the full set of JSON commands for handling various data types, provides practical code examples, and highlights how RedisJSON enables Redis to function as a document, full‑text, and vector search database.
This article introduces graph databases, explains Dgraph concepts and installation via Docker, details Dgraph4j Java client usage including schema setup, querying, mutations, and data monitoring, and explores applications such as equity relationship analysis and data visualization.
Salvatore Sanfilippo, a self‑taught hobbyist who launched Redis in 2009, broke traditional database rules by building an in‑memory NoSQL system, sharing his open‑source philosophy, and shaping a thriving community that powers companies like Uber, Slack, and Twitter.
This article explains what NoSQL databases are, outlines their typical use cases and architectural components, compares them with traditional relational databases across storage, scalability, query, and transaction aspects, and highlights the advantages and trade‑offs to consider when choosing a data solution.
This article surveys emerging database platforms such as PlanetScale, YugaByteDB, Dolt, CockroachDB, Cloudflare D1, Xata, SurrealDB, and FaunaDB, highlighting their architectures, features, pricing, and trade‑offs for developers seeking modern, distributed, or serverless solutions.
Learn how the CAP theorem defines the trade‑offs among consistency, availability, and partition tolerance, and see how different NoSQL databases such as MongoDB (CP) and Cassandra (AP) align with these principles to guide your choice of storage for cloud‑native, distributed applications.
This article explains the principles and practical designs of high‑performance database clusters, covering read/write separation, master‑slave replication lag, distribution mechanisms, sharding (both vertical and horizontal), business‑level partitioning, and the role of NoSQL technologies such as key‑value stores, document databases, columnar databases, and full‑text search engines.
This article introduces NoSQL databases, explains their core concepts, typical use cases, architectural components, and contrasts them with relational databases, helping readers understand when and why to choose NoSQL solutions for large‑scale, unstructured data workloads.
Redis is exploring a disk‑backed tiered storage architecture, allowing infrequently used data to move from memory to flash and back, a move aimed at reducing costs, improving scalability, and expanding its popularity among developers and gaming applications.
This article explains the concepts and advantages of NoSQL databases, categorizes the four main types, introduces MongoDB’s data model and components, and provides step‑by‑step instructions for installing MongoDB 7.0 on CentOS 7 using both yum repository and manual RPM packages.
This article provides a comprehensive overview of Redis, covering its common application scenarios, detailed comparisons with Memcached, internal data structures, implementation details, and practical usage patterns such as caching, leaderboards, counters, and real‑time analytics for modern backend systems.
This article explains how Abase2 enables multi‑region deployment for a high‑scale NoSQL database by adopting CRDT technology, detailing the challenges of cross‑region consistency, the evolution of Abase's architecture, and the engineering solutions that provide Redis‑compatible CRDT command support with low latency and strong eventual consistency.
This article provides a concise yet comprehensive overview of relational, key‑value, document, search‑engine, time‑series, vector, spatial, graph, columnar, and multimodel databases, explaining their data models, typical queries, core advantages, and popular implementations to help developers choose the right storage solution for any project.
A comprehensive benchmark shows RedisJSON (RediSearch) dramatically outperforms MongoDB and ElasticSearch in isolated writes, isolated reads, mixed workloads, and latency, delivering up to 200‑plus times faster writes, over 500‑times faster reads, and significantly lower latency across all operation types.
MongoDB is an open‑source, C++‑based NoSQL document database that stores data as BSON documents in collections, offering schema‑less modeling, embedded structures, high scalability through sharding, and a rich set of CRUD commands for modern application needs.
This article explains key database terminology, traces the history of database technologies, compares relational, NoSQL, NewSQL, OLTP/OLAP, columnar, time‑series and graph databases, and demonstrates practical data‑structure designs such as zipper tables, bit operations, bitmaps, bloom filters, and ring queues for software development.
This article introduces and compares major database categories—including relational, key‑value, document, columnar, graph, and time‑series databases—explaining their structures, typical use cases, and advantages, helping readers understand when to choose each type for various applications.
This article compares SQL and NoSQL databases, outlining their advantages, limitations, and ideal use‑cases, and provides guidance on selecting the appropriate technology based on consistency, scalability, and data model requirements.
MySQL 8.0 introduces a native document store that brings full ACID‑compliant NoSQL capabilities, schema‑less JSON handling, simple CRUD APIs, seamless SQL integration, large‑document support, built‑in security and infrastructure simplification, enabling developers and DBAs to leverage existing MySQL expertise for modern applications.
This guide walks you through the essential stages of database selection—from assessing project requirements and comparing candidate systems to performance testing, long‑term impact analysis, and making the final decision—ensuring you pick a solution that fits both current and future needs.
This article provides a comprehensive introduction to IndexedDB, covering its concepts, key objects, versioning, transactions, and CRUD operations with detailed JavaScript examples, enabling developers to quickly build client‑side databases for modern web applications.
This article explains Redis as an in‑memory key‑value NoSQL database, outlines its data types and high‑availability features, and details common cache problems such as avalanche, penetration and breakdown along with practical mitigation strategies, followed by interview commentary on how to discuss Redis effectively.
This article traces the history of database development from early file systems to relational models, outlines major database categories such as hierarchical, relational, NoSQL, NewSQL, and columnar stores, and explores practical data structure designs like chain tables, bitwise operations, and circular queues for modern software engineering.
This article explains the true meaning of NoSQL, its theoretical foundation in the CAP theorem, the reasons why it excels in large‑scale and high‑concurrency scenarios, compares it with relational databases, and outlines typical use cases and trade‑offs.
The article explains what open‑source databases are, compares community and commercial editions, examines licensing models, reviews major vendors' adoption (Oracle, Microsoft, Amazon, Google), and discusses recent governance disputes and the impact of hybrid licensing on the database ecosystem.
This comprehensive guide explains Redis fundamentals, covering NoSQL basics, the five core data types and their internal storage mechanisms, persistence options (RDB and AOF), replication, Sentinel failover, publish/subscribe patterns, and common cache pitfalls such as penetration, breakdown, and avalanche, providing practical insights for developers and architects.
This article summarizes Guo Bin's DTCC2022 talk on Google Cloud Bigtable, detailing its architecture, performance characteristics, scaling strategies, and how the fully managed NoSQL service powers low‑latency real‑time bidding and user profiling in modern advertising technology.
This article provides a comprehensive overview of Redis as a NoSQL in‑memory database, covering its core data structures, special types, transaction model, RDB and AOF persistence, publish/subscribe, master‑slave replication, Sentinel failover, and common cache pitfalls such as penetration, breakdown and avalanche, with practical mitigation strategies.
This article provides a detailed comparison of two document‑oriented NoSQL databases, MongoDB and CouchDB, covering their architectures, features, replication models, mobile support, query mechanisms, code examples, and guidance on choosing the appropriate system based on application requirements.
This article explains how wide‑column (wide‑table) storage like Bigtable/HBase and full‑text search engines such as Elasticsearch address the massive write, query performance, and reliability demands of write‑intensive and analytics workloads, comparing their architectures, strengths, and limitations.
This article surveys the development of graph databases, describing their underlying data models, storage designs across relational, native, document, and wide‑column systems, and reviewing representative modern distributed graph databases while discussing current challenges and future directions such as GQL standardization and graph‑AI integration.
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 outlines eight comprehensive database optimization techniques—including reducing data volume, using space‑for‑performance methods, and selecting appropriate storage systems—explaining their underlying principles, trade‑offs, and practical implementation steps for backend engineers facing slow queries and high load.
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 examines HBase’s high reliability and performance as a column‑oriented NoSQL store, outlines its advantages and limitations, presents two practical use cases from e‑commerce, and details its data model, architecture components, and design considerations for effective deployment.
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.
An in‑depth look at BitalosDB, the home‑grown NoSQL storage engine behind Zuoyebang’s massive KV traffic, covering its novel IO architecture, KV‑separation design, Raft‑based consistency, multi‑cloud CRDT replication, and benchmark results that show up to 400% cost savings versus standard Redis.
This article chronicles the 30‑year dominance of relational databases, the challenges posed by object‑oriented and high‑concurrency workloads, and how four alternative data‑store families—Redis, MongoDB, Neo4j, and HBase/Cassandra—gave birth to the modern NoSQL movement.
This article explains how to choose storage options, design sharding keys, apply partitioning strategies, modify business code, handle historical data migration, and plan future scaling for large‑scale e‑commerce systems using MySQL, NoSQL, and NewSQL solutions.
This article explains why thoughtful database design is critical for scalable applications and outlines ten best‑practice guidelines—including stakeholder input, choosing the right DB type, consistent naming, normalization, documentation, security, scalability, stored procedures, modeling effort, and thorough testing—to help developers build robust, high‑performance data stores.
This article provides a comprehensive analysis of the Chinese database industry, covering global trends such as the dominance of relational databases and the rise of NoSQL, the impact of cloud vendors and open‑source, domestic market opportunities, classifications of database types, and profiles of leading Chinese vendors, highlighting how big‑data and AI are reshaping demand and creating growth prospects for home‑grown solutions.
This article provides a comprehensive overview of HBase, covering its column‑oriented design, core components such as HMaster, RegionServer and ZooKeeper, the data model with column families and row keys, and detailed step‑by‑step write and read processes for distributed storage.
This article presents a comprehensive framework for selecting a storage solution, covering business and technical metrics, comparing relational databases with various NoSQL types, and offering concrete pros, cons, and scenario‑based recommendations to help engineers make informed data‑store decisions.
A comprehensive benchmark shows RedisJSON delivering dramatically higher throughput and lower latency than MongoDB and ElasticSearch across isolated writes, isolated reads, and mixed workloads, with up to 200‑fold faster writes and 500‑plus‑fold faster reads, while maintaining sub‑millisecond response times.
