This article introduces MongoDB as a popular NoSQL database, explains its schema‑free, collection‑oriented design, storage format, and platform support, and provides a complete PHP class that implements common CRUD and aggregation operations for MongoDB.
RedKV is Xiaohongshu's self‑developed NVMe‑SSD based distributed NoSQL KV store that combines Gossip and Shard control, delivering petabyte‑scale storage, near‑100 million QPS, multi‑cloud elasticity, low‑cost Redis compatibility, and advanced features such as token‑bucket rate limiting, online compression, backup‑read tail‑latency mitigation, and robust backup‑recovery mechanisms.
ByteDance’s NoSQL ecosystem, spanning KV stores like ABase, document databases, columnar systems, and a custom distributed graph database, underpins over 90% of its online services, handling tens of thousands of instances and billions of daily requests, while embracing BASE principles and cloud‑native scalability.
This article presents a systematic guide for backend engineers on diagnosing why databases become slow and offers eight practical optimization solutions—including data reduction, space‑for‑performance techniques, and appropriate storage system choices—organized by the layer of the stack they target.
This article provides a detailed survey of database fundamentals, historical evolution from early relational systems to modern distributed and cloud‑native solutions, explains various architectural components and core technologies such as query processing, indexing, transaction management, and discusses business‑driven classifications, distributed designs, SMP versus MPP, and resource‑access models.
In this interview, Kvrocks PMC Lin Tianyi explains the challenges of large‑scale storage, the architecture and replication model of Kvrocks, its open‑source community roles, Redis‑compatible clustering, migration commands, future roadmap, and his perspective on the Chinese open‑source ecosystem.
RedKV, Xiaohongshu’s self‑developed distributed NoSQL key‑value store, combines a Redis‑compatible interface, RocksDB‑backed storage, and a shard‑managed multi‑cloud architecture that delivers three‑times higher write throughput, 1.5‑times read throughput, 40 % cost savings versus HBase, and sub‑5 ms P999 latency for petabyte‑scale workloads.
This article explains how different data models—from relational tables to JSON documents and graph structures—affect software design, storage, querying, and scalability, illustrating concepts with a resume example and discussing trade‑offs such as impedance mismatch, normalization, and multi‑entity relationships.
Learn why HBase outperforms MySQL for massive, historical data, explore key rowkey design principles such as composite keys, field ordering, length alignment, and hotspot mitigation, and see practical examples like cold‑hot data separation and transaction logs, plus a concise overview of HBase’s core architecture.
The 5th Techo TVP Developer Summit explored the surge of data, the strategic role of NoSQL in digital transformation, presented cutting‑edge trends, performance breakthroughs, cloud‑native multi‑model solutions, and real‑world case studies from finance to gaming, highlighting future directions for database technology.
This article chronicles the architectural evolution of JD Baitiao’s backend—from early MySQL monoliths through Solr‑HBase, MongoDB, and DBRep—to the adoption of Apache ShardingSphere, highlighting the motivations, technical trade‑offs, decoupling strategies, and performance outcomes for a high‑throughput financial service.
This article explains how Aerospike, a high‑performance NoSQL database, achieves millisecond‑level query latency on terabyte‑scale datasets by using a hybrid storage architecture, multi‑level storage tiers, flash optimizations, and a flexible ecosystem that supports diverse real‑time use cases.
This article explains the fundamentals of B+ trees, introduces log‑structured merge (LSM) trees as a modern alternative for write‑intensive workloads, and demonstrates how HBase leverages LSM trees—including MemStore, HFile, compaction, and Bloom filters—to achieve efficient storage and retrieval in NoSQL environments.
This article examines JD Baitiao’s journey from monolithic MySQL to NoSQL and DBRep, detailing how the team evaluated and ultimately adopted Apache ShardingSphere for scalable, low‑coupling database sharding, highlighting performance, maintenance, and business-driven architectural decisions.
This article presents a systematic overview of eight practical database optimization techniques—including data reduction, serialization, archiving, intermediate tables, sharding, distributed caching, master‑slave replication, and appropriate storage selection—to help backend engineers diagnose and resolve the majority of performance bottlenecks caused by data volume, high load, and search complexity.
A comprehensive performance benchmark shows RedisJSON delivering dramatically higher throughput and lower latency than MongoDB and ElasticSearch across isolated writes, isolated reads, and mixed workloads, highlighting its superior scalability and stability for modern data‑intensive applications.
This article explains why databases become slow, introduces a four‑layer thinking model, and presents eight practical solutions—including reducing data volume, leveraging space for performance, and selecting the right storage system—to help backend engineers resolve 80‑90% of common performance problems.
This article outlines a systematic approach for backend engineers to identify root causes of database slowness and presents eight concrete optimization techniques—ranging from data reduction and space‑for‑performance trade‑offs to appropriate storage system selection—while explaining when each method is most effective and what trade‑offs to consider.
The 2022 China Database Industry Analysis Report examines market growth, product diversity, and competitive dimensions, detailing relational, NoSQL, and NewSQL classifications, open‑source concepts, and the challenges and opportunities facing domestic database vendors amid rising investment and geopolitical pressures.
This article provides a comprehensive introduction to Redis, covering its core characteristics, supported data types, caching usage patterns, typical issues such as consistency, avalanche, penetration and breakdown, performance reasons, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability mechanisms.
This article explains why databases become slow, introduces a four‑layer thinking model, and presents eight practical optimization techniques—including data reduction, caching, sharding, master‑slave replication, and CQRS—along with their benefits, drawbacks, and suitable scenarios.
A single Aerospike node experienced extreme XDR replication lag due to a misconfigured forward setting, and the article walks through terminology, monitoring data, root‑cause analysis, and step‑by‑step commands to resolve the issue and improve cluster performance.
This comprehensive guide explores Redis fundamentals, including NoSQL concepts, its five primary data structures, internal storage mechanisms, persistence options, replication, sentinel failover, and strategies to prevent cache penetration, breakdown, and avalanche, providing developers with deep insights for building robust, high‑performance applications.
This article explains Redis's two persistence mechanisms—RDB snapshots and AOF append‑only logs—detailing how they work, how to enable them via commands or configuration, their advantages and drawbacks, and guidance on choosing the right method for your workload.
The article compares MySQL’s relational tables with MongoDB’s schema‑free documents, detailing equivalent concepts, CRUD syntax, and benchmark results that show MongoDB achieving higher write and query throughput, easier schema evolution, and built‑in sharding, recommending it for new agile projects while suggesting MySQL remain viable for legacy systems.
This article compares Redis, Memcached, and MongoDB, outlining each system's strengths and weaknesses, performance characteristics, storage capabilities, availability, reliability, consistency, data analysis features, and typical application scenarios to help developers choose the appropriate NoSQL solution.
This article explains how to evaluate, select, and implement Elasticsearch as a scalable NoSQL search engine for handling tens of millions of reading‑record entries and full‑text work‑search, covering architectural trade‑offs, memory usage, indexing strategies, cluster sharding, pagination limits, server sizing, and .NET integration with code examples.
Redis is an open‑source, in‑memory data‑structure store that serves as a database, cache, and message broker; this guide introduces its fundamentals, explains why NoSQL emerged, details its five primary data types, common commands, and advanced features like transactions and pipelines.
When selecting a storage engine, evaluate business metrics such as user and data volume, read/write bias, and consistency needs, then match those requirements to the strengths and limits of relational databases, key‑value caches, column‑stores, document stores, search engines, and other NoSQL options to achieve the right balance of performance, scalability, and operational cost.
This article explains how domain‑driven design shifts database modeling from a data‑structure‑first approach to a domain‑model‑centric process, covering persistence of domain objects, the conversion of the four classic relational relationships and inheritance into tables, and the distinct strategies required for NoSQL databases.
KeyDB, a multithreaded fork of Redis, offers higher throughput, multi‑master replication, vertical and horizontal scaling, TLS encryption, JavaScript modules, and ARM support, making it a compelling alternative for boosting QPS without additional hardware, while remaining fully compatible with Redis.
CKV+, Tencent’s next‑generation high‑performance NoSQL KV database compatible with Redis, Memcached and ASN, evolves from early CMEM architectures, adopts a multi‑tenant shared‑memory design built on Seastar, integrates DPDK for user‑space networking, and achieves up to ten million QPS with significant cost and latency improvements.
This article explains why a high‑traffic platform migrated from SQL Server to Elasticsearch, covering the technical motivations, performance characteristics, indexing strategies, data synchronization methods, API design, and operational monitoring to achieve a scalable, low‑latency search solution.
This article shares a practical experience of replacing a massive SQL Server data store with Elasticsearch, covering the motivations, architectural design, performance advantages, indexing mechanisms, data synchronization strategies, implementation details, and operational considerations for handling tens of billions of records.
This article details the business background, technical advantages, architecture, indexing mechanisms, clustering, data synchronization strategies, API design, and performance monitoring of Elasticsearch, illustrating how it replaces costly SQL LIKE queries with a scalable, high‑performance search solution for massive user activity data.
The article presents a comprehensive performance benchmark of RedisJSON (with RediSearch) against MongoDB and ElasticSearch, showing RedisJSON’s superior write, read, and mixed‑workload throughput and latency across isolated and mixed scenarios, supported by detailed YCSB test results and analysis.
Amid the rise of the industrial internet, NoSQL databases such as MongoDB, Redis, and Elasticsearch have surged in popularity, with strong SaaS offerings and impressive financial results that illustrate their growing market share, cloud integration, and the broader shift toward open‑source, cloud‑native data solutions.
Benchmark tests reveal that RedisJSON, powered by RediSearch, dramatically outperforms MongoDB and ElasticSearch across isolated reads and writes, mixed workloads, and latency metrics, delivering up to 500‑fold speed gains, lower latency, and higher throughput, making it a compelling choice for modern data‑intensive applications.
The report presents a comprehensive performance benchmark of RedisJSON (RediSearch) against MongoDB and ElasticSearch, showing RedisJSON’s superior write, read, and mixed‑workload throughput and latency across isolated and hybrid scenarios, with detailed test methodology and analysis.
Recent benchmark tests show RedisJSON (with RediSearch) dramatically outperforms MongoDB and ElasticSearch, delivering up to 5.4‑times faster isolated writes, over 200‑times faster than ElasticSearch, 12.7‑times faster reads, and significantly lower latency and higher throughput across mixed workloads.
The report presents extensive YCSB‑based performance benchmarks showing that RedisJSON (with RediSearch) outperforms MongoDB and ElasticSearch by large margins in isolated writes, isolated reads, mixed workloads, and latency across a variety of operation mixes on identical AWS hardware.
Recent benchmark tests show that RedisJSON, powered by RediSearch, dramatically outperforms MongoDB and ElasticSearch in isolated writes, isolated reads, and mixed workloads, delivering up to 12.7× faster reads, 5.4× faster writes, and significantly lower latency across various operation types.
This article provides a comprehensive overview of HBase, covering its origins, column‑family storage model, key components such as HMaster and HRegionServer, data‑location process, row‑key design strategies, practical use cases, and comparisons with relational and other NoSQL databases.
This comprehensive guide covers MongoDB's core concepts—including schema‑free design, high availability, sharding, storage engine internals, indexing, and performance tuning—while providing practical code examples, configuration steps, and best‑practice tips for developers and architects looking to master the database in production environments.
This article explains why MySQL queries become slow, how index design and common pitfalls affect performance, introduces MDL locks and large‑table strategies, and then compares Elasticsearch and HBase as complementary solutions for fast search and massive data storage.
The article explains why the Internet of Things generates massive, diverse data streams that require specialized databases, outlines key selection criteria, describes common IoT data types, and reviews several open‑source databases—InfluxDB, CrateDB, MongoDB, RethinkDB, SQLite, and Cassandra—highlighting their strengths for IoT workloads.
The November 2021 DB-Engines popularity ranking, based on October data, reveals shifts among the top 20 databases, highlights score drops for MySQL, SQL Server and MongoDB, growth for PostgreSQL and MongoDB, and explains the five metrics used to calculate these rankings.
This article explains why database capacity planning is challenging and presents a systematic approach—including workload analysis, performance modeling, consistency considerations, and node scaling decisions—using the open‑source NoSQL database ScyllaDB to guide accurate capacity estimation.
In this article, Poki engineers explain why they abandoned PHP in favor of Go while migrating to a micro‑service architecture, discuss the performance and operational benefits of Go, evaluate NoSQL options like MongoDB and Cassandra, and justify sticking with MySQL for their high‑traffic gaming platform.
This article explains why sizing a database cluster is challenging, outlines a step‑by‑step methodology for estimating workload, configuration and performance, discusses the impact of consistency levels, secondary indexes, materialized views and maintenance, and shows how ScyllaDB can be used to model and simplify capacity planning.
This article explains why sizing a database cluster is challenging, outlines a systematic capacity‑planning process, examines workload characteristics, query‑operation mapping, consistency trade‑offs, and maintenance considerations, and demonstrates how the open‑source NoSQL database ScyllaDB can be used to model and simplify these decisions.
This article describes Baidu's design and implementation of a cloud‑native, high‑performance KV storage platform—UNDB—covering the performance and cloud‑native challenges, engine optimizations, dynamic management, multi‑model architecture, and the resulting cost and reliability improvements for massive search and feed workloads.
The October DB‑Engines popularity ranking shows the same top‑10 databases as September, highlights score gains for MySQL and PostgreSQL, notes a year‑over‑year decline for the three leading systems, and reveals Snowflake’s rise into the top‑20, all based on five key popularity indicators.
This tutorial walks you through installing the Aerospike NoSQL database, tuning Linux system parameters, configuring a three‑node cluster, performing basic AQL operations, and setting up AMC monitoring, all within roughly ten minutes of setup time.
Baidu’s UNDB KV storage system, built with cloud‑native containerization and software‑hardware co‑design, separates key‑value processing to cut write amplification below 1.5×, leverages an OpenChannel SSD engine for over 30% performance gains, scales to 10,000+ servers and 100 PB, handles more than a trillion daily requests across search and feed, and cuts operating costs by nearly half.
The latest DB‑Engines monthly ranking shows the top nine databases unchanged, highlights a swap between Microsoft Access and Cassandra, notes declines for Microsoft SQL Server and MySQL, and analyzes popularity trends that suggest PostgreSQL and MongoDB may soon surpass SQL Server, illustrated with detailed charts.
This article introduces Aerospike, a hybrid‑SSD, micro‑second, petabyte‑scale distributed real‑time NoSQL database, detailing its company background, architectural design, performance optimizations, and ideal use cases such as high‑throughput ad‑tech workloads.
The September 2021 DB‑Engines ranking shows major declines for Oracle, MySQL and SQL Server, rapid gains for Snowflake and MongoDB, and highlights emerging time‑series and key‑value databases like InfluxDB, Redis, and ClickHouse reshaping the database market.
The article explains the rapid rise of graph databases, outlines their core concepts and advantages, compares them with NoSQL and relational databases, presents performance benchmarks, and reviews leading solutions such as Neo4j and JanusGraph, including their data models and query language.
This article explains the rise of graph databases, compares them with traditional relational and NoSQL systems, details the differences between Neo4j and JanusGraph, and demonstrates how the Cypher query language enables efficient relationship queries in complex, large‑scale data environments.
This comprehensive guide covers everything from MongoDB's core concepts, data model, and architecture to practical commands for creating databases, collections, indexes, sharding, replication, aggregation, and troubleshooting, providing clear answers to over fifty frequently asked questions.
This article provides a comprehensive overview of MongoDB, covering its definition, document‑oriented storage, key features, differences from relational databases, sharding and replication mechanisms, common commands, data types, indexing, aggregation, and practical usage scenarios for modern applications.
This tutorial walks you through installing pymongo, connecting to MongoDB with and without authentication, performing insert, update, delete, query, and index operations, and demonstrates each step with clear Python code snippets and illustrative screenshots.
This article details how the logistics HR data preprocessing platform processes around 20 million daily records by adopting HBase for high‑performance, scalable, column‑oriented storage, covering its architecture, read/write mechanisms, best practices, and performance considerations.
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.
This comprehensive guide walks you through MongoDB fundamentals, connection strings, creating databases and collections, inserting, updating, deleting, querying documents with AND/OR logic, advanced range and regex queries, and powerful aggregation pipelines including sorting and grouping.
This article explains the limitations of open‑source Redis, introduces Huawei Cloud's GaussDB for Redis with its compute‑storage separation architecture, details its design, implementation, and disaster‑recovery mechanisms, and summarizes the competitive advantages such as strong consistency, high availability, elastic scaling, and superior performance.
This newsletter compiles the most important updates from June 2021 across RDBMS, NoSQL, NewSQL, time‑series, big‑data, domestic and cloud databases, highlighting new versions, major features, performance improvements and security enhancements for products such as Oracle 21c, MySQL 8.0.24/8.0.25, PostgreSQL 14 Beta, TiDB 5.0, OceanBase V3.1 and many others.
The article outlines a range of techniques—including caching, read‑write splitting, vertical partitioning, sharding, cold‑hot data separation, Elasticsearch, NoSQL and NewSQL—to store and retrieve billions of user‑generated records efficiently while maintaining high throughput and low latency.
This article provides an in‑depth technical overview of HBase, covering its Bigtable origins, distributed column‑store design, core components such as ZooKeeper, HMaster and RegionServer, data flow, storage formats, row‑key design, bulk loading, SQL integration, indexing, coprocessors, and performance tuning for big‑data environments.
This article introduces Redis as a high‑performance, in‑memory key‑value database, explains its core features such as persistence, pub/sub, data structures, and atomic operations, and provides a comprehensive list of commands for keys, strings, hashes, lists, sets, and sorted sets.
This article uses a fictional bank scenario to illustrate how database architectures evolve from a single MySQL instance through caching, master‑slave replication, vertical splitting, horizontal sharding, clustering, and finally the adoption of NoSQL and search engines to handle growing traffic and data complexity.
This article introduces the concept and components of database systems, classifies databases into relational, NoSQL, NewSQL and multi‑model types, explains the CAP theorem and its impact on consistency versus availability, and outlines current market trends and future directions in the database industry.
This article explains what NoSQL means, contrasts it with SQL and relational databases, walks through schema design, primary keys, indexing, and then demonstrates how to implement a lightweight key‑value NoSQL store in pure Python with full command parsing and handling.
This article explains what NoSQL means, contrasts it with relational databases, and guides readers through building a simple Python key‑value NoSQL database, covering schema basics, querying, indexing, command parsing, and the trade‑offs between flexibility and query performance.
This guide introduces Erupt, a low‑code full‑stack Java framework that lets you create enterprise‑level backend management systems without writing any front‑end code, covering installation, SpringBoot integration, CRUD generation, extended modules like scheduling, code generation, monitoring, NoSQL support, and online API development.
This article provides a comprehensive guide to Redis, covering its role as a non‑relational database, detailed data types and commands, persistence mechanisms (RDB and AOF), memory eviction strategies, high‑availability features such as replication, Sentinel and clustering, and solutions to typical cache problems like penetration, avalanche, and hot‑key handling.
This article shares an interview experience that compares MongoDB and MySQL, explains the advantages and disadvantages of document‑oriented and relational databases, introduces other NoSQL solutions such as Elasticsearch, and offers guidance on choosing the right storage technology based on workload characteristics.
This article introduces Redis as an open‑source, in‑memory data‑structure store, explains its role as a NoSQL database, details its five fundamental data types, common commands, expiration handling, pipelines, and transactions, and shows how it fits into modern scaling architectures.
This guide walks through migrating documents from a MongoDB 3.4.1 cluster to an Apache CouchDB 2.2 cluster using a Python script that leverages pymongo and CouchDB’s REST API, covering environment setup, migration steps, code execution, and verification of transferred data.
This article explains Redis persistence mechanisms, detailing how RDB snapshots and AOF append‑only logs work, how to enable and configure them, their advantages and drawbacks, and guidance on choosing the right method for reliable data storage.
This article provides a comprehensive introduction to HBase, covering its origins, column‑oriented NoSQL design, storage on HDFS, logical and physical structures, read/write workflows, performance optimizations, and common interview questions for big‑data engineers.
This article systematically refutes ten widespread misconceptions about MongoDB—covering scalability, versioning, data format, transactions, relationships, sharding, security, data loss, and suitability—while explaining its architecture, BSON storage, and best‑practice usage with illustrative code examples.
This article provides a comprehensive overview of database system design, covering its historical evolution, classification of relational and NoSQL databases, key architectural patterns, consistency models, indexing techniques, storage formats, compression methods, and practical considerations for selecting the right database solution.
This article explains Redis persistence mechanisms, detailing how the in‑memory database can lose data on shutdown and how the two main durability options—RDB snapshots and AOF append‑only logs—work, their configuration commands, their advantages, disadvantages, and guidance on choosing the right approach for production use.
This article provides a thorough overview of Redis, covering its eight core data types, essential commands, practical usage examples, and advanced features such as geospatial indexes, hyperloglog, and bitmap operations, making it a valuable reference for developers and interview preparation.
This guide walks through Redis’s eight core data structures—String, List, Set, Hash, Sorted Set, Geospatial, HyperLogLog, and Bitmap—detailing their commands, practical usage scenarios, and example interactions to help developers ace interview questions and optimize NoSQL solutions.
The 2020 DB-Engines ranking, based on search engine visibility, developer discussions, job postings, professional platform usage, and social media mentions, ranks 363 databases, highlighting Oracle, MySQL and SQL Server as the dominant relational leaders while showcasing the rapid rise of various NoSQL categories.
This guide reviews Redis's eight fundamental data types—String, List, Set, Hash, Sorted Set, Geospatial, HyperLogLog, and Bitmap—explaining their commands, usage scenarios, and practical examples to help developers and interviewees master this popular NoSQL database.
The article explains why relational databases, especially PostgreSQL combined with TimescaleDB, are better suited than NoSQL for handling complex, time‑series IoT data, offering robust SQL ecosystems, schema design, data integrity, and reduced data silos for multi‑user access.
This article introduces Redis as a high‑performance, in‑memory NoSQL database, explains why learning it is valuable, provides step‑by‑step installation instructions, and presents essential commands for keys, strings, lists, sets, sorted sets, and hashes with practical code examples.
The article explains why the massive, diverse data generated by the Internet of Things requires specialized databases, outlines key criteria for selecting a suitable database, and reviews several popular options such as InfluxDB, CrateDB, MongoDB, RethinkDB, SQLite, and Cassandra, highlighting their strengths for IoT workloads.
This article provides a comprehensive guide to Redis persistence mechanisms, detailing how RDB snapshots and AOF logs work, how to configure and use them, their advantages and drawbacks, and how to choose the right approach for reliable data storage.
This article explains the principles, data models, storage choices, synchronization strategies, metadata handling, and scaling considerations for building a high‑performance feed‑stream system that can support millions to billions of users, with practical guidance on using NoSQL or relational databases.
This comprehensive guide explains what NoSQL is, contrasts non‑relational and relational databases, outlines the main representatives of each, dives deep into MongoDB architecture, features, use‑cases and limitations, and also covers MySQL fundamentals, replication, backup, and common monitoring tools like Prometheus and Zabbix.
This article introduces HBase’s column‑oriented architecture, explains the roles of Master, RegionServer, and Zookeeper, and provides detailed environment preparation and installation commands for setting up an HBase cluster on Hadoop.
MongoDB’s flexible, schema‑less design offers performance benefits but can become a maintenance nightmare as projects grow, so the article explains why introducing external schema protocols like Google’s Protobuf or Facebook’s Thrift provides structure, reduces bugs, and balances flexibility with robustness.
The article compares traditional relational databases with NoSQL, explains why NoSQL emerged, outlines its four main categories—key‑value, document‑oriented, column‑family, and graph databases—lists popular implementations, their features, and suitable and unsuitable application scenarios.
This article explains how to evaluate business architecture, data volume, data types, and team expertise to select the most suitable database—whether relational with strong ACID guarantees for OLTP, analytical OLAP systems, or various NoSQL options—while highlighting Huawei Cloud offerings for different scenarios.
The article starts with a job‑interview anecdote and then provides a comprehensive overview of NoSQL, its history, definitions, comparison with MySQL, practical scenarios, and detailed examinations of key NoSQL projects such as LevelDB, RocksDB, SSDB, and Pika, highlighting their architectures and use cases.
