This article explains six key reasons why the ES6 Map data structure is superior to traditional objects, covering flexible key types, order preservation, easy size retrieval, prototype isolation, performance benefits, and built‑in iteration methods, while also noting scenarios where objects remain useful.
This article introduces several lightweight Python databases—including PickleDB, TinyDB, ZODB, Durus, Buzhug, Gadfly, and PyTables—detailing their main features, typical use cases, limitations, and providing basic code examples to help developers choose suitable storage solutions for small projects or learning purposes.
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.
The February 2022 DB‑Engines ranking shows Snowflake’s rapid rise, a sharp decline for Oracle, MySQL and Microsoft SQL Server, stable positions for Elasticsearch, and detailed score trends across relational, key‑value, document, time‑series and graph databases, highlighting five popularity metrics used for the assessment.
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.
This whitepaper explains the concept of in‑memory databases, traces their historical development, outlines core attributes and typical use cases in e‑commerce, live streaming and telecom, compares leading products, and provides technical and management recommendations for hardware and product selection as well as future trends.
This article introduces Redis string data type and explains five basic commands (SET, GET, INCR, DECR, APPEND) as well as five commonly used commands (STRLEN, GETRANGE, SETRANGE, MSET, MGET), providing syntax, behavior, and example outputs.
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.
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.
FeatureKV, WeChat’s high‑performance key‑value store, handles one‑billion queries per second and ingests a billion keys per hour by separating write‑only DataSvr from read‑only KVSvr, supporting in‑memory, indexed, and block‑indexed tables, scaling horizontally, guaranteeing eventual consistency with versioned reads, and delivering up to 11 billion reads per second with sub‑15 ms latency.
This article explains what NoSQL databases are, introduces the CAP theorem that underlies their design trade‑offs, outlines typical application scenarios, discusses their limitations, classifies the main NoSQL families, and reviews four widely used systems—Redis, HBase, Cassandra, and MongoDB.
This article examines the limitations of traditional relational databases and explains how NoSQL solutions—key‑value stores, document databases, columnar databases, and full‑text search engines—address issues like schema rigidity, high I/O, and weak search, helping you choose the right data store.
This article explains what native multi‑model databases are, why they are advantageous, how to model complex hierarchical data such as aircraft maintenance teams, and demonstrates real‑world AQL queries that combine document, key‑value, and graph models within a single engine.
The article details Tencent File System (TFS), describing its platform components, 3D indexing techniques, high‑performance key‑value storage (TSSD) with MHT, dual‑read and smooth scaling mechanisms, hybrid index storage, host‑level FTL, Append‑Only and erasure‑coding storage engines, and how these innovations deliver scalable, low‑cost, high‑performance data storage for massive workloads.
This article explains the limitations of traditional relational databases, introduces the emergence of NoSQL, categorizes its four main types—key‑value, document‑oriented, column‑family, and graph—detailing popular products, typical use cases, and scenarios where each is unsuitable.
This article explains how traditional ACID transactions fall short for modern large‑scale systems and presents lock‑free transaction methods, atomic and partial cache‑switch techniques, and a PostgreSQL‑style MVCC model for key‑value databases, including practical rules and trade‑offs.
This article details the design, implementation, and performance of Lushan, a high‑throughput offline static data storage server built with libevent that supports dynamic library mounting, key‑value indexing, and efficient query handling for large‑scale recommendation workloads.
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.
