1 views collected around this technical thread.
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.
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.
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 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 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.
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.
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.
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 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.
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 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.