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Enterprise-level professional community for Database, BigData, and AIOps. Daily original articles, weekly online tech talks, monthly offline salons, and quarterly XCOPS&DAMS conferences—delivered by industry experts.

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Recent Articles

Latest from dbaplus Community

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dbaplus Community
dbaplus Community
Jan 21, 2026 · Information Security

How Large Language Models Transform Data Security: Frameworks, Challenges, and Real-World Practices

This article reviews the current state, feasibility, industry adoption, concrete deployment scenarios, and future directions of applying large language models to data security, covering technical challenges, architectural designs, prompt engineering, privacy‑preserving techniques, and practical case studies.

AI applicationsInformation SecurityLLM engineering
0 likes · 21 min read
How Large Language Models Transform Data Security: Frameworks, Challenges, and Real-World Practices
dbaplus Community
dbaplus Community
Jan 18, 2026 · Databases

Avoid These Common SQL Performance Pitfalls for Faster Queries

This guide enumerates frequent misconceptions in SQL performance—from over‑indexing and SELECT * misuse to improper transaction handling and outdated monitoring practices—explains why they hurt efficiency, and provides concrete, version‑aware solutions to optimize queries, schema design, and database operations.

MySQLOptimizationPerformance
0 likes · 26 min read
Avoid These Common SQL Performance Pitfalls for Faster Queries
dbaplus Community
dbaplus Community
Jan 17, 2026 · Operations

Why Installing Software on Linux Feels So Much Harder Than on Windows

Switching from Windows to Linux reveals unexpected challenges: software installation methods differ, executable permissions rely on file attributes, and version‑specific library compatibility can break apps, making Linux setups feel far more labor‑intensive than the straightforward Windows experience.

CompatibilitySoftware InstallationSystem Administration
0 likes · 6 min read
Why Installing Software on Linux Feels So Much Harder Than on Windows
dbaplus Community
dbaplus Community
Jan 15, 2026 · Backend Development

How to Design a High‑Throughput Database Architecture for a Billion‑Row Daily Log System

This guide breaks down a real‑world interview scenario where a backend engineer must design a scalable database solution for a billing‑log or feed system handling 100 million daily inserts and 100 k QPS reads, covering partitioning vs sharding, sharding key selection, shard count, read‑write separation, multi‑level caching, consistency patterns, hot‑key mitigation, and online schema changes.

Online Schema ChangeRead-Write Separationbackend architecture
0 likes · 10 min read
How to Design a High‑Throughput Database Architecture for a Billion‑Row Daily Log System
dbaplus Community
dbaplus Community
Jan 13, 2026 · Backend Development

How AI‑Driven DDD Refactoring Cut Service Package Development from Days to Hours

This article presents a detailed case study of using AI to assist domain‑driven design (DDD) for refactoring Taobao's monolithic flash‑sale service‑package system, showing how automated context extraction, code skeleton generation, and AI‑augmented implementation reduced development effort from 5‑8 person‑days to a configurable solution while improving architecture decoupling, code quality, and extensibility.

AIArchitectureDomain-Driven Design
0 likes · 15 min read
How AI‑Driven DDD Refactoring Cut Service Package Development from Days to Hours
dbaplus Community
dbaplus Community
Jan 11, 2026 · Databases

Why Using Only Postgres Can Replace Redis, RabbitMQ, and Elasticsearch

The article argues that a single PostgreSQL instance can handle caching, queuing, full‑text search, and real‑time notifications, eliminating the need for separate services like Redis, RabbitMQ, and Elasticsearch, while reducing cost and complexity.

Database ConsolidationFull-text searchMessage Queue
0 likes · 12 min read
Why Using Only Postgres Can Replace Redis, RabbitMQ, and Elasticsearch
dbaplus Community
dbaplus Community
Jan 8, 2026 · Backend Development

How Big Platforms Verify Username Availability in Milliseconds

This article walks through the layered architecture that large services like Instagram use to instantly check if a username is taken, starting from simple database queries, adding caching, employing Bloom filters, and finally using Trie structures for fast, memory‑efficient lookups.

CachingTriebackend architecture
0 likes · 10 min read
How Big Platforms Verify Username Availability in Milliseconds