<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Architecture on Dev Toolkit</title><link>https://wen.yunshangtool.cn/tags/architecture/</link><description>Recent content in Architecture on Dev Toolkit</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sat, 16 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://wen.yunshangtool.cn/tags/architecture/index.xml" rel="self" type="application/rss+xml"/><item><title>Database Design Patterns for Scalable Applications</title><link>https://wen.yunshangtool.cn/posts/database-design-patterns/</link><pubDate>Sat, 16 May 2026 00:00:00 +0000</pubDate><guid>https://wen.yunshangtool.cn/posts/database-design-patterns/</guid><description>Good database design is foundational to application scalability and performance. Here are proven patterns for different scenarios.
Normalization vs Denormalization: Normalize to reduce redundancy and ensure data integrity. Denormalize for read-heavy workloads where performance matters more than storage efficiency.
Indexing Strategy: Create indexes for frequently queried columns, but avoid over-indexing. Each index adds write overhead. Use composite indexes for multi-column queries.
Partitioning: Split large tables into smaller, more manageable pieces. Range partitioning by date is common for time-series data.</description></item></channel></rss>