惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

G
Google Developers Blog
WordPress大学
WordPress大学
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
小众软件
小众软件
人人都是产品经理
人人都是产品经理
美团技术团队
Blog — PlanetScale
Blog — PlanetScale
S
SegmentFault 最新的问题
博客园 - 【当耐特】
V
V2EX
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 叶小钗
Google DeepMind News
Google DeepMind News
量子位
罗磊的独立博客
月光博客
月光博客
N
Netflix TechBlog - Medium
大猫的无限游戏
大猫的无限游戏
博客园_首页
P
Proofpoint News Feed
Jina AI
Jina AI
云风的 BLOG
云风的 BLOG
博客园 - 司徒正美
腾讯CDC

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Building a SQL Database in Rust: Reducing Memory Usage wi...
Musab Khan · 2026-06-13 · via DEV Community
Cover image for Building a SQL Database in Rust: Reducing Memory Usage with Spans and String Interning

Musab Khan

I have been working on a PostgreSQL compatible database system in Rust and recently made a couple of changes that significantly reduced unnecessary memory allocations.

The first change was in the lexer.

Originally, identifiers were stored as String values inside token variants such as Ident(String) and QuotedIdent(String). This meant every identifier required its own allocation even though the original SQL query already contained that text.

I switched to storing spans instead. A span contains the start and end position along with line and column information. Whenever I need the actual identifier text, I can retrieve it directly from the source query using the span.

Besides reducing allocations, this also improved diagnostics because I always know exactly where a token came from.

The second change was introducing a string interner.

As I started building larger parts of the parser and AST, I noticed that names such as tables, columns, views, and aliases could appear many times throughout a query. Storing the same string repeatedly felt wasteful.

I implemented a simple interner:

pub struct Interner {
    map: HashMap<&'static str, Symbol>,
    strings: Vec<&'static str>,
}

Now identifiers are stored as compact symbols instead of duplicated strings. The actual text is stored only once and can be resolved when needed.

Some benefits of this approach:

  • No duplicate allocations for repeated identifiers
  • Faster identifier comparisons using integer equality
  • Smaller AST nodes
  • Better cache locality

The project currently includes a lexer, parser, binder, query planner, optimizer, catalog, and storage engine.

I am still exploring ways to improve performance and memory efficiency, so I would be interested to hear how others have approached similar problems in compilers, interpreters, or database systems.

Repository: https://github.com/musab05/osirisdb