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

IT之家
IT之家
Y
Y Combinator Blog
月光博客
月光博客
Blog — PlanetScale
Blog — PlanetScale
GbyAI
GbyAI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - 三生石上(FineUI控件)
S
SegmentFault 最新的问题
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
美团技术团队
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
Last Week in AI
Last Week in AI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
有赞技术团队
有赞技术团队
博客园 - 司徒正美
V
Visual Studio Blog
小众软件
小众软件
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
T
Tailwind CSS Blog
Apple Machine Learning Research
Apple Machine Learning Research
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
A
About on SuperTechFans
The Cloudflare Blog

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
I made a free, open source, self-hostable alternative to ...
Graham Sutto · 2026-05-13 · via DEV Community

Graham Sutton

I created Mockyard - a free, open source, and self-hostable alternative to Mockaroo.

Screenshot of Mockyard

Mockyard ships as a Docker container that just runs:

docker run -p 8080:8080 ghcr.io/portside-labs/mockyard

Enter fullscreen mode Exit fullscreen mode

Problem

If you don't know what Mockaroo is, it's an online tool for generating large amounts of mock data in formats like CSV, JSON, SQL, etc.

The catch is that Mockaroo is limited to 1K rows per file on the free tier, and costs $60/year if you want to generate files with up to 100K rows. It's also not open source and not self-hostable.

I built Mockyard for two reasons:

  1. AI has made it possible to build things that used to live in the "I wish I had time for this" category.
  2. I needed to test CSV ingestion pipelines with hundreds of thousands to millions of records, and I wanted something that was fast, memory efficient, easy to use, and didn't require going online or installing a bunch of languages, tools, or packages.

Now it costs me $0 to generate up to 10 million rows per file, every day of the year.

Honestly, I figured someone would have already built this, but either nobody actually has or I'm terrible at Googling.

And yes, I tried generatedata.com too. It's good, but it didn't quite fit the way I needed to generate some of my mock data.

Differences

One thing I wanted was the ability to generate data using weighted enums. In Mockyard, you can specify not just fixed enum values, but also their distribution.

For example:

  • 20% of records should have role = Admin
  • 30% should have role = Manager
  • The remaining 50% should have role = Viewer

Another issue I had was address realism. Seeing things like:

Miami, Yukon Territory, Switzerland

for city, state, and country combinations hurt my eyes.

So Mockyard supports lookup tables so fields can stay logically connected. If a city is selected, the state and country can match appropriately instead of being generated independently and producing nonsense.

You still have to specify your own lookup values, but at least the generated data looks realistic.

Performance

Initial benchmarks for the same CSV with four columns:

Rows Format Time Throughput (rows/sec)
1,000 CSV 0.02s ~50,000
10,000 CSV 0.09s ~111,111
100,000 CSV 0.53s ~188,679
1,000,000 CSV 4.89s ~204,499
10,000,000 CSV 53.61s ~186,532

10 million rows is currently the max in Mockyard.

Anything beyond one million rows and Excel won't even load the whole file anyway (at least on Mac), so this should cover most real-world scenarios.

Web or API - Your Pick

If you want to generate data programmatically, Mockyard also exposes an API endpoint:

Screenshot of the OpenAPI spec

The UI actually uses the exact same API under the hood.

Output

Right now, output is limited to CSV and JSON.

Why?

Because I personally haven't needed anything else yet.

That said, if people actually find this useful and want support for additional formats, feel free to open an issue on the repo.

Repository: https://github.com/portside-labs/mockyard