- Power of plain text:
- Combination of unstructured content and structured data in simple Markdown files.
- Simplicity at core:
- Turn your Markdown files into a queryable, lightweight SQL database.
- Flexible and extendable:
- Bring your own document types, extend your frontmatter with computed fields and check for errors with custom validations.
- Simple API:
- Get a list of all or some Markdown files, filter them by frontmatter fields, and more.
- Do one thing well:
- MarkdownDB just gives you a database and an API — a super-powerful and extensible way to create those from markdown.
- Open source:
- Your content isn't locked away in proprietary platforms. It's open, it's free, it's yours.
- [x] do laundry
{
"metadata": {
"tasks": [{ "description": "do laundry", "checked": true }]
}
}
---
title: Example Post
date: 2023-01-01
---
# Content
{
"metadata": {
"title": "Example Post",
"date": "2023-01-01"
}
}
---
tags: frontmatter_tag_1, frontmatter_tag_2
---
# Some heading
#body_tag
Lorem ipsum #tag1 #tag2 #tag3
{
"metadata": {
"tags": ["frontmatter_tag_1", "frontmatter_tag_2"]
},
"tags": ["body_tag", "frontmatter_tag_1", "frontmatter_tag_2"]
}
const addTitle = (fileInfo, ast) => {
const headerNode = ast.children.find((node) => node.type === "heading");
const title = headerNode
? headerNode.children.map((child) => child.value).join("")
: "";
fileInfo.title = title;
};
client.indexFolder({
folderPath: "PATH_TO_FOLDER",
customConfig: { computedFields: [addTitle] },
});
Our vision
Your content, your database
Markdown files already contain structured data — frontmatter fields, tags, links, tasks. Most tools ignore it. MarkdownDB extracts it, indexes it, and makes it queryable, without touching your files or locking you into a platform.
Plain text in. Rich queryable data out.
Learn more about the Markdown Database Pattern →
Step 1: You have a folder of markdown content
For example, your blog posts. Each file can have a YAML frontmatter header with metadata like title, date, tags, etc.
---
title: My first blog post
date: 2021-01-01
tags: [a, b, c]
author: John Doe
---
# My first blog post
This is my first blog post.
I'm using MarkdownDB to manage my blog posts.
Step 2: Index the files with MarkdownDB
Use the npm mddb package to index Markdown files into an SQLite database. This will create a markdown.db file in the current directory.
# npx mddb <path-to-folder-with-your-md-files>
npx mddb ./blog
SELECT files.*
FROM files
INNER JOIN file_tags ON files._id = file_tags.file
WHERE file_tags.tag = 'a'
Step 4: ...or using MarkdownDB Node.js API
Use our Node API to query your data for your blog, wiki, docs, digital garden, or anything you want!
import { MarkdownDB } from "mddb";
const client = new MarkdownDB({
client: "sqlite3",
connection: { filename: "markdown.db" },
});
const mddb = await client.init();
const blogFiles = await mddb.getFiles({
frontmatter: { draft: false },
});
MarkdownDB released!
First version of the package released under @flowershow/markdowndb with basic functionalities, like indexing files into an SQLite database, extracting frontmatter data and basic JS API.
Version 0.1.0
We added support for extracting and querying forward links and backlinks.
Version 0.2.0
We fixed some annoying bugs and renamed package from @flowershow/markdowndb to just mddb.
Version 0.3.0
Support for querying files by frontmatter field values.
Version 0.4.0
Add Tags Extraction from Markdown Content.
Version 0.5.0
Add tasks extraction from files. e.g - [ ] task
Version 0.6.0
Implement JSON output to disk.
Version 1.0.0
Support for custom document types and computed fields.
Flowershow