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· 5 min read
Rotem Tamir

We recently shipped a new feature in Atlas that enables developers to quickly create ERD (Entity Relationship Diagram) visualizations of their database schemas. Today, I want to show how this feature can be used in tandem with one of Atlas's core capabilities - "Schema Loaders" - to produce visualizations of GORM models.

But first, let's introduce the different characters in this story:

  • Atlas - an open-source tool for managing database schemas.
  • GORM - one of the most popular ORMs for Go.
  • ERD - a diagram that shows the relationships between entities in a database. (we'll see an example in a minute)
  • Schema Loaders - a feature in Atlas that allows users to load their database schemas from different sources.

In this blog post, we will create a toy GORM application, and show how you can use Atlas to visualize the database schema that GORM generates for you.

Step 1: Bootstrap the GORM application

Start by creating a new directory for our project. Then, initialize a new Go module:

mkdir gormviz
cd gormviz
go mod init

Next, we will install the GORM package and the SQLite driver:

go get -u
go get -u

Now, let's create a file that will contain our database models. We will call it models/models.go:

package models

import ""

type User struct {
Name string
Pets []Pet

type Pet struct {
Name string
User User
UserID uint

Step 2: Setup Atlas

Now that we have a GORM application, let's set up Atlas. First, we will install the Atlas CLI:

curl -sSf | sh

For more installation options, see these instructions.

Next, let's install the GORM Atlas provider which will allow us to load our GORM models into Atlas:

go get

Next, to prevent the Go Modules system from dropping this dependency from our go.mod file, let's follow the Go Module's official recommendation for tracking dependencies of tools and add a file named tools.go with the following contents:

//go:build tools
package main

import _ ""

Alternatively, you can simply add a blank import to the models.go file we created above.

Finally, to tidy things up, run:

go mod tidy

After the provider is installed, let's create an Atlas project file to glue everything together.

Create a new file named atlas.hcl with the following contents:

data "external_schema" "gorm" {
program = [
"--path", "./models",
"--dialect", "sqlite",

env "gorm" {
src = data.external_schema.gorm.url
dev = "sqlite://file?mode=memory&_fk=1"

This file defines two things:

  • An External Schema Loader named gorm that will load our GORM models into Atlas by executing the program on our models package.
  • An Environment named gorm that will use the gorm schema loader to load the models into a SQLite database in memory.

Step 3: Visualize the GORM models

Now that we have everything set up, let's run Atlas and see what we get:

atlas schema inspect -w --env gorm --url 'env://src'

Let's break down this command:

  • The schema inspect command is used to inspect a database schema and produce a representation of it.
  • The -w flag tells Atlas to open a web browser and display the visualization.
  • The --env gorm flag tells Atlas to use the gorm environment we defined in the atlas.hcl file.
  • The --url 'env://src' flag tells Atlas to use schema defined in the src attribute of the gorm env.

When we run this command,

Atlas will prompt us to ask if we want this visualization to be shared on the Public Atlas Playground or if we want to keep it private:

? Where would you like to share your schema visualization?:
▸ Publicly (
Your personal workspace (requires 'atlas login')

As this is just a toy example, let's publish it to the public playground by pressing Enter.

Once we do this, Atlas will extract the database schema from our GORM models, and publish it to the playground, printing the URL of the visualization:

Privately sharing visualizations

By publishing visualizations to the public playground, you can easily share them with others by simply sending them the URL. However, if you want to keep your visualizations private, such that they are only accessible to you and your team, you can do so by first logging in to Atlas Cloud:

atlas login

Your browser should open and prompt you to either login or create a free account if you don't already have one. Upon completion, you should see a message in your terminal that looks like this:

You are now connected to rotemtam85 on Atlas Cloud.

Once you are logged in, you can re-run the atlas schema inspect --env gorm --web command and Atlas will prompt you to ask if you want to share the visualization in your personal workspace:

? Where would you like to share your schema visualization?:
Publicly (
▸ Privately (

Select the Privately option and press Enter. Atlas will then publish the visualization to your personal workspace and print the URL of the visualization:

Wrapping up

In this post, we have shown how to use Atlas to visualize the database schema that GORM generates for your application. ERD visualizations are just one of the many features that Atlas provides for working with your database schema. To learn more about what you can do with the Atlas GORM provider, check out the relevant guide.

How can we make Atlas better?

We would love to hear from you on our Discord server ❤️.

· 3 min read

Having a visual representation of your data model can be helpful as it allows for easier comprehension of complex data structures, and enables developers to better understand and collaborate on the data model of the application they are building.

Entity relationship diagrams (ERDs) are a common way to visualize data models, by showing how data is stored in the database. ERDs are graphical representations of the entities, their attributes, and the way these entities are related to each other.

Today we are happy to announce the release of DjangoViz, a new tool for automatically creating ERDs from Django data models.

Django is an open source Python framework for building web applications quickly and efficiently. In this blog post, I will introduce DjangoViz and demonstrate how to use it for generating Django schema visualizations using the Atlas playground.


Django ORM

Django ORM is a built-in module in the Django web framework. It offers a high-level abstraction layer that enables developers to define complex application data models with ease. Unlike traditional ORM frameworks that rely on tables and foreign keys, Django models are defined using Python objects and relationships:

from django.db import models

class User(models.Model):
username = models.CharField(max_length=255)
email = models.EmailField(unique=True)
password = models.CharField(max_length=255)

class Post(models.Model):
title = models.CharField(max_length=255)
content = models.TextField()
author = models.ForeignKey(User, on_delete=models.CASCADE)

When the application runs, Django translates these Python models into database schemas, mapping each model to a corresponding database table and each field to a corresponding column in the table.
When working with schemas and making changes to them, being able to understand the full picture just through code can get complicated very quickly. To help developers better understand their schema, we have created DjangoViz.

Introducing DjangoViz

For the purpose of this demo, we will follow the Django getting started tutorial, and showcase how you can use DjangoViz to visualize the default models included by Django's startproject command.

First, install Django and create a new project:

pip install Django
django-admin startproject atlas_demo
cd atlas_demo

Install the DjangoViz package:

pip install djangoviz

Add DjangoViz to your Django project's INSTALLED_APPS in atlas_demo/


DjangoViz supports either PostgreSQL or MySQL, in this example we will use PostgreSQL:

Install the PostgreSQL driver:

pip install psycopg2-binary

Configure the database to work with PostgreSQL in the file:

"default": {
"ENGINE": "django.db.backends.postgresql_psycopg2",
"NAME": "postgres",
"USER": "postgres",
"PASSWORD": "pass",
"HOST": "",
"PORT": "5432",

Start a PostgreSQL container:

docker run --rm -p 5432:5432  -e POSTGRES_PASSWORD=pass -d postgres:15

Now, you can visualize your schema by running the djangoviz management command from your new project directory:

python djangoviz

You will get a public link to your visualization, which will present an ERD and the schema itself in SQL or HCL:

Here is a public link to your schema visualization:

When clicking on the link you will see the ERD of your new project:


Wrapping up

In this post, we discussed DjangoViz, a new tool that helps to quickly visualize Django schemas. With this tool, you can easily get an overview of the data model and visual of your schema. We would love to hear your thoughts and feedback if you decide to give it a go!

Have questions? Feedback? Find our team on our Discord server ❤️.

· 4 min read
Rotem Tamir

Atlas is most commonly used for managing and applying schema changes to databases, but it can also be used for something else: exploring and understanding database schemas.

With inspection, Atlas connects to your database, analyzes its structure from the metadata tables, and creates a graph data structure that maps all the entities and relations within the database. Atlas can then take this graph and represent it in various formats for users to consume. In this post, I will present two such forms of representation: Entity Relationship Diagrams (ERDs) and JSON documents.

Schemas as ERDs

One of the most useful ways to represent a database schema is using an Entity Relationship Diagram (ERD). This allows developers to see the schema in a visual and intuitive way, making it easy to understand the relationships between different elements of the database. When using ERDs, because the data is presented in a graph format, you can easily navigate through the schema and see how different entities are connected. This can be especially useful when working with complex or large databases, as it allows you to quickly identify patterns and connections that might not be immediately obvious when looking at the raw data.

Using Explore to generate an ERD

To automatically generate an ERD from your database, you can use the Explore feature of Atlas Cloud. To visualize a schema using the Explore feature, you need to provide your database schema in one of two ways:

  1. Provide a connection string to your database. This will allow Atlas Cloud to connect to your database and automatically generate a schema from the metadata tables. Note: this method only works for databases that are publicly accessible via the internet.

  2. Provide the schema as an Atlas HCL file. If you have an existing Atlas project, you can use the atlas schema inspect command to generate the HCL file from your database.

    After installing Atlas, you can run the following command to generate the HCL representation of your database schema:

    # MySQL
    atlas schema inspect -u mysql://root:pass@localhost:3306/db_name

    # PostgreSQL
    atlas schema inspect postgres://postgres:pass@localhost:5432/db_name?sslmode=disable

Schemas as JSON documents

In addition to producing ERDs, Atlas can also produce a JSON document that represents the database schema. One of the key benefits of representing the database schema as a JSON document is that it allows you to use standard tools like jq to analyze the schema programmatically. jq is a popular command-line tool for working with JSON data, and it can be especially useful for exploring and manipulating the schema data generated by Atlas.

With jq, you can easily extract specific information from the schema, such as the names of all the tables in the database or the foreign key relationships between different entities. This makes it easy to write scripts or programs that can automatically analyze the schema and identify potential issues or opportunities for optimization.

To get the JSON representation of your database schema, you can use the atlas schema inspect command with a custom logging format:

atlas schema inspect -u '<url>' --format '{{ json . }}'

This will output the schema as a JSON document:

"schemas": [
"name": "test",
"tables": [
"name": "blog_posts",
"columns": [
"name": "id",
"type": "int"
"name": "title",
"type": "varchar(100)",
"null": true
// .. Truncated for brevity ..

Once your schema is represented as a JSON document, you can use jq to analyze it. For example, to get a list of all the tables that contain a foreign key, run:

atlas schema inspect -u '<url>' --format '{{ json . }}' | jq '.schemas[].tables[] | select(.foreign_keys | length > 0) | .name'

This will output:


Wrapping up

In this blog post, we demonstrated how Atlas can be used as a schema inspection and visualization tool, in addition to its more commonly known use as a schema migration tool. We showed how to use the Explore feature to create an ERD from your database schema, and how to use the atlas schema inspect command to generate a JSON document that can be analyzed using jq and other tools.

Have questions? Feedback? Feel free to reach out on our Discord server.