Load GitHub Data to Supabase Using dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Supabase. You can get the connection string for your Supabase database as described in the Supabase Docs.

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This documentation explains how to load data from GitHub into Supabase using the open-source Python library dlt. The dlt library allows you to extract data on issues, pull requests, or events from any GitHub repository and load it into a destination of your choice, such as Supabase. Supabase is an open-source alternative to Firebase, offering a Postgres database, authentication, instant APIs, edge functions, real-time subscriptions, storage, and vector embeddings. For more detailed information about the GitHub API, visit GitHub's documentation.

dlt Key Features

• Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more
• Run it where Python runs: On Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more
• User-friendly interface: A declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Learn more
• Detailed tutorial available: A step-by-step guide to building a pipeline that loads data from the GitHub API into DuckDB. Follow the tutorial
• Community support: Join the dlt community on Slack for help and support, and give the library a ⭐ on GitHub. Join Slack

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for Supabase:

pip install "dlt[postgres]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from GitHub to Supabase. You can run the following commands to create a starting point for loading data from GitHub to Supabase:

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github postgres
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt[postgres]>=0.3.25

You now have the following folder structure in your project:

github_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── github/                   # folder with source specific files
│   └── ...
├── github_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.github]
access_token = "access_token" # please set me up!

[destination.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 5432
connect_timeout = 15

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations

• Read more about setting up the GitHub source in our docs.
• Read more about setting up the Supabase destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at github_pipeline.py, as well as a folder github that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

import dlt

from github import github_reactions, github_repo_events


def load_duckdb_repo_reactions_issues_only() -> None:
    """Loads issues, their comments and reactions for duckdb"""
    pipeline = dlt.pipeline(
        "github_reactions",
        destination='postgres',
        dataset_name="duckdb_issues",
        full_refresh=True,
    )
    # get only 100 items (for issues and pull request)
    data = github_reactions(
        "duckdb", "duckdb", items_per_page=100, max_items=100
    ).with_resources("issues")
    print(pipeline.run(data))


def load_airflow_events() -> None:
    """Loads airflow events. Shows incremental loading. Forces anonymous access token"""
    pipeline = dlt.pipeline(
        "github_events", destination='postgres', dataset_name="airflow_events"
    )
    data = github_repo_events("apache", "airflow", access_token="")
    print(pipeline.run(data))
    # if you uncomment this, it does not load the same events again
    # data = github_repo_events("apache", "airflow", access_token="")
    # print(pipeline.run(data))


def load_dlthub_dlt_all_data() -> None:
    """Loads all issues, pull requests and comments for dlthub dlt repo"""
    pipeline = dlt.pipeline(
        "github_reactions",
        destination='postgres',
        dataset_name="dlthub_reactions",
        full_refresh=True,
    )
    data = github_reactions("dlt-hub", "dlt")
    print(pipeline.run(data))


if __name__ == "__main__":
    load_duckdb_repo_reactions_issues_only()
    load_airflow_events()
    load_dlthub_dlt_all_data()

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python github_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline github_events info

You can also use streamlit to inspect the contents of your Supabase destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline github_events show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

• Deploy with Github Actions: Learn how to deploy a pipeline using Github Actions.
• Deploy with Airflow: Follow the steps to deploy a pipeline with Airflow and Google Composer.
• Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions.
• Explore other deployment options: Check out other methods to deploy your pipeline here.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operations and quickly identify any issues. Read more
• Set up alerts: Configure alerts to get notified about important events and potential issues in your dlt pipeline. Read more
• Set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipeline, helping you to diagnose and resolve issues efficiently. Read more

Available Sources and Resources

For this verified source the following sources and resources are available

Source github_repo_events

"GitHub repo events source provides data about activities and interactions within a repository."

Resource Name	Write Disposition	Description
repo_events	append	Retrieves all the repository events associated with the GitHub repository. This includes information about the actor (user who triggered the event), organization, payload (specific details about the event), and the repository itself.

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