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Load GitHub Data to DuckDB using Python and dlt Library

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Welcome to our technical guide on how to use dlt to load data from GitHub into DuckDB. This guide will show you how to fetch data on issues, pull requests, or events from any GitHub repository using the GitHub API. We'll then load this data into DuckDB, a fast in-process analytical database with a rich SQL dialect and deep client API integrations. The dlt library, a versatile open-source Python tool, will be our main engine for this process. For more information on the GitHub API, visit https://docs.github.com. Let's get started.

dlt Key Features​

  • Efficient Data Pipeline Creation: The tutorial provides a comprehensive guide on creating data pipelines using dlt. It covers everything from fetching data from the GitHub API to making reusable data sources. Tutorial
  • Quick Start with dlt: The guide provides a quick start to using dlt by demonstrating how to retrieve and load data from the GitHub API into DuckDB. Quick Start
  • DuckDB Destination: The DuckDB destination guide gives detailed instructions on how to install, setup, and use DuckDB as a destination in dlt. It also provides information on supported file formats, column hints, and names normalization. DuckDB Destination
  • Advanced Deployment with dlt init: The guide explains how to use the dlt init command with branches, local folders, or git repositories for advanced deployment scenarios. Advanced Deployment
  • GitHub Verified Source: The GitHub verified source guide provides steps to set up and use the GitHub verified source in dlt. It covers how to grab credentials, initialize the verified source, and add credentials. GitHub Verified Source

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 DuckDB:

pip install "dlt[duckdb]"

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 DuckDB. You can run the following commands to create a starting point for loading data from GitHub to DuckDB:

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github duckdb
# 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[duckdb]>=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!

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 DuckDB 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='duckdb',
        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='duckdb', 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='duckdb',
        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 DuckDB 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: Github Actions is a CI/CD runner that you can use for free. dlt has a deploy command that prepares your pipeline for deployment with Github Actions. You can learn more about this process here.
  • Deploy with Airflow: Airflow is a platform that allows you to programmatically author, schedule, and monitor workflows. dlt allows you to deploy your pipeline with Airflow and Google Composer. You can learn more about this process here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. dlt allows you to deploy your pipeline with Google Cloud Functions. You can learn more about this process here.
  • Other deployment options: dlt offers other deployment options as well. You can learn more about these options here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides detailed information about the execution of your pipelines. This includes the start time, end time, duration, and status of each pipeline run. You can use this information to monitor the health and performance of your pipelines. More details can be found here.
  • Set up alerts: dlt allows you to set up alerts so that you are notified of any issues or failures in your pipelines. This can help you to quickly identify and resolve problems, ensuring that your pipelines continue to run smoothly. More information on setting up alerts can be found here.
  • Set up tracing: Tracing in dlt provides detailed information about the execution of your pipelines. This can help you to identify bottlenecks and optimize your pipelines for better performance. More details on how to set up tracing can be found here.

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 NameWrite DispositionDescription
repo_eventsappendRetrieves 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.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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