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Loading GitHub Data to Local Filesystem with dlt in Python

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This documentation provides a guide on using the dlt library to load data from GitHub repositories into The Local Filesystem. The GitHub verified source allows you to extract data on issues, pull requests, and events using the GitHub API. The data can then be stored in a local folder, creating a data lake with formats such as JSONL, Parquet, or CSV. By leveraging the open-source dlt library, you can efficiently manage and analyze your GitHub data locally. For more detailed information on the GitHub API, visit GitHub Documentation.

dlt Key Features​

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go.
  • Flexible Data Loading: dlt supports various data loading behaviors, such as appending or replacing data, and incrementally loading new data. Learn more about managing data loading behaviors here.
  • Dynamic Data Fetching: Make your data fetch more dynamic and reduce code redundancy with dlt. Find out how to make your data fetch more dynamic here.
  • Secure Secrets Handling: dlt provides a way to securely handle secrets, ensuring your sensitive information is protected. Discover how to handle secrets securely here.
  • Reusable Data Sources: Create configurable and reusable data sources with dlt. This feature allows you to build efficient and maintainable data pipelines. Learn how to create reusable data sources here.

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 The Local Filesystem:

pip install "dlt[filesystem]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github filesystem
# 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[filesystem]>=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.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 The Local Filesystem destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to a local directory, remove the [destination.filesystem.credentials] section from your secrets.toml and provide a local filepath as the bucket_url.

[destination.filesystem] # in ./dlt/secrets.toml
bucket_url="file://path/to/my/output"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem',
        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='filesystem', 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='filesystem',
        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 The Local Filesystem 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 and Google Composer: Follow the guide to deploy a pipeline with Airflow.
  • Deploy with Google Cloud Functions: Discover how to deploy a pipeline using Google Cloud Functions.
  • Explore other deployment options: Check out various 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 in production by following the guide on How to Monitor your pipeline.
  • Set up alerts: Ensure you are notified of any issues with your dlt pipeline by setting up alerts. Follow the detailed instructions in the Set up alerts guide.
  • Set up tracing: Gain insights into the execution of your dlt pipeline by setting up tracing. Check out the step-by-step guide on And set up tracing.

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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