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Loading GitHub Data into ClickHouse Using Python's dlt Library

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Welcome to this technical documentation. Here, we will guide you on how to use the verified source, github, with dlt, an open-source Python library, to load data from any GitHub repository onto clickhouse, a fast, open-source column-oriented database management system. This process involves fetching data on issues, pull requests, or events using the GitHub API and generating analytical data reports in real-time using SQL queries in ClickHouse. For more detailed information about the source, please refer to the official GitHub documentation at https://docs.github.com.

dlt Key Features​

  • Automated Maintenance: dlt provides automated maintenance through schema inference and evolution, and alerts. Its short, declarative code makes maintenance simple. Learn more
  • Scalability and Fine-tuning: dlt offers mechanisms and configuration options to scale up and fine-tune pipelines. It supports running extraction, normalization, and load in parallel, and allows for fine-tuning of memory buffers, intermediary file sizes, and compression options. Read more
  • Governance Support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts, contributing to better data management practices and overall data governance. Read more
  • Data Extraction: With dlt, data extraction is simple and efficient. It leverages iterators, chunking, and parallelization for scalability, and utilizes implicit extraction Directed Acyclic Graphs (DAGs) for efficient API calls for data enrichments or transformations. Learn more
  • DuckDB Destination: dlt supports DuckDB as a destination, with all write dispositions supported and data loading done through large INSERT VALUES statements by default. It also supports various file formats and column hints. Read more

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 ClickHouse:

pip install "dlt[clickhouse]"

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

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

[destination.clickhouse.credentials]
database = "default"
password = "password" # please set me up!
username = "default"
host = "host" # please set me up!
port = 9440
http_port = 8443

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 ClickHouse 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='clickhouse',
        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='clickhouse', 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='clickhouse',
        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 ClickHouse 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: dlt can be deployed using Github Actions. This CI/CD runner allows you to set up automated workflows for your dlt projects.
  • Deploy with Airflow: You can use Airflow, a platform designed to programmatically author, schedule and monitor workflows, to deploy your dlt pipelines.
  • Deploy with Google Cloud Functions: dlt can also be deployed using Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: For more information on how to deploy dlt, check out other deployment options.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a comprehensive monitoring system that allows you to keep track of your data pipeline's performance. From tracking data load times to identifying bottlenecks, you can ensure your pipeline runs smoothly and efficiently. Learn more about it here.
  • Set Up Alerts: Stay informed about your pipeline's status with dlt's alerting feature. You can set up alerts to notify you of any issues that arise during the pipeline's execution, allowing you to address them promptly. Find out how to set up alerts here.
  • Implement Tracing: dlt offers a tracing feature that allows you to track the execution of your pipeline. This feature is particularly useful for debugging and identifying issues in your pipeline. Learn how to set up tracing 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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