Loading GitHub Data to Timescale with dlt in Python

tip

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

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This documentation explains how to use the dlt library to load data from GitHub into Timescale. The GitHub verified source allows you to load data on issues, pull requests, or events from any GitHub repository using the GitHub API. Timescale is engineered to handle demanding workloads, such as time series, vector, events, and analytics data. Built on PostgreSQL, Timescale offers expert support at no extra charge. The dlt library facilitates this process with its open-source capabilities. For further information on the GitHub API, visit here.

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. Getting Started
• Pipeline Metadata: Leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data lineage. Read more about lineage
• Schema Enforcement and Curation: Enforce and curate schemas to ensure data consistency and quality. Adjust a schema
• Scaling and Finetuning: Offers several mechanisms and configuration options to scale up and finetune pipelines. Read more about performance
• Community Support: Join the dlt community on Slack, report issues, and make feature requests on GitHub. Join our Slack community

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 Timescale:

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

# 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 Timescale 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 Timescale 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: Follow this guide to set up and deploy your dlt pipeline using GitHub Actions. Learn more
• Deploy with Airflow and Google Composer: This tutorial walks you through deploying a dlt pipeline using Airflow and Google Composer. Learn more
• Deploy with Google Cloud Functions: Learn how to deploy your dlt pipeline using Google Cloud Functions. Learn more
• Explore other deployment options: Check out various other methods to deploy your dlt pipeline. Learn more

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to keep an eye on your pipeline's performance and health by following the detailed guide on How to Monitor your pipeline.
• Set up alerts: Ensure that you are promptly informed about any issues or important events in your pipeline by setting up alerts. Follow the instructions on Set up alerts.
• And set up tracing: Gain deeper insights into your pipeline's execution and troubleshoot issues effectively by setting up tracing. Check out the comprehensive 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 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.

Additional pipeline guides

• Load data from MySQL to Supabase in python with dlt
• Load data from Spotify to Snowflake in python with dlt
• Load data from Zendesk to Azure Synapse in python with dlt
• Load data from Mux to Neon Serverless Postgres in python with dlt
• Load data from Google Analytics to Neon Serverless Postgres in python with dlt
• Load data from Salesforce to Snowflake in python with dlt
• Load data from Pipedrive to Azure Cloud Storage in python with dlt
• Load data from Spotify to PostgreSQL in python with dlt
• Load data from Jira to Databricks in python with dlt
• Load data from Mux to DuckDB in python with dlt