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Loading Data from mux to postgresql Using Python's dlt Library

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This page provides technical documentation on how to load data from mux, a platform that simplifies the complexities of building video streaming and on-demand video catalogs, to postgresql, an open source object-relational database system renowned for its robustness and ability to handle complex data workloads. To facilitate this process, we utilize dlt, an open source Python library. Detailed information about mux can be found at https://www.mux.com/.

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. Get Started
  • Robust Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Governance Support
  • Data Extraction: Extracting data with dlt is simple and scalable, leveraging iterators, chunking, and parallelization techniques. Data Extraction
  • Postgres Destination: dlt supports Postgres as a destination for data pipelines, providing a comprehensive setup guide for getting started. Postgres Destination
  • DuckDB Destination: dlt also supports DuckDB as a destination, providing a detailed setup guide and additional destination options. DuckDB Destination

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 PostgreSQL:

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

# create a new directory
mkdir mux_pipeline
cd mux_pipeline
# initialize a new pipeline with your source and destination
dlt init mux 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]>=0.3.5

You now have the following folder structure in your project:

mux_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── mux/                   # folder with source specific files
│   └── ...
├── mux_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.mux]
mux_api_access_token = "mux_api_access_token" # please set me up!
mux_api_secret_key = "mux_api_secret_key" # 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 Mux source in our docs.
  • Read more about setting up the PostgreSQL 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 mux_pipeline.py, as well as a folder mux 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 mux import mux_source


def load_yesterday_video_views() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mux", destination='postgres', dataset_name="mux_data"
    )
    load_info = pipeline.run(mux_source())
    print(load_info)


if __name__ == "__main__":
    load_yesterday_video_views()

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

python mux_pipeline.py

4. Inspecting your load result​

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

dlt pipeline mux info

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

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline mux 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 is a CI/CD runner that can be used for free. You need to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy dlt using Airflow. This method will create an Airflow DAG for your pipeline script that you should customize. The DAG uses dlt Airflow wrapper to make this process simple.
  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
  • Other Deployment Methods: There are other methods to deploy dlt as well. You can find more information about these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides you with the ability to monitor your pipeline's performance and ensure it's running as expected. You can inspect the load info and trace, save and alert on schema changes, and inspect the pipeline using the command line. Learn more about it here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This feature allows you to respond quickly to any potential problems and ensure your pipeline is running smoothly. Find out how to set up alerts here.
  • Set Up Tracing: Tracing in dlt gives you insight into the runtime of your pipeline. It contains timing information on extract, normalize and load steps and also all the config and secret values with full information from where they were obtained. Learn more about setting up tracing here.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source mux_source​

"Mux_source" provides data on video content and viewing metrics from the Mux platform.

Resource NameWrite DispositionDescription
assets_resourcemergeFetches metadata about video assets from the Mux API's "assets" endpoint
views_resourceappendFetches data about every video view from yesterday from the Mux API

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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