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Loading Data from notion to clickhouse using Python's dlt Library

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This page provides technical documentation on how to load data from Notion to ClickHouse using the open-source Python library, dlt. Notion is a versatile platform that allows you to think, write, and plan in a single space. It can be used to capture thoughts, manage projects, or even run a company. On the other hand, ClickHouse is a high-speed, column-oriented database management system that is open-source. It enables real-time generation of analytical data reports using SQL queries. The dlt library facilitates this data transfer process. For more information on Notion, visit https://www.notion.so/help/guides/what-is-notion.

dlt Key Features​

  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about it here.
  • Data Extraction: Extracting data with dlt is simple - you simply decorate your data-producing functions with loading or incremental extraction metadata, which enables dlt to extract and load by your custom logic. Find more information here.
  • Data Types: dlt supports a wide range of data types, from text and double to complex and wei. This flexibility allows you to handle diverse data sources. Check out the supported data types here.
  • DuckDB Destination: DuckDB is a supported destination in dlt. You can easily install dlt with DuckDB dependencies and set up a project with a pipeline that loads to DuckDB. Learn how to do it here.
  • Data Lineage: dlt provides identifiers, data lineage, and schema lineage for efficient data tracking and management. You can read more about it 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 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 Notion to ClickHouse. You can run the following commands to create a starting point for loading data from Notion to ClickHouse:

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

You now have the following folder structure in your project:

notion_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── notion/                   # folder with source specific files
│   └── ...
├── notion_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.notion]
api_key = "api_key" # 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 Notion 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 notion_pipeline.py, as well as a folder notion 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 notion import notion_databases


def load_databases() -> None:
    """Loads all databases from a Notion workspace which have been shared with
    an integration.
    """
    pipeline = dlt.pipeline(
        pipeline_name="notion",
        destination='clickhouse',
        dataset_name="notion_data",
    )

    data = notion_databases()

    info = pipeline.run(data)
    print(info)


if __name__ == "__main__":
    load_databases()

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

python notion_pipeline.py

4. Inspecting your load result​

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

dlt pipeline notion 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 notion 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 you can use for free. You can specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy dlt using Airflow. This is a managed Airflow environment provided by Google. It creates an Airflow DAG for your pipeline script that you should customize.
  • Deploy with Google Cloud Functions: dlt can be deployed with Google Cloud Functions. This is a lightweight, event-based, asynchronous compute solution that allows you to create small, single-purpose functions that respond to cloud events without the need to manage a server or a runtime environment.
  • Other Deployment Options: There are several other ways to deploy dlt. You can explore more about these options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: Keep track of your pipeline's performance and progress with dlt's built-in monitoring capabilities. Learn how to effectively monitor your pipeline here.
  • Set Up Alerts: Stay informed about any issues or changes in your pipeline. dlt allows you to set up alerts to notify you about any important events or anomalies. Learn how to set up alerts here.
  • Set Up Tracing: Gain insights into your pipeline's execution with dlt's tracing feature. Tracing allows you to follow the execution of your pipeline and identify any potential bottlenecks or issues. Learn how to set up tracing here.

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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