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Loading Data from google sheets to clickhouse using dlt in Python

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This page provides technical documentation on how to use dlt, an open-source Python library, to load data from Google Sheets into ClickHouse. Google Sheets is a versatile online spreadsheet platform that enables secure, real-time data sharing and editing from any device. On the other hand, ClickHouse is a high-speed, open-source, column-oriented database management system that supports real-time analytical data reporting using SQL queries. By leveraging the capabilities of dlt, you can seamlessly transfer data from Google Sheets to ClickHouse, thus making your data analysis more efficient and insightful. For more details on Google Sheets, please visit here.

dlt Key Features​

  • Governance Support: dlt pipelines offer robust governance support through mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.
  • Scalability and Fine-tuning: dlt provides several mechanisms and configuration options to scale up and fine-tune pipelines, including running extraction, normalization, and load in parallel and fine-tuning memory buffers, intermediary file sizes, and compression options. Read more about performance here.
  • Automated Maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. This makes dlt a powerful tool for data management.
  • Versatility: dlt can be run wherever Python runs - on Airflow, serverless functions, notebooks. No need for external APIs, backends or containers, and it scales on micro and large infra alike.
  • Community Support: dlt has a growing community of users who contribute to its development. Join the dlt community on Slack to find recent releases or discuss what you can build with dlt.

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

# create a new directory
mkdir google_sheets_pipeline
cd google_sheets_pipeline
# initialize a new pipeline with your source and destination
dlt init google_sheets 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:


google-api-python-client
dlt[clickhouse]>=0.3.25

You now have the following folder structure in your project:

google_sheets_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── google_sheets/                   # folder with source specific files
│   └── ...
├── google_sheets_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

[sources.google_sheets]
spreadsheet_url_or_id = "spreadsheet_url_or_id" # please set me up!
range_names = 
["a", "b", "c"] # please set me up!

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.google_sheets.credentials]
client_id = "client_id" # please set me up!
client_secret = "client_secret" # please set me up!
refresh_token = "refresh_token" # please set me up!
project_id = "project_id" # 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 Google Sheets 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 google_sheets_pipeline.py, as well as a folder google_sheets 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:


from typing import Sequence

import dlt

from google_sheets import google_spreadsheet


def load_pipeline_with_ranges(
    spreadsheet_url_or_id: str, range_names: Sequence[str]
) -> None:
    """
    Loads explicitly passed ranges
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='clickhouse',
        full_refresh=True,
        dataset_name="test",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=range_names,
        get_sheets=False,
        get_named_ranges=False,
    )
    info = pipeline.run(data)
    print(info)


def load_pipeline_with_sheets(spreadsheet_url_or_id: str) -> None:
    """
    Will load all the sheets in the spreadsheet, but it will not load any of the named ranges in the spreadsheet.
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='clickhouse',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        get_sheets=True,
        get_named_ranges=False,
    )
    info = pipeline.run(data)
    print(info)


def load_pipeline_with_named_ranges(spreadsheet_url_or_id: str) -> None:
    """
    Will not load the sheets in the spreadsheet, but it will load all the named ranges in the spreadsheet.
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='clickhouse',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        get_sheets=False,
        get_named_ranges=True,
    )
    info = pipeline.run(data)
    print(info)


def load_pipeline_with_sheets_and_ranges(spreadsheet_url_or_id: str) -> None:
    """
    Will load all the sheets in the spreadsheet and all the named ranges in the spreadsheet.
    """
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='clickhouse',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        get_sheets=True,
        get_named_ranges=True,
    )
    info = pipeline.run(data)
    print(info)


def load_with_table_rename_and_multiple_spreadsheets(
    spreadsheet_url_or_id: str, range_names: Sequence[str]
) -> None:
    """Demonstrates how to load two spreadsheets in one pipeline and how to rename tables"""

    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination='clickhouse',
        full_refresh=True,
        dataset_name="sample_google_sheet_data",
    )

    # take data from spreadsheet 1
    data = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=[range_names[0]],
        get_named_ranges=False,
    )

    # take data from spreadsheet 2
    data_2 = google_spreadsheet(
        spreadsheet_url_or_id=spreadsheet_url_or_id,
        range_names=[range_names[1]],
        get_named_ranges=False,
    )
    # apply the table name to the existing resource: the resource name is the name of the range
    data.resources[range_names[0]].apply_hints(table_name="first_sheet_data")
    data_2.resources[range_names[1]].apply_hints(table_name="second_sheet_data")

    # load two spreadsheets
    info = pipeline.run([data, data_2])
    print(info)
    # yes the tables are there
    user_tables = pipeline.default_schema.data_tables()
    # check if table is there
    assert {t["name"] for t in user_tables} == {
        "first_sheet_data",
        "second_sheet_data",
        "spreadsheet_info",
    }


if __name__ == "__main__":
    url_or_id = "1HhWHjqouQnnCIZAFa2rL6vT91YRN8aIhts22SUUR580"
    range_names = ["hidden_columns_merged_cells", "Blank Columns"]

    load_pipeline_with_ranges(url_or_id, range_names)
    load_pipeline_with_sheets(url_or_id)
    load_pipeline_with_named_ranges(url_or_id)
    load_pipeline_with_sheets_and_ranges(url_or_id)
    load_with_table_rename_and_multiple_spreadsheets(url_or_id, range_names)

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

python google_sheets_pipeline.py

4. Inspecting your load result​

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

dlt pipeline google_sheets_pipeline 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 google_sheets_pipeline 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 easily deployed using GitHub Actions. You can specify when the GitHub Action should run using a cron schedule expression. Follow the guide here.
  • Deploy with Airflow: dlt can be deployed using Airflow, a powerful open-source tool to programmatically author, schedule and monitor workflows. To learn more about deploying a pipeline with Airflow, follow this guide.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. dlt can be deployed with Google Cloud Functions as well. Check out this guide for more information.
  • Other Deployment Options: There are other options to deploy dlt as well. You can check them out here.

The running in production section will teach you about:​

  • Monitoring your Pipeline: It's crucial to keep an eye on your pipeline to ensure it's functioning as expected. Learn how to effectively monitor your pipeline with dlt in this guide.
  • Setting up Alerts: Stay informed about any potential issues or updates with your pipeline by setting up alerts. Find out how to set up alerts in dlt using this tutorial.
  • Tracing your Pipeline: Tracing allows you to track the execution of your pipeline and helps in debugging. Learn how to set up tracing in dlt with this guide.

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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