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Loading Clubhouse Data to PostgreSQL Using dlt in Python

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This documentation provides a comprehensive guide for loading data from Clubhouse to PostgreSQL using the open-source Python library dlt. Clubhouse is a social media audio app that offers real-time virtual rooms for user communication via audio. PostgreSQL is a robust, open-source object-relational database system that extends SQL capabilities to handle complex data workloads efficiently. This guide will walk you through the steps to extract data from Clubhouse and load it into PostgreSQL using dlt. For more information on Clubhouse, visit clubhouse.com.

dlt Key Features​

  • Extract: The Python script requests data from an API or a similar source. Once this data is received, the script parses the JSON and provides it to dlt as input, which then normalizes that data. Learn more
  • Normalize: The configurable normalization engine in dlt recursively unpacks this nested structure into relational tables (i.e. inferring data types, linking tables to create parent-child relationships, etc.), making it ready to be loaded. Learn more
  • Load: The data is then loaded into your chosen destination. dlt uses configurable, idempotent, atomic loads that ensure data safely ends up there. Learn more
  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations, dlt notifies stakeholders. Learn more
  • Data Governance: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more

Getting started with your pipeline locally​

OpenAPI Source Generator dlt-init-openapi

This walkthrough makes use of the dlt-init-openapi generator cli tool. You can read more about it here. The code generated by this tool uses the dlt rest_api verified source, docs for this are here.

0. Prerequisites​

dlt and dlt-init-openapi requires Python 3.9 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 and dlt-init-openapi​

First you need to install the dlt-init-openapi cli tool.

pip install dlt-init-openapi

The dlt-init-openapi cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.

# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you 
# can just hit Enter and all will be rendered.
dlt-init-openapi clubhouse --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/clubhouse_api.yaml --global-limit 2
cd clubhouse_pipeline
# install generated requirements
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>=0.4.12

You now have the following folder structure in your project:

clubhouse_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── rest_api/                # The rest api verified source
│   └── ...
├── clubhouse/                
│   └── __init__.py          # TODO: possibly tweak this file
├── clubhouse_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

1.1. Tweak clubhouse/__init__.py​

This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api source set up in a different way. The generated file for the clubhouse source will look like this:

Click to view full file (133 lines)

from typing import List

import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig


@dlt.source(name="clubhouse_source", max_table_nesting=2)
def clubhouse_source(
        base_url: str = dlt.config.value,
) -> List[DltResource]:

    # source configuration
    source_config: RESTAPIConfig = {
        "client": {
            "base_url": base_url,
            "paginator": {
                "type":
                    "page_number",
                "page_param":
                    "page",
                "total_path":
                    "",
                "maximum_page":
                    20,
            },
        },
        "resources": 
        [
        {
            "name": "check_for_update",
            "table_name": "check_for_update",
            "endpoint": {
                "path": "/check_for_update",
                "params": {
                    # the parameters below can optionally be configured
                    # "is_testflight": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_actionable_notification",
            "table_name": "get_actionable_notification",
            "endpoint": {
                "path": "/get_actionable_notifications",
            }
        },
        {
            "name": "get_all_topic",
            "table_name": "get_all_topic",
            "endpoint": {
                "path": "/get_all_topics",
            }
        },
        {
            "name": "get_channel",
            "table_name": "get_channel",
            "endpoint": {
                "path": "/get_channels",
            }
        },
        {
            "name": "get_event",
            "table_name": "get_event",
            "endpoint": {
                "path": "/get_events",
                "params": {
                    # the parameters below can optionally be configured
                    # "is_filtered": "OPTIONAL_CONFIG",
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_notification",
            "table_name": "get_notification",
            "endpoint": {
                "path": "/get_notifications",
                "params": {
                    # the parameters below can optionally be configured
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_setting",
            "table_name": "get_setting",
            "endpoint": {
                "path": "/get_settings",
            }
        },
        {
            "name": "get_suggested_follows_all",
            "table_name": "get_suggested_follows_all",
            "endpoint": {
                "path": "/get_suggested_follows_all",
                "params": {
                    # the parameters below can optionally be configured
                    # "in_onboarding": "OPTIONAL_CONFIG",
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_users_for_topic",
            "table_name": "get_users_for_topic",
            "endpoint": {
                "path": "/get_users_for_topic",
                "params": {
                    # the parameters below can optionally be configured
                    # "topic_id": "OPTIONAL_CONFIG",
                    # "page_size": "OPTIONAL_CONFIG",

                },
            }
        },
        {
            "name": "get_welcome_channel",
            "table_name": "get_welcome_channel",
            "endpoint": {
                "path": "/get_welcome_channel",
            }
        },
        ]
    }

    return rest_api_source(source_config)

2. Configuring your source and destination credentials​

info

dlt-init-openapi will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml.

  • If you know your API needs authentication, but none was detected, you can learn more about adding authentication to the rest_api here.
  • OAuth detection currently is not supported, but you can supply your own authentication mechanism as outlined here.

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​


[runtime]
log_level="INFO"

[sources.clubhouse]
# Base URL for the API
base_url = "https://www.clubhouseapi.com/api/"

generated secrets.toml​


[sources.clubhouse]
# secrets for your clubhouse source
# example_api_key = "example value"

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations

At this time, the dlt-init-openapi cli tool will always create pipelines that load to a local duckdb instance. Switching to a different destination is trivial, all you need to do is change the destination parameter in clubhouse_pipeline.py to postgres and supply the credentials as outlined in the destination doc linked below.

  • Read more about setting up the rest_api 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 clubhouse_pipeline.py, as well as a folder clubhouse 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 clubhouse import clubhouse_source


if __name__ == "__main__":
    pipeline = dlt.pipeline(
        pipeline_name="clubhouse_pipeline",
        destination='duckdb',
        dataset_name="clubhouse_data",
        progress="log",
        export_schema_path="schemas/export"
    )
    source = clubhouse_source()
    info = pipeline.run(source)
    print(info)

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

python clubhouse_pipeline.py

4. Inspecting your load result​

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

dlt pipeline clubhouse_pipeline 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 clubhouse_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: Learn how to deploy your dlt pipeline using GitHub Actions, a free CI/CD runner. Follow the instructions here.
  • Deploy with Airflow and Google Composer: Use Google Composer, a managed Airflow environment, to deploy your dlt pipeline. Detailed steps can be found here.
  • Deploy with Google Cloud Functions: Deploy your dlt pipeline using Google Cloud Functions for serverless execution. Follow the guide here.
  • Explore Other Deployment Options: Discover various other methods to deploy your dlt pipeline, including detailed walkthroughs and guides. Check them out here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operations and quickly identify any issues. Read more
  • Set up alerts: Configure alerts to stay informed about the status of your dlt pipeline and be notified of any critical issues. Read more
  • And set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipeline, helping you troubleshoot and optimize performance. Read more

Available Sources and Resources​

For this verified source the following sources and resources are available

Source Clubhouse​

Clubhouse source provides notifications, user suggestions, events, topics, settings, updates, and channels data.

Resource NameWrite DispositionDescription
get_notificationappendRetrieves notifications for the user.
get_suggested_follows_allappendFetches a list of all suggested users to follow.
get_eventappendRetrieves details about a specific event.
get_users_for_topicappendGets a list of users associated with a specific topic.
get_all_topicappendRetrieves all available topics on the platform.
get_settingappendFetches user settings and preferences.
get_actionable_notificationappendRetrieves actionable notifications that require user interaction.
check_for_updateappendChecks if there are any updates available for the app.
get_channelappendRetrieves information about a specific audio channel.
get_welcome_channelappendFetches details about the welcome channel for new users.

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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