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Python Data Loading from slack to postgresql using dlt Library

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This page provides technical documentation on how to load data from slack, a business messaging app that brings people closer to relevant information, to postgres, an open-source object-relational database system. The process utilizes the dlt library, a Python-based open-source tool. The dlt library simplifies the process of data extraction from slack and its subsequent storage in postgres, ensuring the secure handling of complex data workloads. Detailed information about slack can be accessed at https://slack.com. This guide will take you through the steps of using dlt for data loading from slack to postgres efficiently and effectively.

dlt Key Features​

  • Automated Maintenance: With schema inference and evolution alerts, as well as short declarative code, maintenance becomes simple. Learn more here.
  • Run Anywhere Python Runs: dlt can run on any platform that supports Python, including Airflow, serverless functions, and notebooks. It doesn't require any external APIs, backends, or containers, making it scalable on micro and large infrastructures alike. Check it out here.
  • Slack Integration: dlt provides basic support for sending Slack messages, allowing for easy communication and updates. You can configure Slack incoming hook via secrets.toml or environment variables. Dive into it here.
  • DuckDB Destination: dlt supports DuckDB as a destination for your data. It provides a setup guide and details on data loading, supported file formats, and supported column hints. Explore more here.
  • Postgres Destination: dlt also supports Postgres as a destination for your data. It provides a setup guide, instructions on how to create a new database and user, and details on data loading, supported file formats, and supported column hints. Learn more 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 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 Slack to PostgreSQL. You can run the following commands to create a starting point for loading data from Slack to PostgreSQL:

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack 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]>=0.3.12

You now have the following folder structure in your project:

slack_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── slack/                   # folder with source specific files
│   └── ...
├── slack_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.slack]
access_token = "access_token" # 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 Slack 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 slack_pipeline.py, as well as a folder slack 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:


"""Pipeline to load slack into duckdb."""

from typing import List

import dlt
from pendulum import datetime
from slack import slack_source


def load_all_resources() -> None:
    """Load all resources from slack without any selection of channels."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='postgres', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=1000, start_date=datetime(2023, 9, 1), end_date=datetime(2023, 9, 8)
    )

    # Uncomment the following line to load only the access_logs resource. It is not selectes
    # by default because it is a resource just available on paid accounts.
    # source.access_logs.selected = True

    load_info = pipeline.run(
        source,
    )
    print(load_info)


def select_resource(selected_channels: List[str]) -> None:
    """Execute a pipeline that will load the given Slack list of channels with the selected
    channels incrementally beginning at the given start date."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='postgres', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=20,
        selected_channels=selected_channels,
        start_date=datetime(2023, 9, 1),
        end_date=datetime(2023, 9, 8),
    ).with_resources("channels", "1-announcements", "dlt-github-ci")

    load_info = pipeline.run(
        source,
    )
    print(load_info)


def get_users() -> None:
    """Execute a pipeline that will load Slack users list."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='postgres', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=20,
    ).with_resources("users")

    load_info = pipeline.run(
        source,
    )
    print(load_info)


if __name__ == "__main__":
    # Add your desired resources to the list...
    # resources = ["access_logs", "conversations", "conversations_history"]

    # load_all_resources()
    # select_resource(selected_channels=["dlt-github-ci"])

    # select_resource(selected_channels=["1-announcements", "dlt-github-ci"])

    get_users()

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

python slack_pipeline.py

4. Inspecting your load result​

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

dlt pipeline slack 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 slack 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 allows you to deploy your pipelines using Github Actions. This CI/CD runner is free to use and can be scheduled to run at specific times.
  • Deploy with Airflow: You can also deploy your dlt pipelines with Airflow. This platform is particularly useful for managing and scheduling complex data pipelines.
  • Deploy with Google Cloud Functions: If you're working in the Google Cloud environment, dlt provides support for deploying your pipelines with Google Cloud Functions. This allows you to execute your pipelines in response to specific event triggers.
  • Other Deployment Options: For more ways to deploy your dlt pipelines, check out the additional deployment guides.

The running in production section will teach you about:​

  • How to Monitor Your Pipeline: dlt provides a comprehensive monitoring solution that allows you to keep an eye on your data pipelines. You can track the status of your pipelines, check the data flow, and identify any issues that might arise. Learn more about it here.
  • Set Up Alerts: Stay informed about the status of your pipelines with dlt's alerting capabilities. You can set up alerts to notify you of any changes or issues with your data pipelines. This feature ensures that you are always aware of what's happening with your data. Check out the guide here.
  • Set Up Tracing: dlt allows you to trace the execution of your data pipelines, providing valuable insights into the performance and efficiency of your data processes. With tracing, you can identify bottlenecks, streamline your pipelines, and improve your data operations. Learn how to set it up 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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