Python Data Loading from chess.com to motherduck using dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

Welcome to the technical guide for using the dlt library to load data from Chess.com into MotherDuck. Chess.com is a popular online platform providing a range of services for chess enthusiasts, including games, tournaments, and lessons. MotherDuck is an in-process analytical database known for its speed and feature-rich SQL dialect. This guide will walk you through the process of using dlt, an open-source Python library, to facilitate the data transfer between these platforms. For more details about Chess.com, please visit Chess.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. Getting Started Guide

• Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Governance Support in dlt Pipelines

• Wide Range of Destinations: dlt supports a wide range of destinations like DuckDB, MotherDuck, and AWS S3 filesystem. List of Available Destinations

• dbt Support: This destination integrates with dbt via dbt-duckdb which is a community supported package. dbt version >= 1.5 is required (which is current dlt default.) dbt Support

• Automated Tests: Each destination must pass few hundred automatic tests. MotherDuck is passing those tests (except the transactions OFC). However we encountered issues with ATTACH timeouts when connecting which makes running such number of tests unstable. Tests on CI are disabled. Automated Tests

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 MotherDuck:

pip install "dlt[motherduck]"

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 Chess.com to MotherDuck. You can run the following commands to create a starting point for loading data from Chess.com to MotherDuck:

# create a new directory
mkdir chess_pipeline
cd chess_pipeline
# initialize a new pipeline with your source and destination
dlt init chess motherduck
# 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[motherduck]>=0.3.25

You now have the following folder structure in your project:

chess_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── chess/                   # folder with source specific files
│   └── ...
├── chess_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.chess]
config_int = 0 # 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.chess]
secret_str = "secret_str" # please set me up!

[sources.chess.secret_dict] # please set me up!
key = "value"

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # please set me up!

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations

• Read more about setting up the Chess.com source in our docs.
• Read more about setting up the MotherDuck 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 chess_pipeline.py, as well as a folder chess 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 chess import source


def load_players_games_example(start_month: str, end_month: str) -> None:
    """Constructs a pipeline that will load chess games of specific players for a range of months."""

    # configure the pipeline: provide the destination and dataset name to which the data should go
    pipeline = dlt.pipeline(
        pipeline_name="chess_pipeline",
        destination='motherduck',
        dataset_name="chess_players_games_data",
    )
    # create the data source by providing a list of players and start/end month in YYYY/MM format
    data = source(
        ["magnuscarlsen", "vincentkeymer", "dommarajugukesh", "rpragchess"],
        start_month=start_month,
        end_month=end_month,
    )
    # load the "players_games" and "players_profiles" out of all the possible resources
    info = pipeline.run(data.with_resources("players_games", "players_profiles"))
    print(info)


def load_players_online_status() -> None:
    """Constructs a pipeline that will append online status of selected players"""

    pipeline = dlt.pipeline(
        pipeline_name="chess_pipeline",
        destination='motherduck',
        dataset_name="chess_players_games_data",
    )
    data = source(["magnuscarlsen", "vincentkeymer", "dommarajugukesh", "rpragchess"])
    info = pipeline.run(data.with_resources("players_online_status"))
    print(info)


def load_players_games_incrementally() -> None:
    """Pipeline will not load the same game archive twice"""
    # loads games for 11.2022
    load_players_games_example("2022/11", "2022/11")
    # second load skips games for 11.2022 but will load for 12.2022
    load_players_games_example("2022/11", "2022/12")


if __name__ == "__main__":
    # run our main example
    load_players_games_example("2022/11", "2022/12")
    load_players_online_status()

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

python chess_pipeline.py

4. Inspecting your load result

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

dlt pipeline chess_pipeline info

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

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline chess_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 allows you to easily deploy your pipeline using Github Actions. You simply need to follow the steps outlined in this guide.
• Deploy with Airflow: You can also deploy your pipeline with Airflow. This guide provides step-by-step instructions on how to do so.
• Deploy with Google Cloud Functions: If you prefer, you can deploy your pipeline using Google Cloud Functions. Follow the instructions in this guide to learn how.
• Other Deployment Options: dlt offers a variety of other deployment options. Check out this page for more information.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides built-in tools for monitoring your data pipeline. This feature allows you to keep track of your pipeline's performance and troubleshoot any issues that may arise. You can learn more about it here.
• Set Up Alerts: To ensure that you are notified of any issues in your pipeline promptly, dlt allows you to set up alerts. This feature can help you to respond quickly to any potential problems and maintain the efficiency of your pipeline. Learn how to set it up here.
• Set Up Tracing: Tracing is another powerful feature offered by dlt. It allows you to track the execution of your pipeline and provides valuable insights into its performance. You can find out how to set up tracing here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source chess

The Chess.com source provides data on player profiles, online statuses, and historical game details.

Resource Name	Write Disposition	Description
players_games	append	This resource retrieves players' games that happened between a specified start and end month. It includes various details like accuracy, ratings, results, time control, tournament details, etc. for both the black and white players in each game.
players_online_status	append	This resource checks the current online status of multiple chess players. It retrieves their username, status, last login date, and check time.
players_profiles	replace	This resource retrieves player profiles for a list of player usernames. It includes details like the player's avatar, country, followers, streaming status, join date, last online time, league, location, name, player ID, status, title, URL, username, and verification status.

Additional pipeline guides

• Load data from GitLab to Neon Serverless Postgres in python with dlt
• Load data from Keap to Redshift in python with dlt
• Load data from Clubhouse to Azure Cloud Storage in python with dlt
• Load data from Oracle Database to Timescale in python with dlt
• Load data from The Local Filesystem to AWS S3 in python with dlt
• Load data from Bitbucket to Timescale in python with dlt
• Load data from Box Platform API to Supabase in python with dlt
• Load data from Qualtrics to AWS S3 in python with dlt
• Load data from Oracle Database to The Local Filesystem in python with dlt
• Load data from Aladtec to Redshift in python with dlt