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Python Guide: Loading Chess.com Data to Redshift using dlt

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This page provides technical documentation on using the open-source Python library dlt to load data from Chess.com into Redshift. Chess.com is a comprehensive online platform catering to chess enthusiasts, offering services such as online games, tournaments, lessons, and more. On the other hand, Redshift is Amazon's fully managed, petabyte-scale data warehouse service in the cloud, capable of scaling from a few hundred gigabytes to over a petabyte. Utilizing dlt presents an efficient way to extract data from Chess.com and load it into Redshift for further analysis and utilization. More information about Chess.com can be found at https://www.chess.com/.

dlt Key Features​

  • Amazon Redshift Integration: dlt provides seamless integration with Amazon Redshift, allowing users to easily set up and initialize a new project with Redshift as the destination. Learn more about it here.

  • Robust 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.

  • Scalability and Efficiency: dlt offers scalability through iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs that allow efficient API calls for data enrichments or transformations. Learn more about it here.

  • Schema Adjustment: dlt allows users to make changes in the import schema. It detects any changes to the schemas automatically and propagates them to the pipeline on the next run. Learn more about it here.

  • Next Steps and Advanced Topics: dlt is a constantly growing library that supports many features and use cases needed by the community. After getting started, users can explore more advanced topics and join the dlt community. Learn 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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir chess_pipeline
cd chess_pipeline
# initialize a new pipeline with your source and destination
dlt init chess redshift
# 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[redshift]>=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.redshift.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 = 5439
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 Chess.com source in our docs.
  • Read more about setting up the Redshift 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='redshift',
        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='redshift',
        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 Redshift 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 can be deployed using Github Actions. This is a CI/CD runner that is free to use. You can specify when the GitHub Action should run using a cron schedule expression. The command also takes additional flags: --run-on-push and --run-manually. Learn more about it here.
  • Deploy with Airflow: Airflow is another way to deploy dlt. You can use Google Composer, a managed Airflow environment provided by Google. It will create an Airflow DAG for your pipeline script that you should customize. Follow the guide here to learn more.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a lightweight compute solution for developers to create single-purpose, stand-alone functions that respond to Cloud events without the need to manage a server or runtime environment. Learn how to deploy dlt with Google Cloud Functions here.
  • Other Deployment Options: There are other ways to deploy dlt. You can find more information about these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides functionalities to monitor your pipeline and ensure it is running as expected. The monitoring feature helps you keep track of your data loads, detect any anomalies, and troubleshoot issues promptly. For more details, visit How to Monitor your pipeline.
  • Set Up Alerts: Stay informed about the status of your pipeline with dlt's alerting feature. It allows you to set up alerts that notify you about any significant events or issues in your pipeline, enabling you to take necessary actions promptly. To learn more, check out Set up alerts.
  • Set Up Tracing: dlt offers tracing capabilities that provide insights into the execution of your pipeline. Tracing helps you understand the performance of your pipeline and identify potential bottlenecks or issues. For more information, read Set up tracing.

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 NameWrite DispositionDescription
players_gamesappendThis 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_statusappendThis resource checks the current online status of multiple chess players. It retrieves their username, status, last login date, and check time.
players_profilesreplaceThis 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​

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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