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dlt Python Guide: Load Chess.com Data to AWS Athena

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This document provides technical guidance on how to load data from Chess.com, an online platform for chess enthusiasts, into AWS Athena, a query service for analyzing data in Amazon S3 using standard SQL, using an open-source Python library called dlt. Our implementation with dlt also supports Iceberg tables. For more information about the source, visit Chess.com.

dlt Key Features​

  • AWS Athena / Glue Catalog: dlt allows you to store data as parquet files in S3 buckets and create external tables in AWS Athena. You can query these tables with Athena SQL commands. Learn more

  • Chess.com Verified Source: dlt provides a verified source for the Chess.com API. This allows you to retrieve player profiles, game archives, and player games from the Chess.com platform. Learn more

  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This facilitates better data management practices, compliance adherence, and overall data governance. Learn more

  • Filesystem Destination: dlt allows you to initialize your pipeline with the AWS S3 filesystem as the destination. This allows you to store your data in a secure and scalable storage solution. Learn more

  • Data Lineage and Schema Lineage: dlt provides tracing capabilities that allow you to track the lineage of your data and schemas. This facilitates better understanding of your data's lifecycle and promotes data consistency and traceability. Learn more

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 AWS Athena:

pip install "dlt[athena]"

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

# create a new directory
mkdir chess_pipeline
cd chess_pipeline
# initialize a new pipeline with your source and destination
dlt init chess athena
# 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[athena]>=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.athena]
query_result_bucket = "query_result_bucket" # please set me up!
athena_work_group = "athena_work_group" # please set me up!

[destination.athena.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 AWS Athena 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='athena',
        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='athena',
        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 AWS Athena 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 widely used and basically free. You can set up a schedule for when the action should run using a cron schedule expression. Learn more about how to deploy a pipeline with Github Actions.
  • Deploy with Airflow and Google Composer: Google Composer is a managed Airflow environment provided by Google. dlt provides a command to initialize deployment with Airflow. This will create an Airflow DAG for your pipeline script that you should customize. Learn more about how to deploy a pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. dlt can be deployed on Google Cloud Functions, enabling you to execute your pipeline in response to events without having to manage a server. Learn more about how to deploy a pipeline with Google Cloud Functions.
  • Other Deployment Options: dlt provides flexibility in deployment options. There are various ways to deploy your pipeline, depending on your specific requirements. Learn more about the other deployment options.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to keep track of your pipeline's performance and status. It provides detailed insights into the pipeline's operations, which can help you identify and resolve any issues that may arise. Learn more about how to monitor your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any significant events or changes in your pipeline. This feature ensures that you are always up-to-date with your pipeline's status and can respond promptly to any issues. Find out how to set up alerts here.
  • Set Up Tracing: Tracing in dlt allows you to track the execution of your pipeline and understand its performance. It provides detailed information about each operation in your pipeline, helping you identify bottlenecks and optimize your pipeline. Learn 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 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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