Skip to main content

Loading Data from Chess.com to Neon Serverless Postgres with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Neon Serverless Postgres. You can get the connection string for your Neon Serverless Postgres database as described in the Neon Serverless Postgres Docs.

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.

This documentation provides a guide on loading data from Chess.com to Neon Serverless Postgres using the open-source Python library dlt. Chess.com is a comprehensive online platform for chess enthusiasts, offering features such as online games, tournaments, and lessons. Neon Serverless Postgres is a serverless platform designed to help you build reliable and scalable applications faster, utilizing the PostgreSQL database you know and love. This guide will walk you through the steps to extract data from Chess.com and load it into Neon Serverless Postgres using dlt. For more details about Chess.com, visit their website.

dlt Key Features​

  • Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques, enabling efficient processing of large datasets. Learn more
  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data vaulting. Learn more
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality by adhering to predefined schemas. Learn more
  • Automatic Schema Evolution: dlt enables proactive governance by alerting users to schema changes, ensuring stakeholders can review and validate modifications. Learn more
  • Configurable Normalization Engine: dlt recursively unpacks nested JSON structures into relational tables, making it ready to be loaded into your chosen destination. 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 Neon Serverless Postgres:

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

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

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.postgres]
dataset_name = "dataset_name" # 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 Chess.com source in our docs.
  • Read more about setting up the Neon Serverless Postgres 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='postgres',
        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='postgres',
        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 Neon Serverless Postgres 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: Learn how to set up continuous integration and deployment for your dlt pipeline using GitHub Actions.
  • Deploy with Airflow: Follow this guide to deploy your dlt pipeline using Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Discover how to deploy your dlt pipeline using Google Cloud Functions.
  • Explore other deployment options: Check out additional methods and guides for deploying your dlt pipeline 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 operation and quick identification of issues. How to Monitor your pipeline
  • Set up alerts: Set up notifications to alert you of any issues or important events in your pipeline to maintain seamless data flow. Set up alerts
  • And set up tracing: Implement tracing to get detailed insights into the execution of your pipeline, helping you debug and optimize performance. And 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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.