Python dlt Guide: Loading Data from Chess.com to AWS S3

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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This guide provides technical instructions on how to load data from chess.com, an online hub for chess enthusiasts, to aws s3, a versatile storage service, using the open-source Python library dlt. Chess.com offers a range of services including online games, tournaments, and lessons. Aws s3 is a remote file system and bucket storage service that uses fsspec to abstract file operations. It is primarily used for staging other destinations and can quickly form a data lake. The dlt library facilitates this data transfer process. More information about chess.com can be found at https://www.chess.com/.

dlt Key Features

• Initialise the dlt project: Start by initialising a new dlt project by using a simple command. This command will initialise your pipeline with your chosen source and the AWS S3 filesystem as the destination. Read more
• Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Read more
• Data loading: dlt stores all the files in a single folder with the name of the dataset that you passed to the run or load methods of pipeline. The name of each file contains essential metadata on the content. Read more
• Filesystem & buckets: Filesystem destination stores data in remote file systems and bucket storages like S3, google storage or azure blob storage. Underneath, it uses fsspec to abstract file operations. Read more
• DuckDB: DuckDB is a dlt destination. dlt will load data using large INSERT VALUES statements by default. Loading is multithreaded (20 threads by default). If you are ok with installing pyarrow we suggest to switch to parquet as file format. Read 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 S3:

pip install "dlt[filesystem]"

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 S3. You can run the following commands to create a starting point for loading data from Chess.com to AWS S3:

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

You now have the following folder structure in your project:

my-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:

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!

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.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!

Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Chess.com source in the dlt verifed sources documentation.

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='filesystem',
        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='filesystem',
        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 S3 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 supports deployment with Github Actions, offering a free CI/CD runner. You can schedule your pipeline using a cron schedule expression and run it either on push or manually. Learn more here.
• Deploy with Airflow and Google Composer: dlt provides an easy way to deploy your pipeline with Airflow and Google Composer. It creates an Airflow DAG for your pipeline script and provides a guide on how to add environment variables with secrets to the Airflow. More details can be found here.
• Deploy with Google Cloud Functions: dlt also enables deployment with Google Cloud Functions. This method is ideal for small datasets and allows you to run your pipeline in response to events without needing to manage a server. Find out more here.
• Other Deployment Methods: Besides the above-mentioned methods, dlt supports other deployment methods as well. You can explore them here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides a monitoring system to keep track of your pipeline's performance and status. You can learn more about it here.
• Set Up Alerts: With dlt, you can set up alerts to notify you of any changes or issues in your pipeline. This way, you can quickly respond to any problems that may arise. Find out how to set up alerts here.
• Set Up Tracing: dlt also allows you to set up tracing to record and analyze your pipeline's operations. This can help you identify any bottlenecks or inefficiencies in your pipeline. Learn more about setting 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.

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