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Load data from Rest API to The Local Filesystem using dlt in Python

About our rest_api verified source

This example demonstrates how to use the rest_api to retrieve data from the GitHub Rest API, but will work with any HTTP Rest API. Please read:

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The rest_api verified source supports retrieving data from any HTTP Rest API and loading it into The Local Filesystem. The local filesystem destination stores data in a local folder, allowing you to create datalakes easily. You can store data in formats like JSONL, Parquet, or CSV. This process is facilitated by the open-source Python library called dlt. For more details about the rest_api source, visit this link.

dlt Key Features​

  • 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. Read more.
  • Schema Evolution: dlt alerts users to schema changes, allowing them to review and validate modifications, update downstream processes, or perform impact analysis. Learn more.
  • Scalability and Performance: dlt offers mechanisms and configuration options to scale up and finetune pipelines, including parallel processing and memory optimization. Read more.
  • Advanced Data Extraction: dlt supports scalable data extraction through iterators, chunking, and parallelization, along with implicit extraction DAGs for efficient API calls and transformations. 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 The Local Filesystem:

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 Rest API to The Local Filesystem. You can run the following commands to create a starting point for loading data from Rest API to The Local Filesystem:

# create a new directory
mkdir rest_api_pipeline
cd rest_api_pipeline
# initialize a new pipeline with your source and destination
dlt init rest_api 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]>=0.4.11

You now have the following folder structure in your project:

rest_api_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── rest_api/                   # folder with source specific files
│   └── ...
├── rest_api_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

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.rest_api]
github_token = "github_token" # please set me up!

[destination.filesystem]
dataset_name = "dataset_name" # please set me up!
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!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Rest API source in our docs.
  • Read more about setting up the The Local Filesystem destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to a local directory, remove the [destination.filesystem.credentials] section from your secrets.toml and provide a local filepath as the bucket_url.

[destination.filesystem] # in ./dlt/secrets.toml
bucket_url="file://path/to/my/output"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at rest_api_pipeline.py, as well as a folder rest_api 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:


from typing import Any

import dlt
from rest_api import (
    RESTAPIConfig,
    check_connection,
    rest_api_source,
    rest_api_resources,
)


@dlt.source
def github_source(github_token: str = dlt.secrets.value) -> Any:
    # Create a REST API configuration for the GitHub API
    # Use RESTAPIConfig to get autocompletion and type checking
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.github.com/repos/dlt-hub/dlt/",
            "auth": {
                "type": "bearer",
                "token": github_token,
            },
        },
        # The default configuration for all resources and their endpoints
        "resource_defaults": {
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "params": {
                    "per_page": 100,
                },
            },
        },
        "resources": [
            # This is a simple resource definition,
            # that uses the endpoint path as a resource name:
            # "pulls",
            # Alternatively, you can define the endpoint as a dictionary
            # {
            #     "name": "pulls", # <- Name of the resource
            #     "endpoint": "pulls",  # <- This is the endpoint path
            # }
            # Or use a more detailed configuration:
            {
                "name": "issues",
                "endpoint": {
                    "path": "issues",
                    # Query parameters for the endpoint
                    "params": {
                        "sort": "updated",
                        "direction": "desc",
                        "state": "open",
                        # Define `since` as a special parameter
                        # to incrementally load data from the API.
                        # This works by getting the updated_at value
                        # from the previous response data and using this value
                        # for the `since` query parameter in the next request.
                        "since": {
                            "type": "incremental",
                            "cursor_path": "updated_at",
                            "initial_value": "2024-01-25T11:21:28Z",
                        },
                    },
                },
            },
            # The following is an example of a resource that uses
            # a parent resource (`issues`) to get the `issue_number`
            # and include it in the endpoint path:
            {
                "name": "issue_comments",
                "endpoint": {
                    # The placeholder {issue_number} will be resolved
                    # from the parent resource
                    "path": "issues/{issue_number}/comments",
                    "params": {
                        # The value of `issue_number` will be taken
                        # from the `number` field in the `issues` resource
                        "issue_number": {
                            "type": "resolve",
                            "resource": "issues",
                            "field": "number",
                        }
                    },
                },
                # Include data from `id` field of the parent resource
                # in the child data. The field name in the child data
                # will be called `_issues_id` (_{resource_name}_{field_name})
                "include_from_parent": ["id"],
            },
        ],
    }

    yield from rest_api_resources(config)


def load_github() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rest_api_github",
        destination='filesystem',
        dataset_name="rest_api_data",
    )

    load_info = pipeline.run(github_source())
    print(load_info)


def load_pokemon() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rest_api_pokemon",
        destination='filesystem',
        dataset_name="rest_api_data",
    )

    pokemon_source = rest_api_source(
        {
            "client": {
                "base_url": "https://pokeapi.co/api/v2/",
                # If you leave out the paginator, it will be inferred from the API:
                # paginator: "json_response",
            },
            "resource_defaults": {
                "endpoint": {
                    "params": {
                        "limit": 1000,
                    },
                },
            },
            "resources": [
                "pokemon",
                "berry",
                "location",
            ],
        }
    )

    def check_network_and_authentication() -> None:
        (can_connect, error_msg) = check_connection(
            pokemon_source,
            "not_existing_endpoint",
        )
        if not can_connect:
            pass  # do something with the error message

    check_network_and_authentication()

    load_info = pipeline.run(pokemon_source)
    print(load_info)


if __name__ == "__main__":
    load_github()
    load_pokemon()

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

python rest_api_pipeline.py

4. Inspecting your load result​

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

dlt pipeline rest_api_github info

You can also use streamlit to inspect the contents of your The Local Filesystem destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline rest_api_github 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: Automate your pipeline deployment using GitHub Actions. Follow the detailed guide to set up and schedule your deployments. Learn more
  • Deploy with Airflow: Use Google Composer or any Airflow instance to deploy your pipeline. This guide walks you through the steps to get your pipeline running in an Airflow environment. Learn more
  • Deploy with Google Cloud Functions: Leverage Google Cloud Functions to deploy your pipeline in a serverless environment. This guide provides instructions to set up and manage your deployments. Learn more
  • Other Deployment Options: Explore various other methods to deploy your pipeline, including detailed walkthroughs and examples. Learn more

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth operation and quick identification of issues. How to Monitor your pipeline
  • Set up alerts: Set up alerts to be notified of any issues or important events in your dlt pipeline, ensuring you can respond promptly. Set up alerts
  • Set up tracing: Implement tracing to get detailed insights into the execution of your dlt pipeline, including timing information and configuration details. And set up tracing

Available Sources and Resources​

For this verified source the following sources and resources are available

Source github_source​

"Rest API Source for GitHub, providing detailed data on issues and related comments."

Resource NameWrite DispositionDescription
issue_commentsmergeContains information about the issue comments including the author, body of the comment, created date, and user details among other data.
issuesmergeContains information about the issues including the assignee details, author, body of the issue, comments, created date, and user details among other data.

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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