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Loading Data from rest_api to clickhouse with Python dlt

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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This page provides technical documentation on how to load data from a Rest API into ClickHouse using the open-source Python library dlt. Rest API is a verified source that supports data acquisition from any HTTP Rest API. ClickHouse is a high-performance, open-source, column-oriented database management system that empowers real-time generation of analytical data reports through SQL queries. The dlt library facilitates this data transfer, offering a streamlined way to populate ClickHouse with data fetched from a Rest API. For detailed information on the Rest API source, please visit https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api.

dlt Key Features​

  • Authentication Types: Snowflake destination accepts three authentication types - password authentication, key pair authentication, and external authentication. Learn more about these authentication types here.
  • How dlt Works: dlt turns JSON data into a live dataset stored in the destination of your choice. It does this by extracting the JSON data, normalizing it to a schema, and finally loading it to the location where you will store it. Learn more about this process here.
  • Next Steps: After getting a basic understanding of dlt, you can take full advantage of the dlt library by building your sources out of existing building blocks. Learn more about these steps here.
  • 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. Learn more about these mechanisms here.
  • Extracting Data with dlt: Extracting data with dlt is simple - you simply decorate your data-producing functions with loading or incremental extraction metadata. dlt also offers scalability through iterators, chunking, parallelization, and utilizes implicit extraction DAGs. Learn more about this 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 ClickHouse:

pip install "dlt[clickhouse]"

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

# 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 clickhouse
# 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[clickhouse]>=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.clickhouse]
dataset_name = "dataset_name" # please set me up!

[destination.clickhouse.credentials]
database = "default"
password = "password" # please set me up!
username = "default"
host = "host" # please set me up!
port = 9440
http_port = 8443

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 ClickHouse 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 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='clickhouse',
        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='clickhouse',
        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 ClickHouse 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: dlt provides seamless integration with Github Actions to automate your deployment process.

  • Deploy with Airflow: You can easily deploy your dlt pipelines with Airflow, a platform to programmatically author, schedule and monitor workflows.

  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services.

  • Other Deployment Options: For more options on how to deploy your dlt pipelines, visit this page on the official dlt documentation.

The running in production section will teach you about:​

  • Monitoring Your Pipeline: dlt provides a comprehensive way to monitor your pipeline. You can view the status of your pipeline, check the progress of your data loads, and much more. For more details, check out the guide on how to monitor your pipeline.
  • Setting Up Alerts: With dlt, you can set up alerts to notify you of any changes or issues with your pipeline. This feature allows you to respond quickly to any potential problems and keep your pipeline running smoothly. Learn more about how to set up alerts.
  • Setting Up Tracing: Tracing is a crucial aspect of running a dlt pipeline. It allows you to track the execution of your pipeline and identify any potential bottlenecks or issues. For more information on how to set up tracing, visit the tracing guide.

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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