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Loading Shopify Data to ClickHouse with Python Using dlt

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This page provides technical documentation on how to utilize the open-source Python library dlt for loading data from Shopify to ClickHouse. Shopify is a comprehensive commerce platform enabling anyone to initiate, develop, manage, and expand a business. On the other hand, ClickHouse is a swift open-source column-oriented database management system, specializing in generating analytical data reports in real-time using SQL queries. The dlt library facilitates this data transfer process, making it seamless and efficient. For more information about Shopify, please visit https://www.shopify.com/.

dlt Key Features​

  • Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more

  • Implicit extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. A DAG represents a directed graph without cycles, where each node represents a data source or transformation step. 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. Read more

  • How dlt works: dlt automatically turns JSON returned by any source (e.g. an API) into a live dataset stored in the destination of your choice (e.g. Google BigQuery). It does this by first extracting the JSON data, then normalizing it to a schema, and finally loading it to the location where you will store it. Read more

  • Getting started with dlt: Dive into the Getting started guide for a quick intro to the essentials of dlt. Play with the Google Colab demo to see dlt in action. Read the Tutorial to learn how to build a pipeline that loads data from an API. 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 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 Shopify to ClickHouse. You can run the following commands to create a starting point for loading data from Shopify to ClickHouse:

# create a new directory
mkdir shopify_dlt_pipeline
cd shopify_dlt_pipeline
# initialize a new pipeline with your source and destination
dlt init shopify_dlt 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]>=0.3.8

You now have the following folder structure in your project:

shopify_dlt_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── shopify_dlt/                   # folder with source specific files
│   └── ...
├── shopify_dlt_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.shopify_dlt]
shop_url = "shop_url" # please set me up!
organization_id = "organization_id" # 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.shopify_dlt]
private_app_password = "private_app_password" # please set me up!
access_token = "access_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 Shopify 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 shopify_dlt_pipeline.py, as well as a folder shopify_dlt 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:


"""Pipeline to load shopify data into BigQuery.
"""

import dlt
from dlt.common import pendulum
from typing import List, Tuple
from shopify_dlt import shopify_source, TAnyDateTime, shopify_partner_query


def load_all_resources(resources: List[str], start_date: TAnyDateTime) -> None:
    """Execute a pipeline that will load the given Shopify resources incrementally beginning at the given start date.
    Subsequent runs will load only items updated since the previous run.
    """

    pipeline = dlt.pipeline(
        pipeline_name="shopify", destination='clickhouse', dataset_name="shopify_data"
    )
    load_info = pipeline.run(
        shopify_source(start_date=start_date).with_resources(*resources),
    )
    print(load_info)


def incremental_load_with_backloading() -> None:
    """Load past orders from Shopify in chunks of 1 week each using the start_date and end_date parameters.
    This can useful to reduce the potiential failure window when loading large amounts of historic data.
    Chunks and incremental load can also be run in parallel to speed up the initial load.
    """

    pipeline = dlt.pipeline(
        pipeline_name="shopify", destination='clickhouse', dataset_name="shopify_data"
    )

    # Load all orders from 2023-01-01 to now
    min_start_date = current_start_date = pendulum.datetime(2023, 1, 1)
    max_end_date = pendulum.now()

    # Create a list of time ranges of 1 week each, we'll use this to load the data in chunks
    ranges: List[Tuple[pendulum.DateTime, pendulum.DateTime]] = []
    while current_start_date < max_end_date:
        end_date = min(current_start_date.add(weeks=1), max_end_date)
        ranges.append((current_start_date, end_date))
        current_start_date = end_date

    # Run the pipeline for each time range created above
    for start_date, end_date in ranges:
        print(f"Load orders between {start_date} and {end_date}")
        # Create the source with start and end date set according to the current time range to filter
        # created_at_min lets us set a cutoff to exclude orders created before the initial date of (2023-01-01)
        # even if they were updated after that date
        data = shopify_source(
            start_date=start_date, end_date=end_date, created_at_min=min_start_date
        ).with_resources("orders")

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

    # Continue loading new data incrementally starting at the end of the last range
    # created_at_min still filters out items created before 2023-01-01
    load_info = pipeline.run(
        shopify_source(
            start_date=max_end_date, created_at_min=min_start_date
        ).with_resources("orders")
    )
    print(load_info)


def load_partner_api_transactions() -> None:
    """Load transactions from the Shopify Partner API.
    The partner API uses GraphQL and this example loads all transactions from the beginning paginated.

    The `shopify_partner_query` resource can be used to run custom GraphQL queries to load paginated data.
    """

    pipeline = dlt.pipeline(
        pipeline_name="shopify_partner",
        destination='clickhouse',
        dataset_name="shopify_partner_data",
    )

    # Construct query to load transactions 100 per page, the `$after` variable is used to paginate
    query = """query Transactions($after: String, first: 100) {
        transactions(after: $after) {
            edges {
                cursor
                node {
                    id
                }
            }
        }
    }
    """

    # Configure the resource with the query and json paths to extract the data and pagination cursor
    resource = shopify_partner_query(
        query,
        # JSON path pointing to the data item in the results
        data_items_path="data.transactions.edges[*].node",
        # JSON path pointing to the highest page cursor in the results
        pagination_cursor_path="data.transactions.edges[-1].cursor",
        # The variable name used for pagination
        pagination_variable_name="after",
    )

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


if __name__ == "__main__":
    # Add your desired resources to the list...
    resources = ["products", "orders", "customers"]
    load_all_resources(resources, start_date="2000-01-01")

    # incremental_load_with_backloading()

    # load_partner_api_transactions()

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

python shopify_dlt_pipeline.py

4. Inspecting your load result​

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

dlt pipeline shopify 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 shopify 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 using Github Actions. This allows you to automate your CI/CD workflows and run your pipelines on a schedule, on push, or manually.
  • Deploy with Airflow: You can deploy your dlt pipelines using Airflow. This involves creating an Airflow DAG for your pipeline script and adding necessary environment variables with secrets to the Airflow.
  • Deploy with Google Cloud Functions: dlt also supports deployment using Google Cloud Functions. This allows you to run your pipelines in a serverless environment on Google Cloud.
  • Other Deployment Options: There are other ways to deploy your dlt pipelines too. You can find more information about these other deployment options here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides detailed information on how to monitor your pipeline in production. You can find more details on how to do this here.
  • Set up alerts: With dlt, you can set up alerts to notify you of any issues with your pipeline. Learn how to set up alerts here.
  • Set up tracing: dlt allows you to set up tracing to help you understand the execution flow of your pipeline. Find out how to set this up here.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source shopify​

"Shopify is an e-commerce platform offering data on customer accounts, transactions, and product listings."

Resource NameWrite DispositionDescription
customersmergeIndividuals or entities who have created accounts on a Shopify-powered online store
ordersmergeTransactions made by customers on an online store
productsmergeThe individual items or goods that are available for sale

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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