ADF Workflows Unleashed: Handling POST API Pagination

In the world of APIs, the GET method has long been the go-to for retrieving data. However, a growing trend among third-party tools is using POST requests for data retrieval, especially when dealing with filters and pagination. The difference here is subtle but important: while the API's behavior mirrors that of a GET request (returning data), the parameters are passed in the body of a POST request.

As data engineers and integration specialists, this creates a unique challenge when using tools like Azure Data Factory (ADF). ADF is exceptionally powerful for orchestrating data workflows, but it follows more traditional approaches to pagination—ones that assume GET-style API behavior. In scenarios where APIs handle pagination using POST requests, you may find ADF’s default pagination rules in Copy Data activities fall short. But fear not! There’s a workaround using ADF's flexible Web Activity and Until Activity.

Why Traditional Pagination Fails with POST APIs

Normally, ADF’s pagination feature is designed to work with GET requests, where parameters like pageNumber or pageToken are passed in the URL. For POST requests, pagination information is often embedded within the request body. This discrepancy can lead to problems when automating data extraction using ADF’s Copy Data activity. Since the default pagination rules assume the URL changes with each request, they don't accommodate APIs where pagination is embedded within the request body.

The Solution: Web Activity with Until Activity for Custom Pagination

To overcome this, we leverage ADF’s Web Activity in combination with the Until Activity. This high level approach allows us to dynamically modify the body of the POST request for each subsequent page of data, making it possible to retrieve paginated results efficiently. Here’s how you can implement this custom pagination approach:

Step-by-Step Guide

1. Initialize Variables for Pagination First, we need variables to track the pagination parameters, like the current page or token. For example, you might create two variables: currentPage (starting at 1) and hasMorePages (set to true initially).
2. Set Up Web Activity for Data Retrieval Use the Web Activity to make the initial POST request.
3. Extract Pagination Information from the Response After each POST request, inspect the response to determine whether more data is available. In this case let's say API will return a field such as hasNextPage or provide the totalPages. You can then parse the response using expressions to extract this information and update your variables.
4. Implement the Until Activity The Until Activity will run until the hasMorePages variable becomes false. Inside the loop.
5. Dynamically Adjust the Request Body With each iteration, the currentPage variable gets updated, and the body of your POST request changes dynamically, ensuring that you retrieve the next page of data on each call.
6. Data Storage Optionally, store the retrieved data in Blob Storage, a SQL database, or other services as per your pipeline requirements.

Sample Pipeline Overview

Here’s a visual outline of how the pipeline works:

1. Initialize Variables: Set up pagination variables (currentPage, hasMorePages).
2. Until Activity: This will loop until there are no more pages. Web Activity: Performs a POST request with dynamic pagination parameters. Set Variable Activity: Updates pagination variables based on the API response.

Key Benefits of This Approach

• Customizability: You control how pagination is handled, making it adaptable to a wide variety of API structures.
• Scalability: Handle large datasets efficiently by paginating through API responses without overloading the system.
• Flexibility: This solution is not limited to specific APIs, and can be adapted to any API where pagination is driven through POST requests.

Final Thoughts

Azure Data Factory’s Web Activity and Until Activity offer a powerful, flexible solution for handling APIs that require POST requests for pagination. While ADF’s native Copy Data pagination works well for most GET requests, these custom approaches allow you to extract data from any API, even those that don’t follow the traditional rules.

By applying this approach, you’ll be able to unlock more possibilities in your ETL pipelines, streamline your data extraction processes, and showcase the full potential of ADF as a robust tool for handling complex API workflows.