Скачать или смотреть How to Consume REST API in Azure Databricks with Scala

Learn how to easily consume a REST API in Azure Databricks using Scala, handle JSON data, and implement parallel API calls.
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Unlocking the Power of Azure Databricks: Consuming REST APIs with Scala

As a newcomer to Azure Databricks and Scala, you might find yourself facing a common challenge - consuming HTTP REST APIs that return JSON data. While Databricks is renowned for its data processing capabilities using Apache Spark, navigating its libraries and functionalities to integrate APIs can be daunting. In this guide, we'll explore how to seamlessly work with REST APIs in Databricks and enhance your data processing flexibility.

The Challenge

You may have noticed that the official Databricks documentation lacks comprehensive information on using REST APIs as data sources. This can be particularly frustrating when you want to perform operations such as pagination and parallel API calls. Thankfully, there is a solution that empowers you to leverage the full potential of Spark for this task.

Solution Overview

The approach involves a few straightforward steps:

Performing a GET request to fetch the data from the API endpoint.

Creating an RDD (Resilient Distributed Dataset) from the response you receive.

Reading the RDD as a DataFrame using Spark.

Let's break down this process further.

Step-by-Step Implementation

Step 1: Define the GET Request

First, you need a simple function to perform the GET request. This function will return the contents of the API response as a string. Here’s how you do that:

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Step 2: Set the API URL

Next, specify the URL of your REST API. Remember to replace the placeholder values with actual endpoints.

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Step 3: Fetch and Process the Response

Using the function you defined, make the GET request and store the result. You will also want to ensure the JSON response is correctly formatted by removing any unnecessary line endings.

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Step 4: Create an RDD

With the cleaned JSON response, the next step is to create an RDD. The sc.parallelize() function allows you to create a distributed dataset from a list.

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Step 5: Read as DataFrame

Finally, read the RDD as a DataFrame using the spark.read.json() function. This action allows you to manipulate the data using Spark's powerful data processing capabilities.

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Complete Code Example

Here’s how your complete code looks once combined:

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Conclusion

And that’s it! You have successfully consumed a REST API in Azure Databricks using Scala and returned the data as a DataFrame for additional processing. This method provides a solid foundation for more complex scenarios, such as making multiple API calls in parallel, particularly useful for handling paginated data.

Remember, exploring further functionalities in Databricks can open new doors for efficient data processing and analysis. Embrace the power of Spark and enhance your data workflows today!