How to Split a JSON Array into Multiple JSONs Using Scala Spark

Learn how to efficiently `split a JSON array` into separate JSON files using Scala and Apache Spark. This guide covers step-by-step instructions to help you achieve this in Spark Shell.
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Splitting a JSON Array into Multiple JSON Files with Scala Spark

Working with JSON data is a common task in data processing, especially when using powerful tools like Scala and Apache Spark. One common issue you might encounter is needing to split a JSON array into multiple JSON files. If you're looking for a solution to this problem, you're in the right place! In this guide, we'll explore how to achieve this using a practical example.

Understanding the Problem

Let's break down the situation. Suppose you have a JSON object that contains an array of marks, and you want to split each object in that array into separate JSON files. Here’s an example of how your JSON data might look:

[[See Video to Reveal this Text or Code Snippet]]

In this example, we want each mark associated with its subject to be outputted as a separate JSON file.

The Solution

To tackle the problem, we can utilize Spark's DataFrame capabilities. The key steps involve exploding the array of marks, generating a unique identifier for each JSON object, and then writing them out as separate files. Let’s break this down into clear steps:

Step 1: Explode the JSON Array

The first step is to transform the JSON structure into a flat DataFrame. We will "explode" the marks array to create multiple rows from the JSON objects inside it. This can be done using the selectExpr function combined with the inline function.

Step 2: Add an Unique Identifier

After flattening the DataFrame, we will add a unique ID to each row. This ID will allow us to ensure that each JSON file generated has a unique name or structure. We can achieve this using the monotonically_increasing_id function.

Step 3: Write to Output

Finally, we need to write the resulting DataFrame to JSON files. During this step, we can partition the output by the unique ID, which will effectively create a separate file for each entry.

Example Code

Here's what the complete code looks like in Spark Shell:

[[See Video to Reveal this Text or Code Snippet]]

.withColumn("id", monotonically_increasing_id): This adds a unique identifier to each row.

.repartition(col("id")): This redistributes the data across partitions, which can help with file sizing and performance.

Conclusion

By following the steps outlined in this post, you can efficiently split a JSON array into multiple JSON files using Scala and Apache Spark. This process can be particularly useful when handling large datasets, allowing for easier management and analysis of data. Give it a try, and start simplifying your JSON data processes today!