Introduction

Handling large JSON data in API responses can be challenging, especially when platform limitations restrict the size of data that can be stored in a single JSON field.

This guide explains how to split a large JSON into smaller, manageable chunks and save each chunk separately to avoid data loss or errors.

Using a feature called Data Prep Processing within external request nodes and webhooks, you can efficiently process and store extensive data sets, such as restaurant menus, user feeds, or any large JSON payloads.

Why Is Managing Large JSON Data Important?

Many APIs return massive amounts of data that cannot be stored in a single JSON due to platform constraints. For example:

• Platform limit: 20,000 characters per JSON field.

• API response: Can contain tens or hundreds of items, each with multiple attributes.

• Problem: Saving the entire response in one JSON exceeds limits, leading to empty or incomplete data storage.

Common issues include:

• Data truncation

• Failed saves

• Data loss

• Inability to process large responses

Typical Scenario: Restaurant Menu API

Suppose you call an API that returns a restaurant menu with:

This response might include 270 items, each with multiple nested details, making it impossible to store in a single JSON due to size constraints.

Strategies to Handle Large JSON Data

There are two main approaches:

1. Selective Data Storage

   • Keep only relevant fields (e.g., Dish ID, Name, Price).

   • Disadvantage: Loss of detailed or user-specific data.

2. Splitting Data into Chunks

   • Divide the large JSON into smaller, manageable parts.

   • Store each chunk separately, avoiding size limits.

   • Preferred method for preserving complete data.

Implementing Data Chunking with Data Prep Processing

Step 1: Enable Data Prep Processing

• Available in external request nodes and webhooks.

• Facilitates custom data manipulation before storage.

Step 2: Write JavaScript Code for Chunking

Below is a sample code snippet (provided in the description) that:

• Defines an array called chunks.

• Sets a chunk size (e.g., 50 items).

• Loops through the payload, slicing it into chunks.

• Pushes each chunk into the chunks array.

• Returns the array of chunks for further processing.

const chunks = [];
const chunkSize = 50; // Adjust as needed

for (let i = 0; i < payload.length; i += chunkSize) {
  chunks.push(payload.slice(i, i + chunkSize));
}

return chunks;

Note:

• payload is a system variable containing the parsed JSON response.

• No need to manually parse JSON; it's already parsed in this context.

Step 3: Run the API Call and Observe Results

• The process may take some time due to large data.

• After execution, you'll see multiple JSON chunks (e.g., 6 chunks of 50 items each, plus a remaining chunk).

Visualizing the Chunked Data

Before chunking:

• One large JSON with 270+ items.

• Difficult to store or process due to size.

After chunking:

Each chunk is now under the 20,000-character limit, ensuring safe storage.

Benefits of Chunking Data

• Prevents data truncation due to size limits.

• Enables storage of complete datasets.

• Facilitates easier data retrieval and processing.

• Supports platform constraints without losing information.

Practical Tips for Chunking

• Adjust chunk size based on data size and platform limits.

• Use descriptive variable names for clarity.

• Test with smaller datasets before scaling.

• Automate storage of each chunk into separate fields or databases.

Summary Table: Key Concepts

Final Thoughts

Handling large JSON data effectively is crucial for maintaining data integrity and avoiding platform limitations. By dividing large responses into smaller chunks using Data Prep Processing and JavaScript, you can:

• Ensure complete data storage

• Avoid errors caused by size restrictions

• Maintain data fidelity for further processing

This approach is versatile and applicable across various use cases, from menu APIs to user data feeds.