Understanding Memory Usage When Reading Large JSON Files in Node.js

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The Problem

A user experienced a significant memory usage issue while attempting to read a 274.9 MB JSON file. After reading the file and parsing it into an array of objects, the memory usage jumped to over 1.1 GB. This raised an important question: Why does reading a JSON file of that size use so much memory?

To illustrate this, consider the following code snippet that reads the file and checks the memory usage after loading the objects:

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

The output showed a length of 920,885 objects and reported a memory consumption of 1193.05 MB.

The Solution: Understanding Memory Usage

1. Character Encoding Implications

One crucial detail to consider is how JavaScript manages strings. JavaScript uses UCS-2 (a form of UTF-16), which means that each character in a string takes up 2 bytes of memory. This characteristic is particularly relevant when the JSON file is read and stored as raw strings. Here’s the breakdown of the memory usage:

File Size: The original JSON file is 274.9 MB.

String Encoding: Due to UCS-2 encoding, the effective memory usage for strings will be at least double the file size, given that each character occupies 2 bytes.

2. Memory Heap for Objects

After parsing the JSON file into an array of objects, it’s important to acknowledge the additional memory required for each object:

Overhead of Objects: JavaScript objects are essentially hash maps and carry inherent overhead.

String Storage: If the objects contain string values, each string will again consume double the memory compared to its original size in the file.

3. Live References and Garbage Collection

In the provided code, the raw JSON string is not immediately freed or deleted from memory, which means it retains a reference in the heap memory. This can lead to elevated memory usage for the following reasons:

Memory Not Freed: The JavaScript engine maintains a reference to the string until there are no more references to it.

Garbage Collection Limitation: If the engine cannot determine that the string memory is safe to release, it remains allocated, further contributing to overall memory consumption.

Final Thoughts

String encoding practices where each character can take up 2 bytes.

Overhead associated with storing objects.

The need for garbage collection to manage memory effectively.

Understanding these factors can help developers manage memory consumption better, particularly when working with large datasets. To mitigate high memory usage in the future, consider the following strategies:

Stream Processing: Instead of reading the entire file at once, consider using a stream to process the JSON data in smaller chunks.

Memory Profiling: Use memory profiling tools to analyze and optimize the memory usage of your application.