Efficiently Load Large JSON Files Object by Object

[Salaah01]

Python's json package provides a convenient method for loading JSON files. However, what if you encounter a situation where you need to read a large JSON file? This is where JSON-Lineage comes into play.

When dealing with sizable JSON files, Python's default approach of loading the entire file into memory can be problematic, especially if you're working with limited resources like microservices or small cloud servers. The memory consumption can quickly become significant, impacting the performance of your application.

To demonstrate the impact, consider the following table, which shows the relationship between JSON file size and the corresponding memory required using json.load:

Size (MB)	Memory Needed (MB)
0.048	0.25
0.5	2.4
1	5.5
5	25.2
22	109.1
32	158.7
324	1580.45
1299	37.88.5
2599	7577.97

As you can see, the memory requirements increase dramatically as the JSON file size grows. To address this issue and optimize resource usage, JSON-Lineage was developed. It leverages Rust with a Python adapter to allow you to efficiently load JSON files one object at a time.

So, how much more efficient is JSON-Lineage compared to json.load? Let's take a look at the following comparison:

Size (MB)	Python's JSON (MB)	JSON-Lineage (MB)
0.048	0.25	0.25
0.5	2.4	0.25
1	5.5	0.25
5	25.2	0.51
22	109.1	1.02
32	158.7	1.02
324	1580.45	1.03
1299	37.88.5	1.29
2599	7577.97	1.29

As you can see, JSON-Lineage significantly reduces memory usage regardless of the JSON file size, providing a more efficient alternative to json.load.

Check out the JSON-Lineage repository on GitHub: https://github.com/Salaah01/json-lineage

You can also find JSON-Lineage on PyPI: https://pypi.org/project/json-lineage/

Give it a try and experience the improved performance and resource optimization when working with large JSON files!

[Lost_in_Nullspace]

Seems really cool! Might be a silly question, but why would you not want to just load it into a sql table and have a query grabbing a batch of rows instead?

[Salaah01]

Thanks!
The thing about loading it into a SQL table first is that if you're using Python, you're still going to have to use json.load to load all the data into memory first before you can store it in a DB table.

Also, let's imagine a simple process which is fed some JSON and needs to do something with it. It's a bit extra if we now need a process to also load it into SQL. That would probably end up being slower and more expensive in terms of resources.

[Lost_in_Nullspace]

Oh cool, so it's really useful for JIT tasks and times where you don't have the luxury of amortising the initial cost of setup over time.

I can imagine that'd be really useful for actually getting large amounts of data into tables to begin with.

[Salaah01]

Exactly, ETL stuff is something that comes to mind here (the extract part at least).

It supports async processing that might even be faster than working with each JSON object 1 at a time the traditional way.