orjson has better performance than json and is suitable for big data processing. orjson is a third-party library written in Rust. It supports more data types by default. It is faster serialization/deserialization, especially suitable for performance-sensitive scenarios such as Web API and log processing. json is a standard library that does not require installation and is suitable for projects with simple requirements and high compatibility requirements. When using it, please note: orjson output is bytes and needs to be manually decoded, and does not support custom encoders, and the input must be bytes type.

Python's orjson and standard library's json are both modules used to process JSON data, but they have some key differences in performance, functionality and usage scenarios. If you care about the speed of data serialization/deserialization or memory usage, orjson will be a better choice.

orjson is faster and more suitable for performance-sensitive scenarios

orjson is a third-party library that focuses on high performance. Compared to the built-in json module, orjson is usually much faster in serialization (dumps) and deserialization (loads) operations, especially when processing large amounts of data.

For example, you have a list of thousands of records that need to be frequently converted into JSON strings for transmission or saving. Using orjson.dumps() may be several times faster than json.dumps() . This is because orjson is written in Rust, the underlying optimization is better, and it supports more data types by default (such as dataclass, datetime, etc.) and does not require additional conversions.

json is more general and suitable for simple needs

Although the performance of the json module in the Python standard library is not as good as orjson , its biggest advantage is that it is "out of the box" - no additional dependencies are required, and all Python environments come with it. If you only process small amounts of JSON data occasionally, or the project cannot introduce third-party libraries, then json is enough.

In addition, json 's API design is relatively intuitive, has low learning cost, and is friendly to novices. for example:

 import json

data = {"name": "Alice", "age": 30}
json_str = json.dumps(data)

This writing is common in various tutorials and projects.

Use suggestions: Select modules based on the scene

• If you are developing services with high performance requirements (such as Web API, log processing, data analysis middleware, etc.), you can consider changing to orjson .
• If you are writing scripts, tool applets, or want to ensure maximum compatibility, then continue to use json .
• If you are already using frameworks like FastAPI or Starlette, they use orjson internally to improve response speed by default.

What should be noted is:

• orjson does not support custom JSON encoder (like json.dumps(encoder=...) )
• The string it returns is of type bytes , not str , and sometimes it needs to manually decode it

Small details are easy to ignore

For example, when you are making HTTP requests, if you use requests to send the result of orjson.dumps(data) , remember to add .decode("utf-8") , otherwise an error may occur. Or pass it directly to modern clients such as httpx or aiohttp , which supports bytes better.

For example, although orjson.loads() can also parse ordinary JSON strings, if the input is of type str instead of bytes , it will report an error. This is different from json.loads() , so you need to pay attention to the consistency of the input format.

Basically that's it.

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