Boost Python JSON Performance with UltraJSON: Install, Use, and Advanced Tips

ujson (UltraJSON) is a high‑performance JSON parsing and serialization library for Python, written in C, making it significantly faster than the standard json module when handling large JSON data.

1. Why Choose ujson?

The standard json module is feature‑complete but has average performance, especially with large data volumes or high‑frequency parsing/serialization. ujson offers speed advantages in such scenarios.

2. Installation

pip install ujson

Note: Because ujson is compiled in C, some environments (e.g., Windows) may require a C toolchain such as Visual Studio Build Tools.

3. Basic Usage

ujson’s API mirrors the standard json module, allowing a drop‑in replacement.

Import

import ujson as json

Using ujson as json makes future switching back to the standard library easy.

Serialize (object → JSON string)

data = {
    "name": "Alice",
    "age": 30,
    "is_student": false,
    "hobbies": ["reading", "coding"]
}
json_str = json.dumps(data)
print(json_str)
# Output: {"name":"Alice","age":30,"is_student":false,"hobbies":["reading","coding"]}

Deserialize (JSON string → object)

json_str = '{"name":"Bob","age":25,"is_student":true,"hobbies":["sports","music"]}'
data = json.loads(json_str)
print(data["name"])  # Output: Bob

4. Advanced Features

Pretty‑print with indentation

pretty_json = json.dumps(data, indent=4)
print(pretty_json)

Sort keys

json_str = json.dumps(data, sort_keys=True)
print(json_str)
# Output (keys sorted alphabetically): {"age":30,"hobbies":["reading","coding"],"is_student":false,"name":"Alice"}

Disable ASCII escaping for Chinese characters

chinese_data = {"message": "你好，世界"}
json_str = json.dumps(chinese_data, ensure_ascii=False)
print(json_str)
# Output: {"message":"你好，世界"}

Custom object serialization

class User:
    def __init__(self, name, age):
        self.name = name
        self.age = age

def default_serializer(obj):
    if isinstance(obj, User):
        return {"name": obj.name, "age": obj.age}
    raise TypeError(f"Object of type {type(obj)} is not JSON serializable")

user = User("Charlie", 28)
json_str = json.dumps(user, default=default_serializer)
print(json_str)
# Output: {"name":"Charlie","age":28}

5. Performance Comparison (ujson vs json)

Typical benchmark results (Python 3.10) show that ujson dramatically outperforms the built‑in json module for large payloads and high‑frequency operations.

6. Typical Application Scenarios

7. Packaging Recommendation for Enterprise Projects

Create a thin wrapper to centralize JSON handling, making future engine swaps trivial.

# utils/json_utils.py
import ujson as json

def dumps(data, indent=None, sort_keys=False, ensure_ascii=True, default=None):
    return json.dumps(data, indent=indent, sort_keys=sort_keys, ensure_ascii=ensure_ascii, default=default)

def loads(data):
    return json.loads(data)

Use the wrapper elsewhere: from utils.json_utils import dumps, loads Switching to another JSON engine (e.g., orjson) only requires updating the wrapper.

8. Optional Enhancements (Advanced)

9. Comparison with Other JSON Libraries

10. Summary

ujson provides a fast, C‑based alternative to Python’s json module, with a compatible API, advanced serialization options, and clear performance benefits, making it ideal for backend services that process large or frequent JSON payloads.