Benchmarking python JSON libraries

While reading the FastAPI documentation I came across various python libraries for working with json. This got me thinking which one is the best, performance wise (including the python's built-in json module).

I surfed the net, found some resources and comparisions, and thought of implementing them on my own. So, this article is about the perf benchmarks I got when testing these libraries and the methodology.

Test Setup

• Ubuntu 24.04 LTS
• RAM - 24 GB
• Python version - 3.12

Libraries tested

• json - Built-in Python JSON library; widely used but relatively slow.
• ujson - Fast C-based JSON parser; a drop-in replacement for json.
• orjson - Extremely fast Rust-based library with rich type support.
• rapidjson - Python wrapper for RapidJSON (C++); good performance and flexibility.
• msgspec - Ultra-fast library with optional typed structs for maximum speed.

Results

Takeways

• The top contendors are orjson and msgspec (duh).
• I personally like to use orjson when working with fastAPI as it has builtin support for orjson response format making it a more developer friendly option.
• msgspec on the other hand is like a swiss army knife as it has support for other structs as well like yaml, toml etc. And it has a ton of more validation features like validating with a python class (sort of like pydantic).

Methodology

Generating sample data

A simple script to generate the testing data (can be made complex for better benchmarking).

def generate_sample_data(n): return [ { "id": i, "name": f"User{i}", "active": bool(i % 2), "scores": [random.random() for _ in range(10)], "info": {"age": random.randint(20, 40), "city": "City" + str(i)} } for i in range(n) ] data = generate_sample_data(10_000) 

Benchmarking function

Store the encoding and decoding methods of every library in a dictionary with the lib names as the key and run this function.

def benchmark_json_libs(data, runs=10): results = [] # Pre-serialize data for each lib  pre_serialized = {} for name, funcs in libs.items(): try: pre_serialized[name] = funcs["dumps"](data) except Exception as e: print(f"Serialization failed for {name}: {e}") continue for name, funcs in libs.items(): if name not in pre_serialized: continue try: ser_time = timeit.timeit(lambda: funcs["dumps"](data), number=runs) deser_time = timeit.timeit(lambda: funcs["loads"](pre_serialized[name]), number=runs) results.append({ "Library": name, "Serialization Time (s)": ser_time, "Deserialization Time (s)": deser_time }) except Exception as e: print(f"Benchmarking failed for {name}: {e}") return pd.DataFrame(results) 

Visualization

Visualize the result using seaborn and matplotlib.

fig, axes = plt.subplots(1, 2, figsize=(14, 5)) sns.barplot(x="Library", y="Serialization Time (s)", data=results_df, ax=axes[0]) axes[0].set_title("JSON Serialization Time") sns.barplot(x="Library", y="Deserialization Time (s)", data=results_df, ax=axes[1]) axes[1].set_title("JSON Deserialization Time") plt.tight_layout() plt.show() 

And that's it. Thanks for reading. Bye.