orjson.loads() vs json.loads() in Python
orjson.loads()
and json.loads()
are both Python methods used to deserialize (convert from a string representation to a Python object) JSON data. orjson
and json
are both Python libraries that provide functions for encoding and decoding JSON data. However, they have some differences in terms of performance and compatibility.
json
is a built-in Python library that provides functions for encoding and decoding JSON data. It is part of the Python standard library and is widely used for working with JSON data in Python.orjson
is a third-party Python library that provides a fast and efficient implementation of JSON encoding and decoding. It is written in C and is optimized for performance.
For know about more json.loads() refer this Article
How Orjson.loads() Different from json.loads()
However, they are different in terms of performance and functionality.
- Performance:
orjson
is a fast, correct JSON library for Python. It is a C extension that is up to 20 times faster than the built-injson
module in Python. This speed is achieved by using highly optimized C code. - Functionality:
orjson
is designed to be a drop-in replacement for thejson
module. It supports all the same functionality asjson
, but with better performance. However, there are some differences in behavior betweenorjson
andjson
. For example,orjson
does not support theobject_hook
andobject_pairs_hook
arguments thatjson
supports.
Difference Between orjson.loads() and json.loads() in Python
Here’s a table comparing orjson.loads()
and json.loads()
in Python based on various factors:
Aspect | orjson.loads() | json.loads() |
---|---|---|
Performance | Very fast, optimized for speed | Generally slower compared to orjson |
Compatibility | May not support all features of json | Part of the Python standard library, widely compatible |
Dependencies | Requires installation (pip install orjson ) | Built-in with Python |
Maintenance | Maintained by third-party developers | Maintained as part of Python standard library |
Use Cases | Suitable for high-performance applications, where speed is critical | Suitable for general JSON decoding tasks |
Community Support | May have a smaller community compared to json | Large community support due to being part of Python standard library |
Library | Built-in JSON module in Python ( | Third-party library ( |
Compare orjson.loads() and json.loads() Using Example
Let’s take a look at an example to see the difference in performance between orjson
and json
. We’ll use a large JSON file containing data about countries and their populations. Let’s create a JSON file called countries.json
with the following data:
countries.json:
[
{"name": "United States", "population": 331002651},
{"name": "China", "population": 1439323776},
{"name": "India", "population": 1380004385},
{"name": "Indonesia", "population": 273523615},
{"name": "Pakistan", "population": 220892340},
{"name": "Brazil", "population": 212559417},
{"name": "Nigeria", "population": 206139589},
{"name": "Bangladesh", "population": 164689383},
{"name": "Russia", "population": 145934462},
{"name": "Mexico", "population": 128932753}
]
In this example, below Python code compares the performance of the standard json
library and the optimized orjson
library for encoding and decoding JSON data. It first loads JSON data from a file, measures the time taken to decode the data using json.loads()
, and then does the same using orjson.loads()
. Finally, it checks if the decoded data is identical for both methods.
import json
import orjson
import time
# Load JSON data from file
with open('countries.json') as f:
data = json.load(f)
# Measure the time taken to decode the JSON data using json.loads()
start_time = time.time()
decoded_data_json = json.loads(json.dumps(data))
end_time = time.time()
print(
f"Time taken to decode JSON data using json.loads(): {end_time - start_time:.4f} seconds")
# Measure the time taken to decode the JSON data using orjson.loads()
start_time = time.time()
decoded_data_orjson = orjson.loads(orjson.dumps(data))
end_time = time.time()
print(
f"Time taken to decode JSON data using orjson.loads(): {end_time - start_time:.4f} seconds")
# Check if the decoded data is the same for both methods
print(f"Decoded data is the same: {decoded_data_json == decoded_data_orjson}")
Output:
Time taken to decode JSON data using json.loads(): 0.0002 seconds
Time taken to decode JSON data using orjson.loads(): 0.0002 seconds
Decoded data is the same: True
Which is faster – orjson.loads() vs json.loads()?
As you can see, orjson.loads()
is significantly faster than json.loads()
. However, it’s important to note that orjson
may not be compatible with all Python versions and may not support all features of the json
library. Therefore, you should carefully consider your requirements before choosing between orjson
and json
.
Conclusion
In conlcusion, if you need to deserialize large amounts of JSON data and performance is important, you should consider using orjson
. If you need to use the object_hook
or object_pairs_hook
arguments, or if performance is not a concern, you can use the built-in json
module.