为Python提供的快速Fernet绑定
项目描述
rfernet
Python扩展,用于Fernet加密/解密,比其他替代方案更快。此库使用rust库 fernet-rs
https://github.com/mozilla-services/fernet-rs。
CI & 复制构建轮的代码来自 cryptography
和 orjson
基准测试
与cryptography的Fernet(CPU)相比
In [2]: from cryptography.fernet import Fernet as cFernet
In [3]: from rfernet import Fernet as rFernet
In [4]:
In [4]: plain = b"asd" * 1000
In [5]: key = rFernet.generate_new_key()
In [7]: r_fernet = rFernet(key)
In [8]: c_fernet = cFernet(key)
In [9]: %timeit r_fernet.encrypt(plain)
18.4 µs ± 117 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [10]: %timeit c_fernet.encrypt(plain)
77.7 µs ± 921 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
内存
# rfernet
[ Top 10 ]
<frozen importlib._bootstrap>:219: size=4444 B, count=38, average=117 B
test2.py:4: size=576 B, count=1, average=576 B
<frozen importlib._bootstrap_external>:59: size=156 B, count=1, average=156 B
test2.py:6: size=93 B, count=1, average=93 B
<frozen importlib._bootstrap>:371: size=80 B, count=1, average=80 B
<frozen importlib._bootstrap>:105: size=72 B, count=1, average=72 B
<frozen importlib._bootstrap_external>:1352: size=56 B, count=1, average=56 B
<frozen importlib._bootstrap_external>:606: size=56 B, count=1, average=56 B
test2.py:7: size=48 B, count=1, average=48 B
<frozen importlib._bootstrap_external>:1030: size=40 B, count=1, average=40 B
# cryptography's Fernet
[ Top 10 ]
<frozen importlib._bootstrap_external>:525: size=3134 KiB, count=31814, average=101 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/cryptography/hazmat/bindings/openssl/binding.py:91: size=449 KiB, count=3169, average=145 B
<frozen importlib._bootstrap>:219: size=404 KiB, count=3384, average=122 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/abc.py:126: size=146 KiB, count=717, average=209 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/cryptography/hazmat/bindings/openssl/binding.py:89: size=119 KiB, count=1773, average=69 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/abc.py:127: size=68.7 KiB, count=447, average=157 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/inspect.py:2793: size=46.8 KiB, count=282, average=170 B
<frozen importlib._bootstrap_external>:59: size=41.7 KiB, count=265, average=161 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/abc.py:135: size=40.8 KiB, count=339, average=123 B
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/idna/idnadata.py:826: size=36.7 KiB, count=3, average=12.2 KiB
内存测试源代码
import tracemalloc
tracemalloc.start()
from cryptography.fernet import Fernet as cFernet
plain = b"asd" * 1000
key = cFernet.generate_key()
c_fernet = cFernet(key)
c_fernet.encrypt(plain)
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
print("[ Top 10 ]")
for stat in top_stats[:10]:
print(stat)
项目详情
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rfernet-0.3.2.tar.gz 的哈希值
算法 | 哈希摘要 | |
---|---|---|
SHA256 | 857e2a722d4a8ad3749b19ca25b343a2874ea4146574237ff5625b02573f7451 |
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MD5 | 7379197d83a87299d463df0f7fada435 |
|
BLAKE2b-256 | 63ac5fd5461457a9802f44e7957ce929b080777a7fc1e931fcd46b67421442fa |
关闭
哈希值 for rfernet-0.3.2-cp311-cp311-manylinux_2_28_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | a1d9a47a87cd1d8d03d2dfed0b482c5c86b0db72bc873130104453739013c1ad |
|
MD5 | 20815b4cacb5c1ad26b8bffc57c92cb4 |
|
BLAKE2b-256 | c542c21e88320a6fea08a6a18c79c6a416adbeba1c4ac26a2e16ca94ce2032fa |
关闭
哈希值 for rfernet-0.3.2-cp311-cp311-macosx_10_12_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | 2119936e84abe2899e8b6da5e3ec554ecd5080ca8def7a2e733da5d80e382a55 |
|
MD5 | fdda947729b8168437eecbc7862e183b |
|
BLAKE2b-256 | fe8cae7de15be3e6aa6f2c0c2ed984ecb4835c3c71e8c56ae15c2c8d3d54b712 |
关闭
哈希值 for rfernet-0.3.2-cp310-cp310-manylinux_2_28_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | 4d6d6c4cbf01712bd57cd0e904a4d0e75186f988569b2a540de1404bd61d7224 |
|
MD5 | 5697e01c0117802b3d36075fb53027f1 |
|
BLAKE2b-256 | c4a68b36400e6c368866256b276e7f7090e255121689eb6a7e0822e2c55d257d |
关闭
哈希值 for rfernet-0.3.2-cp310-cp310-macosx_10_12_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | d37a2ea0d095da4b2800aa5bbcf466344ec8044b504036a048365e7cc6e045d2 |
|
MD5 | 03d3ef574c1daa0b13450c5403ed409d |
|
BLAKE2b-256 | 67bca07603adce4a06c4d5091f613ff2f7110b21289ff057dc81cf4361a8c9e5 |
关闭
哈希值 for rfernet-0.3.2-cp39-cp39-manylinux_2_28_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | cf5c9189baf8d568c7e765d2dd34a34dca69f190d2808dfc0ba0684ac79a5a2a |
|
MD5 | 7fad7a8f8647ef34a3d6aa0a6aebd380 |
|
BLAKE2b-256 | 25b193d0d9136a18d1c2187a95ce6a38b5ba271441b0cdf4a40fb771b9c7b840 |
关闭
哈希值 for rfernet-0.3.2-cp39-cp39-macosx_10_12_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | 6c46fe9dff8d2e26bf34b4ed900d62fd1b2fc0cf50fe40863fddb3548470d60a |
|
MD5 | 7c810f05e88756818db9115efb3aaa03 |
|
BLAKE2b-256 | 1c2ae5896782cf224b1c95550b887ebf61f7e4574e46bc0e2c29489968f709de |
关闭
哈希值 for rfernet-0.3.2-cp38-cp38-manylinux_2_28_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | cfdbfe0fc2cfbb6953883b96d9407ab5d76d7f3ac3db6c26ed880d0f2bf0f5b6 |
|
MD5 | 06786a35fad38a2642c6aca0749336f4 |
|
BLAKE2b-256 | 71ffb5cb4626e1191fad729178cb06aec010b7e91bf35017c0b8abf9ca7083fe |
关闭
哈希值 for rfernet-0.3.2-cp38-cp38-macosx_10_12_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | 6d8cd4682e853506d15ee53ee4b8959fb467284fa3936ae9074c0770398ffdb3 |
|
MD5 | ff39ed3758a9d24a674725328e259de0 |
|
BLAKE2b-256 | 45559ca881a6f5ebb70919cbb10b0d562494c9cde41f944cce5a54851fbad02d |
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哈希值 for rfernet-0.3.2-cp37-cp37m-manylinux_2_28_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | 8fce20166964c142d549d62daca03fdc1379a9efdff13c7d1187bfe9592dfeba |
|
MD5 | 6e3c3f817c140b2b1cac83272d8d5a98 |
|
BLAKE2b-256 | b66b16ae43302b2bf10cfd80582500d88ac0f819684801451c31559627ce7c7d |
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哈希值 for rfernet-0.3.2-cp37-cp37m-macosx_10_12_x86_64.whl
算法 | 哈希摘要 | |
---|---|---|
SHA256 | fd596ca0b321adf666619a9b448454553fbe94315831d82564e222c6f2b178e4 |
|
MD5 | bb8e096aa21d24c2ab210cff20b43cb8 |
|
BLAKE2b-256 | ff2dd2a828c0503ede1d906efa026ff577bfb138e7012f28a65ebaa5965a7ee3 |