我不知道这是否是最快的，但你可以试试这样的方法...
将Numpy数组存储到Redis是这样的--见函数
toRedis()
。
检索Numpy数组是这样的--见函数
fromRedis()
。
#!/usr/bin/env python3
import struct
import redis
import numpy as np
def toRedis(r,a,n):
"""Store given Numpy array 'a' in Redis under key 'n'"""
h, w = a.shape
shape = struct.pack('>II',h,w)
encoded = shape + a.tobytes()
# Store encoded data in Redis
r.set(n,encoded)
return
def fromRedis(r,n):
"""Retrieve Numpy array from Redis key 'n'"""
encoded = r.get(n)
h, w = struct.unpack('>II',encoded[:8])
# Add slicing here, or else the array would differ from the original
a = np.frombuffer(encoded[8:]).reshape(h,w)
return a
# Create 80x80 numpy array to store
a0 = np.arange(6400,dtype=np.uint16).reshape(80,80)
# Redis connection
r = redis.Redis(host='localhost', port=6379, db=0)
# Store array a0 in Redis under name 'a0array'
toRedis(r,a0,'a0array')
# Retrieve from Redis
a1 = fromRedis(r,'a0array')
np.testing.assert_array_equal(a0,a1)
你可以通过对Numpy数组的dtype与形状进行编码来增加灵活性。我没有这么做，因为你可能已经知道你所有的数组都是一个特定的类型，那么代码就会毫无理由地变得更大，更难读。
在现代iMac上进行粗略的基准测试:
80x80 Numpy array of np.uint16 => 58 microseconds to write
200x200 Numpy array of np.uint16 => 88 microseconds to write
Keywords:Python, Numpy, Redis, array, serialise, serialize, key, incr, unique
