Where are python bytearrays used?
Question:
I recently came across the dataType called bytearray in python. Could someone provide scenarios where bytearrays are required?
Answers:
A bytearray is very similar to a regular python string (str in python2.x, bytes in python3) but with an important difference, whereas strings are immutable, bytearrays are mutable, a bit like a list of single character strings.
This is useful because some applications use byte sequences in ways that perform poorly with immutable strings. When you are making lots of little changes in the middle of large chunks of memory, as in a database engine, or image library, strings perform quite poorly; since you have to make a copy of the whole (possibly large) string. bytearrays have the advantage of making it possible to make that kind of change without making a copy of the memory first.
But this particular case is actually more the exception, rather than the rule. Most uses involve comparing strings, or string formatting. For the latter, there’s usually a copy anyway, so a mutable type would offer no advantage, and for the former, since immutable strings cannot change, you can calculate a hash of the string and compare that as a shortcut to comparing each byte in order, which is almost always a big win; and so it’s the immutable type (str or bytes) that is the default; and bytearray is the exception when you need it’s special features.
If you look at the documentation for bytearray, it says:
Return a new array of bytes. The bytearray type is a mutable sequence of integers in the range 0 <= x < 256.
In contrast, the documentation for bytes says:
Return a new “bytes” object, which is an immutable sequence of integers in the range 0 <= x < 256. bytes is an immutable version of bytearray – it has the same non-mutating methods and the same indexing and slicing behaviors.
As you can see, the primary distinction is mutability. str methods that “change” the string actually return a new string with the desired modification. Whereas bytearray methods that change the sequence actually change the sequence.
You would prefer using bytearray, if you are editing a large object (e.g. an image’s pixel buffer) through its binary representation and you want the modifications to be done in-place for efficiency.
This answer has been shameless ripped off from here
Example 1: Assembling a message from fragments
Suppose you’re writing some network code that is receiving a large message on a socket connection. If you know about sockets, you know that the recv() operation doesn’t wait for all of the data to arrive. Instead, it merely returns what’s currently available in the system buffers. Therefore, to get all of the data, you might write code that looks like this:
# remaining = number of bytes being received (determined already)
msg = b""
while remaining > 0:
chunk = s.recv(remaining) # Get available data
msg += chunk # Add it to the message
remaining -= len(chunk)
The only problem with this code is that concatenation (+=) has horrible performance. Therefore, a common performance optimization in Python 2 is to collect all of the chunks in a list and perform a join when you’re done. Like this:
# remaining = number of bytes being received (determined already)
msgparts = []
while remaining > 0:
chunk = s.recv(remaining) # Get available data
msgparts.append(chunk) # Add it to list of chunks
remaining -= len(chunk)
msg = b"".join(msgparts) # Make the final message
Now, here’s a third solution using a bytearray:
# remaining = number of bytes being received (determined already)
msg = bytearray()
while remaining > 0:
chunk = s.recv(remaining) # Get available data
msg.extend(chunk) # Add to message
remaining -= len(chunk)
Notice how the bytearray version is really clean. You don’t collect parts in a list and you don’t perform that cryptic join at the end. Nice.
Of course, the big question is whether or not it performs. To test this out, I first made a list of small byte fragments like this:
chunks = [b"x"*16]*512
I then used the timeit module to compare the following two code fragments:
# Version 1
msgparts = []
for chunk in chunks:
msgparts.append(chunk)
msg = b"".join(msgparts)
#Version 2
msg = bytearray()
for chunk in chunks:
msg.extend(chunk)
When tested, version 1 of the code ran in 99.8s whereas version 2 ran in 116.6s (a version using += concatenation takes 230.3s by comparison). So while performing a join operation is still faster, it’s only faster by about 16%. Personally, I think the cleaner programming of the bytearray version might make up for it.
Example 2: Binary record packing
This example is an slight twist on the last example. Suppose you had a large Python list of integer (x,y) coordinates. Something like this:
points = [(1,2),(3,4),(9,10),(23,14),(50,90),...]
Now, suppose you need to write that data out as a binary encoded file consisting of a 32-bit integer length followed by each point packed into a pair of 32-bit integers. One way to do it would be to use the struct module like this:
import struct
f = open("points.bin","wb")
f.write(struct.pack("I",len(points)))
for x,y in points:
f.write(struct.pack("II",x,y))
f.close()
The only problem with this code is that it performs a large number of small write() operations. An alternative approach is to pack everything into a bytearray and only perform one write at the end. For example:
import struct
f = open("points.bin","wb")
msg = bytearray()
msg.extend(struct.pack("I",len(points))
for x,y in points:
msg.extend(struct.pack("II",x,y))
f.write(msg)
f.close()
Sure enough, the version that uses bytearray runs much faster. In a simple timing test involving a list of 100000 points, it runs in about half the time as the version that makes a lot of small writes.
Example 3: Mathematical processing of byte values
The fact that bytearrays present themselves as arrays of integers makes it easier to perform certain kinds of calculations. In a recent embedded systems project, I was using Python to communicate with a device over a serial port. As part of the communications protocol, all messages had to be signed with a Longitudinal Redundancy Check (LRC) byte. An LRC is computed by taking an XOR across all of the byte values.
Bytearrays make such calculations easy. Here’s one version:
message = bytearray(...) # Message already created
lrc = 0
for b in message:
lrc ^= b
message.append(lrc) # Add to the end of the message
Here’s a version that increases your job security:
message.append(functools.reduce(lambda x,y:x^y,message))
And here’s the same calculation in Python 2 without bytearrays:
message = "..." # Message already created
lrc = 0
for b in message:
lrc ^= ord(b)
message += chr(lrc) # Add the LRC byte
Personally, I like the bytearray version. There’s no need to use ord() and you can just append the result at the end of the message instead of using concatenation.
Here’s another cute example. Suppose you wanted to run a bytearray through a simple XOR-cipher. Here’s a one-liner to do it:
>>> key = 37
>>> message = bytearray(b"Hello World")
>>> s = bytearray(x ^ key for x in message)
>>> s
bytearray(b'm@IIJx05rJWIA')
>>> bytearray(x ^ key for x in s)
bytearray(b"Hello World")
>>>
Here is a link to the presentation
Wikipedia provides an example of XOR cipher using Python’s bytearrays (docstrings reduced):
#!/usr/bin/python2.7
from os import urandom
def vernam_genkey(length):
"""Generating a key"""
return bytearray(urandom(length))
def vernam_encrypt(plaintext, key):
"""Encrypting the message."""
return bytearray([ord(plaintext[i]) ^ key[i] for i in xrange(len(plaintext))])
def vernam_decrypt(ciphertext, key):
"""Decrypting the message"""
return bytearray([ciphertext[i] ^ key[i] for i in xrange(len(ciphertext))])
def main():
myMessage = """This is a topsecret message..."""
print 'message:',myMessage
key = vernam_genkey(len(myMessage))
print 'key:', str(key)
cipherText = vernam_encrypt(myMessage, key)
print 'cipherText:', str(cipherText)
print 'decrypted:', vernam_decrypt(cipherText,key)
if vernam_decrypt(vernam_encrypt(myMessage, key),key)==myMessage:
print ('Unit Test Passed')
else:
print('Unit Test Failed - Check Your Python Distribution')
if __name__ == '__main__':
main()
I recently came across the dataType called bytearray in python. Could someone provide scenarios where bytearrays are required?
A bytearray is very similar to a regular python string (str in python2.x, bytes in python3) but with an important difference, whereas strings are immutable, bytearrays are mutable, a bit like a list of single character strings.
This is useful because some applications use byte sequences in ways that perform poorly with immutable strings. When you are making lots of little changes in the middle of large chunks of memory, as in a database engine, or image library, strings perform quite poorly; since you have to make a copy of the whole (possibly large) string. bytearrays have the advantage of making it possible to make that kind of change without making a copy of the memory first.
But this particular case is actually more the exception, rather than the rule. Most uses involve comparing strings, or string formatting. For the latter, there’s usually a copy anyway, so a mutable type would offer no advantage, and for the former, since immutable strings cannot change, you can calculate a hash of the string and compare that as a shortcut to comparing each byte in order, which is almost always a big win; and so it’s the immutable type (str or bytes) that is the default; and bytearray is the exception when you need it’s special features.
If you look at the documentation for bytearray, it says:
Return a new array of bytes. The bytearray type is a mutable sequence of integers in the range 0 <= x < 256.
In contrast, the documentation for bytes says:
Return a new “bytes” object, which is an immutable sequence of integers in the range 0 <= x < 256. bytes is an immutable version of bytearray – it has the same non-mutating methods and the same indexing and slicing behaviors.
As you can see, the primary distinction is mutability. str methods that “change” the string actually return a new string with the desired modification. Whereas bytearray methods that change the sequence actually change the sequence.
You would prefer using bytearray, if you are editing a large object (e.g. an image’s pixel buffer) through its binary representation and you want the modifications to be done in-place for efficiency.
This answer has been shameless ripped off from here
Example 1: Assembling a message from fragments
Suppose you’re writing some network code that is receiving a large message on a socket connection. If you know about sockets, you know that the recv() operation doesn’t wait for all of the data to arrive. Instead, it merely returns what’s currently available in the system buffers. Therefore, to get all of the data, you might write code that looks like this:
# remaining = number of bytes being received (determined already)
msg = b""
while remaining > 0:
chunk = s.recv(remaining) # Get available data
msg += chunk # Add it to the message
remaining -= len(chunk)
The only problem with this code is that concatenation (+=) has horrible performance. Therefore, a common performance optimization in Python 2 is to collect all of the chunks in a list and perform a join when you’re done. Like this:
# remaining = number of bytes being received (determined already)
msgparts = []
while remaining > 0:
chunk = s.recv(remaining) # Get available data
msgparts.append(chunk) # Add it to list of chunks
remaining -= len(chunk)
msg = b"".join(msgparts) # Make the final message
Now, here’s a third solution using a bytearray:
# remaining = number of bytes being received (determined already)
msg = bytearray()
while remaining > 0:
chunk = s.recv(remaining) # Get available data
msg.extend(chunk) # Add to message
remaining -= len(chunk)
Notice how the bytearray version is really clean. You don’t collect parts in a list and you don’t perform that cryptic join at the end. Nice.
Of course, the big question is whether or not it performs. To test this out, I first made a list of small byte fragments like this:
chunks = [b"x"*16]*512
I then used the timeit module to compare the following two code fragments:
# Version 1
msgparts = []
for chunk in chunks:
msgparts.append(chunk)
msg = b"".join(msgparts)
#Version 2
msg = bytearray()
for chunk in chunks:
msg.extend(chunk)
When tested, version 1 of the code ran in 99.8s whereas version 2 ran in 116.6s (a version using += concatenation takes 230.3s by comparison). So while performing a join operation is still faster, it’s only faster by about 16%. Personally, I think the cleaner programming of the bytearray version might make up for it.
Example 2: Binary record packing
This example is an slight twist on the last example. Suppose you had a large Python list of integer (x,y) coordinates. Something like this:
points = [(1,2),(3,4),(9,10),(23,14),(50,90),...]
Now, suppose you need to write that data out as a binary encoded file consisting of a 32-bit integer length followed by each point packed into a pair of 32-bit integers. One way to do it would be to use the struct module like this:
import struct
f = open("points.bin","wb")
f.write(struct.pack("I",len(points)))
for x,y in points:
f.write(struct.pack("II",x,y))
f.close()
The only problem with this code is that it performs a large number of small write() operations. An alternative approach is to pack everything into a bytearray and only perform one write at the end. For example:
import struct
f = open("points.bin","wb")
msg = bytearray()
msg.extend(struct.pack("I",len(points))
for x,y in points:
msg.extend(struct.pack("II",x,y))
f.write(msg)
f.close()
Sure enough, the version that uses bytearray runs much faster. In a simple timing test involving a list of 100000 points, it runs in about half the time as the version that makes a lot of small writes.
Example 3: Mathematical processing of byte values
The fact that bytearrays present themselves as arrays of integers makes it easier to perform certain kinds of calculations. In a recent embedded systems project, I was using Python to communicate with a device over a serial port. As part of the communications protocol, all messages had to be signed with a Longitudinal Redundancy Check (LRC) byte. An LRC is computed by taking an XOR across all of the byte values.
Bytearrays make such calculations easy. Here’s one version:
message = bytearray(...) # Message already created
lrc = 0
for b in message:
lrc ^= b
message.append(lrc) # Add to the end of the message
Here’s a version that increases your job security:
message.append(functools.reduce(lambda x,y:x^y,message))
And here’s the same calculation in Python 2 without bytearrays:
message = "..." # Message already created
lrc = 0
for b in message:
lrc ^= ord(b)
message += chr(lrc) # Add the LRC byte
Personally, I like the bytearray version. There’s no need to use ord() and you can just append the result at the end of the message instead of using concatenation.
Here’s another cute example. Suppose you wanted to run a bytearray through a simple XOR-cipher. Here’s a one-liner to do it:
>>> key = 37
>>> message = bytearray(b"Hello World")
>>> s = bytearray(x ^ key for x in message)
>>> s
bytearray(b'm@IIJx05rJWIA')
>>> bytearray(x ^ key for x in s)
bytearray(b"Hello World")
>>>
Here is a link to the presentation
Wikipedia provides an example of XOR cipher using Python’s bytearrays (docstrings reduced):
#!/usr/bin/python2.7
from os import urandom
def vernam_genkey(length):
"""Generating a key"""
return bytearray(urandom(length))
def vernam_encrypt(plaintext, key):
"""Encrypting the message."""
return bytearray([ord(plaintext[i]) ^ key[i] for i in xrange(len(plaintext))])
def vernam_decrypt(ciphertext, key):
"""Decrypting the message"""
return bytearray([ciphertext[i] ^ key[i] for i in xrange(len(ciphertext))])
def main():
myMessage = """This is a topsecret message..."""
print 'message:',myMessage
key = vernam_genkey(len(myMessage))
print 'key:', str(key)
cipherText = vernam_encrypt(myMessage, key)
print 'cipherText:', str(cipherText)
print 'decrypted:', vernam_decrypt(cipherText,key)
if vernam_decrypt(vernam_encrypt(myMessage, key),key)==myMessage:
print ('Unit Test Passed')
else:
print('Unit Test Failed - Check Your Python Distribution')
if __name__ == '__main__':
main()