Issue1612

It takes ~ 300x longer to create instances of array.array in Jython2.5.1 vs Python2.6 and Python3.1

e.g. the following: 
from array import array
array('b', large_string)

$ python2.6 profile_array.py 
0.0104711055756
$ python3.1 profile_array.py 
0.00699281692505
$ jython profile_array.py 
3.00600004196
$ jython --version
Jython 2.5.1

Did you measure total program time?

The 3s of jython profile_array.py do **NOT** include the JVM start-up time, so it is 'wall-clock' time of the loop.

this is what is in the attached script:
start = time()
for i in range(10000):
    array('b', large_string)
stop = time()

The problem here is that we copy the string. In 2.6 this can be avoided by supporting a string to back an array. This can (and should) be part of a general support for memoryview.

better title - "Jython ____" is just noise here

The reported performance problem is still seen in 2.7.0 beta 4.

In reviewing CPython 2.7's arraymodule.c, I don't see any support for copy-on-write semantics to do this speedup. Instead it's just a straightforward memcpy in the frombytes function.

So the additional overhead here has a simple root cause: unlike CPython, Jython uses the same method, PyArray.fromStream, to read from an input stream into a given array. Although the read should be reasonably fast/inlineable (but more overhead than simply looping through the string), the write performance into the array is very slow since it uses java.lang.reflect.Array, in this case java.lang.reflect.Array#setByte.

Some simple specialization would speed things up considerably, much as was done with CPython.

Changing misleading title! (Copy-on-write would still be interesting, and perhaps more feasible on Jython.)

@zyasoft Something like the patch I have in mind? I can get a better profile number with this naive "bulk" put() implementation sans-copy-on-write optimization, but it's modest at best.