pool

I wanted to know that, is it possible to create a pool of objects? So that I can take an object from the pool and once I'm done with the work, I can put it into the pool. Gray I wanted to know that, is it possible to create a pool of objects? So that I can take an object from the pool and once I'm done with the work, I can put it into the pool. It is possible yes. You can see performance improvements in many situations if the construction of a new object is expensive (like establishing a database connection) or if for other reasons the GC bandwidth is too high (often a problem in Android-land)

I have hundreds of thousands of text files that I want to parse in various ways. I want to save the output to a single file without synchronization problems. I have been using multiprocessing pool to do this to save time, but I can't figure out how to combine Pool and Queue. The following code will save the infile name as well as the maximum number of consecutive "x"s in the file. However, I want all processes to save results to the same file, and not to different files as in my example. Any help on this would be greatly appreciated. import multiprocessing with open('infilenamess.txt') as f:

This question already has an answer here: Python multiprocessing pool.map for multiple arguments 18 answers I need some way to use a function within pool.map() that accepts more than one parameter. As per my understanding, the target function of pool.map() can only have one iterable as a parameter but is there a way that I can pass other parameters in as well? In this case, I need to pass in a few configuration variables, like my Lock() and logging information to the target function. I have tried to do some research and I think that I may be able to use partial functions to get it to work?

I have a function which performs some simulation and returns an array in string format. I want to run the simulation (the function) for varying input parameter values, over 10000 possible input values, and write the results to a single file. I am using multiprocessing, specifically, pool.map function to run the simulations in parallel. Since the whole process of running the simulation function over 10000 times takes a very long time, I really would like to track the process of the entire operation. I think the problem in my current code below is that, pool.map runs the function 10000 times,

(This question is about how to make multiprocessing.Pool() run code faster. I finally solved it, and the final solution can be found at the bottom of the post.) Original Question: I'm trying to use Python to compare a word with many other words in a list and retrieve a list of the most similar ones. To do that I am using the difflib.get_close_matches function. I'm on a relatively new and powerful Windows 7 Laptop computer, with Python 2.6.5. What I want is to speed up the comparison process because my comparison list of words is very long and I have to repeat the comparison process several