keronluck.blogg.se

1. #Bulk unzipper zip file#
2. #Bulk unzipper generator#
3. #Bulk unzipper archive#
4. #Bulk unzipper full#
5. #Bulk unzipper code#

bd: Disable percentage Include filenames

#Bulk unzipper full#

X: eXtract files with full Include eXclude archives

#Bulk unzipper archive#

Here is its output: Usage: 7z Į: Extract files from archive (without using directory names)

#Bulk unzipper zip file#

Here is for the "normal" unzip creating a folder per zip file unzipped: "C:\Program Files (x86)\7-Zip\7z.exe" x *.zipĪnd to have a full details of what you can do with 7z.exe use the -help: "C:\Program Files (x86)\7-Zip\7z.exe" -help however the e in the previous answer export everything in the directory. I wanted to unzip every zip file in a directory into multiple folder. I'm also working on a fastprocesspool module but this is still not finished and buggy, First tests have shown that this module is also faster than multiprocessing.Pool and much fast than ProcessPoolExecutor.My default installation folder was C:\Program Files (x86)\7-Zip so I'm going to go from there. The doc directory contains some benchmark files which show the overhead difference between the 3 thread pool implementations.

#Bulk unzipper code#

For example code please look into benchmark.py.

#Bulk unzipper generator#

It also has the advantage that you can use generator functions as worker which is very useful in certain situations. This module is faster than ThreadPool and much faster than ThreadPoolExecutor. Or you could also try my fastthreadpool module ( ). Use a different thread pool implementation or if you want to stick with ThreadPoolExecutor use map. You should not use ThreadPoolExecutor, because it has a relatively high overhead and is very slow.

It contains a bunch of terrible hacks and ugly stuff but hopefully it's a start. zip file there is 34MB which is relatively small compared to what's happening on the server. I did my benchmarking using a c5.4xlarge EC2 server. I'm going to stick with zipfile.ZipFile(file_buffer).extractall(temp_dir). What if you get the number wrong in a virtual environment? Of if the number is too low and don't benefit any from spreading the workload and now you're just paying for overheads to move the work around? Also, just look at the difference in the code between f1 and f2! With concurrent.futures pool classes you can cap the number of CPUs it's allowed to use but that doesn't feel great either. You're bound to one CPU but the performance is still pretty good. Conclusionĭoing it serially turns out to be quite nice. Since there's other things going on in this server, I'm not sure I'm willing to let on process take over all the other CPUs. What if some of those other CPUs are needed for something else going on in gunicorn? Those other processes would have to patiently wait till there's a CPU available. Perhaps, it could be worth it if the extraction was significantly faster.īut remember! This optimization depends on using up as many CPUs as it possibly can. Not sure what the cost of that it's not likely to be cheap. So in my web server, to use this solution, I'd first have to save the in-memory ZIP file to disk, then invoke this function. The problem with using a pool of processors is that it requires that the original. as_completed ( futures ): total += future. filename, dest, ) ) total = 0 for future in concurrent. ProcessPoolExecutor () as executor : for member in zf.

join ( dest, filename ) return _count_file ( fn ) def f3 ( fn, dest ): with open ( fn, 'rb' ) as f : zf = zipfile. Is there perhaps a way to optimize that? Baseline functionįirst it's these common functions that simulate actually doing something with the files in the zip file:ĭef unzip_member_f3 ( zip_filepath, filename, dest ): with open ( zip_filepath, 'rb' ) as f : zf = zipfile. This worked much better but I still noticed the whole unzipping was taking up a huge amount of time. So, the solution, after much testing, was to dump the zip file to disk (in a temporary directory in /tmp) and then iterate over the files.

First you have the 1GB file in RAM, then you unzip each file and now you have possibly 2-3GB all in memory. That failed spectacularly with various memory explosions and EC2 running out of memory. It's not unusual that each zip file contains 100 files and 1-3 of those make up 95% of the zip file size.Īt first I tried unzipping the file, in memory, and deal with one file at a time. Within them, there are mostly plain text files but there are some binary files in there too that are huge. The average is 560MB but some are as much as 1GB. The challenge is that these zip files that come in are huuuge. In this particular application what it does is that it looks at the file's individual name and size, compares that to what has already been uploaded in AWS S3 and if the file is believed to be different or new, it gets uploaded to AWS S3. So the context is this a zip file is uploaded into a web service and Python then needs extract that and analyze and deal with each file within.