Fastest way to download music on android

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Download Now. Why musicians like you love the Musicnotes app. David Choir Conductor. Emmi Pianist. Suzy Singer. I have been able to transpose music to my range. Remotely, you may be limited in the number of simultaneous connections if all the requests are against one server, or many.

These limitations will probably necessitate that you write the script in such a way as to only poll a small fraction of the URLs at any one time , as another poster mentioned, is probably a decent thread pool size, although you may find that you can successfully deploy many more.

I would suggest you use the threading module. You can use it to launch and track running threads. Python's threading support is bare, but the description of your problem suggests that it is completely sufficient for your needs. Finally, if you'd like to see a pretty straightforward application of a parallel network application written in Python, check out ssh. It's a small library which uses Python threading to parallelize many SSH connections. The design is close enough to your requirements that you may find it to be a good resource.

The overhead associated with thousands of OS threads is non-trivial and the context switching within the Python interpreter adds even more on top of it. Threading will certainly get the job done but I suspect that an asynchronous route will provide better overall performance. It has an admittedly steep learning curve but it quite easy to use once you get a good handle on Twisted's style of asynchronous programming.

Using a thread pool is a good option, and will make this fairly easy. Unfortunately, python doesn't have a standard library that makes thread pools ultra easy.

Create epoll object, open many client TCP sockets, adjust their send buffers to be a bit more than request header, send a request header — it should be immediate, just placing into a buffer, register socket in epoll object, do.

Limit number of sockets opened simultaneously — handle errors when sockets are created. Create a new socket only if another is closed. Adjust OS limits. Try forking into a few not many processes: this may help to use CPU a bit more effectively. Apache Bench is all you need. For your case, threading will probably do the trick as you'll probably be spending most time waiting for a response. There are helpful modules like Queue in the standard library that might help.

I did a similar thing with parallel downloading of files before and it was good enough for me, but it wasn't on the scale you are talking about. Consider using Windmill , although Windmill probably cant do that many threads.

You could do it with a hand rolled Python script on 5 machines, each one connecting outbound using ports , opening , port connections. Also, it might help to do a sample test with a nicely threaded QA app such as OpenSTA in order to get an idea of how much each server can handle.

You'll probably get more performance more connections that way. I found that using the tornado package to be the fastest and simplest way to achieve this:. The easiest way would be to use Python's built-in threading library.

One option is here , but it's trivial to write your own. You can't parallelize all , calls, but you can fire off or so of them at the same time. Stack Overflow for Teams — Collaborate and share knowledge with a private group.

Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Ask Question. Asked 11 years, 7 months ago. Active 3 months ago. Viewed k times. IgorGanapolsky IgorGanapolsky 24k 17 17 gold badges silver badges bronze badges. Make sure that you only do HEAD request so that you don't download the whole document. See: stackoverflow. Excellent point, Kalmi. If all Igor wants is the status of the request, these K requests will go much, much, much quicker.

Much quicker. You don't need threads for this; the most efficient way is likely to use an asynchronous library like Twisted. Show 3 more comments. Active Oldest Votes. HTTPConnection url. Igor Hatarist 4, 2 2 gold badges 29 29 silver badges 41 41 bronze badges. Don't forget to close the connection conn.

Opening too many http connections may halt your script at some point and eats memory. This is Python 2 code. How much faster can you go if you want to talk with the SAME server each time, by persisting the connection?

Can this even be done across threads, or with one persistent connection per thread? Most operations on lists are implicitly thread-safe in CPython and some other python implementations due to the GIL, so this is safe to do. Show 14 more comments. Glen Thompson Glen Thompson 6, 3 3 gold badges 41 41 silver badges 40 40 bronze badges.

It could, but I have found the above to work better. It does work but I just havn't found it to work as well. I get higher error rates when I use it and for the life of me I can't get it to work as well as concurrent futures although in theory It seems that it should work better, see: stackoverflow. I tested your snippet, somehow it executes twice. Am I doing something wrong?