worker | Backstroke worker eats off a queue of link operations | Runtime Evironment library

You function getTab seems not right, you are currently trying to query on the url. Not on the query options. The following function should work.

To avoid the page from crashing, add the user-agent header to the headers= parameter in requests.get(), otherwise, the page thinks that your a bot and will block you.

Send your data to separate processes. I used concurrent.futures because I like the simple interface.

This runs in about 11 seconds on my computer.

Not yet no. Here is a specs issue discussing this very matter, and it is something a lot of actors would like to see, so there is hope it comes, one day.

Note that there is an AudioWorklet API available, which will create its own Worklet (which also works in a parallel thread), but you still need to instantiate it from the UI thread, and you don't have access to everything that can be done in an AudioContext. Still, it may suit your needs.

Also, note that it might be possible to do the computing you have to do in a Worker already, by transferring ArrayBuffers from your UI thread to the Worker's one, or by using SharedArrayBuffers.

I'm struggling to understand how that code fits together, but this part strikes me as definitely wrong:

Redis doesn't define structured serialization formats. It mostly store strings and integers. So you have to choose or define your format for your struct.

A popular one is JSON, as you noticed, but if you just want to (de)serialize simple pairs of (id, description), it's not very readable nor convenient.

In such a case, you can define your own format, for example the id and the description with a dash in between:

https://docs.python.org/3/library/math.html

The math module consists mostly of thin wrappers around the platform C math library functions.

While python itself can only execute a single instruction at a time, a low level c function that is called by python does not have this limitation.
So it's not python that is using multiple cores but your system's well optimized math library that is wrapped by python's math module.

That basically answers both your questions.

Regarding the usefulness of multiprocessing: It is still useful for those cases, where you're trying to parallelize pure python code or code that does not call libraries that already use multiple cores. However, it comes with inter process communication (IPC) overhead that may or may not be larger than the performance gain that you get from using multiple cores. Tuning IPC is therefore often crucial for multiprocessing in python.

The error is caused by the for loop. Try:

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