Consider, for a moment, the shelve and dbm packages for storing things in a “database.” Built-in. Lightweight. The database is essentially a mapping from identifiers to objects. It can be quite nice.

The shelve module directly puts Python objects in a file. It’s an ideal database structure for Python …

Nowadays, we don't have the same compelling reasons to test a random number generator. The intervening decades have seen a lot of fruitful research. Good algorithms.

Looking back to my 1968 self, however, I still feel a need to work out the solution to an old problem. See The Old …

Got this in an email.

"Python's multi-threading module seems not efficient because of the global interpreter lock?"

Yep.

Is the trick is to use "Thread-Local Data"?

Nope.

The multiprocessing module includes a generic Process class, which can be used to wrap a simple function.

The function must be designed to work with Queues or Pipelines or other synchronization techniques.

There's an advantage, however, to defining a class which gracefully handles generator functions. If we have Generator-Aware multi-processing …

The advantage of multiprocessing is to have multiple processes working on a problem. If we break a big problem into small, concurrent steps, we can often get results in less elapsed time by making more effective use of the CPU. Specifically, we want to make use of non-user time where …

Read this: Shared Counter with Python Multiprocessing.

Brilliant. Thank you for this.

Too many of the questions on StackOverflow that include multi-threading are better approached as multi-processing. In Linux, there are times when all threads of a single process are stopped while the process (as a whole) waits for system …