gather | Run SQL queries on Java objects in a collection | SQL Database library

First, an MRE with an emphasis on the M¹:

After contacting Apple Developer Technical Support (DTS) regarding this issue, they provided me with the following guidance.

Regarding "insufficient usage data available" for energy logs accessible via the Xcode organizer:

DTS indicated that they do not publish the thresholds for active users and usage logs would be expected to be present if you have more that a few thousand active users consistently on each version of your app. If your app meets this criteria and still does not show energy logs, DTS recommended opening a bug report with them.

Regarding how to collect energy log data for your app:

DTS recommended using MetricKit to get daily metric payloads. Payloads are delivered to your app every 24 hours and it is then possible to consume them and send them off device.

The instantiation of this is vey basic and can be as simple as:

You should have those two indexes to speed up your query :

While you can't return an impl Future from an impl FnMut, you can return a boxed future, i.e. a dyn Future which must be boxed because it's in return position. After a bit of borrow checker tetris, we arrive to this:

The main difference between these signatures boils down to the fact that ! can coerce into any other type, and thus is compatible with any other type (since this code path is never taken, we can assume it to be of any type we need). It's important when we have multiple possible code paths, such as if-else or match.

For example, consider the following (probably contrived, but hopefully clear enough) code:

Attention:

Here is an inspiring idea which is not fully satisfy OP's requirement (e.g., ID.new and number order) but I think it worth to be recoreded here.

You can turn DT into long format by melt firstly. Then to shift value with the step -nrow(DT) in order to do the minus operation, i.e. NAME1 - NAME2, NAME2 - NAME3, NAME3 - NAME1.

What you're asking about ("get[ing] the last element of a lazy but finite Seq … while keeping the original Seq lazy") isn't possible. I don't mean that it's not possible with Raku – I mean that, in principle, it's not possible for any language that defines "laziness" the way Raku does with, for example, the is-lazy method.

If particular, when a Seq is lazy in Raku, that "means that [the Seq's] values are computed on demand and stored for later use." Additionally, one of the defining features of a lazy iterable is that it cannot know its own length while remaining lazy – that's why calling .elems on a lazy iterable throws an error:

the ZonedDateTime can retrieve the current ZoneId. With the Zone, you can just use the getDisplayName(TextStyle style, Locale locale) getter. Just experiment which TextStyle is the best for you.

Multiprocessing isn't exactly a simple library, but once you're familiar with how it works, it's pretty easy to poke around and figure it out.

You usually want to start with context.py. This is where all the useful classes get bound depending on OS, and... well... the "context" you have active. There are 4 basic contexts: Fork, ForkServer, and Spawn for posix; and a separate Spawn for windows. These in turn each have their own "Popen" (called at start()) to launch a new process to handle the separate implementations.

creating a process literally calls os.fork(), and then in the child organizes to run BaseProcess._bootstrap() which sets up some cleanup stuff then calls self.run() to execute the code you give it. No pickling occurs to start a process this way because the entire memory space gets copied (with some exceptions. see: fork(2)).

I am most familiar with windows, but I assume both the win32 and posix versions operate in a very similar manner. A new python process is created with a simple crafted command line string including a pair of pipe handles to read/write from/to. The new process will import the __main__ module (generally equal to sys.argv[0]) in order to have access to all the needed references. Then it will execute a simple bootstrap function (from the command string) that attempts to read and un-pickle a Process object from its pipe it was created with. Once it has the Process instance (a new object which is a copy; not just a reference to the original), it will again arrange to call _bootstrap().

The first time a new process is created with the "forkserver" context, a new process will be "spawn"ed running a simple server (listening on a pipe) which handles new process requests. Subsequent process requests all go to the same server (based on import mechanics and a module-level global for the server instance). New processes are then "fork"ed from that server in order to save the time of spinning up a new python instance. These new processes however can't have any of the same (as in same object and not a copy) Process objects because the python process they were forked from was itself "spawn"ed. Therefore the Process instance is pickled and sent much like with "spawn". The benefits of this method include: The process doing the forking is single threaded to avoid deadlocks. The cost of spinning up a new python interpreter is only paid once. The memory consumption of the interpreter, and any modules imported by __main__ can largely be shared due to "fork" generally using copy-on-write memory pages.

In all cases, once the split has occurred, you should consider the memory spaces totally separate, and the only communication between them is via pipes or shared memory. Locks and Semaphores are handled by an extension library (written in c), but are basically named semaphores managed by the OS. Queue's, Pipe's and multiprocessing.Manager's use pickling to synchronize changes to the proxy objects they return. The new-ish multiprocessing.shared_memory uses a memory-mapped file or buffer to share data (managed by the OS like semaphores).

the code may have a bug and an object which is supposed to read-only is inadvertently modified, leading to its pickling to be transferred to other processes.

This only really applies to multiprocessing.Manager proxy objects. As everything else requires you to be very intentional about sending and receiveing data, or instead uses some other transfer mechanism than pickling.