lru-cache | :dizzy: A feature complete LRU cache implementation in C++ | Caching library

import sys; print "%s.%s.%s"

That's some good old Python 2 notation if I ever saw some :)

As @ElapsedSoul mentioned. macOS 12.3 removed the Python 2 that came by default for all these years. Therefore, I would personally recommend for others not to upgrade to 12.3 if you need Python 2 until you find an alternative solution.

Some possible alternatives include the following:

Homebrew unfortunately got rid of Python 2 a long time ago. There are however some workarounds to try and get it to work: How to reinstall python@2 from Homebrew?.

I haven't tested this, so it might not work. But if it does, this is likely the best solution for your needs. From the following:

As has been commented in the comments section, the issue has been solved by moving the project folder outside the OneDrive directory.

Because it's a synchronized cloud folder, as soon as you install/add/delete/update anything, it's being updated in the OneDrive cloud so the file/folder it's being used in the background and potentially unreachable. If at this time you try to develop the project (gatsby develop or gatsby build) and the file is being used, you won't be able to run it.

I don't think it's a good practice to use a cloud folder because the amount of data synchronized (mainly because of the node_modules) it's something to care about (it's also ignored in the .gitignore for a reason) so moving it to any other folder outside the OneDrive directory should be enough to run your project because the rest of global dependencies, according to your logs, were successfully installed.

It looks like the executable is actually named x86_64-linux-gnu-gcc, see https://packages.debian.org/bullseye/arm64/gcc-x86-64-linux-gnu/filelist.

functools.cache was newly added in version 3.9.

The documentation states:

Simple lightweight unbounded function cache. Sometimes called “memoize”.

Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit.

Example from the docs:

The issue was that tsconfig.json must use "module": "esnext". Otherwise the compiled code is not compatible.

That's not moving between rax and rdx.

That's indexing into an array pointed to by rax by rdx and putting the result in rax. Probable L1 cache miss.

allkeys-lru is triggered by Redis allocated memory limit. It's a safety feature to avoid crashing Redis entirely. If you rely on only allkeys-lru to cleanup your data then your Redis will run slower because any operation would have to be applied to a bigger DB. And your Redis DB will always be at max size.

Also it makes it harder to monitor your resources during your business growth.

Using TTL on your values is more a technical decision based on your use case. It gives you more control over which events you don't need anymore. TTL uses more memory because it has to store the TTL value for each record, makes sense.

For Redis-Streams, you can use the MAXLEN property to not grow your streams too much, specially when you don't need older data. This property is per stream so it will not increase Redis memory that much. Redis-streams are expired by stream(by key), not by record. So it's not possible to expire old records from Streams based on a TTL/record if you continuously receive new data.

Main conclusion: Use TTL and MAXLEN where possible to cleanup unnecessary data sooner so Redis will not need to do it all at once and you will have more control over your data and resources.

I have solved my issue by adding tf.compat.v1.enable_eager_execution() to import and it works fine