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> For instance, a local cache of some values is basically useless when python is used as backend. Since there will be multiple processes handling the requests (

> For instance, a local cache of some values is basically useless when python is used as backend. Since there will be multiple processes handling the requests (in the same node), they don't share memory. If you want to share low level memory between process, use SharedMemory: https://docs.python.org/3/library/multiprocessing.shared_memory.html https://docs.python.org/3/library/multiprocessing.shared_mem... If you want to share high level data structure between process, use diskcache: http://www.grantjenks.com/docs/diskcache/tutorial.html http://www.grantjenks.com/docs/diskcache/tutorial.html This is, granted, not as straightforward as just sharing a variable between threads, but it will get you far.

goldenkey 5y ago
That's like claiming the ability to share JS ArrayBuffer's between workers supplants the need for the ability to share high level objects like a Map. What I'm getting at is that JS had the exact same problem, and it offers the same ability for sharing blocks of raw memory. But the same pitfalls for any actual structured object. Instead, we have to serialize/unserialize (creating a copy) from a separate worker, process, or disk source, it's performance is shite for objects of large size. Message passing is not ideal for large pieces of data. Shared memory is hundreds of times faster.
- BiteCode_dev 5y ago
  Yes. SharedMemory can share list (https://docs.python.org/3/library/multiprocessing.shared_memory.html#multiprocessing.shared_memory.ShareableList https://docs.python.org/3/library/multiprocessing.shared_mem...) but that's it. That's why diskscache is better as an alternative for complex objects: https://pypi.org/project/diskcache/ https://pypi.org/project/diskcache/ It's not as good as a simple shared variable, but it's surprisingly performant, since it uses memory-mapped files.
  - goldenkey 5y ago
    I see what you mean. Does diskcache still have to do deserialization of the mapped data? Nested objects (large disparate heap pointer graphs) would suffer performance penalties.
    - BiteCode_dev 5y ago
      It does yes, although pickle is pretty fast, it's still a penalty.

goldenkey 5y ago

That's like claiming the ability to share JS ArrayBuffer's between workers supplants the need for the ability to share high level objects like a Map. What I'm getting at is that JS had the exact same problem, and it offers the same ability for sharing blocks of raw memory. But the same pitfalls for any actual structured object. Instead, we have to serialize/unserialize (creating a copy) from a separate worker, process, or disk source, it's performance is shite for objects of large size. Message passing is not ideal for large pieces of data. Shared memory is hundreds of times faster.

BiteCode_dev 5y ago
Yes. SharedMemory can share list (https://docs.python.org/3/library/multiprocessing.shared_memory.html#multiprocessing.shared_memory.ShareableList https://docs.python.org/3/library/multiprocessing.shared_mem...) but that's it. That's why diskscache is better as an alternative for complex objects: https://pypi.org/project/diskcache/ https://pypi.org/project/diskcache/ It's not as good as a simple shared variable, but it's surprisingly performant, since it uses memory-mapped files.
- goldenkey 5y ago
  I see what you mean. Does diskcache still have to do deserialization of the mapped data? Nested objects (large disparate heap pointer graphs) would suffer performance penalties.
  - BiteCode_dev 5y ago
    It does yes, although pickle is pretty fast, it's still a penalty.