Hi folks, as illustrated in faster-cpython#150 [1], we have implemented a
mechanism that supports data persistence of a subset of python date types with
mmap, therefore can reduce package import time by caching code object. This
could be seen as a more eager pyc format, as they are for the same purpose, but
our approach try to avoid [de]serialization. Therefore, we get a speedup in
overall python startup by ~15%.
Currently, we’ve made it a third-party library and have been working on
open-sourcing.
Our implementation (whose non-official name is “pycds”) mainly contains two
parts:
importlib hooks, this implements the mechanism to dump code objects to an
archive and a `Finder` that supports loading code object from mapped memory.
Dumping and loading (subset of) python types with mmap. In this part, we deal
with 1) ASLR by patching `ob_type` fields; 2) hash seed randomization by
supporting only basic types who don’t have hash-based layout (i.e. dict is not
supported); 3) interned string by re-interning strings while loading mmap
archive and so on.
After pycds has been installed, complete workflow of our approach includes
three parts:
Record name of imported packages to heap.lst, `PYCDSMODE=TRACE
PYCDSLIST=heap.lst python run.py`
Dump memory archive of code objects of imported packages, this step does not
involve the python script, `PYCDSMODE=DUMP PYCDSLIST=heap.lst
PYCDSARCHIVE=heap.img python`
Run other python processes with created archive, `PYCDSMODE=SHARE
PYCDSARCHIVE=heap.img python run.py`
We could even make use of immortal objects if PEP 683 [2] was accepted, that
could gives CDS more performance improvements. Currently, any archived object
is virtually immortal, we add rc by 1 to who has been copied to the archive to
avoid being deallocated. However, without changes to CPython, rc fields of
archived object will still be updated, therefore have extra footprint due to
CoW.
More background and detail implementation could be found at [1].
We think it could be an effective way to improve python’s startup performance,
and could even do more like sharing large data between python instances.
We’re welcome for suggestions and questions.
Best,
Yichen Yan
Alibaba Compiler Group
[1] “Faster startup -- Share code objects from memory-mapped file”,
https://github.com/faster-cpython/ideas/discussions/150
[2] PEP 683: "Immortal Objects, Using a Fixed Refcount" (draft),
https://mail.python.org/archives/list/python-...@python.org/message/TPLEYDCXFQ4AMTW6F6OQFINSIFYBRFCR/
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