A simple project to explore the number of GCs when doing basic ORM work.

Overview

Question: Does Python do extremely too many GCs for ORMs?

YES, OMG YES. Check this out

Python Default GC Settings:

  • SQLAlchemy - 20,000 records in one query

    • 1,859 GCs
    • 908ms
    • 78.7 MB mem
  • MongoDB - 20,000 records in one query

    • 463 GCs
    • 593ms
    • 75.8 MB mem

Talk Python-optimized GC Settings:

  • SQLAlchemy - 20,000 records in one query:
    • 29 GCs (64x improvement)
    • 695ms (23% improvement)
    • 76.8 MB mem (surprisingly: 2% improvement with less GC)
  • MongoDB - 20,000 records in one query
    • 10 GCs (46x improvement)
    • 515ms (13%)
    • 72.3 MB mem (surprisingly: 4% improvement with less GC)
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Comments
  • Get CPU overheads estimate

    Get CPU overheads estimate

    This is very instructive, but perhaps could be complemented with CPU overhead estimates to see how they relate to the number of GCs. One way of doing this could be with the GC state sampling option of Austin.

    opened by P403n1x87 7
Owner
Michael Kennedy
A Python enthusiast, and an entrepreneur. Host of @talkpython and pythonbytes, founder of Talk Python Training. Python Software Foundation Fellow.
Michael Kennedy
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