Garbage Collection in Python by Benjamin Peterson

Transcript

1. Garbage Collection Benjamin Peterson
2. Outline ➔ How GC works in various Python implementations ◆ optimizations ➔ GC semantics subtleties
3. Part 1 Implementation Basics
4. Bias & Disclaimer
5. What is GC in Python? ➔ Unused objects are finalized and deallocated. ➔ When? ◆ “eventually”... or never! ◆ not running out of memory is good
6. CPython
7. CPython: reference counting ➔ Every object has a count of how many other objects want to keep it alive. ➔ New objects have ref count 1. ➔ When ref count is 0, the object can be deleted.
8. New object Preexisting objects
9. Preexisting objects New object
10. Preexisting objects Dead object
11. Preexisting objects Dead object Dead object
12. refcounting example
13. Reference Counting’s Major Flaw Reference Cycles
14. Preexisting objects
15. Preexisting objects Cycle keeps itself alive!
16. CPython’s cyclic GC ➔ Detects cycles unreachable from the program and deletes them ➔ Runs every once and while on allocation ➔ on CPython
17. Preexisting objects Cyclic GC subtracts internal references
18. Preexisting objects Cycles are now deleted
20. PyPy review ➔ Interpreter written in RPython ➔ RPython translated to low level language (C) ➔ Interpreter is abstracted from low-level details like GC
21. PyPy has pluggable GCs
22. PyPy GC ➔ GC is simply another low-level transform during translation. ➔ GC algorithm itself is written in RPython. ➔ GC implementation can be selected at translation time. ➔ Current default GC: “minmark”
23. Mark and Sweep ➔ Step 1: Starting from known live objects, recursively traverse objects, marking them as reachable. ➔ Step 2: Walk all allocated objects, deleting that ones that aren’t marked as alive. ➔ No need to worry about reference cycles.
24. Preexisting objects Unreachable object Marking
25. Preexisting objects Unreachable object Marking GC traverses references to C.
26. Preexisting objects Unreachable object Sweeping GC notices that D is unreachable and deletes it.
27. Fancy PyPy GC Optimizations
28. Python allocates constantly
29. “High Infant Mortality”
30. The nursery ➔ Store newly allocated objects in a “nursery” ➔ Collect the “nursery” often and move surviving objects elsewhere (minor collection) ➔ Garbage collect old objects less frequently (major collection)
31. GC Pauses ➔ When the GC is running, the program is not. ➔ Inconvenient for many long running programs like servers. ➔ A deal-breaker for real-time applications like video processing.
32. PyPy incremental GC ➔ In PyPy 2.2 ➔ Major collection split into multiple passes, each lasting only a few milliseconds. ➔ “All in all it was relatively painless work.”
33. GC in PyPy summary ➔ Pluggable ➔ Generational ➔ Incremental ➔ Integrated with the JIT
34. Part 2 GC Semantics
35. kills kittens
36. Cycles with finalizers: a conundrum Which finalizer to run first? Reference to the alive world.
37. What to do about cycles with ? ➔ CPython < 3.4: give up ➔ CPython >= 3.4: PEP 442 ➔ PyPy: sort finalizers into a “reasonable” order and run them
38. PEP 442 - “Safe Object Finalization” ➔ Step 1: Run finalizers on unreachable cycles (arbitrary order). Resurrect any cycles that become reachable again. ➔ Step 2: Break references in remaining cycles.
39. Themes ➔ Garbage Collection is hard. ➔ (PyPy’s) GCs are awesome!
40. Queries? (benjamin@python.org) ➔ PyPy Blog (http://morepypy.blogspot.com) ➔ GC module documentation ➔ Wikipedia article on garbage collection ➔ The source ◆ ◆