Thinking Outside the GIL

Have you ever written a small, elegant application that couldn't keep up with the growth of your data or user demand? Did your beautiful design end up buried in threads and locks? Did Python's very special Global Interpreter Lock make all of this an exercise in futility?

This talk is for you! With the combined powers of AsyncIO and multiprocessing, we'll redesign an old multithreaded application limited by the GIL into a modern solution that scales with the demand using only the standard library. No prior AsyncIO or multiprocessing experience required.

Presented at PyCon US 2018 in Cleveland: https://youtu.be/0kXaLh8Fz3k

Transcript

2. Thinking Outside the GIL With AsyncIO and Multiprocessing John Reese Production Engineer, Facebook @n7cmdr
 github.com/jreese
3. • Global Interpreter Lock • One VM thread at a time • No concurrent memory access • I/O wait releases lock What’s the GIL?
4. • Gather ~100M data points • Process and aggregate anomalies • Easy to add new checks • Simple deployment • Few dependencies Stateful monitoring
8. • One binary • Fetch the world • Process everything • Aggregate results • Thread pool for I/O #impact
9. • Scales in time and memory • Runtime now too slow • Underutilizing hardware • Ultimately limited by the GIL Not aging well
10. Give me options
11. Switch to py3 ~45% memory savings ~20% runtime reduction
12. • Technically correct • Scales with number of workers • Complicated deployments • Communication overhead Sharding
13. • Scales with CPU cores • Automatic IPC • Pool.map is really useful Multiprocessing
16. • Scales with CPU cores • Automatic IPC • Pool.map is really useful • One task per process • Beware forking, pickling Multiprocessing
17. • Based on futures • Faster than threads • Massive I/O concurrency AsyncIO
22. • Based on futures • Faster than threads • Massive I/O concurrency • Processing still limited by GIL • Beware timeouts and queue length AsyncIO
23. Why not both?
24. • Use multiprocessing primitives • Event loop per process • Queues for work/results • Highly parallel workload • Need to do some plumbing Multiprocessing + AsyncIO
25. But How?
26. But How?
27. But How?
43. • Multiple work queues • Combine tasks into batches • Use spawned processes Optimizations
44. • Minimize what you pickle • Prechunk work items • Aggregate results in the child • Use map/reduce Considerations
45. Performance comparison Threads AsyncIO Multiprocessing Multi/Async Naive Multi/Async Tuned Multi/Async Map/Reduce Processed / Second
46. I want it!
47. $ pip install aiomultiprocess
48. • Simple implementation • Emulates multiprocessing API • One shot or process pool • Supports map/reduce workloads aiomultiprocess github.com/jreese/aiomultiprocess
50. Python is slow
51. Python is slow powerful
52. Great tools make complex tasks simple
53. John Reese Production Engineer, Facebook @n7cmdr
 github.com/jreese