Friendly and Modern AsyncIO

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Friendly and Modern AsyncIO John Reese Production Engineer, Python Foundation @n7cmdr  github.com / jreese

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1. What is AsyncIO 2. Why is it useful 3. High level concepts 4. Standard library basics 5. Building friendly, asynchronous APIs Topics

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github.com/jreese/pycon

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github.com/jreese/pycon $ git clone https://github.com/jreese/pycon $ cd pycon/friendly-asyncio $ python3.7 -m venv venv $ source venv/bin/activate $ pip install -Ur requirements.txt

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What is AsyncIO

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• Cooperative multitasking • Built around futures/promises • Single threaded • Optimized for concurrent I/O What is AsyncIO

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• Cooperative multitasking • Built around futures/promises • Single threaded • Optimized for concurrent I/O What is AsyncIO

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• Parallel execution • Multithreaded • Performance/speed improvement • Optimized for computation What AsyncIO is not

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• Parallel execution • Multithreaded • Performance/speed improvement • Optimized for computation What AsyncIO is not

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• Context switching • Thread safety • Control ﬂow • “How many?” Why not threads?

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• Context switching • Thread safety • Control ﬂow • “How many?” Why not threads?

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$ python3.7 1-threads.py 1299 runs of fib in 1.0s: 769.83 usec per run

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156 runs of fibs in 1.0s: 6.42 msec per run 151 runs of fibp in 1.0s: 6.65 msec per run

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156 runs of fibs in 1.0s: 6.44 msec per run 387 runs of fibp in 1.0s: 2.59 msec per run

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But what about I/O?

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• Simple TCP server • Accepts an upper bound k • “Thinks about it” • Returns a random integer [0,k] Randint-as-a-service $ python3.7 server.py listening on 8080...

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$ echo 20 | nc ::1 8080 6 $ echo 20 | nc ::1 8080 17 $ echo 20 | nc ::1 8080 11

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$ python3.7 2-thread-client.py got 19

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10 runs of get_random in 1.0s: 0.104 sec per run

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1 runs of many_random in 3.4s: 3.419 sec per run

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2 runs of many_random in 1.1s: 0.558 sec per run 1 runs of many_random in 1.4s: 1.351 sec per run 1 runs of many_random in 1.2s: 1.241 sec per run 0 0.001 0.002 0.003 0.004 32 64 128 256 512 1024

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$ python3.7 3-asyncio-client.py 10 runs of get_random in 1.0s: 0.105 sec per run

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7 runs of async_random in 1.0s: 0.147 sec per run

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6 runs of async_random in 1.1s: 0.191 sec per run 4 runs of async_random in 1.1s: 0.280 sec per run 3 runs of async_random in 1.4s: 0.467 sec per run 0 0.001 0.002 0.003 0.004 32 64 128 256 512 1024 128 256 512 1024 AsyncIO Threads

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How does AsyncIO work?

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• Object that is not ready, or done • “done” means success or exception • Add callbacks, or periodically check state • Result yields ﬁnal value, or raise exception Back to the futures

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• Object that is not ready, or done • “done” means success or exception • Add callbacks, or periodically check state • Result yields ﬁnal value, or raise exception Back to the futures

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• Like futures, without polling or callbacks • Object with __await__ method • Must be “awaited” via await keyword • Yields value when completed Awaitables

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• Asynchronous function • Dedicated async def syntax • Returns a coroutine object when called • Only executes when awaited Coroutines

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• Event loop controls execution of pending tasks • Tasks wrap and await futures automatically • Tasks are awaitable, yield their wrapped futures Event loops and tasks

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• Executes current task until done or blocked • Next task picked from queue • Tasks can “starve” other tasks Event loops and tasks

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$ python3.7 4-asyncio-basics.py got coroutine: running foo got 1 from coroutine

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$ python3.7 4-asyncio-basics.py got coroutine: running foo got 1 from coroutine

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coro: task: task: result=None>

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coro: task: task: result=None>

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coro: task: task: result=None>

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gather two: [0, 11] gather ten: [1, 8, 13, 4, 19, 2, 5, 4, 20, 20]

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gather two: [0, 11] gather ten: [1, 8, 13, 4, 19, 2, 5, 4, 20, 20]

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$ python3.7 5-starvation.py getting food full now, took 0.1s

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getting food nap time nap over full now, took 2.0s

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getting food nap time nap over full now, took 2.0s

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getting food nap time full now, took 0.1s nap over

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0: counting to 100000 0: ... 99999 ... 100000 1: counting to 100000 1: ... 99999 ... 100000 2: counting to 100000 2: ... 99999 ... 100000 3: counting to 100000 3: ... 99999 ... 100000

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0: counting to 100000 1: counting to 100000 2: counting to 100000 3: counting to 100000 0: ... 99999 ... 100000 1: ... 99999 ... 100000 2: ... 99999 ... 100000 3: ... 99999 ... 100000

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Making AsyncIO useful

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$ curl http://0.0.0.0:8080/randint/25 15 $ curl http://0.0.0.0:8080/randint/2500 2318 $ python 6-aiohttp-server.py ======== Running on http://0.0.0.0:8080 ======== (Press CTRL+C to quit)

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$ python3.7 7-aiohttp-client.py got (200, 14)

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$ python3.7 7-aiohttp-client.py got (200, 14)

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$ python3.7 8-aiosqlite.py sender> Bob recipient> Alice message> I need your help. inserted row id 1

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row #1 from Bob to Alice: I need your help. row #2 from Janet to Nina: What time is the developer sync? row #3 from Nina to Janet: I think it's after the design review.

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Designing Friendly APIs

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• Focus on common use cases • Make interfaces feel natural • Use the most obvious syntax features • Support multiple styles if possible Obvious and natural

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• Common, asynchronous actions • Initializing async objects • Preparing or fetching resources Awaitables

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• Implement the __await__ special method Awaitables

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$ python3.7 9-awaitables.py 7

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$ python3.7 9-awaitables.py 23 5

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• For use in async for loops • Each iteration is an async coroutine • Useful for iteration before all data is ready • Iterate large, async datasets without blocking Async Iterables

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• Similar to normal iterables • Use __aiter__ to create an iterator • Use __anext__ to return the next value • Raise StopAsyncIteration when exhausted Async Iterables

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19 15 1 10 20

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[34, 9, 28]

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0 20 19

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• Ephemeral actions, connections, transactions • Async coroutines at enter and exit • Ensure clean up on exit or exception Async Context Managers

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• Same pattern as regular context managers • __aenter__ called when entering • __aexit__ called at exit or exception Async Context Managers

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$ python3.7 10-contexts.py row #4 from Jack to Jill: I'm out of water. row #5 from Jack to Terry: Do you know where my pail is?

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Mix Paradigms

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Phew...

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Read the [3.7] docs

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Experimentation is key

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Consider your workload

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Don't be afraid to benchmark

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Don't cross the streams

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Don't cross the streams

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Don't cross the streams

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github.com/jreese/pycon

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github.com/jreese/pycon John Reese Production Engineer, Python Foundation @n7cmdr  github.com / jreese