The second day of the 2021 Python Language Summit finished with a series of lightning talks from Ronny Pfannschmidt, Pablo Galindo, Batuhan Taskaya, Luciano Ramalho, Jason R. Coombs, Mark Shannon, and Tobias Kohn.
Annotated Assertions: Debugging With Joy
Ronny Pfannschmidt spoke about annotated assertions. He is a pytest maintainer and loves approachable debugging.
He compared assertions in unittest with assertions in pytest. He remarked that mistakes have been made in the past and are still being made today. Before pytest 2.1, pytest would reinterpret assertions, which was bad for side effects. Today, pytest deals with side effects by handling all of the variables, collecting them, and showing them to you.
Here's what he would like to see in Python:
Here's what he'd like to do:
- Create a PEP or have a PEP sponsor
- Open the implementation of pytest to a wider audience
PEP 657: Fine-Grained Error Locations in Tracebacks
Pablo Galindo and Batuhan Taskaya shared their thoughts on what they want to do and what they don't want to do with PEP 657. The goal of this PEP is to improve the debugging experience by making the information in tracebacks more specific. It would also help with code coverage tools because it would allow expression-level coverage rather than just line-level coverage. JEP 358 has already accomplished something similar.
The speakers want to:
- Keep maintenance costs low
- Keep the size small without overcomplicating the compiler
- Provide an API for tools to consume
- Provide an opt-out mechanism
They want to avoid:
- Adding a new set of .pyc files
- Adding a new debugging info file format
- Having a large number of new flags to customize
- Implementing in memory/size encoding
- Complicating the compiler too much
- Providing more than one opt-out mechanism
- Having manual metadata propagation
For the opt-out mechanism, there will be two ways to deactivate the feature:
- Environment variable: PYNODEBUGRANGES
- Command line option: -Xnodebugranges
Who Speaks for Mort on python-dev?
- Mort is an opportunistic developer who like to create quick solutions for immediate problems. He focuses on productivity and learns as needed.
- Elvis is a pragmatic programmer who likes to create long-lasting solutions. He learns while working on solutions.
- Einstein is a paranoid programmer who likes to create the most efficient solution to a problem. He typically learns before working on the solution.
Annotations as Transforms
Jason R. Coombs shared his thoughts on designating transformation functions to be applied to parameters and return values. He had originally been inspired by the simplicity and power of decorators, and his idea could in theory be applied with decorators today. However, he determined that it would be more elegant to use annotations.
Using this approach would have advantages:
- Elegant, simple declaration of intended behavior
- Clear separation of concerns
- Avoiding rewriting variables in the scope
- Easy reuse of transformations
- Explicit type transformation
However, there would also be challenges:
- Compatibility: Although older versions of Python don't have this functionality, you could implement a compatibility shim.
- Ambiguity between types and transforms: In order to address this concern, you could potentially:
- Require transforming functions to be explicitly created
- Provide a wrapping helper to specify that a type is used as a transform (e.g. -> transform(str))
- Provide a wrapper helper or explicit types for nontransforming type declarations (e.g. Int or strict(int))
Tiers of Execution: Making CPython Execute Efficiently
- Tier 0: The slowest tier, with minimal memory usage and low startup time
- Tier 1: Primary interpreter, the adaptive, specializing interpreter
- Tier 2: Small region, lightweight JIT
- Tier 3: Large region, heavyweight JIT
The higher a tier, the hotter the code that it will execute. Today, CPython is at tier 0.3. It's a compromise between memory use and speed but isn't optimized for either. He said that tier 0 could be considered for Python 3.11 or later. It could:
- Minimize startup time and memory use at the expense of execution speed
- Support a full set of features, including sys.settrace
- Be able to execute from a .pyc file that is mmapped and immutable
- Adaptive, specializing interpreter (PEP 659)
- Possible lack of support for some features, such as sys.settrace
Running Parallel Python Code in the Browser
You could suspend the current task and let everything in the event queue happen so that the message can be processed and then resume your task later on. To do that, you could use the bytecode in Python 3.6+ because the frame already has an index into the bytecode and captures state, to a certain extent. However, some bytecode instructions are too complex. _add_ can execute arbitrary Python code, fail, call _radd_, and execute other Python code. The standard bytecode is insufficient.
He's currently using an MPI interface for parallel processing. There is:
- Early-stage multiprocessing support
- No blocking or freezing of the browser's UI