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Q&A Readable syntax for executing many callables with useful side effects

In Python, multiprocessing is easy to do if you follow a "list projection" paradigm. Say you want to take a list of inputs X and apply some function f to every x_i, such that y_i = f(x_i) and the y...

1 answer · posted 1y ago by matthewsnyder‭ · last activity 1y ago by mr Tsjolder‭

Question functional-programming python-3 multiprocess

#4: Post edited by

matthewsnyder‭ · 2023-08-09T22:53:01Z (over 1 year ago)

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In Python, multiprocessing is easy to do if you follow a "list projection" paradigm. Say you want to take a list of inputs `X` and apply some function `f` to every `x_i`, such that `y_i = f(x_i)` and the `y_i` comprise the output list `Y`:
```python
y = multiprocessing.Pool().map(f, x)
```
Is there a good API to "take a list of **functions** `F`, and for each `f_i` run `f_i()`"?
List comprehension also runs into this issue, and you end up having to do stuff like:
```python
# Bare statement, but still worth executing for the side effects
[f_i() for f_i in F]
```
Multiprocessing pools look similarly silly:
```python
# Trivial lambda to coerce args list into the functions
~~multiprocessing.Pool().map(lambda f: f(), F)~~
```
While these aren't terribly long or complex, they are somewhat confusing at first glance. I worry that I would need 1-3 lines of comments just to ensure that they'll be readable in a few years when I've forgotten it. Often, whenever you need a lot of comments to explain some code, it's a sign that there's a much better way to do it. Is there such a better way in Python?
<details><summary>Context</summary>I want to have a loop which updates various Git repositories. There are differences in how various repos must be updated. Therefore, I want to have a loop that creates a function (no input, no output) tailored to the corner cases of each repo. Then use multiprocessing to actually execute those. This seems to make the actual business logic much simpler than trying to write a generic function that can handle all corner cases of every type of repo.
However, I am interested in the general idea more than the specific case of handling repos.
</details>

In Python, multiprocessing is easy to do if you follow a "list projection" paradigm. Say you want to take a list of inputs `X` and apply some function `f` to every `x_i`, such that `y_i = f(x_i)` and the `y_i` comprise the output list `Y`:
```python
y = multiprocessing.Pool().map(f, x)
```
Is there a good API to "take a list of **functions** `F`, and for each `f_i` run `f_i()`"?
List comprehension also runs into this issue, and you end up having to do stuff like:
```python
# Bare statement, but still worth executing for the side effects
[f_i() for f_i in F]
```
Multiprocessing pools look similarly silly:
```python
# Must be defined in outer scope, otherwise you'll get pickling errors
def execute(f):
f()
multiprocessing.Pool().map(execute, F)
```
While these aren't terribly long or complex, they are somewhat confusing at first glance. I worry that I would need 1-3 lines of comments just to ensure that they'll be readable in a few years when I've forgotten it. Often, whenever you need a lot of comments to explain some code, it's a sign that there's a much better way to do it. Is there such a better way in Python?
<details><summary>Context</summary>I want to have a loop which updates various Git repositories. There are differences in how various repos must be updated. Therefore, I want to have a loop that creates a function (no input, no output) tailored to the corner cases of each repo. Then use multiprocessing to actually execute those. This seems to make the actual business logic much simpler than trying to write a generic function that can handle all corner cases of every type of repo.
However, I am interested in the general idea more than the specific case of handling repos.
</details>

#3: Post edited by

matthewsnyder‭ · 2023-08-09T20:34:29Z (over 1 year ago)

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~~Readable syntax for executing many callables for the side effects~~

Readable syntax for executing many callables with useful side effects

#2: Post edited by

matthewsnyder‭ · 2023-08-09T20:33:22Z (over 1 year ago)

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In Python, multiprocessing is easy to do if you follow a "list projection" paradigm. Ie. say you want to take a list of inputs `X` and apply some function `f` to every `x_i`, such that `y_i = f(x_i)` and the `y_i` comprise the output list `Y`:
~~```~~
y = multiprocessing.Pool().map(f, x)
```
Is there a good API to "take a list of **functions** `F`, and for each `f_i` run `f_i()`"?
List comprehension also runs into this issue, and you end up having to do stuff like:
~~```~~
# Bare statement, but still worth executing for the side effects
[f_i() for f_i in F]
```
Multiprocessing pools look similarly silly:
~~```~~
# Trivial lambda to coerce args list into the functions
multiprocessing.Pool().map(lambda f: f(), F)
```
While these aren't terribly long or complex, they are somewhat confusing at first glance. I worry that I would need 1-3 lines of comments just to ensure that they'll be readable in a few years when I've forgotten it. Often, whenever you need a lot of comments to explain some code, it's a sign that there's a much better way to do it. Is there such a better way in Python?
<details><summary>Context</summary>I want to have a loop which updates various Git repositories. There are differences in how various repos must be updated. Therefore, I want to have a loop that creates a function (no input, no output) tailored to the corner cases of each repo. Then use multiprocessing to actually execute those. This seems to make the actual business logic much simpler than trying to write a generic function that can handle all corner cases of every type of repo.
However, I am interested in the general idea more than the specific case of handling repos.
</details>

In Python, multiprocessing is easy to do if you follow a "list projection" paradigm. Say you want to take a list of inputs `X` and apply some function `f` to every `x_i`, such that `y_i = f(x_i)` and the `y_i` comprise the output list `Y`:
```python
y = multiprocessing.Pool().map(f, x)
```
Is there a good API to "take a list of **functions** `F`, and for each `f_i` run `f_i()`"?
List comprehension also runs into this issue, and you end up having to do stuff like:
```python
# Bare statement, but still worth executing for the side effects
[f_i() for f_i in F]
```
Multiprocessing pools look similarly silly:
```python
# Trivial lambda to coerce args list into the functions
multiprocessing.Pool().map(lambda f: f(), F)
```
While these aren't terribly long or complex, they are somewhat confusing at first glance. I worry that I would need 1-3 lines of comments just to ensure that they'll be readable in a few years when I've forgotten it. Often, whenever you need a lot of comments to explain some code, it's a sign that there's a much better way to do it. Is there such a better way in Python?
<details><summary>Context</summary>I want to have a loop which updates various Git repositories. There are differences in how various repos must be updated. Therefore, I want to have a loop that creates a function (no input, no output) tailored to the corner cases of each repo. Then use multiprocessing to actually execute those. This seems to make the actual business logic much simpler than trying to write a generic function that can handle all corner cases of every type of repo.
However, I am interested in the general idea more than the specific case of handling repos.
</details>

#1: Initial revision by

matthewsnyder‭ · 2023-08-09T20:23:24Z (over 1 year ago)

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Readable syntax for executing many callables for the side effects

In Python, multiprocessing is easy to do if you follow a "list projection" paradigm. Ie. say you want to take a list of inputs `X` and apply some function `f` to every `x_i`, such that `y_i = f(x_i)` and the `y_i` comprise the output list `Y`:

```
y = multiprocessing.Pool().map(f, x)
```

Is there a good API to "take a list of **functions** `F`, and for each `f_i` run `f_i()`"?

List comprehension also runs into this issue, and you end up having to do stuff like:
```
# Bare statement, but still worth executing for the side effects
[f_i() for f_i in F]
```

Multiprocessing pools look similarly silly:
```
# Trivial lambda to coerce args list into the functions
multiprocessing.Pool().map(lambda f: f(), F)
```

While these aren't terribly long or complex, they are somewhat confusing at first glance. I worry that I would need 1-3 lines of comments just to ensure that they'll be readable in a few years when I've forgotten it. Often, whenever you need a lot of comments to explain some code, it's a sign that there's a much better way to do it. Is there such a better way in Python?

<details><summary>Context</summary>I want to have a loop which updates various Git repositories. There are differences in how various repos must be updated. Therefore, I want to have a loop that creates a function (no input, no output) tailored to the corner cases of each repo. Then use multiprocessing to actually execute those. This seems to make the actual business logic much simpler than trying to write a generic function that can handle all corner cases of every type of repo.

However, I am interested in the general idea more than the specific case of handling repos.
</details>

functional-programming python-3 multiprocess

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