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Dive into Python asyncio - part 2
In the second part of this series on deep diving into
asyncio
andasync/await
in Python, we will be looking at the following topics:- task, task groups, task cancellation
- async queues
- async locks and semaphores
- async context managers
- async error handling
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Dive into Python asyncio - part 1
For as long as I have worked in Python land, I never had to touch the async part of the language. I know that
asyncio
library has gotten a lot of love in the past few years. Recently I’ve came across an opportunity to do a lot of IO and non-cpu bound work in Python. I decided to take a deep dive into theasyncio
library and see what it has to offer.In part 1 of this series (I originally just wanted to write one post and realized the scope is way too big), we’ll cover:
- How async code interfaces with synchronous code in Python
- How to convert synchronous code to asynchronous code, including how to prevent blocking of the event loop via custom
ThreadPoolExecutor
- How to use
asyncio
to run multiple tasks concurrently
Basic example, async hello world
import asyncio async def hello_world(): asyncio.sleep(1) print("Hello world") asyncio.run(hello_world()) >>> Hello world
Running two async functions in parallel
import asyncio async def foo(): while True: asyncio.sleep(1) print("foo") async def bar(): while True: asyncio.sleep(1) print("bar") asyncio.run(asyncio.gather(foo(), bar()))
What if I have existing synchronous methods?
We can wrap a synchronous function in an async function, an example implementation would be a decorator (i love decorators, btw):
def async_wrap( loop: Optional[asyncio.BaseEventLoop] = None, executor: Optional[Executor] = None ) -> Callable: def _async_wrap(func: Callable) -> Callable: @wraps(func) async def run(*args, loop=loop, executor=executor, **kwargs): if loop is None: loop = asyncio.get_event_loop() pfunc = partial(func, *args, **kwargs) return await loop.run_in_executor(executor, pfunc) return run return _async_wrap
The above decorator is a higher order decorator (it takes arguments and then generates another decorator), example usage is the following:
import asyncio import time @async_wrap() def foo(): while True: time.sleep(1) print("foo from sync") async def bar(): while True: asyncio.sleep(1) print("bar from async") asyncio.run(asyncio.gather(foo(), bar()))
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What is copiable?
What is copiable anyway?
Python is garbage collected and has a reference counting system. This means that when you create an object, it is stored in memory and a reference to it is stored in a variable. When you assign a variable to another variable, the reference count for the object is incremented. When you delete a variable, the reference count is decremented. When the reference count reaches zero, the object is deleted from memory.
This is a very simple explanation of how Python works. There are many more details that I will not go into here. The point is that when you assign a variable to another variable, you are not creating a copy of the object. You are creating a new reference to the same object. This is important to understand because it can lead to some unexpected behavior.
Questions I had:
- What happens when you assign a variable to another variable?
- What happens when you return a complex object (i.e. a class) as part of a tuple from a function?
- What happens when you spin up a subprocess, call a method you defined in one class, and give it an object as an argument?
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Dependency injection in Python
Since Python type hints are introduced, they have made complex Python code-bases much more readable and easier to maintain - especially combined with newer static analysis tools such as
mypy
orpylint
. However, even with these tools, Python is still a dynamic language. When using a dynamic language on a larger application (>5k LOC), the ability to do whatever we wanted any where and any time is more of a curse than a blessing.In this post, I wanted to discuss several options of implementing loosely coupled code in large Python codebases that I have played around with and the final solution of dependency injection based pattern I ended up deciding on.
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Javascript oddities
A collection of weird things in Javascript:
1.
var
scoping rulesfor (var i = 0; i < 3; ++i) { const log = () => { console.log(`a ${i}`); } setTimeout(log, 100); } for (let i = 0; i < 3; ++i) { const log = () => { console.log(`b ${i}`); } setTimeout(log, 100); }
The output here is:
"a 3" "a 3" "a 3" "b 0" "b 1" "b 2"
Why does
var
cause it to print 3?2.
const
in Javascript does not mean the same as C/C++. Example:const value = 3; value = 4; // error, cannot override a constant value += 3; // error const obj = {a : 3}; obj.a += 3; //allowed obj.a = 5; //allowed
Turns out
const
in Javascript is more of a “const” reference likeconst &
in C++. It does not mean the value itself is constant - just the reference to the array cannot be changed.3. Converting time formats can be tricky
Suppose you have a time in
yyyy-mm-DD
format and you want it inmm/DD/yyyy
format.new Date('2016-06-05'). toLocaleString('en-us', {year: 'numeric', month: '2-digit', day: '2-digit'}) // Output: >>> '06/04/2016'
Wait, what happened?, I asked for 2016-06-05 in
mm/dd/YYYY
but it gave me06/04/2016
instead! This because all dates by default assumes it’s GMT time, when you convert it to a local timezone, you might get a different date.The
moment
library fortunately makes this a lot easier.var date = new Date('10/01/2021'); var formattedDate = moment(date).format('YYYY-MM-DD');
If we don’t want some extra dependency, it’s probably easier to just not convert the date into a Javascript
Date
obj and directly do string operations on it to get it to the format you want. Example:function reformatDateString(dateString) { //reformat date string to from YYYY-MM-DD to MM/DD/YYYY if (dateString && dateString.indexOf('-') > -1) { const dateParts = dateString.split('-'); return `${dateParts[1]}/${dateParts[2]}/${dateParts[0]}`; } return dateString; }
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Rust like enums in C++
While browsing the excellent modern C++ reactive console UI library FTXUI, I noticed this piece of code in how the events are typed/handled.
// event.hpp struct Event { // --- Constructor section --------------------------------------------------- static Event Character(char); static Event CursorReporting(std::string, int x, int y); // Other constructor methods // --- Arrow --- static const Event ArrowLeft; static const Event ArrowRight; static const Event ArrowUp; static const Event ArrowDown; // .... Other definitions etc....
My immediate reaction to this is that this feels weird - partially because I am not used to the author’s C++ style, in addition, the combination of static member variables sharing the parent-type, and static methods for constructors are really confusing to read.
Let’s dive into it to see how things work.
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Python testing ecosystem
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Common, stupid, but non-obvious C++ mistakes I made
Internet consensus tends to label C++ as a hard language; I like to think Cpp is a “deep” language. There are always rooms for improvement - doesn’t matter how long you have been coding in C++. The expressiveness and the depth of the language is double-edged. It is what makes C++ great, but also makes it daunting for new users. These are the mistakes I’ve made in my daily usage of C++. I hope they can be useful for other people to avoid them in the future.
1. Capture by reference on transient objects
Callbacks (lambda functions, function pointers, functors, or
std::bind
on static functions) are a common paradigm when you work with message queues, thread pools, or event based systems. Lambda and closures give you a lot of power - but too much power could often cause problems, consider the following code:
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Rich D3 interactivity in Jekyll posts
This is me trying to reproduce the results in this Stack Overflow post and Dan Cole’s blog.
Things to keep in mind:
- Include morley.csv (Search local JavaScript for
/morley.csv
) - Include D3.js
- Include box.js
- Include the local CSS and JavaScript
Check out the code for this post on GitHub.
- Include morley.csv (Search local JavaScript for
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Maximize the luck surface area
Hacker News recently had a post about “maximizing luck surface area”. Jason Roberts made a point that seemingly random success that people experience that most people attributes to luck, is really a game of probability.
While most people follow the axiom of doing “good work” by spending years going through school, honing their craft, and then practicing those crafts and skills to perfection at work (cough, programming languages, frameworks, design patterns, algorithms, etc.) Many people neglects the other important pre-requisites to success - letting others know about your work and building up the reputation for network effects.