dealing with python reactively - pycon korea 2017

Dealing with Python Reactively PyCon Korea 2017

Kenneth

SPEAKER

Editor

Dev team leader

Kenneth

INTRODUCTION

Ractive Progamming

RxPy

Coroutine / Generator

CH1.

CH2.

CH3.

CH1.

Reactive Programming

What is reactive programming?

CH 1. Reactive Programming

The new paradigm that focused on the asynchronous data flow

Difficult

Key keyword #1


Asynchronous It can be received the data that is not guaranteed

In the asynchronous environment sequentially, And also it can be processed the data flexible.

Key keyword #2


Reactive It is not executed if there is no operation (input) from the outside.

Key keyword #3


Lightweight Data flow can be subdivided for each purpose,

And also it can be merged again. This makes it possible to reduce the weight.

The data flows.


However it does not flow at the same time. So we can not guarantee that the data always comes.


It can not be easy to process the data that comes every another times.


.next() .subscribe()

Controlling multiple data flows. Reactive programming makes it possible.


The data that comes from each different times concurrently,

To be sequentially

It only see the data flows. That’s why it’s intuitive.


Map

Filter

Merge Reduce

On Complete

On Error Retry

Skip Buffer

CH2.

RxPy

ReactiveX (RX)

CH 2. RxPy

Microsoft announced `Volta` project in 2007

It is officially known as `Reactive Extensions` in 2009

It was gradually released as open source since 2012

def observer_generator(observer): # It passes the string “hello” through the observer. observer.on_next("hello") # Likewise, it passes the string "world!" through the observer. observer.on_next("world!") def main(): # Create an observer, passes it to a predefined function, and receives an object that can receive it. observable = Observable.create(observer_generator) # Receive the observer, At this time the observer read the variable that passed at on_next. # Oh! After the below subscribe is started, the above observer_generator will be executed. observable.subscribe(on_next=lambda value: print(value))

hello_world.py

observable.create (Create an observer)

observer_generator

observer

def observer_generator(observer): # It passes the string “hello” through the observer. observer.on_next("hello") # Likewise, it passes the string "world!" through the observer. observer.on_next("world!") def main(): # Create an observer, passes it to a predefined function, and receives an object that can receive it. observable = Observable.create(observer_generator) # Receive the observer, At this time the observer read the variable that passed at on_next. # Oh! After the below subscribe is started, the above observer_generator will be executed. observable.subscribe(on_next=lambda value: print(value))

hello_world.py

observable

1. next(‘hello’)

2. next(‘world!’)

observer

1. print(‘hello’)

2. print(‘world!’)

It passes the data through on_next

Print the message that received from on_next

from rx import Observable, Observer class PrintObserver(Observer): def on_next(self, value): print('on_next value:%s’ % (value)) def on_completed(self): print('on_completed !') def on_error(self, value): print('on_error value:%s’ % (value)) def observer_generator(observer): observer.on_next(“break") observer.on_next(“the ice!") while True: message = input() if message: observer.on_next(message) else: observer.on_completed() break def main(): observable = Observable.create(observer_generator) observable.subscribe(PrintObserver())

ice_breaking.py

Observable (Data forwarder)

Observable (Data receiver)

subscribe()

next(‘break’)

next(‘the ice!’)

next(‘message’)

print()

print()

print()

on_next

on_next

on_next

completed()

on_completed print()

1. You can expand incoming messages by using Predefined object in Subscribe method.

CH3.

Coroutine / Generator

Coroutine?

CH 3. Coroutine / Generator

Unlike functions, Routines whose parents are

“equivalent” to the called function.

Python only: Coroutine can process only the received data.

Coroutine vs General routine


General Routine

Function call

Return

Parameters

Result

Coroutine

Function call

Parameters

Yield

Yield

Yield

Main code Main code

Calculating

Calculating

Calculating

Send

Send

Send

Use Case


Init Data

Caller Coroutine

2. Wait for the caller's input via yield (Will be returned to caller code lines)

1. Inserting initial data to apply to coroutines

3. If you have input to the caller, return to the coroutine code and execute the logic.

If yield appears in the logic, it returns to the parent code again.

(repeat)

next()

Yield

Yield

4. Finally, the caller ends the coroutine. Close

Generator?


If Coroutine is a gourmand, The Generator is the giving tree.

A generator is a `generator` that generates data through yield.

Do you know range function?


def main(): # Use the range function to insert each value from 0 to 2 # into value and repeat it three times. for value in range(3): print(u’current value %d' % (value)) OUTPUT: current_value 0 current_value 1 current_value 2

Making range function by using Generator.


# It is the range function created by using the Generator. def custom_range(number): index = 0 while(index < number): # At this point, we go back to the parent that called this function, # Pass the value, and proceed to the parent's logic until we call this function again. # This is the heart of the generator. Remember this! yield index index += 1

coroutine_generator.py

def main(): # Let's try to use the existing range function. for value in range(3): print(u'original range %d' % (value)) # Insert a line escaping for the division. print(u'\n') # Let's try the function we just created. for value in custom_range(3): print(u'custom range %d' % (value))

OUTPUT original range 0 original range 1 original range 2 custom range 0 custom range 1 custom range 2

Use Case


Large Data

Memory

Process

yield

1. In a real-time cursor, yield returns

every 500 datasets.

2. Actually there are only 500 data in memory, so there is out of memory problem.

3. When 500 data are processed and the process is finished,

500 data will be emptied from the memory. Likewise, there is no out of memory problem.

4. At the end of the process, it again gets 500 data from Large Data.

Conclusion about Generator


Data that comes in real time can be used to stabilize

the memory by using the generator! (Performance difference is insignificant)

What is the difference Between Coroutine and Generator?


Corutine

Function call

Parameters

Yield

Yield

Yield

Main code

Calculating

Calculating

Calculating

Send

Send

Generator

Function call

Parameters

Yield

Yield

Yield

Main code

Calculating

Calculating

Calculating

Return

Return

Return

Send

Question

Thank you

[email protected] http://github.com/KennethanCeyer/pycon-kr-2017

Email GitHub

dealing with python reactively - pycon korea 2017

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