JavaScript Tutorial for Beginners: Thread & Call Stack

JavaScript Under The Hood: Thread & Call Stack

In this tutorial, we will talk about JavaScript's main thread as well as how the call stack works.

Call stack

A call stack is a mechanism for an interpreter (like the JavaScript interpreter in a web browser) to keep track of its place in a script that calls multiple functions — what function is currently being run and what functions are called from within that function, etc.

  • When a script calls a function, the interpreter adds it to the call stack and then starts carrying out the function.
  • Any functions that are called by that function are added to the call stack further up, and run where their calls are reached.
  • When the current function is finished, the interpreter takes it off the stack and resumes execution where it left off in the last code listing.
  • If the stack takes up more space than it had assigned to it, it results in a "stack overflow" error.


function greeting() {
   // [1] Some code here
   // [2] Some code here
function sayHi() {
   return "Hi!";

// Invoke the `greeting` function

// [3] Some code here

The code above would be executed like this:

  1. Ignore all functions, until it reaches the greeting() function invocation.
  2. Add the greeting() function to the call stack list.

Note: Call stack list: - greeting

  1. Execute all lines of code inside the greeting() function.
  2. Get to the sayHi() function invocation.
  3. Add the sayHi() function to the call stack list.

Note: Call stack list: - sayHi - greeting

  1. Execute all lines of code inside the sayHi() function, until reaches its end.
  2. Return execution to the line that invoked sayHi() and continue executing the rest of the greeting() function.
  3. Delete the sayHi() function from our call stack list.

Note: Call stack list: - greeting

  1. When everything inside the greeting() function has been executed, return to its invoking line to continue executing the rest of the JS code.
  2. Delete the greeting() function from the call stack list.

Note: Call stack list: EMPTY

In summary, then, we start with an empty Call Stack. Whenever we invoke a function, it is automatically added to the Call Stack. Once the function has executed all of its code, it is automatically removed from the Call Stack. Ultimately, the Stack is empty again.


Thread in computer science is the execution of running multiple tasks or programs at the same time. Each unit capable of executing code is called a thread.

The main thread is the one used by the browser to handle user events, render and paint the display, and to run the majority of the code that comprises a typical web page or app. Because these things are all happening in one thread, a slow website or app script slows down the entire browser; worse, if a site or app script enters an infinite loop, the entire browser will hang. This results in a frustrating, sluggish (or worse) user experience.

However, modern JavaScript offers ways to create additional threads, each executing independently while possibly communicating between one another. This is done using technologies such as web workers, which can be used to spin off a sub-program which runs concurrently with the main thread in a thread of its own. This allows slow, complex, or long-running tasks to be executed independently of the main thread, preserving the overall performance of the site or app—as well as that of the browser overall. This also allows individuals to take advantage of modern multi-core processors.

A special type of worker, called a service worker, can be created which can be left behind by a site—with the user's permission—to run even when the user isn't currently using that site. This is used to create sites capable of notifying the user when things happen while they're not actively engaged with a site. Such as notifying a user they have received new email even though they're not currently logged into their mail service.

Overall it can be observed these threads within our operating system are extremely helpful. They help minimize the context switching time, enables more efficient communication and allows further use of the multiprocessor architecture.

#javascript #programming 

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JavaScript Tutorial for Beginners: Thread & Call Stack

Pyringe: Debugger Capable Of Attaching to & Injecting Code Into Python

DISCLAIMER: This is not an official google project, this is just something I wrote while at Google.


What this is

Pyringe is a python debugger capable of attaching to running processes, inspecting their state and even of injecting python code into them while they're running. With pyringe, you can list threads, get tracebacks, inspect locals/globals/builtins of running functions, all without having to prepare your program for it.

What this is not

A "Google project". It's my internship project that got open-sourced. Sorry for the confusion.

What do I need?

Pyringe internally uses gdb to do a lot of its heavy lifting, so you will need a fairly recent build of gdb (version 7.4 onwards, and only if gdb was configured with --with-python). You will also need the symbols for whatever build of python you're running.
On Fedora, the package you're looking for is python-debuginfo, on Debian it's called python2.7-dbg (adjust according to version). Arch Linux users: see issue #5, Ubuntu users can only debug the python-dbg binary (see issue #19).
Having Colorama will get you output in boldface, but it's optional.

How do I get it?

Get it from the Github repo, PyPI, or via pip (pip install pyringe).

Is this Python3-friendly?

Short answer: No, sorry. Long answer:
There's three potentially different versions of python in play here:

  1. The version running pyringe
  2. The version being debugged
  3. The version of your build of gdb was linked against

2 Is currently the dealbreaker here. Cpython has changed a bit in the meantime[1], and making all features work while debugging python3 will have to take a back seat for now until the more glaring issues have been taken care of.
As for 1 and 3, the 2to3 tool may be able to handle it automatically. But then, as long as 2 hasn't been taken care of, this isn't really a use case in the first place.

[1] - For example, pendingbusy (which is used for injection) has been renamed to busy and been given a function-local scope, making it harder to interact with via gdb.

Will this work with PyPy?

Unfortunately, no. Since this makes use of some CPython internals and implementation details, only CPython is supported. If you don't know what PyPy or CPython are, you'll probably be fine.

Why not PDB?

PDB is great. Use it where applicable! But sometimes it isn't.
Like when python itself crashes, gets stuck in some C extension, or you want to inspect data without stopping a program. In such cases, PDB (and all other debuggers that run within the interpreter itself) are next to useless, and without pyringe you'd be left with having to debug using print statements. Pyringe is just quite convenient in these cases.

I injected a change to a local var into a function and it's not showing up!

This is a known limitation. Things like inject('var = 2') won't work, but inject('var[1] = 1337') should. This is because most of the time, python internally uses a fast path for looking up local variables that doesn't actually perform the dictionary lookup in locals(). In general, code you inject into processes with pyringe is very different from a normal python function call.

How do I use it?

You can start the debugger by executing python -m pyringe. Alternatively:

import pyringe

If that reminds you of the code module, good; this is intentional.
After starting the debugger, you'll be greeted by what behaves almost like a regular python REPL.
Try the following:

==> pid:[None] #threads:[0] current thread:[None]
>>> help()
Available commands:
 attach: Attach to the process with the given pid.
 bt: Get a backtrace of the current position.
==> pid:[None] #threads:[0] current thread:[None]
>>> attach(12679)
==> pid:[12679] #threads:[11] current thread:[140108099462912]
>>> threads()
[140108099462912, 140108107855616, 140108116248323, 140108124641024, 140108133033728, 140108224739072, 140108233131776, 140108141426432, 140108241524480, 140108249917184, 140108269324032]

The IDs you see here correspond to what threading.current_thread().ident would tell you.
All debugger functions are just regular python functions that have been exposed to the REPL, so you can do things like the following.

==> pid:[12679] #threads:[11] current thread:[140108099462912]
>>> for tid in threads():
...   if not tid % 10:
...     thread(tid)
...     bt()
Traceback (most recent call last):
  File "/usr/lib/python2.7/", line 524, in __bootstrap
  File "/usr/lib/python2.7/", line 551, in __bootstrap_inner
  File "/usr/lib/python2.7/", line 504, in run
    self.__target(*self.__args, **self.__kwargs)
  File "./", line 46, in Idle
  File "./", line 40, in Wait
==> pid:[12679] #threads:[11] current thread:[140108241524480]

You can access the inferior's locals and inspect them like so:

==> pid:[12679] #threads:[11] current thread:[140108241524480]
>>> inflocals()
{'a': <proxy of A object at remote 0x1d9b290>, 'LOL': 'success!', 'b': <proxy of B object at remote 0x1d988c0>, 'n': 1}
==> pid:[12679] #threads:[11] current thread:[140108241524480]
>>> p('a')
<proxy of A object at remote 0x1d9b290>
==> pid:[12679] #threads:[11] current thread:[140108241524480]
>>> p('a').attr
==> pid:[12679] #threads:[11] current thread:[140108241524480]

And sure enough, the definition of a's class reads:

class Example(object):
  cl_attr = False
  def __init__(self):
    self.attr = 'Some_magic_string'

There's limits to how far this proxying of objects goes, and everything that isn't trivial data will show up as strings (like '<function at remote 0x1d957d0>').
You can inject python code into running programs. Of course, there are caveats but... see for yourself:

==> pid:[12679] #threads:[11] current thread:[140108241524480]
>>> inject('import threading')
==> pid:[12679] #threads:[11] current thread:[140108241524480]
>>> inject('print threading.current_thread().ident')
==> pid:[12679] #threads:[11] current thread:[140108241524480]

The output of my program in this case reads:


If you need additional pointers, just try using python's help (pyhelp() in the debugger) on debugger commands.

Author: google
Source Code:
License: Apache-2.0 License


Lowa Alice

Lowa Alice


JavaScript Tutorial for Beginners: Learn JavaScript in 1 Hour

Watch this JavaScript tutorial for beginners to learn JavaScript basics in one hour.
avaScript is one of the most popular programming languages in 2019. A lot of people are learning JavaScript to become front-end and/or back-end developers.

I’ve designed this JavaScript tutorial for beginners to learn JavaScript from scratch. We’ll start off by answering the frequently asked questions by beginners about JavaScript and shortly after we’ll set up our development environment and start coding.

Whether you’re a beginner and want to learn to code, or you know any programming language and just want to learn JavaScript for web development, this tutorial helps you learn JavaScript fast.

You don’t need any prior experience with JavaScript or any other programming languages. Just watch this JavaScript tutorial to the end and you’ll be writing JavaScript code in no time.

If you want to become a front-end developer, you have to learn JavaScript. It is the programming language that every front-end developer must know.

You can also use JavaScript on the back-end using Node. Node is a run-time environment for executing JavaScript code outside of a browser. With Node and Express (a popular JavaScript framework), you can build back-end of web and mobile applications.

If you’re looking for a crash course that helps you get started with JavaScript quickly, this course is for you.


00:00 What is JavaScript
04:41 Setting Up the Development Environment
07:52 JavaScript in Browsers
11:41 Separation of Concerns
13:47 JavaScript in Node
16:11 Variables
21:49 Constants
23:35 Primitive Types
26:47 Dynamic Typing
30:06 Objects
35:22 Arrays
39:41 Functions
44:22 Types of Functions

📺 The video in this post was made by Programming with Mosh
The origin of the article:
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#javascript #javascript tutorial #javascript tutorial for beginners #beginners

Hertha  Mayer

Hertha Mayer


Authentication In MEAN Stack - A Quick Guide

I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.

To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.

Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.

Angular demo runner
Online angular editor for building demo.

Let me show what I mean…

Template parser errors#

There are template parser errors that can be easy catched by stackblitz

It gives me some information but I want the error to be highlighted

#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js

wp codevo

wp codevo


JavaScript Shopping Cart - Javascript Project for Beginners

#shopping cart javascript #hopping cart with javascript #javascript shopping cart tutorial for beginners #javascript cart project #javascript tutorial #shopping cart

Variables Globales De Python: Cómo Definir Un Ejemplo De Variable Glob

En este artículo, aprenderá los conceptos básicos de las variables globales.

Para empezar, aprenderá cómo declarar variables en Python y qué significa realmente el término 'ámbito de variable'.

Luego, aprenderá las diferencias entre variables locales y globales y comprenderá cómo definir variables globales y cómo usar la globalpalabra clave.

¿Qué son las variables en Python y cómo se crean? Una introducción para principiantes

Puede pensar en las variables como contenedores de almacenamiento .

Son contenedores de almacenamiento para almacenar datos, información y valores que le gustaría guardar en la memoria de la computadora. Luego puede hacer referencia a ellos o incluso manipularlos en algún momento a lo largo de la vida del programa.

Una variable tiene un nombre simbólico y puede pensar en ese nombre como la etiqueta en el contenedor de almacenamiento que actúa como su identificador.

El nombre de la variable será una referencia y un puntero a los datos almacenados en su interior. Por lo tanto, no es necesario recordar los detalles de sus datos e información; solo necesita hacer referencia al nombre de la variable que contiene esos datos e información.

Al dar un nombre a una variable, asegúrese de que sea descriptivo de los datos que contiene. Los nombres de las variables deben ser claros y fácilmente comprensibles tanto para usted en el futuro como para los otros desarrolladores con los que puede estar trabajando.

Ahora, veamos cómo crear una variable en Python.

Al declarar variables en Python, no necesita especificar su tipo de datos.

Por ejemplo, en el lenguaje de programación C, debe mencionar explícitamente el tipo de datos que contendrá la variable.

Entonces, si quisiera almacenar su edad, que es un número entero, o inttipo, esto es lo que tendría que hacer en C:

#include <stdio.h>
int main(void)
  int age = 28;
  // 'int' is the data type
  // 'age' is the name 
  // 'age' is capable of holding integer values
  // positive/negative whole numbers or 0
  // '=' is the assignment operator
  // '28' is the value

Sin embargo, así es como escribirías lo anterior en Python:

age = 28

#'age' is the variable name, or identifier
# '=' is the assignment operator
#'28' is the value assigned to the variable, so '28' is the value of 'age'

El nombre de la variable siempre está en el lado izquierdo y el valor que desea asignar va en el lado derecho después del operador de asignación.

Tenga en cuenta que puede cambiar los valores de las variables a lo largo de la vida de un programa:

my_age = 28

print(f"My age in 2022 is {my_age}.")

my_age = 29

print(f"My age in 2023 will be {my_age}.")


#My age in 2022 is 28.
#My age in 2023 will be 29.

Mantienes el mismo nombre de variable my_age, pero solo cambias el valor de 28a 29.

¿Qué significa el alcance variable en Python?

El alcance de la variable se refiere a las partes y los límites de un programa de Python donde una variable está disponible, accesible y visible.

Hay cuatro tipos de alcance para las variables de Python, que también se conocen como la regla LEGB :

  • local ,
  • Encerrando ,
  • globales ,
  • Incorporado .

En el resto de este artículo, se centrará en aprender a crear variables con alcance global y comprenderá la diferencia entre los alcances de variables locales y globales.

Cómo crear variables con alcance local en Python

Las variables definidas dentro del cuerpo de una función tienen alcance local , lo que significa que solo se puede acceder a ellas dentro de esa función en particular. En otras palabras, son 'locales' para esa función.

Solo puede acceder a una variable local llamando a la función.

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#call function


#The best place to learn to code is with freeCodeCamp!

Mire lo que sucede cuando trato de acceder a esa variable con un alcance local desde fuera del cuerpo de la función:

def learn_to_code():
    #create local variable
    coding_website = "freeCodeCamp"
    print(f"The best place to learn to code is with {coding_website}!")

#try to print local variable 'coding_website' from outside the function


#NameError: name 'coding_website' is not defined

Plantea un NameErrorporque no es 'visible' en el resto del programa. Solo es 'visible' dentro de la función donde se definió.

Cómo crear variables con alcance global en Python

Cuando define una variable fuera de una función, como en la parte superior del archivo, tiene un alcance global y se conoce como variable global.

Se accede a una variable global desde cualquier parte del programa.

Puede usarlo dentro del cuerpo de una función, así como acceder desde fuera de una función:

#create a global variable
coding_website = "freeCodeCamp"

def learn_to_code():
    #access the variable 'coding_website' inside the function
    print(f"The best place to learn to code is with {coding_website}!")

#call the function

#access the variable 'coding_website' from outside the function


#The best place to learn to code is with freeCodeCamp!

¿Qué sucede cuando hay una variable global y local, y ambas tienen el mismo nombre?

#global variable
city = "Athens"

def travel_plans():
    #local variable with the same name as the global variable
    city = "London"
    print(f"I want to visit {city} next year!")

#call function - this will output the value of local variable

#reference global variable - this will output the value of global variable
print(f"I want to visit {city} next year!")


#I want to visit London next year!
#I want to visit Athens next year!

En el ejemplo anterior, tal vez no esperaba ese resultado específico.

Tal vez pensaste que el valor de citycambiaría cuando le asignara un valor diferente dentro de la función.

Tal vez esperabas que cuando hice referencia a la variable global con la línea print(f" I want to visit {city} next year!"), la salida sería en #I want to visit London next year!lugar de #I want to visit Athens next year!.

Sin embargo, cuando se llamó a la función, imprimió el valor de la variable local.

Luego, cuando hice referencia a la variable global fuera de la función, se imprimió el valor asignado a la variable global.

No interfirieron entre sí.

Dicho esto, usar el mismo nombre de variable para variables globales y locales no se considera una buena práctica. Asegúrese de que sus variables no tengan el mismo nombre, ya que puede obtener algunos resultados confusos cuando ejecute su programa.

Cómo usar la globalpalabra clave en Python

¿Qué sucede si tiene una variable global pero desea cambiar su valor dentro de una función?

Mira lo que sucede cuando trato de hacer eso:

#global variable
city = "Athens"

def travel_plans():
    #First, this is like when I tried to access the global variable defined outside the function. 
    # This works fine on its own, as you saw earlier on.
    print(f"I want to visit {city} next year!")

    #However, when I then try to re-assign a different value to the global variable 'city' from inside the function,
    #after trying to print it,
    #it will throw an error
    city = "London"
    print(f"I want to visit {city} next year!")

#call function


#UnboundLocalError: local variable 'city' referenced before assignment

Por defecto, Python piensa que quieres usar una variable local dentro de una función.

Entonces, cuando intento imprimir el valor de la variable por primera vez y luego reasignar un valor a la variable a la que intento acceder, Python se confunde.

La forma de cambiar el valor de una variable global dentro de una función es usando la globalpalabra clave:

#global variable
city = "Athens"

#print value of global variable
print(f"I want to visit {city} next year!")

def travel_plans():
    global city
    #print initial value of global variable
    print(f"I want to visit {city} next year!")
    #assign a different value to global variable from within function
    city = "London"
    #print new value
    print(f"I want to visit {city} next year!")

#call function

#print value of global variable
print(f"I want to visit {city} next year!")

Utilice la globalpalabra clave antes de hacer referencia a ella en la función, ya que obtendrá el siguiente error: SyntaxError: name 'city' is used prior to global declaration.

Anteriormente, vio que no podía acceder a las variables creadas dentro de las funciones ya que tienen un alcance local.

La globalpalabra clave cambia la visibilidad de las variables declaradas dentro de las funciones.

def learn_to_code():
   global coding_website
   coding_website = "freeCodeCamp"
   print(f"The best place to learn to code is with {coding_website}!")

#call function

#access variable from within the function


#The best place to learn to code is with freeCodeCamp!


¡Y ahí lo tienes! Ahora conoce los conceptos básicos de las variables globales en Python y puede distinguir las diferencias entre las variables locales y globales.

Espero que hayas encontrado útil este artículo.

Comenzará desde lo básico y aprenderá de una manera interactiva y amigable para principiantes. También construirá cinco proyectos al final para poner en práctica y ayudar a reforzar lo que ha aprendido.

¡Gracias por leer y feliz codificación!