Meaning of the Word Yield

Meaning of the word yield

The word yield doesn't really have any special meaning in the context of Ruby. It means the same thing as it means in every other programming language, or in programming and computer science in general.

It is usually used when some kind of execution context surrenders control flow to a different execution context. For example, in Unix, there is a sched_yield function which a thread can use to give up the CPU to another thread (or process). With coroutines, the term yield is generally used to transfer control from one coroutine to another. In C#, there is a yield keyword, which is used by an iterator method to give up control to the iterating method.

And in fact, this last usage is exactly identical to the usage of the Enumerator::Yielder#yield method in Ruby, which you were asking about. Calling this method will suspend the enumerator and give up control to the enumerating method.

Example:

fibs = Enumerator.new do |y|
  a, b = 0, 1
  y.yield a
  loop do
    y.yield b
    a, b = b, a + b
  end
end

puts fibs.next #  0
puts fibs.next #  1
puts fibs.next #  1
puts fibs.next #  2
puts fibs.next #  3
puts fibs.next #  5
puts fibs.next #  8
puts fibs.next # 13
puts fibs.next # 21

As you see, there is an infinite loop. Obviously, if this loop just ran on its own, it wouldn't be of much use. But since every time it hits the yield method, it gives up control until it is called again, this will produce the Fibonacci numbers one by one, essentially representing an infinitely long list of all Fibonacci numbers.

There is another method, Fiber.yield, which serves a similar purpose. (In fact, I already described it above, because Fiber is just Ruby's name for coroutines.) Inside a Fiber, you call Fiber.yield to give up control back to the execution context that originally gave control to you.

Lastly, there is the yield keyword, which is used inside a method body to give up control to the block that was passed into the method.

Note that, at least in the Enumerator case (i.e. the first example), you can additionally interpret yield as to produce, since the Enumerator produces a new value, every time it calls yield.

What does the yield keyword do?

To understand what yield does, you must understand what generators are. And before you can understand generators, you must understand iterables.

Iterables

When you create a list, you can read its items one by one. Reading its items one by one is called iteration:

>>> mylist = [1, 2, 3]
>>> for i in mylist:
...    print(i)
1
2
3

mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:

>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
...    print(i)
0
1
4

Everything you can use "for... in..." on is an iterable; lists, strings, files...

These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.

Generators

Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:

>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
...    print(i)
0
1
4

It is just the same except you used () instead of []. BUT, you cannot perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.

Yield

yield is a keyword that is used like return, except the function will return a generator.

>>> def create_generator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = create_generator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object create_generator at 0xb7555c34>
>>> for i in mygenerator:
...     print(i)
0
1
4

Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.

To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky.

Then, your code will continue from where it left off each time for uses the generator.

Now the hard part:

The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it'll return the first value of the loop. Then, each subsequent call will run another iteration of the loop you have written in the function and return the next value. This will continue until the generator is considered empty, which happens when the function runs without hitting yield. That can be because the loop has come to an end, or because you no longer satisfy an "if/else".

Your code explained

Generator:

# Here you create the method of the node object that will return the generator
def _get_child_candidates(self, distance, min_dist, max_dist):

    # Here is the code that will be called each time you use the generator object:

    # If there is still a child of the node object on its left
    # AND if the distance is ok, return the next child
    if self._leftchild and distance - max_dist < self._median:
        yield self._leftchild

    # If there is still a child of the node object on its right
    # AND if the distance is ok, return the next child
    if self._rightchild and distance + max_dist >= self._median:
        yield self._rightchild

    # If the function arrives here, the generator will be considered empty
    # there are no more than two values: the left and the right children

Caller:

# Create an empty list and a list with the current object reference
result, candidates = list(), [self]

# Loop on candidates (they contain only one element at the beginning)
while candidates:

    # Get the last candidate and remove it from the list
    node = candidates.pop()

    # Get the distance between obj and the candidate
    distance = node._get_dist(obj)

    # If the distance is ok, then you can fill in the result
    if distance <= max_dist and distance >= min_dist:
        result.extend(node._values)

    # Add the children of the candidate to the candidate's list
    # so the loop will keep running until it has looked
    # at all the children of the children of the children, etc. of the candidate
    candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))

return result

This code contains several smart parts:

The loop iterates on a list, but the list expands while the loop is being iterated. It's a concise way to go through all these nested data even if it's a bit dangerous since you can end up with an infinite loop. In this case, candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) exhausts all the values of the generator, but while keeps creating new generator objects which will produce different values from the previous ones since it's not applied on the same node.
The extend() method is a list object method that expects an iterable and adds its values to the list.

Usually, we pass a list to it:

>>> a = [1, 2]
>>> b = [3, 4]
>>> a.extend(b)
>>> print(a)
[1, 2, 3, 4]

But in your code, it gets a generator, which is good because:

You don't need to read the values twice.
You may have a lot of children and you don't want them all stored in memory.

And it works because Python does not care if the argument of a method is a list or not. Python expects iterables so it will work with strings, lists, tuples, and generators! This is called duck typing and is one of the reasons why Python is so cool. But this is another story, for another question...

You can stop here, or read a little bit to see an advanced use of a generator:

Controlling a generator exhaustion

>>> class Bank(): # Let's create a bank, building ATMs
...    crisis = False
...    def create_atm(self):
...        while not self.crisis:
...            yield "$100"
>>> hsbc = Bank() # When everything's ok the ATM gives you as much as you want
>>> corner_street_atm = hsbc.create_atm()
>>> print(corner_street_atm.next())
$100
>>> print(corner_street_atm.next())
$100
>>> print([corner_street_atm.next() for cash in range(5)])
['$100', '$100', '$100', '$100', '$100']
>>> hsbc.crisis = True # Crisis is coming, no more money!
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> wall_street_atm = hsbc.create_atm() # It's even true for new ATMs
>>> print(wall_street_atm.next())
<type 'exceptions.StopIteration'>
>>> hsbc.crisis = False # The trouble is, even post-crisis the ATM remains empty
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> brand_new_atm = hsbc.create_atm() # Build a new one to get back in business
>>> for cash in brand_new_atm:
...    print cash
$100
$100
$100
$100
$100
$100
$100
$100
$100
...

Note: For Python 3, useprint(corner_street_atm.__next__()) or print(next(corner_street_atm))

It can be useful for various things like controlling access to a resource.

Itertools, your best friend

The itertools module contains special functions to manipulate iterables. Ever wish to duplicate a generator?
Chain two generators? Group values in a nested list with a one-liner? Map / Zip without creating another list?

Then just import itertools.

An example? Let's see the possible orders of arrival for a four-horse race:

>>> horses = [1, 2, 3, 4]
>>> races = itertools.permutations(horses)
>>> print(races)
<itertools.permutations object at 0xb754f1dc>
>>> print(list(itertools.permutations(horses)))
[(1, 2, 3, 4),
 (1, 2, 4, 3),
 (1, 3, 2, 4),
 (1, 3, 4, 2),
 (1, 4, 2, 3),
 (1, 4, 3, 2),
 (2, 1, 3, 4),
 (2, 1, 4, 3),
 (2, 3, 1, 4),
 (2, 3, 4, 1),
 (2, 4, 1, 3),
 (2, 4, 3, 1),
 (3, 1, 2, 4),
 (3, 1, 4, 2),
 (3, 2, 1, 4),
 (3, 2, 4, 1),
 (3, 4, 1, 2),
 (3, 4, 2, 1),
 (4, 1, 2, 3),
 (4, 1, 3, 2),
 (4, 2, 1, 3),
 (4, 2, 3, 1),
 (4, 3, 1, 2),
 (4, 3, 2, 1)]

Understanding the inner mechanisms of iteration

Iteration is a process implying iterables (implementing the __iter__() method) and iterators (implementing the __next__() method).
Iterables are any objects you can get an iterator from. Iterators are objects that let you iterate on iterables.

There is more about it in this article about how for loops work.

What is the yield keyword used for in C#?

The yield contextual keyword actually does quite a lot here.

The function returns an object that implements the IEnumerable<object> interface. If a calling function starts foreaching over this object, the function is called again until it "yields". This is syntactic sugar introduced in C# 2.0. In earlier versions you had to create your own IEnumerable and IEnumerator objects to do stuff like this.

The easiest way understand code like this is to type-in an example, set some breakpoints and see what happens. Try stepping through this example:

public void Consumer()
{
    foreach(int i in Integers())
    {
        Console.WriteLine(i.ToString());
    }
}

public IEnumerable<int> Integers()
{
    yield return 1;
    yield return 2;
    yield return 4;
    yield return 8;
    yield return 16;
    yield return 16777216;
}

When you step through the example, you'll find the first call to Integers() returns 1. The second call returns 2 and the line yield return 1 is not executed again.

Here is a real-life example:

public IEnumerable<T> Read<T>(string sql, Func<IDataReader, T> make, params object[] parms)
{
    using (var connection = CreateConnection())
    {
        using (var command = CreateCommand(CommandType.Text, sql, connection, parms))
        {
            command.CommandTimeout = dataBaseSettings.ReadCommandTimeout;
            using (var reader = command.ExecuteReader())
            {
                while (reader.Read())
                {
                    yield return make(reader);
                }
            }
        }
    }
}

What are the main uses of yield(), and how does it differ from join() and interrupt()?

Source: http://www.javamex.com/tutorials/threads/yield.shtml

Windows

In the Hotspot implementation, the way that Thread.yield() works has
changed between Java 5 and Java 6.

In Java 5, Thread.yield() calls the Windows API call Sleep(0). This
has the special effect of clearing the current thread's quantum and
putting it to the end of the queue for its priority level. In other
words, all runnable threads of the same priority (and those of greater
priority) will get a chance to run before the yielded thread is next
given CPU time. When it is eventually re-scheduled, it will come back
with a full full quantum, but doesn't "carry over" any of the
remaining quantum from the time of yielding. This behaviour is a
little different from a non-zero sleep where the sleeping thread
generally loses 1 quantum value (in effect, 1/3 of a 10 or 15ms tick).

In Java 6, this behaviour was changed. The Hotspot VM now implements
Thread.yield() using the Windows SwitchToThread() API call. This call
makes the current thread give up its current timeslice, but not its
entire quantum. This means that depending on the priorities of other
threads, the yielding thread can be scheduled back in one interrupt
period later. (See the section on thread scheduling for more
information on timeslices.)

Linux

Under Linux, Hotspot simply calls sched_yield(). The consequences of
this call are a little different, and possibly more severe than under
Windows:

a yielded thread will not get another slice of CPU until all other threads have had a slice of CPU;

(at least in kernel 2.6.8 onwards), the fact that the thread has yielded is implicitly taken into account by the scheduler's heuristics
on its recent CPU allocation— thus, implicitly, a thread that has
yielded could be given more CPU when scheduled in the future.

(See the section on thread scheduling for more details on priorities
and scheduling algorithms.)

When to use yield()?

I would say practically never. Its behaviour isn't standardly defined
and there are generally better ways to perform the tasks that you
might want to perform with yield():

if you're trying to use only a portion of the CPU, you can do this in a more controllable way by estimating how much CPU the thread
has used in its last chunk of processing, then sleeping for some
amount of time to compensate: see the sleep() method;

if you're waiting for a process or resource to complete or become available, there are more efficient ways to accomplish this,
such as by using join() to wait for another thread to complete, using
the wait/notify mechanism to allow one thread to signal to another
that a task is complete, or ideally by using one of the Java 5
concurrency constructs such as a Semaphore or blocking queue.

What does 'yield' keyword do in flutter?

yield adds a value to the output stream of the surrounding async* function. It's like return, but doesn't terminate the function.

See https://dart.dev/guides/language/language-tour#generators

Stream asynchronousNaturalsTo(n) async* {
  int k = 0;
  while (k < n) yield k++;
}

When the yield statement executes, it adds the result of evaluating its expression to the stream. It doesn’t necessarily suspend (though in the current implementations it does).

What's the yield keyword in JavaScript?

The MDN documentation is pretty good, IMO.

The function containing the yield keyword is a generator. When you call it, its formal parameters are bound to actual arguments, but its body isn't actually evaluated. Instead, a generator-iterator is returned. Each call to the generator-iterator's next() method performs another pass through the iterative algorithm. Each step's value is the value specified by the yield keyword. Think of yield as the generator-iterator version of return, indicating the boundary between each iteration of the algorithm. Each time you call next(), the generator code resumes from the statement following the yield.

What does the yield keyword do?

To understand what yield does, you must understand what generators are. And before you can understand generators, you must understand iterables.

Iterables

When you create a list, you can read its items one by one. Reading its items one by one is called iteration:

>>> mylist = [1, 2, 3]
>>> for i in mylist:
...    print(i)
1
2
3

mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:

>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
...    print(i)
0
1
4

Everything you can use "for... in..." on is an iterable; lists, strings, files...

These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.

Generators

Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:

>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
...    print(i)
0
1
4

Yield

yield is a keyword that is used like return, except the function will return a generator.

>>> def create_generator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = create_generator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object create_generator at 0xb7555c34>
>>> for i in mygenerator:
...     print(i)
0
1
4

Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.

Then, your code will continue from where it left off each time for uses the generator.

Now the hard part:

Your code explained

Generator:

# Here you create the method of the node object that will return the generator
def _get_child_candidates(self, distance, min_dist, max_dist):

    # Here is the code that will be called each time you use the generator object:

    # If there is still a child of the node object on its left
    # AND if the distance is ok, return the next child
    if self._leftchild and distance - max_dist < self._median:
        yield self._leftchild

    # If there is still a child of the node object on its right
    # AND if the distance is ok, return the next child
    if self._rightchild and distance + max_dist >= self._median:
        yield self._rightchild

    # If the function arrives here, the generator will be considered empty
    # there are no more than two values: the left and the right children

Caller:

# Create an empty list and a list with the current object reference
result, candidates = list(), [self]

# Loop on candidates (they contain only one element at the beginning)
while candidates:

    # Get the last candidate and remove it from the list
    node = candidates.pop()

    # Get the distance between obj and the candidate
    distance = node._get_dist(obj)

    # If the distance is ok, then you can fill in the result
    if distance <= max_dist and distance >= min_dist:
        result.extend(node._values)

    # Add the children of the candidate to the candidate's list
    # so the loop will keep running until it has looked
    # at all the children of the children of the children, etc. of the candidate
    candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))

return result

This code contains several smart parts:

The loop iterates on a list, but the list expands while the loop is being iterated. It's a concise way to go through all these nested data even if it's a bit dangerous since you can end up with an infinite loop. In this case, candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) exhausts all the values of the generator, but while keeps creating new generator objects which will produce different values from the previous ones since it's not applied on the same node.
The extend() method is a list object method that expects an iterable and adds its values to the list.

Usually, we pass a list to it:

>>> a = [1, 2]
>>> b = [3, 4]
>>> a.extend(b)
>>> print(a)
[1, 2, 3, 4]

But in your code, it gets a generator, which is good because:

You don't need to read the values twice.
You may have a lot of children and you don't want them all stored in memory.

You can stop here, or read a little bit to see an advanced use of a generator:

Controlling a generator exhaustion

>>> class Bank(): # Let's create a bank, building ATMs
...    crisis = False
...    def create_atm(self):
...        while not self.crisis:
...            yield "$100"
>>> hsbc = Bank() # When everything's ok the ATM gives you as much as you want
>>> corner_street_atm = hsbc.create_atm()
>>> print(corner_street_atm.next())
$100
>>> print(corner_street_atm.next())
$100
>>> print([corner_street_atm.next() for cash in range(5)])
['$100', '$100', '$100', '$100', '$100']
>>> hsbc.crisis = True # Crisis is coming, no more money!
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> wall_street_atm = hsbc.create_atm() # It's even true for new ATMs
>>> print(wall_street_atm.next())
<type 'exceptions.StopIteration'>
>>> hsbc.crisis = False # The trouble is, even post-crisis the ATM remains empty
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> brand_new_atm = hsbc.create_atm() # Build a new one to get back in business
>>> for cash in brand_new_atm:
...    print cash
$100
$100
$100
$100
$100
$100
$100
$100
$100
...

Note: For Python 3, useprint(corner_street_atm.__next__()) or print(next(corner_street_atm))

It can be useful for various things like controlling access to a resource.

Itertools, your best friend

Then just import itertools.

An example? Let's see the possible orders of arrival for a four-horse race:

>>> horses = [1, 2, 3, 4]
>>> races = itertools.permutations(horses)
>>> print(races)
<itertools.permutations object at 0xb754f1dc>
>>> print(list(itertools.permutations(horses)))
[(1, 2, 3, 4),
 (1, 2, 4, 3),
 (1, 3, 2, 4),
 (1, 3, 4, 2),
 (1, 4, 2, 3),
 (1, 4, 3, 2),
 (2, 1, 3, 4),
 (2, 1, 4, 3),
 (2, 3, 1, 4),
 (2, 3, 4, 1),
 (2, 4, 1, 3),
 (2, 4, 3, 1),
 (3, 1, 2, 4),
 (3, 1, 4, 2),
 (3, 2, 1, 4),
 (3, 2, 4, 1),
 (3, 4, 1, 2),
 (3, 4, 2, 1),
 (4, 1, 2, 3),
 (4, 1, 3, 2),
 (4, 2, 1, 3),
 (4, 2, 3, 1),
 (4, 3, 1, 2),
 (4, 3, 2, 1)]

Understanding the inner mechanisms of iteration

There is more about it in this article about how for loops work.

What does yield mean in PHP?

What is `yield`?

The yield keyword returns data from a generator function:

The heart of a generator function is the yield keyword. In its simplest form, a yield statement looks much like a return statement, except that instead of stopping execution of the function and returning, yield instead provides a value to the code looping over the generator and pauses execution of the generator function.

What is a generator function?

A generator function is effectively a more compact and efficient way to write an Iterator. It allows you to define a function (your xrange) that will calculate and return values while you are looping over it:

function xrange($min, $max) {
    for ($i = $min; $i <= $max; $i++) {
        yield $i;
    }
}

[…]

foreach (xrange(1, 10) as $key => $value) {
    echo "$key => $value", PHP_EOL;
}

This would create the following output:

0 => 1
1 => 2
…
9 => 10

You can also control the $key in the foreach by using

yield $someKey => $someValue;

In the generator function, $someKey is whatever you want appear for $key and $someValue being the value in $val. In the question's example that's $i.

What's the difference to normal functions?

Now you might wonder why we are not simply using PHP's native range function to achieve that output. And right you are. The output would be the same. The difference is how we got there.

When we use range PHP, will execute it, create the entire array of numbers in memory and return that entire array to the foreach loop which will then go over it and output the values. In other words, the foreach will operate on the array itself. The range function and the foreach only "talk" once. Think of it like getting a package in the mail. The delivery guy will hand you the package and leave. And then you unwrap the entire package, taking out whatever is in there.

When we use the generator function, PHP will step into the function and execute it until it either meets the end or a yield keyword. When it meets a yield, it will then return whatever is the value at that time to the outer loop. Then it goes back into the generator function and continues from where it yielded. Since your xrange holds a for loop, it will execute and yield until $max was reached. Think of it like the foreach and the generator playing ping pong.

Why do I need that?

Obviously, generators can be used to work around memory limits. Depending on your environment, doing a range(1, 1000000) will fatal your script whereas the same with a generator will just work fine. Or as Wikipedia puts it:

Meaning of the Word Yield