This blog post is outdated. Please read chapter “Generators” in “Exploring ES6”.

This blog post is part of a series on iteration in ES6:

Generators, a new feature of ECMAScript 6 , are functions that can be paused and resumed. This helps with many applications: iterators, asynchronous programming, etc. This blog post explains how generators work and gives an overview of their applications.

The following GitHub repository contains the example code: generator-examples

Two important applications of generators are:

Implementing iterables

Blocking on asynchronous function calls

The following subsections give brief overviews of these applications, more thorough explanations are provided later (plus discussions of other topics).

Implementing iterables via generators #

The following function returns an iterable over the properties of an object, one [key,value] pair per property:

function * objectEntries ( obj ) { let propKeys = Reflect .ownKeys(obj); for ( let propKey of propKeys) { yield [propKey, obj[propKey]]; } }

How exactly objectEntries() works is explained later. It is used like this:

let jane = { first : 'Jane' , last : 'Doe' }; for ( let [key,value] of objectEntries(jane)) { console .log( ` ${key} : ${value} ` ); }

Blocking on asynchronous function calls #

In the following code, I use the control flow library co to asynchronously retrieve two JSON files. Note how, in line (A), execution blocks (waits) until the result of Promise.all() is ready. That means that the code looks synchronous while performing asynchronous operations.

co( function * ( ) { try { let [croftStr, bondStr] = yield Promise .all([ getFile( 'http://localhost:8000/croft.json' ), getFile( 'http://localhost:8000/bond.json' ), ]); let croftJson = JSON .parse(croftStr); let bondJson = JSON .parse(bondStr); console .log(croftJson); console .log(bondJson); } catch (e) { console .log( 'Failure to read: ' + e); } });

getFile(url) retrieves the file pointed to by url . Its implementation is shown later. I’ll also explain how co works.

What are generators? #

Generators are functions that can be paused and resumed, which enables a variety of applications.

As a first example, consider the following generator function whose name is genFunc :

function * genFunc ( ) { console .log( 'First' ); yield ; console .log( 'Second' ); }

Two things distinguish genFunc from a normal function declaration:

It starts with the “keyword” function* .

. It is paused in the middle via yield .

Calling genFunc does not execute it. Instead, it returns a so-called generator object that lets us control genFunc ’s execution:

> let genObj = genFunc();

genFunc() is initially suspended at the beginning of its body. The method genObj.next() continues the execution of genFunc , until the next yield :

> genObj.next() First { value: undefined, done: false }

As you can see in the last line, genObj.next() also returns an object. Let’s ignore that for now. It will matter once we look at generators as iterators.

genFunc is now paused in line (A). If we call next() again, execution resumes and line (B) is executed:

> genObj.next() Second { value: undefined, done: true }

Afterwards, the function is finished, execution has left the body and further calls of genObj.next() have no effect.

Ways of creating generators #

There are four ways in which you can create generators:

Via a generator function declaration: function * genFunc ( ) { ··· } let genObj = genFunc(); Via a generator function expression: const genFunc = function * ( ) { ··· }; let genObj = genFunc(); Via a generator method definition in an object literal: let obj = { * generatorMethod() { ··· } }; let genObj = obj.generatorMethod(); Via a generator method definition in a class definition (which can be a class declaration or a class expression ): class MyClass { * generatorMethod() { ··· } } let myInst = new MyClass(); let genObj = myInst.generatorMethod();

Roles played by generators #

Generators can play three roles:

Iterators (data producers): Each yield can return a value via next() , which means that generators can produce sequences of values via loops and recursion. Due to generator objects implementing the interface Iterable , these sequences can be processed by any ECMAScript 6 construct that supports iterables. Two examples are: for-of loops and the spread operator ( ... ). Observers (data consumers): yield can also receive a value from next() (via a parameter). That means that generators become data consumers that pause until a new value is pushed into them via next() . Coroutines (data producers and consumers): Given that generators are pausable and can be both data producers and data consumers, not much work is needed to turn them into coroutines (cooperatively multitasked tasks).

The next sections provide deeper explanations of these roles.

Generators as iterators (data production) #

As explained before, generator objects can be data producers, data consumers or both. This section looks at them as data producers, where they implement both the interfaces Iterable and Iterator (shown below). Note that that means that the result of a generator function is both an iterable and an iterator. The full interface of generator objects will be shown later.

interface Iterable { [ Symbol .iterator]() : Iterator; } interface Iterator { next() : IteratorResult; return ?(value? : any) : IteratorResult; } interface IteratorResult { value : any; done : boolean; }

A generator function produces a sequence of values via yield , a data consumer consumes thoses values via the iterator method next() . For example, the following generator function produces the values 'a' and 'b' :

function * genFunc ( ) { yield 'a' ; yield 'b' ; }

This interaction shows how to retrieve the yielded values via the generator object genObj :

> let genObj = genFunc(); > genObj.next() { value: 'a', done: false } > genObj.next() { value: 'b', done: false } > genObj.next() // end of sequence reached ( value: undefined, done: true }

Ways of iterating over a generator #

As generator objects are iterable, ES6 language constructs that support iterables can be applied to them. The following three ones are especially important.

First, the for-of loop:

for ( let x of genFunc()) { console .log(x); }

Second, the spread operator ( ... ), which turns iterated sequences into elements of an array (consult for more information on this operator):

let arr = [...genFunc()];

Third, destructuring :

> let [x, y] = genFunc(); > x 'a' > y 'b'

Returning from a generator #

The previous generator function did not contain an explicit return . An implicit return is equivalent to returning undefined . Let’s examine a generator with an explicit return :

function * genFuncWithReturn ( ) { yield 'a' ; yield 'b' ; return 'result' ; }

The returned value shows up in the last object returned by next() , whose property done is true :

> let genObjWithReturn = genFuncWithReturn(); > genObjWithReturn.next() { value: 'a', done: false } > genObjWithReturn.next() { value: 'b', done: false } > genObjWithReturn.next() { value: 'result', done: true }

However, most constructs that work with iterables ignore the value inside the done object:

for ( let x of genFuncWithReturn()) { console .log(x); } let arr = [...genFuncWithReturn()];

yield* , an operator for making recursive generator calls, does consider values inside done objects. It is explained later.

Example: iterating over properties #

Let’s look at an example that demonstrates how convenient generators are for implementing iterables. The following function, objectEntries() , returns an iterable over the properties of an object:

function * objectEntries ( obj ) { let propKeys = Reflect .ownKeys(obj); for ( let propKey of propKeys) { yield [propKey, obj[propKey]]; } }

This function enables you to iterate over the properties of an object jane via the for-of loop:

let jane = { first : 'Jane' , last : 'Doe' }; for ( let [key,value] of objectEntries(jane)) { console .log( ` ${key} : ${value} ` ); }

For comparison – an implementation of objectEntries() that doesn’t use generators is much more complicated:

function objectEntries ( obj ) { let index = 0 ; let propKeys = Reflect .ownKeys(obj); return { [ Symbol .iterator]() { return this ; }, next() { if (index < propKeys.length) { let key = propKeys[index]; index++; return { value : [key, obj[key]] }; } else { return { done : true }; } } }; }

Recursion via yield* (for output) #

The yield* operator lets you call another generator from within a generator, as if you made a function call. For now, I only explain how this works if both generators produce output, I’ll later explain how things work if input is involved.

How can one generator recursively call another generator? Let’s assume you have written a generator function foo :

function * foo ( ) { yield 'a' ; yield 'b' ; }

How would you call foo from another generator function bar ? The following approach does not work!

function * bar ( ) { yield 'x' ; foo(); yield 'y' ; }

Calling foo() returns an object, but does not actually execute foo() . That’s why ECMAScript 6 has the operator yield* for making recursive generator calls:

function * bar ( ) { yield 'x' ; yield * foo(); yield 'y' ; } let arr = [...bar()];

Internally, yield* works roughly as follows:

function * bar ( ) { yield 'x' ; for ( let value of foo()) { yield value; } yield 'y' ; }

The operand of yield* does not have to be a generator object, it can be any iterable:

function * bla ( ) { yield 'sequence' ; yield * [ 'of' , 'yielded' ]; yield 'values' ; } let arr = [...bla()];

yield* considers end-of-iteration values #

Most constructs that support iterables ignore the value included in the end-of-iteration object (whose property done is true ). Generators provide that value via return . The result of yield* is the end-of-iteration value:

function * genFuncWithReturn ( ) { yield 'a' ; yield 'b' ; return 'The result' ; } function * logReturned ( genObj ) { let result = yield * genObj; console .log(result); }

If we want to get to line (A), we first must iterate over all values yielded by logReturned() :

> [...logReturned(genFuncWithReturn())] The result [ 'a', 'b' ]

Iterating over trees #

Iterating over a tree with recursion is simple, writing an iterator for a tree with traditional means is complicated. That’s why generators shine here: they let you implement an iterator via recursion. As an example, consider the following data structure for binary trees. It is iterable, because it has a method whose key is Symbol.iterator . That method is a generator method and returns an iterator when called.

class BinaryTree { constructor (value, left=null, right=null) { this .value = value; this .left = left; this .right = right; } * [ Symbol .iterator]() { yield this .value; if ( this .left) { yield * this .left; } if ( this .right) { yield * this .right; } } }

The following code creates a binary tree and iterates over it via for-of :

let tree = new BinaryTree( 'a' , new BinaryTree( 'b' , new BinaryTree( 'c' ), new BinaryTree( 'd' )), new BinaryTree( 'e' )); for ( let x of tree) { console .log(x); }

You can only yield in generators #

A significant limitation of generators is that you can only yield while you are (statically) inside a generator function. That is, yielding in callbacks doesn’t work:

function * genFunc ( ) { [ 'a' , 'b' ].forEach( x => yield x); }

yield is not allowed inside non-generator functions, which is why the previous code causes a syntax error. In this case, it is easy to rewrite the code so that it doesn’t use callbacks (as shown below). But unfortunately that isn’t always possibe.

function * genFunc ( ) { for ( let x of [ 'a' , 'b' ]) { yield x; } }

The upsides of this limitation are explained later: they make generators easier to implement and compatible with event loops.

Generators as observers (data consumption) #

As consumers of data, generator objects conform to the second half of the generator interface, Observer :

interface Observer { next(value? : any) : void ; return (value? : any) : void ; throw (error) : void ; }

As an observer, a generator pauses until it receives input. There are three kinds of input, transmitted via the methods specified by the interface:

next() sends normal input.

sends normal input. return() terminates the generator.

terminates the generator. throw() signals an error.

Sending values via next() #

If you use a generator as an observer, you send values to it via next() and it receives those values via yield :

function * dataConsumer ( ) { console .log( 'Started' ); console .log( `1. ${ yield } ` ); console .log( `2. ${ yield } ` ); return 'result' ; }

Let’s use this generator interactively. First, we create a generator object:

> let genObj = dataConsumer();

We now call genObj.next() , which starts the generator. Execution continues until the first yield , which is where the generator pauses. The result of next() is the value yielded in line (A) ( undefined , because yield doesn’t have an operand). In this section, we are not interested in what next() returns, because we only use it to send values, not to retrieve values.

> genObj.next() Started { value : undefined , done : false }

We call next() two more times, in order to send the value 'a' to the first yield and the value 'b' to the second yield :

> genObj.next( 'a' ) 1. a { value : undefined , done : false } > genObj.next( 'b' ) 2. b { value : 'result' , done : true }

The result of the last next() is the value returned from dataConsumer() . done being true indicates that the generator is finished.

Unfortunately, next() is asymmetric, but that can’t be helped: It always sends a value to the currently suspended yield , but returns the operand of the following yield .

The first next() #

When using a generator as an observer, it is important to note that the only purpose of the first invocation of next() is to start the observer. It is only ready for input afterwards, because this first invocation has advanced execution to the first yield . Therefore, you can’t send input via the first next() – you even get an error if you do:

> function* g() { yield } > g().next('hello') TypeError: attempt to send 'hello' to newborn generator

The following utility function fixes this issue:

function coroutine ( generatorFunction ) { return function ( ...args ) { let generatorObject = generatorFunction(...args); generatorObject.next(); return generatorObject; }; }

To see how coroutine() works, let’s compare a wrapped generator with a normal one:

const wrapped = coroutine( function * ( ) { console .log( `First input: ${ yield } ` ); return 'DONE' ; }); const normal = function * ( ) { console .log( `First input: ${ yield } ` ); return 'DONE' ; };

The wrapped generator is immediately ready for input:

> wrapped().next('hello!') First input: hello!

The normal generator needs an extra next() until it is ready for input:

> let genObj = normal(); > genObj.next() { value: undefined, done: false } > genObj.next('hello!') First input: hello! { value: 'DONE', done: true }

yield binds loosely #

yield binds very loosely, so that we don’t have to put its operand in parentheses:

yield a + b + c;

This is treated as:

yield (a + b + c);

Not as:

( yield a) + b + c;

As a consequence, many operators bind more tightly than yield and you have to put yield in parentheses if you want to use it as an operand. For example, you get a SyntaxError if you make an unparenthesized yield an operand of plus:

console .log( 'Hello' + yield ); console .log( 'Hello' + yield 123 ); console .log( 'Hello' + ( yield )); console .log( 'Hello' + ( yield 123 ));

You do not need parens if yield is a direct argument in a function or method call:

foo( yield 'a' , yield 'b' );

You also don’t need parens if you use yield on the right-hand side of an assignment:

let input = yield ;

The need for parens can be seen in the following grammar rules in the ECMAScript 6 specification. These rules describe how expressions are parsed. I list them here from general (loose binding, lower precedence) to specific (tight binding, higher precedence). Wherever a certain kind of expression is demanded, you can also use more specific ones. The opposite is not true. The hierarchy ends with ParenthesizedExpression , which means that you can mention any expression anywhere, if you put it in parentheses.

Expression : AssignmentExpression Expression , AssignmentExpression AssignmentExpression : ConditionalExpression YieldExpression ArrowFunction LeftHandSideExpression = AssignmentExpression LeftHandSideExpression AssignmentOperator AssignmentExpression ··· AdditiveExpression : MultiplicativeExpression AdditiveExpression + MultiplicativeExpression AdditiveExpression - MultiplicativeExpression MultiplicativeExpression : UnaryExpression MultiplicativeExpression MultiplicativeOperator UnaryExpression ··· PrimaryExpression : this IdentifierReference Literal ArrayLiteral ObjectLiteral FunctionExpression ClassExpression GeneratorExpression RegularExpressionLiteral TemplateLiteral ParenthesizedExpression ParenthesizedExpression : ( Expression )

The operands of an AdditiveExpression are an AdditiveExpression and a MultiplicativeExpression . Therefore, using a (more specific) ParenthesizedExpression as an operand is OK, but using a (more general) YieldExpression isn’t.

Let’s recap how next(x) works (after the first invocation):

The generator is currently suspended at a yield operator. Send the value x to that yield , which means that it evaluates to x . Proceed to the next yield or return : yield x leads to next() returning with

leads to returning with return x leads to next() returning with

return() and throw() work similarly to next() , but they do something different in step 2:

return(x) executes return x at the location of yield .

executes at the location of . throw(x) executes throw x at the location of yield .

return() terminates the generator #

return() performs a return at the location of the yield that led to the last suspension of the generator. Let’s use the following generator function to see how that works.

function * genFunc1 ( ) { try { console .log( 'Started' ); yield ; } finally { console .log( 'Exiting' ); } }

In the following interaction, we first use next() to start the generator and to proceed until the yield in line (A). Then we return from that location via return() .

> let genObj1 = genFunc1(); > genObj1.next() Started { value: undefined, done: false } > genObj1.return('Result') Exiting { value: 'Result', done: true }

Preventing termination #

You can prevent return() from terminating the generator if you yield inside the finally clause (using a return statement in that clause is also possible):

function * genFunc2 ( ) { try { console .log( 'Started' ); yield ; } finally { yield 'Not done, yet!' ; } }

This time, return() does not exit the generator function. Accordingly, the property done of the object it returns is false .

> let genObj2 = genFunc2(); > genObj2.next() Started { value: undefined, done: false } > genObj2.return('Result') { value: 'Not done, yet!', done: false }

You can invoke next() one more time. Similarly to non-generator functions, the return value of the generator function is the value that was queued prior to entering the finally clause.

> genObj2.next() { value: 'Result', done: true }

Returning from a newborn generator #

Returning a value from a newborn generator (that hasn’t started yet) is allowed:

> function* genFunc() {} > genFunc().return('yes') { value: 'yes', done: true }

throw() signals an error #

throw() throws an exception at the location of the yield that led to the last suspension of the generator. Let’s examine how that works via the following generator function.

function * genFunc1 ( ) { try { console .log( 'Started' ); yield ; } catch (error) { console .log( 'Caught: ' + error); } }

In the following interaction, we first use next() to start the generator and proceed until the yield in line (A). Then we throw an exception from that location.

> let genObj1 = genFunc1(); > genObj1.next() Started { value : undefined , done : false } > genObj1.throw( new Error ( 'Problem!' )) Caught: Error : Problem! { value : undefined , done : true }

The result of throw() (shown in the last line) stems from us leaving the function with an implicit return .

Uncaught exceptions #

If you don’t catch the exception inside the generator, it is thrown by throw() . For example, the following generator function does not catch exceptions:

function * genFunc2 ( ) { console .log( 'Started' ); yield ; }

If we use throw() to throw an instance of Error at line (A), the method itself throws that error:

> let genObj2 = genFunc2(); > genObj2.next() Started { value: undefined, done: false } > genObj2.throw(new Error('Problem!')) Error: Problem!

Throwing from a newborn generator #

Throwing an exception in a newborn generator (that hasn’t started yet) is allowed:

> function* genFunc() {} > genFunc().throw(new Error('Problem!')) Error: Problem!

Example: processing asynchronously pushed data #

The fact that generators-as-observers pause while they wait for input makes them perfect for on-demand processing of data that is received asynchronously. The pattern for setting up a chain of generators for processing is as follows:

First chain member: a normal function that has a parameter target , which is the generator object of the next element in the chain of generators. The function makes an asynchronous request and pushes the results to the target via target.next() .

Intermediate chain members: generators that have a parameter target . They receive data via yield and send data via target.next() .

Last chain member: a generator that has no parameter target and only receives data.

As an example, let’s chain generators to process a file that is read asynchronously. The code shown in this section is a Node.js script that is executed via babel-node .

The following code sets up the chain, which starts with the normal function readFile , continues with the generators splitLines and numberLines and ends with the generator printLines :

let fileName = process.argv[ 2 ]; readFile(fileName, chain(splitLines, numberLines, printLines));

I’ll explain what these functions do when I show their code. The following helper function sets up a chain of generators: Starting with the last generator, each generator function is called and the resulting generator object is used to start the generator via an initial next() . If a generator has a successor, it receives the successor’s generator object via the parameter target . The result of chain() is the generator object of the first generator function (in our example: splitLines ).

function chain ( ...generatorFuncs ) { if (generatorFuncs.length < 1 ) { throw new Error ( 'Need at least 1 argument' ); } let generatorObject = generatorFuncs[generatorFuncs.length -1 ](); generatorObject.next(); for ( let i=generatorFuncs.length -2 ; i >= 0 ; i--) { let generatorFunction = generatorFuncs[i]; generatorObject = generatorFunction(generatorObject); generatorObject.next(); } return generatorObject; }

readFile() is the non-generator function that starts everything.

import {createReadStream} from 'fs' ; function readFile ( fileName, target ) { let readStream = createReadStream(fileName, { encoding : 'utf8' , bufferSize : 1024 }); readStream.on( 'data' , buffer => { let str = buffer.toString( 'utf8' ); target.next(str); }); readStream.on( 'end' , () => { target.return(); }); }

The chain of generators starts with splitLines :

function * splitLines ( target ) { let previous = '' ; try { while ( true ) { previous += yield ; let eolIndex; while ((eolIndex = previous.indexOf( '

' )) >= 0 ) { let line = previous.slice( 0 , eolIndex); target.next(line); previous = previous.slice(eolIndex+ 1 ); } } } finally { if (previous.length > 0 ) { target.next(previous); } target.return(); } }

Note an important pattern:

readFile uses the generator object method return() to signal the end of the sequence of chunks that it sends.

uses the generator object method to signal the end of the sequence of chunks that it sends. readFile sends that signal while splitLines is waiting for input via yield , inside an infinite loop. return() breaks from that loop.

sends that signal while is waiting for input via , inside an infinite loop. breaks from that loop. splitLines uses a finally clause to handle the end-of-sequence.

The next generator is numberLines :

function * numberLines ( target ) { try { for ( let lineNo = 0 ; ; lineNo++) { let line = yield ; target.next( ` ${lineNo} : ${line} ` ); } } finally { target.return(); } }

The last generator is printLines :

function * printLines ( ) { while ( true ) { let line = yield ; console .log(line); } }

The neat thing about this code is that everything happens lazily (on demand): lines are split, numbered and printed as they arrive; we don’t have to wait for all of the text before we can start printing.

yield* : the full story #

So far, we have only seen one aspect of yield : it propagates yielded values from the callee to the caller. Now that we are interested in generators receiving input, another aspect becomes relevant: yield* also forwards input received by the caller to the callee.

I’ll first explain the complete semantics of yield* by showing how you’d implemented it in JavaScript. Then I give simple examples where input received by the caller via next() , return() and throw() is forwarded to the callee.

The following statement:

let yieldStarResult = yield * calleeFunc();

is roughly equivalent to:

let yieldStarResult; let calleeObj = calleeFunc(); let prevReceived = undefined ; while ( true ) { try { let item = calleeObj.next(prevReceived); if (item.done) { yieldStarResult = item.value; break ; } prevReceived = yield item.value; } catch (e) { if (e instanceof Return) { calleeObj.return(e.returnedValue); return e.returnedValue; } else { calleeObj.throw(e); } } }

To keep things simple, several things are missing in this code:

The operand of yield* can be any iterable value.

return() and throw() are optional iterator methods. We should only call them if they exist.

If an exception is received and throw() does not exist, but return() does then return() is called (before throwing an exception) to give calleeObject the opportunity to clean up.

calleeObj can refuse to close, by returning an object whose property done is false . Then the caller also has to refuse to close and yield* must continue to iterate.

The following generator function caller() invokes the generator function callee() via yield* .

function * callee ( ) { console .log( 'callee: ' + ( yield )); } function * caller ( ) { while ( true ) { yield * callee(); } }

callee logs values received via next() , which allows us to check whether it receives the value 'a' and 'b' that we send to caller .

> let callerObj = caller(); > callerObj.next() // start { value: undefined, done: false } > callerObj.next('a') callee: a { value: undefined, done: false } > callerObj.next('b') callee: b { value: undefined, done: false }

Let’s use the following code to demonstrate how throw() works while yield* is delegating to another generator.

function * callee ( ) { try { yield 'b' ; yield 'c' ; } finally { console .log( 'finally callee' ); } } function * caller ( ) { try { yield 'a' ; yield * callee(); yield 'd' ; } catch (e) { console .log( '[caller] ' + e); } }

We first create a generator object and advance until line (A).

> let genObj = caller(); > genObj.next().value 'a' > genObj.next().value 'b'

In that line, we throw an exception:

> genObj.throw(new Error('Problem!')) finally callee [caller] Error: Problem! { value: undefined, done: true }

callee doesn’t catch the exception, which is why it is propagated into caller , where it is logged before caller finishes.

Let’s use the following code to demonstrate how return() works while yield* is delegating to another generator.

function * callee ( ) { try { yield 'b' ; yield 'c' ; } finally { console .log( 'finally callee' ); } } function * caller ( ) { try { yield 'a' ; yield * callee(); yield 'd' ; } finally { console .log( 'finally caller' ); } }

Destructuring closes an iterator via return() if one doesn’t iterate until the end:

let [x, y] = caller();

Interestingly, the return() is sent to caller and forwarded to callee (which it terminates early), but then also terminates caller (which is what someone invoking return() would expect). That is, return is propagated much like an exception.

How to think about yield* #

There are two ways to think about yield* :

As a function call from generator to generator.

In order to understand return() , it helps to ask yourself: what should happen if I copy-pasted the code of the callee function into the code of the caller function.

Generators as coroutines (cooperative multitasking) #

We have seen generators being used as either sources or sinks of data. For many applications, it’s good practice to strictly separate these two roles, because it keeps things simpler. This section describes the full generator interface (which combines both roles) and one application where both roles are needed: cooperative multitasking, where tasks must be able to both send and receive information.

The full generator interface #

The full interface of generator objects, Generator , handles both output and input and combines two interfaces that we have seen previously: Iterator for output and Observer for input.

interface Iterator { next() : IteratorResult; return ?(value? : any) : IteratorResult; } interface Observer { next(value? : any) : void ; return (value? : any) : void ; throw (error) : void ; }

This is the full interface of generator objects (as described by the ECMAScript language specification):

interface Generator { next(value? : any) : IteratorResult; throw (value? : any) : IteratorResult; return (value? : any) : IteratorResult; } interface IteratorResult { value : any; done : boolean; }

Cooperative multitasking #

Cooperative multitasking is an application of generators where we need them to handle both output and input. Before we get into how that works, let’s first review the current state of parallelism in JavaScript .

JavaScript runs in a single process. There are two ways in which this limitation is being abolished:

Multiprocessing: Web Workers let you run JavaScript in multiple processes. Shared access to data is one of the biggest pitfalls of multiprocessing. Web Workers avoid it by not sharing any data. That is, if you want a Web Worker to have a piece of data, you must send it a copy or transfer your data to it (after which you can’t access it anymore).

Cooperative multitasking: There are various patterns and libraries that experiment with cooperative multitasking. Multiple tasks are run, but only one at a time. Each task must explicitly suspend itself, giving it full control over when a task switch happens. In these experiments, data is often shared between tasks. But due to explicit suspension, there are few risks.

Two use cases benefit from cooperative multitasking, because they involve control flows that are mostly sequential, anyway, with occasional pauses:

Asynchronous computations: A task blocks (pauses) until it receives the result of a long-running computation.

Streams: A task sequentially processes a stream of data and pauses if there is no data available.

For binary streams, WHATWG is currently working on a standard proposal that is based on callbacks and promises.

For streams of data, Communicating Sequential Processes (CSP) are an interesting solution. A generator-based CSP library is covered later in this blog post.

For asynchronous computations, Promises have become popular and are included in ECMAScript 6.

Simplifying asynchronous code via generators #

Several promise-based libraries simplify asynchronous code via generators. Generators are ideal as clients of promises, because they can be suspended until a result arrives.

The following example demonstrates what that looks like if one uses the library co by T.J. Holowaychuk. We need two libraries (if we run Node.js code via babel-node ):

require ( 'isomorphic-fetch' ); let co = require ( 'co' );

co is the actual library for cooperative multitasking, isomorphic-fetch is a polyfill for the new promise-based fetch API (a replacement of XMLHttpRequest ; read “That's so fetch!” by Jake Archibald for more information). fetch makes it easy to write a function getFile that returns the text of a file at a url via a Promise:

function getFile ( url ) { return fetch(url) .then( request => request.text()); }

We now have all the ingredients to use co . The following task reads the texts of two files, parses the JSON inside them and logs the result.

co( function * ( ) { try { let [croftStr, bondStr] = yield Promise .all([ getFile( 'http://localhost:8000/croft.json' ), getFile( 'http://localhost:8000/bond.json' ), ]); let croftJson = JSON .parse(croftStr); let bondJson = JSON .parse(bondStr); console .log(croftJson); console .log(bondJson); } catch (e) { console .log( 'Failure to read: ' + e); } });

Note how nicely synchronous this code looks, even though it makes an asynchronous call in line (A). A generator-as-task makes an async call by yielding a promise to the scheduler function co . The yielding pauses the generator. Once the promise returns a result, the scheduler resumes the generator by passing it the result via next() . A simple version of co looks as follows.

function co ( genFunc ) { let genObj = genFunc(); run(); function run ( promiseResult = undefined ) { let item = genObj.next(promiseResult); if (!item.done) { item.value .then( result => run(result)) .catch( error => { genObj.throw(error); }); } } }

The limitations of cooperative multitasking via generators #

Coroutines are cooperatively multitasked tasks that have no limitations: Inside a coroutine, any function can suspend the whole coroutine (the function activation itself, the activation of the function’s caller, the caller’s caller, etc.).

In contrast, you can only suspend a generator from directly within a generator and only the current function activation is suspended. Due to these limitations, generators are occasionally called shallow coroutines .

The benefits of the limitations of generators #

The limitations of generators have two main benefits:

Generators are compatible with event loops, which provide simple cooperative multitasking in browsers. I’ll explain the details momentarily.

Generators are relatively easy to implement, because only a single function activation needs to be suspended and because browsers can continue to use event loops.

JavaScript already has a very simple style of cooperative multitasking: the event loop , which schedules the execution of tasks in a queue. Each task is started by calling a function and finished once that function is finished (that’s a simplification of how things actually work, but it’ll do for now). Events, setTimeout() and other mechanisms add tasks to the queue.

This style of multitasking makes one important guarantee: run to completion; every function can rely on not being interrupted by another task until it is finished. Functions become transactions and can perform complete algorithms without anyone seeing the data they operate on in an itermediate state. Concurrent access to shared data makes multitasking complicated and is not allowed by JavaScript’s concurrency model. That’s why run to completion is a good thing.

Alas, coroutines prevent run to completion, because any function could suspend its caller. For example, the following algorithm consists of multiple steps:

step1(sharedData); step2(sharedData); lastStep(sharedData);

If step2 was to suspend the algorithm, other tasks could run before the last step of the algorithm is performed. Those tasks could contain other parts of the application which would see sharedData in an unfinished state.

Generators preserve run to completion, they only suspend themselves and return to their caller. co and similar libraries give you most of the power of coroutines:

They provide schedulers for tasks defined via generators.

Tasks start with generators and can thus be fully suspended.

A recursive (generator) function call is only suspendable if it is done via yield* . That gives callers control over suspension.

Examples of generators #

This section gives several examples of what generators can be used for.

Implementing iterables via generators #

In the blog post “Iterables and iterators in ECMAScript 6”, I implemented several iterables “by hand”. In this section, I use generators, instead.

The iterable combinator take() #

take() converts a (potentially infinite) sequence of iterated values into a sequence of length n :

function * take ( n, iterable ) { for ( let x of iterable) { if (n <= 0 ) return ; n--; yield x; } }

The following is an example of using it:

let arr = [ 'a' , 'b' , 'c' , 'd' ]; for ( let x of take( 2 , arr)) { console .log(x); }

An implementation of take() without generators is more complicated:

function take ( n, iterable ) { let iter = iterable[ Symbol .iterator](); return { [ Symbol .iterator]() { return this ; }, next() { if (n > 0 ) { n--; return iter.next(); } else { iter.return() return { done : true }; } }, return () { n = 0 ; iter.return(); } }; }

Note that the iterable combinator zip() does not profit much from being implemented via a generator, because multiple iterables are involved (and for-of can’t be used).

Infinite iterables #

naturalNumbers() returns an iterable over all natural numbers:

function * naturalNumbers ( ) { for ( let n= 0 ;; n++) { yield n; } }

This function is often used in conjunction with a combinator:

for ( let x of take( 3 , naturalNumbers())) { console .log(x); }

One last time, I show the non-generator implementation, so you can compare:

function naturalNumbers ( ) { let n = 0 ; return { [ Symbol .iterator]() { return this ; }, next() { return { value : n++ }; } } }

Array-inspired iterable combinators: map , filter #

Arrays can be transformed via the methods map and filter . Those methods can be generalized to have iterables as input and iterables as output.

This is the generalized version of map :

function * map ( iterable, mapFunc ) { for ( let x of iterable) { yield mapFunc(x); } } let arr = [...take( 4 , map(naturalNumbers(), x => x * x))];

This is the generalized version of filter :

function * filter ( iterable, filterFunc ) { for ( let x of iterable) { if (filterFunc(x)) { yield x; } } } let arr = [...take( 4 , filter(naturalNumbers(), x => (x % 2 ) === 0 ))];

Generators for lazy evaluation #

The next two examples show how generators can be used to process a stream of characters.

The input is a stream of characters.

Step 1 – tokenize (characters → words): The characters are grouped into words, strings that match the regular expression /^[A-Za-z0-9]$/ . Non-word characters are ignored, but they separate words. The input of this step is a stream of characters, the output a stream of words.

Step 2 – extract numbers (words → numbers): only keep words that match the regular expression /^[0-9]+$/ and convert them to numbers.

Step 3 – add numbers (numbers → number): produce a single number by computing the total of all numbers in a stream.

The neat thing is that everything is computed lazily (incrementally and on demand): computation starts as soon as the first character arrives. For example, we don’t have to wait until we have all characters to get the first word.

Lazy pull (generators as iterators) #

Step 1 – tokenize. The following trick makes the code a bit simpler: the end-of-sequence iterator result (whose property done is false ) is converted into the sentinel value END_OF_SEQUENCE .

function * tokenize ( chars ) { let iterator = chars[ Symbol .iterator](); let ch; do { ch = getNextItem(iterator); if (isWordChar(ch)) { let word = '' ; do { word += ch; ch = getNextItem(iterator); } while (isWordChar(ch)); yield word; } } while (ch !== END_OF_SEQUENCE); } const END_OF_SEQUENCE = Symbol (); function getNextItem ( iterator ) { let item = iterator.next(); return item.done ? END_OF_SEQUENCE : item.value; } function isWordChar ( ch ) { return typeof ch === 'string' && /^[A-Za-z0-9]$/ .test(ch); }

Let’s try out tokenization. Note that the spaces and the dot are non-words. They are ignored, but they separate words. We use the fact that strings are iterables over characters (Unicode code points). The final result is an iterable over words, which we turn into an array via the spread operator ( ... ).

> [...tokenize('2 apples and 5 oranges.')] [ '2', 'apples', 'and', '5', 'oranges' ]

Step 2 – extract numbers: This step is relatively simple, we only yield words that contain nothing but digits, after converting them to numbers via Number() .

function * extractNumbers ( words ) { for ( let word of words) { if ( /^[0-9]+$/ .test(word)) { yield Number (word); } } }

The following example shows the transformation steps: characters → words → numbers.

> [...extractNumbers(tokenize('2 apples and 5 oranges.'))] [ 2, 5 ]

Step 3 – add numbers: This last step is performed by a normal function that pulls the results and reports their total.

function summarize ( numbers ) { let result = 0 ; for ( let n of numbers) { result += n; } return result; }

The final result shows us that there are 7 things in the input sentence.

> summarize(extractNumbers(tokenize('2 apples and 5 oranges.'))) 7

Lazy push (generators as observables) #

Not much work is needed to convert the previous pull-based algorithm into a push-based one. The steps are the same. But instead of finishing via pulling, we start via pushing (in both cases, non-generator functions are used).

As previously explained, if generators receive input via yield , the first invocation of next() on the generator object doesn’t do anything. That’s why we again use the following helper function:

function coroutine ( generatorFunction ) { return function ( ...args ) { let generatorObject = generatorFunction(...args); generatorObject.next(); return generatorObject; }; }

The following function send() wraps the chain of generators. Its parameter receiver holds the generator object of the first generator in the chain. send() pushes the contents of iterable to the receiver , via next() .

function send ( iterable, receiver ) { for ( let x of iterable) { receiver.next(x); } receiver.return(); }

When a generator processes a stream, it needs to be aware of the end of the stream, so that it can clean up properly. For pull, we did this via a special end-of-stream sentinel. For push, the end-of-stream is signaled via return() .

Let’s test send() via a generator that simply outputs everything it receives:

const logItems = coroutine( function * ( ) { try { while ( true ) { let item = yield ; console .log(item); } } finally { console .log( 'DONE' ); } });

Let’s send logItems() three “characters” via a string (which is an iterable over Unicode code points).

> send('abc', logItems()); a b c DONE

Step 1 – tokenize. Note how this generator reacts to the end of the stream (as signaled via return() ) in two finally clauses. We depend on return() being sent to either one of the two yield s. Otherwise, the generator would never terminate, because the infinite loop starting in line (A) would never terminate.

const tokenize = coroutine( function * ( receiver ) { try { while ( true ) { let ch = yield ; if (isWordChar(ch)) { let word = '' ; try { do { word += ch; ch = yield ; } while (isWordChar(ch)); } finally { receiver.next(word); } } } } finally { receiver.return(); } }); function isWordChar ( ch ) { return /^[A-Za-z0-9]$/ .test(ch); }

tokenize() demonstrates that generators work well as implementations of linear state machines. In this case, the machine has two states: “inside a word” and “not inside a word”.

Let’s tokenize a string:

> send('2 apples and 5 oranges.', tokenize(logItems())); 2 apples and 5 oranges

Step 2 – extract numbers: This step is straightforward.

const extractNumbers = coroutine( function * ( receiver ) { try { while ( true ) { let word = yield ; if ( /^[0-9]+$/ .test(word)) { receiver.next( Number (word)); } } } finally { receiver.return(); } });

Let’s log the numbers appearing in a string:

> send('2 apples and 5 oranges.', tokenize(extractNumbers(logItems()))); 2 5 DONE

Step 3 – add numbers: This time, we react to the end of the stream by pushing a single value and then closing the receiver.

const addNumbers = coroutine( function * ( receiver ) { let result = 0 ; try { while ( true ) { result += yield ; } } finally { receiver.next(result); receiver.return(); } });

Let’s sum up the numbers appearing inside a string:

> send('2 apples and 5 oranges.', tokenize(extractNumbers(addNumbers(logItems())))); 7 DONE

As mentioned before, one benefit of a push-based approach is that it allows you to process data that you receive asynchronously.

Cooperative multi-tasking via generators #

Pausing long-running tasks #

In this example, we create a counter that is displayed on a web page. We improve an initial version until we have a cooperatively multitasked version that doesn’t block the main thread and the user interface.

This is the part of the web page in which the counter should be displayed:

< body > Counter: < span id = "counter" > </ span > </ body >

This function displays a counter that counts up forever (well, until the number overflows):

function countUp ( start = 0 ) { const counterSpan = document .querySelector( '#counter' ); while ( true ) { counterSpan.textContent = String (start); start++; } }

If you ran this function, it would completely block the user interface thread in which it runs and its tab would become unresponsive.

Let’s implement the same functionality via a generator that periodically pauses via yield (a scheduling function for running this generator is shown at the end):

function * countUp ( start = 0 ) { const counterSpan = document .querySelector( '#counter' ); while ( true ) { counterSpan.textContent = String (start); start++; yield ; } }

Let’s add one small improvement. We move the update of the user interface to another generator, displayCounter , which we call via yield* . As it is a generator, it can also take care of pausing.

function * countUp ( start = 0 ) { while ( true ) { start++; yield * displayCounter(start); } } function * displayCounter ( counter ) { const counterSpan = document .querySelector( '#counter' ); counterSpan.textContent = String (counter); yield ; }

Lastly, this is a scheduling function that we can use to run countUp() . Each execution step of the generator is handled by a separate task, which is created via setTimeout() . That means that the user interface can schedule other tasks in between and will remain responsive.

function run ( generatorObject ) { if (!generatorObject.next().done) { setTimeout( function ( ) { run(generatorObject); }, 1000 ); } }

With the help of run , we get a (nearly) infinite count-up that doesn’t block the user interface:

run(countUp());

Cooperative multitasking with generators and Node.js-style callbacks #

If you call a generator function (or method), it does not have access to its generator object; its this is the this it would have if it were a non-generator function. A work-around is to pass the generator object into the generator function via yield .

The following Node.js script uses this technique, but wraps the generator object in a callback ( next , line (A)). It must be run via babel-node .

import {readFile} from 'fs' ; let fileNames = process.argv.slice( 2 ); console .log(fileNames, readFile); run( function * ( ) { let next = yield ; for ( let f of fileNames) { let contents = yield readFile(f, { encoding : 'utf8' }, next); console .log( '-------------' , f); console .log(contents); } });

In line (A), we get a callback that we can use with functions that follow Node.js callback conventions. The callback uses the generator object to wake up the generator, as you can see in the implementation of run() :

function run ( generatorFunction ) { let generatorObject = generatorFunction(); generatorObject.next(); let nextFunction = createNextFunction(generatorObject); generatorObject.next(nextFunction); } function createNextFunction ( generatorObject ) { return function ( error, result ) { if (error) { generatorObject.throw(error); } else { generatorObject.next(result); } }; }

Communicating Sequential Processes (CSP) #

The library js-csp brings Communicating Sequential Processes (CSP) to JavaScript, a style of cooperative multitasking that is similar to ClojureScript’s core.async and Go’s goroutines. js-csp has two abstractions:

Processes: are cooperatively multitasked tasks and implemented by handing a generator function to the scheduling function go() .

. Channels: are queues for communication between processes. Channels are created by calling chan() .

As an example, let’s use CSP to handle DOM events, in a manner reminiscent of Functional Reactive Programming. The following code uses the function listen() (which is shown later) to create a channel that outputs mousemove events. It then continuously retrieves the output via take , inside an infinite loop. Thanks to yield , the process blocks until the channel has output.

import csp from 'js-csp' ; csp.go( function * ( ) { let element = document .querySelector( '#uiElement1' ); let channel = listen(element, 'mousemove' ); while ( true ) { let event = yield csp.take(channel); let x = event.layerX || event.clientX; let y = event.layerY || event.clientY; element.textContent = ` ${x} , ${y} ` ; } });

listen() is implemented as follows.

function listen ( element, type ) { let channel = csp.chan(); element.addEventListener(type, event => { csp.putAsync(channel, event); }); return channel; }

This example is taken from the blog post “Taming the Asynchronous Beast with CSP Channels in JavaScript” by James Long. Consult this blog post for more information on CSP.

This is a diagram of how various objects are connected in ECMAScript 6 (it is based on Allen Wirf-Brock’s diagram in the ECMAScript specification):

Legend:

The white (hollow) arrows express the has-prototype relationship (inheritance) between objects. In other words: a white arrow from x to y means that Object.getPrototypeOf(x) === y .

to means that . Parentheses indicate that an object exists, but is not accessible via a global variable.

An instanceof arrow from x to y means that x instanceof y . Remember that o instanceof C is equivalent to C.prototype.isPrototypeOf(o) .

arrow from to means that . A prototype arrow from x to y means that x.prototype === y .

The diagram reveals two interesting facts:

First, a generator function g works very much like a constructor (you can even invoke it via new ): The generator objects it creates are instances of it, methods added to g.prototype become prototype methods, etc.:

> function * g ( ) {} > g.prototype.hello = function ( ) { return 'hi!' }; > let obj = g(); > obj instanceof g true > obj.hello() 'hi!'

Second, if you want to make methods available for all generator objects, it’s best to add them to (Generator.object) . One way of accessing that object is as follows:

> let Generator_prototype = Object .getPrototypeOf( function * ( ) {}).prototype; > Generator_prototype.hello = function ( ) { return 'hi!' }; > let generatorObject = ( function * ( ) {})(); > generatorObject.hello() 'hi!'

There is no (Iterator) in the diagram, because no such object exists. But, given how instanceof works and because (IteratorPrototype) is a prototype of g1() , you could still say that g1() is an instance of Iterator .

All iterators in ES6 have (IteratorPrototype) in their prototype chain. That object is iterable, because it has the following method. Therefore, all ES6 iterators are iterable (as a consequence, you can apply for-of etc. to them).

[ Symbol .iterator]() { return this ; }

The specification recommends to use the following code to access (IteratorPrototype) :

const proto = Object .getPrototypeOf.bind( Object ); let IteratorPrototype = proto(proto([][ Symbol .iterator]()));

You could also use:

let IteratorPrototype = proto(proto( function * ( ) {}.prototype));

Quoting the ECMAScript 6 specification:

ECMAScript code may also define objects that inherit from IteratorPrototype . The IteratorPrototype object provides a place where additional methods that are applicable to all iterator objects may be added.

IteratorPrototype will probably become directly accessible in an upcoming version of ECMAScript and contain tool methods such as map() and filter() (source).

The value of this in generators #

A generator function combines two concerns:

It is a function that sets up and returns a generator object. It contains the code that the generator object steps through.

That’s why it’s not immediately obvious what the value of this should be inside a generator.

In function calls and method calls, this is what it would be if gen() wasn’t a generator function, but a normal function:

function * gen ( ) { ; yield this ; } let [functionThis] = gen(); console .log(functionThis); let obj = { method : gen }; let [methodThis] = obj.method(); console .log(methodThis === obj);

If you access this in a generator that was invoked via new , you get a ReferenceError (source: ES6 spec):

function * gen ( ) { console .log( this ); } new gen();

We have previously seen a simple work-around: wrap the generator in a normal function that hands the generator its generator object via yield .

Style consideration: whitespace before and after the asterisk #

Reasonable – and legal – variations of formatting the asterisk are:

A space before and after it:

function * foo(x, y)

A space before it:

function *foo(x, y)

A space after it:

function* foo(x, y)

No whitespace before and after it:

function*foo(x, y)

Let’s figure out which of these variations make sense for which constructs and why.

Generator function declarations and expressions #

Here, the star is only used because generator (or something similar) isn’t available as a keyword. If it were, then a generator function declaration would look like this:

generator foo(x, y) { ... }

Instead of generator , ECMAScript 6 marks the function keyword with an asterisk. Thus, function* can be seen as a synonym for generator , which suggests writing generator function declarations as follows.

function * foo ( x, y ) { ... }

Anonymous generator functions would be formatted like this:

const foo = function * ( x, y ) { ... }

Concise generator method definitions #

When writing a concise generator method definitions, I recommend to format the asterisk as follows.

let obj = { * generatorMethod(x, y) { ... } };

There are three arguments in favor of writing a space after the asterisk.

First, the asterisk shouldn’t be part of the method name. On one hand, it isn’t part of the name of a generator function. On the other hand, the asterisk is only mentioned when defining a generator, not when using it.

Second, a concise generator method definition is an abbreviation for the following syntax. (To make my point, I’m redundantly giving the function expression a name, too.)

let obj = { generatorMethod : function * generatorMethod ( x, y ) { ... } };

If concise method definitions are about omitting the function keyword then the asterisk should probably be followed by a space.

Third, generator method definitions are syntactically similar to getters and setters (which are already available in ECMAScript 5):

let obj = { get foo() { ... } set foo(value) { ... } }

The keywords get and set can be seen as modifiers of a normal concise method definition. Arguably, an asterisk is also such a modifier.

Recursive yield #

The following is an example of a generator function yielding its own yielded values recursively:

function * foo ( x ) { ... yield* foo(x - 1 ); ... }

The asterisk marks a different kind of yield operator, which is why the above way of writing it makes sense.

Documenting generator functions and methods #

Kyle Simpson (@getify) proposed something interesting: Given that we often append parentheses when we write about functions and methods such as Math.max() , wouldn’t it make sense to prepend an asterisk when writing about generator functions and methods? For example: should we write *foo() to refer to the generator function in the previous subsection? Let me argue against that.

When it comes to writing a function that returns an iterable, a generator is only one of the several options. I think it is better to not give away this implementation detail via marked function names.

Furthermore, you don’t use the asterisk when calling a generator function, but you do use parentheses.

Lastly, the asterisk doesn’t provide useful information – yield* can also be used with functions that return an iterable. But it may make sense to mark the names of functions and methods that return iterables (including generators). For example, via the suffix Iter .

I hope that this blog post convinced you that generators are a useful and versatile tool.

I like that generators let you implement cooperatively multitasked tasks that block while making asynchronous function calls. In my opinion that’s the right mental model for async calls. I hope that JavaScript goes further in this direction in the future. If one generator async-calls another generator, the indirection via promises is not needed. It could be avoided by the generator-based async functions that have been proposed for ECMAScript 2016.

Acknowledgement: items 1–3 are sources of this blog post.