vasync: observable asynchronous control flow
This module provides several functions for asynchronous control flow. There are
many modules that do this already (notably async.js). This one's claim to fame
is improved debuggability.
Observability is important
Working with Node's asynchronous, callback-based model is much easier with a
handful of simple control-flow abstractions, like:
- waterfalls and pipelines (which invoke a list of asynchronous callbacks
sequentially)
- parallel pipelines (which invoke a list of asynchronous callbacks in parallel
and invoke a top-level callback when the last one completes).
- queues
- barriers
But these structures also introduce new types of programming errors: failing to
invoke the callback can cause the program to hang, and inadvertently invoking it
twice can cause all kinds of mayhem that's very difficult to debug.
The functions in this module keep track of what's going on so that you can
figure out what happened when your program goes wrong. They generally return an
object describing details of the current state. If your program goes wrong, you
have several ways of getting at this state:
- On illumos-based systems, use MDB to find the status object
and then print it out.
- Provide an HTTP API (or AMQP, or whatever) that returns these pending status
objects as JSON (see kang).
- Incorporate a REPL into your program and print out the status object.
- Use the Node debugger to print out the status object.
Functions
- parallel: invoke N functions in
parallel (and merge the results)
- forEachParallel:
invoke the same function on N inputs in parallel
- pipeline: invoke
N functions in series (and stop on failure)
- forEachPipeline:
invoke the same function on N inputs in series (and stop on failure)
- waterfall:
like pipeline, but propagating results between stages
- barrier: coordinate
multiple concurrent operations
- queue/queuev: fixed-size worker queue
parallel: invoke N functions in parallel
Synopsis: parallel(args, callback)
This function takes a list of input functions (specified by the "funcs" property
of "args") and runs them all. These input functions are expected to be
asynchronous: they get a "callback" argument and should invoke it as
callback(err, result)
. The error and result will be saved and made available
to the original caller when all of these functions complete.
This function returns the same "result" object it passes to the callback, and
you can use the fields in this object to debug or observe progress:
operations
: array corresponding to the input functions, with
func
: input function,status
: "pending", "ok", or "fail",err
: returned "err" value, if any, andresult
: returned "result" value, if any
successes
: "result" field for each of "operations" where
"status" == "ok" (in no particular order)ndone
: number of input operations that have completednerrors
: number of input operations that have failed
This status object lets you see in a debugger exactly which functions have
completed, what they returned, and which ones are outstanding.
All errors are combined into a single "err" parameter to the final callback (see
below).
Example usage:
console.log(mod_vasync.parallel({
'funcs': [
function f1 (callback) { mod_dns.resolve('joyent.com', callback); },
function f2 (callback) { mod_dns.resolve('github.com', callback); },
function f3 (callback) { mod_dns.resolve('asdfaqsdfj.com', callback); }
]
}, function (err, results) {
console.log('error: %s', err.message);
console.log('results: %s', mod_util.inspect(results, null, 3));
}));
In the first tick, this outputs:
status: { operations:
[ { func: [Function: f1], status: 'pending' },
{ func: [Function: f2], status: 'pending' },
{ func: [Function: f3], status: 'pending' } ],
successes: [],
ndone: 0,
nerrors: 0 }
showing that there are three operations pending and none has yet been started.
When the program finishes, it outputs this error:
error: first of 1 error: queryA ENOTFOUND
which encapsulates all of the intermediate failures. This model allows you to
write the final callback like you normally would:
if (err)
return (callback(err));
and still propagate useful information to callers that don't deal with multiple
errors (i.e. most callers).
The example also prints out the detailed final status, including all of the
errors and return values:
results: { operations:
[ { func: [Function: f1],
funcname: 'f1',
status: 'ok',
err: null,
result: [ '165.225.132.33' ] },
{ func: [Function: f2],
funcname: 'f2',
status: 'ok',
err: null,
result: [ '207.97.227.239' ] },
{ func: [Function: f3],
funcname: 'f3',
status: 'fail',
err: { [Error: queryA ENOTFOUND] code: 'ENOTFOUND',
errno: 'ENOTFOUND', syscall: 'queryA' },
result: undefined } ],
successes: [ [ '165.225.132.33' ], [ '207.97.227.239' ] ],
ndone: 3,
nerrors: 1 }
You can use this if you want to handle all of the errors individually or to get
at all of the individual return values.
Note that "successes" is provided as a convenience and the order of items in
that array may not correspond to the order of the inputs. To consume output in
an ordered manner, you should iterate over "operations" and pick out the result
from each item.
forEachParallel: invoke the same function on N inputs in parallel
Synopsis: forEachParallel(args, callback)
This function is exactly like parallel
, except that the input is specified as
a single function ("func") and a list of inputs ("inputs"). The function is
invoked on each input in parallel.
This example is exactly equivalent to the one above:
console.log(mod_vasync.forEachParallel({
'func': mod_dns.resolve,
'inputs': [ 'joyent.com', 'github.com', 'asdfaqsdfj.com' ]
}, function (err, results) {
console.log('error: %s', err.message);
console.log('results: %s', mod_util.inspect(results, null, 3));
}));
pipeline: invoke N functions in series (and stop on failure)
Synopsis: pipeline(args, callback)
The named arguments (that go inside args
) are:
funcs
: input functions, to be invoked in seriesarg
: arbitrary argument that will be passed to each function
The functions are invoked in order as func(arg, callback)
, where "arg" is the
user-supplied argument from "args" and "callback" should be invoked in the usual
way. If any function emits an error, the whole pipeline stops.
The return value and the arguments to the final callback are exactly the same as
for parallel
. The error object for the final callback is just the error
returned by whatever pipeline function failed (if any).
This example is similar to the one above, except that it runs the steps in
sequence and stops early because pipeline
stops on the first error:
console.log(mod_vasync.pipeline({
'funcs': [
function f1 (_, callback) { mod_fs.stat('/tmp', callback); },
function f2 (_, callback) { mod_fs.stat('/noexist', callback); },
function f3 (_, callback) { mod_fs.stat('/var', callback); }
]
}, function (err, results) {
console.log('error: %s', err.message);
console.log('results: %s', mod_util.inspect(results, null, 3));
}));
As a result, the status after the first tick looks like this:
{ operations:
[ { func: [Function: f1], status: 'pending' },
{ func: [Function: f2], status: 'waiting' },
{ func: [Function: f3], status: 'waiting' } ],
successes: [],
ndone: 0,
nerrors: 0 }
Note that the second and third stages are now "waiting", rather than "pending"
in the parallel
case. The error and complete result look just like the
parallel case.
forEachPipeline: invoke the same function on N inputs in series (and stop on failure)
Synopsis: forEachPipeline(args, callback)
This function is exactly like pipeline
, except that the input is specified as
a single function ("func") and a list of inputs ("inputs"). The function is
invoked on each input in series.
This example is exactly equivalent to the one above:
console.log(mod_vasync.forEachPipeline({
'func': mod_dns.resolve,
'inputs': [ 'joyent.com', 'github.com', 'asdfaqsdfj.com' ]
}, function (err, results) {
console.log('error: %s', err.message);
console.log('results: %s', mod_util.inspect(results, null, 3));
}));
waterfall: invoke N functions in series, stop on failure, and propagate results
Synopsis: waterfall(funcs, callback)
This function works like pipeline
except for argument passing.
Each function is passed any values emitted by the previous function (none for
the first function), followed by the callback to invoke upon completion. This
callback must be invoked exactly once, regardless of success or failure. As
conventional in Node, the first argument to the callback indicates an error (if
non-null). Subsequent arguments are passed to the next function in the "funcs"
chain.
If any function fails (i.e., calls its callback with an Error), then the
remaining functions are not invoked and "callback" is invoked with the error.
The only difference between waterfall() and pipeline() are the arguments passed
to each function in the chain. pipeline() always passes the same argument
followed by the callback, while waterfall() passes whatever values were emitted
by the previous function followed by the callback.
Here's an example:
mod_vasync.waterfall([
function func1(callback) {
setImmediate(function () {
callback(null, 37);
});
},
function func2(extra, callback) {
console.log('func2 got "%s" from func1', extra);
callback();
}
], function () {
console.log('done');
});
This prints:
func2 got "37" from func1
better stop early
barrier: coordinate multiple concurrent operations
Synopsis: barrier([args])
Returns a new barrier object. Like parallel
, barriers are useful for
coordinating several concurrent operations, but instead of specifying a list of
functions to invoke, you just say how many (and optionally which ones) are
outstanding, and this object emits 'drain'
when they've all completed. This
is syntactically lighter-weight, and more flexible.
Example: printing sizes of files in a directory
var mod_fs = require('fs');
var mod_path = require('path');
var mod_vasync = require('../lib/vasync');
var barrier = mod_vasync.barrier();
barrier.on('drain', function () {
console.log('all files checked');
});
barrier.start('readdir');
mod_fs.readdir(__dirname, function (err, files) {
barrier.done('readdir');
if (err)
throw (err);
files.forEach(function (file) {
barrier.start('stat ' + file);
var path = mod_path.join(__dirname, file);
mod_fs.stat(path, function (err2, stat) {
barrier.done('stat ' + file);
console.log('%s: %d bytes', file, stat['size']);
});
});
});
This emits:
barrier-readdir.js: 602 bytes
foreach-parallel.js: 358 bytes
barrier-basic.js: 552 bytes
nofail.js: 384 bytes
pipeline.js: 490 bytes
parallel.js: 481 bytes
queue-serializer.js: 441 bytes
queue-stat.js: 529 bytes
all files checked
queue/queuev: fixed-size worker queue
Synopsis: queue(worker, concurrency)
Synopsis: queuev(args)
This function returns an object that allows up to a fixed number of tasks to be
dispatched at any given time. The interface is compatible with that provided
by the "async" Node library, except that the returned object's fields represent
a public interface you can use to introspect what's going on.
If the tasks are themselves simple objects, then the entire queue may be
serialized (as via JSON.stringify) for debugging and monitoring tools. Using
the above fields, you can see what this queue is doing (worker_name), which
tasks are queued, which tasks are being processed, and so on.
Example 1: Stat several files
Here's an example demonstrating the queue:
var mod_fs = require('fs');
var mod_vasync = require('../lib/vasync');
var queue;
function doneOne()
{
console.log('task completed; queue state:\n%s\n',
JSON.stringify(queue, null, 4));
}
queue = mod_vasync.queue(mod_fs.stat, 2);
console.log('initial queue state:\n%s\n', JSON.stringify(queue, null, 4));
queue.push('/tmp/file1', doneOne);
queue.push('/tmp/file2', doneOne);
queue.push('/tmp/file3', doneOne);
queue.push('/tmp/file4', doneOne);
console.log('all tasks dispatched:\n%s\n', JSON.stringify(queue, null, 4));
The initial queue state looks like this:
initial queue state:
{
"nextid": 0,
"worker_name": "anon",
"npending": 0,
"pending": {},
"queued": [],
"concurrency": 2
}
After four tasks have been pushed, we see that two of them have been dispatched
and the remaining two are queued up:
all tasks pushed:
{
"nextid": 4,
"worker_name": "anon",
"npending": 2,
"pending": {
"1": {
"id": 1,
"task": "/tmp/file1"
},
"2": {
"id": 2,
"task": "/tmp/file2"
}
},
"queued": [
{
"id": 3,
"task": "/tmp/file3"
},
{
"id": 4,
"task": "/tmp/file4"
}
],
"concurrency": 2
}
As they complete, we see tasks moving from "queued" to "pending", and completed
tasks disappear:
task completed; queue state:
{
"nextid": 4,
"worker_name": "anon",
"npending": 1,
"pending": {
"3": {
"id": 3,
"task": "/tmp/file3"
}
},
"queued": [
{
"id": 4,
"task": "/tmp/file4"
}
],
"concurrency": 2
}
When all tasks have completed, the queue state looks like it started:
task completed; queue state:
{
"nextid": 4,
"worker_name": "anon",
"npending": 0,
"pending": {},
"queued": [],
"concurrency": 2
}
Example 2: A simple serializer
You can use a queue with concurrency 1 and where the tasks are themselves
functions to ensure that an arbitrary asynchronous function never runs
concurrently with another one, no matter what each one does. Since the tasks
are the actual functions to be invoked, the worker function just invokes each
one:
var mod_vasync = require('../lib/vasync');
var queue = mod_vasync.queue(
function (task, callback) { task(callback); }, 1);
queue.push(function (callback) {
console.log('first task begins');
setTimeout(function () {
console.log('first task ends');
callback();
}, 500);
});
queue.push(function (callback) {
console.log('second task begins');
process.nextTick(function () {
console.log('second task ends');
callback();
});
});
This example outputs:
$ node examples/queue-serializer.js
first task begins
first task ends
second task begins
second task ends