In using Plone 4, I have successfully created a subscriber event to do extra processing when a custom content type is saved. This I accomplished by using the Products.Archetypes.interfaces.IObjectInitializedEvent interface.
configure.zcml
<subscriber
for="mycustom.product.interfaces.IRepositoryItem
Products.Archetypes.interfaces.IObjectInitializedEvent"
handler=".subscribers.notifyCreatedRepositoryItem"
/>
subscribers.py
def notifyCreatedRepositoryItem(repositoryitem, event):
"""
This gets called on IObjectInitializedEvent - which occurs when a new object is created.
"""
my custom processing goes here. Should be asynchronous
However, the extra processing can sometimes take too long, and I was wondering if there is a way to run it in the background i.e. asynchronously.
Is it possible to run subscriber events asynchronously for example when one is saving an object?
Not out of the box. You'd need to add asynch support to your environment.
Take a look at plone.app.async; you'll need a ZEO environment and at least one extra instance. The latter will run async jobs you push into the queue from your site.
You can then define methods to be executed asynchronously and push tasks into the queue to execute such a method asynchronously.
Example code, push a task into the queue:
from plone.app.async.interfaces import IAsyncService
async = getUtility(IAsyncService)
async.queueJob(an_async_task, someobject, arg1_value, arg2_value)
and the task itself:
def an_async_task(someobject, arg1, arg2):
# do something with someobject
where someobject is a persistent object in your ZODB. The IAsyncService.queueJob takes at least a function and a context object, but you can add as many further arguments as you need to execute your task. The arguments must be pickleable.
The task will then be executed by an async worker instance when it can, outside of the context of the current request.
Just to give more options, you could try collective.taskqueue for that, really simple and really powerful (and avoid some of the drawbacks of plone.app.async).
The description on PyPI already has enough to get you up to speed in no time, and you can use redis for the queue management which is a big plus.
Related
This is my code
def long_stage_task(node, deployment_folder_name, stage_s3_bucket):
global workers
logging.info("starting....")
work = StageOS(node, deployment_folder_name, stage_s3_bucket)--> class
work.stagestart()--> class method
executor = ProcessPoolExecutor(5)
executor.submit(long_stage_task, i, deployment_folder_name, stage_s3_bucket)
Now how can i stop a particular process/pid.
Is there any way to pass globals or shared state btw them, i don't see any thing in the doc.
https://docs.python.org/3/library/concurrent.futures.html
You could pass to the workers a list of Events and set them when you want the worker to stop. This implies your long_stage_task function periodically checks its own Event.
If what you are after is stopping a task which is taking too long, you can take a look at pebble. It allows to set timeouts to function calls as well as to cancel ongoing tasks.
TL;DR
How to safely await on function execution (takes str and int as arguments and doesn't require any other context) in a separate process?
Long story
I have aiohtto.web web API that uses Boost.Python wrapper for C++ extension, run under gunicorn (and I plan to deploy it on Heroku), tested by locust.
About extension: it have just one function that does non-blocking operation - takes one string (and one integer for timeout management), does some calculations with it and returns a new string. And for every input string, it is only one possible output (except timeout, but in that case, C++ exception must be raised and translated by Boost.Python to a Python-compatible one).
In short, a handler for specific URL executes the code below:
res = await loop.run_in_executor(executor, func, *args)
where executor is the ProcessPoolExecutor instance, and func -function from C++ extension module. (in the real project, this code is in the coroutine method of the class, and func - it's classmethod that only executes C++ function and returns the result)
Error catching
When a new request arrives, I extract it's POST data by request.post() and then storing it's data to the instance of the custom class named Call (because I have no idea how to name it in another way). So that call object contains all input data (string), request receiving time and unique id that comes with the request.
Then it proceeds to class named Handler (not the aiohttp request handler), that passes it's input to another class' method with loop.run_in_executor inside. But Handler has a logging system that works like a middleware - reads id and receiving time of every incoming call object and logging it with a message that tells you either it just starting to execute, successfully executed or get in trouble. Also, Handler have try/except and stores all errors inside the call object, so that logging middleware knows what error occurred, or what output extension had returned
Testing
I have the unit test that just creates 256 coroutines with this code inside and executor that have 256 workers and it works well.
But when testing with Locust here comes a problem. I use 4 Gunicorn workers and 4 executor workers for this kind of testing. At some time application just starts to return wrong output.
My Locust's TaskSet is configured to log every fault response with all available information: output string, error string, input string (that was returned by the application too), id. All simulated requests are the same, but id is unique for every.
The situation is better when setting Gunicorn's max_requests option to 100 requests, but failures still come.
Interesting thing is, that sometimes I can trigger "wrong output" period by simply stopping and starting Locust's test.
I need a 100% guarantee that my web API works as I expect.
UPDATE & solution
Just asked my teammate to review the C++ code - the problem was in global variables. In some way, it wasn't a problem for 256 parallel coroutines, but for Gunicorn was.
I have an ActiveJob that's supposed to load a piece of data from an external system over HTTP. When that job completes, I want to queue a second job that does some postprocessing and then submits the data to a different external system.
I don't want the first job to know about the second job, because
encapsulation
reusability
it's none of the first job's business, basically
Likewise, I don't want the first job to care what happens next if the data-loading fails -- maybe the user gets notified, maybe we retry after a timeout, maybe we just log it and throw up our hands -- again it could vary based on the details of the exception, and there's no need for the job to include the logic for that or the connections to other systems to handle it.
In Java (which is where I have the most experience), I could use something like Guava's ListenableFuture to add success and failure callbacks after the fact:
MyDataLoader loader = new MyDataLoader(someDataSource)
ListenableFuture<Data> future = executor.submit(loader);
Futures.addCallback(future, new FutureCallback<Data>() {
public void onSuccess(Data result) {
processData(result);
}
public void onFailure(Throwable t) {
handleFailure(t);
}
});
ActiveJob, though, doesn't seem to provide this sort of external callback mechanism -- as best I can make out from relevant sections in "Active Job Basics", after_perform and rescue_from are only meant to be called from within the job class. And after_peform isn't meant to distinguish between success and failure.
So the best I've been able to come up with (and I'm not claiming it's very good) is to pass a couple of lambdas into the job's perform method, thus:
class MyRecordLoader < ActiveJob::Base
# Loads data expensively (hopefully on a background queue) and passes
# the result, or any exception, to the appropriate specified lambda.
#
# #param data_source [String] the URL to load data from
# #param on_success [-> (String)] A lambda that will be passed the record
# data, if it's loaded successfully
# #param on_failure [-> (Exception)] A lambda that will be passed any
# exception, if there is one
def perform(data_source, on_success, on_failure)
begin
result = load_data_expensively_from data_source
on_success.call(result)
rescue => exception
on_failure.call(exception)
end
end
end
(Side note: I have no idea what the yardoc syntax is for declaring lambdas as parameters. Does this look correct, or, failing that, plausible?)
The caller would then have to pass these in:
MyRecordLoader.perform_later(
some_data_source,
method(:process_data),
method(:handle_failure)
)
That's not terrible, at least on the calling side, but it seems clunky, and I can't help but suspect there's a common pattern for this that I'm just not finding. And I'm somewhat concerned that, as a Ruby/Rails novice, I'm just bending ActiveJob to do something it was never meant to do in the first place. All the ActiveJob examples I'm finding are 'fire and forget' -- asynchronously "returning" a result doesn't seem to be an ActiveJob use case.
Also, it's not clear to me that this will work at all in the case of a back-end like Resque that runs the jobs in a separate process.
What's "the Ruby way" to do this?
Update: As hinted at by dre-hh, ActiveJob turned out not to be the right tool here. It was also unreliable, and overcomplicated for the situation. I switched to Concurrent Ruby instead, which fits the use case better, and which, since the tasks are mostly IO-bound, is fast enough even on MRI, despite the GIL.
ActiveJob is not an async Library like a future or promise.
It is just an interface for performing tasks in a background. The current thread/process receives no result of this operation.
For example when using Sidekiq as ActiveJob queue, it will serialize the parameters of the perform method into the redis store. Another daemon process running within the context of your rails app will be watching the redis queue and instantiate your worker with the serialized data.
So passing callbacks might be alright, however why having them as methods on another class. Passing callbacks would make sense if those are dynamic (changing on different invocation). However as you have them implemented on the calling class, consider just moving those methods into your job worker class.
I'm using Sidekiq to perform some heavy processing in the background. I looked online but couldn't find the answers to the following questions. I am using:
Class.delay.use_method(listing_id)
And then, inside the class, I have a
self.use_method(listing_id)
listing = Listing.find_by_id listing_id
UserMailer.send_mail(listing)
Class.call_example_function()
Two questions:
How do I make this function idempotent for the UserMailer sendmail? In other words, in case the delayed method runs twice, how do I make sure that it only sends the mail once? Would wrapping it in something like this work?
mail_sent = false
if !mail_sent
UserMailer.send_mail(listing)
mail_sent = true
end
I'm guessing not since the function is tried again and then mail_sent is set to false for the second run through. So how do I make it so that UserMailer is only run once.
Are functions called within the delayed async method also asynchronous? In other words, is Class.call_example_function() executed asynchronously (not part of the response / request cycle?) If not, should I use Class.delay.call_example_function()
Overall, just getting familiar with Sidekiq so any thoughts would be appreciated.
Thanks
I'm coming into this late, but having been around the loop and had this StackOverflow entry appearing prominently via Google, it needs clarification.
The issue of idempotency and the issue of unique jobs are not the same thing. The 'unique' gems look at the parameters of job at the point it is about to be processed. If they find that there was another job with the same parameters which had been submitted within some expiry time window then the job is not actually processed.
The gems are literally what they say they are; they consider whether an enqueued job is unique or not within a certain time window. They do not interfere with the retry mechanism. In the case of the O.P.'s question, the e-mail would still get sent twice if Class.call_example_function() threw an error thus causing a job retry, but the previous line of code had successfully sent the e-mail.
Aside: The sidekiq-unique-jobs gem mentioned in another answer has not been updated for Sidekiq 3 at the time of writing. An alternative is sidekiq-middleware which does much the same thing, but has been updated.
https://github.com/krasnoukhov/sidekiq-middleware
https://github.com/mhenrixon/sidekiq-unique-jobs (as previously mentioned)
There are numerous possible solutions to the O.P.'s email problem and the correct one is something that only the O.P. can assess in the context of their application and execution environment. One would be: If the e-mail is only going to be sent once ("Congratulations, you've signed up!") then a simple flag on the User model wrapped in a transaction should do the trick. Assuming a class User accessible as an association through the Listing via listing.user, and adding in a boolean flag mail_sent to the User model (with migration), then:
listing = Listing.find_by_id(listing_id)
unless listing.user.mail_sent?
User.transaction do
listing.user.mail_sent = true
listing.user.save!
UserMailer.send_mail(listing)
end
end
Class.call_example_function()
...so that if the user mailer throws an exception, the transaction is rolled back and the change to the user's flag setting is undone. If the "call_example_function" code throws an exception, then the job fails and will be retried later, but the user's "e-mail sent" flag was successfully saved on the first try so the e-mail won't be resent.
Regarding idempotency, you can use https://github.com/mhenrixon/sidekiq-unique-jobs gem:
All that is required is that you specifically set the sidekiq option
for unique to true like below:
sidekiq_options unique: true
For jobs scheduled in the future it is possible to set for how long
the job should be unique. The job will be unique for the number of
seconds configured or until the job has been completed.
*If you want the unique job to stick around even after it has been successfully processed then just set the unique_unlock_order to
anything except :before_yield or :after_yield (unique_unlock_order =
:never)
I'm not sure I understand the second part of the question - when you delay a method call, the whole method call is deferred to the sidekiq process. If by 'response / request cycle' you mean that you are running a web server, and you call delay from there, so all the calls within the use_method are called from the sidekiq process, and hence outside of that cycle. They are called synchronously relative to each other though...
I'd like to understand some details of the relations between command handlers, aggregates, the repository and the event store in CQRS-based systems.
What I've understood so far:
Command handlers receive commands from the bus. They are responsible for loading the appropriate aggregate from the repository and call the domain logic on the aggregate. Once finished, they remove the command from the bus.
An aggregate provides behavior and an internal state. State is never public. The only way to change state is by using the behavior. The methods that model this behavior create events from the command's properties, and apply these events to the aggregate, which in turn call an event handlers that sets the internal state accordingly.
The repository simply allows loading aggregates on a given ID, and adding new aggregates. Basically, the repository connects the domain to the event store.
The event store, last but not least, is responsible for storing events to a database (or whatever storage is used), and reloading these events as a so-called event stream.
So far, so good.
Now there are some issues that I did not yet get:
If a command handler is to call behavior on a yet existing aggregate, everything is quite easy. The command handler gets a reference to the repository, calls its loadById method and the aggregate is returned. But what does the command handler do when there is no aggregate yet, but one should be created? From my understanding the aggregate should later-on be rebuilt using the events. This means that creation of the aggregate is done in reply to a fooCreated event. But to be able to store any event (including the fooCreated one), I need an aggregate. So this looks to me like a chicken-and-egg problem: I can not create the aggregate without the event, but the only component that should create events is the aggregate. So basically it comes down to: How do I create new aggregates, who does what?
When an aggregate triggers an event, an internal event handler responses to it (typically by being called via an apply method) and changes the aggregate's state. How is this event handed over to the repository? Who originates the "please send the new events to the repository / event store" action? The aggregate itself? The repository by watching the aggregate? Someone else who is subscribed to the internal events? ...?
Last but not least I have a problem understanding the concept of an event stream correctly: In my imagination, it's simply something like an ordered list of events. What's of importance is that it's "ordered". Is this right?
The following is based on my own experience and my experiments with various frameworks like Lokad.CQRS, NCQRS, etc. I'm sure there are multiple ways to handle this. I'll post what makes most sense to me.
1. Aggregate Creation:
Every time a command handler needs an aggregate, it uses a repository. The repository retrieves the respective list of events from the event store and calls an overloaded constructor, injecting the events
var stream = eventStore.LoadStream(id)
var User = new User(stream)
If the aggregate didn't exist before, the stream will be empty and the newly created object will be in it's original state. You might want to make sure that in this state only a few commands are allowed to bring the aggregate to life, e.g. User.Create().
2. Storage of new Events
Command handling happens inside a Unit of Work. During command execution every resulting event will be added to a list inside the aggregate (User.Changes). Once execution is finished, the changes will be appended to the event store. In the example below this happens in the following line:
store.AppendToStream(cmd.UserId, stream.Version, user.Changes)
3. Order of Events
Just imagine what would happen, if two subsequent CustomerMoved events are replayed in the wrong order.
An Example
I'll try to illustrate the with a piece of pseudo-code (I deliberately left repository concerns inside the command handler to show what would happen behind the scenes):
Application Service:
UserCommandHandler
Handle(CreateUser cmd)
stream = store.LoadStream(cmd.UserId)
user = new User(stream.Events)
user.Create(cmd.UserName, ...)
store.AppendToStream(cmd.UserId, stream.Version, user.Changes)
Handle(BlockUser cmd)
stream = store.LoadStream(cmd.UserId)
user = new User(stream.Events)
user.Block(string reason)
store.AppendToStream(cmd.UserId, stream.Version, user.Changes)
Aggregate:
User
created = false
blocked = false
Changes = new List<Event>
ctor(eventStream)
isNewEvent = false
foreach (event in eventStream)
this.Apply(event, isNewEvent)
Create(userName, ...)
if (this.created) throw "User already exists"
isNewEvent = true
this.Apply(new UserCreated(...), isNewEvent)
Block(reason)
if (!this.created) throw "No such user"
if (this.blocked) throw "User is already blocked"
isNewEvent = true
this.Apply(new UserBlocked(...), isNewEvent)
Apply(userCreatedEvent, isNewEvent)
this.created = true
if (isNewEvent) this.Changes.Add(userCreatedEvent)
Apply(userBlockedEvent, isNewEvent)
this.blocked = true
if (isNewEvent) this.Changes.Add(userBlockedEvent)
Update:
As a side note: Yves' answer reminded me of an interesting article by Udi Dahan from a couple of years ago:
Don’t Create Aggregate Roots
A small variation on Dennis excellent answer:
When dealing with "creational" use cases (i.e. that should spin off new aggregates), try to find another aggregate or factory you can move that responsibility to. This does not conflict with having a ctor that takes events to hydrate (or any other mechanism to rehydrate for that matter). Sometimes the factory is just a static method (good for "context"/"intent" capturing), sometimes it's an instance method of another aggregate (good place for "data" inheritance), sometimes it's an explicit factory object (good place for "complex" creation logic).
I like to provide an explicit GetChanges() method on my aggregate that returns the internal list as an array. If my aggregate is to stay in memory beyond one execution, I also add an AcceptChanges() method to indicate the internal list should be cleared (typically called after things were flushed to the event store). You can use either a pull (GetChanges/Changes) or push (think .net event or IObservable) based model here. Much depends on the transactional semantics, tech, needs, etc ...
Your eventstream is a linked list. Each revision (event/changeset) pointing to the previous one (a.k.a. the parent). Your eventstream is a sequence of events/changes that happened to a specific aggregate. The order is only to be guaranteed within the aggregate boundary.
I almost agree with yves-reynhout and dennis-traub but I want to show you how I do this. I want to strip my aggregates of the responsibility to apply the events on themselves or to re-hydrate themselves; otherwise there is a lot of code duplication: every aggregate constructor will look the same:
UserAggregate:
ctor(eventStream)
foreach (event in eventStream)
this.Apply(event)
OrderAggregate:
ctor(eventStream)
foreach (event in eventStream)
this.Apply(event)
ProfileAggregate:
ctor(eventStream)
foreach (event in eventStream)
this.Apply(event)
Those responsibilities could be left to the command dispatcher. The command is handled directly by the aggregate.
Command dispatcher class
dispatchCommand(command) method:
newEvents = ConcurentProofFunctionCaller.executeFunctionUntilSucceeds(tryToDispatchCommand)
EventDispatcher.dispatchEvents(newEvents)
tryToDispatchCommand(command) method:
aggregateClass = CommandSubscriber.getAggregateClassForCommand(command)
aggregate = AggregateRepository.loadAggregate(aggregateClass, command.getAggregateId())
newEvents = CommandApplier.applyCommandOnAggregate(aggregate, command)
AggregateRepository.saveAggregate(command.getAggregateId(), aggregate, newEvents)
ConcurentProofFunctionCaller class
executeFunctionUntilSucceeds(pureFunction) method:
do this n times
try
call result=pureFunction()
return result
catch(ConcurentWriteException)
continue
throw TooManyRetries
AggregateRepository class
loadAggregate(aggregateClass, aggregateId) method:
aggregate = new aggregateClass
priorEvents = EventStore.loadEvents()
this.applyEventsOnAggregate(aggregate, priorEvents)
saveAggregate(aggregateId, aggregate, newEvents)
this.applyEventsOnAggregate(aggregate, newEvents)
EventStore.saveEventsForAggregate(aggregateId, newEvents, priorEvents.version)
SomeAggregate class
handleCommand1(command1) method:
return new SomeEvent or throw someException BUT don't change state!
applySomeEvent(SomeEvent) method:
changeStateSomehow() and not throw any exception and don't return anything!
Keep in mind that this is pseudo code projected from a PHP application; the real code should have things injected and other responsibilities refactored out in other classes. The ideea is to keep aggregates as clean as possible and avoid code duplication.
Some important aspects about aggregates:
command handlers should not change state; they yield events or
throw exceptions
event applies should not throw any exception and should not return anything; they only change internal state
An open-source PHP implementation of this could be found here.