According to the google doc, a service running in the flexible enviroment can be the target of a push task:
Outside of the standard environment, you can't add tasks to push
queues, but a service running in the flexible environment can be the
target of a push task. You can specify this using the target parameter
when adding a task to queue or by specifying the default target for
the queue in queue.yaml.
However, when I tried to do it I get 404 errors in the flexible service.
That's totally normal due to the required endpoint (/_ah/queue/deferred) for task queues is it not defined in the flexible service.
How do I become a flexible service in a valid target for task queues?
Do I have to define that endpoint in my code in some way?
Usually, you'll need to write a handler in your worker service to do the processing after receiving a task. In the case of push tasks, the service will send HTTP requests to your whatever url you specify. If no url is specified the default URL /_ah/queue/[QUEUE_NAME] will be used.
Now, from the endpoint you mention, it seems you are using deferred tasks, which are a somewhat special kind. Please, see this thread for a workaround by adding the needed url entry. It mentions Managed VMS but it should still work.
Related
I am using Camunda workflows to automate various processes. I have come across a scenario where the process is not moving from a service task. Usually, we call the task/{taskid}/complete to complete the task, but since the process is stuck on a service task, I am not able to complete that task. Can anybody help me find a way to complete the service task?
You are using a service task. That basically means "a machine should do something". The "normal" implementation is to provide code (a java Delegate or a connector endpoint) that is called by the process engine to execute this task.
The alternativ is to use the "external task" pattern. Think of external tasks as "user tasks for computers". So the process waits, tells subscribed clients that a job is to be done and waits for their completion.
I suppose your process uses the second option? (you can check in the modeler under "Implementation"). So completion can be done through the external task API, see docs.
/external-task/{id}/complete
If it is a connector then you likely will see when checking the log that retries have occurred and that the transaction rolled back. After addressing the underlying issue the service task (email) should be sent without explicitly triggering the service task and the following user task (Approval) should be created.
I would like to know if ethtx tasks can be used with webhook jobs. I tried to run the job from the operator UI and it returns an internal server error, when I look up the logs it says that
Expected at least one task to be final pipeline/common.go:212 logger=1.2.1#168d34a stacktrace=github.com/smartcontractkit/chainlink/core/services/pipeline.TaskRunResults.FinalResult
/chainlink/core/services/pipeline/common.go:212
which I assume it means it cannot work unless ethtx is a final task such as jsonparse/multiply etc
Webhook jobs are limited in the task flow I believe, such that there is no support for webhook jobs and on-chain tx's related to that webhook job. In general, node operators utilize webhook jobs, to verify there job-spec code within the "observationSource" such that a bridge api endpoint is properly connected, and filtering to the correct path that the downstream user wants. Because there is no on-chain tx recorded, the testing can be done immediately after job-creation and no test smart contract to ping the oracle/node.
https://docs.chain.link/docs/jobs/types/webhook/
I registered my task app in Spring Cloud Data Flow, created a definition for it and the status shows 'unknown'. I created the stream and trying to launch the task through task-sink and I get an error:
java.lang.IllegalStateException: failed to resolve MavenResource:
How to launch a task from the task-sink? Am I missing something? Any help is appreciated. Another question I have is how do I access the payload sent via TaskLaunchRequest in my task?
S1 http | step1: transformer-rabbit | log
S2 :S1.step1 > filter --expression=payload.contains('CUSTADDRMODRQ_V15') | task-processor | task-sink
task-sink is launching the task provided by the uri in the TaskLaunchRequest. It is looking for the resource as shown in the log
OUT Using manager EnhancedLocalRepositoryManager with priority 10.0 for /home/vcap/.m2/repository
OUT Using transporter HttpTransporter with priority 5.0 for https://repo.spring.io/libs-snapshot and finally failing.
The task is deployed in our repository and as mentioned I registered and created the definition for it as well.
This one is in cf environment and I am using SCDF server 1.0.0.M4.
In the application.properties for the task-sink i am providing maven.remote.repositories.snapshots.url=**
task create fis-ifx-event-task --definition "fis-event-task"
My goal is launching the task from the stream.
Thanks for the information. I am in fact using the BUILD-SNAPSHOT as I am unable to enable taks in 1.0.0M4 version. Here is the one I am using spring-cloud-dataflow-server-cloudfoundry-1.0.0.BUILD-20160808.144306-116. I am able to register and create task definitions. The status of the task definition is showing as 'unknown' even when I am using the sample task module provided by your team. But when I initiate the flow of the stream and when task-sink tries to launch the task, it is unable to find the maven resource. When I create the task definition, does the task module gets deployed? I don't see any app in Pivotal Apps Manager. As mentioned earlier, I provided maven.remote.repositories.snapshot.url in the application.properties file for the task-sink application. Another thing I observed is when I launch the task manually from dataflow shell it gives an error CF-UnprocessableEntity(10008): The request is semantically invalid: Unknown field(s): 'staging_disk_in_mb', 'staging_memory_in_mb' and also a message saying 'Source is empty'. Presently the task is supposed to print the timestamp and is not dependent on any input.
TaskProcessor code:
#EnableBinding(Processor.class)
#EnableConfigurationProperties(TaskProcessorProperties.class)
public class TaskProcessor {
#Autowired
private TaskProcessorProperties processorProperties;
public TaskProcessor() {
}
#Transformer(inputChannel = Processor.INPUT, outputChannel = Processor.OUTPUT)
#ELI(level = "info", eventType = ELIEventType.INBOUND)
public Object setupRequest(String message) {
Map<String, String> properties = new HashMap<String, String>();
properties.put("payload", message);
TaskLaunchRequest request = new TaskLaunchRequest(processorProperties.getUri(), null, properties, null);
return new GenericMessage<>(request);
}
}
TaskSink code:
#SpringBootApplication
#EnableTaskLauncher
#EnableBinding(Sink.class)
#EnableConfigurationProperties(TaskSinkProperties.class)
public class FisIfxEventTaskSinkApplication {
public static void main(String[] args) {
SpringApplication.run(FisIfxEventTaskSinkApplication.class, args);
}
}
I provided the stream I am using earlier in the post. Sink is receiving the TaskLaunchRequest with uri and payload as you can see here and unable to launch the task.
OUT registering [40, java.io.File] with serializer org.springframework.integration.codec.kryo.FileSerializer
2016-08-10T16:08:55.02-0600 [APP/0]
OUT Launching Task for the following resource TaskLaunchRequest{uri='maven://com.xxx:fis.ifx.event-task:jar:1.0-SNAPSHOT', commandlineArguments=[], environmentProperties={payload={"statusCode":0,"fisT
opic":"CustomerDataUpdated","payloadId":"CUSTADDRMODR``Q_V15","customerIds":[1597304]}}, deploymentProperties={}}
Before I begin, you have a number of questions here. In the future, it's better to break them up into multiple questions so that they are easier to find by other users and easier to answer. That being said:
A little context on the current state of things
In order to understand how things will work, it's important to understand the current state of things. The current releases of the software involved are:
Pivotal Cloud Foundry (PCF) - 1.7.12. This version is required for any task support.
Spring Cloud Task (SCT) - 1.0.2.RELEASE
Spring Cloud Data Flow CF (SCDF) - 1.0.0.BUILD-SNAPSHOT (current as of the date of this post).
Currently PCF 1.7.12+ has all the capabilities to run tasks. You can create v3 applications (the type of application used to launch a task), run it as a task, etc. However, the tooling around that functionality is not currently complete. There is no support for v3 applications in Apps Manager or the CLI. There is a plugin for the CLI that is more of a dev tool that can be used to help with some functions (it will show you logs, etc), but it is not fully functional and requires a specific version of the CLI to work [1]. This is one of the reasons that the task functionality within PCF is still considered experimental.
Spring Cloud Task is currently GA and supports all the functionality needed to effectively run tasks on CF. However, it's important to note that SCT doesn't handle orchestration so the actual launching of tasks on CF is the responsibility of either the user, or Spring Cloud Data Flow (the easier route).
Spring Cloud Data Flow's Cloud Foundry server implementation currently has functionality to launch tasks on PCF in the latest snapshots. We have validated this against 1.7.12 as well as the development branch of 1.8.
The task workflow within SCDF
Tasks are fundamentally different from stream applications within the context of SCDF. When you create a stream definition, you are given the option to deploy it. What this does is it actually downloads the Spring Boot über jars and deploys them to PCF as long running processes. If they go down, PCF, will relaunch them as expected, etc.
Tasks on the other hand, are not deployed. They are launched. The difference is that while you create a task definition, there is nothing deployed until you click launch. And when the task completes, the software is shut down and cleaned up. So while a stream definition may have states, it's really a one to one relationship between the definition and the deployed software. Where with a task, you can launch a task definition as many times as you want.
Your issues
Reading through your post, I see a few things that you are struggling with. Let me see if I can help:
Task Definitions within SCDF and launching them via a stream - When launching a task from a stream, the task registry within SCDF is not used. The sink expects the URL for the resource to be within the TaskLauchRequest.
Apps Manager and tasks - As mentioned above, there is no support for v3 applications in Apps Manager yet so you won't be able to see your tasks there.
Viewing the logs - In order to debug what's going wrong with launching your task on CF, you're going to want to view the logs. To do so, use the v3 CLI plugin mentioned above to view them. It's important to note that you can only tail live logs with the plugin, not view logs that have previously been rendered. Because of that, when testing, you'll want to tail the logs as soon as the app is created, before it's launched.
Error in SCDF Shell - The error you received from the SCDF shell (CF-UnprocessableEntity(10008):...) leads me to wonder if you have both the correct version of PCF (1.7.12+) and the correct version of the following other libraries:
spring-cloud-deployer-cloudfoundry - The latest snapshots
cf-java-client - 2.0.0.M10+
reactor-core - 3.0.0.RC1+
I hope this helps!
[1] https://github.com/cloudfoundry/v3-cli-plugin
Task support is not available in 1.0.0.M4 release of SCDF's CF-server. In this release, the task commands/REST-APIs should be disabled - see here. And for that reason, you wouldn't see any docs related to Tasks in the 1.0.0.M4 reference guide.
That said, the Task support is available/enabled in the BUILD-SNAPSHOT release. If you're locally building the CF-server and upon pushing it to CF, you could take advantage the task commands in the shell to create and launch task definitions.
Following is my use case
Bunch of applications enqueue messages in Kafka under different topics.
Have consumer of each topic distribute the work to a worker in a cluster. The work can be classified as long running, memory intensive, simple etc and the worker is chosen accordingly.
This has me exploring Akka cluster for work distribution, routing and scaling. I can use Akka "Supervisor" as a Kafka consumer and assign incoming work to the appropriate worker based on its classification.
But what I am still trying to understand is the correct way to implement a resilient way of communication between the supervisor and workers in the Akka cluster. Because as soon as the supervisor consumes the message from Kafka, the Kafka offset is committed. If some error happens in processing after the offset commit, is the following acceptable way to recover and start from where it was last left?
Make the supervisor a persistent actor by using durable mailbox backed by Kafka. Supervisor enqueues work in Kafka and worker gets its work from Kafka and commits its offset only after completing the work.
As said by Jaakko, it really depends on the third-part library you are using.
As far as I'm concerned I have successfully used Akka Streams Kafka although I did enable offset auto-commit.
However, this library may meet your needs since it allows you to customize offset commit (see sections External Offset Storage and Offset Storage in Kafka).
The documentation says:
The Consumer.committableSource makes it possible to commit offset positions to Kafka. Compared to auto-commit this gives exact control of when a message is considered consumed.
In order to disable auto-commit, you have to complete your Akka application.conf file by adding an akka.kafka.consumer section:
akka.kafka.consumer {
# Properties defined by org.apache.kafka.clients.consumer.ConsumerConfig
# can be defined in this configuration section.
kafka-clients {
# Disable auto-commit by default
enable.auto.commit = false
}
}
Last version of akka-stream-kafka_2.11 (version 0.16) is compatible with Akka 2.5.x but you have to override akka-stream_2.11 dependency with the one of the Akka toolkit. Currently, I am using this library with Akka 2.5.3 and it works really well.
Hope you will find what your are looking for :)
One of the characteristics I love most about Google's Task Queue is its simplicity. More specifically, I love that it takes a URL and some parameters and then posts to that URL when the task queue is ready to execute the task.
This structure means that the tasks are always executing the most current version of the code. Conversely, my gearman workers all run code within my django project -- so when I push a new version live, I have to kill off the old worker and run a new one so that it uses the current version of the code.
My goal is to have the task queue be independent from the code base so that I can push a new live version without restarting any workers. So, I got to thinking: why not make tasks executable by url just like the google app engine task queue?
The process would work like this:
User request comes in and triggers a few tasks that shouldn't be blocking.
Each task has a unique URL, so I enqueue a gearman task to POST to the specified URL.
The gearman server finds a worker, passes the url and post data to a worker
The worker simply posts to the url with the data, thus executing the task.
Assume the following:
Each request from a gearman worker is signed somehow so that we know it's coming from a gearman server and not a malicious request.
Tasks are limited to run in less than 10 seconds (There would be no long tasks that could timeout)
What are the potential pitfalls of such an approach? Here's one that worries me:
The server can potentially get hammered with many requests all at once that are triggered by a previous request. So one user request might entail 10 concurrent http requests. I suppose I could have a single worker with a sleep before every request to rate-limit.
Any thoughts?
As a user of both Django and Google AppEngine, I can certainly appreciate what you're getting at. At work I'm currently working on the exact same scenario using some pretty cool open source tools.
Take a look at Celery. It's a distributed task queue built with Python that exposes three concepts - a queue, a set of workers, and a result store. It's pluggable with different tools for each part.
The queue should be battle-hardened, and fast. Check out RabbitMQ for a great queue implementation in Erlang, using the AMQP protocol.
The workers ultimately can be Python functions. You can trigger workers using either queue messages, or perhaps more pertinent to what you're describing - using webhooks
Check out the Celery webhook documentation. Using all these tools you can build a production ready distributed task queue that implements your requirements above.
I should also mention that in regards to your first pitfall, celery implements rate-limiting of tasks using a Token Bucket algorithm.