We have a Reactive REST API using Spring Data Neo4j (SpringBoot v2.7.5) deployed to Kubernetes. When running a stress test to determine the breaking point, once the volume of requests that the service can handle has been breached, the service does not auto-recover, even after the load has dropped to a level at which the service can handle.
After the service has fallen over the Neo4J health indicator shows:
“org.neo4j.driver.exceptions.ClientException: Unable to acquire connection from the pool within configured maximum time of 60000ms”
With respect to connection/configuration settings we are using defaults configured by SDN.
Observations:
Up until the point at which the service breaks only a small number of connections are utilised, at the point at which it breaks the connections in use jumps up to the max pool size and the above mentioned error is observed. No matter how much time passes (even well beyond the max connection lifetime) the service is unable to acquire a connection from the pool. Upon manually shutting down and restarting the service/pod the service returns to a healthy state.
As an interim solution we now check the Neo4J health indicator, if the mentioned error is present the liveness state is set to down which triggers Kubernetes to restart the service automatically. However, I’m wondering if there is an underlying issue with the connections in the pool not getting ‘cleaned up’?
You can take a look at this discussion https://github.com/spring-projects/spring-data-neo4j/issues/2632
I had the same issue. The problem is that either Spring Framework or Neo4j reactive transaction manager doesn't close connections in a complex reactive flow e.g. when there are a lot of inner calls/mappings and somewhere inside an exception is thrown.
So as a workaround you can add #Transactional in such places to avoid multiple transactions to be created.
I have deployed an application (frontend and backend) in App Engine. First of all, I am using the free tier and I chose the default F1 for the frontend and B2 for the backend. I don't exactly understand the difference between B and F instances but based on their names, I chose them for backend and frontend respectively.
My backend is a Flask application that reads some data from Firestore on #app.before_first_request and "pre-caches" it for all future requests. This takes about 20-30 seconds before the first request is served so I really don't want the backend instance to become undeployed all the time.
Right now, my backend successfully serves one request (that I am making from the browser) and then immediately gets undeployed (basically I see no active instances in App Engine dashboard after the request is served). This means that every request once again has the same long delay upon server start that I don't want. I am not sure why this is happening because I've set idle timeout to 5 minutes. I know it is not a problem with my Flask application because it does not crash after a request on a local machine and I've done its memory profiling which is in bounds of B2 limits. This is my app.yaml for the backend:
runtime: python38
service: api
env_variables:
PORT: 8080
instance_class: B2
basic_scaling:
max_instances: 1
idle_timeout: 5m
Any insight would be appreciated!
Based on the information and behavior that you are exposing, please allow me to explain to you that both Scaling models are behaving as they are designed to do so.
“Automatic Scaling: It creates instances based on request rate, response latencies, and other application metrics. You can specify thresholds for each of these metrics, and a minimum number instances to keep running always.
Basic Scaling: Basic scaling creates instances only when your application receives requests. Each instance will be shut down when the application becomes idle. Basic scaling is ideal for work that is intermittent or driven by user activity.”
Use the following URL’s documentation as reference for those models and more of them How Instances are Managed.
Information added on 10/12/2021:
Hi,
I think the correct term is “shutdown” instead of “undeployed” Disabling your application. Looking at Instance States "an instance of a manual or basic scaled service can be either running or stopped. All instances of the same service and version share the same state." then looking at Scaling types "Basic scaling creates instances when your application receives requests. Each instance will be shut down when the application becomes idle. Basic scaling is ideal for work that is intermittent or driven by user activity." and the table's Startup and shutdown row for basic scaling "Instances are created on demand to handle requests and automatically shut down when idle, based on the idle_timeout configuration parameter. An instance that is manually stopped has 30 seconds to finish handling requests before it is forcibly terminated." and Scaling down "You can specify a minimum number of idle instances. Setting an appropriate number of idle instances for your application based on request volume allows your application to serve every request with little latency".
Could you please verify:
that the instance was not manually halted?
that instance is becoming idle?
that there are no background threads?
if functionality is the same when setting the max_instances to 2
that there are no logs showcasing an instance shutdown
that they are reaching the version with the updated the idle_timeout set
Is it possible to setup an alert based on the status of a custom service. For example, stackdriver-agent service crashed at one point. When running 'service stackdriver-agent status" I receive an 'Active: inactive (dead)' response.
Is it possible to setup an alert based on the condition above? The stackdriver-agent service is just an example. In theory, I would like to setup this alert condition on any service.
The answer is yes. In Stackdriver you can set up an alarm for any process in your machine. Selecting the option Add Process Health Condition you can configure alarms to receive notifications if your process starts or stops. Bear in mind that you first have to set up the Stackdriver Agent in your machine and that this option is only available in Stackdriver premium.
Thrahir's answer is a good one, though the UI has changed since then (click the right arrow next to "Metric" and "Uptime Check" to see other condition types; "Process Health" is the very last one).
If your service is a server, you might rather use an uptime check (https://cloud.google.com/monitoring/uptime-checks/) to monitor its state; that gives you a better analog to what the service's users will see than directly monitoring your processes does.
Aaron Sher, Stackdriver engineer
In our beta stack, we have a single EC2 instance listening to a tasklist. Sometimes another developer in the team start's his own instance for testing purposes and forget to turn it off. This creates problems for the next developer who tries to start an activity only for it to be taken up by the last developer's machine. Is there a way to get the hostnames of all activity workers listening to a particular tasklist ?
It is not currently possible to get a list of pollers waiting on a task list through the SWF API. The workaround is to look at the identity field on the ActivityExecutionStarted event after it was picked up by the wrong worker.
One way to avoid this issue is always use a task list name that is specific to a machine or developer to avoid collisions.
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.