If the stateful stream application is started with 6 threads on a single node, would the above exception occur?
Is there any process that needs to be followed, if a stateful stream application started on node 1 consuming a particular topic, is made to run on different node?
If the stateful stream application is started on 2 nodes and if the above exception occurs, would the stream application terminate immediately?
If yes, where can this exception be caught in a try-catch block?
If the exception can be caught, and if we add sleep for 10 mins, would the store automatically gets to valid state?
If not, is there a method that can be used to check the store state and wait until it becomes valid?
Follow-up:
If the stateful stream application is started with 6 threads on a single node, would the above exception occur?
It can
Essentially I was wondering if we keep the entire topic consumption on a single node, would it avoid re-building the store from an internal topic if a re-balancing occurs, due to one of the thread going down/terminates?
store is not ready yet: you can wait until the store is ready -- best to register a restore callback (check the docs for details) to get informed when restore is finished and you can retry to query the store.
Sorry, just to be clear on the above, is it StateRestoreCallback OR StateRestoreListener? I assume it is the later one. Also, is it required to override StateRestoreCallback and include logic to restore the store?
InvalidStateStoreException can have different causes, thus, it's hard to answer your question without more context.
If the stateful stream application is started with 6 threads on a single node, would the above exception occur?
It can.
Is there any process that needs to be followed, if a stateful stream application started on node 1 consuming a particular topic, is made to run on different node?
No.
If the stateful stream application is started on 2 nodes and if the above exception occurs, would the stream application terminate immediately?
Depends where the exception it thrown:
Either, the corresponding StreamThread would die, but the application would not terminate automatically. You should register an uncaught exception handler on the KafkaStreams instance and react to an dying thread with custom code (like, terminating the application).
If it is thrown from KafkaStreams using interactive queries, StreamThread would not be affected.
Where can this exception be caught in a try-catch block?
Usually yes, especially if you refer to interactive queries feature.
if we add sleep for 10 mins, would the store automatically gets to valid state?
If you refer to interactive queries feature, sleeping is not a good strategy. There are multiple causes for the exception and you need to react accordingly:
store is not local but on different node: you can figure this out by check the store metadata.
store is not ready yet: you can wait until the store is ready -- best to register a restore listener (check the docs for details) to get informed when restore is finished and you can retry to query the store.
Update
Essentially I was wondering if we keep the entire topic consumption on a single node, would it avoid re-building the store from an internal topic if a re-balancing occurs, due to one of the thread going down/terminates?
Yes (for non-EOS case). Other threads would detect the local store and reuse it.
StateRestoreCallback OR StateRestoreListener
Yes, it's StateRestoreListener. You would implement StateRestoreCallback only if you write a custom state store.
Related
I am currently developing a kinda complex workflow with camunda. The goal of this workflow is to orchestrate the execution of different external business processes. Which includes start, overwatch and synchronize these workflows. Everything besides the synchronization works as expected.
Example:
My example has one main workflow which starts multiple sub workflows. The main workflow has to be aware when all sub workflows are finished. Every sub workflow is triggered by a message and sends a message back to the main workflow at the end of execution. Therefore, all sub workflows should be synchronized in the main workflow.
Xml can be accessed on this site: https://pastebin.com/2aj4z0zU
Unfortunately, this leads to numerous message correlation exceptions at the choke point in the main workflow (1st lane, after the first parallel gateway). I am using the following code to correlate the messages:
this.runtimeService.createMessageCorrelation(messageName)
.processInstanceId(processInstanceId)
.setVariables(payload)
.correlate();
The whole workflow is executable and runs without errors, but only if one example_workflow at a time is executed. Starting multiple example_workflows quickly one after another results in this type of exception randomly for every message type:
ENGINE-16004 Exception while closing command context: Cannot correlate message 'PROCESS_B_FINISHED': No process definition or execution matches the parameters org.camunda.bpm.engine.MismatchingMessageCorrelationException: Cannot correlate message 'PROCESS_B_FINISHED': No process definition or execution matches the parameters
at org.camunda.bpm.engine.impl.cmd.CorrelateMessageCmd.execute(CorrelateMessageCmd.java:88) ~[camunda-engine-7.14.0.jar!/:7.14.0]
Currently, the correlation exceptions occur if a postgresql database is used. The same workflow runs much better, but not perfect, when we use a h2 file-based database. All receive tasks are not configured asynchronously, only send tasks are (async before + exclusive).
Questions:
Is this already the best practice to synchronize multiple messages in one workflow?
What could be the reason for the correlation exceptions while using a postgresql database?
Used software:
spring boot application [Version:2.3.4]
camunda [Version:7.14.0]
h2 [Version:1.4.200]
postgresql [Version:42.2.22]
the process model seems to contain sequences where it can run into a deadlock (What if blue is followed directly by green? Or yellow?) or where you have race conditions. If the process has not reached a state where it is in a receiving state for the message, then the message delivery will fail (as indicated in the error message you shared)
(The reason you are observing the CorellationException more frequently on postgresql if the race condition. With this external database some operations take slightly more time, increasing the chance of the race condition occurring).
The process engine needs to be able to match a message to a unique receiver. If there are multiple potential receivers for the same message name, and no other correlation criteria creating a unique match is provided, then the delivery will also fail. You either need to use unique message names per instance or better use a businessKey or a process data which is unique per instance as additional correlation criteria. This is why it does not work when you run multiple process instances.
Modelling a workflow with this parallel message bottleneck leads to a race condition, as mentioned by #rob2universe's post.
To solve this problem, I had firstly to correlate the messages directly. I did this by adding a unique identifier to every message, which was not a big deal due to the fact that an item ID was defined within the payload of every message. Secondly, I had to remove all asynchronous and exclusive markers for every receive task and connected gateways. And thirdly, I had to reset the job executor properties to default values. Limiting the pool size and jobs per acquisition did not benefit the workflow execution.
After all these changes, my workflow now runs as expected with no errors. Unfortunately, due to the described bottleneck optimistic logging exceptions are common, but the workflow engine handles these exceptions without further errors.
we are currently working in a message driven Microservice environment and some of our messages/events are event sourced (using Apache Kafka). Now we are struggling with implementing more complex business requirements, were we have to take multiple events into account to create new events and side effects.
In the current situation we are working with devices that can produce errors and we already process them and have a single topic which contains ERROR_OCCURRED and ERROR_RESOLVED events (so they are in order). We also make sure, that all messages regarding a specific device always go onto the same partition. And both messages share an ID that identifies that specific error incident. We already have a projection that consumes those events and provides an API for our customers, s.t. they can see all occurred errors and their current state.
Now we have to deal with the following requirement:
Reporting Errors
We need a push system that reports errors of devices to our external partners, but only after 15 minutes and if they have not been resolved in that timeframe. Our first approach was to consume all ERROR_RESOLVED events, store the IDs and have another consumer that is handling the ERROR_OCCURRED events in a delayed fashion (e.g. by only consuming the next ERROR_OCCURRED event on the topic if its timestamp is at least 15 minutes old). We would then be able to know if that particular error has already been resolved and does not need to be reported (since they share a common ID with the corresponding ERROR_RESOLVED event). Otherwise we send an HTTP request to our external partner and create an ERROR_REPORTED event on a new topic. Is there any better approach for delayed and conditional message processing?
We also have to take the following special use cases into account:
Service restarts: currently we are planning to keep the list of resolved errors in memory, so if a service restarts, that list has to be created from scratch. We could just replay the ERROR_RESOLVED messages, but that may take some time and in that time no ERROR_OCCURRED events should be processed because that may result in reporting errors that have been resolved in less then 15 minutes, but we are just not aware of it. Are there any good practices regarding replay vs. "normal" processing?
Scaling: we may increase or decrease the number of instances of our service at any time, so the partition assignment may change during runtime. That should not be a problem if we create a consumer group for each service instance when consuming the ERROR_RESOLVED events, s.t. every instance knows all resolved errors while still only handling the ERROR_OCCURRED events of its assigned partitions (in another consumer group which is shared by all instances). Is there a better approach for handling partition reassignment and internal state?
Thanks in advance!
For side effects, I would record all "side" actions in the event store. In your particular example, when it is time to send a notification, I would call SEND_NOTIFICATION command that emit NOTIFICATION_SENT event. These events would be processed by some worker process that does actual HTTP request.
Actually I would elaborate this even furter, since notifications could fail, so I would have, say, two events NOTIFICATION_REQUIRED, and NORIFICATION_SENT, so we can retry failed notifications.
And finally your logic would be "if error was not resolved in 15 minutes and notification was not sent - send a notification (or just discard if it missed its timeframe)"
We are using microservices, cqrs, event store using nodejs cqrs-domain, everything works like a charm and the typical flow goes like:
REST->2. Service->3. Command validation->4. Command->5. aggregate->6. event->7. eventstore(transactional Data)->8. returns aggregate with aggregate ID-> 9. store in microservice local DB(essentially the read DB)-> 10. Publish Event to the Queue
The problem with the flow above is that since the transactional data save i.e. persistence to the event store and storage to the microservice's read data happen in a different transaction context if there is any failure at step 9 how should i handle the event which has already been propagated to the event store and the aggregate which has already been updated?
Any suggestions would be highly appreciated.
The problem with the flow above is that since the transactional data save i.e. persistence to the event store and storage to the microservice's read data happen in a different transaction context if there is any failure at step 9 how should i handle the event which has already been propagated to the event store and the aggregate which has already been updated?
You retry it later.
The "book of record" is the event store. The downstream views (the "published events", the read models) are derived from the book of record. They are typically behind the book of record in time (eventual consistency) and are not typically synchronized with each other.
So you might have, at some point in time, 105 events written to the book of record, but only 100 published to the queue, and a representation in your service database constructed from only 98.
Updating a view is typically done in one of two ways. You can, of course, start with a brand new representation and replay all of the events into it as part of each update. Alternatively, you track in the metadata of the view how far along in the event history you have already gotten, and use that information to determine where the next read of the event history begins.
Inside your event store, you could track whether read-side replication was successful.
As soon as step 9 suceeds, you can flag the event as 'replicated'.
That way, you could introduce a component watching for unreplicated events and trigger step 9. You could also track whether the replication failed multiple times.
Updating the read-side (step 9) and flagigng an event as replicated should happen consistently. You could use a saga pattern here.
I think i have now understood it to a better extent.
The Aggregate would still be created, answer is that all the validations for any type of consistency should happen before my aggregate is constructed, it is in case of a failure beyond the purview of the code that a failure exists while updating the read side DB of the microservice which needs to be handled.
So in an ideal case aggregate would be created however the event associated would remain as undispatched unless all the read dependencies are updated, if not it remains as undispatched and that can be handled seperately.
The Event Store will still have all the event and the eventual consistency this way is maintained as is.
I have core data nested contexts setup. Main queue context for UI and saving to SQLite persistent store. Private queue context for syncing data with the web service.
My problem is the syncing process can take a long time and there are the chance that the syncing object is deleted in the Main queue context. When the private queue is saved, it will crash with the "Core Data could not fulfill faulted" exception.
Do you have any suggestion on how to check this issue or the way to configure the context for handle this case?
There is no magic behind nested contexts. They don't solve a lot of problems related to concurrency without additional work. Many people (you seem to be one of those people) expect things to work out of the box which are not supposed to work. Here is a little bit of background information:
If you create a child context using the private queue concurrency type then Core Data will create a queue for this context. To interact with objects registered at this context you have to use either performBlock: or performBlockAndWait:. The most important thing those two methods do is to make sure to invoke the passed block on the queue of the context. Nothing more - nothing less.
Think about this for a moment in the context of a non Core Data based application. If you want to do something in the background you could create a new queue and schedule blocks to do work on that queue in the background. If your job is done you want to communicate the result of the background operations to another layer inside your app logic. What happens when the user deleted the object/data in the meantime which is related to the results from the background operation? Basically the same: A crash.
What you experience is not a Core Data specific problem. It is a problem you have as soon you introduce concurrency. What you need is to think about a policy or some kind of contract between your child and parent contexts. For example, before you delete the object from the root context you should cancel all of the operations/blocks which are running on other queues and wait for the cancellation to finish before you actually delete the object.
Application server creates a new transaction before calling MDB's onMessage method. Also I am processing database update in onMessage method. Transactions create additional overhead and processing several message in one transaction could increase performance.
Is it possible to make App server to use one transaction for several messages. Or maybe there are other approaches to this problem?
And, by the way, I can't use multiple instances, cause I need to preserve the sequence order.
I guess you can store the messages in a list and depending upon how many messages you want to process in one transaction you can check the size of the list and process the messages.