DB Transaction and Integrations Events dispatch - how to make it atomic? - asynchronous

I'm designing a system with multiple bounded contexts (microservices). I will have 2 kind of events.
Domain Events, which happens "in memory" within single transaction (sync)
Integration Events, which are used between bounded contexts (async)
My problem is, how to make sure that once transaction is committed (at this point I'm sure all Domain Events were processed successfully) that Integration Events are successful as well.
When my Transaction is committed, normally I will dispatch Integration Events (e.g. to the queue), but there is possibility that this queue is down as well, so previously just-committed transaction has to be "reverted". How?
The only solution that comes to my mind is to store Integration Events to the same DB, within the same Transaction, and then process the Integration Events records and push them to the queue - this would be something like "using current DB, as a pre-queue, before pushing it to The Real Queue (however I read that using DB for this is an anti-pattern).
Is there any pattern (reliable approach) to make sure both: Transaction commit and Message pushed to the queue is an atomic operation?
EDIT
After reading https://devblogs.microsoft.com/cesardelatorre/domain-events-vs-integration-events-in-domain-driven-design-and-microservices-architectures/ , the author actually suggests the approach of "pre-queue" in same DB (he calls it “ready to publish the event”).

Checkout transactional outbox pattern.
This pattern does create a pre-queue. But the nice part is that pushing messages from pre-queue to real queue is fully decoupled. Instead you have a middleman called, a message relay that reads your transaction logs and pushes your event from to the real queue. Now since sending message and your domain events are fully decoupled, you can do all your domain events in a single transaction.
And make sure you that all your services are idempontent(same result despite duplicate calls). This transactional outbox patter does guarantee that messages are published, but in case when the message relay fails just after publishing(before acknowledging) it would publish the same event again.
Idempotent services is also necessary in other scenarios. As the event bus(the real queue) could have the same issue. Event bus propagates events, services acknowledge, then network error, then since the event bus is not acknowledged, the same event would be sent again.
Hmm actually idempotence alone could solve the whole issue. After the domain events computation completes(single transaction), if publishing message fails the service can simply throw an error without roll back. Since the event is not acknowledged the event bus will send the same event again. Now since the service is idempotent, the same database transaction will not happen twice, it will basically overwrite or better(should) skip and directly move to message publishing and acknowledging.

Related

Out-of-the-box capabilities for Spring-Kafka consumer to avoid duplicate message processing

I stumbled over Handling duplicate messages using the Idempotent consumer pattern :
Similar, but slightly different is the Transactional Inbox Pattern which acknowledges the kafka message receipt after the transaction INSERT into messages (no business transaction) concluded successfully and having a background polling to detect new messages in this table and trigger the real business logic (i.e. the message listener) subsequently.
Now I wonder, if there is a Spring magic to just provide a special DataSource config to track all received messages and discard duplicated message deliveries?
Otherwise, the application itself would need to take care to ack the kafka message receipt, message state changes and data cleanup of the event table, retry after failure and probably a lot of other difficult things that I did not yet thought about.
The framework does not provide this out of the box (there is no general solution that will work for all), but you can implement it via a filter, to avoid putting this logic in your listener.
https://docs.spring.io/spring-kafka/docs/2.7.9/reference/html/#filtering-messages

Axon4 - Re-queue failed messages

In below scenario, what would be the bahavior of Axon -
Command Bus recieved the command
It creates an event
However messaging infra is down (say kafka)
Does Axon has re-queing capability for event or any other alternative to handle this scenario.
If you're using Axon, you know it differentiates between Command, Event and Query messages. I'd suggest to be specific in your question which message type you want to retry.
However, I am going to make the assumption it's about events, as your stating Kafka.
If this is the case, I'd highly recommend reading the reference guide on the matter, as it states how you can uncouple Kafka publication from actual event storage in Axon.
Simply put, use a TrackingEventProcessor as the means to publish events on Kafka, as this will ensure a dedicate thread is used for publication instead of the same thread storing the event. Added, the TrackingEventProcessor can be replayed, thus "re-process" events.

Datastore: Failed transactions and rollbacks: What happens if rollback is not called or fails?

What happens if a transaction fails and the application crashes for other reasons and the transaction is not rolled back?
Also, what happens and how should rollback failures be treated?
You don't have to worry about the impact of your app's crashes on transaction rollbacks (or any other stateful datastore operation).
The application just sends RPC requests for the operations. The actual operation steps/sequence execution, happens on the datastore backend side, not inside your application.
From Life of a Datastore Write:
We'll dive into a bit more detail in terms of what new data is placed
in the datastore as part of write operations such as inserts,
deletions, updates, and transactions. The focus is on the backend work
that is common to all of the runtimes.
...
When we call put or makePersistent, several things happen behind
the scenes before the call returns and sets the entity's key:
The my_todo object is converted into a protocol buffer.
The appserver makes an RPC call to the datastore server, sending the entity data in a protocol buffer.
If a key name is not provided, a unique ID is determined for this entity's key. The entity key is composed of app ID | ancestor keys |
kind name | key name or ID.
The datastore server processes the request in two phases that are executed in order: commit, then apply. In each phase, the datastore
server identifies the Bigtable tablet servers that should receive
the data.
Now, depending on the client library you use, transaction rollback could be entirely automatic (in the ndb python client library, for example) or could be your app's responsibility. But even if it is your app's responsibility, it's a best-effort attempt anyways. Crashing without requesting a rollback would simply mean that some potentially pending operations on the backend side will eventually time out instead of being actively ended. See also related GAE: How to rollback a transaction?

Recommended way(s) to log events in Corda

We are capturing a new committed state in the vault through vaultTrack method on Corda RPC proxy for using in the logs recording. Although it’s working properly, we thinks it might have cause some overhead for network connection. So, we decided to try using ServiceHub in the CorDapp for capturing the new event instead. Unfortunately, the event keep occurring every time when the flow is called (based on observable concept?). Maybe we did not set up properly?. Based on your experience and expertise, could you
Suggest what went wrong; and
The corresponding solutions?
More details here:
As we are using the logs of CorDapp for a performance benchmark. Therefore, we are focusing only new committed state event. In API endpoint where we had started, we are using VaultTrack in RPC to record each new committed state event as shown in the example below:
Although the API seems to work properly but we think it might consume RPC connection in the overall performance since the observable is called every time a new state is committed. Please correct us if we're wrong. As such we decided to change to logging the events in the flow instead.
In CorDapp, we are using VaultService in ServiceHub to record each new committed state event in the ‘call function’ of flow initiator as shown in the example below:
We found that the logs recording in CorDapp i.e. in the flow (from the serviceHub mentioned above) keep gaining duplicated log every time the flow is called. From our initial investigation, we found that the problem is "vaultService" keep getting subscribed every time the flow is initiated. Therefore, we switched back to use the API endpoint method. Please could you advise us the right way to capture the event in CorDapp. To log the event of a newly committed state during our performance testing.
The approach of subscribing to a vault observable within a flow will not work. Once the flow ends, the subscription will not be terminated. Every time you run the flow, an additional subscriber will be added. This will degrade performance (although the RPC overhead is generally quite low as long as the states serialise quickly enough).
You should observe updates to the vault using an RPC client instead. Here is an example:
val client = CordaRPCClient(nodeAddress)
val proxy = client.start(rpcUserUsername, rpcUserPassword).proxy
// Track IOUState updates in the vault
val (snapshot, updates) = proxy.vaultTrack(IOUState::class.java)
// Log the existing IOUStates and listen for new ones.
snapshot.states.forEach { logState(it) }
updates.toBlocking().subscribe { update ->
update.produced.forEach { logState(it) }
}
When you call start on the CordaRPCClient, you will connect to the node's Artemis message queue. This message queue will be used to stream updates from the vault back to the client over time.
In the example above, the vault updates are simply logged. You can change this behaviour as required (e.g. to call an API whenever an update is produced).

Is it possible to suspend a flow such that it can be resumed with an RPC-call?

I am trying to implement the following use-case in Corda:
FlowA has been invoked on PartyA via startFlowDynamic. FlowA creates a partially signed transaction and invokes FlowB on PartyB via sendAndReceive. A human user shall now review and manually approve this transaction. Ideally FlowB should suspend after receiving the transaction. I would like to be able to query for suspended instances of FlowB via RPC, and present those (or rather some representation of the transaction therein) to the user in my UI. Then, after the user actions his approval, I would like to resume FlowB via RPC, which would then sign the transaction and return it to FlowA on PartyA.
I noticed that I can inspect suspended flows to some degree via CordaRPCOps.stateMachineAndUpdates and I read the tutorial on progress tracking, but it doesn't quite suffice for my case. I also read that interacting with people from flows is listed as a future feature, I just wondered if there isn't already some way to accomplish this ?
See the Negotiation Cordapp sample for an example of how this would work in practice here.
Corda doesn't currently support suspending a flow for user interaction.
However, you can support this kind of workflow as follows. Suppose you're writing a CorDapp for loan applications. You could have an initial flow that agrees the creation of a loanApplicationstate between two parties. From there, the approver can inspect the loan application, and either kick off an approve flow that creates a transaction to transform the loanApplication into an approvedLoan state, or kick off a reject flow to consume the loanApplication state without issuing an approvedLoan state.
Equally, you could add a status field to the loan state, specifying whether the loan is approved or not. Initially, the loan state would have the field set to unapproved. Then the approver could kick off one of two flows to update the loan state, to either have an approved or a rejected status.
I'm not sure if this is a "recommended approach" but I implemented a Quasar compatible AsynchListenableFuture in my flow as someone else had described here.
I needed to suspend a flow and wait for the production of a state from another flow (in response to a user interaction). It seems to work, but suspect it could be regarded as rather off-piste(?!).
Splitting the activities into atomic flows invoked directly by UI interaction is fine, but I needed a sort of "monitoring" flow to wait for an external (e.g. user) event before determining which sub flow to initiate next, and this needed to happen automatically and from within a flow already invoked prior to the the user interaction - the flow logic is then conditional on a state change which may arise from a user interaction or an incoming transaction from another node. In my case, this high level monitoring flow detects the consumption of a known state on the node, then invokes a subflow in response. The high level flow waits on the AsynchListenableFuture as described in the answer referenced above. I created a composite VaultQuery on an attribute of states of contract state types of interest (e.g custom field X = Y), and converted the returned observable (returned from trackBy.future) to a Quasar compatible AsynchListenableFuture. When the state is consumed by a transaction created by a flow triggered by the external action, the future returns and the automatic event (in my case the creation of an other transaction with another party) is executed.
I'm only experimenting / evaluating Corda, not sure how robust this approach would be in production reality, but it seems to work OK, hope this helps in some way.
Some form of higher level workflow flows in Corda, which can wait on external events and conditionally invoke other flows depending on the external action would be of real interest in my context.

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