I am looking into building a simple solution where producer services push events to a message queue and then have a streaming service make those available through gRPC streaming API.
Cloud Pub/Sub seems well suited for the job however scaling the streaming service means that each copy of that service would need to create its own subscription and delete it before scaling down and that seems unnecessarily complicated and not what the platform was intended for.
On the other hand Kafka seems to work well for something like this but I'd like to avoid having to manage the underlying platform itself and instead leverage the cloud infrastructure.
I should also mention that the reason for having a streaming API is to allow for streaming towards a frontend (who may not have access to the underlying infrastructure)
Is there a better way to go about doing something like this with the GCP platform without going the route of deploying and managing my own infrastructure?
If you essentially want ephemeral subscriptions, then there are a few things you can set on the Subscription object when you create a subscription:
Set the expiration_policy to a smaller duration. When a subscriber is not receiving messages for that time period, the subscription will be deleted. The tradeoff is that if your subscriber is down due to a transient issue that lasts longer than this period, then the subscription will be deleted. By default, the expiration is 31 days. You can set this as low as 1 day. For pull subscribers, the subscribers simply need to stop issuing requests to Cloud Pub/Sub for the timer on their expiration to start. For push subscriptions, the timer starts based on when no messages are successfully delivered to the endpoint. Therefore, if no messages are published or if the endpoint is returning an error for all pushed messages, the timer is in effect.
Reduce the value of message_retention_duration. This is the time period for which messages are kept in the event a subscriber is not receiving messages and acking them. By default, this is 7 days. You can set it as low as 10 minutes. The tradeoff is that if your subscriber disconnects or gets behind in processing messages by more than this duration, messages older than that will be deleted and the subscriber will not see them.
Subscribers that cleanly shut down could probably just call DeleteSubscription themselves so that the subscription goes away immediately, but for ones that shut down unexpectedly, setting these two properties will minimize the time for which the subscription continues to exist and the number of messages (that will never get delivered) that will be retained.
Keep in mind that Cloud Pub/Sub quotas limit one to 10,000 subscriptions per topic and per project. Therefore, if a lot of subscriptions are created and either active or not cleaned up (manually, or automatically after expiration_policy's ttl has passed), then new subscriptions may not be able to be created.
I think your original idea was better than ephemeral subscriptions tbh. I mean it works, but it feels totally unnatural. Depending on what your requirements are. For example, do clients only need to receive messages while they're connected or do they all need to get all messages?
Only While Connected
Your original idea was better imo. What I probably would have done is to create a gRPC stream service that clients could connect to. The implementation is essentially an observer pattern. The consumer will receive a message and then iterate through the subscribers to do a "Send" to all of them. From there, any time a client connects to the service, it just registers itself with that observer collection and unregisters when it disconnects. Horizontal scaling is passive since clients are sticky to whatever instance they've connected to.
Everyone always get the message, if eventually
The concept is similar to the above but the client doesn't implicitly un-register from the observer on disconnect. Instead, it would register and un-register explicitly (through a method/command designed to do so). Modify the 'on disconnected' logic to tell the observer list that the client has gone offline. Then the consumer's broadcast logic is slightly different. Now it iterates through the list and says "if online, then send, else queue", and send the message to a ephemeral queue (that belongs to the client). Then your 'on connect' logic will send all messages that are in queue to the client before informing the consumer that it's back online. Basically an inbox. Setting up ephemeral, self-deleting queues is really easy in most products like RabbitMQ. I think you'll have to do a bit of managing whether or not it's ok to delete a queue though. For example, never delete the queue unless the client explicitly unsubscribes or has been inactive for so long. Fail to do that, and the whole inbox idea falls apart.
The selected answer above is most similar to what I'm subscribing here in that the subscription is the queue. If I did this, then I'd probably implement it as an internal bus instead of an observer (since it would be unnecessary) - You create a consumer on demand for a connecting client that literally just forwards the message. The message consumer subscribes and unsubscribes based on whether or not the client is connected. As Kamal noted, you'll run into problems if your scale exceeds the maximum number of subscriptions allowed by pubsub. If you find yourself in that position, then you can unshackle that constraint by implementing the pattern above. It's basically the same pattern but you shift the responsibility over to your infra where the only constraint is your own resources.
gRPC makes this mechanism pretty easy. Alternatively, for web, if you're on a Microsoft stack, then SignalR makes this pretty easy too. Clients connect to the hub, and you can publish to all connected clients. The consumer pattern here remains mostly the same, but you don't have to implement the observer pattern by hand.
(note: arrows in diagram are in the direction of dependency, not data flow)
Related
Problem Statement
Informal State
We have some scenarios where the integration layer (a combination of AWS SNS/SQS components and etc.) is also responsible for the data distribution to target systems. Those are mostly async flows. In this case, we send a confirmation to a caller that we have received the data and will take a responsibility for the data delivery. Here, although the data is not originated from the integration layer we are still holding it and need to make sure that the data is not lost, for example, if the consumers are down or if messages, on-error, are sent to the DLQs and hence being automatically deleted after the retention period.
Solution Design
Currently my idea was to proceed with a back-up of the SQS/DLQ queues based upon CloudWatch configured alerts using ApproximateAgeOfOldestMessage metric with some applied threshold (something like the below):
Msg Expiration Event if ApproximateAgeOfOldestMessage / Message retention > Threshold
Now, more I go forward with this idea and more I doubt that this might be actually the right approach…
In particular, I would like to build something unobtrusive that can be "attached" to our SQS queues and dump the messages that are about to expire in some repository, like for example the AWS S3. Then have a procedure to recover the messages from S3 to the same original queue.
The above procedure contains many challenges like: message identification and consumption (receive message is not design to "query" for specific messages), message dump in the repository with a reference to the source queue, etc. which would suggest to me that the above approach might be a complex over-kill.
That being said, I'm aware of other "alternatives" (such as this) but I would appreciate if you could answer to the specific technical details described above, without trying to challenge the "need" instead.
Similar to Mark B's suggestion, you can use the SQS extended client (https://github.com/awslabs/amazon-sqs-java-extended-client-lib) to send all your messages through S3 (which is a configuration knob: https://github.com/awslabs/amazon-sqs-java-extended-client-lib/blob/master/src/main/java/com/amazon/sqs/javamessaging/ExtendedClientConfiguration.java#L189).
The extended client is a drop-in replacement for the AmazonSQS interface so it minimizes the intrusion on business logic - usually it's a matter of just changing your dependency injection.
I am looking into ways to order list of messages from google cloud pub/sub. The documentation says:
Have a way to determine from all messages it has currently received whether or not there are messages it has not yet received that it needs to process first.
...is possible by using Cloud Monitoring to keep track of the pubsub.googleapis.com/subscription/oldest_unacked_message_age metric. A subscriber would temporarily put all messages in some persistent storage and ack the messages. It would periodically check the oldest unacked message age and check against the publish timestamps of the messages in storage. All messages published before the oldest unacked message are guaranteed to have been received, so those messages can be removed from persistent storage and processed in order.
I tested it locally and this approach seems to be working fine.
I have one gripe with it however, and this is not something easily testable by myself.
This solution relies on server-side assigned (by google) publish_time attribute. How does Google avoid the issues of skewed clocks?
If my producer publishes messages A and then immediately B, how can I be sure that A.publish_time < B.publish_time is true? Especially considering that the same documentation page mentions internal load-balancers in the architecture of the solution. Is Google Pub/Sub using atomic clocks to synchronize time on the very first machines which see messages and enrich those messages with the current time?
There is an implicit assumption in the recommended solution that the clocks on all the servers are synchronized. But the documentation never explains if that is true or how it is achieved so I feel a bit uneasy about the solution. Does it work under very high load?
Notice I am only interested in relative order of confirmed messages published after each other. If two messages are published simultaneously, I don't care about the order of them between each other. It can be A, B or B, A. I only want to make sure that if B is published after A is published, then I can sort them in that order on retrieval.
Is the aforementioned solution only "best-effort" or are there actual guarantees about this behavior?
There are two sides to ordered message delivery: establishing an order of messages on the publish side and having an established order of processing messages on the subscribe side. The document to which you refer is mostly concerned with the latter, particularly when it comes to using oldest_unacked_message_age. When using this method, one can know that if message A has a publish timestamp that is less than the publish timestamp for message B, then a subscriber will always process message A before processing message B. Essentially, once the order is established (via publish timestamps), it will be consistent. This works if it is okay for the Cloud Pub/Sub service itself to establish the ordering of messages.
Publish timestamps are not synchronized across servers and so if it is necessary for the order to be established by the publishers, it will be necessary for the publishers to provide a timestamp (or sequence number) as an attribute that is used for ordering in the subscriber (and synchronized across publishers). The subscriber would sort message by this user-provided timestamp instead of by the publish timestamp. The oldest_unacked_message_age will no longer be exact because it is tied to the publish timestamp. One could be more conservative and only consider messages ordered that are older than oldest_unacked_message_age minus some delta to account for this discrepancy.
Google Cloud Pub-sub does not guarantee order of events receive to consumers as they were produced. Reason behind that is Google Cloud Pub-sub also running on a cluster of nodes. The possibility is there an event B can reach the consumer before event A. To Ensure ordering you have to make changes on both producer and consumer to identify the order of events. Here is section from docs.
I am implementing a process in my AWS based hosting business with an event driven architecture on AWS SNS. This is largely a learning experience with a new architecture, programming and hosting paradigm for me.
I have considered AWS Step functions, but have decided to implement a Message Bus with AWS SNS topic(s), because I want to understand the underlying event driven programming model.
Nearly all actions are performed by lambda functions and steps are coupled via SNS and/or SQS.
I am undecided if to implement the process with one or many SNS topics and if I should subscribe the core logic to the message bus(es) with one or many lambda functions.
One or many message buses
My core process currently consist of 9 events which of which 2 sets of 2 can be parallel, the remaining 4 are sequential. Subscribing these all to the same message bus is easier to set up, but requires each lambda function to check if the message is relevant to it, which seems like a waste of resources.
On the other hand I could have 6 message buses and be sure that a notified resource has something to do with the message.
One or many lambda functions
If all lambda functions are subscribed to the same message bus, it may be easier to package them all up with a dispatcher function in a single lambda function. It would also reduce the amount of code to upload to lambda, albeit I don't have to pay for that.
On the other hand I would loose the ability to control the timeout for the lambda function and any changes to the order of events is now dependent on the dispatcher code.
I would still have the ability to scale each process part, as any parts that contain repeating elements are seperated by SQS queues.
You should always emit each type of message to it's own topic, as this allows other services to consume these events without tightly coupling the two services.
Likewise, each worker that wants to consume messages should have it's own queue with it's own subscription to the topic.
Doing the following allows you to add new message consumers for a given event without having to modify the upstream service. Furthermore, responsibility over each component is clear - the service producing messages to a topic owns that topic (and the message format), whereas the consumer owns its queue and event handling semantics.
Your consumer can specify a message filter when subscribing to a topic, so it can only receive messages it cares about (documentation).
For example, a process that sends a customer survey after the customer has received their order would subscribe its queue to the Order Status Changed event with the filter set to only receive events where the new_status field is equal to shipment-received).
The above reflects principles of Service-Oriented architecture - and there's plenty of good material out there elaborating the points above.
In the context of writing a Messenger chat bot in a cloud environment, I'm facing some concurrency issues.
Specifically, I would like to ensure that incoming messages from the same conversation are processed one after the other.
As a constraint, I'm processing the messages with workers in a Cloud environment (i.e the worker pool is of variable size and worker instances are potentially short-lived and may crash). Also, low latency is important.
So abstracting a little, my requirements are:
I have a stream of incoming messages
each of these messages has a 'topic key' (the conversation id)
the set of topics is not known ahead-of-time and is virtually infinite
I want to ensure that messages of the same topic are processed serially
on a cluster of potentially ephemeral workers
if possible, I would like reliability guarantees e.g making sure that each message is processed exactly once.
My questions are:
Is there a name for this concurrency scenario?.
Are there technologies (message brokers, coordination services, etc.) which implement this out of the box?
If not, what algorithms can I use to implement this on top of lower-level concurrency tools? (distributed locks, actors, queues, etc.)
I don't know of a widely-accepted name for the scenario, but a common strategy to solve that type of problem is to route your messages so that all messages with the same topic key end up at the same destination. A couple of technologies that will do this for you:
With Apache ActiveMQ, HornetQ, or Apache ActiveMQ Artemis, you could use your topic key as the JMSXGroupId to ensure all messages with the same topic key are processed in-order by the same consumer, with failover
With Apache Kafka, you could use your topic key as the partition key, which will also ensure all messages with the same topic key are processed in-order by the same consumer
Some message broker vendors refer to this requirement as Message Grouping, Sticky Sessions, or Sticky Message Load Balancing.
Another common strategy on messaging systems with weaker delivery/ordering guarantees (like Amazon SQS) is to simply include a sequence number in the message and leave it up to the destination to resequence and request redelivery of missing messages as needed.
I think you can fix this by using a queue and a set. What I can think of is sending every message object in queue and processing it as first in first out. But while adding it in queue add topic name in set and while taking it out for processing remove topic name from set.
So now if you have any topic in set then don't add another message object of same topic in queue.
I hope this will help you. All the best :)
Should WS-notification (WS Notification) be used to just notify or should the data also be transmitted with the payload to save an extra call(back).
Use Case:
A customer's record has changed. Need to notify other systems. Sends a notification.
Scenario 1.
Send the notification with customer record changes. Could be bad since each listening system might do a different action or may or may not need the customer record.
Scenario 2.
Just send the notification. Means that each listening system will have to "react" in some way. Responsibility is on the listening system.
Two methods.
Pub/Sub Push and Pub/Sub Pull.
Pub/Sub Push is to push out the full data.
Pub/Sub Pull is to send enough data for the target app to call back and request the full data. This allows better control of information passed than the pub/sub push method.
Pub/Sub Push method is the easiest to implement.
Pub-sub kind of implies that the notification consumers are already interested in the topic in question by virtue of the fact that they have subscribed. However, as you say, they may not need to respond. So if you consider the notification to be a true event then the notifying system is saying, "here is a notification that my state has changed". If the notification consumer is interested it can use request-response to get that new state. This would be more flexible and lightweight.
Notifications are inherently more event-oriented and therefore using them to push state should be considered carefully. Particularly as with pub-sub you seldom have an idea as to how many subscribers you have at run time - then capacity planning can be difficult and peak load spikes are not uncommon.
So keep the notifications lightweight. Let the consumers decide if they're to act on the event. You're on your way to a true EDA!