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.
Related
I want to achieve batch consuming of a PubSub subscription, retrieving all the messages that were in the subscription at the begining of my process. To do so, I use PubSub's asynchronous pulling for Java, and the consumer.ack() and consumer.nack() functions to process exactly the number of messages that I want, and make the subscription redeliver the messages that I have received but not processed yet. My problem being that I did not managed to find a way to retrieve the real time count of messages in my subscription.
I have started to request pubsub.googleapis.com/subscription/num_undelivered_messages metric from Google Cloud Monitoring, but unfortunately the metric has a ~3 minutes latency with the real count of undelivered messages in the subscription.
Is there any way to retrieve this message count on real time ?
There is no way to retrieve the message count in real time, no. Also keep in mind that such a number would not be sufficient to retrieve all of the messages that were in the subscription at the beginning of the process unless you can guarantee that no publishing is happening at the same time.
If there is publishing, then your subscriber could get those messages before messages published earlier, unless you are using ordered message delivery and even still, those delivery guarantees are per ordering key, not a total ordering guarantee. If you can guarantee that there are no publishes during this time and/or you are only bringing the subscriber up periodically, then it sounds more like a batch case, which means you may want to consider a database or a GCS file as an alternative place to store the messages for processing.
We have a project which receives data from sensors and then we send this data to GCP. For this we have used GCP's Pub/Sub model. Issue here is when we pull the messages, they are not in ordered manner. So we are not able to verify that the data we have sent to GCP has reached there or not.
Also GCP has mentioned that they don't guarantee the order of messages https://cloud.google.com/pubsub/docs/ordering
Any better way to verify this messages, other than the solutions recommended by GCP.
Ordering is not guaranteed in general in Pub/Sub, it is true. However, when using ordering keys as described in the ordering documentation to which you link, ordering is guaranteed. You would need to set an ordering key on published messages and enable message ordering on your subscription. Right now, the documentation only shows how to do this in Java, though other language examples will be coming soon.
Without using ordering, you could potentially monitor the backlog to see when num_undelivered_messages is 0. However, this has some drawbacks:
You would have to continuously query the metric to see its value.
The delay in computing the metric is O(minutes) and so it may be stale, resulting in either not tracking messages that were very recently published (resulting in it showing a value less than the actual size of the backlog) or not recording the fact that some messages were delivered and acked (resulting in it showing a value greater than the actual size of the backlog).
In general, it is preferred with Pub/Sub that your subscribers are always running and ready to receive data when it is published. Cloud Pub/Sub guarantees that messages successfully published will be received by subscribers, assuming subscribers are able to receive the messages within the message retention duration, which defaults to seven days.
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)
Background:
We configured cloud pubsub topic to interact within multiple app engine services,
There we have configured push based subscribers. We have configured its acknowledgement deadline to 600 seconds
Issue:
We have observed pubsub has pushed same message twice (more than twice from some other topics) to its subscribers, Looking at the log I can see this message push happened with the gap of just 1 Second, Ideally as we have configured ackDeadline to 600 seconds, pubsub should re-attempt message delivery only after 600 seconds.
Need following answers:
Why same message has got delivered more than once in 1 second only
Does pubsub doesn’t honors ackDeadline configuration before
reattempting message delivery?
References:
- https://cloud.google.com/pubsub/docs/subscriber
Message redelivery can happen for a couple of reasons. First of all, it is possible that a message got published twice. Sometimes the publisher will get back an error like a deadline exceeded, meaning the publish took longer than anticipated. The message may or may not have actually been published in this situation. Often, the correct action is for the publisher to retry the publish and in fact that is what the Google-provided client libraries do by default. Consequently, there may be two copies of the message that were successfully published, even though the client only got confirmation for one of them.
Secondly, Google Cloud Pub/Sub guarantees at-least-once delivery. This means that occasionally, messages can be redelivered, even if the ackDeadline has not yet passed or an ack was sent back to the service. Acknowledgements are best effort and most of the time, they are successfully processed by the service. However, due to network glitches, server restarts, and other regular occurrences of that nature, sometimes the acknowledgements sent by the subscriber will not be processed, resulting in message redelivery.
A subscriber should be designed to be resilient to these occasional redeliveries, generally by ensuring that operations are idempotent, i.e., that the results of processing the message multiple times are the same, or by tracking and catching duplicates. Alternatively, one can use Cloud Dataflow as a subscriber to remove duplicates.
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 :)