It is mentioned in the documentation that we cannot change the partition count on service fabric statefull services after the service is deployed. Is it the similar case if named partitions are used. Can we add or remove new named partitions to the statefull service in its lifetime, is there a side effect ?
As a general rule, no, the partitions cannot be modified after the service is created regardless of how you approach it (e.g. Int64, Named, or Singleton). The definition is indicated in your ApplicationManifest.xml and should not change.
That said if you estimated the partition count incorrectly when you originally designed the solution, there's no particular reason you can't simply create a v2 of your service with the newly realized partition count and either introduce logic in it or create a (likely singleton) stateless migration service to move your data from one service to the other, mapping the data between the partitions as you see fit.
Once migrated, you could save a backup of your state in either service (just in case), update the references in your solution to point to the new service and delete the old.
It's not as clean as some provided migration experience, but it does get the job done.
Goal: is to organize blue/green deployment for the particular spring-boot service which is using Kafka.(I'm not interested how to solve B/G on REST or DB layer so lets assume that this part of B/G is already done on LB)
I want: to run two instances of service in docker swarm cluster simultaneously. But one of them must be in sleep mode i.e. do not produce and consume any messages.
Biggest problem: it is not a big deal to set scale = 2 for my service. However in this case each instance of service will consume events and process them. This leads to disaster. So I need simple and transparent mechanism for turning off all services' producers and consumers and restore them with specific offset
I`m looking for an example or suggestions how to achieve that.
Current idea: is to store current offsets in zookeeper and write custom layer that will pool those configs and manage consumers and producers based on that. However, I believe some better and simpler way/framework exists.
There are several approaches:
1. Pause and resume - https://docs.spring.io/spring-kafka/reference/html/_reference.html#pause-resume
For each topic split to 2 topics: active/non-active, and switch between them
If you use public cloud - https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-kafka-on-aws/
I have a Session enabled Azure servicebus queue. I need some form of service that can read from the queue and process them and save the result (in memory for later retrieval). We are using azure servicefabric in our current architecture. I got few questions regarding which one to choose Stateful or Stateless service.
If I use Stateful service, then based on the documentation my understanding is, service will be running on 1 primary node (assuming 1 partition) and 2 active secondary nodes. That means, if I have a 10 node Service fabric cluster, then this stateful service will be utilizing only one node (VM) primarily.
So if I add a listener to this stateful service to read messages from Queues then that service on primary node will read messages from queues and all other remaining 9 nodes wont be able to utilized. Is this correct?
Whereas if I use Stateless service, I can create instances on all 10 nodes and all of them could listen to the message in Queues and process them in parallel. However, I will loose the option to save the results.
Please advise.
So if I add a listener to this stateful service to read messages from Queues then that service on primary node will read messages from queues and all other remaining 9 nodes wont be able to utilized. Is this correct?
That is correct. With stateful service scenario, only the primary replica will have it's listener executed, and work will be done. Other replicas can be used in read-only mode, but they would not be writing anything into reliable collections.
Whereas if I use Stateless service, I can create instances on all 10 nodes and all of them could listen to the message in Queues and process them in parallel.
Exactly. Stateless services can perform their work in parallel and no state is persisted. That's also the reason whey there's no reliable collection available for this Service Fabric model.
However, I will loose the option to save the results.
Not necessarily true. You could still save your data in a centralized/shared DB, just like you'd do with stateless solutions in the past (for example Cloud Services, or a Azure WebApp).
What you should ask yourself is what problem are you solving. If you have data sharding, the Statful makes more sense. If you don't have data sharding and/or you need to scale out your processing power, rather that scale up, Stateless is a better approach.
When I create a new Service Fabric actor the underlying (auto generated) actor service is configured to use 10 partitions.
I'm wondering how much I need to care about this value?
In particular, I wonder whether the Actor Runtime has support for changing the number of partitions of an actor service on a running cluster.
The Partition Service Fabric reliable services topic says:
In rare cases, you may end up needing more partitions than you have initially chosen. As you cannot change the partition count after the fact, you would need to apply some advanced partition approaches, such as creating a new service instance of the same service type. You would also need to implement some client-side logic that routes the requests to the correct service instance, based on client-side knowledge that your client code must maintain.
However, due to the nature of Actors and that they are managed by the Actor Runtime I'm tempted to believe that it would indeed be possible to do this. -- That the Actor Runtime would be able to take care of all the heavylifting required to re-partition actor instances.
Is that at all possible?
The number of partitions in a running service cannot be changed. This is true of Actors as well as Reliable Services. Typically, you would want to pick a large number of partitions (more than the number of nodes) up front and then scale out the number of nodes in the cluster instead of trying to repartition your data on the fly. Take a look at Abhishek and Matthew's comments in the discussion here for some ideas on how to estimate how many partitions you might need.
I've created a stateless service within Service Fabric. It has a SingletonPartition, but multiple instances (InstanceCount is -1 in my case).
I want to communicate with a specific replica of this service. To find all replica's I use:
var fabricClient = new FabricClient();
var serviceUri = new Uri(SERVICENAME);
Partition partition = (await fabricClient.QueryManager.GetPartitionListAsync(serviceUri)).First();
foreach(Replica replica in await fabricClient.QueryManager.GetReplicaListAsync(partition.PartitionInformation.Id))
{
// communicate with this replica, but how to construct the proxy?
//var eventHandlerServiceClient = ServiceProxy.Create<IService>(new Uri(replica.ReplicaAddress));
}
The problem is that there is no overload of the ServiceProxy to create one to the replica. Is there another way to communicate with a specific replica?
Edit
The scenario we are building is the following. We have different moving parts with counter information: 1 named partitioned stateful service (with a couple of hundred partitions), 1 int64 partitioned stateful service, and 1 actor with state. To aggregate the counter information, we need to reach out to all service-partitions and actor-instances.
We could of course reverse it and let everyone send there counts to a single (partitioned) service. But that would add a network call in the normal flow (and thus overhead).
Instead, we came up with the following. The mentioned services&actors are combined into one executable and one servicemanifest. Therefore they are in the same process. We add a stateless service with instancecount -1 to the mentioned services&actors. All counter information is stored inside a static variable. The stateless service can read this counter information.
Now, we only need to reach out to the stateless service (which has an upper limit of the number of nodes).
Just to get some terminology out of the way first, "replica" only applies to stateful services where you have a unique replica set for each partition of a service and replicate state between them for HA. Stateless services just have instances, all of which are equal and identical.
Now to answer your actual question: ServiceProxy doesn't have an option to connect to a specific instance of a deployed stateless service. You have the following options:
Primary replica: connect to the primary replica of a stateful service partition.
Random instance: connect to a random instance of a stateless service.
Random replica: connect to a random replica - regardless of its role - of a stateful service partition.
Random secondary replica - connect to a random secondary replica of a stateful service partition.
E.g.:
ServiceProxy.Create<IMyService>(serviceUri, partitionKey, TargetReplicaSelector.RandomInstance)
So why no option to connect to a specific stateless service instance?
Well, I would turn this question around and ask why would you want to connect to a specific stateless service instance? By definition, each stateless instance should be identical. If you are keeping some state in there - like user sessions - then now you're stateful and should use stateful services.
You might think of intelligently deciding which instance to connect to for load balancing, but again since it's stateless, no instance should be doing more work than any other as long as requests are distributed evenly. And for that, Service Proxy has the random distribution option.
With that in mind, if you still have some reason to seek out specific stateless service instances, you can always use a different communication stack - like HTTP - and do whatever you want.
"Well, I would turn this question around and ask why would you want to connect to a specific stateless service instance?"
One example would be if you have multiple (3x) stateless service instances all having WebSocket connections to different clients, let's say 500 each. And you want to notify all 1500 (500x3) users of the same message, if it was possible to connect directly to a specific instance (which I would expect was possible, since I can query for those instances using the FabricClient), I could send a message to each instance which would redirect it to all connected clients.
Instead we have to come up with any of multiple workarounds:
Have all instances connect to some evented system that allows them to trigger on incoming message, e.g. Azure Event Hubs, Azure Service Bus, RedisCache.
Host an additional endpoint, as mentioned here, which makes it 3 endpoints pr service instance: WCF, WebSocket, HTTP.
Change to a stateful partitioned service which doesn't hold any state or any replicas, but simply allows to call partitions.
Currently having some serious issues with RedisCache so migrating away from that, and would like to avoid external dependencies such as Event Hubs and Service Bus just for this scenario.
Sending many messages each second, which will give additional overhead when having to call HTTP, and then the request need to transition over to the WebSocket context.
In order to target a specific instance of stateless service you can use named partitions. You can have a single instance per partition and use multiple Named partitions. For example, you can have 5 named partitions [0,1,2,3,4] each will have only one instance of the "service". Then you can call it like this
ServiceProxy.Create<IMyService>(serviceUri, partitionKey, TargetReplicaSelector.RandomInstance)
where partitionKey parameter will have one of values [0,1,2,3,4].
the real example would be
_proxyFactory.CreateServiceProxy<IMyService>(
_myServiceUri,
new ServicePartitionKey("0"), // One of "0,1,2,3,4"
TargetReplicaSelector.Default,
MyServiceEndpoints.ServiceV1);
This way you can choose one of 5 instances. But all 5 instancies may not be always available. For example during startup or when the service dies and SF is recreating or it is in InBuild stage... So for this reason you should run Partition discovery