My Understanding :- A distributed destination is a single, logical(not physical) destination to a client which internally contains set of physical destinations (queues or topics) .
It helps in scalable applications in terms of High availability(HA) and Load Balancing(LB).
So when i do distributedQueue.put(someObject), distributed queue will put the object on one of the phyicalQueue and also maintains some meta data to record which
object lies on which on which queue
Now when i do distributedQueue.receive() , it will refer metadata , poll the data from right queue and serve it to client.
Is that correct ?
That would be one way of implementing a distributed queue, yes.
However, in your implementation the metadata store will very quickly become a bottleneck/hot-spot.
Related
I am new for Vert.x. When I read writestream in the article from https://vertx.io/docs/vertx-core/java/ as following:
setWriteQueueMaxSize: set the number of object at which the write queue is
considered full, and the method writeQueueFull returns true. Note that,
when the write queue is considered full, if write is called the data will
still be accepted and queued. The actual number depends on the stream
implementation, for Buffer the size represents the actual number of bytes
written and not the number of buffers.
especially this statement - "Note that, when the write queue is considered full, if write is called the data will still be accepted and queued.", from this statement, I have a few of questions:
(1) Is there any size limitation for writing to stream? I mean, such as event bus, how many messages can be written to event bus? Does it depend on memory? suppose I keep on writing messages to event bus and message doesn't be consumed, does it cause Out of Memory in Java?
(2) If there is some limitation for writing, where and how can I check default queue size? Such as, I want to know the default queue size of Vert.x KafkaProducer, where can I check it?
Any ideas are appreciated.
There are actually three separate questions there, but I'll give it a shot anyway:
Is there any size limitation for writing to stream?
That depends on the stream implementation. But I've yet to see one implementation that actually uses WriteQueueMaxSize
how many messages can be written to event bus? Does it depend on memory?
EventBus is a special case. If nobody is consuming from EventBus, it will simply drop messages, so in such a trivial case, an infinite number could be written. But if there are consumers, and they're slow, yes, eventually you'll run out of memory. EventBus implementation currently doesn't do anything with WriteQueueMaxSize
If there is some limitation for writing, where and how can I check default queue size? Such as, I want to know the default queue size of Vert.x KafkaProducer, where can I check it?
All Vert.x project are open source, so you'll usually find them on GitHub (or other open source repo, but I don't remember any that are not on GitHub, actually).
Particularly for Vert.x KafkaProducer, you can see the code here:
https://github.com/vert-x3/vertx-kafka-client/blob/1720d5a6792f70509fd7249a47ab50b930cee7a7/src/main/java/io/vertx/kafka/client/producer/impl/KafkaProducerImpl.java#L217
i was wondering if there is a way to implement metadata or even multiple metadata to a service bus queue message to be used later on in an application to sort on but still maintaining FIFO in the queue.
So in short, what i want to do is:
Maintaining Fifo, that s First in First Out structure in the queue, but as the messages are coming and inserted to the queue from different Sources i want to be able to sort from which source the message came from with for example metadata.
I know this is possible with Topics where you can insert a property to the message, but also i am unsure if it is possible to implement multiple properties into the topic message.
Hope i made my self clear on what i am asking is possible.
I assume you use .NET API. If this case you can use Properties dictionary to write and read your custom metadata:
BrokeredMessage message = new BrokeredMessage(body);
message.Properties.Add("Source", mySource);
You are free to add multiple properties too. This is the same for both Queues and Topics/Subscriptions.
i was wondering if there is a way to implement metadata or even multiple metadata to a service bus queue message to be used later on in an application to sort on but still maintaining FIFO in the queue.
To maintain FIFO in the queue, you'd have to use Message Sessions. Without message sessions you would not be able to maintain FIFO in the queue itself. You would be able to set a custom property and use it in your application and sort out messages once they are received out of order, but you won't receive message in FIFO order as were asking in your original question.
If you drop the requirement of having an order preserved on the queue, the the answer #Mikhail has provided will be suitable for in-process sorting based on custom property(s). Just be aware that in-process sorting will be not a trivial task.
I have an application where multiple users can send REST operations to modify the state of shared objects.
When an object is modified, then multiple actions will happen (DB, audit, logging...).
Not all the operations are valid for example you can not Modify an object after it was Deleted.
Using Kafka I was thinking about the following architecture:
Rest operations are queuing in a Kafka topic.
Operations to the same object are going to the same partition. So all the object's operations will be in sequence and processed by a consumer
Consumers are listening to a partition and validate the operation using an in-memory database
If the operation was valid then is sent to a "Valid operation topic" otherways is sent to an "Invalid operation topic"
Other consumers (db, log, audit) are listening to the "Valid operation topic"
I am not very sure about point number 3.
I don't like the idea to keep the state of all my objects. (I have billions of objects and even if an object can be of 10mb in size, what I need to store to validate its state is just few Kbytes...)
However, is this a common pattern? Otherwise how can you verify the validity of certain operations?
Also what would do you use as a in-memory database? Surely it has to be highly available, fault-tolerant and support transaction (read and write).
I believe this is a very valid pattern, and is essentially a variation to an event-sourced CQRS pattern.
For example, Lagom implements their CQRS persistence in a very similar fashion (although based on completely different toolset)
A few points:
you are right about the need for sequencial operations: since all your state mutations need to be based on the result of the previous mutation, there must be a strong order in their execution. This is very often the case for such things, so we like to be able to scale those operations horizontally as much as possible so that each of those sequences operations is happening in parallel to many other sequences. In your case we have one such sequence per shared object.
Relying on Kafka partitioning by key is a good way to achieve that (assuming you do not set max.in.flight.requests.per.connection higher than the default value 1). Here again Lagom has a similar approach by having their persistent entity distributed and single-threaded. I'm not saying Lagom is better, I'm just comforting you in the fact that is approach is used by others :)
a key aspect of your pattern is the transformation of a Command into an Event: in that jargon a command is seen as a request to impact the state and may be rejected for various reasons. An event is a description of a state update that happened in the past and is irrefutable from the point of view of those who receive it: a event always tells the truth. The process you are describing would be a controller that is at the boundary between the two: it is responsible for transforming commands into events.
In that sense the "Valid operation topic" you mention would be an event-sourced description of the state updates of your process. Since it's all backed by Kafka it would be arbitrarily partionable and thus scalable, which is awesome :)
Don't worry about the size of the sate of all your object, it must sit somewhere somehow. Since you have this controller that transforms the commands into events, this one becomes the primary source of truth related to that object, and this one is responsible for storing it: this controller handles the primary storage for your events, so you must cater space for it. You can use Kafka Streams's Key value store: those are local to each of your processing instance, though if you make them persistent they have no problem in handling data much bigger that the available RAM. Behind the scene data is spilled to disk thanks to RocksDB, and even more behind the scene it's all event-sourced to a kafka topic so your state store is replicated and will be transparently re-created on another machine if necessary
I hope this helps you finalise your design :)
I frequently see queues in software architecture, especially those called "scalable" with prominent representative of Actor from Akka.io multi-actor platform. However, how can queue be scalable, if we have to synchronize placing messages in queue (and therefore operate in single thread vs multi thread) and again synchronize taking out messages from queue (to assure, that message it taken exactly once)? It get's even more complicated, when those messages can change state of (actor) system - in this case even after taking out message from queue, it cannot be load balanced, but still processed in single thread.
Is it correct, that putting messages in queue must be synchronized?
Is it correct, that putting messages out of queue must be synchronized?
If 1 or 2 is correct, then how is queue scalable? Doesn't synchronization to single thread immediately create bottleneck?
How can (actor) system be scalable, if it is statefull?
Does statefull actor/bean mean, that I have to process messages in single thread and in order?
Does statefullness mean, that I have to have single copy of bean/actor per entire system?
If 6 is false, then how do I share this state between instances?
When I am trying to connect my new P2P node to netowrk, I believe I have to have some "server" that will tell me, who are other peers, is that correct? When I am trying to download torrent, I have to connect to tracker - if there is "server" then we do we call it P2P? If this tracker will go down, then I cannot connect to peers, is that correct?
Is synchronization and statefullness destroying scalability?
Is it correct, that putting messages in queue must be synchronized?
Is it correct, that putting messages out of queue must be synchronized?
No.
Assuming we're talking about the synchronized java keyword then that is a reenetrant mutual exclusion lock on the object. Even multiple threads accessing that lock can be fast as long as contention is low. And each object has its own lock so there are many locks, each which only needs to be taken for a short time, i.e. it is fine-grained locking.
But even if it did, queues need not be implemented via mutual exclusion locks. Lock-free and even wait-free queue data structures exist. Which means the mere presence of locks does not automatically imply single-threaded execution.
The rest of your questions should be asked separately because they are not about message queuing.
Of course you are correct in that a single queue is not scalable. The point of the Actor Model is that you can have millions of Actors and therefore distribute the load over millions of queues—if you have so many cores in your cluster. Always remember what Carl Hewitt said:
One Actor is no actor. Actors come in systems.
Each single actor is a fully sequential and single-threaded unit of computation. The whole model is constructed such that it is perfectly suited to describe distribution, though; this means that you create as many actors as you need.
I am working on my bc thesis project which should be a Minecraft server written in scala and Akka. The server should be easily deployable in the cloud or onto a cluster (not sure whether i use proper terminology...it should run on multiple nodes). I am, however, newbie in akka and i have been wondering how to implement such a thing. The problem i'm trying to figure out right now, is how to share state among actors on different nodes. My first idea was to have an Camel actor that would read tcp stream from minecraft clients and then send it to load balancer which would select a node that would process the request and then send some response to the client via tcp. Lets say i have an AuthenticationService implementing actor that checks whether the credentials provided by user are valid. Every node would have such actor(or perhaps more of them) and all the actors should have exactly same database (or state) of users all the time. My question is, what is the best approach to keep this state? I have came up with some solutions i could think of, but i haven't done anything like this so please point out the faults:
Solution #1: Keep state in a database. This would probably work very well for this authentication example where state is only represented by something like list of username and passwords but it probably wouldn't work in cases where state contains objects that can't be easily broken into integers and strings.
Solution #2: Every time there would be a request to a certain actor that would change it's state, the actor will, after processing the request, broadcast information about the change to all other actors of the same type whom would change their state according to the info send by the original actor. This seems very inefficient and rather clumsy.
Solution #3: Having a certain node serve as sort of a state node, in which there would be actors that represent the state of the entire server. Any other actor, except the actors in such node would have no state and would ask actors in the "state node" everytime they would need some data. This seems also inefficient and kinda fault-nonproof.
So there you have it. Only solution i actually like is the first one, but like i said, it probably works in only very limited subset of problems (when state can be broken into redis structures). Any response from more experienced gurus would be very appriciated.
Regards, Tomas Herman
Solution #1 could possibly be slow. Also, it is a bottleneck and a single point of failure (meaning the application stops working if the node with the database fails). Solution #3 has similar problems.
Solution #2 is less trivial than it seems. First, it is a single point of failure. Second, there are no atomicity or other ordering guarantees (such as regularity) for reads or writes, unless you do a total order broadcast (which is more expensive than a regular broadcast). In fact, most distributed register algorithms will do broadcasts under-the-hood, so, while inefficient, it may be necessary.
From what you've described, you need atomicity for your distributed register. What do I mean by atomicity? Atomicity means that any read or write in a sequence of concurrent reads and writes appears as if it occurs in single point in time.
Informally, in the Solution #2 with a single actor holding a register, this guarantees that if 2 subsequent writes W1 and then W2 to the register occur (meaning 2 broadcasts), then no other actor reading the values from the register will read them in the order different than first W1 and then W2 (it's actually more involved than that). If you go through a couple of examples of subsequent broadcasts where messages arrive to destination at different points in time, you will see that such an ordering property isn't guaranteed at all.
If ordering guarantees or atomicity aren't an issue, some sort of a gossip-based algorithm might do the trick to slowly propagate changes to all the nodes. This probably wouldn't be very helpful in your example.
If you want fully fault-tolerant and atomic, I recommend you to read this book on reliable distributed programming by Rachid Guerraoui and Luís Rodrigues, or the parts related to distributed register abstractions. These algorithms are built on top of a message passing communication layer and maintain a distributed register supporting read and write operations. You can use such an algorithm to store distributed state information. However, they aren't applicable to thousands of nodes or large clusters because they do not scale, typically having complexity polynomial in the number of nodes.
On the other hand, you may not need to have the state of the distributed register replicated across all of the nodes - replicating it across a subset of your nodes (instead of just one node) and accessing those to read or write from it, providing a certain level of fault-tolerance (only if the entire subset of nodes fails, will the register information be lost). You can possibly adapt the algorithms in the book to serve this purpose.