Consider a small chat server. In this server, the actual processing of messages is done by nodes of a service called "chat". Communications of this service along with a "user" service are then aggregated via a "gateway" service in front that is the only service that actually communicates with the users and is in charge of passing requests received to other services via the RabbitMQ channel they share.
In a system designed like this, each user is connected to one of the instances of the "gateway" service and when sending and receiving messages indirectly communicates with the private "chat" or "user" services behind. To load balance this, we have an Nginx reverse-proxy on the edge that tries to distribute requests to different "gateway" instances. But since WebSocket connection is real-time, "chat" instances should also be able to send messages to the right instance of the "gateway" in charge of that specific user for user-specific messages and to all "gateway" instances for site-wide messages. This is a problem since with RabbitMQ I don't believe we can target a specific subscriber and even if we could, we don't know to which instance that specific user is connected right now.
Therefore, since we are using Socket.io for WebSocket connection, I am thinking of adding a new Redis node to the stack to allow this communication between different instances of the "gateway" service. This is directly supported by Socket.io and works alright and removes all sorts of limitations imposed by the RabbitMQ, however, we are still using RabbitMQ to route a message from a "chat" instance to a "gateway" instance that then will propagate through the Redis service and when the right "gateway" instance having access to the user is found, delivered to them.
This adds unnecessary lag to user-specific outbound messages. So here I am asking if anyone has a better idea of how this problem should be approached and how to decrease this lag.
Personally, I have this idea of adding Socket.io to "chat" services (with no client access) and use its backend to send the message directly to the Redis store so that the instance of the "gateway" connected to it can route it directly to the user, going over the whole RabbitMQ thing for this type of messages.
It might be important to mention that none of these services are here just to do this specific thing, RabbitMQ is heavily used for communication between different services acting as the message broker and the "gateway" service works with multiple other services for data aggregation, authentication and data validation and transformation. The above example was a simplified version of the problem at hand with the minimum number of moving parts that I could easily describe here.
Edit: To send messages directly to socket.io redis store, the following library can be used apparently not to load the whole socket.io library:
https://github.com/socketio/socket.io-redis-emitter
Related
I'm currently running microservices in my company. They are not api servers, just processes that communicate with each other. So the implementation to communicate with each other is RabbitMQ.
Now I'm trying to implement a health checker to know if a server has restarted or crashed.
But I'm only familiar using a health check by calling a specific api in the server. But our services aren't an api server so they don't have any ports to imply. And I also don't wan't to add an api server for just to implement a health checker.
So I'm searching about any use cases about implementing health checks by sending messages (health check signals) to the health checker by a message broker such as RabbitMQ instead of using APIs.
Does anyone have some ideas?
Sounds like an obvious and easy mechanism for a system like yours that already relies on message queuing. Implement any architecture you want from publishing specific messages to each service - either on a single exchange where every service (as client) looks for himself as the topic, or on an exchange-per-service - or you could simply have an exchange that's read from by the health-check service and all services emit messages periodically (dead man style) to that exchange - and that service just makes sure it hears from anyone once in awhile.
Consider also using rabbit event exchange at your health-check service - so it'll be able to keep track of service connect/disconnects from the channel the service is talking to the exchange with. Channels are suppose to stay up all the time, so a disconnect indicates trouble of some kind - especially if it wasn't preceded by a service (as client) sent message indicating a normal going-down event. In other words, as a health "protocol" - instead of getting polled by a health service, each microservice would be proactive about sending "coming up", "ready", "healthy" (periodically), and "going down" messages to the health service.
As a general comment: In my opinion message queues are very much underutilized. There are many use cases they're more appropriate for than other techniques (e.g., more popular techniques like REST over HTTP). They provide distinct benefits which are built-in to the message queuing/message broker concept which you might very well otherwise need to provide for yourself for your use case (or use a "framework" which has provided it). I'd always consider the role - all the roles! - of a message broker in a system architecture and use it where it fits.
I am currently developing a typical IoT service. At the moment multiple devices connect to one MQTT broker (mosquitto) and my java backend also connects to the broker (Paho).
The problem i see is the following:
When i am going to have multiple instances of my java backend every backend will receive and process every message received. That`s a big issue. I just want to deliver a message to only one java backend. Anybody an idea how to deal with this problem?
Btw: Java backends will be added or removed depending on the load.
There are a couple of options
Place a queuing system between your application and the MQTT broker, possibly something like Apache Kafka
HiveMQ and IBM MessageSight brokers support (different implementations) of something called shared subscriptions. This allows messages to be shared out between more than one client. Shared subscriptions is likely to be formally added to the MQTTv5 spec which should mean that it will be added to more broker and have a standard implementation.
We usually use message passing to send messages to decoupled services. This makes service discovery a non-issue, because (with AMQP in RabbitMQ for instance) you can use the broker's routing capability to dispatch messages to the right queues that feed the correct services. Load balancing is also handled by the message broker.
Enter kubernetes.
The use case that is usually laid out when talking about service replication and re-spawning failing services, is when your clients use some active protocol like http to contact a service, even if this service handles requests asynchronously. In this context, it is a natural fit to have replication controllers, that manage a group of services and a single entry point to load balance between them.
I like kubernetes' intuitive concepts, like rolling deployments, but how to you control this beasts that don't have an http interface ?
UPDATE:
I am not trying to set up a cluster of message brokers. I am looking at message consumers as services. Service clients don't connect directly to the services, they send messages to the message broker. The message broker acts as a load balancer in a way, and dispatches the messages to the subscribed queue consumers. These consumers implement the service.
My question gravitates around the fact that most usage patterns in demos handle services that are called via http, and kubernetes does a good job here to create a service proxy for these services, and a replication controller. Is it possible to create replication controllers for my kind of service, which does not have a http interface per se, and have all the benefits of rolling updates, and minimum instances?
I'm not sure I entirely understand the question. Are you asking how to use RabbitMQ with Kubernetes? Or how to set up a RabbitMQ cluster: https://www.rabbitmq.com/clustering.html? Or how rolling updates interact with RabbitMQ? Or something else?
I think you should be able to create one service and one replication controller per server, and then use the service DNS names in the cluster configuration file. This is the current approach used to run Zookeeper, also. We have a long-standing TODO to make this less verbose (https://github.com/GoogleCloudPlatform/kubernetes/issues/260), but the current approach should be straightforward. You do lose the ability to use a single kubectl rolling-update command to update the cluster, but it's also straightforward to update the instances individually.
I'm trying to get some feedback on the recommendations for a service 'roster' in my specific application. I have a server app that maintains persistant socket connections with clients. I want to further develop the server to support distributed instances. Server "A" would need to be able to broadcast data to the other online server instances. Same goes for all other active instances.
Options I am trying to research:
Redis / Zookeeper / Doozer - Each server instance would register itself to the configuration server, and all connected servers would receive configuration updates as it changes. What then?
Maintain end-to-end connections with each server instance and iterate over the list with each outgoing data?
Some custom UDP multicast, but I would need to roll my own added reliability on top of it.
Custom message broker - A service that runs and maintains a registry as each server connects and informs it. Maintains a connection with each server to accept data and re-broadcast it to the other servers.
Some reliable UDP multicast transport where each server instance just broadcasts directly and no roster is maintained.
Here are my concerns:
I would love to avoid relying on external apps, like zookeeper or doozer but I would use them obviously if its the best solution
With a custom message broker, I wouldnt want it to become a bottleneck is throughput. Which would mean I might have to also be able to run multiple message brokers and use a load balancer when scaling?
multicast doesnt require any external processes if I manage to roll my own, but otherwise I would need to maybe use ZMQ, which again puts me in the situation of depends.
I realize that I am also talking about message delivery, but it goes hand in hand with the solution I go with.
By the way, my server is written in Go. Any ideas on a best recommended way to maintain scalability?
* EDIT of goal *
What I am really asking is what is the best way to implement broadcasting data between instances of a distributed server given the following:
Each server instance maintains persistent TCP socket connections with its remote clients and passes messages between them.
Messages need to be able to be broadcasted to the other running instances so they can be delivered to relavant client connections.
Low latency is important because the messaging can be high speed.
Sequence and reliability is important.
* Updated Question Summary *
If you have multiple servers / multiple end points that need to pub/sub between each other, what is a recommended mode of communication between them? One or more message brokers to re-pub messages to a roster of the discovered servers? Reliable multicast directly from each server?
How do you connect multiple end points in a distributed system while keeping latency low, speed high, and delivery reliable?
Assuming all of your client-facing endpoints are on the same LAN (which they can be for the first reasonable step in scaling), reliable UDP multicast would allow you to send published messages directly from the publishing endpoint to any of the endpoints who have clients subscribed to the channel. This also satisfies the low-latency requirement much better than proxying data through a persistent storage layer.
Multicast groups
A central database (say, Redis) could track a map of multicast groups (IP:PORT) <--> channels.
When an endpoint receives a new client with a new channel to subscribe, it can ask the database for the channel's multicast address and join the multicast group.
Reliable UDP multicast
When an endpoint receives a published message for a channel, it sends the message to that channel's multicast socket.
Message packets will contain ordered identifiers per server per multicast group. If an endpoint receives a message without receiving the previous message from a server, it will send a "not acknowledged" message for any messages it missed back to the publishing server.
The publishing server tracks a list of recent messages, and resends NAK'd messages.
To handle the edge case of a server sending only one message and having it fail to reach a server, server can send a packet count to the multicast group over the lifetime of their NAK queue: "I've sent 24 messages", giving other servers a chance to NAK previous messages.
You might want to just implement PGM.
Persistent storage
If you do end up storing data long-term, storage services can join the multicast groups just like endpoints... but store the messages in a database instead of sending them to clients.
All,
I'm looking for advice over the following scenario:
I have a component running in one part of the corporate network that sends messages to an application logic component for processing. These components might reside on the same server, different servers in the same network (LAN ot WAN) or live outside in the cloud. The application server should be scalable and resilient.
The messages are related in that the sequence they arrive is important. They are time-stamped with the client timestamp.
My thinking is that I'll get the clients to use WCF basicHttpBinding (some are based on .NET CF which only has basic) to send messages to the Application Server (this is because we can guarantee port 80/443 will be open for outgoing connections). Server accepts these, and writes these into a queue. This queue can be scaled out if needed over multiple machines.
I'm hesitant to use MSMQ for the queue though as to properly scale out we are going to have to install seperate private queues on each application server and round-robin monitor the queues. I'm concerned though that we could lose a message on a server that's gone down until the server is restored, and we could end up processing a later message from a different server and disrupt the sequence.
What I'd prefer is a central queue (e.g. a database table) that all application servers monitor.
With this in mind, what I'd like to do is to create a custom WCF binding, similar to netMsmqBinding, but that uses the DB table instead but I'm confused as to whether I can simply create a custom transport or a I need a full binding, and whether the binding will allow the client to send over HTTP. I've looked around the internet but I'm a little confused as to where to start.
I could not bother with the custom WCF binding but it seems a good way to introduce scalability if I do need to seperate the servers.
Any suggestions please would be helpful, including alternatives.
Many thanks
I would start with MSMQ because it is exactly for this purpouse. Use single transactional queue on clustered machine and let application servers to take messages for processing from this queue. Each message processing has to be part of distributed transaction (MSDTC).
This scenario will ensure:
clustered queue host will ensure that if one cluster node fails the other will still be able to handle requests
sending each message as recoverable - it means that message will be persisted on hard drive (not only in memory) so in critical failure of the whole cluster you will still have all messages.
transactional queue will ensure that all message transport operations will be atomic - moving message from outgoing queue to destination queue will be processed as transaction. It means that original message from outgoing queue will be kept in queue until ack from destination queue arrives. Transactional processing can ensure in order delivery.
Distributed transaction will allow application servers consuming messages in transaction. Message will not be deleted from queue until application server commits transaction or transaction time outs.
MSMQ is also available on .NET CF so you can send messages directly to queue without intermediate non-reliable web service layer.
It should be possible to configure MSMQ over HTTP (but I have never used it so I'm not sure how it cooperates with previous mentioned features).
Your proposed solution will be pretty hard. You will end up in building BizTalk's MessageBox. But if you really want to do it, check Omar's post about building database queue table.