I'm using SignalR 1.0.4 and have a hub that returns a ChannelReader created from an observable by an extension.
A typescript client (also 1.0.4) is forced to connect using websockets only, and streams data from this channel fine.
Now I'm testing scale out using 2 instances of the hub, both using the same Redis connection. I'm emitting values from the channel's observable on both instances but the client only appears to be receiving data from the instance it is connected to. My conclusion is that the channel reader data is not broadcast to other channels via Redis.
I've tried to replicate this using the SignalRSamples by replicating the project and giving the copy different host IPs to emulate 2 load-balanced instances. I add the same Redis connection to both projects and start both up.
Regular websocket connections via the hubs.html have no problem broadcasting data across instances. The streaming.html doesn't replicate data for observable or channel reader.
Are channel readers meant to be used in this way, i.e. can they scale out?
ChannelReaders are meant to stream data down to the caller of the method. They don't participate in scale-out at all. Consider them the same as standard return values, SignalR just supports enumerating items off them over time. The programming model is very similar to how iterators work in C# (methods that use the yield keyword). If you want to broadcast messages to other clients, you should just use the Clients property on the Hub base class and send messages to those clients.
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I want to use the gRPC streaming mechanism for the clients to get notified when the system has changed.
E.g. The db stores users. Clients can add and delete users via gRPC unary calls. There are also streaming methods such that the clients can get notified when other client has added or deleted a user.
In case that I have several intances of my gRPC service (e.g. in k8s) how can Client1 that has a durable connection to instance1 gets notified when a client2 that makes a unary delete call to an instance 3?
You need a way to publish events between all instances. One way is to use your database. Or use a messaging solution that supports publish/subscribe. A lightweight solution could be redis.
I am trying to connect multiple Nodemcu with Ubidots and finally add one master device that can read all the from other slave devices. Can I do it directly from Ubidots IoT Platform?
Yes, you can do it directly with Ubidots. I recommend you to manage your data using MQTT as a communication protocol. Check out Ubidots Docs for detailed info.
You have to use the publish example for the slave nodes, and from the master node, you must use the subscribe example to get the values from the different variables.
The code of this project can serve as a reference for you to subscribe to multiple variables at the same time.
Im learning akka streams but obviously its relevant to any streaming framework :)
quoting akka documentation:
Reactive Streams is just to define a common mechanism of how to move
data across an asynchronous boundary without losses, buffering or
resource exhaustion
Now, from what I understand is that if up until before streams, lets take an http server for example, the request would come and when the receiver wasent finished with a request, so the new requests that are coming will be collected in a buffer that will hold the waiting requests, and then there is a problem that this buffer have an unknown size and at some point if the server is overloaded we can loose requests that were waiting.
So then stream processing came to play and they bounded this buffer to be controllable...so we can predefine the number of messages (requests in my example) we want to have in line and we can take care of each at a time.
my question, if we implement that a source in our server can have a 3 messages at most, so if the 4th id coming what happens with it?
I mean when another server will call us and we are already taking care of 3 requests...what will happened to he's request?
What you're describing is not actually the main problem that Reactive Streams implementations solve.
Backpressure in terms of the number of requests is solved with regular networking tools. For example, in Java you can configure a thread pool of a networking library (for example Netty) to some parallelism level, and the library will take care of accepting as much requests as possible. Or, if you use synchronous sockets API, it is even simpler - you can postpone calling accept() on the server socket until all of the currently connected clients are served. In either case, there is no "buffer" on either side, it's just until the server accepts a connection, the client will be blocked (either inside a system call for blocking APIs, or in an event loop for async APIs).
What Reactive Streams implementations solve is how to handle backpressure inside a higher-level data pipeline. Reactive streams implementations (e.g. akka-streams) provide a way to construct a pipeline of data in which, when the consumer of the data is slow, the producer will slow down automatically as well, and this would work across any kind of underlying transport, be it HTTP, WebSockets, raw TCP connections or even in-process messaging.
For example, consider a simple WebSocket connection, where the client sends a continuous stream of information (e.g. data from some sensor), and the server writes this data to some database. Now suppose that the database on the server side becomes slow for some reason (networking problems, disk overload, whatever). The server now can't keep up with the data the client sends, that is, it cannot save it to the database in time before the new piece of data arrives. If you're using a reactive streams implementation throughout this pipeline, the server will signal to the client automatically that it cannot process more data, and the client will automatically tweak its rate of producing in order not to overload the server.
Naturally, this can be done without any Reactive Streams implementation, e.g. by manually controlling acknowledgements. However, like with many other libraries, Reactive Streams implementations solve this problem for you. They also provide an easy way to define such pipelines, and usually they have interfaces for various external systems like databases. In particular, such libraries may implement backpressure on the lowest level, down to to the TCP connection, which may be hard to do manually.
As for Reactive Streams itself, it is just a description of an API which can be implemented by a library, which defines common terms and behavior and allows such libraries to be interchangeable or to interact easily, e.g. you can connect an akka-streams pipeline to a Monix pipeline using the interfaces from the specification, and the combined pipeline will work seamlessly and supporting all of the backpressure features of Reacive Streams.
I am designing a whatsapp like messenger application for the desktop using WPF and .Net. Now, when a user creates a group I want other members of the group to receive a notification that they were added to a group. My frontend is built in C#.Net, which is connected to a RESTful Webservice (Ruby on Rails). I am using Postgres for the database. I also have a Redis layer to cache my rails models.
I am considering the following options.
1) Use Postgres's inbuilt NOTIFY/LISTEN mechanism which the clients can subscribe to directly. I foresee two issues here
i) Postgres might not be able to handle 10000's of clients subscribed directly.
ii) There is no guarantee of delivery if the client is disconnected
2) Use Redis' Pub/Sub mechanism to which the clients can subscribe. I am still concerned with no guarantee of delivery here.
3) Use a messaging queue like RabbitMQ. The producer of this queue will be postgres which will push in messages through triggers. The consumer of-course will be the .Net clients.
So far, I am inclined to use the 3rd option.
Does anyone have any suggestions how to design this?
In an application like WhatsApp itself, the client running in your phone is an integral part of a large and complex event-based, distributed system.
Without more context, it would be impossible to point in the right direction. That said:
For option 1: You seem to imply that each client, as in a WhatsApp client, would directly (or through some web service) communicate with Postgres as an event bus, which is not sound and would not scale because you can only have ONE Postgres instance.
For option 2: You have the same problem that in option 1 with worse failure modes.
For option 3: RabbitMQ seems like a reasonable ally here. It is distributed in nature and scales well. As a matter of fact, it runs on erlang just as most of WhatsApp does. Using triggers inside Postgres to publish messages however does not make a lot of sense.
You need a message bus because you would have lots of updates to do in the background, not to directly connect your users to each other. As you said, clients can be offline.
Architecture is more about deferring decisions than taking them.
I suggest that you start simple. Build a small, monolithic, synchronous system first, pushing updates as persisted data to all the involved users. For example; In a group of n users, just write n records to a table. It is already complicated to reliably keep track of who has received and read what.
This heavy "group" updates can then be moved to long-running processes using RabbitMQ or the like, but a system with several thousand users can very well work without such thing, especially because a simple message from user A to user B would not need many writes.
I have to develop a message bus for processes to send, receive messages from each other. Currently, we are running on Linux with the view of porting to other platforms later.
For this, I am using ZeroMQ over TCP. The pattern is PUB-SUB with a forwarder. My bus runs as a separate process and all clients connect to SUB port to receive messages and PUB to send messages. Each process subscribes to messages by a unique tag. A send call from a process sends messages to all. A receive call will fetch that process the messages marked with the tag of that process. This is working fine.
Now I need to wrap the ZeroMQ stuff. My clients only need to supply a unique tag. I need to maintain a global list of tags vs. ZeroMQ context and sockets details. When a client say,
initialize_comms("name"); the bus needs to check if this name is unique, create ZeroMQ contexts and sockets. Similarly, if a client say receive("name"); the bus needs to fetch messages with that tag.
To summarize the problems I am facing;
Is there anyway to achieve this using facilities provided by ZeroMQ?
Is ZeroMQ the right tool for this, or should I look for something like nanomsg?
Is PUB-SUB with forwarder the right pattern for this?
Or, am I missing something here?
Answers
Yes, ZeroMQ is capable of serving this need
Yes. ZeroMQ is a right tool ( rather a powerful tool-box of low-latency components ) for this. While nanomsg has a straight primitive for bus, the core distributed logic can be integrated in ZeroMQ framework
Yes & No. PUB-SUB as given above may serve for emulation of the "shout-cast"-to-bus and build on a SUB side-effect of using a subscription key(s). The WHOLE REST of the logic has to be re-thought and designed so as the whole scope of the fabrication meets your plans (ref. below). Also kindly bear in mind, that initial versions of ZeroMQ operated PUB/SUB primitive as "subscription filtering" of the incoming stream of messages being done on receiver side, so massive designs shall check against traffic-volumes / risk-of-flooding / process-inefficiency on the massive scale...
Yes. ZeroMQ is rather a well-tuned foundation of primitive elements ( as far as the architecture is discussed, not the power & performance thereof ) to build more clever, more robust & almost-linearly-scaleable Formal Communication Pattern(s). Do not get stuck to PUB/SUB or PAIR primitives once sketching Architecture. Any design will remain poor if one forgets where the True Powers comes from.
A good place to start a next step forward towards a scaleable & fault-resilient Bus
Thus a best next step one may do is IMHO to get a bit more global view, which may sound complicated for the first few things one tries to code with ZeroMQ, but if you at least jump to the page 265 of the Code Connected, Volume 1, if it were not the case of reading step-by-step thereto.
The fastest-ever learning-curve would be to have first an un-exposed view on the Fig.60 Republishing Updates and Fig.62 HA Clone Server pair for a possible High-availability approach and then go back to the roots, elements and details.
Here is what I ended up designing, if anyone is interested. Thanks everyone for the tips and pointers.
I have a message bus implemented using ZeroMQ (and CZMQ) running as a separate process.
The pattern is PUBLISHER-SUBSCRIBER with a LISTENER. They are connected using a PROXY.
In addition, there is a ROUTER invoked using a newly forked thread.
These three endpoints run on TCP and are bound to predefined ports which the clients know of.
PUBLISHER accepts all messages from clients.
SUBSCRIBER sends messages with a unique tag to the client who have subscribed to that tag.
LISTENER listens to all messages passing through. currently, this is for logging testing and purposes.
ROUTER provides a separate comms channel to clients. Messages such as control commands are directed here so that they will not get passed downstream.
Clients connect to,
PUBLISHER to send messages.
SUBSCRIBER to receive messages. Subscription is using unique tags.
ROUTER to send commands (check tag uniqueness etc.)
I am still doing implementation so there may be unseen problems, but right now it works fine. Also, there may be a more elegant way but I didn't want to throw away the PUB-SUB thing I had built.