I am building an app with several Node.js instances as a Backend (http server, socket server and several a pool of domain servers). Now I am trying to cover several communication and configuration aspects and am wondering if redis makes an appropriate solution.
So, I would use it for the following purposes:
Implementation of a shared run-time lookup table. It's a table of several hundreds of relativelly simple records, accessed and manipulated by 2 node-instances.
Implementation of message queues. Each domain server receives commands from the http server and should execute them sequentially. Domain server should be able to listen on a redis-event, and execute each new command upon its arival
socket sever also has a regis message queue and listen to its event, in order to push notification to connected clients
Is redis "too heavy" for such a purpose?
Does it offer all needed functionality?
I can definitelly implement a look-up in a file and/or memory and a queue using sockets. However, it might make a code cleaner and a solution more robust with redis.
Redis is definitely not a heavy solution, on the contrary.
It's small, insanely fast (when using pipelining), easy to deploy. I consider it as a light solution, a kind of swiss knife that may solves many problems.
Redis based message queues are OK if you don't expect any guarantee on the message delivery. That is to say Redis based queues can't assure you the client has received the message. If it's a problem for your application you should consider using an heavier solution, like 0mq or Rabbitmq.
Related
I'm trying to get my head around MassTransit in combination with RabbitMQ.
The basic concepts are working in a test project, but what I need is the following:
My system will have one or more servers that react to real life events (telephony). These events wil, by means of MassTransit and RabbitMQ, translate into messages that will be picked up by one or more receivers via a separate server, set up as RabbitMQ host. So far so good.
However, I cannot assume that I always have a connection between the publisher and the host machines. Just assume that the publishing server will continue to consume the real life events, but now cannot publish it's messages.
So, the question is: Does MassTransit have some kind of mechanism to store messages locally some way until the connection is re-established?
Or should I install RabbitMQ on every publishing server as well, in order to create a local exchange? Then I have to make the exchanges synchronize themselves after a reconnect.
Probably you have to implement a store and forward policy. Instead of publishing directly your message through MassTransit and RabbitMQ, you can store the message in a persistence repository (a local database) and delegate to some other process the notification through Masstransit of the messages stored before. This approach is often referred as "Client High Availability". This does not substitute the standard HA (High Availability) on server like the one implemented by RabbitMQ. But it's a good approach to use in a distributed system (like the one you described) because it could help you a lot in scenarios of server failure (e.g. an issue on RabbitMQ server that causes some loss of messages that you still have inside the store of some client and therefore you can make it process again).
I am building a web app which has two parts. In one part it uses a real time connection between the server and the client and in the other part it does some cpu intensive task to provide relevant data.
Implementing the real time communication in nodejs and the cpu intensive part in python/java. What is the best way the nodejs server can participate in a duplex communication with the other server ?
For a basic solution you can use Socket.IO if you are already using it and know how it works, it will get the job done since it allows for communication between a client and server where the client can be a different server in a different language.
If you want a more robust solution with additional options and controls or which can handle higher traffic throughput (though this shouldn't be an issue if you are ultimately just sending it through the relatively slow internet) you can look at something like ØMQ (ZeroMQ). It is a messaging queue which gives you more control and lots of different communications methods beyond just request-response.
When you set either up I would recommend using your CPU intensive server as the stable end(server) and your web server(s) as your client. Assuming that you are using a single server for your CPU intensive tasks and you are running several NodeJS server instances to take advantage of multi-cores for your web server. This simplifies your communication since you want to have a single point to connect to.
If you foresee needing multiple CPU servers you will want to setup a routing server that can route between multiple web servers and multiple CPU servers and in this case I would recommend the extra work of learning ØMQ.
You can use http.request method provided to make curl request within node's code.
http.request method is also used for implementing Authentication api.
You can put your callback in the success of request and when you get the response data in node, you can send it back to user.
While in backgrount java/python server can utilize node's request for CPU intensive task.
I maintain a node.js application that intercommunicates among 34 tasks spread across 2 servers.
In your case, for communication between the web server and the app server you might consider mqtt.
I use mqtt for this kind of communication. There are mqtt clients for most languages, including node/javascript, python and java. In my case I publish json messages using mqtt 'topics' and any task that has registered to subscribe to a 'topic' receives it's data when published. If you google "pub sub", "mqtt" and "mosquitto" you'll find lots of references and examples. Mosquitto (now an Eclipse project) is only one of a number of mqtt brokers that are available. Another very good broker that is written in Java is called hivemq.
This is a very simple, reliable solution that scales well. In my case literally millions of messages reliably pass through mqtt every day.
You must be looking for socketio
Socket.IO enables real-time bidirectional event-based communication.
It works on every platform, browser or device, focusing equally on reliability and speed.
Sockets have traditionally been the solution around which most
realtime systems are architected, providing a bi-directional
communication channel between a client and a server.
Trying most of the websocket engines I've concluded that best way is using Primus (a universal wrapper for real-time frameworks) so to be able to test any websocket framework that may come around without changing my functionality.
Even though that Primus does what it says, i've found myself in the situation were I wanted to scale .
Primus has many plugins and two of those are : primus-cluster and primus-redis-rooms.
These two are the ones that use Redis pub-sub in order to scale when you have many node processes. The problem that I faced with both plugins is that I cannot send a message to an individual socket - spark . Meaning that sparks are not saved - passed to Redis so that each process knows how many sparks are in total .
Does anyone have an idea on how to implement this?
The problem with primus-redis and primus-redis rooms is that only implements broadcasting and not from one server -> a spark on a different server messaging.
As for the rooms hack that you suggest is an "ok" alternative but it's definitely a hack and provides a lot of overhead. I don't think it's that hard to create a plugin which:
adds the spark.id to redis (spark.id -> server address) for each connection that it accepts.
removes the spark.id from redis when the connection disconnects.
adds pub/sub channel (server address) for the server so it can receive messages.
make this channel listen to messages with spark.ids and finds the sparks on the Primus server and writes the message.
write a method that finds the spark.id in redis, so it knows the server address and does a PUBLISH to the channel with the message that needs to be written together with the spark.id.
publish module to npm and receive a lot of free beer ;-)
It might take a bit longer to write then the hack you suggested but It would probably be worth the effort.
I'm using Backbone.iobind to bind my client Backbone models over socket.io to the back-end server which in turn store it all to MongoDB.
I'm using socket.io so I can synchronize changes back to other clients Backbone models.
The problems starts when I try to run the same thing over a cluster of node.js servers.
Setting a session store was easy using connect-mongo which stores the session to MongoDB.
But now I can't inform all the clients on every change, since the clients are distributed between the different node.js servers.
The only solution I found is to set a pub/sub queue between the different node.js servers (e.g. mubsub), which seems like a very heavy weight solution that will trigger an event on all the servers for every change.
How did you reach the conclusion that pub/sub is a "very heavy weight solution"?
Sounds like you got it right up until that part :-)
Oh, and pub/sub is not a queue.
Let's examine that claim:
The nice thing about pub/sub is that you publish and subscribe to channels/topics.
So, using the classic chat server example, let's say you have a million users connected in total, but #myroom only has 50 users in it.
When a message is sent to #myroom, it's being published once. No duplication whatsoever.
In most use-cases you won't even need to store it on disk/RAM, so we're mostly looking at network/bandwidth here. And, I mean, you're probably throwing more data (probably over the wire?) to MongoDB already, so I assume that's not your bottleneck.
If you also use socket.io's rooms features (which is basically its own pub/sub mechanism), that means only 5 users will have that message emitted to them over the websocket.
And no, socket.io won't iterate over 1M clients to find out which of them are in room #myroom ;-)
So the message is published once, each subscriber (node.js instance) will get notified once, and only the relevant clients -- socket.io won't waste CPU cycles in order to find them as it keeps track of them when they join() or leave() a room -- will receive the message.
Doesn't that sound pretty efficient and light-weight?
Give Redis a shot.
It's really simple to set-up, runs entirely in memory, blazing-fast, replication is extremely simple, etc.
That's the way socket.io recommends passing events between nodes.
You can find more information/code here.
Additionally, if MongoDB can't handle the load at any point, you can use Redis as your session-store as well.
Hope this helps!
We are developing a Javascript control which should be constantly connected to a server for receiving animation updates.
We are planning to host this stuff on an Amazon cloud.
The scenario is like this: server connects to activemq queue waiting for updates, for each update it broadcasts it to all connected clients.
Is it even possible to handle such load with node.js + socket.io?
Will a single node.js server be able to handle such load?
How to organize fast transport between different nodes if we will have to use more than one node?
Will single node.js server be able to handle such load?.. How to organize fast transport between different nodes if we will have to use more than one node
You say that you are planning to host on Amazon. So first off, nothing should be scoped for a single server. Amazon machines will simply "disappear", you have to assume that you are going to use multiple computers.
...handling 50k simultaneous clients
So to start with, 50k connections for a single box is a very big number. Here's a very detailed blog post discussing "getting to 10k" with node.js+socket.io.
Here's a very telling quote:
it seemed as though 10,000 clients simply required more serialization
than my server was able to handle.
So a key component to "getting to 50k" is going to be the amount of work required just pushing data over the wire.
How to organize fast transport between different nodes if we will have to use more than one node.
That blog post is the first of 3. When you're done the first, read the other two. That should point you in the right direction.