As the Doc says:
Some applications need multiple logical connections to the broker. However, it is undesirable to
keep many TCP connections open at the same time because doing so consumes
system resources and makes it more difficult to configure firewalls.
AMQP 0-9-1 connections are multiplexed with channels that can be thought of as
"lightweight connections that share a single TCP connection".
Every protocol operation performed by a client happens on a channel.
Communication on a particular channel is completely separate from communication on another
channel, therefore every protocol method also carries a channel ID (a.k.a. channel number),
an integer that both the broker and clients use to figure out which channel the method is for.
And in spring-amqp doc,it says:
It is important to understand that the cache size is (by default) not a limit but is merely the number of
channels that can be cached. With a cache size of, say, 10, any number of channels can actually be in use.
If more than 10 channels are being used and they are all returned to the cache,
10 go in the cache. The remainder are physically closed.
I'm a little confused since channels are virtual connections,what does physically closed mean?
Based on my understanding, different channels are really just different TCP packets(Identified by a different channel ID)? And if so, Why CachingConnectionFactory cache channels ？ (I know my understanding must be wrong。。。)
We are using redis message bus and handling messages using a channel. But if our application is deployed in multiple instances then the request and response is passed to all the instances. To avoid this scenario which of the below approach is better?
Create a channel for each instance of the application
Create a channel for each user
Any suggestions will be highly appreciated
The limiting factor here is the number of subscribers to the same channel. Number of channels can be large as such. So you can choose the granularity accordingly. Read more here:
https://groups.google.com/forum/#!topic/redis-db/R09u__3Jzfk
All the complexity on the end is on the PUBLISH command, that performs
an amount of work that is proportional to:
a) The number of clients receiving the message.
b) The number of clients subscribed to a pattern, even if they'll not
match the message.
This means that if you have N clients subscribed to 100000 different
channels, everything will be super fast.
If you have instead 10000 clients subscribed to the same channel,
PUBLISH commands against this channel will be slow, and take maybe a
few milliseconds (not sure about the actual time taken). Since we have
to send the same message to everybody.
Similar question asked before : How does Redis PubSub subscribe mechanism works?
I have a test app (first with RabbitMQ) which runs on partially trusted clients (in that i don't want them creating queues on their own), so i will look into the security permissions of the queues and credentials that the clients connect with.
For messaging there are mostly one-way broadcasts from server to clients, and sometimes a query from server to a specific client (over which the replies will be sent on a replyTo queue which is dedicated to that client on which the server listens for responses).
I currently have a receive function on the server which looks out for "Announce" broadcast from clients:
agentAnnounceListener.Received += (model, ea) =>
{
var body = ea.Body;
var props = ea.BasicProperties;
var message = Encoding.UTF8.GetString(body);
Console.WriteLine(
"[{0}] from: {1}. body: {2}",
DateTimeOffset.FromUnixTimeMilliseconds(ea.BasicProperties.Timestamp.UnixTime).Date,
props.ReplyTo,
message);
// create return replyTo queue, snipped in next code section
};
I am looking to create the return to topic in the above receive handler:
var result = channel.QueueDeclare(
queue: ea.BasicProperties.ReplyTo,
durable: false,
exclusive: false,
autoDelete: false,
arguments: null);
Alternatively, i could store the received announcements in a database, and on a regular timer run through this list and declare a queue for each on every pass.
In both scenarioes this newly created channel would then be used at a future point by the server to send queries to the client.
My questions are please:
1) Is it better to create a reply channel on the server when receiving the message from client, or if i do it externally (on a timer) are there any performance issues for declaring queues that already exist (there could be thousands of end points)?
2) If a client starts to miss behave, is there any way that they can be booted (in the receive function i can look up how many messages per minute and boot if certain criteria are met)? Are there any other filters that can be defined prior to receive in the pipeline to kick clients who are sending too many messages?
3) In the above example notice my messages continuously come in each run (the same old messages), how do i clear them out please?
I think preventing clients from creating queues just complicates the design without much security benefit.
You are allowing clients to create messages. In RabbitMQ, its not very easy to stop clients from flooding your server with messages.
If you want to rate-limit your clients, RabbitMQ may not be the best choice. It does rate-limiting automatically when servers starts to struggle with processing all the messages, but you can't set a strict rate limit on per-client basis on the server using out-of-the-box solution. Also, clients are normally allowed to create queues.
Approach 1 - Web App
Maybe you should try to use web application instead:
Clients authenticate with your server
To Announce, clients send a POST request to a certain endpoint, ie /api/announce, maybe providing some credentials that allow them to do so
To receive incoming messages, GET /api/messages
To acknowledge processed message: POST /api/acknowledge
When client acknowledges receipt, you delete your message from database.
With this design, you can write custom logic to rate-limit or ban clients that misbehave and you have full control of your server
Approach 2 - RabbitMQ Management API
If you still want to use RabbitMQ, you can potentially achieve what you want by using RabbitMQ Management API
You'll need to write an app that will query RabbitMQ Management API on timer basis and:
Get all the current connections, and check message rate for each of them.
If message rate exceed your threshold, close connection or revoke user's permissions using /api/permissions/vhost/user endpoint.
In my opinion, web app may be easier if you don't need all the queueing functionality like worker queues or complicated routing that you can get out of the box with RabbitMQ.
Here are some general architecture/reliability ideas for your scenario. Responses to your 3 specific questions are at the end.
General Architecture Ideas
I'm not sure that the declare-response-queues-on-server approach yields performance/stability benefits; you'd have to benchmark that. I think the simplest topology to achieve what you want is the following:
Each client, when it connects, declares an exclusive and/or autodelete anonymous queue. If the clients' network connectivity is so sketchy that holding open a direct connection is undesirable, so something similar to Alex's proposed "Web App" above, and have clients hit an endpoint that declares an exclusive/autodelete queue on their behalf, and closes the connection (automatically deleting the queue upon consumer departure) when a client doesn't get in touch regularly enough. This should only be done if you can't tune the RabbitMQ heartbeats from the clients to work in the face of network unreliability, or if you can prove that you need queue-creation rate limiting inside the web app layer.
Each client's queue is bound to a broadcast topic exchange, which the server uses to communicate broadcast messages (wildcarded routing key) or specifically targeted messages (routing key that only matches one client's queue name).
When the server needs to get a reply back from the clients, you could either have the server declare the response queue before sending the "response-needed" message, and encode the response queue in the message (basically what you're doing now), or you could build semantics in your clients in which they stop consuming from their broadcast queue for a fixed amount of time before attempting an exclusive (mutex) consume again, publish their responses to their own queue, and ensure that the server consumes those responses within the allotted time, before closing the server consume and restoring normal broadcast semantics. That second approach is much more complicated and likely not worth it, though.
Preventing Clients Overwhelming RabbitMQ
Things that can reduce the server load and help prevent clients DoSing your server with RMQ operations include:
Setting appropriate, low max-length thresholds on all the queues, so the amount of messages stored by the server will never exceed a certain multiple of the number of clients.
Setting per-queue expirations, or per-message expirations, to make sure that stale messages do not accumulate.
Rate-limiting specific RabbitMQ operations is quite tricky, but you can rate-limit at the TCP level (using e.g. HAProxy or other router/proxy stacks), to ensure that your clients don't send too much data, or open too many connections, at a time. In my experience (just one data point; if in doubt, benchmark!) RabbitMQ cares less about the count of messages ingested per time than it does the data volume and largest possible per-message size ingested. Lots of small messages are usually OK; a few huge ones can cause latency spikes, otherwise, rate-limiting the bytes at the TCP layer will probably allow you to scale such a system very far before you have to re-assess.
Specific Answers
In light of the above, my answers to your specific questions would be:
Q: Should you create reply queues on the server in response to received messages?
A: Yes, probably. If you're worried about the queue-creation rate
that happens as a result of that, you can rate-limit per server instance. It looks like you're using Node, so you should be able to use one of the existing solutions for that platform to have a single queue-creation rate limiter per node server instance, which, unless you have many thousands of servers (not clients), should allow you to reach a very, very large scale before re-assessing.
Q: Are there performance implications to declaring queues based on client actions? Or re-declaring queues?
A: Benchmark and see! Re-declares are probably OK; if you rate-limit properly you may not need to worry about this at all. In my experience, floods of queue-declare events can cause latency to go up a bit, but don't break the server. But that's just my experience! Everyone's scenario/deployment is different, so there's no substitute for benchmarking. In this case, you'd fire up a publisher/consumer with a steady stream of messages, tracking e.g. publish/confirm latency or message-received latency, rabbitmq server load/resource usage, etc. While some number of publish/consume pairs were running, declare a lot of queues in high parallel and see what happens to your metrics. Also in my experience, the redeclaration of queues (idempotent) doesn't cause much if any noticeable load spikes. More important to watch is the rate of establishing new connections/channels. You can also rate-limit queue creations very effectively on a per-server basis (see my answer to the first question), so I think if you implement that correctly you won't need to worry about this for a long time. Whether RabbitMQ's performance suffers as a function of the number of queues that exist (as opposed to declaration rate) would be another thing to benchmark though.
Q: Can you kick clients based on misbehavior? Message rates?
A: Yes, though it's a bit tricky to set up, this can be done in an at least somewhat elegant way. You have two options:
Option one: what you proposed: keep track of message rates on your server, as you're doing, and "kick" clients based on that. This has coordination problems if you have more than one server, and requires writing code that lives in your message-receive loops, and doesn't trip until RabbitMQ actually delivers the messages to your server's consumers. Those are all significant drawbacks.
Option two: use max-length, and dead letter exchanges to build a "kick bad clients" agent. The length limits on RabbitMQ queues tell the queue system "if more messages than X are in the queue, drop them or send them to the dead letter exchange (if one is configured)". Dead-letter exchanges allow you to send messages that are greater than the length (or meet other conditions) to a specific queue/exchange. Here's how you can combine those to detect clients that publish messages too quickly (faster than your server can consume them) and kick clients:
Each client declares it's main $clientID_to_server queue with a max-length of some number, say X that should never build up in the queue unless the client is "outrunning" the server. That queue has a dead-letter topic exchange of ratelimit or some constant name.
Each client also declares/owns a queue called $clientID_overwhelm, with a max-length of 1. That queue is bound to the ratelimit exchange with a routing key of $clientID_to_server. This means that when messages are published to the $clientID_to_server queue at too great a rate for the server to keep up, the messages will be routed to $clientID_overwhelm, but only one will be kept around (so you don't fill up RabbitMQ, and only ever store X+1 messages per client).
You start a simple agent/service which discovers (e.g. via the RabbitMQ Management API) all connected client IDs, and consumes (using just one connection) from all of their *_overwhelm queues. Whenever it receives a message on that connection, it gets the client ID from the routing key of that message, and then kicks that client (either by doing something out-of-band in your app; deleting that client's $clientID_to_server and $clientID_overwhelm queues, thus forcing an error the next time the client tries to do anything; or closing that client's connection to RabbitMQ via the /connections endpoint in the RabbitMQ management API--this is pretty intrusive and should only be done if you really need to). This service should be pretty easy to write, since it doesn't need to coordinate state with any other parts of your system besides RabbitMQ. You'll lose some messages from misbehaving clients with this solution, though: if you need to keep them all, remove the max-length limit on the overwhelm queue (and run the risk of filling up RabbitMQ).
Using that approach, you can detect spamming clients as they happen according to RabbitMQ, not just as they happen according to your server. You could extend it by also adding a per-message TTL to messages sent by the clients, and triggering the dead-letter-kick behavior if messages sit in the queue for more than a certain amount of time--this would change the pseudo-rate-limiting from "when the server consumer gets behind by message count" to "when the server consumer gets behind by message delivery timestamp".
Q: Why do messages get redelivered on each run, and how do I get rid of them?
A: Use acknowledgements or noack (but probably acknowledgements). Getting a message in "receive" just pulls it into your consumer, but doesn't pop it from the queue. It's like a database transaction: to finally pop it you have to acknowledge it after you receive it. Altnernatively, you could start your consumer in "noack" mode, which will cause the receive behavior to work the way you assumed it would. However, be warned, noack mode imposes a big tradeoff: since RabbitMQ is delivering messages to your consumer out-of-band (basically: even if your server is locked up or sleeping, if it has issued a consume, rabbit is pushing messages to it), if you consume in noack mode those messages are permanently removed from RabbitMQ when it pushes them to the server, so if the server crashes or shuts down before draining its "local queue" with any messages pending-receive, those messages will be lost forever. Be careful with this if it's important that you don't lose messages.
My app has multiple threads that publish messages to a single RabbitMQ cluster.
Reading the rabbit docs: i read the following:
For applications that use multiple threads/processes for processing, it is very common to open a new channel per thread/process and not share channels between them.
And I understand that instead of opening multiple connection (expensive)
it is better to open multiple channels.
But why not use a single channel to all threads?
What are the benefits of using multiple channels over a single channel?
AMQP has the concept of Channel to provide more flexibility over reliable TCP connections. Opening a TCP connection per message would be extremely expensive, so they came up with the idea of logical Channels within a connection.
It is not a good idea to use a Channel for all the threads because if anything fails in a particular thread and the Channel dies, the rest of the threads will throw the exception AlreadyClosedException. A channel can die for multiple reasons: for example for trying to declare something that is already declared with other parameters or trying to cancel a consumer which doesn't exist, publishing to an exchange that doesn't exist, etc...
My best advice would be to have an object that holds a Channel in a local variable and also implements ShutdownListener interface, so every time the channel fails, it is able to recover and create a new one from a connection. So I would say that the main benefit is failure tolerance and scalability, since if a Channel dies it won't affect the rest.
Book Essential WCF claims that NetTcpBinding.MaxConnections limits the number of connections to an endpoint. Thus if property is set to value of 10, then only 10 concurrent connections will be allowed to that endpoint.
But the following blog http://kennyw.com/work/indigo/181 claims this property this property doesn’t limit the number of concurrent connections, but instead only specifies max number of connections that will be cached and reused by another channel:
MaxConnections for TCP is not a hard
and fast limit, but rather a knob on
the connections that we will cache in
our connection pool. That is, if you
set MaxConnections=2, you can still
open 4 client channels on the same
factory simultaneously. However, when
you close all of these channels, we
will only keep two of these
connections around (subject to
IdleTimeout of course) for future
channel usage. This helps performance
in cases where you are creating and
disposing client channels. This knob
will also apply to the equivalent
usage on the server-side as well (that
is, when a server-side channel is
closed, if we have less than
MaxConnections in our server-side pool
we will initiate I/O to look for
another new client channel).
So which is true?
EDIT:
First of all, you mean NetTcpBinding.MaxConnections, right?
Yes, thank you ... I've corrected the typo
See official docs at http://msdn.microsoft.com/en-us/library/system.servicemodel.nettcpbinding.maxconnections.aspx and especially http://msdn.microsoft.com/en-us/library/ms731078.aspx - the behavior is actually different depending if it's the server or the client, but in no case is it a hard limit on the number of connections. (On the client, it's a limit on the connections that are pooled, and on the server it's a limit on connections that haven't been accepted yet by the ServiceModel layer).
a) I assume by “pooled” you mean number of connection that will be reused by other channels. But the blog says this is the case for both client and the server, while if I understand you correctly, you’re saying on server it means number of connections waiting to be accepted by ServiceModel layer?
Thus if property is set to 10, then only 10 connections will be allowed to wait to be accepted and if another connection tries to wait, it will immediately be rejected?
First of all, you mean NetTcpBinding.MaxConnections, right?
See official docs at http://msdn.microsoft.com/en-us/library/system.servicemodel.nettcpbinding.maxconnections.aspx and especially http://msdn.microsoft.com/en-us/library/ms731078.aspx - the behavior is actually different depending if it's the server or the client, but in no case is it a hard limit on the number of connections. (On the client, it's a limit on the connections that are pooled, and on the server it's a limit on connections that haven't been accepted yet by the ServiceModel layer).