My project dependencies spring-boot-starter-parent:2.5.12, master-slave ActiveMQ as BrokerRelay. I manually implemented failover, but there are some problems.
Here is my configuration:
I use events to traverse the ActiveMQ service. The problem is that when a connection is unavailable and the connection has been difficult to use, this event will not be issued, so I cannot change the index and keep sending connection requests. The reason for this has also been found:
When brokeravailable is always false, this event will not be issued, so I can't change the index. Now what should I do? Because this is reactor's knowledge, I can't do anything about it. I don't know how to solve this problem, and I can't rewrite this method. I hope you can give me a suggestion.
Related
I realize there is a method to set on MQConnectionFactory to attempt to reconnect if the connection of a consumer or producer is broken. However, I'm wondering if one can do something similar for an application that is starting up and setting up consumers and producers. The code I have right now will not recover if the server is down when my client application comes up.
Is there a common/recommended practice here?
My recommendation would simply be to use the tools that are provided in the Java language itself. For example, you could write a loop with exception handling to retry the initial connection or JNDI lookup a configurable number of times. It's hard to provide more specific recommendations when you haven't provided any client code of your own.
I am running an Apache server that receives HTTP requests and connects to a daemon script over ZeroMQ. The script implements the Multithreaded Server pattern (http://zguide.zeromq.org/page:all#header-73), it successfully receives the request and dispatches it to one of its worker threads, performs the action, responds back to the server, and the server responds back to the client. Everything is done synchronously as the client needs to receive a success or failure response to its request.
As the number of users is growing into a few thousands, I am looking into potentially improving this. The first thing I looked at is the different patterns of ZeroMQ, and whether what I am using is optimal for my scenario. I've read the guide but I find it challenging understanding all the details and differences across patterns. I was looking for example at the Load Balancing Message Broker pattern (http://zguide.zeromq.org/page:all#header-73). It seems quite a bit more complicated to implement than what I am currently using, and if I understand things correctly, its advantages are:
Actual load balancing vs the round-robin task distribution that I currently have
Asynchronous requests/replies
Is that everything? Am I missing something? Given the description of my problem, and the synchronous requirement of it, what would you say is the best pattern to use? Lastly, how would the answer change, if I want to make my setup distributed (i.e. having the Apache server load balance the requests across different machines). I was thinking of doing that by simply creating yet another layer, based on the Multithreaded Server pattern, and have that layer bridge the communication between the web server and my workers.
Some thoughts about the subject...
Keep it simple
I would try to keep things simple and "plain" ZeroMQ as long as possible. To increase performance, I would simply to change your backend script to send request out from dealer socket and move the request handling code to own program. Then you could just run multiple worker servers in different machines to get more requests handled.
I assume this was the approach you took:
I was thinking of doing that by simply creating yet another layer, based on the Multithreaded Server pattern, and have that layer bridge the communication between the web server and my workers.
Only problem here is that there is no request retry in the backend. If worker fails to handle given task it is forever lost. However one could write worker servers so that they handle all the request they got before shutting down. With this kind of setup it is possible to update backend workers without clients to notice any shortages. This will not save requests that get lost if the server crashes.
I have the feeling that in common scenarios this kind of approach would be more than enough.
Mongrel2
Mongrel2 seems to handle quite many things you have already implemented. It might be worth while to check it out. It probably does not completely solve your problems, but it provides tested infrastructure to distribute the workload. This could be used to deliver the request to be handled to multithreaded servers running on different machines.
Broker
One solution to increase the robustness of the setup is a broker. In this scenario brokers main role would be to provide robustness by implementing queue for the requests. I understood that all the requests the worker handle are basically the same type. If requests would have different types then broker could also do lookups to find correct server for the requests.
Using the queue provides a way to ensure that every request is being handled by some broker even if worker servers crashed. This does not come without price. The broker is by itself a single point of failure. If it crashes or is restarted all messages could be lost.
These problems can be avoided, but it requires quite much work: the requests could be persisted to the disk, servers could be clustered. Need has to be weighted against the payoffs. Does one want to use time to write a message broker or the actual system?
If message broker seems a good idea the time which is required to implement one can be reduced by using already implemented product (like RabbitMQ). Negative side effect is that there could be a lot of unwanted features and adding new things is not so straight forward as to self made broker.
Writing own broker could covert toward inventing the wheel again. Many brokers provide similar things: security, logging, management interface and so on. It seems likely that these are eventually needed in home made solution also. But if not then single home made broker which does single thing and does it well can be good choice.
Even if broker product is chosen I think it is a good idea to hide the broker behind ZeroMQ proxy, a dedicated code that sends/receives messages from the broker. Then no other part of the system has to know anything about the broker and it can be easily replaced.
Using broker is somewhat developer time heavy. You either need time to implement the broker or time to get use to some product. I would avoid this route until it is clearly needed.
Some links
Comparison between broker and brokerless
RabbitMQ
Mongrel2
I am working with someone who is trying to achieve a load-balancing behavior using JMS Queues with IBM Websphere MQ. As such, they have multiple Camel JMS consumers configured to read from the same Queue. Despite that this behavior is undefined according to the JMS spec (last time I looked anyway), they expect a sort of round-robin / load-balancing behavior. And, while the spec leaves this undefined, I'm led to believe that the normal behavior of Websphere MQ is to deliver the message to only one of the consumers, and that it may do some type of load-balancing. See here, for example: When multi MessageConsumer connect to same queue(Websphere MQ),how to load balance message-consumer?
But in this particular case, it appears that both consumers are receiving the same message.
Can anyone who is more of an expert with Websphere MQ shed any light on this? Is there any situation where this behavior is expected? Is there any configuration change that can alleviate this?
I'm leaning towards telling everyone here to use the native Websphere MQ clustering facility and go away from having multiple consumers pointing at the same Queue, but that will be a big change for them, so I'd love to discover a way to make this work.
Not that I'm a fan of relying on anything that's undefined, but if they're willing to rely on IBM specific behavior, I'll leave that up to them.
The only way for them to both receive the same messages are:
There are multiple copies of the message.
The apps are browsing the message without a lock, then circling back to delete it.
The apps are backing out a transaction and making the message available again.
The connection is severed before the app acknowledges the message.
Having multiple apps compete for messages in a queue is a recommended practice. If one app goes down the queue is still served. In a cluster this is crucial because the cluster will continue to direct messages to the un-served queue instance until it fills up.
If it's a Dev system, install SupportPac MA0W and tell it to trace just that one queue and you will be able to see exactly what is happening.
See the JMS spec in section 4.4. The provider must never deliver a second copy of an acknowledged message. Exception is made for session handling in 4.4.13 which I cover in #4 above. That's pretty unambiguous and part of the official spec so not an IBM-specific behavior.
I am wondering how we can ensure message durability when using websphere MQ and WCF. I want to be able to have my WCF process pick messages off of the queue and if there is an issue that the applciation encounters (power outage, etc) I don't lose the messages. I also would like to not have to use a transaction if at all possible because I want to eliminate distributed transactions.
Thanks,
S
Well, there's transactions and there's distributed transactions. The "right" answer is to use the WMQ 1-phase commit here. That doesn't have the complexity of XA transactions but it does give you the ability to roll back a message without losing it. In fact, when using clients you really should be using at least 1-phase commit just to prevent loss of messages.
Short of that there is always the "browse-with-lock, delete-message-under-cursor" method. I'm pretty sure everything you need to do the browseing, locking and deleting is exposed under .NET but perhaps Shashi will comment and confirm.
WebSphere MQ WCF custom channel has a feature "Assured Delivery" that guarantees that a service request or reply is actioned and not lost. This is the 1-phase commit (also known as SYNC_POINT in) WMQ.
"Assuered Delivery" is a service contract attribute. Here are more details about the feature.
Im a web developer ended up in some j2ee development (newbie). I sincerely need this theory confirmed.
I been given the privilege to deliver a message from our system (producer) to the SOA Enterprice service bus (consumer) when the user hits the save button. The information can not be missed or not delivered and the delivery order must be kept.
Environment:
Jboss eap 5.1 as the producer.
JNDI server is the ESB (maybe standard).
Jboss ESB as the consumer.
My weapon of choice is JMS, p2p, due to the asynchronous nature.
When the producer is abut to send the message some problems can occur:
ESB is down causing JNDI exception
Queue manager is for some reason not awake or wrongly configured. This should cause some JMS exception.
Network hickup, causing a JMS error.
So Im looking for some failover pattern. Here is my suggestion:
Add a internal JMS queue to which the message is initially added.
Add a MDB that listen to the internal queue and tries to send it to the target queue (ESB).
If failing in any way log fatal and send email to cool support people.
This should generate a reliable pattern where a message remains on the internal que until processed by the MDB.
Please advice.
Best Regards
ds
Well a 'temporary' queue is not a totally bad idea, but during the time from moving data from one queue to putting it on another you'll have a potential window of risk. Even though that window is close to nothing, what would happen if you got some failure right there and then? -You'd have to put the message back on the queue (and there you'd get into the problem with getting it in the correct order - nasty stuff!) or hold on to it in some way until you put it the other queue (which in turn can be cumbersome if you'd e g get into some failure-situaton.
A more stable solution would be to put data in a db with a queue-order column. You can then select your data in the correct order, send it to the new queue, and finally flag it as 'done' or something or even (better?) remove the data in the db.