We have developed SMPP application. It's SMS receiving speed is mere 16 SMS per second.
How can i increase this speed?
First and foremost, I recommend getting JRat to profile the application. You need to know where to optimize before optimizing.
That being said, I went through this as well. The biggest bottleneck I encountered was the ServerPDUEventListener implementation - in my first version, I was treating all incoming PDUs in that class - which serialized access to them - and some were doing database access! The way I solved this is by spawning threads for the PDUs which I actually wanted to process in a more detailed manner - in my case this was the DELIVER_SM PDUs and the SUBMIT_SM_RESP PDUs but that depends on the actual application you are developing. Handling them in separate threads meant my main ServerPDUEventListener was free to keep processing the next PDUs. The bottleneck is similar to implementing a server socket - whenever you accept a client socket, you want to return to listening for other incoming connections and handle the communication in a separate thread.
What kindof application it is, written in Java?
Couple of things
1. See where it is taking most of the time in processing. This would lead to a solution
2. Can optimize the processing flow to queue and process the messages
There are other factor involved as well, like hardware configuration etc, but normal hardware gives a decent performance.
First of all process all incoming and outgoing sms in the asynchronous mode. For example in the jsmpp lib you can process all traffic in the asynchronous and synchronous mode. The first mode is mach faster.
If possible process all your heavy business logic in the separate threads and if possible for example in the enterprise java beans. If your traffic is very big and business logic too heavy then using asynchronous mode in the smpp and enterprise java beans for business logic can very improve your application architecture.
Related
I haven't found an existing post asking this but apologize if I missed it.
I'm trying to get my head round microservices and have come across articles where RabbitMQ is used. I'm confused why RabbitMQ is needed. Is the intention that the services will use a web api to communicate with the outside world and RabbitMQ to communicate with each other?
In Microservices architecture you have two ways to communicate between the microservices:
Synchronous - that is, each service calls directly the other microservice , which results in dependency between the services
Asynchronous - you have some central hub (or message queue) where you place all requests between the microservices and the corresponding service takes the request, process it and return the result to the caller. This is what RabbitMQ (or any other message queue - MSMQ and Apache Kafka are good alternatives) is used for. In this case all microservices know only about the existance of the hub.
microservices.io has some very nice articles about using microservices
A message queue provide an asynchronous communications protocol - You have the option to send a message from one service to another without having to know if another service is able to handle it immediately or not. Messages can wait until the responsible service is ready. A service publishing a message does not need know anything about the inner workings of the services that will process that message. This way of handling messages decouple the producer from the consumer.
A message queue will keep the processes in your application separated and independent of each other; this way of handling messages could create a system that is easy to maintain and easy to scale.
Simply put, two obvious cases can be used as examples of when message queues really shine:
For long-running processes and background jobs
As the middleman in between microservices
For long-running processes and background jobs:
When requests take a significant amount of time, it is the perfect scenario to incorporate a message queue.
Imagine a web service that handles multiple requests per second and cannot under any circumstances lose one. Plus the requests are handled through time-consuming processes, but the system cannot afford to be bogged down. Some real-life examples could include:
Images Scaling
Sending large/many emails (like newsletters)
Search engine indexing
File scanning
Video encoding
Delivering notifications
PDF processing
Calculations
The middleman in between microservices:
For communication and integration within and between applications, i.e. as the middleman between microservices, a message queue is also useful. Think of a system that needs to notify another part of the system to start to work on a task or when there are a lot of requests coming in at the same time, as in the following scenarios:
Order handling (Order placed, update order status, send an order, payment, etc.)
Food delivery service (Place an order, prepare an order, deliver food)
Any web service that needs to handle multiple requests
Here is a story explaining how Parkster (a digital parking service) are breaking down their system into multiple microservices by using RabbitMQ.
This guide follow a scenario where a web application allows users to upload information to a web site. The site will handle this information and generate a PDF and email it back to the user. Handling the information, generating the PDF and sending the email will in this example case take several seconds and that is one of the reasons of why a message queue will be used.
Here is a story about how and why CloudAMQP used message queues and RabbitMQ between microservices.
Here is a story about the usage of RabbitMQ in an event-based microservices architecture to support 100 million users a month.
And finally a link to Kontena, about why they chose RabbitMQ for their microservice architecture: "Because we needed a stable, manageable and highly-available solution for messaging.".
Please note that I work for the company behind CloudAMQP (hosting provider of RabbitMQ).
The same question can be why REST is necessary for microservices? Microservice concept is not something new under moon. A long time distribution of workflow was used for backend engineering and asynchronous request processing, Microservice is the same component in a separated jvm which matches with S(single responsibility) in SOLID. What makes it micro SERVICE - is that it is balanced. And that is the all! Particularly (!), it can be REST Service on Spring Cloud/REST base, which is registered by Eureka, has proxy gateway and load balancing over Zuul and Ribbon. But it is not the whole world of microservices!By the way, asynchronous distributed processing is one of tasks which microservices are used for. Long time ago services(components) in separated JVM was integrated over any messaging and the pattern is known as ESB. Microservices are the same subjects the pattern. Due to fashion for Spring Cloud REST seems like it is the only way of microservices. Nope! Message based asynchronous microservice architecture is supported by Vertx https://dzone.com/articles/asynchronous-microservices-with-vertx, for example. Why not to use RabbitMQ as message channel? In this case load balancing can be provided by building RabbitMQ cluster. For example:https://codeburst.io/using-rabbitmq-for-microservices-communication-on-docker-a43840401819. So, world is much wide more.
FYI: This will be my first real foray into Async/Await; for too long I've been settling for the familiar territory of BackgroundWorker. It's time to move on.
I wish to build a WCF service, self-hosted in a Windows service running on a remote machine in the same LAN, that does this:
Accepts a request for a single .ZIP archive
Creates the archive and packages several files
Returns the archive as its response to the request
I have to support archives as large as 10GB. Needless to say, this scenario isn't covered by basic WCF designs; we must take additional steps to meet the requirement. We must eliminate timeouts while the archive is building and memory errors while it's being sent. Both of these occur under basic WCF designs, depending on the size of the file returned.
My plan is to proceed using task-based asynchronous WCF calls and streaming mode.
I have two concerns:
Is this the proper approach to the problem?
Microsoft has done a nice job at abstracting all of this, but what of the underlying protocols? What goes on 'under the hood?' Does the server keep the connection alive while the archive is building (could be several minutes) or instead does it close the connection and initiate a new one once the operation is complete, thereby requiring me to properly route the request through the client machine firewall?
For #2, clearly I'm hoping for the former (keep-alive). But after some searching I'm not easily finding an answer. Perhaps you know.
You need streaming for big payloads. That is the right approach. This has nothing at all to do with asynchronous IO. The two are independent. The client cannot even tell that the server is async internally.
I'll add my standard answers for whether to use async IO or not:
https://stackoverflow.com/a/25087273/122718 Why does the EF 6 tutorial use asychronous calls?
https://stackoverflow.com/a/12796711/122718 Should we switch to use async I/O by default?
Each request runs over a single connection that is kept alive. This goes for both streaming big amounts of data as well as big initial delays. Not sure why you are concerned about routing. Does your router kill such connections? That's a problem.
Regarding keep alive, there is nothing going over the wire to do that. TCP sessions can stay open indefinitely without any kind of wire traffic.
I'm setting up a web service with pyramid. A typical request for a view will be very long, about 15 min to finish. So my idea was to queue jobs with celery and a rabbitmq broker.
I would like to know what would be the best way to ensure that bad things cannot happen.
Specifically I would like to prevent the task queue from overflow for example.
A first mesure will be defining quotas per IP, to limit the number of requests a given IP can submit per hour.
However I cannot predict the number of involved IPs, so this cannot solve everything.
I have read that it's not possible to limit the queue size with celery/rabbitmq. I was thinking of retrieving the queue size before pushing a new item into it but I'm not sure if it's a good idea.
I'm not used to good practices in messaging/job scheduling. Is there a recommended way to handle this kind of problems ?
RabbitMQ has flow control built into the QoS. If RabbitMQ cannot handle the publishing rate it will adjust the TCP window size to slow down the publishers. In the event of too many messages being sent to the server it will also overflow to disk. This will allow your consumer to be a bit more naive although if you restart the connection on error and flood the connection you can cause problems.
I've always decided to spend more time making sure the publishers/consumers could work with multiple queue servers instead of trying to make them more intelligent about a single queue server. The benefit is that if you are really overloading a single server you can just add another one (or another pair if using RabbitMQ HA. There is a useful video from Pycon about Messaging at Scale using Celery and RabbitMQ that should be of use.
On my team at work, we use the IBM MQ technology a lot for cross-application communication. I've seen lately on Hacker News and other places about other MQ technologies like RabbitMQ. I have a basic understanding of what it is (a commonly checked area to put and get messages), but what I want to know what exactly is it good at? How will I know where I want to use it and when? Why not just stick with more rudimentary forms of interprocess messaging?
All the explanations so far are accurate and to the point - but might be missing something: one of the main benefits of message queueing: resilience.
Imagine this: you need to communicate with two or three other systems. A common approach these days will be web services which is fine if you need an answers right away.
However: web services can be down and not available - what do you do then? Putting your message into a message queue (which has a component on your machine/server, too) typically will work in this scenario - your message just doesn't get delivered and thus processed right now - but it will later on, when the other side of the service comes back online.
So in many cases, using message queues to connect disparate systems is a more reliable, more robust way of sending messages back and forth. It doesn't work well for everything (if you want to know the current stock price for MSFT, putting that request into a queue might not be the best of ideas) - but in lots of cases, like putting an order into your supplier's message queue, it works really well and can help ease some of the reliability issues with other technologies.
MQ stands for messaging queue.
It's an abstraction layer that allows multiple processes (likely on different machines) to communicate via various models (e.g., point-to-point, publish subscribe, etc.). Depending on the implementation, it can be configured for things like guaranteed reliability, error reporting, security, discovery, performance, etc.
You can do all this manually with sockets, but it's very difficult.
For example: Suppose you want to processes to communicate, but one of them can die in the middle and later get reconnected. How would you ensure that interim messages were not lost? MQ solutions can do that for you.
Message queueuing systems are supposed to give you several bonuses. Among most important ones are monitoring and transactional behavior.
Transactional design is important if you want to be immune to failures, such as power failure. Imagine that you want to notify a bank system of ATM money withdrawal, and it has to be done exactly once per request, no matter what servers failed temporarily in the middle. MQ systems would allow you to coordinate transactions across multiple database, MQ and other systems.
Needless to say, such systems are very slow compared to named pipes, TCP or other non-transactional tools. If high performance is required, you would not allow your messages to be written thru disk. Instead, it will complicate your design - to achieve exotic reliable AND fast communication, which pushes the designer into really non-trivial tricks.
MQ systems normally allow users to watch the queue contents, write plugins, clear queus, etc.
MQ simply stands for Message Queue.
You would use one when you need to reliably send a inter-process/cross-platform/cross-application message that isn't time dependent.
The Message Queue receives the message, places it in the proper queue, and waits for the application to retrieve the message when ready.
reference: web services can be down and not available - what do you do then?
As an extension to that; what if your local network and your local pc is down as well?? While you wait for the system to recover the dependent deployed systems elsewhere waiting for that data needs to see an alternative data stream.
Otherwise, that might not be good enough 'real time' response for today's and very soon in the future Internet of Things (IOT) requirements.
if you want true parallel, non volatile storage of various FIFO streams(at least at some point along the signal chain) use an FPGA and FRAM memory. FRAM runs at clock speed and FPGA devices can be reprogrammed on the fly adding and taking away however many independent parallel data streams are needed(within established constraints of course).
I have a web tier that forwards calls onto an application tier. The web tier uses a shared, cached channel to do so. The application tier services in question are stateless and have concurrency enabled.
But they are not being called concurrently.
If I alter the web tier to create a new channel on every call, then I do get concurrent calls onto the application tier. But I want to avoid that cost since it is functionally unnecessary for my scenario. I have no session state, and nor do I need to re-authenticate the caller each time. I understand that the creation of the channel factory is far more expensive than than the creation of the channels, but it is still a cost I'd like to avoid if possible.
I found this article on MSDN that states:
While channels and clients created by
the channels are thread-safe, they
might not support writing more than
one message to the wire concurrently.
If you are sending large messages,
particularly if streaming, the send
operation might block waiting for
another send to complete.
Firstly, I'm not sending large messages (just a lot of small ones since I'm doing load testing) but am still seeing the blocking behavior. Secondly, this is rather open-ended and unhelpful documentation. It says they "might not" support writing more than one message but doesn't explain the scenarios under which they would support concurrent messages.
Can anyone shed some light on this?
Addendum: I am also considering creating a pool of channels that the web server uses to fulfill requests. But again, I see no reason why my existing approach should block and I'd rather avoid the complexity if possible.
After much ado, this all came down to the fact that I wasn't calling Open explicitly on the channel before using it. Apparently an implicit Open can preclude concurrency in some scenarios.
You can cache the WCF proxy, but still create a channel for each service call - this will ensure concurrency, is not very expensive in comparison to creating a channel from scratch, and re-authentication for each call will not be necessary. This is explained on Wenlong Dong's blog - "Performance Improvement for WCF Client Proxy Creation in .NET 3.5 and Best Practices" (a much better source of WCF information and guidance than MSDN).
Just for completeness: Here is a blog entry explaining the observed behavior of request serialization when not opening the channel explicitly:
http://blogs.msdn.com/b/wenlong/archive/2007/10/26/best-practice-always-open-wcf-client-proxy-explicitly-when-it-is-shared.aspx