Category: Rpc vs message queue

rpc vs message queue

In computer sciencemessage queues and mailboxes are software-engineering components used for inter-process communication IPCor for inter- thread communication within the same process. Group communication systems provide similar kinds of functionality. Message queues provide an asynchronous communications protocolmeaning that the sender and receiver of the message do not need to interact with the message queue at the same time.

Messages placed onto the queue are stored until the recipient retrieves them. Message queues have implicit or explicit limits on the size of data that may be transmitted in a single message and the number of messages that may remain outstanding on the queue.

Many implementations of message queues function internally: within an operating system or within an application. Such queues exist for the purposes of that system only. Other implementations allow the passing of messages between different computer systems, potentially connecting multiple applications and multiple operating systems. There is a Java standard called Java Message Servicewhich has several proprietary and free software implementations. Implementations exist as proprietary software, provided as a serviceopen source software, or a hardware-based solution.

Proprietary options have the longest history, and include products from the inception of message queuing, such as IBM MQ, and those tied to specific operating systems, such as Microsoft Message Queuing. IBM also offers its MQ software on an appliance. Most real-time operating systems RTOSessuch as VxWorks and QNXencourage the use of message queueing as the primary inter-process or inter-thread communication mechanism.

The resulting tight integration between message passing and CPU scheduling is attributed as a main reason for the usability of RTOSes for real time applications.

rpc vs message queue

The Erlang programming language uses processes to provide concurrency; these processes communicate asynchronously using message queuing. In a typical message-queueing implementation, a system administrator installs and configures message-queueing software a queue manager or brokerand defines a named message queue. Or they register with a message queuing service.

An application then registers a software routine that "listens" for messages placed onto the queue. The queue-manager software stores the messages until a receiving application connects and then calls the registered software routine. The receiving application then processes the message in an appropriate manner. These are all considerations that can have substantial effects on transaction semantics, system reliability, and system efficiency.

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Historically, message queuing has used proprietary, closed protocols, restricting the ability for different operating systems or programming languages to interact in a heterogeneous set of environments.

This allowed Java developers to switch between providers of message queuing in a fashion similar to that of developers using SQL databases. In practice, given the diversity of message queuing techniques and scenarios, this wasn't always as practical as it could be. These protocols are at different stages of standardization and adoption.

This is because it is always possible to layer asynchronous behaviour which is what is required for message queuing over a synchronous protocol using request-response semantics. However, such implementations are constrained by the underlying protocol in this case and may not be able to offer the full fidelity or set of options required in message passing above. Many of the more widely known communications protocols in use operate synchronously.

However, scenarios exist in which synchronous behaviour is not appropriate. Google uses this approach for their Google Suggesta search feature which sends the user's partially typed queries to Google's servers and returns a list of possible full queries the user might be interested in the process of typing.Prerequisites This tutorial assumes RabbitMQ is installed and running on localhost on standard port In case you use a different host, port or credentials, connections settings would require adjusting.

If you're having trouble going through this tutorial you can contact us through the mailing list. In the second tutorial we learned how to use Work Queues to distribute time-consuming tasks among multiple workers. But what if we need to run a function on a remote computer and wait for the result?

Well, that's a different story. As we don't have any time-consuming tasks that are worth distributing, we're going to create a dummy RPC service that returns Fibonacci numbers.

To illustrate how an RPC service could be used we're going to create a simple client class. It's going to expose a method named call which sends an RPC request and blocks until the answer is received:. Although RPC is a pretty common pattern in computing, it's often criticised. The problems arise when a programmer is not aware whether a function call is local or if it's a slow RPC.

Confusions like that result in an unpredictable system and adds unnecessary complexity to debugging. Instead of simplifying software, misused RPC can result in unmaintainable spaghetti code. When in doubt avoid RPC.

If you can, you should use an asynchronous pipeline - instead of RPC-like blocking, results are asynchronously pushed to a next computation stage. A client sends a request message and a server replies with a response message.

In order to receive a response we need to send a 'callback' queue address with the request. We can use the default queue. Let's try it:. The AMQP protocol predefines a set of 14 properties that go with a message. Most of the properties are rarely used, with the exception of the following:. In the method presented above we suggest creating a callback queue for every RPC request. That's pretty inefficient, but fortunately there is a better way - let's create a single callback queue per client.

That raises a new issue, having received a response in that queue it's not clear to which request the response belongs. We're going to set it to a unique value for every request.

Microsoft Message Queuing (Performance)

Later, when we receive a message in the callback queue we'll look at this property, and based on that we'll be able to match a response with a request. You may ask, why should we ignore unknown messages in the callback queue, rather than failing with an error? It's due to a possibility of a race condition on the server side. Although unlikely, it is possible that the RPC server will die just after sending us the answer, but before sending an acknowledgment message for the request.

If that happens, the restarted RPC server will process the request again. That's why on the client we must handle the duplicate responses gracefully, and the RPC should ideally be idempotent.Prerequisites This tutorial assumes RabbitMQ is installed and running on localhost on standard port In case you use a different host, port or credentials, connections settings would require adjusting.

If you're having trouble going through this tutorial you can contact us through the mailing list. In the second tutorial we learned how to use Work Queues to distribute time-consuming tasks among multiple workers.

rpc vs message queue

But what if we need to run a function on a remote computer and wait for the result? Well, that's a different story. As we don't have any time-consuming tasks that are worth distributing, we're going to create a dummy RPC service that returns Fibonacci numbers. To illustrate how an RPC service could be used we're going to create a simple client class. It's going to expose a method named call which sends an RPC request and blocks until the answer is received:.

Although RPC is a pretty common pattern in computing, it's often criticised. The problems arise when a programmer is not aware whether a function call is local or if it's a slow RPC. Confusions like that result in an unpredictable system and adds unnecessary complexity to debugging.

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Instead of simplifying software, misused RPC can result in unmaintainable spaghetti code. When in doubt avoid RPC. If you can, you should use an asynchronous pipeline - instead of RPC-like blocking, results are asynchronously pushed to a next computation stage. A client sends a request message and a server replies with a response message.

In order to receive a response the client needs to send a 'callback' queue address with the request. Let's try it:. The AMQP protocol predefines a set of 14 properties that go with a message.

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Most of the properties are rarely used, with the exception of the following:. In the method presented above we suggest creating a callback queue for every RPC request.

That's pretty inefficient, but fortunately there is a better way - let's create a single callback queue per client. That raises a new issue, having received a response in that queue it's not clear to which request the response belongs. We're going to set it to a unique value for every request. Later, when we receive a message in the callback queue we'll look at this property, and based on that we'll be able to match a response with a request.

You may ask, why should we ignore unknown messages in the callback queue, rather than failing with an error?

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It's due to a possibility of a race condition on the server side. Although unlikely, it is possible that the RPC server will die just after sending us the answer, but before sending an acknowledgment message for the request.

If that happens, the restarted RPC server will process the request again. That's why on the client we must handle the duplicate responses gracefully, and the RPC should ideally be idempotent. The presented design is not the only possible implementation of a RPC service, but it has some important advantages:.

Our code is still pretty simplistic and doesn't try to solve more complex but important problems, like:.

If you want to experiment, you may find the management UI useful for viewing the queues. Please keep in mind that this and other tutorials are, well, tutorials. They demonstrate one new concept at a time and may intentionally oversimplify some things and leave out others. For example topics such as connection management, error handling, connection recovery, concurrency and metric collection are largely omitted for the sake of brevity.

Such simplified code should not be considered production ready.Message Queuing MSMQ lets users communicate across networks and systems regardless of the current state of the communicating applications and systems.

Applications send and receive messages through message queues that MSMQ maintains. The message queues continue to function even when the client or server application is not running. Message queuing provides:. Later versions of Windows do not support RPC message queuing. Skip to main content. Exit focus mode. Message queuing provides: Asynchronous messaging. With MSMQ asynchronous messaging, a client application can send a message to a server and return immediately, even if the target computer or server program is not responding.

Guaranteed message delivery. When an application sends a message through MSMQ, the message will reach its destination even if the destination application is not running at the same time or the networks and systems are offline. Routing and dynamic configuration. MSMQ provides flexible routing over heterogeneous networks. The configuration of such networks can be changed dynamically without any major changes to systems and networks themselves.

Connectionless messaging. Applications using MSMQ do not need to set up direct sessions with target applications. Prioritized Messaging.

MSMQ transfers messages across networks based on priority, allowing faster communication for critical applications. Is this page helpful? Yes No. Any additional feedback?

Skip Submit.I am currently in the process of moving a single endpoint out of a large, monolithic Ruby on Rails app. That endpoint does the following, in order. The current implementation does all of the work on the request thread.

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Moving the database writes off the request thread would help only slightly. The real issue is that most of the time is spent marshalling all the data into YAML. This means that an enormous amount of CPU time is spent generating that data. This holds open a connection on the server which is limited to a fixed number of connections at any one time.

The upside to this is that the actual communication performed is machine-to-machine. The individual latency of any connection is not particularly relevant, but the required overall throughput is high. I began rewriting this single endpoint as a seperate piece of software in Go. The current architecture already uses a load balancer, so it can be configured to send requests for this endpoint to a separate set of HTTP servers. In the process of rewriting this I discovered a large amount of code that predated myself.

This code is difficult to port because the people who have written it have long since moved on. Additionally, I am not enthused about creating an implementation to write to an ActiveRecord database from a Go application.

Introduction

In the end I made the decision to keep a large portion of the existing implementation. This of course meant that I needed to come up with a way to call the Ruby code from Go.

I chose this solution because we already have a large AMQP cluster in our production environment that is under-utilized. We also have several servers that do not serve HTTP requests, but instead are reserved for all other work such as periodic tasks. By running the Ruby portion of my new hybrid-solution on these servers, the servers that process HTTP requests are completely unburdened by this service.

My findings are presented here. The basic concept of RPC is that some information is sent and later some information is received.

There is a one to one correlation between those two categories of information under normal circumstances. This mirrors the basic paradigm of imperative programming: the function call. The difference is RPC is typically used to exchange information between two systems separated by a network connection or another communication medium.

The two systems may not even be written in the same programming language. Broker mediated messages allow a single producer to deliver to messages to zero or more consumers.

This is distinct from using a communication protocol like TCP to exchange messages because the broker can make certain guarantees about how each message is handled.And adding components to a software system is one of the things that adds a significant amount of complexity.

Message Queues are systems that let you have fault-tolerant, distributed, decoupled, etc, etc. That sounds good on paper.

Message queues may fit in several use-cases in your application. You can check this nice article about the benefits of MQs of what some use-cases might be. So you post a message to a message queue, then the email processing system picks it up and sends the emails. How would you do that in a monolithic, single classpath application?

Just make your order processing service depend on an email service, and call sendEmail. What is the practical difference? Not much, if any. But then you probably want to be able to add another consumer that does additional thing with a given message? Coupled — yes. But not inconveniently coupled. What if you want to handle spikes? Message queues give you the ability to put requests in a persistent queue and process all of them.

The servlet container thread pool can be used as sort-of queue — response will be served eventually, but the user will have to wait if the thread acquisition timeout is too small, requests will be dropped, though. Or you can use an in-memory queue for the heavier requests that are handled in the UI background. And note that by default your MQ might not be highly-availably. Which leads us to asynchronous processing — this is indeed a useful feature.

Here comes another aspect — does it matter if a message is lost? If you application node, processing the request, dies, can you recover? So, just asynchronously handling heavier invocations might work well. A scheduled job runs, picks all unprocessed ones and processes them asynchronously. Then, when processing is finished, set the flag to true. Regardless of whether you are using an MQ or not. Temporary in-memory processing queues are not persistent state. Because if chosen for the wrong reason, an MQ can be a burden.

They are not as easy to use as it sounds. Generally, the more separate integrated components you have, the more problems may arise. And how does your application node connect to the MQ? Via a refreshing connection pool, using a short-lived DNS record, via a load balancer?

If you overuse your MQ, then it adds latency to your system. You see, it adds a lot of complexity and things to take care of.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Already on GitHub? Sign in to your account. I've found some Blazor examples with gRPC service. That is remote access data. I know the event handling on Blazor client side are implemented by SignalR. How about chatting app or stock dashboard? Which technology would you choice? There is not a silver bullet in software, everything has its strengths and weaknesses.

SignalR will be sufficient for most of scenarios when building either Client or Server-Side Blazor apps. The most common usages like updating DOM dynamically, chat messages, notifications, message queues, logging, smallish data chunks exchange SignalR is less complex to set-up, deploy and run as it's already baked in which results in a rather pleasant and timely rewarding experience. Skip to content.

Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue. Jump to bottom. What do you think?

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Copy link Quote reply. Since the Blazor, we should take gRPC? This comment has been minimized. Sign in to view. You can multicast with grpc with custom code. The most common usages like updating DOM dynamically, chat messages, notifications, message queues, logging, smallish data chunks exchange SignalR is less complex to set-up, deploy and run as it's already baked in which results in a rather pleasant and timely rewarding experience ; gRPC comes in really handy in slightly more advanced scenarios such as: when you need to communicate between different systems relying on generation and consumption of proto contracts e.

ServiceWorkers or Dashboards, when you really require the top-notch performance-volume mixture. The binary format gRPC uses comes to the rescure. Just remember what the RPC stands for, its impact and things to consider e. HTML2 vs.

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Again, I'm sure there are gaps in my knowledge here. Sign up for free to join this conversation on GitHub. Already have an account?

rpc vs message queue

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