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Learn 0MQs main patterns: request-reply, publish-subscribe and pipeline. Work with 0MQ sockets and patterns by building several small applications. Explore advanced uses of 0MQs request-reply pattern through working examples. Build reliable request-reply patterns that keep working when code or hardware fails. Extend 0MQs core pub-sub patterns for performance, reliability, state distribution and monitoring. Learn techniques for building a distributed architecture with 0MQ. Discover what's required to build a general-purpose framework for distributed applications.
About the Author Pieter Hintjens started his first business making video games 30 years ago and has been building software products since then. Binding: Paperback. Condition Type: New. Country Origin: India. Edition : 1. Add to cart. Be the first to write a review About this product. With this quick-paced guide, you'll learn hands-on how to use this scalable, lightweight, and highly flexible networking tool for exchanging messages among clusters, the cloud, and other multi-system environments.
Additional Product Features Dewey Edition. Show More Show Less. New New. No ratings or reviews yet. Be the first to write a review. Best Selling in Nonfiction See all. The Book of Enoch by Enoch , Paperback As we see, round-tripping in the simplest case is 20 times slower than the asynchronous, "shove it down the pipe as fast as it'll go" approach. Let's see if we can apply this to Majordomo to make it faster. It's literally a few minutes' work to refactor the synchronous client API to become asynchronous:. And here's the corresponding client test program, which sends , messages and then receives , back:.
The broker and worker are unchanged because we've not modified the protocol at all. We see an immediate improvement in performance. Here's the synchronous client chugging through K request-reply cycles:. It isn't fully asynchronous because workers get their messages on a strict last-used basis. But it will scale better with more workers.
On my PC, after eight or so workers, it doesn't get any faster. Four cores only stretches so far. But we got a 4x improvement in throughput with just a few minutes' work. The broker is still unoptimized. It spends most of its time copying message frames around, instead of doing zero-copy, which it could. However, the asynchronous Majordomo pattern isn't all roses.
It has a fundamental weakness, namely that it cannot survive a broker crash without more work. If you look at the mdcliapi2 code you'll see it does not attempt to reconnect after a failure. A proper reconnect would require the following:. It's not a deal breaker, but it does show that performance often means complexity.
Is this worth doing for Majordomo?
It depends on your use case. For a name lookup service you call once per session, no. For a web frontend serving thousands of clients, probably yes. So, we have a nice service-oriented broker, but we have no way of knowing whether a particular service is available or not. We know whether a request failed, but we don't know why. It is useful to be able to ask the broker, "is the echo service running? Another option is to do what email does, and ask that undeliverable requests be returned. This can work well in an asynchronous world, but it also adds complexity.
We need ways to distinguish returned requests from replies and to handle these properly. Let's try to use what we've already built, building on top of MDP instead of modifying it. Service discovery is, itself, a service. It might indeed be one of several management services, such as "disable service X", "provide statistics", and so on. What we want is a general, extensible solution that doesn't affect the protocol or existing applications.
We already implemented it in the broker, though unless you read the whole thing you probably missed that. I'll explain how it works in the broker:. Try this with and without a worker running, and you should see the little program report "" or "" accordingly. The implementation of MMI in our example broker is flimsy. For example, if a worker disappears, services remain "present". In practice, a broker should remove services that have no workers after some configurable timeout. Idempotency is not something you take a pill for.
What it means is that it's safe to repeat an operation. Checking the clock is idempotent.
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Lending ones credit card to ones children is not. While many client-to-server use cases are idempotent, some are not. Examples of idempotent use cases include:. When our server applications are not idempotent, we have to think more carefully about when exactly they might crash. If an application dies when it's idle, or while it's processing a request, that's usually fine.
We can use database transactions to make sure a debit and a credit are always done together, if at all. If the server dies while sending its reply, that's a problem, because as far as it's concerned, it has done its work. If the network dies just as the reply is making its way back to the client, the same problem arises. The client will think the server died and will resend the request, and the server will do the same work twice, which is not what we want. To handle non-idempotent operations, use the fairly standard solution of detecting and rejecting duplicate requests.
This means:. Once you realize that Majordomo is a "reliable" message broker, you might be tempted to add some spinning rust that is, ferrous-based hard disk platters. After all, this works for all the enterprise messaging systems. It's such a tempting idea that it's a little sad to have to be negative toward it.
But brutal cynicism is one of my specialties. So, some reasons you don't want rust-based brokers sitting in the center of your architecture are:. Having said this, however, there is one sane use case for rust-based reliability, which is an asynchronous disconnected network. It solves a major problem with Pirate, namely that a client has to wait for an answer in real time. If clients and workers are only sporadically connected think of email as an analogy , we can't use a stateless network between clients and workers.
We have to put state in the middle. So, here's the Titanic pattern, in which we write messages to disk to ensure they never get lost, no matter how sporadically clients and workers are connected. As we did for service discovery, we're going to layer Titanic on top of MDP rather than extend it. It's wonderfully lazy because it means we can implement our fire-and-forget reliability in a specialized worker, rather than in the broker. This is excellent for several reasons:. The only downside is that there's an extra network hop between broker and hard disk. The benefits are easily worth it.
There are many ways to make a persistent request-reply architecture. We'll aim for one that is simple and painless. The simplest design I could come up with, after playing with this for a few hours, is a "proxy service". That is, Titanic doesn't affect workers at all. If a client wants a reply immediately, it talks directly to a service and hopes the service is available.
Review: ZeroMQ: Messaging for Many Applications by Pieter Hintjens
If a client is happy to wait a while, it talks to Titanic instead and asks, "hey, buddy, would you take care of this for me while I go buy my groceries? Titanic is thus both a worker and a client. The dialog between client and Titanic goes along these lines:. You can work through this and the possible failure scenarios. If a worker crashes while processing a request, Titanic retries indefinitely. If a reply gets lost somewhere, Titanic will retry.
If the request gets processed but the client doesn't get the reply, it will ask again. If Titanic crashes while processing a request or a reply, the client will try again. As long as requests are fully committed to safe storage, work can't get lost. The handshaking is pedantic, but can be pipelined, i.
We need some way for a client to request its replies. We'll have many clients asking for the same services, and clients disappear and reappear with different identities. Here is a simple, reasonably secure solution:. In a realistic case, the client would want to store its request UUIDs safely, e.
Before we jump off and write yet another formal specification fun, fun! One way is to use a single service and send it three different request types. Another way, which seems simpler, is to use three services:. We'll just make a multithreaded worker, which as we've seen from our multithreading experience with ZeroMQ, is trivial.
However, let's first sketch what Titanic would look like in terms of ZeroMQ messages and frames. Here's the shortest robust "echo" client example:. Of course this can be, and should be, wrapped up in some kind of framework or API. It's not healthy to ask average application developers to learn the full details of messaging: it hurts their brains, costs time, and offers too many ways to make buggy complexity. Additionally, it makes it hard to add intelligence. For example, this client blocks on each request whereas in a real application, we'd want to be doing useful work while tasks are executed.
This requires some nontrivial plumbing to build a background thread and talk to that cleanly. It's the kind of thing you want to wrap in a nice simple API that the average developer cannot misuse. It's the same approach that we used for Majordomo. Here's the Titanic implementation. This server handles the three services using three threads, as proposed. It does full persistence to disk using the most brutal approach possible: one file per message.
It's so simple, it's scary.
The only complex part is that it keeps a separate queue of all requests, to avoid reading the directory over and over:. To test this, start mdbroker and titanic , and then run ticlient. Now start mdworker arbitrarily, and you should see the client getting a response and exiting happily.
The important thing about this example is not performance which, although I haven't tested it, is surely terrible , but how well it implements the reliability contract. To try it, start the mdbroker and titanic programs. Then start the ticlient, and then start the mdworker echo service. You can run all four of these using the -v option to do verbose activity tracing. You can stop and restart any piece except the client and nothing will get lost. If you want to use Titanic in real cases, you'll rapidly be asking "how do we make this faster?
And so on. You will pay a steep price for the abstraction, ten to a thousand times over a raw disk file.
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If you want to make Titanic even more reliable , duplicate the requests to a second server, which you'd place in a second location just far away enough to survive a nuclear attack on your primary location, yet not so far that you get too much latency. If you want to make Titanic much faster and less reliable , store requests and replies purely in memory. This will give you the functionality of a disconnected network, but requests won't survive a crash of the Titanic server itself.
The Binary Star pattern puts two servers in a primary-backup high-availability pair. At any given time, one of these the active accepts connections from client applications. The other the passive does nothing, but the two servers monitor each other.
ZeroMQ - Messaging for Many Application by Peter Hintjens; O'Reilly Media
If the active disappears from the network, after a certain time the passive takes over as active. We designed it:. Assuming we have a Binary Star pair running, here are the different scenarios that will result in a failover:. Recovery to using the primary server as active is a manual operation. Painful experience teaches us that automatic recovery is undesirable.
There are several reasons:. Having said this, the Binary Star pattern will fail back to the primary server if this is running again and the backup server fails. In fact, this is how we provoke recovery. Stopping the active and then the passive server with any delay longer than the failover timeout will cause applications to disconnect, then reconnect, and then disconnect again, which may disturb users. Binary Star is as simple as it can be, while still working accurately. In fact, the current design is the third complete redesign. Each of the previous designs we found to be too complex, trying to do too much, and we stripped out functionality until we came to a design that was understandable, easy to use, and reliable enough to be worth using.
The main tuning concern is how frequently you want the servers to check their peering status, and how quickly you want to activate failover. In our example, the failover timeout value defaults to 2, msec. If you reduce this, the backup server will take over as active more rapidly but may take over in cases where the primary server could recover. For example, you may have wrapped the primary server in a shell script that restarts it if it crashes. In that case, the timeout should be higher than the time needed to restart the primary server.
It's not trivial work, and we'd usually wrap this in an API that hides it from real end-user applications. Split-brain syndrome occurs when different parts of a cluster think they are active at the same time. It causes applications to stop seeing each other.
Binary Star has an algorithm for detecting and eliminating split brain, which is based on a three-way decision mechanism a server will not decide to become active until it gets application connection requests and it cannot see its peer server. However, it is still possible to mis design a network to fool this algorithm. A typical scenario would be a Binary Star pair, that is distributed between two buildings, where each building also had a set of applications and where there was a single network link between both buildings.
Breaking this link would create two sets of client applications, each with half of the Binary Star pair, and each failover server would become active. To prevent split-brain situations, we must connect a Binary Star pair using a dedicated network link, which can be as simple as plugging them both into the same switch or, better, using a crossover cable directly between two machines.
We must not split a Binary Star architecture into two islands, each with a set of applications. While this may be a common type of network architecture, you should use federation, not high-availability failover, in such cases.
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