When your client and server don't agree on the string format, you will get weird results. When you receive string data from ZeroMQ in C, you simply cannot trust that it's safely terminated. Every single time you read a string, you should allocate a new buffer with space for an extra byte, copy the string, and terminate it properly with a null. So let's establish the rule that ZeroMQ strings are length-specified and are sent on the wire without a trailing null. In the simplest case and we'll do this in our examples , a ZeroMQ string maps neatly to a ZeroMQ message frame, which looks like the above figure—a length and some bytes.
Here is what we need to do, in C, to receive a ZeroMQ string and deliver it to the application as a valid C string:. The result is zhelpers. It is a fairly long source, and only fun for C developers, so read it at leisure. ZeroMQ does come in several versions and quite often, if you hit a problem, it'll be something that's been fixed in a later version. So it's a useful trick to know exactly what version of ZeroMQ you're actually linking with. The second classic pattern is one-way data distribution, in which a server pushes updates to a set of clients.
Let's see an example that pushes out weather updates consisting of a zip code, temperature, and relative humidity. We'll generate random values, just like the real weather stations do. Here is the client application, which listens to the stream of updates and grabs anything to do with a specified zip code, by default New York City because that's a great place to start any adventure:. If you don't set any subscription, you won't get any messages. It's a common mistake for beginners. The subscriber can set many subscriptions, which are added together.
That is, if an update matches ANY subscription, the subscriber receives it. The subscriber can also cancel specific subscriptions.
A subscription is often, but not always, a printable string. Trying to send a message to a SUB socket will cause an error. In theory with ZeroMQ sockets, it does not matter which end connects and which end binds. However, in practice there are undocumented differences that I'll come to later. There is one more important thing to know about PUB-SUB sockets: you do not know precisely when a subscriber starts to get messages.
Even if you start a subscriber, wait a while, and then start the publisher, the subscriber will always miss the first messages that the publisher sends. This is because as the subscriber connects to the publisher something that takes a small but non-zero time , the publisher may already be sending messages out.
This "slow joiner" symptom hits enough people often enough that we're going to explain it in detail. Say you have two nodes doing this, in this order:. Then the subscriber will most likely not receive anything. You'll blink, check that you set a correct filter and try again, and the subscriber will still not receive anything. Making a TCP connection involves to and from handshaking that takes several milliseconds depending on your network and the number of hops between peers.
In that time, ZeroMQ can send many messages. For sake of argument assume it takes 5 msecs to establish a connection, and that same link can handle 1M messages per second. During the 5 msecs that the subscriber is connecting to the publisher, it takes the publisher only 1 msec to send out those 1K messages. In Sockets and Patterns we'll explain how to synchronize a publisher and subscribers so that you don't start to publish data until the subscribers really are connected and ready.
There is a simple and stupid way to delay the publisher, which is to sleep. Don't do this in a real application, though, because it is extremely fragile as well as inelegant and slow. Use sleeps to prove to yourself what's happening, and then wait for Sockets and Patterns to see how to do this right.
The alternative to synchronization is to simply assume that the published data stream is infinite and has no start and no end. One also assumes that the subscriber doesn't care what transpired before it started up. This is how we built our weather client example. So the client subscribes to its chosen zip code and collects updates for that zip code. That means about ten million updates from the server, if zip codes are randomly distributed.
You can start the client, and then the server, and the client will keep working. You can stop and restart the server as often as you like, and the client will keep working. When the client has collected its hundred updates, it calculates the average, prints it, and exits. This is how long it takes to receive and filter 10M messages on my laptop, which is an era Intel i5, decent but nothing special:. As a final example you are surely getting tired of juicy code and want to delve back into philological discussions about comparative abstractive norms , let's do a little supercomputing.
Then coffee. Our supercomputing application is a fairly typical parallel processing model. We have:. In reality, workers run on superfast boxes, perhaps using GPUs graphic processing units to do the hard math. Here is the ventilator. It generates tasks, each a message telling the worker to sleep for some number of milliseconds:. Here is the worker application. It receives a message, sleeps for that number of seconds, and then signals that it's finished:.
Here is the sink application. It collects the tasks, then calculates how long the overall processing took, so we can confirm that the workers really were running in parallel if there are more than one of them:. The average cost of a batch is 5 seconds. When we start 1, 2, or 4 workers we get results like this from the sink:. The pipeline pattern also exhibits the "slow joiner" syndrome, leading to accusations that PUSH sockets don't load balance properly.
If you are using PUSH and PULL, and one of your workers gets way more messages than the others, it's because that PULL socket has joined faster than the others, and grabs a lot of messages before the others manage to connect. If you want proper load balancing, you probably want to look at the load balancing pattern in Advanced Request-Reply Patterns. Having seen some examples, you must be eager to start using ZeroMQ in some apps.
Before you start that, take a deep breath, chillax, and reflect on some basic advice that will save you much stress and confusion. ZeroMQ applications always start by creating a context , and then using that for creating sockets. You should create and use exactly one context in your process.
Technically, the context is the container for all sockets in a single process, and acts as the transport for inproc sockets, which are the fastest way to connect threads in one process. If at runtime a process has two contexts, these are like separate ZeroMQ instances. If that's explicitly what you want, OK, but otherwise remember:. In general, you want to do interesting ZeroMQ stuff in the children, and boring process management in the parent. Classy programmers share the same motto as classy hit men: always clean-up when you finish the job.
When you use ZeroMQ in a language like Python, stuff gets automatically freed for you. But when using C, you have to carefully free objects when you're finished with them or else you get memory leaks, unstable applications, and generally bad karma. Memory leaks are one thing, but ZeroMQ is quite finicky about how you exit an application. The ZeroMQ objects we need to worry about are messages, sockets, and contexts. Luckily it's quite simple, at least in simple programs:.
This is at least the case for C development. In a language with automatic object destruction, sockets and contexts will be destroyed as you leave the scope. If you use exceptions you'll have to do the clean-up in something like a "final" block, the same as for any resource. If you're doing multithreaded work, it gets rather more complex than this. We'll get to multithreading in the next chapter, but because some of you will, despite warnings, try to run before you can safely walk, below is the quick and dirty guide to making a clean exit in a multithreaded ZeroMQ application.
First, do not try to use the same socket from multiple threads. Please don't explain why you think this would be excellent fun, just please don't do it. Next, you need to shut down each socket that has ongoing requests. If your language binding doesn't do this for you automatically when you destroy a context, I'd suggest sending a patch. Finally, destroy the context. This will cause any blocking receives or polls or sends in attached threads i. Catch that error, and then set linger on, and close sockets in that thread, and exit.
Do not destroy the same context twice. It's complex and painful enough that any language binding author worth his or her salt will do this automatically and make the socket closing dance unnecessary. Many applications these days consist of components that stretch across some kind of network, either a LAN or the Internet. So many application developers end up doing some kind of messaging. These protocols are not hard to use, but there is a great difference between sending a few bytes from A to B, and doing messaging in any kind of reliable way. Let's look at the typical problems we face when we start to connect pieces using raw TCP.
Any reusable messaging layer would need to solve all or most of these:. I see it's efficient because it uses poll instead of select. But really, Zookeeper should be using a generic messaging layer and an explicitly documented wire level protocol. It is incredibly wasteful for teams to be building this particular wheel over and over.
But how to make a reusable messaging layer? Why, when so many projects need this technology, are people still doing it the hard way by driving TCP sockets in their code, and solving the problems in that long list over and over? It turns out that building reusable messaging systems is really difficult, which is why few FOSS projects ever tried, and why commercial messaging products are complex, expensive, inflexible, and brittle.
AMQP works better than many other designs, but remains relatively complex, expensive, and brittle. It takes weeks to learn to use, and months to create stable architectures that don't crash when things get hairy. Most messaging projects, like AMQP, that try to solve this long list of problems in a reusable way do so by inventing a new concept, the "broker", that does addressing, routing, and queuing. Brokers are an excellent thing in reducing the complexity of large networks. But adding broker-based messaging to a product like Zookeeper would make it worse, not better.
It would mean adding an additional big box, and a new single point of failure. A broker rapidly becomes a bottleneck and a new risk to manage. If the software supports it, we can add a second, third, and fourth broker and make some failover scheme. People do this. It creates more moving pieces, more complexity, and more things to break. And a broker-centric setup needs its own operations team.
You literally need to watch the brokers day and night, and beat them with a stick when they start misbehaving. You need boxes, and you need backup boxes, and you need people to manage those boxes. It is only worth doing for large applications with many moving pieces, built by several teams of people over several years. So small to medium application developers are trapped. Either they avoid network programming and make monolithic applications that do not scale. Or they jump into network programming and make brittle, complex applications that are hard to maintain.
Or they bet on a messaging product, and end up with scalable applications that depend on expensive, easily broken technology. There has been no really good choice, which is maybe why messaging is largely stuck in the last century and stirs strong emotions: negative ones for users, gleeful joy for those selling support and licenses. What we need is something that does the job of messaging, but does it in such a simple and cheap way that it can work in any application, with close to zero cost.
It should be a library which you just link, without any other dependencies. No additional moving pieces, so no additional risk. It should run on any OS and work with any programming language. And this is ZeroMQ: an efficient, embeddable library that solves most of the problems an application needs to become nicely elastic across a network, without much cost. Actually ZeroMQ does rather more than this. It has a subversive effect on how you develop network-capable applications. But message processing rapidly becomes the central loop, and your application soon breaks down into a set of message processing tasks.
It is elegant and natural. And it scales: each of these tasks maps to a node, and the nodes talk to each other across arbitrary transports. Two nodes in one process node is a thread , two nodes on one box node is a process , or two nodes on one network node is a box —it's all the same, with no application code changes. Let's see ZeroMQ's scalability in action. Here is a shell script that starts the weather server and then a bunch of clients in parallel:.
As the clients run, we take a look at the active processes using the top command', and we see something like on a 4-core box :. Let's think for a second about what is happening here. The weather server has a single socket, and yet here we have it sending data to five clients in parallel. We could have thousands of concurrent clients. The server application doesn't see them, doesn't talk to them directly.
So the ZeroMQ socket is acting like a little server, silently accepting client requests and shoving data out to them as fast as the network can handle it. And it's a multithreaded server, squeezing more juice out of your CPU. For applications that want to run on both v2. Traditional network programming is built on the general assumption that one socket talks to one connection, one peer. There are multicast protocols, but these are exotic. We create threads of logic where each thread work with one socket, one peer. We place intelligence and state in these threads. In the ZeroMQ universe, sockets are doorways to fast little background communications engines that manage a whole set of connections automagically for you.
You can't see, work with, open, close, or attach state to these connections. Whether you use blocking send or receive, or poll, all you can talk to is the socket, not the connections it manages for you. The connections are private and invisible, and this is the key to ZeroMQ's scalability. This is because your code, talking to a socket, can then handle any number of connections across whatever network protocols are around, without change. A messaging pattern sitting in ZeroMQ scales more cheaply than a messaging pattern sitting in your application code.
So the general assumption no longer applies. As you read the code examples, your brain will try to map them to what you know. You will read "socket" and think "ah, that represents a connection to another node". That is wrong. You will read "thread" and your brain will again think, "ah, a thread represents a connection to another node", and again your brain will be wrong. If you're reading this Guide for the first time, realize that until you actually write ZeroMQ code for a day or two and maybe three or four days , you may feel confused, especially by how simple ZeroMQ makes things for you, and you may try to impose that general assumption on ZeroMQ, and it won't work.
And then you will experience your moment of enlightenment and trust, that zap-pow-kaboom satori paradigm-shift moment when it all becomes clear. In this chapter, we're going to get our hands dirty and start to learn how to use these tools in real programs. To be perfectly honest, ZeroMQ does a kind of switch-and-bait on you, for which we don't apologize.
It's for your own good and it hurts us more than it hurts you. ZeroMQ presents a familiar socket-based API, which requires great effort for us to hide a bunch of message-processing engines. However, the result will slowly fix your world view about how to design and write distributed software. Sockets are the de facto standard API for network programming, as well as being useful for stopping your eyes from falling onto your cheeks. One thing that makes ZeroMQ especially tasty to developers is that it uses sockets and messages instead of some other arbitrary set of concepts.
Kudos to Martin Sustrik for pulling this off. Like a favorite dish, ZeroMQ sockets are easy to digest. Sockets have a life in four parts, just like BSD sockets:. Note that sockets are always void pointers, and messages which we'll come to very soon are structures. As a mnemonic, realize that "in ZeroMQ, all your sockets are belong to us", but messages are things you actually own in your code. Creating, destroying, and configuring sockets works as you'd expect for any object.
But remember that ZeroMQ is an asynchronous, elastic fabric. This has some impact on how we plug sockets into the network topology and how we use the sockets after that. Thus we say that we "bind a socket to an endpoint" and "connect a socket to an endpoint", the endpoint being that well-known network address. The main notable differences are:. There are sometimes issues of addressing: servers will be visible to clients, but not necessarily vice versa. It also depends on the kind of sockets you're using, with some exceptions for unusual network architectures. We'll look at socket types later.
Now, imagine we start the client before we start the server. In traditional networking, we get a big red Fail flag. But ZeroMQ lets us start and stop pieces arbitrarily.
A server node can bind to many endpoints that is, a combination of protocol and address and it can do this using a single socket. This means it will accept connections across different transports:.
With most transports, you cannot bind to the same endpoint twice, unlike for example in UDP. The ipc transport does, however, let one process bind to an endpoint already used by a first process. It's meant to allow a process to recover after a crash. Although ZeroMQ tries to be neutral about which side binds and which side connects, there are differences.
We'll see these in more detail later. The upshot is that you should usually think in terms of "servers" as static parts of your topology that bind to more or less fixed endpoints, and "clients" as dynamic parts that come and go and connect to these endpoints. Then, design your application around this model. The chances that it will "just work" are much better like that. Sockets have types. The socket type defines the semantics of the socket, its policies for routing messages inwards and outwards, queuing, etc.
You can connect certain types of socket together, e. Sockets work together in "messaging patterns". We'll look at this in more detail later. It's the ability to connect sockets in these different ways that gives ZeroMQ its basic power as a message queuing system. There are layers on top of this, such as proxies, which we'll get to later. But essentially, with ZeroMQ you define your network architecture by plugging pieces together like a child's construction toy.
It does not block except in some exception cases. ZeroMQ provides a set of unicast transports inproc , ipc , and tcp and multicast transports epgm, pgm. Multicast is an advanced technique that we'll come to later. Don't even start using it unless you know that your fan-out ratios will make 1-to-N unicast impossible. For most common cases, use tcp , which is a disconnected TCP transport. It is elastic, portable, and fast enough for most cases.
We call this disconnected because ZeroMQ's tcp transport doesn't require that the endpoint exists before you connect to it.
Clients and servers can connect and bind at any time, can go and come back, and it remains transparent to applications. The inter-process ipc transport is disconnected, like tcp. It has one limitation: it does not yet work on Windows. By convention we use endpoint names with an ". On UNIX systems, if you use ipc endpoints you need to create these with appropriate permissions otherwise they may not be shareable between processes running under different user IDs. You must also make sure all processes can access the files, e.
The inter-thread transport, inproc , is a connected signaling transport. It is much faster than tcp or ipc. This transport has a specific limitation compared to tcp and ipc : the server must issue a bind before any client issues a connect. This is something future versions of ZeroMQ may fix, but at present this defines how you use inproc sockets.
We create and bind one socket and start the child threads, which create and connect the other sockets. The answer used to be "this is not how it works". ZeroMQ is not a neutral carrier: it imposes a framing on the transport protocols it uses. This framing is not compatible with existing protocols, which tend to use their own framing. But it would not be HTTP. Since v3. You could use this to read and write proper HTTP requests and responses. Hardeep Singh contributed this change so that he could connect to Telnet servers from his ZeroMQ application.
At time of writing this is still somewhat experimental, but it shows how ZeroMQ keeps evolving to solve new problems. Maybe the next patch will be yours. We've seen that one socket can handle dozens, even thousands of connections at once. This has a fundamental impact on how you write applications. A traditional networked application has one process or one thread per remote connection, and that process or thread handles one socket. ZeroMQ lets you collapse this entire structure into a single process and then break it up as necessary for scaling.
If you are using ZeroMQ for inter-thread communications only i. It's not a significant optimization though, more of a curiosity. If you have a background in enterprise messaging, or know UDP well, these will be vaguely familiar. But to most ZeroMQ newcomers, they are a surprise. We're so used to the TCP paradigm where a socket maps one-to-one to another node. Let's recap briefly what ZeroMQ does for you. It delivers blobs of data messages to nodes, quickly and efficiently.
You can map nodes to threads, processes, or nodes. ZeroMQ gives your applications a single socket API to work with, no matter what the actual transport like in-process, inter-process, TCP, or multicast. It automatically reconnects to peers as they come and go. It queues messages at both sender and receiver, as needed.
It limits these queues to guard processes against running out of memory. It handles socket errors. It uses lock-free techniques for talking between nodes, so there are never locks, waits, semaphores, or deadlocks. But cutting through that, it routes and queues messages according to precise recipes called patterns. It is these patterns that provide ZeroMQ's intelligence. They encapsulate our hard-earned experience of the best ways to distribute data and work.
ZeroMQ's patterns are hard-coded but future versions may allow user-definable patterns. ZeroMQ patterns are implemented by pairs of sockets with matching types. In other words, to understand ZeroMQ patterns you need to understand socket types and how they work together. Mostly, this just takes study; there is little that is obvious at this level. We looked at the first three of these in Chapter 1 - Basics , and we'll see the exclusive pair pattern later in this chapter. These are the socket combinations that are valid for a connect-bind pair either side can bind :.
Any other combination will produce undocumented and unreliable results, and future versions of ZeroMQ will probably return errors if you try them. You can and will, of course, bridge other socket types via code, i. These four core patterns are cooked into ZeroMQ. On top of those, we add high-level messaging patterns. We build these high-level patterns on top of ZeroMQ and implement them in whatever language we're using for our application. They are not part of the core library, do not come with the ZeroMQ package, and exist in their own space as part of the ZeroMQ community.
One of the things we aim to provide you with in this book are a set of such high-level patterns, both small how to handle messages sanely and large how to make a reliable pub-sub architecture. The libzmq core library has in fact two APIs to send and receive messages. On the wire, ZeroMQ messages are blobs of any size from zero upwards that fit in memory.
You do your own serialization using protocol buffers, msgpack, JSON, or whatever else your applications need to speak. It's wise to choose a data representation that is portable, but you can make your own decisions about trade-offs. Here are the basic ground rules for using ZeroMQ messages in C:.
This does not copy the data but copies a reference. You can then send the message twice or more, if you create more copies and the message will only be finally destroyed when the last copy is sent or closed. ZeroMQ also supports multipart messages, which let you send or receive a list of frames as a single on-the-wire message. This is widely used in real applications and we'll look at that later in this chapter and in Advanced Request-Reply Patterns. Frames also called "message parts" in the ZeroMQ reference manual pages are the basic wire format for ZeroMQ messages.
A frame is a length-specified block of data. The length can be zero upwards. If you've done any TCP programming you'll appreciate why frames are a useful answer to the question "how much data am I supposed to read of this network socket now? If you're interested in how this works, the spec is quite short. We later extended this with multipart messages, which are quite simply series of frames with a "more" bit set to one, followed by one with that bit set to zero.
The ZeroMQ API then lets you write messages with a "more" flag and when you read messages, it lets you check if there's "more". So here's a useful lexicon:. This is a zero-copy method and is guaranteed to create trouble for you. There are far more important things to learn about ZeroMQ before you start to worry about shaving off microseconds. This rich API can be tiresome to work with. The methods are optimized for performance, not simplicity. If you start using these you will almost definitely get them wrong until you've read the man pages with some care.
So one of the main jobs of a good language binding is to wrap this API up in classes that are easier to use. What if we want to read from multiple endpoints at the same time? The simplest way is to connect one socket to all the endpoints and get ZeroMQ to do the fan-in for us. Let's start with a dirty hack, partly for the fun of not doing it right, but mainly because it lets me show you how to do nonblocking socket reads.
Here is a simple example of reading from two sockets using nonblocking reads. This rather confused program acts both as a subscriber to weather updates, and a worker for parallel tasks:. The cost of this approach is some additional latency on the first message the sleep at the end of the loop, when there are no waiting messages to process.
This would be a problem in applications where submillisecond latency was vital. Also, you need to check the documentation for nanosleep or whatever function you use to make sure it does not busy-loop. You can treat the sockets fairly by reading first from one, then the second rather than prioritizing them as we did in this example. ZeroMQ lets us compose a message out of several frames, giving us a "multipart message".
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Realistic applications use multipart messages heavily, both for wrapping messages with address information and for simple serialization. We'll look at reply envelopes later. What we'll learn now is simply how to blindly and safely read and write multipart messages in any application such as a proxy that needs to forward messages without inspecting them.
Here is how we send the frames in a multipart message we receive each frame into a message object :. ZeroMQ aims for decentralized intelligence, but that doesn't mean your network is empty space in the middle. It's filled with message-aware infrastructure and quite often, we build that infrastructure with ZeroMQ. The ZeroMQ plumbing can range from tiny pipes to full-blown service-oriented brokers. The messaging industry calls this intermediation , meaning that the stuff in the middle deals with either side. In ZeroMQ, we call these proxies, queues, forwarders, device, or brokers, depending on the context.
This pattern is extremely common in the real world and is why our societies and economies are filled with intermediaries who have no other real function than to reduce the complexity and scaling costs of larger networks. Real-world intermediaries are typically called wholesalers, distributors, managers, and so on. One of the problems you will hit as you design larger distributed architectures is discovery.
That is, how do pieces know about each other? It's especially difficult if pieces come and go, so we call this the "dynamic discovery problem". There are several solutions to dynamic discovery. The simplest is to entirely avoid it by hard-coding or configuring the network architecture so discovery is done by hand.
That is, when you add a new piece, you reconfigure the network to know about it. In practice, this leads to increasingly fragile and unwieldy architectures. Let's say you have one publisher and a hundred subscribers. You connect each subscriber to the publisher by configuring a publisher endpoint in each subscriber. That's easy. Subscribers are dynamic; the publisher is static. Now say you add more publishers. Suddenly, it's not so easy any more. If you continue to connect each subscriber to each publisher, the cost of avoiding dynamic discovery gets higher and higher.
There are quite a few answers to this, but the very simplest answer is to add an intermediary; that is, a static point in the network to which all other nodes connect. In classic messaging, this is the job of the message broker. ZeroMQ doesn't come with a message broker as such, but it lets us build intermediaries quite easily.
You might wonder, if all networks eventually get large enough to need intermediaries, why don't we simply have a message broker in place for all applications? For beginners, it's a fair compromise. Just always use a star topology, forget about performance, and things will usually work. However, message brokers are greedy things; in their role as central intermediaries, they become too complex, too stateful, and eventually a problem.
It's better to think of intermediaries as simple stateless message switches. A good analogy is an HTTP proxy; it's there, but doesn't have any special role. Adding a pub-sub proxy solves the dynamic discovery problem in our example. We set the proxy in the "middle" of the network. Then, all other processes connect to the proxy, instead of to each other. It becomes trivial to add more subscribers or publishers. The proxy has to forward these subscription messages from subscriber side to publisher side, by reading them from the XPUB socket and writing them to the XSUB socket. However, in real cases we usually need to allow multiple services as well as multiple clients.
This lets us scale up the power of the service many threads or processes or nodes rather than just one. The only constraint is that services must be stateless, all state being in the request or in some shared storage such as a database. There are two ways to connect multiple clients to multiple servers. The brute force way is to connect each client socket to multiple service endpoints. One client socket can connect to multiple service sockets, and the REQ socket will then distribute requests among these services.
Let's say you connect a client socket to three service endpoints; A, B, and C. The client makes requests R1, R2, R3, R4. This design lets you add more clients cheaply. You can also add more services. Each client will distribute its requests to the services. But each client has to know the service topology.
If you have clients and then you decide to add three more services, you need to reconfigure and restart clients in order for the clients to know about the three new services. That's clearly not the kind of thing we want to be doing at 3 a. Too many static pieces are like liquid concrete: knowledge is distributed and the more static pieces you have, the more effort it is to change the topology.
What we want is something sitting in between clients and services that centralizes all knowledge of the topology. Ideally, we should be able to add and remove services or clients at any time without touching any other part of the topology. So we'll write a little message queuing broker that gives us this flexibility.
The broker binds to two endpoints, a frontend for clients and a backend for services. It doesn't actually manage any queues explicitly—ZeroMQ does that automatically on each socket. The client sends a request. The service reads the request and sends a reply. The client then reads the reply. If either the client or the service try to do anything else e. But our broker has to be nonblocking.
The request-reply broker binds to two endpoints, one for clients to connect to the frontend socket and one for workers to connect to the backend. To test this broker, you will want to change your workers so they connect to the backend socket. Here is a client that shows what I mean:.
The only static node is the broker in the middle. It turns out that the core loop in the previous section's rrbroker is very useful, and reusable. It lets us build pub-sub forwarders and shared queues and other little intermediaries with very little effort. The two or three sockets, if we want to capture data must be properly connected, bound, and configured. If you're like most ZeroMQ users, at this stage your mind is starting to think, "What kind of evil stuff can I do if I plug random socket types into the proxy?
The simple answer is to build a bridge. A protocol interpreter, if you like. A common bridging problem in ZeroMQ is to bridge two transports or networks. As an example, we're going to write a little proxy that sits in between a publisher and a set of subscribers, bridging two networks. The frontend socket SUB faces the internal network where the weather server is sitting, and the backend PUB faces subscribers on the external network. It subscribes to the weather service on the frontend socket, and republishes its data on the backend socket.
It looks very similar to the earlier proxy example, but the key part is that the frontend and backend sockets are on two different networks. We can use this model for example to connect a multicast network pgm transport to a tcp publisher. ZeroMQ's error handling philosophy is a mix of fail-fast and resilience. Processes, we believe, should be as vulnerable as possible to internal errors, and as robust as possible against external attacks and errors.
To give an analogy, a living cell will self-destruct if it detects a single internal error, yet it will resist attack from the outside by all means possible. Assertions, which pepper the ZeroMQ code, are absolutely vital to robust code; they just have to be on the right side of the cellular wall. And there should be such a wall. If it is unclear whether a fault is internal or external, that is a design flaw to be fixed. In other languages, you may get exceptions or halts. When ZeroMQ detects an external fault it returns an error to the calling code. In some rare cases, it drops messages silently if there is no obvious strategy for recovering from the error.
In most of the C examples we've seen so far there's been no error handling. Real code should do error handling on every single ZeroMQ call. If you're using a language binding other than C, the binding may handle errors for you. In C, you do need to do this yourself. It looks neat; then the optimizer removes all the asserts and the calls you want to make, and your application breaks in impressive ways. Let's see how to shut down a process cleanly.
We'll take the parallel pipeline example from the previous section. If we've started a whole lot of workers in the background, we now want to kill them when the batch is finished. Let's do this by sending a kill message to the workers. The best place to do this is the sink because it really knows when the batch is done. How do we connect the sink to the workers? We could switch to another socket type, or we could mix multiple socket flows. Let's try the latter: using a pub-sub model to send kill messages to the workers:. Here is the modified sink application.
When it's finished collecting results, it broadcasts a kill message to all workers:. By default, these simply kill the process, meaning messages won't be flushed, files won't be closed cleanly, and so on. Thanks to your signal handler, your application will not die automatically. Instead, you have a chance to clean up and exit gracefully. You have to now explicitly check for an interrupt and handle it properly. This sets up the signal handling. The interrupt will affect ZeroMQ calls as follows:. Any long-running application has to manage memory correctly, or eventually it'll use up all available memory and crash.
If you use a language that handles this automatically for you, congratulations. ZeroMQ is perhaps the nicest way ever to write multithreaded MT applications. Whereas ZeroMQ sockets require some readjustment if you are used to traditional sockets, ZeroMQ multithreading will take everything you know about writing MT applications, throw it into a heap in the garden, pour gasoline over it, and set it alight.
It's a rare book that deserves burning, but most books on concurrent programming do. To make utterly perfect MT programs and I mean that literally , we don't need mutexes, locks, or any other form of inter-thread communication except messages sent across ZeroMQ sockets. By "perfect MT programs", I mean code that's easy to write and understand, that works with the same design approach in any programming language, and on any operating system, and that scales across any number of CPUs with zero wait states and no point of diminishing returns.
If you've spent years learning tricks to make your MT code work at all, let alone rapidly, with locks and semaphores and critical sections, you will be disgusted when you realize it was all for nothing. It's like two drunkards trying to share a beer. It doesn't matter if they're good buddies. Sooner or later, they're going to get into a fight. And the more drunkards you add to the table, the more they fight each other over the beer. The tragic majority of MT applications look like drunken bar fights. The list of weird problems that you need to fight as you write classic shared-state MT code would be hilarious if it didn't translate directly into stress and risk, as code that seems to work suddenly fails under pressure.
A large firm with world-beating experience in buggy code released its list of "11 Likely Problems In Your Multithreaded Code", which covers forgotten synchronization, incorrect granularity, read and write tearing, lock-free reordering, lock convoys, two-step dance, and priority inversion. Yeah, we counted seven problems, not eleven.
That's not the point though. The point is, do you really want that code running the power grid or stock market to start getting two-step lock convoys at 3 p. Who cares what the terms actually mean? This is not what turned us on to programming, fighting ever more complex side effects with ever more complex hacks.
Some widely used models, despite being the basis for entire industries, are fundamentally broken, and shared state concurrency is one of them. Code that wants to scale without limit does it like the Internet does, by sending messages and sharing nothing except a common contempt for broken programming models. If you need to start more than one proxy in an application, for example, you will want to run each in their own thread.
It is easy to make the error of creating the proxy frontend and backend sockets in one thread, and then passing the sockets to the proxy in another thread. This may appear to work at first but will fail randomly in real use. Remember: Do not use or close sockets except in the thread that created them.
If you follow these rules, you can quite easily build elegant multithreaded applications, and later split off threads into separate processes as you need to. Application logic can sit in threads, processes, or nodes: whatever your scale needs. ZeroMQ uses native OS threads rather than virtual "green" threads. The advantage is that you don't need to learn any new threading API, and that ZeroMQ threads map cleanly to your operating system.
You can use standard tools like Intel's ThreadChecker to see what your application is doing. The disadvantages are that native threading APIs are not always portable, and that if you have a huge number of threads in the thousands , some operating systems will get stressed.
Let's see how this works in practice. We'll turn our old Hello World server into something more capable. The original server ran in a single thread. But realistic servers have to do nontrivial work per request. A single core may not be enough when 10, clients hit the server all at once.
So a realistic server will start multiple worker threads. It then accepts requests as fast as it can and distributes these to its worker threads. The worker threads grind through the work and eventually send their replies back. You can, of course, do all this using a proxy broker and external worker processes, but often it's easier to start one process that gobbles up sixteen cores than sixteen processes, each gobbling up one core.
Further, running workers as threads will cut out a network hop, latency, and network traffic. The MT version of the Hello World service basically collapses the broker and workers into a single process:. Note that creating threads is not portable in most programming languages. Here the "work" is just a one-second pause. We could do anything in the workers, including talking to other nodes.
When you start making multithreaded applications with ZeroMQ, you'll encounter the question of how to coordinate your threads. Though you might be tempted to insert "sleep" statements, or use multithreading techniques such as semaphores or mutexes, the only mechanism that you should use are ZeroMQ messages. Let's make three threads that signal each other when they are ready.
In this example, we use PAIR sockets over the inproc transport:. Note that multithreading code using this pattern is not scalable out to processes.
If you use inproc and socket pairs, you are building a tightly-bound application, i. Do this when low latency is really vital. The other design pattern is a loosely bound application, where threads have their own context and communicate over ipc or tcp. You can easily break loosely bound threads into separate processes. This is the first time we've shown an example using PAIR sockets.
Why use PAIR? Other socket combinations might seem to work, but they all have side effects that could interfere with signaling:. When you want to coordinate a set of nodes on a network, PAIR sockets won't work well any more. This is one of the few areas where the strategies for threads and nodes are different.
Principally, nodes come and go whereas threads are usually static. PAIR sockets do not automatically reconnect if the remote node goes away and comes back. The second significant difference between threads and nodes is that you typically have a fixed number of threads but a more variable number of nodes. Let's take one of our earlier scenarios the weather server and clients and use node coordination to ensure that subscribers don't lose data when starting up. Here is the publisher:.
There are no guarantees that outbound connects will finish in any order whatsoever, if you're using any transport except inproc. ZeroMQ's message API lets you send and receive messages directly from and to application buffers without copying data. We call this zero-copy , and it can improve performance in some applications.
You should think about using zero-copy in the specific case where you are sending large blocks of memory thousands of bytes , at a high frequency. For short messages, or for lower message rates, using zero-copy will make your code messier and more complex with no measurable benefit. Like all optimizations, use this when you know it helps, and measure before and after. When you create the message, you also pass a function that ZeroMQ will call to free the block of data, when it has finished sending the message.
This is the simplest example, assuming buffer is a block of 1, bytes allocated on the heap:. There is no way to do zero-copy on receive: ZeroMQ delivers you a buffer that you can store as long as you wish, but it will not write data directly into application buffers. On writing, ZeroMQ's multipart messages work nicely together with zero-copy. In traditional messaging, you need to marshal different buffers together into one buffer that you can send.
That means copying data. With ZeroMQ, you can send multiple buffers coming from different sources as individual message frames. Send each field as a length-delimited frame. To the application, it looks like a series of send and receive calls. But internally, the multiple parts get written to the network and read back with single system calls, so it's very efficient. In the pub-sub pattern, we can split the key into a separate message frame that we call an envelope. If you want to use pub-sub envelopes, make them yourself.
It's optional, and in previous pub-sub examples we didn't do this. Using a pub-sub envelope is a little more work for simple cases, but it's cleaner especially for real cases, where the key and the data are naturally separate things. Subscriptions do a prefix match. That is, they look for "all messages starting with XYZ". The obvious question is: how to delimit keys from data so that the prefix match doesn't accidentally match data. The best answer is to use an envelope because the match won't cross a frame boundary. Here is a minimalist example of how pub-sub envelopes look in code.
This publisher sends messages of two types, A and B. This example shows that the subscription filter rejects or accepts the entire multipart message key plus data. You won't get part of a multipart message, ever. If you subscribe to multiple publishers and you want to know their address so that you can send them data via another socket and this is a typical use case , create a three-part message.
When you can send messages rapidly from process to process, you soon discover that memory is a precious resource, and one that can be trivially filled up. A few seconds of delay somewhere in a process can turn into a backlog that blows up a server unless you understand the problem and take precautions. System architecting is simply the process of creating and describing a system architecture, which we regard as a process within Systems Engineering see Page 4.
Architecting can be more or less systematic but typically involves: understanding context; exploration of alternatives; understanding trades; supporting decision making; and so on. There are many good guiding principles for architecting, including modularity, high cohesion, loose coupling, etc. As an overall principle. System architecting is the process of creating a system architecture.
The A to Z of Architecture (The A to Z Guide Series) [Allison Lee Palmer] on equmoqenyk.gq *FREE* shipping on qualifying offers. Architecture, which can be. Allison Lee Palmer: The A to Z of Architecture (The A to Z Guide Series) before purchasing it in order to gage whether or not it would be worth my time, and all.
What are the different types of architecture? There are many types of architecture in use, each of which may be focussed on a particular topic of interest, or on a specific purpose, or on a specific set of systems. Some examples of architectures with a focus on specific topics are: Operational; Programme; Security; Information. Some examples of architectures with specific purposes are: Integration; Problem Domain Definition; Design-Controlling.
Some examples of architectures addressing a specific set of systems are: System of Systems; Product Family; Enterprise. Some practitioners regard each type of architecture as a view point on to a single inderlying architecture ie: a single system could have a security architecture and an information architecture, etc - more on this in the section on Architecture Frameworks See Below.
What is the role of architecture? In use, architectural descriptions will usually have a primary role or purpose and a multitude of secondary ones. Some examples are:. A well crafted architecture should deliver the desirable outcomes benefits associated with each of its primary and secondary roles. How is architecting related to Systems Engineering. The answer to the question clearly depends on how one defines Systems Engineering.
In the UK, Systems Engineering has historically been considered broadly, applying at all levels and embracing both synthetic and analytical methods. Hence, we advocate the view that Architecting is best regarded as a subset of a broadly drawn Systems Engineering. Architecture Framework. In recent years there have been several significant attempts to provide assistance to architects, particularly in relation to what kind of system descriptions might be relevant for system architecture.