Distributed Systems with ZeroMQ
java.dzone.com
java.dzone.com
In the specific case of ZeroMQ, there is a deep underlying assumption that logical queues are fundamentally independent things. As a corollary, scheduling what work is done on which queues is of no consequence as long as the contract of the individual queues is upheld.
For any non-trivial distributed system, the above assumption is not true. Correct and scalable scheduling of operations is a function of the current status of all logical queues visible to a process. The processing priority of one queue is dependent on the current status of all other queues, which can change from operation to operation. Distributed systems are cooperatively scheduled and much of the self-balancing behavior of good distributed system designs come from this adaptive scheduling behavior. Unfortunately, systems like ZeroMQ intentionally hide and encapsulate all of the properties of the network transport that would be used to inform the scheduling of operations over a set of logical queues. And if you assume logical queues are fundamentally independent from a scheduling perspective then that is a good design.
ZeroMQ is a good for moving bits over a network but it is often not a correct choice for distributed systems.
Are you thinking of RabbitMQ and distributed queues?
Is it about all the distributed processes seeing a consistent queue state, or are you talking in general about having a quick feedback 'loading' value for each worker (as each grabs an item of the the queue, they are busy and will be less likely to grab one, so other less busy ones can take over).?
I'm not sure what you mean. Please provide one or more concrete examples.
> ZeroMQ is a good for moving bits over a network but it is often not a correct choice for distributed systems.
It seems to me that distributed systems are defined by moving bits over a network.
You're right that there is some confusion regarding what ZeroMQ actually us. This is in no small part due to it's unfortunate name: ZeroMQ isn't a queue. It is implemented with queues, but that's because it sends and receives discrete messages instead of bytes, like it's underlying protocols. The underlying transport protocols use "buffers" to store bytes on their way from your application to their destination. When you have a buffer of messages, that's called a queue!
ZeroMQ is a network protocol for messaging. If you need custom scheduling, load balancing, etc, you can send and receive control messages on a secondary channel. The ZeroMQ Guide [1] is extremely enlightening in this regard. It's worth reading even if you never use ZeroMQ because all of the underlying principals apply to any network transport.
Sorta. People say "distributed system" to mean a single, logical system - typically an application, or maybe some middleware that executes applications - that runs on many machines. So, yes, a distributed system will have bits moving over the network. But the point that jandrewrogers is making is that in a distributed system, the network connections are often dependent on each other. That is, sending data on one connection will impact the sending of data on another connection.
When someone says "distributed systems," you're probably thinking of a client application talking to a server, and maybe that server talking to some backend. That's not what people mean when they use the term. They mean, in the large case, thousands of processes running across hundreds of hosts, and all of those processes may communicate with each other.
In the client-server-backend case, it's okay to assume that each of those connections are independent, and to reason about them as such. In the thousand-to-thousand case in a distributed system, it's often not okay to assume that each of those connections is independent.
In most production distributed systems, there is an agreed upon common RPC protocol and a variety of ad-hoc messaging protocols. Often, this is HTTP for RPC coupled with a variety of job worker systems, maybe an "enterprise" message broker, and line-oriented protocol or two, and a few other custom services.
ZeroMQ isn't trying to replace unix sockets. Nor is it trying to be a panacea for distributed systems. It's trying to standardize a set of sound topology patterns with robust implementations over a common protocol. It's a damn useful tool for distributed systems. If you're system has everyone all chatting at once and saturating your links, that's not ZeroMQ's problem any more than it's unix sockets' problem.
The "cost" of a message operation is not fixed, and pathological operations are much more expensive by definition, so always selecting the least expensive operation that can be run at any point in time from the perspective of the node maximizes the aggregate throughput of the system. Of course, it also has the side effect of applying negative feedback to pathological positive feedback loops.
This is actually a mathematics consequence, so there are limited workarounds. Nor is it intuitive. Making parts of the system slower, with no other changes, can increase the aggregate throughput of the distributed system. For non-trivial distributed systems, you are unlikely to produce a robustly optimal design by accident.
Robust, high-throughput distributed systems are built around the concept of Nash equilibria. To ensure that, there is an optimal ordering to the set of possible message operations over those queues. It gives them a priority and you attempt to schedule operations in approximately optimal order. The ordering is not fixed; every message operation on a queue may have side effects that alters the total ordering of message operations for any process associated with that queue. Consequently, the schedule of operations over the entire set of message queues must be adaptively and dynamically imposed in order to approximate a good Nash behavior.
In real software, this is usually just a simple state machine that adaptively prioritizes operations based on the aggregate state of those messages and queues. Also, we usually approximate ideal scheduling for performance reasons (and good approximations are usually good enough). Priority modifying side effects propagate throughout the distributed system; local modifications in priority provide stabilizing negative feedback to the global behavior. (See also: greedy routing optimization problems etc. This is a pretty complex area of mathematics.)
Consequently, there are some popular design choices for network server systems that tend to work poorly for distributed systems, either because they prevent the creation of an effective operation scheduler or because they hinder the propagation of negative feedback that prevents pathological interactions.
Giving every socket its own OS thread or similar is right out. That lets the operating system decide when operations are scheduled and the operating system has no concept of prioritization in the sense that is important for distributed systems. Trying to use thread interlocks to impose an order leads to pathological context-switching storms. High-performance schedulers are cooperative. (You can design a schedule with multiple threads that executes with minimal interlocks, but it is not trivial.)
Unlimited buffering and hard-limited buffering are also poor abstractions because they obscure the state of a queue. Robust schedulers often prioritize queues based in part on how it reduces total buffering. For some good designs, you can prove the existence of a reasonable upper bound on the total buffers required for a node or queue with no hard limits on any particular buffer. It is an elegant side effect of scheduling interactions in a distributed system but requires a scheduler that is aware of many aspects of buffer state.
In principle, it should be possible to design a message queue abstraction designed for the requirements of distributed systems. It would not be as simple as ZMQ though. ZMQ is not designed to support that use case, so in cases where I am building a non-trivial distributed system protocol, we build on top of epoll directly. It is not a knock against ZMQ, it was designed for other purposes.
I've worked with some pretty big systems that use a model that look an awful lot like 0mq and perform splendidly. I've also worked on several smaller systems (games) that needed to use UDP and all kinds of custom semantics for reliability to accomplish their task. I'd imagine that if such a thing happens when you scale down, what you're saying is possible when you scale up.
To anyone reading this thread: If you don't understand all the complexities of jandrewrogers' response, just assume YAGNI. In that case, if your problem lends itself to a stream oriented protocol, then ZeroMQ is a great choice. You can deal with the other complexities if the need arises.
Lots of things work very well with ZMQ, just not distributed systems. If your application scales efficiently on ZMQ then it is a giant pile of computers and not a "distributed system". I've seen systems that scale to thousands of nodes and also seen systems that won't scale past a few machines on ZMQ. System architects should be able to distinguish the two cases without standing up a cluster to see if it fails. All it requires is one part of your system to not fit the ZMQ case for things to start falling apart. That this is an expected result is not exotic computer science.
At this point, I'm invoking Poe's law.
What is interesting about the literature on designing systems with robustly stable and efficient network behaviors is how restrictive the known solutions to this problem are in terms of how a protocol must be designed. At the same time, it is enlightening to realize that every robust large-scale distributed system employs the mathematics pervasively. Email spam is a predictable failure mode for the SMTP protocol per the mathematics, once you start thinking about it that way.
The most important counter-intuitive idea is that robust distributed protocols are subtractive. You can't design a distributed system around creating good properties per se, you can only design a distributed system that suppresses bad properties. An optimal distributed protocol is one in which the various well-known pathologies are naturally and effectively suppressed by the behavior of the protocol without suppressing aggregate throughput.
Links: - http://zerorpc.dotcloud.com - https://github.com/dotcloud/zerorpc-python
ZeroMQ was chosen as the communication library because it simply works and isn't bloated. No need to implement the state-machines for common patterns in our sockets, ZMQ does it and fast. It doesn't replace a standard socket, though, it only add a layer of functionalities over it.
While using it, we found some minor negative point such as delays needed by the socket upon shutdown, which require sleeps between unit tests, or the random port connector that is not random... But overall, ZMQ is a tool that saved us much developing time and should not be overlooked by distribution systems.