Distributed Systems with ZeroMQ

http://java.dzone.com/articles/distributed-systems-zeromq

by jpro • 14 years ago

65 17 14 years ago

java.dzone.com

If you like this, you should check out ZeroRPC, it handles a lot of the boilerplate code you would need to write by hand.

Links: - http://zerorpc.dotcloud.com - https://github.com/dotcloud/zerorpc-python

jandrewrogers5• 14 years ago

To make a distinction that the article does not (but should), ZeroMQ is a network transport abstraction layer. It is not useful for many distributed systems despite the title for the same reason. For non-trivial distributed systems, behaviors that are correct for network transport use cases that are built into ZeroMQ are pathological for distributed system use cases.

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.

rdtsc• 14 years ago

All good points. Never thought of it that way.

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).?

snprbob864• 14 years ago

> For non-trivial distributed systems, behaviors that are correct for network transport use cases that are built into ZeroMQ are pathological for distributed system use cases.

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.

[1] http://zguide.zeromq.org/page:all

scott_s2• 14 years ago

It seems to me that distributed systems are defined by moving bits over a network.

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.

jandrewrogers5• 14 years ago

You are not understanding the nature of the problem. I have not only used ZMQ extensively in distributed systems but have modified the internals for some purposes. The problem is intrinsic to its design.

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.

soravux• 14 years ago

We are currently using ZeroMQ in our distributed task framework in Python, SCOOP (http://scoop.googlecode.com).

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.

nathancahill• 14 years ago

Required reading for anyone learning Python

Distributed Systems with ZeroMQ

Related Stories