Posted on

Service-Disoriented Architecture

“You can have a second computer once you’ve shown you know how to use the first one.” -Paul Barham

The first rule of distributed systems is don’t distribute your system until you have an observable reason to. Teams break this rule on the regular. People have been talking about service-oriented architecture for a long time, but only recently have microservices been receiving the hype.

The problem, as Martin Fowler observes, is that teams are becoming too eager to adopt a microservice architecture without first understanding the inherent overheads. A contributing factor, I think, is you only hear the success stories from companies who did it right, like Netflix. However, what folks often fail to realize is that these companies—in almost all cases—didn’t start out that way. There was a long and winding path which led them to where they are today. The inverse of this, which some refer to as microservice envy, is causing teams to rush into microservice hell. I call this service-disoriented architecture (or sometimes disservice-oriented architecture when the architecture is DOA).

The term “monolith” has a very negative connotation—unscalable, unmaintainable, unresilient. These things are not intrinsically tied to each other, however, and there’s no reason a single system can’t be modular, maintainable, and fault tolerant at reasonable scale. It’s just less sexy. Refactoring modular code is much easier than refactoring architecture, and refactoring across service boundaries is equally difficult. Fowler describes this as monolith-first, and I think it’s the right approach (with some exceptions, of course).

Don’t even consider microservices unless you have a system that’s too complex to manage as a monolith. The majority of software systems should be built as a single monolithic application. Do pay attention to good modularity within that monolith, but don’t try to separate it into separate services.

Service-oriented architecture is about organizational complexity and system complexity. If you have both, you have a case to distribute. If you have one of the two, you might have a case (although if you have organizational complexity without system complexity, you’ve probably scaled your organization improperly). If you have neither, you do not have a case to distribute. State, specifically distributed state, is hell, and some pundits argue SOA is satan—perhaps a necessary evil.

There are a lot of motivations for microservices: anti-fragility, fault tolerance, independent deployment and scaling, architectural abstraction, and technology isolation. When services are loosely coupled, the system as a whole tends to be less fragile. When instances are disposable and stateless, services tend to be more fault tolerant because we can spin them up and down, balance traffic, and failover. When responsibility is divided across domain boundaries, services can be independently developed, deployed, and scaled while allowing the right tools to be used for each.

We also need to acknowledge the disadvantages. Adopting a microservice architecture does not automatically buy you anti-fragility. Distributed systems are incredibly precarious. We have to be aware of things like asynchrony, network partitions, node failures, and the trade-off between availability and data consistency. We have to think about resiliency but also the business and UX implications. We have to consider the boundaries of distributed systems like CAP and exactly-once delivery.

When distributing, the emphasis should be on resilience engineering and adopting loosely coupled, stateless components—not microservices for microservices’ sake. We need to view eventual consistency as a tool, not a side effect. The problem I see is that teams often end up with what is essentially a complex, distributed monolith. Now you have two problems. If you’re building a microservice which doesn’t make sense outside the context of another system or isn’t useful on its own, stop and re-evaluate. If you’re designing something to be fast and correct, realize that distributing it will frequently take away both.

Like anti-fragility, microservices do not automatically buy you better maintainability or even scalability. Adopting them requires the proper infrastructure and organization to be in place. Without these, you are bound to fail. In theory, they are intended to increase development velocity, but in many cases the microservice premium ends up slowing it down while creating organizational dependencies and bottlenecks.

There are some key things which must be in place in order for a microservice architecture to be successful: a proper continuous-delivery pipeline, competent DevOps and Ops teams, and prudent service boundaries, to name a few. Good monitoring is essential. It’s also important we have a thorough testing and integration story. This isn’t even considering the fundamental development complexities associated with SOA mentioned earlier.

The better strategy is a bottom-up approach. Start with a monolith or small set of coarse-grained services and work your way up. Make sure you have the data model right. Break out new, finer-grained services as you need to and as you become more confident in your ability to maintain and deploy discrete services. It’s largely about organizational momentum. A young company jumping straight to a microservice architecture is like a golf cart getting on the freeway.

Microservices offer a number of advantages, but for many companies they are a bit of a Holy Grail. Developers are always looking for a silver bullet, but there is always a cost. What we need to do is minimize this cost, and with microservices, this typically means easing our way into it rather than diving into the deep end. Team autonomy and rapid iteration are noble goals, but if we’re not careful, we can end up creating an impedance. Microservices require organization and system maturity. Otherwise, they end up being a premature architectural optimization with a lot of baggage. They end up creating a service-disoriented architecture.

Posted on

If State Is Hell, SOA Is Satan

More and more companies are describing their success stories regarding the switch to a service-oriented architecture. As with any technological upswing, there’s a clear and palpable hype factor involved (Big Data™ or The Cloud™ anyone?), but obviously it’s not just puff.

While microservices and SOA have seen a staggering rate of adoption in recent years, the mindset of developers often seems to be stuck in the past. I think this is, at least in part, because we seek a mental model we can reason about. It’s why we build abstractions in the first place. In a sense, I would argue there’s a comparison to be made between the explosion of OOP in the early 90’s and today’s SOA trend. After all, SOA is as much about people scale as it is about workload scale, so it makes sense from an organizational perspective.

The Perils of Good Abstractions

While systems are becoming more and more distributed, abstractions are attempting to make them less and less complex. Mesosphere is a perfect example of this, attempting to provide the “datacenter operating system.” Apache Mesos allows you to “program against your datacenter like it’s a single pool of resources.” It’s an appealing proposition to say the least. PaaS like Google App Engine and Heroku offer similar abstractions—write your code without thinking about scale. The problem is you absolutely have to think about scale or you’re bound to run into problems down the road. And while these abstractions are nice, they can be dangerous just the same. Welcome to the perils of good abstractions.

I like to talk about App Engine because I have firsthand experience with it. It’s an easy sell for startups. It handles spinning up instances when you need them, turning them down when you don’t. It’s your app server, database, caching, job scheduler, task queue all in one, and it does it at scale. There’s vendor lock-in, sure, yet it means no ops, no sysadmins, no overhead. Push to deploy. But it’s a leaky abstraction. It has to be. App Engine scales because it’s distributed, but it allows—no, encourages—you to write your system as a monolith. The datastore, memcache, and task queue accesses are masked as RPCs. This is great for our developer mental model, but it will bite you if you’re not careful. App Engine imposes certain limitations to encourage good design; for instance, front-end requests and datastore calls are limited to 60 seconds (it used to be much less), but the leakiness goes beyond that.

RPC is consistently at odds with distributed systems. I would go so far as to say it’s an anti-pattern in many cases. RPC encourages writing synchronous code, but distributed systems are inherently asynchronous. The network is not reliable. The network is not fast. The network is not your friend. Developers who either don’t understand this or don’t realize what’s happening when they make an RPC will write code as if they were calling a function. It will sure as hell look like just calling a function. When we think synchronously, we end up with systems that are slow, fault intolerant, and generally not scalable. To be quite honest, however, this is perfectly acceptable for 90% of startups as they are getting off the ground because they don’t have workloads at meaningful scale.

There’s certainly some irony here. One of the selling points of App Engine is its ability to scale to large amounts of traffic, yet the vast majority of startups would be perfectly suited to scaling up rather than out, perhaps with some failover in place for good measure. Stack Overflow is the poster child of scale-up architecture. In truth, your architecture should be a function of your access patterns, not the other way around (and App Engine is very much tailored to a specific set of access patterns). Nonetheless, it shows that vertical scaling can work. I would bet a lot of startups could sufficiently run on a large, adequately specced machine or maybe a small handful of them.

The cruel irony is that once you hit a certain scale with App Engine, both in terms of your development organization and user base, you’ve reached a point where you have to migrate off it. And if your data model isn’t properly thought out, you will without a doubt hit scale problems. It’s to the point where you need someone with deep knowledge of how App Engine works in order to build quality systems on it. Good luck hiring a team of engineers who understand it. GAE is great at accelerating you to 100 mph, but you better have some nice airbags for the brick wall it launches you into. In fairness, this is a problem every org hits—Conway’s law is very much a reality and every startup has growing pains. To be clear, this isn’t a jab at GAE, which is actually very effective at accelerating a product using little capital and can sustain long-term success given the right use case. Instead, I use it to illustrate a point.

Peering Through the Abstraction

Eventually SOA makes sense, but our abstractions can cause problems if we don’t understand what’s going on behind the curtain (hence the leakiness). Partial failure is all but guaranteed, and latency, partitioning, and other network pressure happens all the time.

Ken Arnold is famed with once saying “state is hell” in reference to designing distributed systems. In the past, I’ve written how scaling shared data is hard, but with SOA it’s practically a requirement. Ken is right though—state is hell, and SOA is fundamentally competing with consistency. The FLP Impossibility result and the CAP theorem can prove it formally, but really this should be intuitively obvious if we accept the laws of physics.

On the other hand, if you store information that I can’t reconstruct, then a whole host of questions suddenly surface. One question is, “Are you now a single point of failure?” I have to talk to you now. I can’t talk to anyone else. So what happens if you go down?

To deal with that, you could be replicated. But now you have to worry about replication strategies. What if I talk to one replicant and modify some data, then I talk to another? Is that modification guaranteed to have already arrived there? What is the replication strategy? What kind of consistency do you need—tight or loose? What happens if the network gets partitioned and the replicants can’t talk to each other? Can anybody proceed?

Essentially, the more stateful your system is, the harder it’s going to be to scale it because distributing that state introduces a rich tapestry of problems. In practice, we often can’t eliminate state wholesale, but basically everything that can be stateless should be stateless.

Making servers disposable allows you a great deal of flexibility. Former Netflix Cloud Architect Adrian Cockcroft articulates this idea well:

You want to think of servers like cattle, not pets. If you have a machine in production that performs a specialized function, and you know it by name, and everyone gets sad when it goes down, it’s a pet. Instead you should think of your servers like a herd of cows. What you care about is how many gallons of milk you get. If one day you notice you’re getting less milk than usual, you find out which cows aren’t producing well and replace them.

This is effectively how App Engine achieves its scalability. With lightweight, stateless, and disposable instances, it can spin them up and down on the fly without worrying about being in an invalid state.

App Engine also relies on eventual consistency as the default model for datastore interactions. This makes queries fast and highly available, while snapshot isolation can be achieved using entity-group transactions if necessary. The latter, of course, can result in a lot of contention and latency. Yet, people seem to have a hard time grappling with the reality of eventual consistency in distributed systems. State is hell, but calling SOA “satan” is clearly a hyperbole. It is a tough problem nevertheless.

A State of Mind

In the situations where we need state, we have to reconcile with the realities of distributed systems. This means understanding the limitations and accepting the complexities, not papering over them. It doesn’t mean throwing away abstractions. Fortunately, distributed computing is the focus of a lot of great research, so there are primitives with which we can build: immutability, causal ordering, eventual consistency, CRDTs, and other ideas.

As long as we recognize the trade-offs, we can design around them. The crux is knowing they exist in the first place. We can’t have ACID semantics while remaining highly available, but we can use Highly Available Transactions to provide strong-enough guarantees. At the same time, not all operations require coordination or concurrency control. The sooner we view eventual consistency as a solution and not a consequence, the sooner we can let go of this existential crisis. Other interesting research includes BOOM, which seeks to provide a high-level, declarative approach to distributed programming.

State might be hell, but it’s a hell we have to live. I don’t advocate an all-out microservice architecture for a company just getting its start. The complications far outweigh any benefits to be gained, but it becomes a necessity at a certain point. The key is having an exit strategy. PaaS providers make this difficult due to vendor lock-in and architectural constraints. Weigh their advantages carefully.

Once you do transition to a SOA, make as many of those services, or the pieces backing them, as stateless as possible. For those which aren’t stateless, know that the problem typically isn’t novel. These problems have been solved or are continuing to be solved in new and interesting ways. Academic research is naturally at the bleeding edge with industry often lagging behind. OOP concepts date back to as early as the 60’s but didn’t gain widespread adoption until several decades later. Distributed computing is no different. SOA is just a state of mind.

Posted on

Iris Decentralized Cloud Messaging

A couple weeks ago, I published a rather extensive analysis of numerous message queues, both brokered and brokerless. Brokerless messaging is really just another name for peer-to-peer communication. As we saw, the difference in message latency and throughput between peer-to-peer systems and brokered ones is several orders of magnitude. ZeroMQ and nanomsg are able to reliably transmit millions of messages per second at the expense of guaranteed delivery.

Peer-to-peer messaging is decentralized, scalable, and fast, but it brings with it an inherent complexity. There is a dichotomy between how brokerless messaging is conceptualized and how distributed systems are actually built. Distributed systems are composed of services like applications, databases, caches, etc. Services are composed of instances or nodes—individually addressable hosts, either physical or virtual. The key observation is that, conceptually, the unit of interaction lies at the service level, not the instance level. We don’t care about which database server we interact with, we just want to talk to a database server (or perhaps multiple). We’re concerned with logical groups of nodes.

While traditional socket-queuing systems like ZeroMQ solve the problem of scaling, they bring about a certain coupling between components. System designers are forced to build applications which communicate with nodes, not services. We can introduce load balancers like HAProxy, but we’re still addressing specific locations while creating potential single points of failure. With lightweight VMs and the pervasiveness of elastic clouds, IP addresses are becoming less and less static—they come and go. The canonical way of dealing with this problem is to use distributed coordination and service discovery via ZooKeeper, et al., but this introduces more configuration, more moving parts, and more headaches.

The reality is that distributed systems are not built with the instance as the smallest unit of composition in mind, they’re built with services in mind. As discussed earlier, a service is simply a logical grouping of nodes. This abstraction is what we attempt to mimic with things like etcd, ZooKeeper and HAProxy. These assemblies are proven, but there are alternative solutions that offer zero configuration, minimal network management, and overall less complexity. One such solution that I want to explore is a distributed messaging framework called Iris.

Decentralized Messaging with Iris

Iris is posited as a decentralized approach to backend messaging middleware. It looks to address several of the fundamental issues with traditional brokerless systems, like tight coupling and security.

In order to avoid the problem of addressing instances, Iris considers clusters to be the smallest logical blocks of which systems are composed. A cluster is a collection of zero or more nodes which are responsible for a certain service sub-task. Clusters are then assembled into services such that they can communicate with each other without any regard as to which instance is servicing their requests or where it’s located. Lastly, services are composed into federations, which allow them to communicate across different clouds.

This form of composition allows Iris to use semantic or logical addressing instead of the standard physical addressing. Nodes specify the name of the cluster they wish to participate in, while Iris handles the intricacies of routing and balancing. For example, you might have three database servers which belong to a single cluster called “databases.” The cluster is reached by its name and requests are distributed across the three nodes. Iris also takes care of service discovery, detecting new clusters as they are created on the same cloud.

With libraries like ZeroMQ, security tends to be an afterthought. Iris has been built from the ground-up with security in mind, and it provides a security model that is simple and fast.

Iris uses a relaxed security model that provides perfect secrecy whilst at the same time requiring effectively zero configuration. This is achieved through the observation that if a node of a service is compromised, the whole system is considered undermined. Hence, the unit of security is a service – opposed to individual instances – where any successfully authenticated node is trusted by all. This enables full data protection whilst maintaining the loosely coupled nature of the system.

In practice, what this means is that each cluster uses a single private key. This encryption scheme not only makes deployment trivial, it minimizes the effect security has on speed.

Like ZeroMQ and nanomsg, Iris offers a few different messaging patterns. It provides the standard request-reply and publish-subscribe schemes, but it’s important to remember that the smallest addressable unit is the cluster, not the node. As such, requests are targeted at a cluster and subsequently relayed on to a member in a load-balanced fashion. Publish-subscribe, on the other hand, is not targeted at a single cluster. It allows members of any cluster to subscribe and publish to a topic.

Iris also implements two patterns called “broadcast” and “tunnel.” While request-reply forwards a message to one member of a cluster, broadcast forwards it to all members. The caveat is that there is no way to listen for responses to a broadcast.

Tunnel is designed to address the problem of stateful or streaming transactions where a communication between two endpoints may consist of multiple data exchanges which need to occur as an atomic operation. It provides the guarantee of in-order and throttled message delivery by establishing a channel between a client and a node.

Performance Characteristics

According to its author, Iris is still in a “feature phase” and hasn’t been optimized for speed. Since it’s written in Go, I’ve compared its pub/sub benchmark performance with other Go messaging libraries, NATS and NSQ. As before, these benchmarks shouldn’t be taken as gospel, the code is available here, and pull requests are welcome.

We can see that Iris is comparable to NSQ on the sending side and about 4x on the receiving side, at least out of the box.

Conclusion

Brokerless systems like ZeroMQ and nanomsg offer considerably higher throughput and less latency than classical message-oriented middleware but require greater orchestration of network topologies. They offer high scalability but can lead to tighter coupling between components. Traditional brokered message queues, like those of the AMQP variety, tend to be slower while providing more guarantees and reduced coupling. However, they are also more prone to scale problems like availability and partitioning.

In terms of its qualities, Iris appears to be a reasonable compromise between the decentralized nature of the brokerless systems and the minimal-configuration and management of the brokered ones. Its intrinsic value lies in its ability to hide the complexities of the underlying infrastructure behind distributed systems. Rather, Iris lends itself to building large-scale systems the way we conceptualize and reason about them—by using services as the building blocks, not instances.

Posted on

Dissecting Message Queues

Disclaimer (10/29/20) – The benchmarks and performance analysis presented in this post should not be relied on. This post was written roughly six years ago, and at the time, was just the result of my exploration of various messaging systems. The benchmarks are not implemented in a meaningful way, which I discussed in a follow-up post. This post will remain for posterity and learning purposes, but I do not claim that this information is accurate or useful.

Continuing my series on message queues, I spent this weekend dissecting various libraries for performing distributed messaging. In this analysis, I look at a few different aspects, including API characteristics, ease of deployment and maintenance, and performance qualities. The message queues have been categorized into two groups: brokerless and brokered. Brokerless message queues are peer-to-peer such that there is no middleman involved in the transmission of messages, while brokered queues have some sort of server in between endpoints.

The systems I’ll be analyzing are:

Brokerless
nanomsg
ZeroMQ

Brokered
ActiveMQ
NATS
Kafka
Kestrel
NSQ
RabbitMQ
Redis
ruby-nats

To start, let’s look at the performance metrics since this is arguably what people care the most about. I’ve measured two key metrics: throughput and latency. All tests were run on a MacBook Pro 2.6 GHz i7, 16GB RAM. These tests are evaluating a publish-subscribe topology with a single producer and single consumer. This provides a good baseline. It would be interesting to benchmark a scaled-up topology but requires more instrumentation.

The code used for benchmarking, written in Go, is available on GitHub. The results below shouldn’t be taken as gospel as there are likely optimizations that can be made to squeeze out performance gains. Pull requests are welcome.

Throughput Benchmarks

Throughput is the number of messages per second the system is able to process, but what’s important to note here is that there is no single “throughput” that a queue might have. We’re sending messages between two different endpoints, so what we observe is a “sender” throughput and a “receiver” throughput—that is, the number of messages that can be sent per second and the number of messages that can be received per second.

This test was performed by sending 1,000,000 1KB messages and measuring the time to send and receive on each side. Many performance tests tend to use smaller messages in the range of 100 to 500 bytes. I chose 1KB because it’s more representative of what you might see in a production environment, although this varies case by case. For message-oriented middleware systems, only one broker was used. In most cases, a clustered environment would yield much better results.

Unsurprisingly, there’s higher throughput on the sending side. What’s interesting, however, is the disparity in the sender-to-receiver ratios. ZeroMQ is capable of sending over 5,000,000 messages per second but is only able to receive about 600,000/second. In contrast, nanomsg sends shy of 3,000,000/second but can receive almost 2,000,000.

Now let’s take a look at the brokered message queues.

Intuitively, we observe that brokered message queues have dramatically less throughput than their brokerless counterparts by a couple orders of magnitude for the most part. Half the brokered queues have a throughput below 25,000 messages/second. The numbers for Redis might be a bit misleading though. Despite providing pub/sub functionality, it’s not really designed to operate as a robust messaging queue. In a similar fashion to ZeroMQ, Redis disconnects slow clients, and it’s important to point out that it was not able to reliably handle this volume of messaging. As such, we consider it an outlier. Kafka and ruby-nats have similar performance characteristics to Redis but were able to reliably handle the message volume without intermittent failures. The Go implementation of NATS, gnatsd, has exceptional throughput for a brokered message queue.

Outliers aside, we see that the brokered queues have fairly uniform throughputs. Unlike the brokerless libraries, there is little-to-no disparity in the sender-to-receiver ratios, which themselves are all very close to one.

Latency Benchmarks

The second key performance metric is message latency. This measures how long it takes for a message to be transmitted between endpoints. Intuition might tell us that this is simply the inverse of throughput, i.e. if throughput is messages/second, latency is seconds/message. However, by looking closely at this image borrowed from a ZeroMQ white paper, we can see that this isn’t quite the case.

The reality is that the latency per message sent over the wire is not uniform. It can vary wildly for each one. In truth, the relationship between latency and throughput is a bit more involved. Unlike throughput, however, latency is not measured at the sender or the receiver but rather as a whole. But since each message has its own latency, we will look at the averages of all of them. Going further, we will see how the average message latency fluctuates in relation to the number of messages sent. Again, intuition tells us that more messages means more queueing, which means higher latency.

As we did before, we’ll start by looking at the brokerless systems.

In general, our hypothesis proves correct in that, as more messages are sent through the system, the latency of each message increases. What’s interesting is the tapering at the 500,000-point in which latency appears to increase at a slower rate as we approach 1,000,000 messages. Another interesting observation is the initial spike in latency between 1,000 and 5,000 messages, which is more pronounced with nanomsg. It’s difficult to pinpoint causation, but these changes might be indicative of how message batching and other network-stack traversal optimizations are implemented in each library. More data points may provide better visibility.

We see some similar patterns with brokered queues and also some interesting new ones.

Redis behaves in a similar manner as before, with an initial latency spike and then a quick tapering off. It differs in that the tapering becomes essentially constant right after 5,000 messages. NSQ doesn’t exhibit the same spike in latency and behaves, more or less, linearly. Kestrel fits our hypothesis.

Notice that ruby-nats and NATS hardly even register on the chart. They exhibited surprisingly low latencies and unexpected relationships with the number of messages.

Interestingly, the message latencies for ruby-nats and NATS appear to be constant. This is counterintuitive to our hypothesis.

You may have noticed that Kafka, ActiveMQ, and RabbitMQ were absent from the above charts. This was because their latencies tended to be orders-of-magnitude higher than the other brokered message queues, so ActiveMQ and RabbitMQ were grouped into their own AMQP category. I’ve also included Kafka since it’s in the same ballpark.

Here we see that RabbitMQ’s latency is constant, while ActiveMQ and Kafka are linear. What’s unclear is the apparent disconnect between their throughput and mean latencies.

Qualitative Analysis

Now that we’ve seen some empirical data on how these different libraries perform, I’ll take a look at how they work from a pragmatic point of view. Message throughput and speed is important, but it isn’t very practical if the library is difficult to use, deploy, or maintain.

ZeroMQ and Nanomsg

Technically speaking, nanomsg isn’t a message queue but rather a socket-style library for performing distributed messaging through a variety of convenient patterns. As a result, there’s nothing to deploy aside from embedding the library itself within your application. This makes deployment a non-issue.

Nanomsg is written by one of the ZeroMQ authors, and as I discussed before, works in a very similar way to that library. From a development standpoint, nanomsg provides an overall cleaner API. Unlike ZeroMQ, there is no notion of a context in which sockets are bound to. Furthermore, nanomsg provides pluggable transport and messaging protocols, which make it more open to extension. Its additional built-in scalability protocols also make it quite appealing.

Like ZeroMQ, it guarantees that messages will be delivered atomically intact and ordered but does not guarantee the delivery of them. Partial messages will not be delivered, and it’s possible that some messages won’t be delivered at all. The library’s author, Martin Sustrik, makes this abundantly clear:

Guaranteed delivery is a myth. Nothing is 100% guaranteed. That’s the nature of the world we live in. What we should do instead is to build an internet-like system that is resilient in face of failures and routes around damage.

The philosophy is to use a combination of topologies to build resilient systems that add in these guarantees in a best-effort sort of way.

On the other hand, nanomsg is still in beta and may not be considered production-ready. Consequently, there aren’t a lot of resources available and not much of a development community around it.

ZeroMQ is a battle-tested messaging library that’s been around since 2007. Some may perceive it as a predecessor to nanomsg, but what nano lacks is where ZeroMQ thrives—a flourishing developer community and a deluge of resources and supporting material. For many, it’s the de facto tool for building fast, asynchronous distributed messaging systems that scale.

Like nanomsg, ZeroMQ is not a message-oriented middleware and simply operates as a socket abstraction. In terms of usability, it’s very much the same as nanomsg, although its API is marginally more involved.

ActiveMQ and RabbitMQ

ActiveMQ and RabbitMQ are implementations of AMQP. They act as brokers which ensure messages are delivered. ActiveMQ and RabbitMQ support both persistent and non-persistent delivery. By default, messages are written to disk such that they survive a broker restart. They also support synchronous and asynchronous sending of messages with the former having substantial impact on latency. To guarantee delivery, these brokers use message acknowledgements which also incurs a massive latency penalty.

As far as availability and fault tolerance goes, these brokers support clustering through shared storage or shared nothing. Queues can be replicated across clustered nodes so there is no single point of failure or message loss.

AMQP is a non-trivial protocol which its creators claim to be over-engineered. These additional guarantees are made at the expense of major complexity and performance trade-offs. Fundamentally, clients are more difficult to implement and use.

Since they’re message brokers, ActiveMQ and RabbitMQ are additional moving parts that need to be managed in your distributed system, which brings deployment and maintenance costs. The same is true for the remaining message queues being discussed.

NATS and Ruby-NATS

NATS (gnatsd) is a pure Go implementation of the ruby-nats messaging system. NATS is distributed messaging rethought to be less enterprisey and more lightweight (this is in direct contrast to systems like ActiveMQ, RabbitMQ, and others). Apcera’s Derek Collison, the library’s author and former TIBCO architect, describes NATS as “more like a nervous system” than an enterprise message queue. It doesn’t do persistence or message transactions, but it’s fast and easy to use. Clustering is supported so it can be built on top of with high availability and failover in mind, and clients can be sharded. Unfortunately, TLS and SSL are not yet supported in NATS (they are in the ruby-nats) but on the roadmap.

As we observed earlier, NATS performs far better than the original Ruby implementation. Clients can be used interchangeably with NATS and ruby-nats.

Kafka

Originally developed by LinkedIn, Kafka implements publish-subscribe messaging through a distributed commit log. It’s designed to operate as a cluster that can be consumed by large amounts of clients. Horizontal scaling is done effortlessly using ZooKeeper so that additional consumers and brokers can be introduced seamlessly. It also transparently takes care of cluster rebalancing.

Kafka uses a persistent commit log to store messages on the broker. Unlike other durable queues which usually remove persisted messages on consumption, Kafka retains them for a configured period of time. This means that messages can be “replayed” in the event that a consumer fails.

ZooKeeper makes managing Kafka clusters relatively easy, but it does introduce yet another element that needs to be maintained. That said, Kafka exposes a great API and Shopify has an excellent Go client called Sarama that makes interfacing with Kafka very accessible.

Kestrel

Kestrel is a distributed message queue open sourced by Twitter. It’s intended to be fast and lightweight. Because of this, it has no concept of clustering or failover. While Kafka is built from the ground up to be clustered through ZooKeeper, the onus of message partitioning is put upon the clients of Kestrel. There is no cross-communication between nodes. It makes this trade-off in the name of simplicity. It features durable queues, item expiration, transactional reads, and fanout queues while operating over Thrift or memcache protocols.

Kestrel is designed to be small, but this means that more work must be done by the developer to build out a robust messaging system on top of it. Kafka seems to be a more “all-in-one” solution.

NSQ

NSQ is a messaging platform built by Bitly. I use the word platform because there’s a lot of tooling built around NSQ to make it useful for real-time distributed messaging. The daemon that receives, queues, and delivers messages to clients is called nsqd. The daemon can run standalone, but NSQ is designed to run in as a distributed, decentralized topology. To achieve this, it leverages another daemon called nsqlookupd. Nsqlookupd acts as a service-discovery mechanism for nsqd instances. NSQ also provides nsqadmin, which is a web UI that displays real-time cluster statistics and acts as a way to perform various administrative tasks like clearing queues and managing topics.

By default, messages in NSQ are not durable. It’s primarily designed to be an in-memory message queue, but queue sizes can be configured such that after a certain point, messages will be written to disk. Despite this, there is no built-in replication. NSQ uses acknowledgements to guarantee message delivery, but the order of delivery is not guaranteed. Messages can also be delivered more than once, so it’s the developer’s responsibility to introduce idempotence.

Similar to Kafka, additional nodes can be added to an NSQ cluster seamlessly. It also exposes both an HTTP and TCP API, which means you don’t actually need a client library to push messages into the system. Despite all the moving parts, it’s actually quite easy to deploy. Its API is also easy to use and there are a number of client libraries available.

Redis

Last up is Redis. While Redis is great for lightweight messaging and transient storage, I can’t advocate its use as the backbone of a distributed messaging system. Its pub/sub is fast but its capabilities are limited. It would require a lot of work to build a robust system. There are solutions better suited to the problem, such as those described above, and there are also some scaling concerns with it.

These matters aside, Redis is easy to use, it’s easy to deploy and manage, and it has a relatively small footprint. Depending on the use case, it can be a great choice for real-time messaging as I’ve explored before.

Conclusion

The purpose of this analysis is not to present some sort of “winner” but instead showcase a few different options for distributed messaging. There is no “one-size-fits-all” option because it depends entirely on your needs. Some use cases require fast, fire-and-forget messages, others require delivery guarantees. In fact, many systems will call for a combination of these. My hope is that this dissection will offer some insight into which solutions work best for a given problem so that you can make an intelligent decision.

Posted on

A Look at Nanomsg and Scalability Protocols (Why ZeroMQ Shouldn’t Be Your First Choice)

Earlier this month, I explored ZeroMQ and how it proves to be a promising solution for building fast, high-throughput, and scalable distributed systems. Despite lending itself quite well to these types of problems, ZeroMQ is not without its flaws. Its creators have attempted to rectify many of these shortcomings through spiritual successors Crossroads I/O and nanomsg.

The now-defunct Crossroads I/O is a proper fork of ZeroMQ with the true intention being to build a viable commercial ecosystem around it. Nanomsg, however, is a reimagining of ZeroMQ—a complete rewrite in C ((The author explains why he should have originally written ZeroMQ in C instead of C++.)). It builds upon ZeroMQ’s rock-solid performance characteristics while providing several vital improvements, both internal and external. It also attempts to address many of the strange behaviors that ZeroMQ can often exhibit. Today, I’ll take a look at what differentiates nanomsg from its predecessor and implement a use case for it in the form of service discovery.

Nanomsg vs. ZeroMQ

A common gripe people have with ZeroMQ is that it doesn’t provide an API for new transport protocols, which essentially limits you to TCP, PGM, IPC, and ITC. Nanomsg addresses this problem by providing a pluggable interface for transports and messaging protocols. This means support for new transports (e.g. WebSockets) and new messaging patterns beyond the standard set of PUB/SUB, REQ/REP, etc.

Nanomsg is also fully POSIX-compliant, giving it a cleaner API and better compatibility. No longer are sockets represented as void pointers and tied to a context—simply initialize a new socket and begin using it in one step. With ZeroMQ, the context internally acts as a storage mechanism for global state and, to the user, as a pool of I/O threads. This concept has been completely removed from nanomsg.

In addition to POSIX compliance, nanomsg is hoping to be interoperable at the API and protocol levels, which would allow it to be a drop-in replacement for, or otherwise interoperate with, ZeroMQ and other libraries which implement ZMTP/1.0 and ZMTP/2.0. It has yet to reach full parity, however.

ZeroMQ has a fundamental flaw in its architecture. Its sockets are not thread-safe. In and of itself, this is not problematic and, in fact, is beneficial in some cases. By isolating each object in its own thread, the need for semaphores and mutexes is removed. Threads don’t touch each other and, instead, concurrency is achieved with message passing. This pattern works well for objects managed by worker threads but breaks down when objects are managed in user threads. If the thread is executing another task, the object is blocked. Nanomsg does away with the one-to-one relationship between objects and threads. Rather than relying on message passing, interactions are modeled as sets of state machines. Consequently, nanomsg sockets are thread-safe.

Nanomsg has a number of other internal optimizations aimed at improving memory and CPU efficiency. ZeroMQ uses a simple trie structure to store and match PUB/SUB subscriptions, which performs nicely for sub-10,000 subscriptions but quickly becomes unreasonable for anything beyond that number. Nanomsg uses a space-optimized trie called a radix tree to store subscriptions. Unlike its predecessor, the library also offers a true zero-copy API which greatly improves performance by allowing memory to be copied from machine to machine while completely bypassing the CPU.

ZeroMQ implements load balancing using a round-robin algorithm. While it provides equal distribution of work, it has its limitations. Suppose you have two datacenters, one in New York and one in London, and each site hosts instances of “foo” services. Ideally, a request made for foo from New York shouldn’t get routed to the London datacenter and vice versa. With ZeroMQ’s round-robin balancing, this is entirely possible unfortunately. One of the new user-facing features that nanomsg offers is priority routing for outbound traffic. We avoid this latency problem by assigning priority one to foo services hosted in New York for applications also hosted there. Priority two is then assigned to foo services hosted in London, giving us a failover in the event that foos in New York are unavailable.

Additionally, nanomsg offers a command-line tool for interfacing with the system called nanocat. This tool lets you send and receive data via nanomsg sockets, which is useful for debugging and health checks.

Scalability Protocols

Perhaps most interesting is nanomsg’s philosophical departure from ZeroMQ. Instead of acting as a generic networking library, nanomsg intends to provide the “Lego bricks” for building scalable and performant distributed systems by implementing what it refers to as “scalability protocols.” These scalability protocols are communication patterns which are an abstraction on top of the network stack’s transport layer. The protocols are fully separated from each other such that each can embody a well-defined distributed algorithm. The intention, as stated by nanomsg’s author Martin Sustrik, is to have the protocol specifications standardized through the IETF.

Nanomsg currently defines six different scalability protocols: PAIR, REQREP, PIPELINE, BUS, PUBSUB, and SURVEY.

PAIR (Bidirectional Communication)

PAIR implements simple one-to-one, bidirectional communication between two endpoints. Two nodes can send messages back and forth to each other.

REQREP (Client Requests, Server Replies)

The REQREP protocol defines a pattern for building stateless services to process user requests. A client sends a request, the server receives the request, does some processing, and returns a response.

PIPELINE (One-Way Dataflow)

PIPELINE provides unidirectional dataflow which is useful for creating load-balanced processing pipelines. A producer node submits work that is distributed among consumer nodes.

BUS (Many-to-Many Communication)

BUS allows messages sent from each peer to be delivered to every other peer in the group.

PUBSUB (Topic Broadcasting)

PUBSUB allows publishers to multicast messages to zero or more subscribers. Subscribers, which can connect to multiple publishers, can subscribe to specific topics, allowing them to receive only messages that are relevant to them.

SURVEY (Ask Group a Question)

The last scalability protocol, and the one in which I will further examine by implementing a use case with, is SURVEY. The SURVEY pattern is similar to PUBSUB in that a message from one node is broadcasted to the entire group, but where it differs is that each node in the group responds to the message. This opens up a wide variety of applications because it allows you to quickly and easily query the state of a large number of systems in one go. The survey respondents must respond within a time window configured by the surveyor.

Implementing Service Discovery

As I pointed out, the SURVEY protocol has a lot of interesting applications. For example:

  • What data do you have for this record?
  • What price will you offer for this item?
  • Who can handle this request?

To continue exploring it, I will implement a basic service-discovery pattern. Service discovery is a pretty simple question that’s well-suited for SURVEY: what services are out there? Our solution will work by periodically submitting the question. As services spin up, they will connect with our service discovery system so they can identify themselves. We can tweak parameters like how often we survey the group to ensure we have an accurate list of services and how long services have to respond.

This is great because 1) the discovery system doesn’t need to be aware of what services there are—it just blindly submits the survey—and 2) when a service spins up, it will be discovered and if it dies, it will be “undiscovered.”

Here is the ServiceDiscovery class:

The discover method submits the survey and then collects the responses. Notice we construct a SURVEYOR socket and set the SURVEYOR_DEADLINE option on it. This deadline is the number of milliseconds from when a survey is submitted to when a response must be received—adjust it accordingly based on your network topology. Once the survey deadline has been reached, a NanoMsgAPIError is raised and we break the loop. The resolve method will take the name of a service and randomly select an available provider from our discovered services.

We can then wrap ServiceDiscovery with a daemon that will periodically run discover.

The discovery parameters are configured through environment variables which I inject into a Docker container.

Services must connect to the discovery system when they start up. When they receive a survey, they should respond by identifying what service they provide and where the service is located. One such service might look like the following:

Once again, we configure parameters through environment variables set on a container. Note that we connect to the discovery system with a RESPONDENT socket which then responds to service queries with the service name and address. The service itself uses a REP socket that simply responds to any requests with “The answer is 42,” but it could take any number of forms such as HTTP, raw socket, etc.

The full code for this example, including Dockerfiles, can be found on GitHub.

Nanomsg or ZeroMQ?

Based on all the improvements that nanomsg makes on top of ZeroMQ, you might be wondering why you would use the latter at all. Nanomsg is still relatively young. Although it has numerous language bindings, it hasn’t reached the maturity of ZeroMQ which has a thriving development community. ZeroMQ has extensive documentation and other resources to help developers make use of the library, while nanomsg has very little. Doing a quick Google search will give you an idea of the difference (about 500,000 results for ZeroMQ to nanomsg’s 13,500).

That said, nanomsg’s improvements and, in particular, its scalability protocols make it very appealing. A lot of the strange behaviors that ZeroMQ exposes have been resolved completely or at least mitigated. It’s actively being developed and is quickly gaining more and more traction. Technically, nanomsg has been in beta since March, but it’s starting to look production-ready if it’s not there already.