tag
Distributed Systems
- #28 3 min
Sometimes Kill -9 Isn’t Enough
If there’s one thing to know about distributed systems, it’s that they have to be designed with the expectation of failure. It’s also safe to say that most software these days is, in some form, distributed—whether it’s a database, mobile app, or enterprise SaaS. If you have two different processes talking to each other, you have a distributed system, and it doesn’t matter if those processes are local or intergalactically displaced.
- #27 1 min
From Mainframe to Microservice: An Introduction to Distributed Systems
I gave a talk at Iowa Code Camp this weekend on distributed systems. It was primarily an introduction to them, so it explored some core concepts at a high level. We looked at why distributed systems are difficult to build (right), the CAP theorem, consensus, scaling shared data and CRDTs. There was some interest in making the slides available online. I’m not sure how useful they are without narration, but here they are anyway for posterity.
- #26 5 min
Scaling Shared Data in Distributed Systems
Sharing mutable data at large scale is an exceedingly difficult problem. In their seminal paper CRDTs: Consistency without concurrency control, Shapiro et al. describe why the CAP theorem demands a give and take between scalability and consistency. In general, CAP requires us to choose between CP and AP. The former requires serializing every write, which doesn’t scale beyond a small cluster. The latter ensures scalability by giving up consistency. Sharing Data in Centralized Systems We tend to prefer weaker consistency models because they mean lower latency and higher availability. To highlight this point, consider the fact that the memory models for most programming languages are not serializable by default. More concisely, programs with shared memory are not inherently thread-safe. This is a conscious design decision because enforcing memory serializability incurs a significant latency penalty. Instead, programming languages require explicit memory barriers which can be used around the critical sections which need this property.
- #25 6 min
Understanding Consensus
A classical problem presented within the field of distributed systems is the Byzantine Generals Problem. In it, we observe two allied armies positioned on either side of a valley. Within the valley is a fortified city. Each army has a general with one acting as commander. Both armies must attack at the same time or face defeat by the city’s defenders. In order to come to an agreement on when to attack, messengers must be sent through the valley, risking capture by the city’s patrols. Consider the diagram below illustrating this problem.
- #23 6 min
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.