(C)onsistency: “every read sees the most recent write”.It has commonly been expressed as a pick-two-out-of-three type of trade-off with the following properties being the ones to “choose from”: There are many clever algorithms for building distributed state stores, but they are all forced by the CAP theorem to make a fundamental design trade-off between availability and consistency in the event of a network partition. In the particular case of distributed databases, these circumstances make it really hard for a collection of database replicas to determine which transctions to accept, and in what order. Neither is it possible to use the system clock to order events between processes, since there is no upper limit on the amount of clock skew between two hosts (it can, not uncommonly, be around 250ms). Notably, there is no way for a process to distinguish a failed peer from one that is merely delayed by a slow network/processor/etc. That is, having a group of network-distanced processes agree on a (set of) value(s) in the presence of unreliable components (networks, hosts, processes) and the lack of a synchronized global clock. Why is this so?ĭistributed state really boils down to solving the consensus problem. The good news is that high availability is really easy to achieve. Therefore, to achieve those extra nines of availability we should make ourĪpplication geo-distributed. Region outages do happen, for example due to catastrophic events or poor failure-isolation on part of your cloud provider. However, to make your system even more robust, you should try to spread your application over a larger geographical area, spanning several regions, not just zones within a single region. Since zones are located rather nearby (in the same cloud region), network latency is low. So, the recommendation is often to run your application in several of these zones, so that your application can tolerate a zone outage (which is rare, but does happen). Each such zone is essentially a data center with separate network cables and power supplies and, therefore, independent failure domain from the other zones. Most cloud operators have several “zones” in each geographical region they service. This typically means using multiple copies of each component, preferably scattered over different “zones”. So, how does one achieve high availability? A good start is to make your system redundant.
#Syncthing kubernetes full#
For example, 99.9% availability means that you will experience no more than about 9 hours of service downtime during a full year. Instead, availability is measured in nines. What does it mean for a system to be highly available anyway? It’s a relative concept, since in real life one cannot guarantee 100% availability. WordPress also serves as a good use case for our little experiment, since it is well-used (serving the content of 30 percent of all websites, and has non-trivivial state needs (more on that later).īut let’s start with some theory! High availability – just more of the same really However if you, like many others, use WordPress to service the customers of your e-commerce store, then every second of downtime means lost revenue. The average reader of your relative’s cat blog may not be too upset by some service downtime. Okay, so you may (rightfully) ask why it’s important to have a blog/website engine achieve serious levels of availability.
#Syncthing kubernetes how to#
In this post, we’ll examine how to build a geo-distributed application for a non-trivial application. If these claims are true, the road towards geo-distributed applications for the masses appears to lie wide open. Advances that appear to contradict the CAP theorem by promising both strong consistency and high availability at the same time (more on this later). In recent years, a lot of advances have been made in the so-called NewSQL segment of databases. It appears as though the pendulum has started to swing back. This caused a movement towards NoSQL databases which, as we’ll discuss later, come with a different set of limitations. Essentially, relying on an “SQL database” has prevented you from achieving the so highly desired scalability and availability that NoSQL databases (such as Cassandra) offer.
#Syncthing kubernetes code#
If you can endure some theory, you will be richly awarded in the end with sample code to run your WordPress-powered blog or CMS across three (!) Kubernetes clusters in three (!) different geographical regions.įor many years, running an application that requires traditional ACID-style database semantics has translated into running a single fat database or, at best, a clustered solution where the replicas are within close proximity of each-other. This article will explore building geo-distributed applications, with a particular focus on using NewSQL databases.
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