Data Science is an emerging field that is plagued by lurid, often inconsequential reports of success. The press has been all too happy to predict the future demise of the human race. But sifting through chaff, we do see some genuinely interesting reports of work that affects both bottom-line profit and top-line revenue. Location discovery service Foursquare, using a range of data science techniques, predicted that Chipotle’s Q1 2016 revenue would drop by 30%, compared to the previous year (Chipotle is a US fast-food outlet) .
Have you ever wondered what cloud-native actually is? Are you confused about how to use data science to improve your business? Check out these informative blogs to help you get started.
Cloud-Native, a collection of tools and best practices, disrupts the ideas behind traditional software development. I am a firm believer of the core concepts, which include visibility, repeatability, resiliency and robustness.
The idea begins in 2015 when the Linux Foundation formed the Cloud-Native Computing Foundation. The idea was to collect the tools and processes that are often employed to develop cloud-based software.
However, the result was a collection of best practices which extend well beyond the realms of the cloud. This post introduces the essential components: DevOps, continuous delivery, microservices and containers.
The terms “Cloud” or “Cloud Services” have become so laden with buzz that they would be happy to compete with Apollo 11 or Toy Story. But the hype often hides the most important aspects that you need to know. Like how it works, or what you can do with it. This is the first of several introductory pieces that focus on the very basics of modern applications.
In one of my applications, for various reasons, we now have a batch like process and a HTTP based REST application running inside the same binary. Today I came up against an issue where HTTP latencies were around 10 seconds when the batch process was running.
After some debugging, the reason for this is that although the two are running in separate Go routines, the batch process is not allowing the scheduler to schedule the HTTP request until the batch process has finished.
Go introduced vendoring into version 1.5 of the language. The vendor folder is used as a dependency cache for a project. Because of the unique way Go handles dependencies, the cache is full code from an entire repository; worts and all. Go will search the vendor folder for its dependencies before it searches the global GOPATH. Tools have emerged to corral the vendor folder and one of my favourites is glide.
The testing of microservices is inherently more difficult than testing monoliths due to the distributed nature of the code under test. But distributed applications are worth pursuing because by definition they are decoupled and scalable.
With planning, the result is a pipeline that automatically ensures quality. The automated assurance of quality becomes increasingly important in larger projects, because no one person wants to or is able to ensure the quality of the application as a whole.
This article provides some guidelines that I have developed whilst working on a range of software with microservice architectures. I attempt to align the concepts with best practice (references at the end of this article), but some of the terminology is my own.
I recently undertook a time-boxed four hour spike to investigate another Go microservices framework. Go-Micro is a “RPC framework for microservices”. It aims to provide common components that are often used in microservice deployments. It advertises itself as providing a pluggable architecture and boasts a long list of compatibilities.