345 Tech Talks

The highlights of the podcast are: Kubernetes contributes to 3 of the 7 Outcomes, (https://www.345.systems/how-we-help/) specifically Rapid Delivery, Avalilable & Scalable and Costs Optimised. We briefly cover the concept of Microservices: breaking an application into small units that are independently deployable and scalable. This reduces the complexity of our applications and reduces the regression burden as our services are isolated. Containerising applications means that your application is separated from other applications running on the same machine. Basic Kubernetes terms: Cluster: A group of machines working together to host Kubernetes. Nodes: A machine in the cluster. Master node: A machine running Kubernetes services, which control, monitor and coordinate the applications running on the cluster. Worker node: A machine that hosts applications, that has work assigned to it by the master nodes. Pod: A unit of deployment that can be one or more containers. Pods are scalable. Manifest: A file that descri

In this episode we’re talking database technology. Specifically, we’re talking about how we have moved to NoSQL databases as standard as we are designing for load. There’s a lot of ground to cover ranging from the underlying computer science to the choices you can make to get started. The highlights of the podcast are: We begin by referencing the 7 Outcomes, especially those that are most relevant to NoSQL technology. Depending on the use case, NoSQL can assist with Rapid Delivery. Usually the drivers for NoSQL are Available & Scalable and Costs Optimised. We explore relational databases, their strengths and how they achieve a highly consistent, high-quality data model. We look at the problems that come with traditional relational databases, especially the issues of scale and availability. Horizontal scale is only achieved through a partitioning strategy. These strategies used to be implemented through system design but over time we have been able to take advantage of technologies that have already solved the

This is the inaugural episode of the 345 Tech Talks podcast. In this episode Andrew and Paul discuss the issue of tracing and debugging microservices in Kubernetes. This is a technical deep dive into a subject that can make or break your ability to build, test and operate a large production system. A while back we wrote an article “Best Practices for Tracing and Debugging Microservices (https://www.345.systems/technical/best-practices-tracing-debugging-microservices/) ” that has turned out to be our most viewed web page ever on the 345 site. The original article is a brief look at some of the main considerations, so when we were looking for a subject for our first podcast episode this was an ideal candidate. Some of the main points from the episode: Building applications in Kubernetes helps with 3 of the 7 Outcomes for Success: Rapid Delivery, Availability & Scalability and Cost Optimised. The ability to read detailed diagnostic information is essential if you are going to build large scale distributed applic