When building and running a system deployed as a monolith, we are able to make a certain set of assumptions about the behaviour of the overall application. One of the biggest assumptions we make is that the memory space for the application all resides on the same machine. With this model, function and library calls can assume that their view of the data structures for the application are accurate, and that you can retrieve or mutate that data immediately and deterministically (leaving the thornier issues of multi-threaded applications aside for a minute). These assumptions allow teams of programmers to work effectively and efficiently across multiple packages, libraries, classes and functions. ...

In a shopping cart, the swivel wheels of the cart are set in the front, and the fixed wheels are set in the back. Now picture yourself pushing a shopping cart backwards. Almost naturally, you swivel the cart to move the front end to one side or the other before beginning to push the cart. Now picture yourself pushing a shopping cart backwards, on an ice rink. Here, the cart keeps sliding around even after you’ve stopped pushing it. Instead of going straight from one end of the rink to the other, the cart makes giant S-shaped turns. ...

In The Cathedral and the Bazaar, Eric Raymond recounts the early years of Linux. Specifically questioning how the “part-time hacking of several thousands of developers scattered all over the planet” could create perhaps the most complex, functioning application in history. How could a “babbling bazaar of differing agendas and approaches” coalesce into a coherent and stable system without some form of centralized planning? The fact that this bazaar style seemed to work, and work well, came as a distinct shock. As I learned my way around, I worked hard not just at individual projects, but also at trying to understand why the Linux world not only didn’t fly apart in confusion but seemed to go from strength to strength at a speed barely imaginable to cathedral-builders. ...

Many organizations have multiple applications that are being built independently as a microservices-based platform. By its nature, a microservice platform is a distributed system running on multiple processes or services, across multiple servers or hosts. Building a successful platform on top of microservices requires integrating these disparate services and systems to produce a unified set of functionality. It is naïve to think that we can only choose one communication style to solve all problems; if integration needs were always the same, there would be only one style and we would all be happy with it. In reality, a single service can can communicate using many different styles, each one targeting a different scenario and goals. This challenge is what brings me to the topic of this blog post: when and why do we use different communication standards? ...

With so many people running microservice workloads, it is inevitable that organizations keep bumping into the same set of problems: state management, resiliency, event-handling, and more. Dapr exists to help codify the best practices for building microservice applications into building blocks that enable you to build portable applications with the language and framework of your choice. Each building block is completely independent and you can use one, some, or all of them in your application to solve common microservice problems. ...

In my last post I detailed how to setup a local Kubernetes development cluster using kind. This post shows how to leverage this new cluster when developing a system or application that relies on multiple microservices using tilt. If you haven’t setup your local environment with kind yet, refer back to my last post before continuing on. Installing Tilt You can install the tilt binary using Homebrew or through curl and an installation script: ...

kind is a tool built for running local Kubernetes clusters using Docker containers as nodes. kind was primarily designed for testing Kubernetes itself, but it is actually quite useful for creating a Kubernetes environment for local development, QA, or CI/CD. This blog post shows you how to setup a kind-based environment for local development that can mimic a production Kubernetes environment. A fully functioning environment using kind includes a few different components. For our purposes, we will install the following list of software. ...

It’s a dangerous business, Frodo, going out your door. Kubernetes and public cloud infrastructure introduce a few layers of abstraction between users and our services. This article unravels some of those layers to help understand what, exactly, happens between the time a user makes a request to a Kubernetes service running in AWS and when the user receives a response. It’s helpful to start by framing a request in terms of the network boundaries involved, so let’s start there. ...

VoltDB is an in-memory database borne out of the H-Store research project spearheaded by Michael Stonebraker. Within that project, Michael started with the premise of building a fully transactional database with the best possible performance by using insights gathered from an in-depth study on database performance that completely removed disk access — the primary limiting factor on database performance. By removing disk access completely, we end up with a completely in-memory database where we can make additional optimizations like removing write-ahead logging, buffer management, and locks and latches. This effort resulted in the research database H-Store which was commercialized as the in-memory database VoltDB. This article takes a deeper dive into VoltDB to understand how it works and where you may benefit from this approach. ...

When working with Spring Boot, some auto-configuration can happen seemingly by magic. For example, in a traditional Spring application with Java-based configuration, you might configure an H2 database using the following block of code to set the type of the database and run some scripts to initialize it: @Bean public DataSource dataSource() { return new EmbeddedDataSourceBuilder() .setType(H2) .addScript("taco_schema.sql") .addScripts("user_data.sql", "ingredient_data.sql") .build(); } With Spring Boot, you can remove this entire block of code and use auto-configuration. If the H2 dependency is available in the classpath, Spring Boot will automatically create the DataSource and add it as a Bean to Spring’s application context and apply SQL scripts as long as they are named schema.sql or data.sql. Spring Boot does this auth-configuration by leveraging the Spring environment that collects the set of properties available to a Java application and uses those properties to configure beans in Spring’s application context. The following figure from Spring in Action shows how the Spring environment is generated by some of the available configuration property sources available in a typical Java application. ...