You can see the sample code for this post on Github.

Consul Watches offer a way to hook into changes to the Consul cluster state at runtime.The specific type of changes we will be looking at hooking into in this post are checks. Whenever a node or service comes online and registers to Consul, whenever an existing node or service leaves Consul, or whenever an existing node or service becomes unresponsive, Consul will emit a check event. This check event can invoke a process to monitor the health of our services, alerting human being that action might soon be necessary.

The source code for this blog post can be found on GitHub.

Consul can run in either client or server mode. As far as Consul is concerned, the primary difference between client and server mode are that Consul Servers participate in the consensus quorum, store cluster state, and handle queries. Consul Agents are often deployed to act as middle-men between the services and the Consul Servers, which need to be highly available by design.

You can see the source code for this post on GitHub.

We spent the last post figuring out how to migrate an embedded PostgreSQL database using Spring, while trying to side-step the extra magic that comes along with the framework. Here, we are going to build on that work to migrate a real PostgreSQL instance, which we will build in a local Vagrant Virtual Machine.

The source code for this post can be found on Github.

In my last post, I provided an example for how to set up an Nginx Reverse Proxy for a Spring MVC application. One such reason to set up a reverse proxy is to utilize caching of resources. If you have dynamically generated content that doesn’t change very often, then adding caching at the site entry point can dramatically improve site responsiveness and reduce load on critical resources.

Nginx is a popular webserver, excellent at serving up static content, and commonly used as a load balancer or reverse proxy. This post will set up a basic Spring Boot MVC web application, and use Nginx as a reverse proxy. The source code can be found on GitHub.

You can view the sample code associated with this post on GitHub.

In a previous post on Provisioning a Server with Kibana, we saw that it’s very straightforward to get kibana on a box.

You can see the source code for this post on GitHub.

In a previous post on How to Provision Multiple Logstash Hosts Using Ansible, we saw that provisioning logstash is pretty straightforward. However, what do we do with it after it’s been out there transforming messages this entire time? Given that elastic comes out with a new version of Logstash every fifteen or twenty minutes, a wise person would look to automate the upgrade process as soon as possible.

You can see the source code for this blog post on GitHub.

In a previous post, we saw how to provision a multi-node elasticsearch cluster using ansible. The problem with that post is that, by the time I was done writing it, Elastic had already come out with a new version of elasticsearch. I’m being mildly facetious, but not really. They release new versions very quickly, even by the standards of modern software engineering.

The corresponding source code for this post is available on GitHub.

Kibana is a fancy pants web application that tries to make data in Elasticsearch user-friendly. Rounding out the previous two posts on how to install an elasticsearch cluster and how to install multiple logstash hosts, I will now show you how to stack kibana on top of them.

The source code for this post can be found on GitHub.

Logstash primarily exists to extract useful information out of plain-text logs. Most applications have custom logs which are in whatever format the person writing them thought would look reasonable…usually to a human, and not to a machine. While countless future developer hours would be preserved if everything were just in JSON, that is sadly not even remotely the case, and in particular it’s not the case for log files. Logstash aims to be the intermediary between the various log formats and Elasticsearch, which is the document database provided by Elastic as well.

You can see the sample code for this tutorial on GitHub.

Elasticsearch is a distributed, NoSQL, document database, built on top of Lucene. There are so many things I could say about Elasticsearch, but instead I’ll focus on how to install a simple 3-node cluster with an Ansible role. The following example will not have any security baked into it, so it’s really just a starting point to get you up and running.

You can see the sample code for this tutorial on GitHub.

 Molecule is primarily a way to manage the testing of infrastructure automation code. At its core, it wraps around various providers like Vagrant, Docker, or VMWare, and provides relatively simple integration with testing providers, notably TestInfra. Molecule is a great tool, but in my opinion there are not enough resources, by way of examples, to provide an adequate getting started guide. This post is meant to help fill that void.