Quite often building a VM from scratch is not very wise. Unless server configuration is trivial, its provisioning might take significant amount of time. For example, creating an instance of a build server for my current project takes about 40 minutes. This includes installing updates, various SDKs and other dependencies. How is it possible then that I can add new build server to a cluster in about two or three minutes?
The secret is that the most of the software is baked into a VM image, so I never start from scratch. New VM still needs some steps like final configuration and registering within the cluster, but that’s fast. The slowest part is allocating resources for VM, not provisioning.
Historically, we’ve been using own tools for that, but there’re also free and open source ones. Like Packer. Continue reading “Building VM image with Packer”
Using Vagrant for creating Consul cluster on Linux probably was fun. But what about Windows hosts? Believe it or not, but more than half of developers are actually using Windows, so for most of the folks seeing how Vagrant creates Linux VMs is pretty useless.
However, you can create and provision Windows VMs with Vagrant with little to no problem. In fact, Windows support has been around for years. But there’re some things to keep in mind though. Continue reading “Using Vagrant for Windows VMs provisioning”
Last two articles about Consul service discovery involved one simple but extremely boring manual task: creating and configuring a cluster. In fact, I had to do it twice. I had to create three virtual machines, download and unpack Consul on them, find out their IP addresses, add configuration files and finally launch the binaries.
It’s dull. It’s boring. Humans shouldn’t do that kinds of things by hand. Seeing how easily we can automate creation of Docker containers with Dockerfile and docker-compose makes me wonder if we can do the same for hosts. Continue reading “How to use Vagrant to create Consul cluster”
Imagine your distributed app has two kinds of services:
db. Both of them are replicated for higher availability, live on different hosts, go online and offline whenever they like. So, here’s a question: how do
Obvious solution would be to come up with some sort of reliable key-value storage, and whenever service comes online, it would register itself with the address in the store. But what happens when service goes offline? It probably could notify the store just before that, but c’mon, it’s internet: things can go offline without any warning. OK, then we could implement some sort of service health checks to ensure that they are still available… By the way, did you notice how quickly the simple idea of using external store for service discovery started to become a reasonably large infrastructure project?
Service discovery is something very hard to do. But we don’t have to – there’re tools for that, and Consul is one of them. Continue reading “Using Consul for Service Discovery”
Today we’ll take a look at the last component of Elastic’s ELK stack – Kibana. Even though Logstash does a great job of processing logs and other data streams, and Elasticsearch is a powerful hybrid of a search index and a storage for them, these tools do not provide graphical user interface for analyzing the data. For some tasks otherwise convenient command line interface is just not enough. This is where Kibana steps in.
Continue reading “Visualize Elasticsearch data with Kibana”
Last time we talked out about Elaticsearch – a hybrid of NoSQL database and a search engine. Today we’ll continue with Elastic’s ELK stack and will take a look at the tool called Logstash.
Continue reading “Processing logs with Logstash”
So far we’ve been dealing with name-value kind of monitoring data. However, what works well for numeric readings isn’t necessarily useful for textual data. In fact, Grafana, Graphite and Prometheus are useless for other kind of monitoring records – logs and traces.
There’re many, many tools for dealing with those, but I decided to take a look at Elastic’s ELK stack: Elasticsearch, Logstash and Kibana – storage, data processor and visualization tool. And today we’ll naturally start with the first letter of the stack: “E”.
Elasticsearch is fast, horizontally scalable open source search engine. It provides HTTP API for storing and indexing JSON documents and with default configuration it behaves a little bit like searchable NoSQL database.
Continue reading “Quick intro to Elasticsearch”
There’re two conceptually different approaches in collecting application metrics. There’s PUSH approach, when metrics storage sits somewhere and waits until metrics source pushes some data into it. For instance, Graphite doesn’t do any collection on its own, it waits until somebody like collectd does the delivery.
There’s second approach – PULL. In this approach metrics sources don’t try to be smart and just provide their readings on demand. Whoever needs those metrics can make a call, e.g. HTTP request, in order to get some.
Prometheus collects metrics using the second approach. Continue reading “Scraping application metrics with Prometheus”
Even though Graphite does very decent job in displaying individual metrics graphs, its dashboards support is quite limited. Of cause, we could take its powerful Render URL API and build anything we like in good old HTML, but on the other hand, there’s Grafana.
Continue reading “Building dashboards with Grafana”
I mentioned in previous post that collectd uses rrdtool for saving its data by default. It results
.rrd file for each metric, which later can be rendered using very same rrdtool. RRD files are not something most of the people are familiar with and the tool itself isn’t particularly easy to use, so why such an easy to use tool as collectd would choose it?
For a number of reasons. Continue reading “Quick intro to rrdtool”