Quick intro to Elasticsearch

ElasticsearchSo 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”.

What’s Elasticsearch

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.

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Tracking application events in Graphite

I don’t know if that’s a coincidence or not, but drastic changes in application metrics usually happen soon after a product upgrade was made. In fact, whenever I have to deal with new issue on production server, the first thing I do is checking if it was recently updated. No wonder it makes sense to record such events along with other monitoring data.

But assuming our monitoring data is in Graphite, how would we do that?

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Scraping application metrics with Prometheus

Prometheus logoThere’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”

Building dashboards with Grafana

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.

Grafana dashboard

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Creating collectd data source in JavaScript

Builds graph with data collected by JavaScript app

Exec Plugin

In the variety of collectd plugins there’s one ‘to rule them all’. If due to some course of events all collectd plugins except for Exec would be taken from you, you’d still be able to restore all its functionality with Exec.

As the name suggests, Exec starts external program or script and interprets its output as source of data. To be specific, it looks for lines that follow this scheme:

To be even more specific, these lines would work:

What’s interesting, Exec doesn’t specify in what language script should be written, so anything, including JavaScript, might work. In fact, using JavaScript would be beneficial in some scenarios, e.g. when dealing with RESTful services returning JSON.

Before we try JavaScript app as data source for collectd, let’s talk about PUTVAL lines a little bit more. Continue reading “Creating collectd data source in JavaScript”

Quick intro to rrdtool

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”

Host monitoring with collectd

collectdDistributed apps introduce a challenge that we usually could avoid in monolithic ones: how do we say that app is performing well? I’m not talking about it being user-friendly or providing business value. How do you tell that components of your distributed app are actually running? Which services are overutilized? Underutilized? Run out of disk space?

There’re tools to get that answers and collectd is one of them.

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Highly available Kafka cluster in Docker

Apache Kafka cluster in Docker

Up until now we’ve been experimenting with Apache Kafka, a tool build with cluster and high availability in mind, but using exactly one host and availability settings which only few very optimistic people would call high.

Not today.

Today we’re going to spin up multi-host Kafka cluster and we’ll replicate topic in it, so if one host goes down, data and its availability won’t suffer.

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