An Introduction to ELK Stack
Wth the pace of business today, it’s easy to lose track of what’s going on. It’s also becoming increasingly difficult to derive value from data quickly enough that the data is still relevant. Oftentimes companies struggle with a situation where by the time the data has been crunched and visualized in a meaningful way, the optimal window for taking action has already come and gone.
One of the strategies that many organizations are using to make sense of the vast amounts of helpful data their infrastructure generates is by collecting all the logging information from various infrastructure components, crunching it by correlating time stamps and using heuristics that take relationships between infrastructure entities into account, and presenting it in a report or dashboard that brings the important metrics to the surface.
ELK Stack is one way modern organizations choose to accomplish this. As the name (“stack”) implies, ELK is not actually a tool in itself, but rather a useful combination of three different tools – Elasticsearch, Logstash, and Kibana – hence ELK. All three are open source projects maintained by Elastic. The descriptions of each tool from Elastic on their website are great, so I have opted not to re-write them. Elastic says they are:
- Elasticsearch: A distributed, open source search and analytics engine, designed for horizontal scalability, reliability, and easy management. It combines the speed of search with the power of analytics via a sophisticated, developer-friendly query language covering structured, unstructured, and time-series data.
- Logstash: A flexible, open source data collection, enrichment, and transportation pipeline. With connectors to common infrastructure for easy integration, Logstash is designed to efficiently process a growing list of log, event, and unstructured data sources for distribution into a variety of outputs, including Elasticsearch.
- Kibana: An open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. From histograms to geomaps, Kibana brings your data to life with visuals that can be combined into custom dashboards that help you share insights from your data far and wide.
Put simply, the tools respectively provide fast searching over a large data set, collect and distribute large amounts of log data, and visualize the collected and processed data. Getting started with ELK stack isn’t too difficult, but there are ways that community members have contributed their efforts to make it even easier. Friend of the IT community Larry Smith wrote a really helpful guide to deploying a highly available ELK stack environment that you can use to get going. Given a little bit of determination, you can use Larry’s guide to get a resilient ELK stack deployment running in your lab in an evening after work!
Alternatively, if you’re looking to get going on an enterprise-class deployment of these tools and don’t have time for fooling around, you could consider whether hosted ELK stack services would meet your needs. Depending on your budget and skills, it could make sense to let someone else do the heavy lifting, and that’s where services like Qbox come in. I’ve not used the service myself and I’m not necessarily endorsing this one, but I’ve seen manages services like this one be very successful in meeting other pressing needs in the past.
If you check this out and ELK Stack doesn’t meet your data insight requirements, there are other awesome options as well. There’s also the ongoing debate about proprietary vs. open source software and you’ll find that there are log collection/search/visualization tools for both sides of the matter. If you’re looking for something different, you may want to consider:
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