The ELK stack is the most widely used log analytics platform around the globe today, serving as a complete and economical solution. It is also known as the Elastic stack and its popularity is growing alongside the adoption of public clouds. Cloud based IT infrastructures have greater difficulty reaching performance isolation, especially when data-flow is heavy. Developers and DevOp engineers can gain valuable insight of their applications via ELK’s end-to-end search, analyzation, and visualization of log generated from different machines.
What is the ELK Stack made of?
As a programmer it’s natural to be curious about contemporary buzzwords circulating the IT industry. Whether you intend to use ELK stack or not, you can’t say unless you know it in and out. Thereby, ELK stack is basically a combination of three dynamic open source projects, namely Elasticsearch, Logstash, and Kibana. Despite being three independent technologies, they work exceptionally as a team. To get a bigger grip on the concepts, let us study these components individually…
Elasticsearch forms the core of ELK; it is one of the best NoSQL database systems available at the moment. It is a modern search + analytics engine based on Apache Lucene and built with Java. It has an extensive REST API which helps strengthen it’s search capability and features. In collaboration with other elements of the tech stack, it handles data indexing and storage.
Logstash is the component of the ELK that is responsible for parsing/defining data. It is a server-side data processing pipeline that collects data from multiple sources, transforms it and delivers. With over 200 built-in plugins, it supports data in various formats. As part of the stack, it is in charge of processing log messages, enhancing them, and finally dispatching them or stashing them at a reserved location.
Kibana is a data visualization and exploration tool; it is installed in the browser’s UI where it can be used to search and analyze data stored in Elasticsearch indices. It is well known for its high quality graphical and visualization features that enables users to analyze large volumes of data. As part of the ELK stack, it helps to efficiently monitor and query data.
Getting Started with ELK
ELK stack can be configured in a variety of operating systems or environments. Using Docker and configuration management systems like Puppet or Chef, it can be installed locally and on the cloud. The installation method varies for different systems, but there are tutorials available online for every OS. Aside from three main open source components, you’ll need a few paid products to complete the stack, i.e X-Pack and Beats. X-Pack is an extension that provides security, alerting, monitoring, reporting machine learning and several other functions. Beats are lightweight data shippers that send data from numerous machines/systems to Logstash or Elasticsearch.
Why use ELK?
Considering the current state of affairs, huge amounts of data constantly flow through systems on a daily basis, regardless of the business scale. As data sets expand, analytics become sluggish resulting in compromised acumen. The major part of the data consists of a company’s web server logs which are rather useful sources of information. Being unstructured, this data is mostly ignored; only if we had something to make sense of it and use it to our advantage! ELK is the answer to managing all your Big Data, processing it and providing detailed logical analysis reports.
Big corporations or organizations have to face more complications as they are running hundreds of servers altogether which run multiple applications and what not! This adds up to humongous mountains of data; having the power to interpret and narrow it down would certainly be favorable. Logs and metrics help clarify the equation – you can decipher the history, the present, and more or less the future. A technology called Splunk used to be the king of log management, but ever since the emergence of ELK, they’re history. Splunk has about 15,000 active users, which is nothing compared to the millions of downloads achieved by the successor. ELK dominates its competitors by providing all the desirable features for a fraction of the cost.
ELK has become a necessity for ensuring that applications are problem free and running smoothly. A log analytics system needs to function around the clock in order to troubleshoot production issues. Otherwise, you can prepare yourselves to suffer downtime, performance degradation or even a security breach. As your system grows, you consume greater amounts of CPU, memory and storage; as a result the frequency of logs will also multiply. In order to avoid system failures or disconnections, it is crucial to adopt an elastic log management system. Despite being easy to set up, handling different components of the stack can be challenging when complexity rises within a system. You will make mistakes and get flustered, but at the end of the day you will learn from them and move forwards.
Here’s a list of some Big Names that use ELK in their IT management system: