Last updated 27/07/2021
The ELK stack is an acronym used to describe a stack that comprises of three popular open-source projects: Elasticsearch, Logstash, and Kibana. Often referred to as Elasticsearch, the ELK stack gives you the ability to aggregate logs from all your systems and applications, analyze these logs, and create visualizations for application and infrastructure monitoring, faster troubleshooting, security analytics, and more.
E = Elasticsearch
Elasticsearch is an open-source, RESTful, distributed search and analytics engine built on Apache Lucene. Support for various languages, high performance, and schema-free JSON documents makes Elasticsearch an ideal choice for various log analytics and search use cases.
L = Logstash
Logstash is an open-source data ingestion tool that allows you to collect data from a variety of sources, transform it, and send it to your desired destination. With pre-built filters and support for over 200 plugins, Logstash allows users to easily ingest data regardless of the data source or type.
K = Kibana
Kibana is an open-source data visualization and exploration tool for reviewing logs and events. Kibana offers easy-to-use, interactive charts, pre-built aggregations and filters, and geospatial support and making it the preferred choice for visualizing data stored in Elasticsearch.
Why is the ELK stack so popular?
The ELK Stack is popular because it fulfills a need in the log analytics space. As more and more of your IT infrastructure move to public clouds, you need log management and analytics solution to monitor this infrastructure as well as process any server logs, application logs, and clickstreams. The ELK stack provides a simple yet robust log analysis solution for your developers and DevOps engineers to gain valuable insights on failure diagnosis, application performance, and infrastructure monitoring – at a fraction of the price.
Elasticsearch is an open-source, RESTful, distributed search and analytics engine built on Apache Lucene. Since its release in 2010, Elasticsearch has quickly become the most popular search engine, and is commonly used for log analytics, full-text search, security intelligence, business analytics, and operational intelligence use cases.
You can send data in the form of JSON documents to Elasticsearch using the API or ingestion tools such as Logstash and Amazon Kinesis Firehose. Elasticsearch automatically stores the original document and adds a searchable reference to the document in the cluster’s index. You can then search and retrieve the document using the Elasticsearch API. You can also use Kibana, an open-source visualization tool, with Elasticsearch to visualize your data and build interactive dashboards.
Yes, Elasticsearch is a free, open source software. You can run Elasticsearch on-premises, on Amazon EC2, or on Amazon Elasticsearch Service. With on-premises or Amazon EC2 deployments, you are responsible for installing Elasticsearch and other necessary software, provisioning infrastructure, and managing the cluster. Amazon Elasticsearch Service, on the other hand, is a fully managed service, so you don’t have to worry about time-consuming cluster management tasks such as hardware provisioning, software patching, failure recovery, backups, and monitoring.
Elasticsearch offers simple REST-based APIs, a simple HTTP interface, and uses schema-free JSON documents, making it easy to get started and quickly build applications for a variety of use-cases.
The distributed nature of Elasticsearch enables it to process large volumes of data in parallel, quickly finding the best matches for your queries.
Elasticsearch comes integrated with Kibana, a popular visualization and reporting tool. It also offers integration with Beats and Logstash, while enabling you to easily transform source data and load it into your Elasticsearch cluster. You can also use a number of open-source Elasticsearch plugins such as language analyzers and suggesters to add rich functionality to your applications.
Elasticsearch operations such as reading or writing data usually take less than a second to complete. This lets you use Elasticsearch for near real-time use cases such as application monitoring and anomaly detection.
Elasticsearch provides support for various languages including Java, Python, PHP, JavaScript, Node.js, Ruby, and many more.
Kibana is an open-source data visualization and exploration tool used for log and time-series analytics, application monitoring, and operational intelligence use cases. It offers powerful and easy-to-use features such as histograms, line graphs, pie charts, heat maps, and built-in geospatial support. Also, it provides tight integration with Elasticsearch, a popular analytics and search engine, which makes Kibana the default choice for visualizing data stored in Elasticsearch.
Yes, Kibana is a free, open-source visualization tool. You can run Kibana on-premises, on Amazon EC2, or on Amazon Elasticsearch Service. With on-premises or Amazon EC2 deployments, you are responsible for provisioning the infrastructure, installing Kibana software, and managing the cluster. With Amazon Elasticsearch Service, Kibana is deployed automatically with your domain as a fully managed service, automatically taking care of all the heavy lifting to manage the cluster.
Kibana offers intuitive charts and reports that you can use to interactively navigate through large amounts of log data. You can dynamically drag time windows, zoom in and out of specific data subsets, and drill down on reports to extract actionable insights from your data.
Kibana comes with powerful geospatial capabilities so you can seamlessly layer in geographical information on top of your data and visualize results on maps.
Using Kibana’s pre-built aggregations and filters, you can run a variety of analytics like histograms, top-N queries, and trends with just a few clicks.
You can easily set up dashboards and reports and share them with others. All you need is a browser to view and explore the data.
Logstash is a light-weight, open-source, server-side data processing pipeline that allows you to collect data from a variety of sources, transform it on the fly, and send it to your desired destination. It is most often used as a data pipeline for Elasticsearch, an open-source analytics and search engine. Because of its tight integration with Elasticsearch, powerful log processing capabilities, and over 200 pre-built open-source plugins that can help you easily index your data, Logstash is a popular choice for loading data into Elasticsearch.
Logstash allows you to easily ingest unstructured data from a variety of data sources including system logs, website logs, and application server logs.
Logstash offers pre-built filters, so you can readily transform common data types, index them in Elasticsearch, and start querying without having to build custom data transformation pipelines.
With over 200 plugins already available on Github, it is likely that someone has already built the plugin you need to customize your data pipeline. But if none is available that suits your requirements, you can easily create one yourself.
The ELK Stack is most commonly used as a log analytics tool. Its popularity lies in the fact that it provides a reliable and relatively scalable way to aggregate data from multiple sources, store it and analyze it. As such, the stack is used for a variety of different use cases and purposes, ranging from development to monitoring, to security and compliance, to SEO and BI.
Before you decide to set up the stack, understand your specific use case first. This directly affects almost all the steps implemented along the way — where and how to install the stack, how to configure your Elasticsearch cluster and which resources to allocate to it, how to build data pipelines, how to secure the installation — the list is endless.
So, what are you going to be using ELK for?
Here are two examples of how the ELK Stack can be implemented as part of a security-first deployment.
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