Elastic Stack Overview

- 8 mins
elasticsearch kibana logstash xpack beats

The Elastic Stack is a suite of powerful tools developed and maintained by the creators of Elasticsearch.

At the core of this stack is Elasticsearch, the search and analytics engine that powers data retrieval and analysis. Surrounding it are complementary technologies designed to enhance its capabilities, though some can also be used independently.

While using Elasticsearch alone is possible, these tools work seamlessly together, creating a robust ecosystem for data ingestion, visualization, and monitoring.

You can access Elastic Demos here

Elasticsearch - The Core

Elasticsearch is a powerful open-source search and analytics engine widely used to enhance application search functionality. It allows you to build advanced search features like autocomplete, typo correction, synonym handling, and relevance tuning.

But Elasticsearch isn’t just for full-text search—it also excels at querying structured data, aggregating large datasets, and providing valuable insights. With its ability to process and visualize data, Elasticsearch can be used for analytics, dashboards, and real-time monitoring.

A common use case is Application Performance Management (APM), where logs and system metrics are stored, analyzed, and visualized to track errors, server health, and resource usage. Another popular application is event tracking, such as monitoring sales data from multiple stores to identify trends.

Elasticsearch also integrates machine learning for anomaly detection, automatically identifying unusual patterns without requiring manual rule-setting. Whether you’re handling search, business analytics, or predictive insights, Elasticsearch offers a versatile solution for working with large-scale data.

Data Storage

In Elasticsearch, data is stored as documents, which serve as the fundamental units of information. Think of a document as the equivalent of a row in a relational database—it can represent anything from a person to a sales transaction. Each document contains fields, similar to database columns, and is structured as a JSON object. This means adding data to Elasticsearch is as simple as sending a JSON payload that describes the entity you want to store.

Querying Documents

To retrieve data from Elasticsearch, you interact with its REST API, using JSON-based queries. This makes the API intuitive and easy to work with, especially for developers familiar with JSON.

Under the hood, Elasticsearch is built in Java on top of Apache Lucene, a powerful search library. Its popularity stems from its flexibility, scalability, and high-speed performance. While it’s easy to get started with basic queries, unlocking its full potential requires a deeper understanding of its features.

Since Elasticsearch is distributed by design, it can handle vast amounts of data while maintaining lightning-fast search speeds. Even when working with millions of documents, Elasticsearch ensures efficient querying and rapid results.

Kibana - Visualizing and Analyzing Elasticsearch Data

Kibana is an analytics and visualization platform designed to make sense of data stored in Elasticsearch.

Kibana is like an interactive dashboard for Elasticsearch, where you can create visualizations like pie charts, line charts, maps, and more. For example, you can:

Beyond visualizations, Kibana also serves as a management interface for Elasticsearch, providing tools for authentication, authorization, and data exploration. It interacts with Elasticsearch via the REST API, making it easy to build queries and display results without writing custom code.

One of Kibana’s most powerful features is its dashboards, which allow different teams to monitor key metrics:

While Elasticsearch is often associated with search functionality, many organizations use it primarily as an analytics platform—and Kibana is the perfect tool to unlock its full potential.

Logstash: The Data Processing Pipeline

Logstash is a powerful data processing pipeline originally designed for log management—hence the name. While it remains a popular tool for collecting and shipping application logs to Elasticsearch, it has evolved into a general-purpose data processing engine capable of handling various types of data, such as:

Logstash processes data as events, which are then transformed and sent to one or more destinations, including:

The Logstash Pipeline

A Logstash pipeline consists of three stages:

  1. Inputs → How Logstash receives data
  2. Filters → How Logstash processes the data
  3. Outputs → Where Logstash sends the processed data

Each stage uses plugins to handle specific tasks:

Example: Processing Web Server Logs Imagine we want to process web server access logs. The pipeline would work as follows:

  1. Input: Logstash reads logs line by line using the file input plugin.
  2. Filter: Since logs are just raw text, we use a Grok pattern (similar to regex) to extract meaningful fields, such as:
    • Status code (e.g., 200, 404)
    • Request path (e.g., /home, /about)
    • IP address of the requester
  3. Output: The structured data is sent to Elasticsearch for further analysis.

The result is a well-structured document in Elasticsearch instead of unstructured log text.

Logstash Scalability & Flexibility

X-Pack: Extending Elasticsearch & Kibana

X-Pack is a collection of features that enhances Elasticsearch and Kibana by adding additional functionality in key areas.

1. Security

X-Pack introduces authentication and authorization for both Kibana and Elasticsearch:

2. Monitoring

X-Pack enables real-time monitoring of the Elastic Stack, including:

This helps proactively detect issues and optimize system performance.

3. Alerting

X-Pack allows you to set up alerts based on specific conditions. Examples include:

Notifications can be sent via email, Slack, or other integrations.

4. Reporting

X-Pack supports exporting Kibana visualizations and dashboards to:

Reports can be generated on-demand or scheduled (daily, weekly, etc.). Also, custom triggers can generate reports based on predefined conditions.

5. Machine Learning

X-Pack provides the machine learning capabilities integrated into Kibana, enableing anomaly detection and predictive analytics. It helps detect trends and patterns in large datasets.

X-Pack: Graph

Graph is a feature in X-Pack that focuses on uncovering relationships in your data. It helps identify relevant rather than just popular connections. For example:

How Graph Works

X-Pack: SQL

The SQL feature in X-Pack allows users to query Elasticsearch using SQL instead of the native Elasticsearch native Query DSL, which is a JSON-based query language.

Beats: Lightweight Data Shippers

Beats is a collection of lightweight data shippers that send data to Logstash or Elasticsearch. They are small, specialized agents that run on servers and collect various types of data. There are different types of beats, these are the most commonly used:

1. Filebeat

2. Metricbeat

The Elastic Stack is a powerful set of tools for data ingestion, storage, search, and visualization. With X-Pack, you gain essential features like security, monitoring, alerting, reporting, machine learning, Graph, and SQL support. Meanwhile, Beats simplifies data collection by providing lightweight agents for shipping logs and metrics to your Elastic Stack. By leveraging these tools effectively, you can build a secure, scalable, and insightful data pipeline tailored to your needs. 🚀

Lais Ziegler

Lais Ziegler

Dev in training... 👋