Elasticsearch is a distributed, open-source search and analytics engine built on top of Apache Lucene.

It is designed to provide a scalable search solution that can be easily deployed and maintained. Elasticsearch is based on a distributed architecture, which stores and processes data across various machines. This allows it to handle huge volumes of data and provide fast search results.

Elasticsearch is also highly flexible, as it can be used to index and search various data types, including text, numerical, and geospatial data.

It also provides a robust set of APIs that allow developers to build custom search and analytics applications on top of the Elasticsearch engine.

In less technical jargon, Elasticsearch is a tool that allows you to search through large amounts of data quickly. It is often used to power search features on websites and applications to help users find the information they want.

For example, if you use an online store, the search bar at the top of the page might be powered by Elasticsearch. When you type in a search query, Elasticsearch will quickly look through all of the products in the store and return a list of results that match your search. Elasticsearch is also used for other types of data analysis, such as analyzing logs to identify trends or patterns.

Uses of Elasticsearch

Although the usage of Elasticsearch may vary depending on the application, implementation, etc. All the uses fall among these categories:

  1. Full-text search
  2. Structured search
  3. Faceted search
  4. Geospatial search
  5. Data analytics
  6. Real-time search
  7. Auto-complete
  8. Spell-check
  9. Recommendation engines
  10. Text analysis.

Building Blocks of Elasticsearch

Without diving much into the details and technical configuration of how Elasticsearch works, we can define the architecture of Elasticsearch as shown:

The building blocks of Elasticsearch are:

  1. Index - An index is a collection of documents that have similar characteristics. For example, an index might contain all the products in an online store or all the user profiles in a social media application.
  2. Document - An Elasticsearch document is a unit of data stored in an index. Elasticsearch documents are in the form of a document that is a JSON object. Each document correlates a set of keys (names of fields or properties) with their corresponding values (strings, numbers, Booleans, dates, arrays of values, geolocations, or other data types).
  3. Shard - An index can be divided into shards representing smaller pieces of the index that can be stored on different machines. This allows Elasticsearch to scale horizontally and distribute the load across a cluster of machines.
  4. Node - On the other hand, a node is a single-machine part of an Elasticsearch cluster. Each node stores a subset of the data in the cluster and participates in the indexing and search process.
  5. Cluster - A cluster is a collection of nodes that store and index the data in an Elasticsearch index.
  6. Replica - An Elasticsearch replica refers to a copy of a shard stored on a different node. They are mainly used to provide redundancy and improve the reliability of the Elasticsearch cluster.

Elasticsearch Pairing

Although Elasticsearch is a powerful tool, it does not work as a standalone unit. You will often find Elasticsearch paired with Kibana and Logstash.

These tools: Elasticsearch, Logstash, and Kibana, make up what is commonly known as the ELK Stack or Elasticsearch Stack.

What is Logstash?

Logstash is an open-source data processing pipeline that ingests, transforms, and sends data to a specified output. It is a part of the Elastic Stack, a collection of tools for storing, searching, and analyzing data.

Logstash can process data from various sources, such as log files, system metrics, and external APIs. It has a plugin ecosystem that allows users to extend its functionality and can be used to connect to a wide range of different data stores and search engines.

In simpler terms, Logstash allows you to process and transport data from one place to another. It is often used to import data from multiple sources into a central data store, such as Elasticsearch, or stream real-time data to other systems.

For example, we might use Logstash to collect log files from an Apache server and send them to Elasticsearch for storage and analysis.

What is Kibana?

Let us now talk about Kibana.

Kibana, on the other hand, is a powerful data visualization platform. It is used to create and share interactive charts, graphs, and dashboards based on data stored in Elasticsearch.

This allows you to quickly and efficiently explore and analyze their data in near real-time. It also offers an incredibly easy-to-use and intuitive platform for creating visualizations and reports that help you understand and gain insights from your data.

Kibana has a user-friendly interface that allows you to easily create and customize visualizations and build complex dashboards by combining multiple visualizations on a single page. You will often find visualizations such as line graphs, heat maps, pie charts, etc.

Do I need Elasticsearch?

Unfortunately, we cannot provide a yes or no answer to whether or not you need to use Elasticsearch. However, if you find yourself performing many text searches or creating complex regular expressions to sort through some data, it may be a sign you need Elasticsearch.

Some other uses of Elasticsearch include:

Here are some reasons to use Elasticsearch:

  1. Full-text search
  2. Structured search
  3. Real-time search
  4. Faceted search
  5. Geospatial search
  6. Scalability
  7. High availability
  8. Advanced analytics
  9. Data visualization
  10. Customizability and extensibility
  11. Powerful and Heavily Customizable REST API
  12. Free and Open Source...

Luckily, with modern technology infrastructure, you can run a simple ELK stack on your machine, test it, and learn all about it before committing.

You can also look into hosted options such as Elastic Cloud when ready.


That concludes our introduction to Elasticsearch, Logstash, and Kibana. We hope you learned something from this tutorial. If you are ready to learn more about Elasticsearch, check out our Elasticsearch tutorials to expand your knowledge.

If you cannot find the topic you are looking for, you can Submit a Request, and we will do our best to publish one for you.

You can also comment below for technical support on Elasticsearch or any tech-related field. We will do our best to assist you.

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