In this tutorial, we are going to talk about segments. This is one of those internal concepts that you do not strictly need to know to use Elasticsearch effectively, but understanding it gives you a much clearer picture of why Elasticsearch behaves the way it does, especially when it comes to performance, updates, and deletions.…

This tutorial is a quick but very important clarification, because many people casually use the terms inverted index and term dictionary interchangeably. While this is common in conversations, they are not exactly the same thing. To build a correct mental model of how Elasticsearch works internally (through Apache Lucene), we must clearly understand how these…

In the next few tutorials, we will gradually build an understanding of how Elasticsearch works internally. The goal here is not to overload you with theory all at once, but to give you a clear, high-level mental model of how search engines work in general. Once this foundation is strong, many advanced Elasticsearch concepts will…

In this tutorial, we move beyond simple field replacement and explore scripted updates, which allow you to update a document dynamically based on its current values. This concept is very similar to how updates are performed in SQL, where you can increment or modify a column using its existing value rather than explicitly calculating the…

In this tutorial, we focus on updating only specific fields of an existing document in Elasticsearch, rather than replacing the entire document. Although many people casually refer to this as a PATCH-style update, Elasticsearch actually performs this operation using a POST request to a dedicated _update endpoint. Understanding this distinction is important, because the behavior…

In this tutorial, we will understand how to update documents in Elasticsearch.This is an important topic because updating data behaves slightly differently compared to traditional relational databases. Step 1: First, Read the Existing Document Before updating anything, it is always a good idea to read the document first. Let’s assume we already have a document…

1. Getting a Document by ID This is the simplest and fastest way to read data from Elasticsearch. Syntax Example: Fetch a Book with ID = 3 This is a simple GET request where: Response Explanation You will get a response similar to this: Let’s understand what this means: This _source section is what we…

In this short but important section, we will understand how Elasticsearch internally stores documents, and how this structure differs from what we usually see in a relational database. This concept is extremely important because once you understand how Elasticsearch represents data internally, everything else—searching, updating, deleting, and versioning—starts making much more sense. How Data Looks…

Understanding What “Creating a Document” Means in Elasticsearch In Elasticsearch, data is stored in the form of documents, and these documents are stored inside an index. You can think of this analogy: However, Elasticsearch is schema-flexible, meaning it does not strictly require a predefined structure like relational databases. Basic Idea of Adding a Document To…

In this section, we will start interacting directly with Elasticsearch by using the Index API. This is an extremely important step because it allows us to create, inspect, and delete indices, which form the foundation of everything we do in Elasticsearch. Opening Kibana Dev Tools To begin, we need access to the Kibana Dev Tools…