Elasticsearch is a powerful open-source search and analytics engine built on Apache Lucene. It's renowned for its speed, scalability, and ability to handle vast datasets. This blog series will guide you through the process of building a custom search engine using Elasticsearch. We'll explore the essential concepts, steps, and code examples to help you get started.
Before diving into code, let's install Elasticsearch. You can download the latest version from the official website (https://www.elastic.co/downloads/elasticsearch). Once downloaded, extract the archive and follow the instructions for your operating system to start Elasticsearch. You can verify if it's running by accessing the default port (9200) in your browser. If you see a JSON response, Elasticsearch is up and running.
An index in Elasticsearch is like a database in traditional relational databases. To create an index, use the following curl command:
curl -X PUT "http://localhost:9200/my-index" -H 'Content-Type: application/json' -d'
{
"settings": {
"number_of_shards": 5,
"number_of_replicas": 1
}
}
'
This creates an index named "my-index" with 5 shards and 1 replica. Shards distribute data across multiple nodes for better performance and scalability. Replicas provide redundancy in case of node failure.
To index data into Elasticsearch, you'll need to prepare it in a structured format. JSON (JavaScript Object Notation) is commonly used for its flexibility and human-readability. For example, here's a JSON document representing a product:
{
"product_id": "12345",
"name": "Laptop",
"brand": "Acer",
"price": 799.99,
"category": "Electronics"
}
You can add this document to the "my-index" using curl:
curl -X POST "http://localhost:9200/my-index/_doc" -H 'Content-Type: application/json' -d'
{
"product_id": "12345",
"name": "Laptop",
"brand": "Acer",
"price": 799.99,
"category": "Electronics"
}
'
This will create a new document in the "my-index" index. You can repeat this process for all your data.