Inverted index data structure in Elasticsearch - Mini Project: Build & Apply

Building an Inverted Index in Elasticsearch

📖 Scenario: You are creating a simple search system for a small library. The library wants to quickly find books by keywords in their titles and descriptions. To do this, you will build an inverted index using Elasticsearch, which helps find documents containing specific words fast.

🎯 Goal: Build an inverted index in Elasticsearch by creating an index with documents representing books. Then configure the index to analyze text fields properly, add documents, and finally verify the inverted index structure.

📋 What You'll Learn

Create an Elasticsearch index named library with appropriate mappings for text fields.

Add a setting to use the standard analyzer for text fields.

Index three book documents with exact titles and descriptions provided.

Use the _termvectors API to retrieve the inverted index data for a specific document.

💡 Why This Matters

🌍 Real World

Inverted indexes are the backbone of search engines, enabling fast keyword searches in large text collections like libraries, websites, and databases.

💼 Career

Understanding how to build and query inverted indexes in Elasticsearch is essential for roles in search engineering, data engineering, and backend development.

1

Create the Elasticsearch index with mappings

Create an Elasticsearch index called library with mappings for two fields: title and description. Both fields should be of type text.

Elasticsearch

{
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "description": { "type": "text" }
    }
  }
}
# Your code here to create the index named 'library' with these mappings

Need a hint?

Use the PUT method to create the index with the mappings section defining the fields.

2

Add analyzer settings to the index

Update the library index creation to include settings that specify the standard analyzer for both title and description fields.

Elasticsearch

PUT /library
{
  "settings": {
    "analysis": {
      "analyzer": {
        "default": {
          "type": "standard"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "description": { "type": "text" }
    }
  }
}
# Your code here to add settings with standard analyzer

Need a hint?

Include a settings section before mappings with the analyzer configuration.

3

Index three book documents

Add three documents to the library index with these exact data:
1. {"title": "Elasticsearch Basics", "description": "Learn the basics of Elasticsearch."}
2. {"title": "Advanced Elasticsearch", "description": "Deep dive into Elasticsearch features."}
3. {"title": "Search Engines", "description": "Overview of search engine technologies."}
Use document IDs 1, 2, and 3 respectively.

Elasticsearch

PUT /library/_doc/1
{
  "title": "Elasticsearch Basics",
  "description": "Learn the basics of Elasticsearch."
}

PUT /library/_doc/2
{
  "title": "Advanced Elasticsearch",
  "description": "Deep dive into Elasticsearch features."
}

PUT /library/_doc/3
{
  "title": "Search Engines",
  "description": "Overview of search engine technologies."
}
# Your code here to index these three documents

Need a hint?

Use PUT /library/_doc/{id} to add each document with the exact fields and values.

4

Retrieve inverted index data for a document

Use the _termvectors API to get the inverted index terms for the document with ID 1 in the library index. Request term vectors for the title and description fields.

Elasticsearch

GET /library/_termvectors/1
{
  "fields": ["title", "description"]
}
# Your code here to retrieve term vectors for document 1

Need a hint?

Use the GET /library/_termvectors/1 API with a JSON body specifying the fields.