Full-text search engine concept in Elasticsearch

Introduction

A full-text search engine helps you find words or phrases inside large amounts of text quickly and easily.

You want users to search articles or blog posts by keywords.

You need to find specific information inside product descriptions on a website.

You want to build a search box that can handle typos and different word forms.

You want to rank search results by how relevant they are to the query.

Syntax

Elasticsearch

{
  "query": {
    "match": {
      "field_name": "search text"
    }
  }
}

This is a basic search query in Elasticsearch using the match keyword.

Replace field_name with the text field you want to search.

Examples

Searches the title field for the words 'quick brown fox'.

Elasticsearch

{
  "query": {
    "match": {
      "title": "quick brown fox"
    }
  }
}

Searches the content field for the phrase 'Elasticsearch tutorial'.

Elasticsearch

{
  "query": {
    "match": {
      "content": "Elasticsearch tutorial"
    }
  }
}

Searches the description field for the words 'fast search'.

Elasticsearch

{
  "query": {
    "match": {
      "description": "fast search"
    }
  }
}

Sample Program

This example searches the summary field in the books index for the words 'adventure' and 'mystery'.

Elasticsearch will return books whose summaries contain these words, ranked by relevance.

Elasticsearch

POST /books/_search
{
  "query": {
    "match": {
      "summary": "adventure mystery"
    }
  }
}

Important Notes

Full-text search breaks text into words and finds matches, not just exact phrases.

It can handle different word forms, like 'run' and 'running'.

Search results are ranked by how well they match the query.

Summary

Full-text search helps find words inside large text quickly.

Elasticsearch uses queries like match to search text fields.

Results are ranked by relevance to show the best matches first.

Practice

1. What is the main purpose of a full-text search engine like Elasticsearch?

easy

A. To create user interfaces for websites

B. To store images and videos efficiently

C. To quickly find words or phrases inside large amounts of text

D. To perform mathematical calculations on numbers

Solution

1. Step 1: Understand full-text search

   Full-text search engines are designed to search text data quickly and efficiently.

2. Step 2: Identify the main use case

   They help find words or phrases inside large text collections, unlike storing media or calculations.

3. Final Answer:

   To quickly find words or phrases inside large amounts of text -> Option C

4. Quick Check:

   Full-text search = find words fast [OK]

Hint: Full-text search = fast text lookup [OK]

Common Mistakes:

• Confusing full-text search with media storage
• Thinking it does math calculations
• Assuming it builds website interfaces

2. Which Elasticsearch query is commonly used to search text fields for matching words?

easy

A. term

B. match

C. range

D. exists

Solution

1. Step 1: Review query types

   Elasticsearch has different queries: term for exact matches, range for numbers, exists for presence.

2. Step 2: Identify text search query

   The match query is designed to find words inside text fields using full-text search.

3. Final Answer:

   match -> Option B

4. Quick Check:

   Text search query = match [OK]

Hint: Use match query for text search in Elasticsearch [OK]

Common Mistakes:

• Using term query for full-text search
• Confusing range query with text search
• Using exists query to find words

3. Given this Elasticsearch query snippet, what does it do?

{
  "query": {
    "match": { "description": "fast car" }
  }
}

medium

A. Searches documents where the description field exists

B. Searches documents where the description contains the exact phrase "fast car"

C. Searches documents where the description contains both words "fast" and "car" in order

D. Searches documents where the description contains either "fast" or "car"

Solution

1. Step 1: Understand match query behavior

   The match query analyzes the input text and searches for documents containing any of the words.

2. Step 2: Analyze the query text "fast car"

   It looks for documents containing "fast" or "car", not necessarily as an exact phrase or order.

3. Final Answer:

   Searches documents where the description contains either "fast" or "car" -> Option D

4. Quick Check:

   Match query finds any matching words [OK]

Hint: Match query finds any word in text [OK]

Common Mistakes:

• Assuming match query searches exact phrase
• Thinking it requires words in order
• Confusing with exists query

4. You wrote this Elasticsearch query but it returns no results:

{
  "query": {
    "match": { "title": "" }
  }
}

What is the likely problem?

medium

A. The match query cannot search empty strings

B. The field name "title" is invalid syntax

C. Elasticsearch requires a range query for text fields

D. The query must use term instead of match

Solution

1. Step 1: Check the query input

   The match query is given an empty string "" to search, which means no words to match.

2. Step 2: Understand match query behavior with empty input

   Match queries need some text to find matches; empty strings return no results.

3. Final Answer:

   The match query cannot search empty strings -> Option A

4. Quick Check:

   Empty match query = no results [OK]

Hint: Match query needs non-empty text [OK]

Common Mistakes:

• Thinking field name syntax is wrong
• Using range query for text fields
• Confusing term and match query roles

5. You want to find documents where the text field "content" contains the word "apple" but not the word "fruit". Which Elasticsearch query structure should you use?

hard

A. { "query": { "bool": { "must": { "match": { "content": "apple" } }, "must_not": { "match": { "content": "fruit" } } } } }

B. { "query": { "match": { "content": "apple fruit" } } }

C. { "query": { "term": { "content": "apple" } } }

D. { "query": { "range": { "content": { "gte": "apple", "lte": "fruit" } } } }

Solution

1. Step 1: Understand the requirement

   You want documents containing "apple" but excluding those containing "fruit" in the same field.

2. Step 2: Use bool query with must and must_not

   The bool query allows combining conditions: must for required matches, must_not to exclude matches.

3. Step 3: Analyze options

   { "query": { "bool": { "must": { "match": { "content": "apple" } }, "must_not": { "match": { "content": "fruit" } } } } } uses bool with must match "apple" and must_not match "fruit", which fits the requirement.

4. Final Answer:

   { "query": { "bool": { "must": { "match": { "content": "apple" } }, "must_not": { "match": { "content": "fruit" } } } } } -> Option A

5. Quick Check:

   Bool must + must_not = include apple, exclude fruit [OK]

Hint: Use bool must and must_not for include/exclude text [OK]

Common Mistakes:

• Using match with both words includes fruit
• Using term query ignores text analysis
• Using range query on text fields is invalid