Full-text search engine concept in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Elasticsearch query?

Given the following Elasticsearch query, what will be the total number of hits returned?

Elasticsearch

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

A The query returns documents only if the description field contains the word "quick" and "fox" but not "brown".

B The query returns all documents containing the exact phrase "quick brown fox".

C The query returns documents that do not contain any of the words "quick", "brown", or "fox".

D The query returns documents containing any of the words "quick", "brown", or "fox" in the description field.

💡 Hint

Remember that match queries analyze the text and look for any matching terms.

The match query analyzes the input text and searches for documents containing any of the words. It does not require the exact phrase.

🧠 Conceptual

intermediate

Which Elasticsearch component is responsible for scoring documents?

In Elasticsearch, which component calculates how relevant a document is to a search query?

A The Scoring Algorithm (e.g., BM25)

B The Inverted Index

C The Document Store

D The Query Parser

💡 Hint

Think about how Elasticsearch ranks documents by relevance.

The scoring algorithm, such as BM25, calculates a relevance score for each document based on the query terms and document content.

🔧 Debug

advanced

Why does this Elasticsearch query return zero hits?

Consider this query that returns zero hits even though documents exist with the term "Elasticsearch" in the title:

{
  "query": {
    "term": {
      "title": "Elasticsearch"
    }
  }
}

Why does it return zero hits?

A Because the <code>term</code> query only works on numeric fields, not text fields.

B Because the query syntax is invalid and causes a parsing error.

C Because the <code>term</code> query does not analyze the input and the field is analyzed, so the exact term "Elasticsearch" does not match the indexed tokens.

D Because the field "title" does not exist in the index mapping.

💡 Hint

Think about how term queries differ from match queries regarding analysis.

The term query looks for exact terms without analyzing the input. If the field is analyzed (broken into tokens and lowercased), the exact term "Elasticsearch" may not exist as is in the index.

📝 Syntax

advanced

Which option is a valid Elasticsearch bool query combining must and must_not?

Choose the correct JSON syntax for a bool query that must match "python" in the content field and must not match "java" in the content field.

A { "bool": { "must": [ { "match": { "content": "python" } } ], "must_not": [ { "match": { "content": "java" } } ] } }

B { "bool": { "must": { "match": { "content": "python" } }, "must_not": [ { "match": { "content": "java" } } ] } }

C { "bool": { "must": { "match": { "content": "python" } }, "must_not": { "match": { "content": "java" } } } }

D { "bool": { "must": [ { "match": { "content": "python" } } ], "must_not": { "match": { "content": "java" } } } }

💡 Hint

Remember that must and must_not expect arrays of queries.

The must and must_not clauses require arrays of query objects, even if there is only one query.

🚀 Application

expert

How to implement a phrase search with slop in Elasticsearch?

You want to find documents where the phrase "quick fox" appears in the text field, but you want to allow up to 2 words between "quick" and "fox". Which query achieves this?

A { "match": { "text": { "query": "quick fox", "slop": 2 } } }

B { "match_phrase": { "text": { "query": "quick fox", "slop": 2 } } }

C { "term": { "text": "quick fox" } }

D { "match_phrase": { "text": { "query": "quick fox", "boost": 2 } } }

💡 Hint

Use the query type that supports phrase matching with slop.

The match_phrase query supports the slop parameter to allow words between the phrase terms. The match query does not support slop.

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