Full-text search engine concept in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a full-text search engine?

A full-text search engine helps find words or phrases inside large amounts of text quickly. It looks inside documents, not just titles or tags.

intermediate

What is an inverted index in full-text search?

An inverted index is like a big dictionary that tells which documents contain each word. It helps the search engine find matches fast.

intermediate

How does Elasticsearch handle text analysis before searching?

Elasticsearch breaks text into words (tokens), lowers case, removes common words (stop words), and may stem words to their root form to improve search matches.

intermediate

What is relevance scoring in full-text search?

Relevance scoring ranks search results by how well they match the query. Higher scores mean better matches, so the best results show first.

beginner

Why is full-text search different from simple keyword search?

Full-text search understands language better. It can find words inside text, handle word forms, and rank results by importance, unlike simple keyword search that looks for exact matches only.

What does an inverted index store?

A Ranking scores only

B Documents and their sizes

C User search history

D Words and the documents they appear in

An inverted index maps each word to the list of documents containing it, enabling fast search.

Which process breaks text into smaller pieces for searching?

A Indexing

B Tokenization

C Ranking

D Caching

Tokenization splits text into words or tokens, which are then indexed for search.

What is the purpose of stop words in full-text search?

A Common words removed to improve search speed and quality

B Words that boost ranking

C Words that are always searched

D Words that are misspelled

Stop words like 'the' or 'and' are often removed because they don't help find meaningful results.

How does Elasticsearch improve search result relevance?

A By scoring and ranking results based on match quality

B By showing results randomly

C By only showing exact matches

D By ignoring word order

Elasticsearch uses scoring algorithms to rank results so the best matches appear first.

Which feature allows full-text search to find different forms of a word?

A Indexing

B Caching

C Stemming

D Tokenization

Stemming reduces words to their root form, so 'running' and 'runs' match the same base word.

Explain how an inverted index helps a full-text search engine find results quickly.

Think about a dictionary that tells you where to find each word.

Describe the steps Elasticsearch takes to analyze text before searching.

Consider how raw text is prepared to match search queries better.

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