Full-text search engine concept in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an index named 'books'.

Elasticsearch

PUT /[1]

Drag options to blanks, or click blank then click option'

Abooks

Blibrary

Ctexts

Ddocuments

Attempts:

3 left

💡 Hint

Common Mistakes

Using a name unrelated to the data like 'library' or 'texts'.

Forgetting to use the PUT method.

The command PUT /books creates an index named 'books' in Elasticsearch.

2fill in blank

medium

Complete the code to add a document with id '1' to the 'books' index.

Elasticsearch

PUT /books/_doc/[1]
{
  "title": "Elasticsearch Basics",
  "author": "John Doe"
}

Drag options to blanks, or click blank then click option'

A1

Bdoc1

Cbook1

D001

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string like 'doc1' instead of the numeric id '1'.

Omitting the document id.

The document id is '1', so the URL should end with /_doc/1.

3fill in blank

hard

Fix the error in the search query to find documents with 'Elasticsearch' in the title.

Elasticsearch

GET /books/_search
{
  "query": {
    "match": {
      "title": "[1]"
    }
  }
}

Drag options to blanks, or click blank then click option'

AElastic search

Belastic_search

CElasticsearch

Delasticsearch basics

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Elastic search' with a space breaks the match.

Using underscores or extra words changes the search meaning.

The correct search term is exactly 'Elasticsearch' to match the title field.

4fill in blank

hard

Fill both blanks to create a query that searches for 'John' in the author field and sorts results by 'publish_date' descending.

Elasticsearch

GET /books/_search
{
  "query": {
    "match": {
      "[1]": "John"
    }
  },
  "sort": [
    {"[2]": {"order": "desc"}}
  ]
}

Drag options to blanks, or click blank then click option'

Aauthor

Btitle

Cpublish_date

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Searching in the wrong field like 'title'.

Sorting by a non-existent or incorrect field.

The query searches the 'author' field for 'John' and sorts by 'publish_date' in descending order.

5fill in blank

hard

Fill the blanks to create an aggregation that counts documents per 'genre' and filters genres with more than 5 documents.

Elasticsearch

GET /books/_search
{
  "size": 0,
  "aggs": {
    "genres_count": {
      "terms": {
        "field": "[1]",
        "min_doc_count": [2]
      }
    }
  },
  "query": {
    "match_all": {}
  }
}

Drag options to blanks, or click blank then click option'

Aauthor

Bgenre

C5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong field like 'author' for aggregation.

Setting 'min_doc_count' too high or too low.

The aggregation counts documents by 'genre' field and filters to only include genres with at least 5 documents.

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