Inverted index data structure in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the primary purpose of an inverted index in Elasticsearch?

Elasticsearch uses an inverted index to improve search speed. What does this index mainly help with?

A Storing documents in the order they were added

B Mapping terms to the documents that contain them

C Indexing documents by their creation date

D Encrypting document contents for security

💡 Hint

Think about how search engines find documents quickly by words.

An inverted index maps each term to the list of documents containing that term, enabling fast full-text search.

❓ query_result

intermediate

What documents will match this Elasticsearch query?

Given an inverted index with documents:
Doc1: "apple banana"
Doc2: "banana cherry"
Doc3: "apple cherry"

Which documents will match the query {"match": {"content": "apple"}}?

A Doc2 only

B Doc3 only

C Doc1, Doc2, and Doc3

D Doc1 and Doc3

💡 Hint

Look for documents containing the word 'apple'.

The query matches documents containing 'apple', which are Doc1 and Doc3.

📝 Syntax

advanced

Identify the syntax error in this Elasticsearch query

Which option contains a syntax error in the Elasticsearch query?

{"query": {"match": {"content": "apple"}}}

A {"query": {"match": {"content": apple}}}

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

C }}}"elppa" :"tnetnoc"{ :"hctam"{ :"yreuq"{

D {"query": {"match": {"content": "apple"}}}

💡 Hint

Check if all string values are properly quoted.

Option A misses quotes around the term 'apple', causing a syntax error.

❓ optimization

advanced

Which approach optimizes inverted index size in Elasticsearch?

To reduce the size of the inverted index, which method is best?

A Using stop words to exclude common terms

B Indexing all fields without filtering

C Storing full document source in the index

D Disabling compression on the index files

💡 Hint

Think about removing unnecessary words that appear very often.

Stop words are common words excluded from the index to save space and improve search speed.

🔧 Debug

expert

Why does this Elasticsearch query return no results despite matching documents existing?

Given documents indexed with terms 'apple' and 'banana', this query returns no results:

{"query": {"match": {"content": "Apple"}}}

Why?

A The inverted index is corrupted

B The field 'content' does not exist in the index

C The query is case-sensitive and 'Apple' does not match 'apple'

D The query syntax is invalid

💡 Hint

Consider how Elasticsearch handles case in text analysis.

Elasticsearch lowercases terms during indexing and querying by default, but if custom analyzers are used that preserve case, queries become case-sensitive.

Practice

1. What is the main purpose of an inverted index in Elasticsearch?

easy

A. To store documents in the order they were added

B. To map words to the documents where they appear

C. To compress data for storage efficiency

D. To encrypt documents for security

Solution

1. Step 1: Understand the role of inverted index

   An inverted index links each word to the documents containing it, enabling fast search.

2. Step 2: Compare options with definition

   Only To map words to the documents where they appear describes this mapping; others describe unrelated functions.

3. Final Answer:

   To map words to the documents where they appear -> Option B

4. Quick Check:

   Inverted index = word-to-document map [OK]

Hint: Remember: inverted index links words to documents [OK]

Common Mistakes:

• Thinking it stores documents in order
• Confusing with data compression
• Assuming it encrypts data

2. Which of the following is the correct way Elasticsearch uses an inverted index?

easy

A. It stores documents in a tree structure

B. It stores a list of words for each document

C. It stores documents as plain text files

D. It stores a list of documents for each unique word

Solution

1. Step 1: Recall inverted index structure

   Inverted index keeps a list of documents for each unique word found in the data.

2. Step 2: Match options to structure

   It stores a list of documents for each unique word matches this exactly; others describe different or incorrect storage methods.

3. Final Answer:

   It stores a list of documents for each unique word -> Option D

4. Quick Check:

   Inverted index = word to document list [OK]

Hint: Think: word first, then documents list [OK]

Common Mistakes:

• Confusing inverted index with forward index
• Assuming documents are stored as trees
• Thinking documents are stored as plain text only

3. Given these documents indexed in Elasticsearch:
Doc1: "apple banana apple"
Doc2: "banana orange"
What will the inverted index entry for the word banana contain?

medium

A. [Doc1, Doc2]

B. [Doc1]

C. [Doc2]

D. [] (empty list)

Solution

1. Step 1: Identify documents containing 'banana'

   Doc1 contains "banana" once, Doc2 contains "banana" once.

2. Step 2: List documents in inverted index for 'banana'

   Both Doc1 and Doc2 should be listed for the word "banana".

3. Final Answer:

   [Doc1, Doc2] -> Option A

4. Quick Check:

   banana appears in Doc1 and Doc2 [OK]

Hint: Check all docs containing the word, not just one [OK]

Common Mistakes:

• Listing only one document
• Confusing word frequency with document list
• Leaving the list empty

4. You have an inverted index that incorrectly shows the word orange linked only to Doc1, but Doc2 also contains "orange". What is the likely cause?

medium

A. The indexing process missed updating the inverted index for Doc2

B. The word "orange" does not exist in Doc2

C. Elasticsearch does not support multiple documents per word

D. The inverted index only stores the first document per word

Solution

1. Step 1: Verify word presence in documents

   Since "orange" is in Doc2, it should appear in the inverted index for that word.

2. Step 2: Identify indexing error

   If Doc2 is missing, likely the indexing process did not update the inverted index correctly for Doc2.

3. Final Answer:

   The indexing process missed updating the inverted index for Doc2 -> Option A

4. Quick Check:

   Missing doc means indexing error [OK]

Hint: Missing docs usually mean indexing skipped them [OK]

Common Mistakes:

• Assuming word is not in Doc2
• Thinking Elasticsearch limits docs per word
• Believing inverted index stores only one doc

5. You want to optimize search speed for a large text dataset in Elasticsearch. Which approach using the inverted index is best?

hard

A. Disable the inverted index to save storage space

B. Store all documents as plain text and scan each on search

C. Use the inverted index to quickly find documents containing search words

D. Index only the first word of each document

Solution

1. Step 1: Understand inverted index purpose

   It allows fast lookup of documents by words, speeding up search.

2. Step 2: Evaluate options for search speed

   Use the inverted index to quickly find documents containing search words uses the inverted index as intended; others slow search or lose data.

3. Final Answer:

   Use the inverted index to quickly find documents containing search words -> Option C

4. Quick Check:

   Inverted index speeds search by word lookup [OK]

Hint: Use inverted index for fast word-based search [OK]

Common Mistakes:

• Thinking scanning all text is faster
• Disabling inverted index to save space
• Indexing only part of documents