Inverted index data structure in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an inverted index in Elasticsearch?

An inverted index is a data structure that maps each word (term) to the list of documents that contain it. It helps Elasticsearch quickly find documents matching search terms.

intermediate

How does an inverted index differ from a forward index?

A forward index maps documents to the words they contain, while an inverted index maps words to the documents they appear in. Elasticsearch uses inverted indexes for fast search.

beginner

Why is an inverted index efficient for search queries?

Because it directly links terms to documents, Elasticsearch can quickly find all documents containing a search term without scanning every document.

beginner

What role do tokens play in building an inverted index?

Tokens are the individual words or terms extracted from documents. Elasticsearch breaks text into tokens to build the inverted index mapping tokens to documents.

advanced

How does Elasticsearch handle updates to documents in the inverted index?

Elasticsearch creates a new segment with updated data and marks old documents as deleted. The inverted index is updated incrementally without rebuilding everything.

What does an inverted index map in Elasticsearch?

A Documents to users

B Documents to terms

C Terms to documents

D Terms to users

An inverted index maps each term (word) to the documents that contain it.

Why is an inverted index useful for search?

A It stores documents alphabetically

B It links terms directly to documents

C It compresses documents

D It sorts documents by date

Linking terms directly to documents allows fast retrieval of matching documents.

What is a token in the context of an inverted index?

A A word extracted from text

B A document ID

C A database key

D A user ID

Tokens are words or terms extracted from text to build the inverted index.

How does Elasticsearch update its inverted index when documents change?

A Rebuilds the entire index

B Deletes the old index and creates a new one

C Does not update the index

D Creates new segments and marks old documents deleted

Elasticsearch updates by adding new segments and marking old documents deleted for efficiency.

Which data structure is primarily used by Elasticsearch for fast text search?

A Inverted index

B Forward index

C Hash table

D Binary tree

Elasticsearch uses an inverted index to quickly find documents containing search terms.

Explain how an inverted index works in Elasticsearch and why it is important for search performance.

Think about how you find a word in a book index.

Describe the process Elasticsearch uses to update the inverted index when documents are added or changed.

Consider how a library adds new books without reorganizing all shelves.

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