Search functionality design in LLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the primary goal of search functionality in a system?

To quickly and accurately find relevant information from a large set of data based on user queries.

intermediate

Explain the role of an inverted index in search systems.

An inverted index maps each word to the list of documents containing it, enabling fast full-text search by avoiding scanning all documents.

beginner

What is the difference between synchronous and asynchronous search queries?

Synchronous queries block the user until results return, while asynchronous queries allow the user to continue interacting while results load in the background.

beginner

Why is relevance ranking important in search results?

It orders results so the most useful or relevant items appear first, improving user satisfaction and efficiency.

intermediate

Name two common techniques to scale search functionality for large data volumes.

1. Sharding the index across multiple servers. 2. Caching frequent queries and results.

What data structure is commonly used to speed up full-text search?

A Linked list

B Binary tree

C Inverted index

D Hash map

An inverted index maps words to documents, enabling fast full-text search.

Which of the following improves search result relevance?

A Caching

B Data encryption

C Load balancing

D Relevance ranking

Relevance ranking orders results by usefulness to the user.

What is a benefit of asynchronous search queries?

A User can continue interacting while results load

B Results are always more accurate

C Uses less memory

D Blocks user input until complete

Asynchronous queries allow non-blocking user experience.

Which technique helps scale search for very large datasets?

A Using a single large server

B Sharding the index

C Removing caching

D Increasing query complexity

Sharding splits data across servers to handle large volumes.

What does an inverted index map?

A Words to documents

B Documents to users

C Queries to servers

D Users to sessions

It maps each word to the documents containing it.

Describe the key components and flow of a basic search functionality design.

Think about how a user’s search request travels through the system to get results.

Explain how you would scale search functionality to handle millions of queries per day.

Consider both data storage and query handling.

Practice

1. What is the main purpose of building an index in a search functionality system?

easy

A. To compress data for storage

B. To store user passwords securely

C. To display images faster on the screen

D. To quickly find data entries matching search keywords

Solution

1. Step 1: Understand the role of an index in search

   An index maps keywords to data entries, enabling fast lookup instead of scanning all data.

2. Step 2: Identify the correct purpose

   Since search needs to find matching data quickly, the index helps achieve this by direct access.

3. Final Answer:

   To quickly find data entries matching search keywords -> Option D

4. Quick Check:

   Index = Fast keyword lookup [OK]

Hint: Index means fast lookup, not storage or compression [OK]

Common Mistakes:

• Confusing index with data compression
• Thinking index stores passwords
• Assuming index speeds up image display

2. Which data structure is commonly used to implement a search index for keyword lookup?

easy

A. Hash map

B. Linked list

C. Stack

D. Queue

Solution

1. Step 1: Recall common data structures for fast lookup

   Hash maps provide average O(1) time for key-based access, ideal for mapping keywords to data.

2. Step 2: Eliminate other options

   Linked lists, stacks, and queues do not provide efficient direct lookup by key.

3. Final Answer:

   Hash map -> Option A

4. Quick Check:

   Hash map = Fast key lookup [OK]

Hint: Hash maps give fast key-based access, perfect for indexes [OK]

Common Mistakes:

• Choosing linked list which is slow for lookup
• Confusing stack or queue with key-value storage
• Ignoring average O(1) lookup time of hash maps

3. Consider a search system where the index maps keywords to document IDs. If the keyword 'apple' maps to [1, 3, 5] and 'banana' maps to [2, 3], what is the result of searching for documents containing both 'apple' and 'banana'?

medium

A. [1, 2, 3, 5]

B. [3]

C. [1, 5]

D. [2, 3, 5]

Solution

1. Step 1: Identify documents for each keyword

   'apple' maps to documents [1, 3, 5], 'banana' maps to [2, 3].

2. Step 2: Find intersection of document lists

   Documents containing both keywords are in both lists. Intersection of [1, 3, 5] and [2, 3] is [3].

3. Final Answer:

   [3] -> Option B

4. Quick Check:

   Intersection = [3] [OK]

Hint: Search AND means intersection of document lists [OK]

Common Mistakes:

• Merging lists instead of intersecting
• Confusing union with intersection
• Ignoring common documents

4. A search system uses a hash map to store keyword to document ID mappings. The code snippet below has a bug:

index = {}
keywords = ['apple', 'banana', 'apple']
docs = [1, 2, 3]
for i in range(len(keywords)):
    index[keywords[i]] = docs[i]
print(index)

What is the bug in this code?

medium

A. It overwrites previous document IDs for duplicate keywords

B. It uses a list instead of a dictionary

C. It does not initialize the index

D. It uses wrong loop range

Solution

1. Step 1: Analyze how index is updated

   The loop assigns index[keyword] = doc, so duplicate keywords overwrite previous values.

2. Step 2: Identify the bug

   For 'apple', first doc 1 is stored, then overwritten by doc 3, losing doc 1.

3. Final Answer:

   It overwrites previous document IDs for duplicate keywords -> Option A

4. Quick Check:

   Duplicate keys overwrite values in hash map [OK]

Hint: Duplicate keys overwrite values unless stored as list [OK]

Common Mistakes:

• Thinking loop range is wrong
• Assuming index is not initialized
• Confusing data structure type

5. You are designing a search system for a large online store with millions of products. To support fast search by keywords and handle high user traffic, which combination of design choices is best?

hard

A. Use a simple list of products and filter by keywords on the client side

B. Store all product data in a single SQL table and scan it for each search

C. Use an inverted index stored in a distributed NoSQL database with caching layers

D. Build an index only for the top 10 products and search others sequentially

Solution

1. Step 1: Consider scalability and speed needs

   Millions of products and high traffic require fast, scalable search with distributed storage and caching.

2. Step 2: Evaluate options

   Use an inverted index stored in a distributed NoSQL database with caching layers uses inverted index (fast keyword lookup), distributed NoSQL (scalable), and caching (speed). Others are slow or incomplete.

3. Final Answer:

   Use an inverted index stored in a distributed NoSQL database with caching layers -> Option C

4. Quick Check:

   Inverted index + distributed storage + cache = scalable fast search [OK]

Hint: Combine inverted index, distributed DB, and cache for scale [OK]

Common Mistakes:

• Choosing full table scan for large data
• Filtering on client side for millions of items
• Indexing only a small subset of data