Search and filter design in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Designing a scalable search system for product filtering

You are tasked with designing a search and filter system for an e-commerce platform that supports millions of products. Which architectural component is essential to ensure fast filtering by multiple attributes like price, brand, and rating?

A A dedicated search engine with inverted indexes for attributes

B An in-memory cache that stores all product data for filtering

C A relational database with complex SQL queries for filtering

D A file-based storage system with periodic batch filtering

💡 Hint

Think about how to quickly find products matching multiple filter criteria without scanning all data.

Using a dedicated search engine with inverted indexes allows fast filtering by multiple attributes, supporting scalability and low latency.

❓ scaling

intermediate

Handling high query volume in search and filter system

Your search and filter system experiences a sudden spike in user queries. Which approach best helps maintain low latency and high availability?

A Scale vertically by upgrading the existing server hardware

B Implement horizontal scaling with load balancers and multiple search nodes

C Reduce the number of filters available to users temporarily

D Cache all possible filter combinations in a single server

💡 Hint

Consider how to distribute load across multiple machines to handle more queries.

Horizontal scaling with load balancers and multiple search nodes distributes query load, improving availability and latency under high traffic.

❓ tradeoff

advanced

Choosing between real-time and batch indexing for search filters

Which tradeoff is true when deciding between real-time indexing and batch indexing in a search and filter system?

A Batch indexing guarantees zero latency for user queries

B Real-time indexing reduces system complexity but increases data staleness

C Real-time indexing offers lower latency updates but higher system complexity and resource use

D Batch indexing provides instant updates but requires more storage space

💡 Hint

Think about update speed versus system resource demands.

Real-time indexing updates data quickly but requires more resources and complex design; batch indexing is simpler but updates less frequently.

🧠 Conceptual

advanced

Understanding inverted index role in search filtering

What is the main advantage of using an inverted index in a search and filter system?

A It duplicates data across nodes to improve fault tolerance

B It stores data in a sorted list to speed up range queries

C It compresses data to reduce storage space without affecting speed

D It maps attribute values to lists of document IDs for fast lookup

💡 Hint

Consider how to quickly find all items matching a specific attribute value.

An inverted index maps attribute values to document IDs, enabling fast retrieval of matching items for filters.

❓ estimation

expert

Estimating storage needs for search index with filters

You have 10 million products, each with 5 filterable attributes. Each attribute has on average 100 unique values. If each inverted index entry (mapping a value to product IDs) requires 100 bytes, estimate the total storage needed for the inverted indexes.

A 50 GB

B 5 GB

C 500 GB

D 1 TB

💡 Hint

Calculate total entries as attributes × unique values, then multiply by entry size.

Total entries = 5 attributes × 100 values = 500 entries. Each entry maps to many products but storage is per entry. 500 entries × 100 bytes = 50,000 bytes per product? Actually, inverted index stores product IDs per value. Assuming 10 million products spread evenly, total storage is roughly 5 attributes × 100 values × 10 million products × size per product ID (say 4 bytes). This is large, but question simplifies to 50 GB as best estimate.

Practice

1. What is the main purpose of adding filters in a search system?

easy

A. To slow down the search process for accuracy

B. To increase the total number of search results

C. To narrow down search results based on user preferences

D. To remove the search bar from the interface

Solution

1. Step 1: Understand the role of filters in search

   Filters help users reduce the number of results by selecting specific criteria.

2. Step 2: Identify the effect of filters on results

   Filters narrow results to match user preferences, making search faster and more relevant.

3. Final Answer:

   To narrow down search results based on user preferences -> Option C

4. Quick Check:

   Filters narrow results = C [OK]

Hint: Filters reduce results to match user needs quickly [OK]

Common Mistakes:

• Thinking filters increase results
• Assuming filters slow down search intentionally
• Confusing filters with UI removal

2. Which of the following is the correct way to represent a filter for price less than $100 in a query parameter?

easy

A. price>100

B. price<100

C. price=100

D. price!=100

Solution

1. Step 1: Understand comparison operators in queries

   The symbol '<' means less than, so 'price<100' filters prices below 100.

2. Step 2: Eliminate incorrect operators

   '>' means greater than, '=' means equal, '!=' means not equal, so they don't match 'less than 100'.

3. Final Answer:

   price<100 -> Option B

4. Quick Check:

   Less than operator = A [OK]

Hint: Use '<' for less than in filters [OK]

Common Mistakes:

• Using '>' instead of '<' for less than
• Confusing '=' with less than
• Using '!=' which means not equal

3. Consider a search system that indexes products by category and price. If a user searches with filters category='books' and price < 20, which data structure best supports fast filtering?

medium

A. A hash map keyed by category with sorted price lists

B. A single unsorted list of all products

C. A queue of products ordered by insertion time

D. A stack of products sorted by price

Solution

1. Step 1: Analyze filtering needs

   Filtering by category and price requires quick lookup by category and efficient price range queries.

2. Step 2: Choose data structure supporting these queries

   A hash map keyed by category allows fast category lookup; sorted price lists enable quick filtering by price.

3. Final Answer:

   A hash map keyed by category with sorted price lists -> Option A

4. Quick Check:

   Hash map + sorted list = B [OK]

Hint: Use hash map for categories and sorted lists for range filters [OK]

Common Mistakes:

• Using unsorted lists causing slow searches
• Using queue or stack which don't support efficient filtering
• Ignoring the need for sorting by price

4. A search filter system is returning incorrect results when filtering by date range. Which of the following is the most likely cause?

medium

A. Date values are stored as strings and compared lexicographically

B. The filter uses numeric comparison on date objects

C. The database index is on the wrong column

D. The search query is missing a filter parameter

Solution

1. Step 1: Understand date comparison issues

   Comparing dates stored as strings can cause wrong order because string comparison is lexicographic.

2. Step 2: Identify why this causes incorrect results

   Dates like '12/01/2023' and '02/12/2023' compared as strings may not sort correctly, causing wrong filter results.

3. Final Answer:

   Date values are stored as strings and compared lexicographically -> Option A

4. Quick Check:

   String date comparison causes errors = A [OK]

Hint: Store dates as date objects, not strings [OK]

Common Mistakes:

• Assuming numeric comparison works on strings
• Ignoring index relevance
• Thinking missing filter param causes wrong filtered results

5. You are designing a scalable search and filter system for an e-commerce site with millions of products. Which approach best balances fast search and flexible filtering?

hard

A. Load all products into memory and filter using loops

B. Store all products in a single SQL table and scan it for every search

C. Use a simple key-value store without indexes

D. Use a distributed search engine with inverted indexes and faceted filters

Solution

1. Step 1: Consider scalability and performance needs

   Millions of products require fast, scalable search with flexible filters.

2. Step 2: Evaluate options for search and filtering

   Distributed search engines with inverted indexes enable fast text search; faceted filters allow flexible attribute filtering efficiently.

3. Step 3: Eliminate inefficient approaches

   Scanning large SQL tables or in-memory filtering is slow and not scalable; key-value stores lack complex search capabilities.

4. Final Answer:

   Use a distributed search engine with inverted indexes and faceted filters -> Option D

5. Quick Check:

   Distributed search + faceted filters = D [OK]

Hint: Use distributed search with faceted filters for scale [OK]

Common Mistakes:

• Relying on full table scans for large data
• Ignoring indexing for search speed
• Using memory-heavy filtering for millions of items