Search and filter design in LLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of search and filter design in a system?

To help users quickly find relevant information by allowing them to search using keywords and narrow down results using filters.

intermediate

Name two common data structures used to optimize search operations.

Inverted indexes and tries are commonly used to speed up search queries by organizing data for quick lookup.

beginner

Why is pagination important in search and filter design?

Pagination limits the number of results shown at once, improving performance and user experience by avoiding overwhelming the user and reducing load on the system.

intermediate

Explain the difference between client-side and server-side filtering.

Client-side filtering happens in the user's browser after data is loaded, suitable for small datasets. Server-side filtering happens on the server before sending data, better for large datasets to reduce data transfer and improve speed.

intermediate

What role does caching play in search and filter systems?

Caching stores frequent search results temporarily to quickly serve repeated queries without reprocessing, improving response time and reducing server load.

Which data structure is best suited for full-text search optimization?

A Hash map

B Binary search tree

C Linked list

D Inverted index

Inverted indexes map words to their locations in documents, making full-text search efficient.

What is the main benefit of server-side filtering over client-side filtering?

A Improves browser rendering speed

B Reduces data sent over the network

C Allows offline filtering

D Simplifies UI design

Server-side filtering sends only relevant data, reducing network load and improving performance.

Why is pagination used in search results?

A To display all results on one page

B To filter results by date

C To limit the number of results shown at once

D To sort results alphabetically

Pagination breaks results into pages to improve usability and system performance.

Which technique helps speed up repeated search queries?

A Caching

B Sorting

C Filtering

D Indexing

Caching stores results of frequent queries to serve them faster next time.

What is a common challenge when designing filters for search systems?

A Ensuring filters are intuitive and relevant

B Making filters invisible to users

C Avoiding any user input

D Showing all data without filtering

Filters should be easy to use and help users find what they want quickly.

Describe how you would design a scalable search and filter system for an online store.

Think about how to handle large data and fast user queries.

Explain the trade-offs between client-side and server-side filtering in search systems.

Consider data size and where filtering happens.

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