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LLDsystem_design~10 mins

Search and filter design in LLD - Interactive Code Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to define the main component responsible for handling search queries.

LLD
class [1]Handler:
    def __init__(self, data_source):
        self.data_source = data_source

    def search(self, query):
        # Implementation to search data_source
        pass
Drag options to blanks, or click blank then click option'
AQuery
BFilter
CSearch
DResult
Attempts:
3 left
💡 Hint
Common Mistakes
Using generic names like 'ResultHandler' which do not clearly indicate search responsibility.
2fill in blank
medium

Complete the code to add a filter method that narrows down results based on a condition.

LLD
class FilterHandler:
    def __init__(self, items):
        self.items = items

    def filter(self, condition):
        return [item for item in self.items if item.[1](condition)]
Drag options to blanks, or click blank then click option'
Afilters
Bmatches
Cequals
Dcontains
Attempts:
3 left
💡 Hint
Common Mistakes
Using 'filters' which is not a valid method on items.
Using 'equals' which checks for exact match, not containment.
3fill in blank
hard

Fix the error in the code to correctly combine search and filter results.

LLD
def search_and_filter(data, query, filter_condition):
    search_results = [item for item in data if query in item]
    filtered_results = [item for item in search_results if item.[1](filter_condition)]
    return filtered_results
Drag options to blanks, or click blank then click option'
Afilter
Bcontains
Cequals
Dmatches
Attempts:
3 left
💡 Hint
Common Mistakes
Using 'filter' which is not a method on string or list items.
Using 'equals' which requires exact match.
4fill in blank
hard

Fill both blanks to create a dictionary comprehension that maps items to their lengths only if length is greater than 3.

LLD
length_map = {item: len(item) for item in items if len(item) [1] 3 and 'a' [2] item}
Drag options to blanks, or click blank then click option'
A>
B<
Cin
Dnot in
Attempts:
3 left
💡 Hint
Common Mistakes
Using '<' instead of '>' for length comparison.
Using 'not in' instead of 'in' for membership check.
5fill in blank
hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 10.

LLD
filtered_dict = {item[1]: value for item, value in data.items() if value [2] 10 and item[3]('x')}
Drag options to blanks, or click blank then click option'
A.upper()
B>
Cendswith
Dstartswith
Attempts:
3 left
💡 Hint
Common Mistakes
Using 'endswith' instead of 'startswith' for key filtering.
Using '<' instead of '>' for value comparison.

Practice

(1/5)
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