Search and recommendation in HLD - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main component responsible for storing searchable data in a search system.

HLD

The component that stores searchable data for quick retrieval is called the [1].

Drag options to blanks, or click blank then click option'

AIndex

BQuery Processor

CCache

DLoad Balancer

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing the query processor with the index.

Thinking cache stores searchable data permanently.

The Index stores documents to enable fast search retrieval.

2fill in blank

medium

Complete the code to specify the component that ranks search results based on relevance.

HLD

The component responsible for ordering search results by relevance score is the [1].

Drag options to blanks, or click blank then click option'

ARanker

BCrawler

CIndexer

DLoad Balancer

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the indexer with the ranker.

Assuming the crawler ranks results.

The Ranker assigns scores to results and sorts them to show the most relevant first.

3fill in blank

hard

Fix the error in the description of the recommendation system's data source.

HLD

A recommendation system primarily uses [1] data to suggest items to users.

Drag options to blanks, or click blank then click option'

Arandom

Btransactional

Cirrelevant

Dstatic

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing random data which is not meaningful.

Confusing static data with dynamic user data.

Recommendation systems rely on transactional data like user purchases or clicks to make suggestions.

4fill in blank

hard

Fill both blanks to complete the description of a common recommendation algorithm.

HLD

The [1] filtering algorithm recommends items based on [2] similarities between users.

Drag options to blanks, or click blank then click option'

ACollaborative

Bcontent

Cbehavioral

Ddemographic

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing content filtering with collaborative filtering.

Using demographic instead of behavioral similarities.

Collaborative filtering uses behavioral similarities (like past purchases) between users to recommend items.

5fill in blank

hard

Fill all three blanks to complete the architecture description for a scalable search system.

HLD

User requests first hit the [1], then are routed to the [2] which queries the [3] for results.

Drag options to blanks, or click blank then click option'

ALoad Balancer

BSearch Server

CIndex

DCache

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up cache with index as the main data store.

Confusing search server with load balancer.

Requests go to the Load Balancer, then to the Search Server, which queries the Index to find results.