Search functionality design in LLD - Architecture Diagram

System Overview - Search functionality design

This system allows users to search for information quickly and accurately. It must handle many users at once and return results fast, even when the data is large and frequently updated.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Search Service <-> Cache
  |
  v
Search Index
  |
  v
Primary Database

Components

User

client

Initiates search requests

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Routes requests to Search Service and handles authentication

Search Service

service

Processes search queries, checks cache, queries search index

Cache

cache

Stores recent search results for fast retrieval

Search Index

search_index

Optimized data structure for fast full-text search

Primary Database

database

Stores the original data and updates the search index

Request Flow - 11 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Search Service

Search Service → Cache

Cache → Search Service

Search Service → Search Index

Search Index → Search Service

Search Service → Cache

Search Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Search Service cannot retrieve cached results, causing increased load on Search Index and higher latency

Mitigation:System continues to function by querying Search Index directly; cache is rebuilt as new queries come in

Architecture Quiz - 3 Questions

Which component is responsible for distributing incoming user requests evenly?

A API Gateway

B Search Service

C Load Balancer

D Cache

Design Principle

This design uses caching to reduce load on the search index and speed up responses. The load balancer and API gateway ensure requests are managed efficiently and securely. Separating the search index from the primary database allows optimized search performance without affecting data storage.

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