Design a web crawler in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Identify the main components of a web crawler architecture

Which of the following lists correctly represents the essential components of a scalable web crawler system?

A Parser, Renderer, User Tracker, URL Frontier, Logger

B User Interface, Database, Cache, Logger, Scheduler

C Load Balancer, API Gateway, Authentication, Fetcher, Parser

D URL Frontier, Fetcher, Parser, URL Filter, Storage

💡 Hint

Think about the components that handle URL management, downloading pages, and storing data.

A web crawler needs to manage URLs to visit (URL Frontier), download pages (Fetcher), extract links and content (Parser), filter URLs to avoid duplicates or unwanted sites (URL Filter), and store the data (Storage).

❓ scaling

intermediate

Scaling the URL Frontier in a distributed crawler

What is the best approach to scale the URL Frontier component to handle billions of URLs efficiently?

A Partition URLs by domain and distribute queues across multiple servers

B Store all URLs in a relational database with ACID transactions

C Use a centralized queue stored on a single server with large memory

D Keep URLs in local files on each crawler node without coordination

💡 Hint

Consider how to avoid bottlenecks and balance load across servers.

Partitioning URLs by domain and distributing queues allows parallel processing and avoids a single bottleneck, making the system scalable.

❓ tradeoff

advanced

Tradeoffs in politeness and crawling speed

Which option best describes the tradeoff between politeness (respecting website rules) and crawling speed in a web crawler?

A Ignoring robots.txt and crawling aggressively increases speed but risks IP bans

B Politeness has no impact on crawling speed or system design

C Crawling slowly with delays respects politeness but reduces throughput

D Using multiple IPs allows fast crawling without any politeness concerns

💡 Hint

Think about how respecting website rules affects how fast you can crawl.

Respecting politeness means adding delays between requests to the same site, which reduces crawling speed but avoids overloading servers or getting banned.

🧠 Conceptual

advanced

Handling duplicate content in web crawling

What is the most effective method to detect and avoid storing duplicate pages in a web crawler system?

A Use hash functions on page content and compare hashes

B Compare full page content byte-by-byte before storing

C Store all pages and remove duplicates later manually

D Ignore duplicates and rely on URL uniqueness only

💡 Hint

Think about a fast way to check if content is the same without storing everything twice.

Using hash functions like MD5 or SHA on page content allows quick comparison to detect duplicates without storing full content multiple times.

❓ estimation

expert

Estimating storage needs for a large-scale web crawler

If a web crawler downloads 1 billion pages per year, each averaging 500 KB, what is the approximate storage needed per year (in terabytes) to store raw pages without compression?

A Approximately 180 TB

B Approximately 500 TB

C Approximately 15 TB

D Approximately 1000 TB

💡 Hint

Calculate total bytes: pages × size, then convert to terabytes (1 TB = 10^12 bytes).

1 billion pages × 500 KB = 10^9 × 500 × 10^3 bytes = 5 × 10^14 bytes. 5 × 10^14 / 10^12 = 500 TB.