Practice
1. A web application needs to ensure that cache entries are updated immediately after every write to the database to prevent stale reads. Which caching pattern best fits this requirement?
easy
Solution
Step 1: Identify cache update timing
Write-Through updates cache and database synchronously, ensuring cache is always fresh.Step 2: Eliminate other patterns
Cache Aside updates cache only on misses, risking stale cache after writes; Read-Through only loads on reads; Write-Behind delays DB update, risking stale cache.Final Answer:
Option B -> Option BQuick Check:
Write-Through synchronously updates cache and DB [check]
Hint: Write-Through syncs cache and DB on writes [check]
Common Mistakes:
2. In the video transcoding pipeline, at step 4 the Transcoding Worker checks the job queue for new tasks. What happens if the queue is empty at this step?
easy
Solution
Step 1: Understand Transcoding Worker queue interaction
Transcoding Workers typically use message brokers with blocking or long-polling to avoid busy-waiting. Immediate shutdown (A) wastes throughput. Polling with delay (B) causes unnecessary load and latency. Backup queue in DB (D) is inefficient and not standard.Step 2: Recognize blocking on broker notification
Blocking on a message broker notification (e.g., Kafka consumer poll or RabbitMQ consumer wait) is efficient and standard to wait for new tasks.Final Answer:
Option D -> Option DQuick Check:
Blocking on broker notification avoids busy polling [check]
Hint: Workers block on broker notification, not busy poll [check]
Common Mistakes:
3. A distributed system requires a lock mechanism that ensures mutual exclusion across multiple Redis instances with minimal risk of deadlocks. Which component or approach best handles this requirement?
easy
Solution
Step 1: Understand distributed lock requirements
Single Redis instance with SETNX risks single point of failure and split-brain issues.Step 2: Evaluate Redlock's multi-instance coordination
Redlock uses multiple Redis masters to reduce risk of deadlocks and ensure safety under partial failures.Final Answer:
Option A -> Option AQuick Check:
Redlock coordinates multiple Redis masters for safer distributed locks [check]
Hint: Multiple Redis masters coordinated -> safer distributed locks [check]
Common Mistakes:
4. You need to model a social media feed with millions of users and high write throughput. Which data modeling approach is best to optimize for write scalability and read performance?
medium
Solution
Step 1: Analyze write scalability and read patterns
High write throughput and millions of users require scalable writes and efficient feed reads.Step 2: Evaluate referencing with denormalization
Referencing separates concerns; denormalizing popular posts into feeds optimizes read performance without large documents.Final Answer:
Option D -> Option DQuick Check:
Referencing plus denormalization balances write scale and read speed [check]
Hint: Reference plus denormalize popular data for scalable writes and fast reads [check]
Common Mistakes:
5. At 10x scale, which component is most likely to fail first due to caching, and why?
medium
Solution
Step 1: Identify scaling impact on cache
At 10x scale, cache memory pressure increases due to more data and entries.Step 2: Evaluate failure likelihood
Cache memory exhaustion is common before DB or network fail due to caching.Final Answer:
Option C -> Option CQuick Check:
Cache memory limits cause failure first at scale [check]
Hint: Cache memory exhaustion causes failure first at scale [check]
Common Mistakes: