InterviewBit Problems   Design Cache

Is Latency a very important metric for us? (5)
Cached data is replicated as well? (1)
What QPS would a machine have to handle if we shard in blocks of 72GB? (7)
Is this something related to web cache? (1)
What will be more interesting to have expire time as one more dimension to the problem? (3)
One point is missing - How to handle cache eviction policy (2)
Using Go constructs (1)
How did we come up with the conclusion that 174us is less enough for a hashmap read here? (1)
How do we compute QPS (1)
How to mark these questions as done (1)
Are we expected to do these calculation (1)
Number of machines required per shard (2)
How should we modify our approach if we also have to evict keys at some stage? (6)
Query per second with retries (1)
What is QPS in the above problem? (2)
I have a doubt, please correct me if I am wrong. We divided 30 TB of data into 42 (4)
When the Cache is full, which element should be removed first? (2)
So what are the features? (1)
How did the factor of 4 is arrived? (2)
Locks on doubly link list (1)
Latency is always an important metric for distributed and scalable systems. Can t (3)
How would you break down cache write and read into multiple instructions? (3)
What is actually meaning of distributed here? all data spread across multiple nod (1)
Now that we have sorted how things look on a single server, how do we shard? (1)
Partitioning and fault tolerance are important topics that must have been covered (1)
I clicked on one of the links for the implementation of the least recently used c (1)
It is somewhat same as designing a distributed hash table (1)
I think in most of the interviews, candidate is expected to calculate Storage, RA (1)