Database & Storage Deep Dive

From a leaderless KV store to an object store to a global event log: how data is stored, replicated, and retrieved at scale across fundamentally different storage engines.

After this path you will be able to

Explain the difference between a cache, a log, an object store, and a relational DB, and know which one to reach for given a workload's access patterns, consistency requirements, and scale.

Interview approach for this path

1.Start by characterizing the workload: read-heavy vs write-heavy, random vs sequential access, hot spots or uniform distribution.
2.Pick the right storage engine and explain why: relational for joins and transactions, key-value for sub-millisecond lookups, wide-column for time-series writes, object store for large blobs.
3.Address replication upfront: how many copies, sync or async, and what is the RPO if the primary dies?
4.Explain your sharding strategy: what is the partition key, why does it distribute load evenly, and how do you handle hot keys?
5.Discuss consistency requirements: does every read need the latest write, or is eventual consistency acceptable for this workload?
6.Address indexes: which columns, what type (B-tree vs LSM), and what is the write amplification cost?

Systems in this path

4 total

Concepts reinforced throughout

Replication Consistent Hashing Sharding / Data Partitioning Message Queues CAP Theorem

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How users find things: from the instant suggestions in a search box to the inverted index behind full-text search, location-based ranking, and the crawler that feeds it all.

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