Explain LSM Tree vs B-Tree.
LSM trees vs B-trees – LSM (Log-Structured Merge) trees optimize for high write throughput by batching updates, while B-trees balance reads and writes through in-place updates and structured indexing.
When to use/Use Cases
LSM trees are best for write-heavy workloads such as time-series data, logs, or messaging systems. B-trees excel in read-heavy or mixed workloads, such as relational databases and file indexing.
Example
A social platform storing millions of user actions daily benefits from an LSM tree for fast writes, while a banking system prefers a B-tree for reliable read performance.
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Why Is It Important
Understanding index structures directly impacts system scalability, latency, and throughput. Knowing both shows strong database design skills during interviews.
Interview Tips
Emphasize that LSM trees use write buffers, compaction, and Bloom filters, while B-trees rely on balanced nodes for quick lookups. Mention real-world usage patterns and when each is ideal.
Trade-offs
LSM trees: superior writes, slower reads. B-trees: faster lookups, costly random I/O on heavy writes.
Pitfalls
Avoid assuming one structure always wins.
Poorly tuned LSM compaction can hurt reads, while B-trees degrade under sustained high writes. Always align the index choice to workload.
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