Guided Lab Brief

Storage Engines & Indexing Deep Dive

Model OLTP B-Tree paths, LSM-heavy ingestion, and secondary index tradeoffs in one architecture.

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Overview

Model OLTP B-Tree paths, LSM-heavy ingestion, and secondary index tradeoffs in one architecture.

Storage engine choices decide whether your system is read-fast, write-fast, or operationally painful.

You will build 6 architecture steps that model production dependencies.

You will run 1 failure experiment to observe bottlenecks and recovery behavior.

Success target: Read path stays stable under wave traffic while writes remain durable and index-backed queries stay fast.

Learning Objectives

  • Can explain B-Tree vs LSM tradeoffs with concrete read/write paths
  • Can justify when to add separate search index
  • Can defend cache hit-rate targets with latency impact

Experiments

  1. Drop cache hit rate to see storage amplification on read-heavy paths

Failure Modes to Trigger

  • Trigger: Drop cache hit rate to see storage amplification on read-heavy paths

    Observe: With low cache efficiency, most reads fall through to OLTP/search. Query latency climbs and storage pressure spikes.

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