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Database Performance and Indexing ​

Database Performance and Indexing Graphics Coverage ​

Primary chapter graphic: Database Performance Investigation Map, SQL Query Execution Pipeline, Database Index Structure Choices, SQL Logical Clause Order. Accepted graphics: 4. Reviewed non-signal pages: 2. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.

Corpus pages: p. 47, p. 155, p. 202-203, p. 227, p. 233, p. 240, p. 414 Coverage: 8 pages

This chapter is part of Marius's owned architecture build corpus. The text routes decisions; durable implementation signal is carried by accepted graphics, reviewed non-signal decisions, and the linked QA audit.

Chapter Visuals ​

Accepted graphics carry the canonical design signal for this chapter. Each selected source page is either accepted as a graphic or explicitly marked non-signal in the source-faithful ledger. Review and QA state live in visual inventory, visual review ledger, and graphics audit.

Database Performance Investigation Map ​

Database Performance Investigation Map

SQL Query Execution Pipeline ​

SQL Query Execution Pipeline

Database Index Structure Choices ​

Database Index Structure Choices

SQL Logical Clause Order ​

SQL Logical Clause Order

Open Review Queue ​

  • none

Reviewed Non-Signal Pages ​

  • Algorithms And Decision Models: Index + Map Map: source p. 241; batch 37; status non-signal/reviewed; ledger reason in visual-review-ledger.json
  • Database Performance And Indexing: Index + Map Map: source p. 414; batch 41; status non-signal/reviewed; ledger reason in visual-review-ledger.json

Use When ​

  • Database latency, throughput, query shape, or scaling pressure affects product behavior.

Avoid When ​

  • The data model is wrong and needs redesign before tuning.

Core Model ​

  • Performance follows access patterns. Indexes, partitions, pools, and replicas optimize known paths.
  • Prefer explicit ownership over accidental coupling. Every boundary should say who owns correctness, cost, data, recovery, and change.
  • Use corpus page pointers for inspection, and keep the chapter notes focused on reusable design decisions.

Implementation Guidance ​

  • Capture the slow query, expected cardinality, filters, sort order, write cost, and freshness need.
  • Write the smallest useful design note: purpose, inputs, outputs, state, failure behavior, observability, and rollback.
  • Choose the first implementation that can be tested against the real workflow without hiding a known production risk.

Tradeoffs ​

  • Indexes speed reads but add write cost and migration complexity.
  • Centralization reduces duplicated work but can become a bottleneck when every team needs exceptions.
  • Specialized infrastructure helps at scale, but it must earn its operational cost.

Failure Modes ​

  • A query is optimized for a demo-sized table and collapses under real cardinality.
  • The diagram shows boxes but not ownership, retry behavior, data freshness, or user-visible failure.
  • The system has no proof path for the highest-risk assumption.

Decision Checklist ​

  • Inspect execution plans, add targeted indexes, cap result size, and monitor write amplification.
  • Name the owner, source of truth, timeout, retry policy, and evidence that the path works.
  • Add one regression check for the failure mode most likely to recur.

Neutral Automation Examples ​

  • A customer list adds a compound index for tenant and updated time instead of scanning all records.
  • A neutral internal automation starts with fixtures, then adds credentials, permissions, and production scheduling only after the boundary is tested.
  • A customer-facing workflow keeps irreversible actions behind explicit approval until metrics show it is safe to automate further.