# A Philosophy of Software Design vs Designing Data-Intensive Applications

Status: reviewed
Research basis: mini-only

Verdict: ✅ Complementary

Conflict: 10%
Overlap: 35%
Complementarity: 80%

## Loading Decision

Use together when the task changes state, events, schemas, queues, projections, caches, consistency, or ownership while also needing A Philosophy of Software Design pressure.

## Book A Pressure

- A Philosophy of Software Design should drive tasks that need module-depth, API-shape, information-hiding, and complexity-reduction judgment.
- Evidence: `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 3-5: applies to module design, API changes, decomposition, refactoring, naming, comments, tests, performance work, and changes where complexity spreads.

## Book B Pressure

- Designing Data-Intensive Applications should drive tasks where source of truth, consistency, durability, replay, schemas, replication, partitioning, or distributed failure dominate.
- Evidence: `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 3-5: applies where correctness depends on data ownership, consistency, durability, replication, partitioning, schema evolution, event flow, replay, or derived data.

## Complementary Forces

- Claim: A Philosophy of Software Design contributes module-depth, API-shape, information-hiding, and complexity-reduction pressure; Designing Data-Intensive Applications contributes source-of-truth, consistency, replay, schema-evolution, partitioning, and distributed-failure pressure. Together they are useful only where both scopes are active.
- Evidence:
  - `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 13-20: makes reduced complexity, deep modules, caller-oriented interfaces, hidden volatile details, downward-pulled complexity, right-sized generality, and complexity-based split/merge decisions central.
  - `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 34-42: fires on write paths, derived data, schema/API/event changes, retries/jobs/queues/replay, replica reads, partitioning, isolation choices, clock/lock/consensus assumptions, and data-intensive review risks.

## Overlap

- Claim: They overlap where both affect boundaries, explicit responsibilities, tests, coupling reduction, and avoiding hidden assumptions; the overlap score reflects how often an agent would receive similar pressure from both.
- Evidence:
  - `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 42-46: finishes by checking understanding effort, interface value, localized decisions, protected internals, and non-duplicative names/comments.
  - `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 46-55: checks source of truth, consistency/durability/staleness/conflicts, retry/replay/reordering, safe evolution, workload-matched storage, invariant-protecting isolation, rebuildable streams/projections, ownership-aligned services, observability, and no exactly-once wishful thinking.

## Conflicts

- Claim: The tension is over-modeling: the non-data rule set may improve structure, but DDIA requires explicit data semantics before abstractions hide source-of-truth or failure behavior.
- Evidence:
  - `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 7-10: corrects the false belief that small pieces, wrappers, patterns, or documentation are simple when they increase cognitive load.
  - `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 34-42: fires on write paths, derived data, schema/API/event changes, retries/jobs/queues/replay, replica reads, partitioning, isolation choices, clock/lock/consensus assumptions, and data-intensive review risks.

## Use Together When

- Use together when the other design concern changes source of truth, consistency, schema evolution, event flow, replay, derived data, partitions, or ownership boundaries.

## Prefer One When

- Prefer DDIA when consistency, schemas, replay, ordering, source of truth, or distributed data failure is the hard part; prefer the other book when those data semantics are not in scope.

## Source Basis

- `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 3-5: applies to module design, API changes, decomposition, refactoring, naming, comments, tests, performance work, and changes where complexity spreads.
- `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 7-10: corrects the false belief that small pieces, wrappers, patterns, or documentation are simple when they increase cognitive load.
- `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 13-20: makes reduced complexity, deep modules, caller-oriented interfaces, hidden volatile details, downward-pulled complexity, right-sized generality, and complexity-based split/merge decisions central.
- `a-philosophy-of-software-design/a-philosophy-of-software-design.mini.md` lines 42-46: finishes by checking understanding effort, interface value, localized decisions, protected internals, and non-duplicative names/comments.
- `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 3-5: applies where correctness depends on data ownership, consistency, durability, replication, partitioning, schema evolution, event flow, replay, or derived data.
- `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 7-9: corrects local-happy-path thinking about writes, reads, queues, caches, replicas, clocks, and downstream side effects.
- `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 34-42: fires on write paths, derived data, schema/API/event changes, retries/jobs/queues/replay, replica reads, partitioning, isolation choices, clock/lock/consensus assumptions, and data-intensive review risks.
- `designing-data-intensive-applications/designing-data-intensive-applications.mini.md` lines 46-55: checks source of truth, consistency/durability/staleness/conflicts, retry/replay/reordering, safe evolution, workload-matched storage, invariant-protecting isolation, rebuildable streams/projections, ownership-aligned services, observability, and no exactly-once wishful thinking.

## Review Notes

- External context was not used as decisive evidence for A Philosophy of Software Design vs Designing Data-Intensive Applications; the verdict is based on the cited local `mini` line ranges.
