---
title: Document Key Decisions with Context
impact: LOW
impactDescription: preserves decision rationale for future reference
tags: doc, decisions, adr, context
---

## Document Key Decisions with Context

Record significant decisions with their context, options considered, and rationale. Without decision records, teams re-debate settled issues, and new members don't understand why things are done certain ways.

**Incorrect (decisions without context):**

```markdown
## Notes

- Decided to use PostgreSQL
- Will implement caching
- Chose React for frontend

// 6 months later:
// New developer: "Why PostgreSQL instead of MongoDB?"
// Team: "I think there was a reason..."
// Result: Re-evaluate decision, waste time, or wrong assumptions
```

**Correct (Architecture Decision Records):**

```markdown
## ADR-005: Database Selection for User Dashboard

### Status
**Accepted** - 2024-02-15

### Context
We need to select a database for the new User Dashboard feature. The
dashboard displays aggregated analytics data with the following
characteristics:
- Read-heavy workload (95% reads, 5% writes)
- Complex aggregation queries (time series, grouping)
- 10M+ rows expected within 1 year
- Need for ACID transactions for billing data
- Team expertise primarily in relational databases

### Decision Drivers
1. Query complexity (aggregations, joins)
2. Team expertise and hiring pool
3. Operational complexity
4. Cost at scale
5. Ecosystem and tooling

### Options Considered

#### Option A: PostgreSQL
**Pros:**
- Excellent aggregation support (window functions, CTEs)
- Strong ACID compliance
- Team has 5+ years experience
- Rich ecosystem (pgAdmin, pgBouncer, Citus for scale)
- Active community, well-documented

**Cons:**
- Scaling requires more planning than NoSQL
- Connection pooling needed at scale

#### Option B: MongoDB
**Pros:**
- Flexible schema for evolving requirements
- Built-in sharding
- Good for document-oriented data

**Cons:**
- Aggregation pipeline less intuitive than SQL
- Team would need training
- ACID only at document level
- Joins require application-level handling

#### Option C: TimescaleDB (PostgreSQL extension)
**Pros:**
- Optimized for time-series data
- Compatible with PostgreSQL
- Automatic partitioning

**Cons:**
- Additional operational complexity
- May be premature optimization
- Smaller community than vanilla PostgreSQL

### Decision
**Selected: Option A - PostgreSQL**

### Rationale
1. **Query needs:** Our analytics require complex joins and aggregations
   that PostgreSQL handles natively with excellent performance.

2. **Team expertise:** Our team has deep PostgreSQL knowledge. MongoDB
   would require 2-3 weeks of training with ongoing learning curve.

3. **ACID requirements:** Billing data requires strong consistency.
   PostgreSQL provides this across tables, not just documents.

4. **Scale path:** At our projected 10M rows, PostgreSQL performs well.
   If we grow beyond, options include read replicas, Citus extension,
   or migration to TimescaleDB (compatible).

5. **Ecosystem:** Better tooling for our use case (Metabase, Grafana
   integration, migration tools).

### Consequences

**Positive:**
- Team can start immediately without training
- Leverage existing monitoring and backup infrastructure
- Straightforward aggregation query development

**Negative:**
- Must plan connection pooling for high concurrency
- May need to revisit for time-series optimization later
- Schema migrations require more care than schemaless DB

### Related Decisions
- ADR-003: Caching Strategy (impacts read patterns)
- ADR-007: Data Retention Policy (impacts table size)

### Review Date
Re-evaluate in 12 months or when data exceeds 50M rows.

---

## ADR Template

```markdown
## ADR-[NUMBER]: [TITLE]

### Status
[Proposed | Accepted | Deprecated | Superseded by ADR-XXX]

### Context
[What is the situation? What forces are at play?]

### Decision Drivers
[What criteria matter most?]

### Options Considered
[List options with pros/cons]

### Decision
[What was decided?]

### Rationale
[Why this option over others?]

### Consequences
[What are the positive and negative results?]

### Related Decisions
[Links to related ADRs]

### Review Date
[When to revisit this decision]
```
```

**Decision record requirements:**
- Record significant technical and product decisions
- Include context and constraints
- Document options considered
- Explain rationale clearly
- Note consequences and review timeline

Reference: [ADR GitHub - Architecture Decision Records](https://adr.github.io/)
