tomevault-io System Architecture
Design systems for change. Every architecture decision answers: "How will this scale and evolve?"
Core Principle
There are no best practices—only trade-offs in context. The best architecture is the simplest one that meets current needs while enabling future growth.
Critical Rules
| Rule | Enforcement |
|---|---|
| Trade-offs over absolutes | No "best" - only "best for this context" |
| Simplicity that scales | Earn complexity, don't assume it |
| Decisions with rationale | ADRs for significant choices |
| Boundaries and contracts | Enable teams to move independently |
Architecture Evaluation
When evaluating architecture, always:
Understand context first
- Business requirements
- Team capabilities
- Constraints (time, budget, skills)
Identify 2-3 valid approaches
- Never present only one option
Analyze trade-offs explicitly
- What do you gain?
- What do you give up?
Think long-term
- What will be hard to change later?
Document the decision
- Use ADR format
Architecture Patterns
When to Use What
| Pattern | When | Trade-offs |
|---|---|---|
| Monolith | Small team, unclear domain boundaries, speed matters | Simple deployment, harder to scale teams |
| Modular Monolith | Growing team, clearer boundaries, want deployment simplicity | Structure without operational complexity |
| Microservices | Large org, independent team deployment, clear bounded contexts | Team autonomy, operational complexity |
| Serverless | Event-driven, variable load, minimal ops desire | Scaling built-in, cold start latency |
WRONG - Follow the Trend
"We should use microservices because that's what Netflix does."
Problem: Following trends without understanding context.
CORRECT - Context-Driven Decision
Given:
- Team of 5 developers
- Single deployment target
- Unclear domain boundaries still evolving
Recommendation: Modular monolith
Rationale: Microservices would add operational complexity
(service mesh, distributed tracing, deployment coordination)
without the benefit of independent team scaling.
When to revisit: If team grows >15 or we identify clear
bounded contexts with different scaling requirements.
Trade-Off Analysis Framework
For every significant decision, document:
| Dimension | Option A | Option B |
|---|---|---|
| Development speed | ||
| Operational complexity | ||
| Team independence | ||
| Consistency guarantees | ||
| Scaling characteristics | ||
| Cost (infra + people) |
ADR Template
# ADR-XXX: [Decision Title]
**Status:** Proposed | Accepted | Deprecated | Superseded
**Date:** YYYY-MM-DD
## Context
[What issue are we facing? What constraints exist?]
## Decision
[What did we decide?]
## Consequences
### Positive
- [Benefit]
### Negative
- [Drawback]
## Alternatives Considered
### [Option Name]
**Why rejected:** [Reason]
Example ADR
# ADR-001: Use PostgreSQL for primary data store
**Status:** Accepted
**Date:** 2024-01-15
## Context
We need a primary data store for user data, orders, and inventory.
Requirements: ACID transactions, complex queries, team familiarity.
## Decision
Use PostgreSQL 15 as the primary data store.
## Consequences
### Positive
- ACID guarantees for financial data
- Team has 5+ years PostgreSQL experience
- Rich ecosystem (PostGIS, pg_trgm, etc.)
- Proven at our expected scale (100k users)
### Negative
- Vertical scaling limits (can address with read replicas)
- Schema migrations require coordination
## Alternatives Considered
### MongoDB
**Why rejected:** Team lacks experience, eventual consistency
problematic for order processing.
### DynamoDB
**Why rejected:** Complex queries (reporting) would require
additional infrastructure. Cost unpredictable with access patterns.
Database Selection
| Type | Use When | Trade-offs |
|---|---|---|
| Relational (Postgres) | ACID needed, complex queries | Scaling complexity |
| Document (MongoDB) | Flexible schemas, embedded data | Weaker consistency |
| Key-Value (Redis) | Caching, sessions, fast lookups | Limited queries |
| Graph (Neo4j) | Relationship-heavy queries | Specialized |
| Time-Series (InfluxDB) | Metrics, events, IoT | Append-optimized |
Scalability Patterns
Order of Consideration
- Vertical scaling - Bigger machine (simplest)
- Caching - CDN → Application → Database
- Read replicas - Separate read/write traffic
- Horizontal scaling - Multiple instances
- Sharding - Partition data (most complex)
Resilience Patterns
| Pattern | Purpose |
|---|---|
| Retry with backoff | Handle transient failures |
| Circuit breaker | Prevent cascade failures |
| Bulkhead | Isolate failure domains |
| Timeout | Bound waiting time |
| Graceful degradation | Partial service over no service |
API Design Principles
REST
| Principle | Requirement |
|---|---|
| Resource modeling | Nouns, not verbs |
| HTTP semantics | GET reads, POST creates, PUT replaces |
| Versioning | URI (/v1/) or header |
| Pagination | Cursor-based for large sets |
| Error responses | Problem Details (RFC 7807) |
Event-Driven
| Consideration | Guidance |
|---|---|
| Event schema | Version events, use schema registry |
| Ordering | Partition key for ordering guarantees |
| Idempotency | Handle duplicate delivery |
| Dead letter | Handle poison messages |
Distributed Systems Fundamentals
CAP Theorem
Choose two: Consistency, Availability, Partition Tolerance.
In practice: During network partition, choose consistency OR availability.
Consistency Models
| Model | Meaning | Use When |
|---|---|---|
| Strong | All reads see latest write | Financial data |
| Eventual | All reads eventually see latest | Social feeds, caches |
| Causal | Cause-effect ordering preserved | Collaborative editing |
Integration
| Skill | Relationship |
|---|---|
design-principles |
Apply to architecture decisions |
pattern-enforcement |
Enforce boundaries with tooling |
documentation-standards |
Document architectural decisions |
Anti-Patterns
| Anti-Pattern | Why It's Wrong |
|---|---|
| Architecture astronauting | Designing for problems you don't have |
| Premature optimization | Optimize without data |
| Trend following | "Netflix does it" isn't a reason |
| Undocumented decisions | Become mysterious legacy constraints |
| Over-engineering | Complexity without justification |
| Ignoring team capabilities | Architecture must match team |
Decision Checklist
When making architecture decisions:
- Did I understand the context first?
- Did I identify multiple valid approaches?
- Did I analyze trade-offs explicitly?
- Did I consider what's hard to change later?
- Did I document the rationale (ADR)?
- Does this match team capabilities?
- Is this the simplest solution that works?
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