# FraiseQL v1 - Vision & Master Plan

**Purpose**: Rebuild FraiseQL as a showcase-quality Python GraphQL framework for Staff+ engineering interviews
**Goal**: Hiring at top companies (demonstrate architectural mastery)
**Strategy**: Clean rebuild from scratch in `fraiseql-v1/`
**Timeline**: 8 weeks to interview-ready
**Status**: Planning complete, ready for implementation

---

## 🎯 Why This Rebuild?

### **Primary Goal: Land Staff+ Engineering Roles**

This rebuild demonstrates mastery of:
1. **CQRS Architecture** - Command/query separation at database level
2. **Database Performance** - JSONB optimization, Trinity identifiers (10x faster joins)
3. **Rust Integration** - 40x speedup on critical path
4. **API Design** - Clean, intuitive decorator patterns
5. **Systems Thinking** - Database-first optimization, not ORM-centric
6. **Stored Procedures** - Business logic in PostgreSQL functions

**Target Audience**: Senior/Staff/Principal engineers at top companies
**Perfect For**: Architecture discussions, system design interviews

---

## 📐 Core Architecture Patterns (DEFAULT)

### **Pattern 1: Trinity Identifiers**

**The Problem**: Single-ID systems force trade-offs
- SERIAL: Fast joins, but exposes growth rate, not globally unique
- UUID: Secure, but slow joins, random order
- Slug: SEO-friendly, but can't use as PK, not all entities have one

**The Solution**: Use all three, each for its purpose

```sql
-- Command Side (tb_*)
CREATE TABLE tb_user (
    pk_user INT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,  -- Fast internal joins
    fk_organisation INT NOT NULL           -- Fast foreign keys (10x faster than UUID)
        REFERENCES tb_organisation(pk_organisation),
    id UUID DEFAULT gen_random_uuid()      -- Public API (secure, doesn't leak count)
        UNIQUE NOT NULL,
    identifier TEXT UNIQUE NOT NULL,       -- Human-readable (username, slug)
    name TEXT NOT NULL,
    email TEXT UNIQUE NOT NULL
);

-- Query Side (tv_*)
CREATE TABLE tv_user (
    id UUID PRIMARY KEY,                   -- Clean GraphQL API (just "id")
    identifier TEXT UNIQUE NOT NULL,
    data JSONB NOT NULL,
    updated_at TIMESTAMPTZ DEFAULT NOW()
);
```

**Naming Convention**:
- `pk_*` = INT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY (internal, fast INT joins)
- `fk_*` = INT FOREIGN KEY (references pk_*)
- `id` = UUID (public API, exposed in GraphQL)
- `identifier` = TEXT (human-readable: username, slug, etc.)

**Benefits**:
- Fast database joins (SERIAL integers, ~10x faster than UUID)
- Secure public API (UUID doesn't expose sequential count)
- Human-friendly URLs (identifier/slug)
- Clean GraphQL schema (just "id", no "pkUser" ugliness)

---

### **Pattern 2: Mutations as PostgreSQL Functions**

**The Problem**: Python-heavy mutations are:
- Not reusable (can't call from psql, cron, triggers)
- Manual transaction management (easy to mess up)
- Hard to test (need Python app running)
- Multiple round-trips (slow)

**The Solution**: All business logic in PostgreSQL functions

```sql
-- All validation, business logic, transactions in database
CREATE FUNCTION fn_create_user(
    p_organisation_identifier TEXT,  -- Human-friendly!
    p_identifier TEXT,
    p_name TEXT,
    p_email TEXT
) RETURNS UUID AS $$
DECLARE
    v_fk_organisation INT;
    v_id UUID;
BEGIN
    -- Resolve organisation by identifier (not internal pk!)
    SELECT pk_organisation INTO v_fk_organisation
    FROM tb_organisation WHERE identifier = p_organisation_identifier;

    IF NOT FOUND THEN
        RAISE EXCEPTION 'Organisation not found: %', p_organisation_identifier;
    END IF;

    -- Validation
    IF p_email !~ '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}$' THEN
        RAISE EXCEPTION 'Invalid email format';
    END IF;

    -- Insert (transaction is automatic)
    INSERT INTO tb_user (fk_organisation, identifier, name, email)
    VALUES (v_fk_organisation, p_identifier, p_name, p_email)
    RETURNING id INTO v_id;

    -- Sync to query side (explicit, same transaction)
    PERFORM fn_sync_tv_user(v_id);

    -- Return public UUID
    RETURN v_id;
END;
$$ LANGUAGE plpgsql;
```

**Python becomes trivial** (3 lines per mutation):
```python
import fraiseql

@fraiseql.mutation
async def create_user(info, organisation: str, identifier: str, name: str, email: str) -> User:
    db = info.context["db"]
    id = await db.fetchval("SELECT fn_create_user($1, $2, $3, $4)", organisation, identifier, name, email)
    return await QueryRepository(db).find_one("tv_user", id=id)
```

**Benefits**:
- Business logic reusable (psql, cron, other services)
- Automatic transactions (PostgreSQL guarantees ACID)
- Testable in SQL (no Python needed: `psql -f tests/test_mutations.sql`)
- Single round-trip (1 DB call, not 3-5)
- Versioned with migrations (schema changes track logic changes)

---

### **Pattern 3: CQRS with Explicit Sync**

**Command Side** (`tb_*`): Normalized tables, fast writes
**Query Side** (`tv_*`): Denormalized JSONB, fast reads
**Sync Functions** (`fn_sync_tv_*`): Explicit, no triggers

```sql
-- Sync function (called explicitly from mutations)
CREATE FUNCTION fn_sync_tv_user(p_id UUID) RETURNS void AS $$
BEGIN
    INSERT INTO tv_user (id, identifier, data, updated_at)
    SELECT
        u.id,
        u.identifier,
        jsonb_build_object(
            'id', u.id::text,
            'identifier', u.identifier,
            'name', u.name,
            'email', u.email,
            'organisation', (
                SELECT jsonb_build_object(
                    'id', o.id::text,
                    'identifier', o.identifier,
                    'name', o.name
                )
                FROM tb_organisation o
                WHERE o.pk_organisation = u.fk_organisation  -- Fast INT join!
            )
        ),
        NOW()
    FROM tb_user u
    WHERE u.id = p_id
    ON CONFLICT (id) DO UPDATE
    SET data = EXCLUDED.data, updated_at = NOW();
END;
$$ LANGUAGE plpgsql;
```

**Benefits**:
- No N+1 queries (data pre-joined in JSONB)
- Fast reads (single JSONB lookup, no joins)
- Fast writes (normalized tables, no denormalization overhead)
- Explicit control (you see when sync happens)
- No trigger complexity (easier to debug)

---

## 🏗️ V1 Architecture

### **What to Keep from v0**

**Production-Quality Components** (~2,300 LOC):

1. **Type System** (`types/`) - 800 LOC ✅
   - Clean decorator API (`@type`, `@input`, `@field`)
   - Comprehensive scalars (UUID, DateTime, CIDR, LTree)
   - Port with minimal changes

2. **Where Clause Builder** (`sql/where/`) - 500 LOC ✅
   - "Marie Kondo clean" (actual comment in code!)
   - Function-based, testable, composable
   - Enhance for JSONB support

3. **Rust Transformer** (`core/rust_transformer.py`) - 200 LOC Python + Rust ✅
   - 40x speedup (killer feature)
   - Clean Python/Rust bridge
   - Make it central to architecture

4. **Decorator System** (`decorators.py`) - 400 LOC ✅
   - Clean API (`@query`, `@mutation`, `@field`)
   - Simplify, remove N+1 tracking complexity

5. **Repository Core Logic** (`cqrs/repository.py`) - 400 LOC ✅
   - Rebuild with Trinity + Functions pattern
   - Remove `qm_*` references (obsolete)
   - Simplify to core patterns only

**Total to Port**: ~2,300 LOC

### **What to Remove (Feature Bloat)**

Skip these for v1 (focus on core value):
- `analysis/` - Complexity analysis (nice-to-have)
- `audit/` - Audit logging (v1.1)
- `cache/` + `caching/` - Two caching modules! (v1.1)
- `debug/` - Debug mode (v1.1)
- `ivm/` - Incremental View Maintenance (too complex)
- `monitoring/` - Metrics (v1.1, keep error tracking only)
- `tracing/` - OpenTelemetry (v1.1)
- `turbo/` - TurboRouter (v1.1)
- `migration/` - Migrations (v2 with Confiture integration)

**Philosophy**: Ship tight, focused core. Extensions come later.

**v0 LOC**: ~50,000 lines
**v1 Target**: ~3,000 lines (94% reduction)

---

## 📦 V1 Project Structure

```
fraiseql-v1/
├── README.md                          # Impressive overview
├── pyproject.toml                     # Clean dependencies
├── docs/                              # Philosophy-driven docs
│   ├── README.md
│   ├── philosophy/                    # Why FraiseQL exists
│   │   ├── WHY_FRAISEQL.md
│   │   ├── CQRS_FIRST.md
│   │   ├── RUST_ACCELERATION.md
│   │   └── TRINITY_IDENTIFIERS.md
│   ├── architecture/                  # Technical deep dives
│   │   ├── OVERVIEW.md
│   │   ├── NAMING_CONVENTIONS.md
│   │   ├── COMMAND_QUERY_SEPARATION.md
│   │   ├── SYNC_STRATEGIES.md
│   │   └── MUTATIONS_AS_FUNCTIONS.md
│   ├── guides/                        # How-to
│   │   ├── QUICK_START.md
│   │   ├── DATABASE_SETUP.md
│   │   ├── WRITING_QUERIES.md
│   │   ├── WRITING_MUTATIONS.md
│   │   └── PERFORMANCE.md
│   └── api/                           # API reference
│       ├── DECORATORS.md
│       ├── REPOSITORY.md
│       └── TYPE_SYSTEM.md
├── examples/                          # Working examples
│   ├── quickstart/                    # 5-minute hello world
│   ├── blog/                          # Full blog with CQRS
│   └── ecommerce/                     # Product catalog
├── src/fraiseql/                      # Core library (~3,000 LOC)
│   ├── __init__.py                    # Clean public API
│   ├── types/                         # Type system (800 LOC)
│   ├── decorators/                    # @query, @mutation (400 LOC)
│   ├── repositories/                  # Command/Query/Sync (600 LOC)
│   ├── sql/                           # WHERE builder (500 LOC)
│   ├── core/                          # Rust transformer (300 LOC)
│   └── gql/                           # Schema generation (400 LOC)
├── fraiseql_rs/                       # Rust crate
│   ├── Cargo.toml
│   └── src/
│       ├── lib.rs
│       ├── transform.rs
│       └── case_conversion.rs
└── tests/                             # 100% coverage on core
    ├── unit/
    └── integration/
```

---

## 🔧 Core Components (5 Total)

### **Component 1: Type System** (800 LOC)

**Purpose**: Clean decorator API for GraphQL types

```python
import fraiseql
from uuid import UUID

@fraiseql.type
class User:
    id: UUID
    identifier: str
    name: str
    email: str

    @field
    async def posts(self, info) -> list["Post"]:
        return await QueryRepository(info.context["db"]).find("tv_post", where={"userId": self.id})

@input
class CreateUserInput:
    organisation: str  # Organisation identifier
    identifier: str    # Username
    name: str
    email: str
```

**Port From**: `src/fraiseql/types/` (simplify, keep core)

---

### **Component 2: Repositories** (600 LOC)

**Purpose**: Command/Query separation with Trinity support

```python
class CommandRepository:
    """Thin wrapper - calls database functions"""
    async def execute(self, sql: str, *params) -> Any:
        return await self.db.fetchval(sql, *params)

class QueryRepository:
    """Reads from tv_* views"""
    async def find_one(self, view: str, id: UUID = None, identifier: str = None) -> dict:
        if id:
            return await self.db.fetchrow(f"SELECT data FROM {view} WHERE id = $1", id)
        elif identifier:
            return await self.db.fetchrow(f"SELECT data FROM {view} WHERE identifier = $1", identifier)
```

**Port From**: `src/fraiseql/cqrs/repository.py` (rebuild with new pattern)

---

### **Component 3: Decorators** (400 LOC)

**Purpose**: Auto-register queries and mutations

```python
import fraiseql

@fraiseql.query
async def user(info, id: UUID = None, identifier: str = None) -> User:
    """Get user by UUID or identifier"""
    repo = QueryRepository(info.context["db"])
    if id:
        return await repo.find_one("tv_user", id=id)
    elif identifier:
        return await repo.find_one("tv_user", identifier=identifier)

@fraiseql.mutation
async def create_user(info, organisation: str, identifier: str, name: str, email: str) -> User:
    """Create user (business logic in database function)"""
    db = info.context["db"]
    id = await db.fetchval("SELECT fn_create_user($1, $2, $3, $4)", organisation, identifier, name, email)
    return await QueryRepository(db).find_one("tv_user", id=id)
```

**Port From**: `src/fraiseql/decorators.py` (simplify)

---

### **Component 4: WHERE Builder** (500 LOC)

**Purpose**: Type-safe, composable filters for JSONB

```python
# Simple equality
where = {"status": "active"}
# → data->>'status' = 'active'

# Operators
where = {
    "age": {"gt": 18},
    "name": {"contains": "john"}
}
# → data->>'age' > '18' AND data->>'name' LIKE '%john%'
```

**Port From**: `src/fraiseql/sql/where/` (already clean!)

---

### **Component 5: Rust Integration** (300 LOC Python + 200 LOC Rust)

**Purpose**: 40x speedup on JSON transformation

```python
# Transparent - user doesn't see this
result = await query_repo.find_one("tv_user", id=user_id)
# ↑ Automatically runs through Rust transformer
# Snake case DB → CamelCase GraphQL, field selection, type coercion
```

**Port From**: `src/fraiseql/core/rust_transformer.py` + `fraiseql_rs/`

---

## 🎯 Success Criteria

### **Technical**
- [ ] < 1ms query latency (with Rust transform)
- [ ] 40x speedup over traditional GraphQL (benchmarked)
- [ ] 100% test coverage on core (5 components)
- [ ] Clean public API (< 20 exports in `__init__.py`)
- [ ] Zero configuration for quickstart
- [ ] ~3,000 LOC total (vs 50,000 in v0)

### **Documentation**
- [ ] Philosophy docs explain WHY (not just HOW)
- [ ] Architecture diagrams for visual clarity
- [ ] 3 working examples (quickstart, blog, ecommerce)
- [ ] API reference for all public functions
- [ ] Benchmarks vs competitors (Strawberry, Graphene, Hasura)

### **Portfolio Impact**
- [ ] GitHub README with impressive benchmarks
- [ ] "Built with FraiseQL" showcase apps
- [ ] Blog post: "Building the Fastest Python GraphQL Framework"
- [ ] Tech talk slides ready

### **Interview Ready**
- [ ] Can explain architecture in 15 min
- [ ] Have diagrams ready to show
- [ ] Know trade-offs and limitations
- [ ] Have benchmark numbers memorized
- [ ] Can walk through code confidently

---

## 📅 8-Week Implementation Timeline

### **Week 1-2: Documentation Foundation**
**Philosophy First** - creates interview narrative

1. Write `WHY_FRAISEQL.md` (300 lines)
   - The problem (GraphQL is slow)
   - The solution (CQRS + Rust)
   - When to use (honest assessment)

2. Write `CQRS_FIRST.md` (400 lines)
   - Command/query separation
   - Why database-level, not app-level
   - Trinity identifiers deep dive

3. Write `MUTATIONS_AS_FUNCTIONS.md` (350 lines)
   - Why PostgreSQL functions
   - Benefits over Python logic
   - Testing strategies

4. Write `RUST_ACCELERATION.md` (300 lines)
   - Performance bottleneck analysis
   - 40x speedup explanation
   - Benchmarks

**Deliverable**: Can discuss architecture for 30+ minutes (interview prep!)

---

### **Week 3-4: Core Implementation**
**Build the Foundation** - Type System + Decorators

1. **Type System** (Week 3)
   - Port `types/fraise_type.py`
   - Port `types/fraise_input.py`
   - Port `types/scalars/`
   - Tests: 50+ type mapping scenarios

2. **Decorators** (Week 3-4)
   - Port `decorators.py` (simplified)
   - Registry pattern
   - Schema generation
   - Tests: 30+ decorator scenarios

3. **GraphQL Schema Builder** (Week 4)
   - Convert Python → GraphQL types
   - Auto-generate schema
   - Tests: 20+ schema generation tests

**Deliverable**: Can define types and queries (no data yet)

---

### **Week 5-6: CQRS Implementation**
**Build Repositories** - Command/Query/Sync

1. **CommandRepository** (Week 5)
   - Thin wrapper for mutations
   - Call PostgreSQL functions
   - Transaction support
   - Tests: 20+ mutation tests

2. **QueryRepository** (Week 5-6)
   - Read from `tv_*` views
   - Trinity identifier support (id + identifier lookups)
   - WHERE clause integration
   - Pagination (cursor-based)
   - Tests: 40+ query tests

3. **WHERE Clause Builder** (Week 6)
   - Port from v0 (already clean)
   - Enhance for JSONB
   - Operators: eq, ne, gt, lt, contains, in
   - Tests: 30+ operator tests

**Deliverable**: Full CQRS working end-to-end

---

### **Week 6-7: Rust Integration**
**Port Performance Layer**

1. **Rust Transformer** (Week 6-7)
   - Port Rust crate from v0
   - JSON transformation (snake_case → camelCase)
   - Field selection
   - Type coercion
   - Tests: 25+ transformation tests

2. **Performance Benchmarks** (Week 7)
   - Rust vs Python comparison
   - vs Strawberry benchmark
   - vs Graphene benchmark
   - Document 40x speedup

**Deliverable**: Sub-1ms queries proven

---

### **Week 7-8: Examples & Polish**
**Build Showcase Apps**

1. **Quickstart Example** (Week 7)
   - 50-line hello world
   - Trinity identifiers
   - 1 query, 1 mutation
   - README with setup

2. **Blog Example** (Week 7-8)
   - Organisation → User → Post hierarchy
   - Full CQRS
   - Mutations as functions
   - README with architecture explanation

3. **E-commerce Example** (Week 8)
   - Product catalog
   - Complex filters
   - Performance showcase
   - README with benchmarks

4. **Documentation Polish** (Week 8)
   - Review all docs
   - Architecture diagrams
   - Quick start guide
   - API reference

5. **README.md** (Week 8)
   - Impressive benchmarks
   - Clear value proposition
   - Architecture highlights
   - "Why FraiseQL" section

**Deliverable**: Interview-ready, showcaseable project

---

## 🎓 Interview Talking Points

### **60-Second Pitch** (Memorize This!)

> "I built FraiseQL to solve a real problem: GraphQL in Python was too slow for production use at scale. Traditional frameworks like Strawberry suffer from N+1 query problems and Python's object creation overhead.
>
> I took a systems-level approach. Instead of adding DataLoaders at the application layer, I implemented CQRS at the database level. The read side uses PostgreSQL's JSONB with a Trinity identifier pattern - SERIAL for fast joins, UUID for secure APIs, and slugs for user-friendly URLs. This eliminated N+1 queries entirely.
>
> But Python's JSON transformation was still a bottleneck. So I wrote a Rust extension that handles snake_case to camelCase conversion, field selection, and type coercion. This gave us a 40x speedup.
>
> The result: sub-1ms query latency, from 60ms with traditional approaches. All business logic lives in PostgreSQL functions, making it reusable, testable in SQL, and transactionally safe.
>
> This demonstrates CQRS, database optimization, Rust integration, and stored procedures - production patterns for high-scale systems."

**Time that**: Should be ~60 seconds

---

### **Key Architectural Decisions** (15-Minute Deep Dive)

**1. CQRS at Database Level**
- "Why database, not app? Data locality and consistency guarantees"
- "Command side: normalized for writes. Query side: denormalized for reads"
- "Explicit sync functions - no magic triggers. You control when it happens"

**2. Trinity Identifiers**
- "One ID type forces trade-offs. I use three, each for its purpose"
- "SERIAL pk_* for 10x faster joins, UUID for secure APIs, slug for SEO"
- "Shows understanding of database internals vs API design"

**3. Mutations as Functions**
- "Business logic in PostgreSQL, not Python. Why? Reusability and atomicity"
- "Can test in SQL without Python app running"
- "Single round-trip, automatic transactions, versioned with migrations"

**4. Rust Integration**
- "Profiling showed 30% of request time in JSON transformation"
- "Rust gave 40x speedup. When to use systems language? Critical path only"
- "Graceful fallback if Rust unavailable - Python still works"

---

### **Trade-offs & Limitations** (Honesty = Credibility)

**When NOT to use FraiseQL**:
- "If you need real-time subscriptions out of the box (v1.1 feature)"
- "If team isn't comfortable with PostgreSQL functions (training required)"
- "If you need federation (single service only in v1)"
- "If you're just prototyping (overhead of CQRS not worth it)"

**When to use FraiseQL**:
- "High read throughput (100K+ QPS)"
- "Complex queries (multi-level nesting)"
- "Need sub-1ms latency at scale"
- "Team values database-first architecture"

---

## 💡 Competitive Positioning

### **vs Strawberry**
- ✅ 40x faster (Rust transformation)
- ✅ CQRS built-in (vs manual DataLoaders)
- ✅ JSONB-first (vs ORM overhead)
- ❌ Less batteries-included (Strawberry easier for simple apps)

### **vs Graphene**
- ✅ Modern async/await
- ✅ Database-level optimization
- ✅ Production patterns included
- ❌ Smaller ecosystem (Graphene more mature)

### **vs PostGraphile**
- ✅ Python ecosystem (not Node.js)
- ✅ Explicit schema (vs auto-generated)
- ✅ Rust acceleration
- ❌ PostGraphile auto-generates from DB (faster setup)

### **vs Hasura**
- ✅ Python code (vs config-driven)
- ✅ More control over logic
- ✅ Lighter weight (no Haskell runtime)
- ❌ Hasura has built-in auth/authz

**Unique Value**: "The only Python GraphQL framework built for sub-1ms queries at scale through database-level CQRS and Rust acceleration"

---

## 🚀 Getting Started (Action Plan)

### **Immediate Next Step: Week 1**

```bash
# 1. Create docs structure
cd /home/lionel/code/fraiseql/fraiseql-v1
mkdir -p docs/{philosophy,architecture,guides,api}

# 2. Start with WHY_FRAISEQL.md (Day 1-2)
code docs/philosophy/WHY_FRAISEQL.md

# Template:
# - The Problem: GraphQL is slow in Python (100-500ms queries)
# - The Root Causes: N+1, object creation, JSON serialization
# - The Solution: CQRS + JSONB + Rust
# - Performance Results: 0.5-2ms queries (table with numbers)
# - When to Use / When Not to Use (honesty!)

# 3. Write CQRS_FIRST.md (Day 3-4)
code docs/philosophy/CQRS_FIRST.md

# Template:
# - What is CQRS?
# - Why database-level, not app-level?
# - Trinity identifiers deep dive
# - Command/query separation benefits
# - Diagram: tb_* → fn_sync_tv_* → tv_*

# 4. Write MUTATIONS_AS_FUNCTIONS.md (Day 5-6)
code docs/philosophy/MUTATIONS_AS_FUNCTIONS.md

# Template:
# - The Problem: Python business logic
# - The Solution: PostgreSQL functions
# - Complete example (fn_create_user)
# - Benefits table (vs Python)
# - Testing strategies (pgTAP)

# 5. Write RUST_ACCELERATION.md (Day 7)
code docs/philosophy/RUST_ACCELERATION.md

# Template:
# - Profiling results (where time goes)
# - Why Rust for this specific use case
# - Benchmark: Python vs Rust (40x)
# - When to use systems languages
# - Graceful fallback strategy

# 6. Practice your pitch! (Day 7)
# Read all 4 docs out loud
# Time yourself: should be 15-20 min total
# This is your technical narrative!
```

**Week 1 Deliverable**: 4 philosophy docs (~1,350 lines total)
**Interview Impact**: Can discuss FraiseQL architecture for 20+ minutes

---

### **Week 2: Architecture Docs**

Continue with:
- `OVERVIEW.md` - High-level architecture diagram
- `NAMING_CONVENTIONS.md` - Trinity identifiers reference
- `COMMAND_QUERY_SEPARATION.md` - CQRS implementation details
- `SYNC_STRATEGIES.md` - Explicit vs trigger-based

---

### **Week 3+: Implementation**

Follow the 8-week timeline above.

---

## 📊 Progress Tracking

### **Phase 1: Planning** ✅ COMPLETE
- [x] Code audit
- [x] Architecture patterns finalized (Trinity + Functions)
- [x] Component PRDs written
- [x] Vision synthesized

### **Phase 2: Documentation** ⏳ NEXT (Week 1-2)
- [ ] WHY_FRAISEQL.md
- [ ] CQRS_FIRST.md
- [ ] MUTATIONS_AS_FUNCTIONS.md
- [ ] RUST_ACCELERATION.md
- [ ] Architecture docs (5 files)
- [ ] Guide docs (5 files)

### **Phase 3: Implementation** (Week 3-6)
- [ ] Type System (800 LOC)
- [ ] Decorators (400 LOC)
- [ ] Repositories (600 LOC)
- [ ] WHERE Builder (500 LOC)
- [ ] Rust Integration (500 LOC)

### **Phase 4: Examples** (Week 7-8)
- [ ] Quickstart example
- [ ] Blog example
- [ ] E-commerce example

### **Phase 5: Polish** (Week 8)
- [ ] README.md with benchmarks
- [ ] Documentation review
- [ ] Architecture diagrams
- [ ] Blog post draft
- [ ] Tech talk slides

---

## 📚 Reference Documents

**Primary Sources** (synthesized into this vision):
- `FRAISEQL_V1_BLUEPRINT.md` - Original vision
- `V1_COMPONENT_PRDS.md` - Component specifications
- `V1_ADVANCED_PATTERNS.md` - Trinity + Functions patterns
- `V1_NEXT_STEPS.md` - Action planning

**Archived** (production-focused, for v2):
- `V1_TDD_PLAN.md` → Actually about v0 production readiness
- `ROADMAP_V1_UPDATED.md` → Production evolution strategy (v2 material)

**This Document**: Single source of truth for FraiseQL v1 rebuild

---

## 🎯 Final Checklist: Interview Ready?

Before considering v1 "done":

**Can you answer these in an interview?**
- [ ] Why did you build FraiseQL? (2 min)
- [ ] Explain CQRS at database level (5 min)
- [ ] Why Trinity identifiers? (3 min)
- [ ] Why PostgreSQL functions for mutations? (4 min)
- [ ] Show me the benchmarks (2 min)
- [ ] What are the trade-offs? (3 min)
- [ ] When would you NOT use this? (2 min)
- [ ] Walk me through the code (15 min)

**Can you demonstrate?**
- [ ] Run quickstart example (< 5 min setup)
- [ ] Show a query execution (< 1ms)
- [ ] Explain the Rust integration
- [ ] Walk through a mutation function
- [ ] Show the CQRS sync process

**Do you have artifacts?**
- [ ] GitHub repo (public, impressive README)
- [ ] Live demo (deployed somewhere)
- [ ] Blog post (explains architecture)
- [ ] Diagrams (architecture visuals)
- [ ] Benchmarks (data-driven proof)

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**You're ready to build something impressive!** 🚀

**Status**: Vision complete, documentation plan ready, implementation path clear
**Next Step**: Start `docs/philosophy/WHY_FRAISEQL.md` (Week 1, Day 1)
**Timeline**: 8 weeks to interview-ready showcase
**Goal**: Land Staff+ engineering role at top company

Let's build this. 💪
