# Chaos Engineering Test Suite - Phased Implementation Plan

**Document Version**: 1.0
**Date**: December 21, 2025
**Framework**: FraiseQL v1.8.9+ with Rust pipeline
**Status**: Ready for Implementation

---

## 🎯 Executive Summary

This plan establishes a comprehensive chaos engineering test suite to validate FraiseQL's resilience, fault tolerance, and recovery capabilities under adverse conditions. The suite will systematically inject failures into:

- Database connectivity and responsiveness
- Network latency and packet loss
- Authentication and authorization failures
- Cache invalidation and corruption
- Memory and resource exhaustion
- Concurrent request handling
- Rust pipeline failures
- PostgreSQL query execution failures

**Expected Outcome**: Production-hardened FraiseQL that gracefully handles failures and recovers automatically.

**Timeline**: 5 phases over 4-6 weeks
**Effort**: ~100-150 developer hours
**Infrastructure**: Existing test infrastructure + chaos injection tools

---

## 📋 Phase Breakdown

### Phase 0: Chaos Engineering Foundation
**Duration**: 3-4 days
**Effort**: 15-20 hours
**Objective**: Set up infrastructure and baseline measurements

#### 0.1 - Chaos Tool Selection & Setup
**Objective**: Evaluate and install chaos engineering tools

**Tasks**:
- [ ] Evaluate chaos tools for Python/Rust integration:
  - `pytest-chaos` - Native pytest chaos injection
  - `chaos-toolkit` - Declarative chaos experiments
  - `locust` - Load testing and chaos simulation
  - `toxiproxy` - Network chaos (latency, packet loss)
  - Custom `chaosmonkey` module in fraiseql
- [ ] Select primary tool stack: `pytest-chaos` + `toxiproxy` + custom decorators
- [ ] Install and configure tools in test environment
- [ ] Create chaos fixtures and context managers
- [ ] Document tool usage patterns

**Acceptance Criteria**:
- ✅ Chaos tools installed and working
- ✅ Can inject failures in controlled manner
- ✅ Failures can be measured quantitatively
- ✅ Tests reproducible and deterministic

**Files to Create**:
```
fraiseql/chaos/
├── __init__.py
├── fixtures.py           # Chaos fixtures for pytest
├── decorators.py         # @chaos_inject, @fault_tolerant decorators
├── toxiproxy.py          # Toxiproxy integration
├── injectors.py          # Failure injection strategies
└── metrics.py            # Measurement and observability
```

---

#### 0.2 - Baseline Performance Metrics
**Objective**: Establish healthy-state performance baseline

**Tasks**:
- [ ] Measure normal query execution times:
  - Simple queries (SELECT * FROM users)
  - Complex nested queries
  - Mutations
  - Aggregations
- [ ] Measure database connection pool metrics:
  - Connection time
  - Pool utilization
  - Idle timeout behavior
- [ ] Measure authentication performance:
  - Token validation time (cached)
  - Token validation time (uncached)
  - JWKS fetch time
- [ ] Measure Rust pipeline performance:
  - JSON transformation speed
  - Schema registry lookup speed
  - Response building time
- [ ] Document all baselines in metrics.json

**Acceptance Criteria**:
- ✅ Baseline file with 50+ metrics
- ✅ All metrics measurable within ±5%
- ✅ Repeatability: Same query ±3% variance
- ✅ Baseline used as reference for chaos tests

**Expected Baselines** (FraiseQL v1.8.9):
- Simple query: 15-25ms
- Nested query (3 levels): 40-60ms
- Token validation (cached): <1ms
- Token validation (uncached): 50-200ms
- JWKS fetch (cached): <10ms
- Rust JSON transform: <5ms

---

#### 0.3 - Chaos Test Infrastructure
**Objective**: Build reusable chaos testing framework

**Tasks**:
- [ ] Create `ChaosTestCase` base class:
  - Setup/teardown for chaos injection
  - Failure injection helpers
  - Recovery verification
  - Metrics collection
- [ ] Create `ChaosMetrics` dataclass:
  - Success/failure counts
  - Latency percentiles (p50, p95, p99)
  - Error types and frequencies
  - Recovery time measurements
- [ ] Create `FailureScenario` dataclass:
  - Failure type (network, db, auth, etc.)
  - Duration and intensity
  - Target component
  - Expected behavior
- [ ] Create result comparison utilities:
  - Compare against baseline
  - Statistical significance testing
  - Report generation

**Acceptance Criteria**:
- ✅ Base class usable for all chaos tests
- ✅ Metrics collected automatically
- ✅ Results stored in structured format (JSON/CSV)
- ✅ Comparison tools show % deviation from baseline

**Example Usage**:
```python
class TestDatabaseChaos(ChaosTestCase):
    @chaos_inject(
        failure_type="network_latency",
        duration=30,
        latency_ms=500
    )
    def test_query_with_db_latency(self):
        metrics = self.run_queries(count=100)
        self.assert_within_baseline(metrics, tolerance=2.0)  # 2x baseline acceptable
        self.assert_recovery_time(max_ms=5000)
```

---

### Phase 1: Network & Connectivity Chaos
**Duration**: 5-6 days
**Effort**: 25-30 hours
**Objective**: Validate behavior under network failures

#### 1.1 - Database Connection Failures
**Test Suite**: `tests/chaos/network/test_db_connection_chaos.py`

**Failure Scenarios**:

1. **Database Unavailable**
   - Inject: Connection refused, TCP port closed
   - Duration: 5-30 seconds
   - Verify:
     - Connection pool detects failure quickly
     - Queries return error (not hang)
     - Error message is clear
     - Connection pool recovers when DB comes back
   - Metrics: Time to detect, error rate, recovery time

2. **Connection Pool Exhaustion**
   - Inject: Force all connections to be in use
   - Duration: 10-60 seconds
   - Verify:
     - New queries wait in queue (not rejected)
     - Queue depth reported correctly
     - Oldest queries processed first
     - No deadlocks
   - Metrics: Queue depth, wait time, throughput impact

3. **Slow Connection Establishment**
   - Inject: Connection takes 2-5 seconds
   - Duration: 30 seconds
   - Verify:
     - Queries don't timeout waiting for connection
     - Connection timeout configurable
     - Retries work correctly
   - Metrics: Connection establishment time, timeout frequency

4. **Connection Drops Mid-Query**
   - Inject: Kill connection after 500ms
   - Duration: Continuous for 30 seconds
   - Verify:
     - Partial results detected
     - Error returned to client
     - Connection pool removes dead connections
     - Retry successful on new connection
   - Metrics: % queries affected, retry success rate

**Test Count**: 12-15 tests
**Expected Duration**: Each test 30-120 seconds
**Success Criteria**: All scenarios handled gracefully with <2x baseline latency

---

#### 1.2 - Network Latency Injection
**Test Suite**: `tests/chaos/network/test_network_latency_chaos.py`

**Failure Scenarios**:

1. **Gradual Latency Increase**
   - Inject: 0ms → 100ms → 500ms → 2000ms over 60 seconds
   - Verify:
     - Queries still succeed (not timeout)
     - Latency increases proportionally
     - Client receives results (no hangs)
   - Metrics: Latency percentiles, timeout rate

2. **Consistent High Latency**
   - Inject: Constant 1000ms added to all DB operations
   - Duration: 60 seconds
   - Verify:
     - Queries succeed but slower
     - No timeout errors (configurable timeout should be >1.5x injected)
     - Cache effectiveness verified (cached queries unaffected)
   - Metrics: Cache hit rate under latency

3. **Jittery Latency**
   - Inject: Random latency 10-500ms per request
   - Duration: 60 seconds
   - Verify:
     - No timeouts for outliers
     - Percentile metrics show distribution
   - Metrics: Latency distribution, max latency

4. **Asymmetric Latency (Request vs Response)**
   - Inject: High latency on request, low on response (or vice versa)
   - Duration: 30 seconds
   - Verify:
     - Behavior consistent with one-way latency
   - Metrics: Request/response latency separately

**Test Count**: 8-10 tests
**Expected Duration**: Each test 60-120 seconds
**Success Criteria**: System remains responsive under 2000ms latency

---

#### 1.3 - Packet Loss & Corruption
**Test Suite**: `tests/chaos/network/test_packet_loss_chaos.py`

**Failure Scenarios**:

1. **Packet Loss (1%, 5%, 10%)**
   - Inject: Random packet drops
   - Duration: 60 seconds for each % level
   - Verify:
     - TCP retransmit recovers losses
     - Queries succeed despite losses
     - No application-level corruption
   - Metrics: Effective success rate, retry frequency, latency impact

2. **Duplicate Packets**
   - Inject: Random packet duplication (0.5%, 2%)
   - Duration: 30 seconds
   - Verify:
     - Protocol layers handle duplicates
     - No data corruption
     - No duplicate query execution (idempotency)
   - Metrics: Duplicate detection, query idempotency verified

3. **Out-of-Order Packets**
   - Inject: Random packet reordering
   - Duration: 30 seconds
   - Verify:
     - TCP layer reorders correctly
     - Data integrity verified
   - Metrics: Reordering frequency, recovery time

4. **Corrupted Packets**
   - Inject: Bit flips in 0.1% of packets
   - Duration: 30 seconds
   - Verify:
     - TCP checksum detects corruption
     - Packet retransmitted
     - No silent data corruption
   - Metrics: Corruption detection rate, retry rate

**Test Count**: 10-12 tests
**Expected Duration**: Each test 30-120 seconds
**Success Criteria**: No data corruption, graceful error handling

---

### Phase 2: Database & Query Chaos
**Duration**: 6-7 days
**Effort**: 30-40 hours
**Objective**: Validate resilience to database-level failures

#### 2.1 - Query Execution Failures
**Test Suite**: `tests/chaos/database/test_query_failure_chaos.py`

**Failure Scenarios**:

1. **Query Timeout**
   - Inject: PostgreSQL query takes 5-30 seconds
   - Duration: 30 seconds
   - Verify:
     - Query timeout triggers after configured time
     - Connection released back to pool
     - Client receives clear timeout error
     - Subsequent queries work normally
   - Metrics: Timeout accuracy, connection recovery time

2. **Query Syntax/Semantic Errors**
   - Inject: Corrupted query sent to PostgreSQL
   - Verify:
     - Error message returned (not hung)
     - Error is descriptive
     - Connection remains usable
   - Metrics: Error message quality, recovery speed

3. **Constraint Violations**
   - Inject: Insert duplicate primary key
   - Verify:
     - Constraint error returned to client
     - Transaction rolled back correctly
     - Data consistency maintained
   - Metrics: Consistency verification

4. **Insufficient Permissions**
   - Inject: Query with insufficient role permissions
   - Verify:
     - Permission error returned
     - Connection remains usable
   - Metrics: Error handling consistency

5. **Resource Limits Exceeded**
   - Inject: PostgreSQL hits statement timeout, memory limit
   - Verify:
     - Clear error returned
     - No cascading failures
   - Metrics: Resource limit detection

**Test Count**: 12-15 tests
**Expected Duration**: Each test 30-60 seconds
**Success Criteria**: All errors handled gracefully with clear messages

---

#### 2.2 - Data Consistency Failures
**Test Suite**: `tests/chaos/database/test_data_consistency_chaos.py`

**Failure Scenarios**:

1. **Stale Read (Dirty Read)**
   - Inject: Read uncommitted data from parallel transaction
   - Verify:
     - Isolation level prevents dirty reads (if using READ_COMMITTED or higher)
     - Or document if dirty reads are possible
   - Metrics: Data consistency violations (should be zero)

2. **Write Skew Anomaly**
   - Inject: Concurrent writes that violate application invariants
   - Verify:
     - Application logic catches invariant violations
     - Retry on conflict succeeds
   - Metrics: Conflict detection rate, retry success rate

3. **Non-Repeatable Read**
   - Inject: Same query returns different results when repeated
   - Verify:
     - Behavior matches configured isolation level
     - Document expected behavior
   - Metrics: Consistency violations per transaction type

4. **Phantom Reads**
   - Inject: Insert/delete rows that change range query results
   - Verify:
     - Behavior matches isolation level
     - Results consistent within transaction
   - Metrics: Phantom read frequency

**Test Count**: 8-10 tests
**Expected Duration**: Each test 10-30 seconds (data ops are fast)
**Success Criteria**: Zero data corruption, consistency rules upheld

---

#### 2.3 - PostgreSQL Failure Modes
**Test Suite**: `tests/chaos/database/test_postgres_failures.py`

**Failure Scenarios**:

1. **Table Locked (Long-Running Transaction)**
   - Inject: Lock table for 30-60 seconds
   - Verify:
     - Queries wait for lock (configurable timeout)
     - Lock release allows queries through
     - No deadlocks
   - Metrics: Lock wait time, timeout frequency

2. **Index Corruption**
   - Inject: Disable index (simulate corruption)
   - Verify:
     - Query succeeds via table scan (slower)
     - Results correct despite index being unavailable
   - Metrics: Performance impact (expected 2-10x slower)

3. **Memory Pressure in PostgreSQL**
   - Inject: Reduce PostgreSQL work_mem (forces sorts to disk)
   - Verify:
     - Queries still succeed (slower)
     - Correct results despite disk-based operations
   - Metrics: Performance impact

4. **Connection Limit Hit**
   - Inject: Reduce max_connections, fill all slots
   - Verify:
     - New connections rejected (not hung)
     - Error message clear
     - Pool handles rejection correctly
   - Metrics: Connection rejection rate, error clarity

**Test Count**: 8-10 tests
**Expected Duration**: Each test 30-60 seconds
**Success Criteria**: Graceful degradation, no silent failures

---

### Phase 3: Cache & Auth Chaos
**Duration**: 5-6 days
**Effort**: 25-35 hours
**Objective**: Validate resilience to cache and authentication failures

#### 3.1 - Cache Failure Scenarios
**Test Suite**: `tests/chaos/cache/test_cache_failures.py`

**Failure Scenarios**:

1. **Cache Hit Rate Degradation**
   - Inject: Invalidate cache, reduce TTL to 1 second
   - Duration: 60 seconds
   - Verify:
     - Queries still succeed (fallback to DB)
     - Latency increases proportionally
     - Cache rebuilds automatically when TTL expires
   - Metrics: Cache hit rate, latency increase, rebuild frequency

2. **Partial Cache Invalidation**
   - Inject: Invalidate only specific cache keys
   - Verify:
     - Invalidated keys recomputed
     - Other cached values still used
     - Data consistency maintained
   - Metrics: Selective invalidation coverage, consistency

3. **Cache Data Corruption**
   - Inject: Corrupt cached data (modify JSON fields)
   - Verify:
     - Queries detect corruption or fail gracefully
     - Fallback to database
     - No silent return of corrupted data
   - Metrics: Corruption detection rate, fallback frequency

4. **Cache Eviction Under Memory Pressure**
   - Inject: Reduce cache size, force LRU eviction
   - Verify:
     - Least recently used items evicted
     - Most used items retained
     - Query results still correct
   - Metrics: Eviction rate, working set vs cache size

5. **Cache Write Failures**
   - Inject: Cache write succeeds partially (network split)
   - Verify:
     - Query succeeds even if cache write fails
     - Consistency is maintained
   - Metrics: Write failure rate, consistency maintained

**Test Count**: 12-15 tests
**Expected Duration**: Each test 30-120 seconds
**Success Criteria**: Cache failures never cause query failures

---

#### 3.2 - JWKS & Token Cache Failures
**Test Suite**: `tests/chaos/auth/test_jwks_cache_failures.py`

**Failure Scenarios**:

1. **JWKS Fetch Failure**
   - Inject: JWKS server returns 500 error
   - Verify:
     - Cached JWKS used for token validation
     - New tokens (not in cache) fail with clear error
     - Retry on JWKS server recovery succeeds
   - Metrics: Fallback usage, cache hit rate during failure

2. **JWKS Key Rotation Not Detected**
   - Inject: JWKS server changes keys without invalidating cache
   - Duration: 60 seconds
   - Verify:
     - Tokens signed with old key still validate (cache)
     - After cache TTL expires, old tokens rejected
     - New tokens validate immediately
   - Metrics: Key rotation detection time, cache TTL accuracy

3. **Token Cache Corruption**
   - Inject: Corrupt cached token validation result
   - Verify:
     - Corruption detected
     - Token revalidated from JWKS
     - Correct result returned
   - Metrics: Corruption detection rate

4. **High JWKS Fetch Latency**
   - Inject: JWKS server responds in 5-10 seconds
   - Duration: 30 seconds
   - Verify:
     - First token (uncached) times out at configured limit
     - Subsequent tokens use cache
     - Cached tokens unaffected
   - Metrics: Latency impact, cache effectiveness

**Test Count**: 8-10 tests
**Expected Duration**: Each test 30-120 seconds
**Success Criteria**: Auth failures are explicit, not silent

---

#### 3.3 - Authentication Failure Modes
**Test Suite**: `tests/chaos/auth/test_auth_failures.py`

**Failure Scenarios**:

1. **Expired Token During Request**
   - Inject: Token expires while request is in flight
   - Verify:
     - Token validation catches expiration
     - Clear 401 error returned
     - Request not partially executed
   - Metrics: Expiration detection accuracy

2. **Invalid Signature (Key Mismatch)**
   - Inject: Token signed with unknown key
   - Verify:
     - Signature validation fails
     - Request rejected before execution
   - Metrics: Signature validation performance

3. **Insufficient Permissions (Phase 11)**
   - Note: Will be tested in Phase 11 RBAC tests
   - Verify:
     - User can't access unauthorized fields
     - Error returned without executing query
   - Metrics: Permission check performance

4. **Auth Bypass Attempts**
   - Inject: Missing auth header, malformed token, empty token
   - Verify:
     - All attempts rejected
     - No accidental bypass
   - Metrics: Bypass prevention effectiveness

**Test Count**: 8-10 tests
**Expected Duration**: Each test 10-30 seconds
**Success Criteria**: No auth bypasses, clear error messages

---

### Phase 4: Resource & Concurrency Chaos
**Duration**: 6-7 days
**Effort**: 35-45 hours
**Objective**: Validate behavior under high load and resource constraints

#### 4.1 - Memory & Resource Constraints
**Test Suite**: `tests/chaos/resources/test_memory_chaos.py`

**Failure Scenarios**:

1. **Application Memory Limit**
   - Inject: Python process memory capped at 512MB
   - Duration: 60 seconds of queries
   - Verify:
     - Queries succeed despite memory pressure
     - No OOM killer, no crashes
     - Graceful degradation (slower is OK)
   - Metrics: Memory usage, GC frequency, latency impact

2. **Rust Extension Memory Pressure**
   - Inject: Limit Rust pipeline memory to 256MB
   - Duration: 60 seconds
   - Verify:
     - Large JSON transformations succeed (or fail gracefully)
     - No memory leaks
     - Recovery after pressure relieved
   - Metrics: Memory allocations, peak usage

3. **Connection Pool Memory**
   - Inject: Reduce pool memory budget
   - Verify:
     - Fewer connections allowed (documented)
     - Queue depth increases
     - No crashes
   - Metrics: Memory efficiency, queue depth

4. **CPU Throttling**
   - Inject: Limit process CPU to 1 core (or 25%)
   - Duration: 60 seconds
   - Verify:
     - Queries still succeed (slower)
     - No timeouts for reasonable load
   - Metrics: Latency increase, throughput reduction

**Test Count**: 8-10 tests
**Expected Duration**: Each test 60-120 seconds
**Success Criteria**: No crashes, graceful degradation under resource constraints

---

#### 4.2 - High Concurrency Chaos
**Test Suite**: `tests/chaos/concurrency/test_concurrent_chaos.py`

**Failure Scenarios**:

1. **Connection Pool Saturation**
   - Inject: 1000 concurrent queries, pool size = 20
   - Duration: 30 seconds
   - Verify:
     - Queue backs up (expected)
     - All queries eventually succeed
     - No deadlocks
     - Fair scheduling (FIFO)
   - Metrics: Queue depth, wait time, throughput

2. **Race Conditions in Cache**
   - Inject: 100 parallel queries for same uncached key
   - Verify:
     - Cache computed once (not 100 times)
     - All queries get correct result
     - No cache inconsistency
   - Metrics: Cache computation count, correctness

3. **Concurrent Mutations**
   - Inject: 50 concurrent INSERT/UPDATE/DELETE on same table
   - Verify:
     - All succeed or all fail together (transactional)
     - Data consistency maintained
     - No partial updates
   - Metrics: Transaction abort rate, consistency

4. **Thundering Herd (Cache Invalidation)**
   - Inject: 1000 queries hit expired cache simultaneously
   - Duration: 5 seconds
   - Verify:
     - Cache recomputed once (not 1000 times)
     - All queries served correctly
     - Database not overwhelmed
   - Metrics: Duplicate computation prevented, load on DB

5. **Lock Contention**
   - Inject: 100 parallel queries updating same rows
   - Duration: 30 seconds
   - Verify:
     - Locks protect data
     - No data corruption
     - Acceptable latency
   - Metrics: Lock wait time, contention frequency

**Test Count**: 10-12 tests
**Expected Duration**: Each test 30-120 seconds
**Success Criteria**: Correct behavior under 100-1000 concurrent requests

---

#### 4.3 - Cascading Failure Chaos
**Test Suite**: `tests/chaos/cascading/test_cascading_failures.py`

**Failure Scenarios**:

1. **Database Down → Cache Fallback → Graceful Degradation**
   - Inject: Database unavailable, cache available
   - Verify:
     - Queries served from cache (stale OK)
     - Clear indication that data may be stale
     - Recovery when DB comes back
   - Metrics: Fallback activation, staleness duration

2. **Cache Down + Database Slow**
   - Inject: Both cache and database degraded (5s latency each)
   - Verify:
     - Queries still succeed (just slow)
     - No timeout if overall latency < configured timeout
   - Metrics: Combined latency, timeout rate

3. **Auth Down + Critical Query**
   - Inject: JWKS server down, query needs validation
   - Verify:
     - Request rejected with clear auth error
     - Not "database unavailable"
     - Error doesn't cascade to other queries
   - Metrics: Error clarity, failure isolation

4. **Memory Pressure + High Concurrency**
   - Inject: Both conditions simultaneously
   - Verify:
     - System still processes requests (maybe slow)
     - No crashes
     - No starvation
   - Metrics: Fairness, worst-case latency

5. **Network Partitions (Byzantine Failures)**
   - Inject: Split between app and database
   - Verify:
     - Requests timeout (not hang forever)
     - Split resolved → reconciliation works
     - Data consistency maintained
   - Metrics: Partition detection time, recovery time

**Test Count**: 8-10 tests
**Expected Duration**: Each test 30-120 seconds
**Success Criteria**: Failures cascade predictably, recovery is automatic

---

### Phase 5: Monitoring & Observability Chaos
**Duration**: 4-5 days
**Effort**: 20-25 hours
**Objective**: Validate that failures are detectable and observable

#### 5.1 - Metrics & Observability Under Chaos
**Test Suite**: `tests/chaos/observability/test_metrics_chaos.py`

**Failure Scenarios**:

1. **Metric Collection During High Load**
   - Inject: High concurrency + collect metrics
   - Verify:
     - Metrics don't affect query latency >5%
     - No metric data loss
     - Accurate percentile calculations
   - Metrics: Metric collection overhead, accuracy

2. **Log Volume Under Chaos**
   - Inject: 1000 errors per second
   - Verify:
     - Logs not lost (ring buffer or async)
     - Log volume doesn't impact performance
     - Errors correctly categorized
   - Metrics: Log volume, latency impact, categorization accuracy

3. **Trace Data During Failures**
   - Inject: Network failure + enable tracing
   - Verify:
     - Full trace captured despite failure
     - Root cause identifiable from trace
     - Trace overhead <10%
   - Metrics: Trace completeness, overhead

4. **Alert Triggering (Integration)**
   - Inject: Database unavailable
   - Verify:
     - Alert generated within 5 seconds
     - Alert contains root cause
     - False positive rate <5%
   - Metrics: Alert latency, accuracy

**Test Count**: 8-10 tests
**Expected Duration**: Each test 30-60 seconds
**Success Criteria**: All failures observable and actionable

---

#### 5.2 - Chaos Test Report Generation
**Test Suite**: `tests/chaos/reporting/test_report_generation.py`

**Deliverables**:

1. **Chaos Test Summary Report**
   - Format: HTML + JSON
   - Contents:
     - Total tests run: X
     - Passed: Y
     - Failed: Z
     - Inconclusive: W
     - Total run time
   - Metrics: Success rate, coverage, execution time

2. **Per-Test Detailed Reports**
   - Contents:
     - Test name and failure scenario
     - Injected failure details
     - Baseline vs. actual performance
     - Metrics collected
     - Pass/fail decision
     - Root cause analysis (if failed)
   - Format: Per-test JSON file + HTML summary

3. **Comparative Analysis**
   - Compare against previous runs
   - Trend analysis (improving/degrading)
   - Performance regression detection
   - Recommendation for follow-up tests

4. **Chaos Test Dashboard (Optional for Phase 5)**
   - Real-time test execution visualization
   - Failure injection timeline
   - Metric graphs with failure periods highlighted
   - Recovery timeline

**Test Count**: 5-6 tests for report generation
**Success Criteria**: Comprehensive, actionable reports generated automatically

---

## 📊 Implementation Timeline

| Phase | Duration | Effort | Start | End |
|-------|----------|--------|-------|-----|
| Phase 0 | 3-4 days | 15-20h | Week 1 | Week 1 |
| Phase 1 | 5-6 days | 25-30h | Week 2 | Week 3 |
| Phase 2 | 6-7 days | 30-40h | Week 3 | Week 4 |
| Phase 3 | 5-6 days | 25-35h | Week 4 | Week 5 |
| Phase 4 | 6-7 days | 35-45h | Week 5 | Week 6 |
| Phase 5 | 4-5 days | 20-25h | Week 6 | Week 7 |
| **Total** | **4-6 weeks** | **100-150h** | | |

---

## 🛠️ Technical Architecture

### Chaos Injection Layers

```
User Requests
     ↓
Python FastAPI Layer (Chaos can inject auth failures)
     ↓
Database Layer (Chaos can inject connection failures, latency)
     ↓
PostgreSQL (Chaos can inject query timeouts, locks)
     ↓
Network Layer (Chaos can inject packet loss, latency via toxiproxy)
     ↓
Rust Pipeline (Chaos can inject memory pressure, slow transforms)
     ↓
Response Encoding (Chaos can inject serialization failures)
     ↓
Client
```

### Test Infrastructure

```
tests/chaos/
├── conftest.py                    # Shared fixtures
├── __init__.py
├── base.py                        # ChaosTestCase base class
├── metrics.py                     # Metrics collection/comparison
├── fixtures.py                    # Reusable chaos fixtures
├── decorators.py                  # @chaos_inject, etc.
│
├── network/
│   ├── test_db_connection_chaos.py
│   ├── test_network_latency_chaos.py
│   └── test_packet_loss_chaos.py
│
├── database/
│   ├── test_query_failure_chaos.py
│   ├── test_data_consistency_chaos.py
│   └── test_postgres_failures.py
│
├── cache/
│   ├── test_cache_failures.py
│   └── test_token_cache_failures.py
│
├── auth/
│   ├── test_jwks_cache_failures.py
│   └── test_auth_failures.py
│
├── resources/
│   └── test_memory_chaos.py
│
├── concurrency/
│   └── test_concurrent_chaos.py
│
├── cascading/
│   └── test_cascading_failures.py
│
├── observability/
│   ├── test_metrics_chaos.py
│   └── test_report_generation.py
│
└── baseline_metrics.json          # Performance baselines
```

---

## 📋 Success Criteria

### Phase 0
- [ ] Chaos tools installed and functional
- [ ] Baseline metrics documented (50+ metrics)
- [ ] Chaos framework ready for tests

### Phase 1
- [ ] 30+ network chaos tests passing
- [ ] All connection failures handled gracefully
- [ ] System recovers automatically

### Phase 2
- [ ] 35+ database chaos tests passing
- [ ] Zero data corruption under failures
- [ ] Clear error messages to users

### Phase 3
- [ ] 30+ cache/auth tests passing
- [ ] Cache never returns stale/corrupted data
- [ ] Auth failures explicit (not silent)

### Phase 4
- [ ] 35+ resource/concurrency tests passing
- [ ] No deadlocks under high concurrency
- [ ] Fair resource allocation verified

### Phase 5
- [ ] 20+ observability tests passing
- [ ] All failures detectable and loggable
- [ ] Reports generated automatically

### Overall (All Phases)
- [ ] 150+ chaos tests all passing
- [ ] Production readiness verified
- [ ] Recovery procedures documented
- [ ] Runbook generated for operators

---

## 🎯 Key Metrics & KPIs

### Reliability Metrics
- **Recovery Time**: Time from failure injection to normal operation
  - Target: <5 seconds for most failures
- **Data Loss Rate**: % of data lost during failures
  - Target: 0%
- **Crash Rate**: % of failures that cause crashes
  - Target: 0%

### Performance Metrics
- **Graceful Degradation**: Max latency increase under failures
  - Target: <3x baseline for most failures
- **Throughput Under Load**: Requests/sec with concurrency + failures
  - Target: ≥80% of normal throughput
- **Memory Efficiency**: Memory usage under constraints
  - Target: <500MB for normal query load

### Observability Metrics
- **Failure Detection Latency**: Time to detect failure
  - Target: <1 second
- **Alert Accuracy**: % of alerts that are true positives
  - Target: >95%
- **Error Message Clarity**: % of errors with actionable messages
  - Target: 100%

---

## 🚀 Running the Chaos Tests

### Single Test
```bash
pytest tests/chaos/network/test_db_connection_chaos.py::TestDatabaseConnection::test_connection_refused -xvs
```

### All Tests in Phase
```bash
pytest tests/chaos/network/ -v --tb=short
```

### All Chaos Tests
```bash
pytest tests/chaos/ -v --chaos-report=chaos_report.html
```

### With Specific Failure Injection
```bash
pytest tests/chaos/ -v -m "failure_type:network_latency" --chaos-duration=120
```

### Generate Comparison Report
```bash
pytest tests/chaos/ -v --baseline=baseline_metrics.json --report=comparison.html
```

---

## 📚 Dependencies & Tools

### Required
- `pytest` (already have)
- `toxiproxy` (network chaos)
- `pytest-asyncio` (async test support)
- `locust` (load generation, optional)

### Recommended
- `pytest-benchmark` (performance baselines)
- `psutil` (resource monitoring)
- `memory-profiler` (memory usage analysis)
- `pympler` (heap analysis for memory leaks)

### Installation
```bash
pip install pytest-chaos toxiproxy pytest-asyncio locust pytest-benchmark psutil memory-profiler pympler
```

---

## 🔍 Pre-Requisites

Before starting Phase 0, ensure:

- [ ] Test environment stable (all 6088 tests passing)
- [ ] PostgreSQL test database accessible
- [ ] Network access to test environment controlled
- [ ] Can start/stop services (PostgreSQL, app)
- [ ] Can measure resource usage (CPU, memory, network)
- [ ] Team trained on chaos engineering principles
- [ ] Runbooks prepared for common failures

---

## 🤝 Team Roles

### Chaos Engineer (Lead)
- Designs chaos scenarios
- Implements test suite
- Analyzes results
- Generates reports

### QA/Test Engineer
- Executes tests
- Documents failures
- Validates recovery procedures
- Tests edge cases

### DevOps/SRE (Support)
- Manages test infrastructure
- Monitors resource usage
- Handles toxiproxy setup
- Troubleshoots environmental issues

### Product/Architecture (Stakeholder)
- Reviews scenarios
- Sets acceptance criteria
- Prioritizes failures to test
- Approves production deployment

---

## 📝 Documentation & Deliverables

### Per Phase
- [ ] Test plan (this document)
- [ ] Test cases (in code comments)
- [ ] Baseline metrics (JSON)
- [ ] Chaos injection helpers (reusable code)
- [ ] Test results (HTML report)

### Final Deliverables (After All Phases)
1. **Chaos Test Suite**: 150+ reproducible tests
2. **Baseline Metrics**: Reference performance under normal conditions
3. **Runbook**: How to interpret test results
4. **Troubleshooting Guide**: Common failures and recovery steps
5. **Architecture Document**: How chaos is injected at each layer
6. **Performance Report**: System behavior under various failures
7. **Recommendations**: Next steps for improvement

---

## ⚠️ Risks & Mitigation

| Risk | Mitigation |
|------|-----------|
| Tests destabilize production | Use isolated test environment only |
| False positives in tests | Implement statistical significance testing |
| Long test execution | Parallelize tests across cores |
| Memory/resource issues in tests | Cap test duration, kill hung tests |
| Flaky tests (intermittent failures) | Run tests multiple times, document variance |
| Test maintenance overhead | Use clear test patterns, reusable fixtures |

---

## 🎓 Learning Resources

### Chaos Engineering Principles
- Netflix Chaos Monkey (concept origin)
- Principles of Chaos Engineering (chaosengineering.org)
- "Release It!" by Michael Nygard (circuit breakers, bulkheads)

### Tools Documentation
- Toxiproxy: https://github.com/Shopify/toxiproxy
- pytest-chaos: Community library, custom implementation
- Locust: https://locust.io/

### FraiseQL Specific
- Connection pool configuration
- JWKS cache strategy
- PostgreSQL timeout settings
- Rust pipeline memory management

---

## ✅ Approval Checklist

- [ ] Technical lead reviews and approves plan
- [ ] QA lead confirms test coverage is comprehensive
- [ ] DevOps confirms infrastructure can support testing
- [ ] Product agrees on success criteria and KPIs
- [ ] Team has required skills and training
- [ ] Timeline and effort estimates are realistic
- [ ] All prerequisites met

---

## 📅 Next Steps

1. **Week 1**: Present plan to team for feedback
2. **Week 1-2**: Secure approval and allocate resources
3. **Week 2**: Begin Phase 0 (infrastructure setup)
4. **Week 2-6**: Execute Phases 1-5 sequentially
5. **Week 7**: Compile final report and recommendations
6. **Post-delivery**: Ongoing chaos test maintenance

---

*Plan Version: 1.0*
*Last Updated: December 21, 2025*
*Status: Ready for Team Review*
