Skip to content

PHASE3_IMPLEMENTATION.md

Latest commit

 

History

History
394 lines (307 loc) · 9.16 KB

PHASE3_IMPLEMENTATION.md

File metadata and controls

394 lines (307 loc) · 9.16 KB
Raw

Phase 3: Implementation Guide

Duration: Weeks 11-16 (6 weeks)
Modules to Add: 4 new Rust modules + Python bindings
Code Size: ~3,000 new lines of Rust code
Tests: 50+ new test cases

Module 1: Streaming (Base Infrastructure)

File: src/streaming.rs (✅ Started)
Status: Foundation complete, ready for protocol implementations

What's Implemented

✅ StreamingConfig - configuration management
✅ StreamingStats - metrics collection
✅ StreamingBatchProcessor - batch processing with buffer
✅ DeadLetterQueue - failed message handling
✅ RateLimiter - token bucket rate limiting
✅ EnrichmentResult - result model

What's Needed

Week 11.1: Kafka Adapter

pub struct KafkaEnricher {
    consumer: rdkafka::consumer::StreamConsumer,
    producer: rdkafka::producer::FutureProducer,
    enricher: Arc<Enricher>,
    processor: StreamingBatchProcessor,
}

impl KafkaEnricher {
    pub async fn start_streaming(&self, input_topic: &str, output_topic: &str) -> Result<()>
    pub async fn consume_message(&self) -> Result<EnrichmentResult>
    pub async fn produce_result(&self, result: EnrichmentResult) -> Result<()>
}

Checkpoint: Kafka enrichment produces results to output topic with <100ms latency

Week 11.2: MQTT Adapter

pub struct MqttEnricher {
    client: rumqttc::AsyncClient,
    enricher: Arc<Enricher>,
}

impl MqttEnricher {
    pub async fn subscribe(&self, topic: &str) -> Result<()>
    pub async fn handle_message(&self, payload: &[u8]) -> Result<EnrichmentResult>
    pub async fn publish_result(&self, result: &EnrichedRow) -> Result<()>
}

Checkpoint: IoT devices can send sensor data → enriched with weather → publish results

Week 11.3: HTTP Webhook Server

pub async fn start_webhook_server(addr: &str, enricher: Arc<Enricher>) -> Result<()>

pub async fn enrich_webhook(
    State(enricher): State<Arc<Enricher>>,
    Json(rows): Json<Vec<Row>>,
) -> Json<Vec<EnrichedRow>>

Checkpoint: REST API accepts POST requests, enriches synchronously

Module 2: Advanced Weather Data

File: src/weather/advanced.rs (New)
Status: To be created

Week 12.1: Air Quality Integration

pub struct AirQualityFetcher;

impl AirQualityFetcher {
    pub async fn fetch(location: &Location) -> Result<AirQuality>
    pub async fn fetch_with_components(location: &Location) -> Result<PollutionDetail>
}

#[derive(Serialize, Deserialize)]
pub struct AirQuality {
    pub aqi: u32,              // 1-5
    pub pm25: f32,             // µg/m³
    pub pm10: f32,
    pub no2: f32,
    pub o3: f32,
    pub so2: f32,
    pub co: f32,
}

Data Sources:

  • OpenWeather Air Pollution API (free + paid)
  • Alternative: Waqi API (World Air Quality Index)

Checkpoint: AQI data enriched for healthcare use cases

Week 12.2: Disaster & Alerts

pub struct DisasterMonitor;

impl DisasterMonitor {
    pub async fn get_active_alerts(location: &Location) -> Result<Vec<DisasterAlert>>
    pub async fn check_heatwave_risk(location: &Location, temp: f32) -> Result<HeatwaveRisk>
    pub async fn check_flood_risk(location: &Location, rainfall_24h: f32) -> Result<FloodRisk>
}

pub enum DisasterType {
    Heatwave, Coldwave, Flood, Storm, Cyclone, Drought, Wildfire,
}

pub struct DisasterAlert {
    pub alert_type: DisasterType,
    pub severity: u8,
    pub description: String,
    pub issued_at: DateTime<Utc>,
    pub expires_at: DateTime<Utc>,
}

Data Sources:

  • OpenWeather alerts API
  • NOAA (US)
  • Local emergency management APIs

Checkpoint: Logistics platforms can re-route based on disaster alerts

Week 12.3: Climate Context

pub struct ClimateContext {
    pub seasonal_phase: SeasonalPhase,
    pub deviation_from_normal: f32,
    pub monsoon_intensity: Option<f32>,
    pub el_nino_status: Option<String>,
    pub drought_risk: f32,
    pub flood_risk: f32,
}

impl ClimateContext {
    pub async fn fetch(location: &Location) -> Result<Self>
}

Data Sources:

  • NOAA Climate Prediction Center
  • India Meteorological Department
  • Climate indices (El Niño, NAO, IOD)

Checkpoint: Agriculture & energy companies get seasonal context

Week 12.4: Forecast Integration

pub struct WeatherForecast {
    pub forecast_date: DateTime<Utc>,
    pub forecast_hours: Vec<HourlyForecast>,
    pub confidence: f32,
}

impl ForecastFetcher {
    pub async fn fetch_forecast(
        location: &Location,
        hours_ahead: u32,  // 1-384 (16 days max)
    ) -> Result<WeatherForecast>
}

Checkpoints by forecast range:

  • 1-3 hours: 90%+ accuracy
  • 4-24 hours: 80-90% accuracy
  • 1-5 days: 70-80% accuracy
  • 5-14 days: 60-70% accuracy

Module 3: Operational Features

File: src/operations/mod.rs (New)
Status: To be created

Week 13: Error Recovery & Backfill

pub struct EnrichmentRecovery {
    failed_records: Vec<FailedRecord>,
    retry_policy: RetryPolicy,
}

impl EnrichmentRecovery {
    pub async fn retry_failed(&mut self, enricher: &Enricher) -> Result<()>
    pub async fn export_to_dlq(&self, path: &str) -> Result<()>
    pub async fn resume_from_checkpoint(&self, checkpoint: &str) -> Result<()>
}

Features:

  • Exponential backoff (2^n seconds)
  • Configurable max retries (default 3)
  • Dead-letter queue archival
  • Checkpoint/resume capability

Checkpoint: Failed records can be reprocessed later

Week 14: Multi-Tenancy

pub struct TenantManager {
    tenants: HashMap<String, TenantConfig>,
    rate_limiters: HashMap<String, RateLimiter>,
}

pub struct TenantConfig {
    pub tenant_id: String,
    pub api_key: String,
    pub rate_limit: RateLimit,
    pub cache_ttl: Duration,
}

impl TenantManager {
    pub async fn enrich_for_tenant(
        &self,
        tenant_id: &str,
        rows: Vec<Row>,
    ) -> Result<Vec<EnrichedRow>>
}

Features:

  • Per-tenant API keys
  • Rate limiting (requests/sec)
  • Isolated caching
  • Usage tracking

Checkpoint: SaaS platform can support multiple customers

Week 15: Audit & Compliance

pub struct AuditLogger {
    storage: Box<dyn AuditStorage>,
}

pub struct AuditLog {
    pub timestamp: DateTime<Utc>,
    pub tenant_id: String,
    pub action: AuditAction,
    pub row_count: usize,
    pub cost_usd: f64,
    pub errors: Vec<String>,
}

impl AuditLogger {
    pub async fn log(&self, audit: AuditLog) -> Result<()>
}

pub trait AuditStorage: Send + Sync {
    async fn store(&self, log: AuditLog) -> Result<()>;
    async fn query(&self, filters: AuditFilter) -> Result<Vec<AuditLog>>;
}

Compliance Standards:

  • GDPR: Data retention policies
  • SOC2: Access logging
  • HIPAA: Audit trails
  • PCI-DSS: API key rotation

Checkpoint: Enterprise compliance requirements met

Week 16: Testing & Release

  • 50+ test cases for streaming
  • Performance benchmarks (100K events/sec)
  • Integration tests with Kafka/MQTT
  • Documentation & deployment guides
  • Beta release notes

Python Bindings (Phase 3)

File: src/python/streaming.py (New)

from pyweatherenriched import PyWeatherEnriched, StreamingConfig

# Kafka enrichment
enricher = PyWeatherEnriched(api_key="...")
streaming = enricher.create_kafka_stream(
    bootstrap_servers=["kafka:9092"],
    input_topic="operational_data",
    output_topic="enriched_data",
    config=StreamingConfig(batch_size=1000)
)
streaming.start()

# MQTT enrichment
mqtt_enricher = enricher.create_mqtt_stream(
    broker_address="mqtt.example.com",
    topic="sensors/+/data",
)
mqtt_enricher.start()

# Webhook server
server = enricher.create_webhook_server(
    port=8080,
    path="/enrich"
)
server.start()

Testing Strategy

Unit Tests (Week 15)

  • Streaming buffer tests (5)
  • Rate limiter tests (4)
  • Dead-letter queue tests (3)
  • AQI fetcher tests (5)
  • Disaster alert tests (4)
  • Climate context tests (3)
  • Forecast tests (4)
  • Tenant isolation tests (4)
  • Audit logging tests (4)
  • Total: 40+ tests

Integration Tests (Week 16)

  • Kafka end-to-end (3)
  • MQTT end-to-end (3)
  • HTTP webhook (3)
  • Error recovery (2)
  • Multi-tenant isolation (2)
  • Total: 13 integration tests

Performance Tests (Week 16)

  • 100K events/sec throughput
  • <100ms latency percentile
  • Cache hit rate > 70%
  • Memory usage < 500MB

Dependencies to Add

# Phase 3 additions
rdkafka = "0.36"           # Kafka
rumqttc = "0.24"           # MQTT
axum = "0.7"               # HTTP server
tokio-stream = "0.1"       # Streaming utilities
prometheus = "0.13"        # Metrics
opentelemetry = "0.21"     # Tracing
tracing = "0.1"            # Logging

Rollout Strategy

Week 11: Kafka + MQTT + Webhooks (beta)
Week 12: Advanced weather (production)
Week 13: Operational features (production)
Week 14-15: Multi-tenancy + Audit (production)
Week 16: Full release

Success Criteria

✅ 100K events/second throughput
✅ <100ms end-to-end latency
✅ 99.9% uptime SLA
✅ Zero message loss (exactly-once)
✅ GDPR/SOC2 compliance
✅ 50+ test cases passing
✅ Complete documentation

Next Phase: Phase 4 - Geo-spatial & Enterprise (8 weeks)