Weather Sensor Metrics API

Production-ready REST API for high-throughput time-series weather data ingestion, aggregation, and analytics with PostgreSQL optimization.

📊 Project Management

Methodology: Agile with Kanban board for task tracking and sprint management.

Project Structure

The project was organized using Epic → Story → Task hierarchy following Clean Architecture layers:

📦 Weather Sensor API (7 Epics, 18 Stories, 41 Tasks)
│
├─ 🔷 EPIC 1: Infrastructure Setup (3 Stories, 6 Tasks)
│  ├─ 📘 Story: Project Initialization
│  ├─ 📘 Story: Database Infrastructure
│  └─ 📘 Story: Project Dependencies
│
├─ 🔷 EPIC 2: Domain Layer (3 Stories, 6 Tasks)
│  ├─ 📘 Story: Core Entities
│  ├─ 📘 Story: Repositories
│  └─ 📘 Story: Specifications Pattern
│
├─ 🔷 EPIC 3: Application Layer (3 Stories, 5 Tasks)
│  ├─ 📘 Story: DTOs
│  ├─ 📘 Story: Business Services
│  └─ 📘 Story: MapStruct Mappers
│
├─ 🔷 EPIC 4: Web Layer (2 Stories, 4 Tasks)
│  ├─ 📘 Story: REST Controllers
│  └─ 📘 Story: OpenAPI Documentation
│
├─ 🔷 EPIC 5: Validation & Error Handling (2 Stories, 3 Tasks)
│  ├─ 📘 Story: Custom Validators
│  └─ 📘 Story: Global Exception Handler
│
├─ 🔷 EPIC 6: Testing (2 Stories, 5 Tasks)
│  ├─ 📘 Story: Unit Tests (MetricQueryService, MetricIngestionService)
│  └─ 📘 Story: Integration Tests (TestContainers)
│
├─ 🔷 EPIC 7: Quick Wins - Production Readiness (3 Stories, 14 Tasks)  v1.1.0
│  ├─ 📘 Story: Observability & Monitoring
│  └─ 📘 Story: Async Processing
└─ 🔷 EPIC 8: API Protection & Rate Limiting (1 Story, 6 Tasks) ✨ v1.2.0
   └─ 📘 Story: Rate Limiting with Bucket4j

Story Points Distribution

Epic	Stories	Tasks	Story Points	Status
Epic 1: Infrastructure	3	6	9	✅ Done
Epic 2: Domain Layer	3	6	8	✅ Done
Epic 3: Application Layer	3	5	10	✅ Done
Epic 4: Web Layer	2	4	5	✅ Done
Epic 5: Validation	2	3	4	✅ Done
Epic 6: Testing	2	5	8	✅ Done
Epic 7: Quick Wins	3	14	13	✅ Done
Epic 8: Rate Limiting	1	6	5	✅ Done
TOTAL	19	47	62	100%

Jira Board Views

Backlog View: Organized by Epics with all Stories and Tasks visible

Board View (Kanban): All tasks moved to "Done" column after completion

Epic Details: Example: Infrastructure Setup Epic with linked Stories

Task Tracking Example

Epic 2: Domain Layer Implementation

📘 Story: Core Entities (3 Story Points)
├─ ☑️ Task: Create Sensor entity
│  ├─ Description: JPA entity with @ManyToOne relationship
│  ├─ Commit: 423ff54
│  └─ Status: Done
├─ ☑️ Task: Create MetricData entity  
│  ├─ Description: Time-series entity with composite index
│  ├─ Commit: 423ff54
│  └─ Status: Done
└─ ☑️ Task: Create MetricType enum
   ├─ Description: Enum with unit and displayName
   ├─ Commit: 423ff54
   └─ Status: Done

Development Phases

Phase	Duration	Epics Completed	Key Deliverables
Setup	Day 1 (4h)	Epic 1	Database + Flyway + Dependencies
Core	Day 1 (6h)	Epic 2-3	Domain + Services + DTOs
API	Day 2 (3h)	Epic 4	Controllers + Swagger
Quality	Day 2 (4h)	Epic 5-6	Validators + Tests
Production	Day 3 (2h)	Epic 7	Observability + Async + Docs
Protection	Day 3 (1.5h)	Epic 8	Rate Limiting + Tests

Total Development Time: ~20.5 hours across 3 days Project Status: 100% Complete ✅

Quick Start

# Clone and run
git clone https://github.com/[your-username]/weather-sensor-api.git
cd weather-sensor-api

# Start PostgreSQL
docker compose up -d

# Run application
./gradlew bootRun

# Access API docs
open http://localhost:8080/swagger-ui.html

# Check health & metrics (NEW in v1.1.0)
curl http://localhost:8080/actuator/health
curl http://localhost:8080/actuator/metrics
curl http://localhost:8080/actuator/prometheus

Problem & Solution

Challenge: Store and query millions of time-series sensor measurements with sub-100ms response times and high ingestion throughput.

Solution: PostgreSQL with BRIN indexes + JPA Specifications + database-level aggregations + async processing.

Key Requirements:

✅ Ingest metrics from distributed sensors (sync & async)
✅ High-throughput async ingestion (~2000 req/s)
✅ Query with multiple aggregations (MIN, MAX, AVG, SUM)
✅ Filter by sensor(s), metric type(s), date range
✅ Date range validation (1 day - 1 month)
✅ Production-ready monitoring and observability

Architecture

Clean Architecture with clear separation of concerns:

┌─────────────────────────────────────────────────┐
│  WEB LAYER                                      │
│  Controllers → OpenAPI/Swagger                  │
├─────────────────────────────────────────────────┤
│  APPLICATION LAYER                              │
│  Services → DTOs → MapStruct → Validators       │
│  Event Listeners → Async Executors           │
├─────────────────────────────────────────────────┤
│  DOMAIN LAYER                                   │
│  Entities → Repositories → Specifications       │
│  Domain Events                               │
├─────────────────────────────────────────────────┤
│  INFRASTRUCTURE                                 │
│  PostgreSQL → Flyway → Actuator  → Config    │
└─────────────────────────────────────────────────┘

Patterns Applied:

Repository Pattern (data access abstraction)
Specification Pattern (dynamic queries)
Strategy Pattern (pluggable aggregations)
DTO Pattern (API decoupling)
Event-Driven Architecture (domain events)
Async Processing (CompletableFuture + Thread Pool)

Tech Stack

Layer	Technology	Rationale
Language	Java 17	Modern features, LTS support
Framework	Spring Boot 3.2.1	Production-ready, extensive ecosystem
Database	PostgreSQL 15	BRIN indexes for time-series, ACID guarantees
ORM	Spring Data JPA	Repository abstraction, Specification API
Build	Gradle 8.5	Faster than Maven, better caching
Migrations	Flyway	Version control for schema
Mapping	MapStruct 1.5.5	Compile-time, zero reflection
Testing	JUnit 5 + TestContainers	Real database integration tests
Docs	SpringDoc OpenAPI	Interactive API documentation
Monitoring	Actuator + Micrometer	Prometheus-compatible metrics, health checks
Async	CompletableFuture + ThreadPool	High-throughput non-blocking ingestion

🎯 New Features (v1.1.0)

Production-readiness improvements added after initial release.

Observability & Monitoring

Production-ready monitoring with Spring Boot Actuator and Micrometer.

Endpoints:

GET /actuator/health - Application health with custom indicators
GET /actuator/metrics - Available metrics list
GET /actuator/prometheus - Prometheus scraping format
GET /actuator/info - Application information

Custom Health Indicators:

{
  "status": "UP",
  "components": {
    "database": {
      "status": "UP",
      "details": {
        "database": "PostgreSQL",
        "total_metrics": 256000,
        "checked_at": "2025-10-15T10:30:00"
      }
    },
    "sensors": {
      "status": "UP",
      "details": {
        "active_sensors": 12,
        "total_sensors": 15,
        "availability_percentage": 80.0
      }
    }
  }
}

Metrics Tracked:

metric.query.requests - Query counter (tags: statistic, sensor_count)
metric.query.time - Query execution histogram
metric.query.execution - Timer with success/failure
metric.ingestion.success - Ingestion counter (tags: mode, metric_type, sensor)
metric.ingestion.errors - Error counter (tags: mode, error)
metric.ingestion.batch - Batch counter

Example:

curl http://localhost:8080/actuator/metrics/metric.ingestion.success

{
  "name": "metric.ingestion.success",
  "measurements": [{"statistic": "COUNT", "value": 15420.0}],
  "availableTags": [
    {"tag": "mode", "values": ["sync", "async", "batch"]},
    {"tag": "metric_type", "values": ["TEMPERATURE", "HUMIDITY", "WIND_SPEED"]}
  ]
}

⚡ Async Metric Ingestion

High-throughput non-blocking ingestion with CompletableFuture.

Thread Pool Configuration:

Core threads: 10 (always alive)
Max threads: 50 (scales under load)
Queue capacity: 500 (burst buffer)
Rejection policy: CallerRunsPolicy (backpressure)

Performance:

Sync: ~500 req/s
Async: ~2000 req/s (4x improvement)

Event-Driven Architecture:

MetricIngestedEvent → MetricIngestedEventListener
  ↓
- Audit logging
- Real-time alerts (future)
- Cache invalidation (future)
- External publishing (Kafka/SQS - future)

New Endpoint:

POST /api/v1/metrics/async
{
  "sensorId": 1,
  "metricType": "TEMPERATURE",
  "value": 23.5,
  "timestamp": "2025-10-22T10:30:00"
}

# Response: 202 Accepted (fire-and-forget)

🛡️ Rate Limiting

API protection against abuse using token bucket algorithm.

Configuration:

Sustained rate: 100 requests/minute per IP
Burst capacity: 20 requests/second
In-memory storage (ConcurrentHashMap)
Protection on /api/v1/metrics/** endpoints

Rate Limit Headers:

X-RateLimit-Limit: 100             # Max requests per minute
X-RateLimit-Remaining: 95          # Tokens remaining
X-RateLimit-Retry-After-Seconds: 60  # Wait time (on 429)

Example - Rate Limit Exceeded:

POST /api/v1/metrics
# After 20 requests in 1 second...

HTTP/1.1 429 Too Many Requests
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 0
X-RateLimit-Retry-After-Seconds: 60

{
  "error": "Too Many Requests",
  "message": "Rate limit exceeded. Try again in 60 seconds.",
  "status": 429
}

Protected Endpoints:

POST /api/v1/metrics (sync ingestion)
POST /api/v1/metrics/async (async ingestion)
POST /api/v1/metrics/batch (batch ingestion)

Excluded from Rate Limiting:

/actuator/** (monitoring)
/swagger-ui/** (documentation)

Configuration (application.yml):

rate-limit:
  enabled: true
  requests-per-minute: 100
  burst-capacity: 20

Database Schema

-- Optimized for time-series queries
CREATE TABLE metric_data (
    id BIGSERIAL PRIMARY KEY,
    sensor_id BIGINT NOT NULL,
    metric_type VARCHAR(50) NOT NULL,
    value DECIMAL(10,2) NOT NULL,
    timestamp TIMESTAMP NOT NULL,
    CONSTRAINT fk_sensor FOREIGN KEY (sensor_id) REFERENCES sensors(id)
);

-- Composite index for common query pattern: sensor + type + time range
CREATE INDEX idx_metric_composite
    ON metric_data(sensor_id, metric_type, timestamp DESC);

-- BRIN index for time-range scans (10x smaller than B-tree)
CREATE INDEX idx_metric_timestamp_brin
    ON metric_data USING BRIN (timestamp);

Index Strategy:

Composite index covers 90% of queries
BRIN index: 1% storage overhead vs B-tree, optimal for sequential time-series
Tested with EXPLAIN ANALYZE: 25x faster on 1M records

API Endpoints

Ingestion Endpoints

1. Ingest Metric (Synchronous)

POST /api/v1/metrics
Content-Type: application/json

{
  "sensorId": 1,
  "metricType": "TEMPERATURE",
  "value": 23.5,
  "timestamp": "2025-10-22T10:30:00"
}

# Response: 201 Created
{
  "id": 12345,
  "sensorId": 1,
  "metricType": "TEMPERATURE",
  "value": 23.5,
  "timestamp": "2025-10-22T10:30:00"
}

Use when: You need the generated ID or immediate confirmation.

2. Ingest Metric (Asynchronous) NEW

POST /api/v1/metrics/async
Content-Type: application/json

{
  "sensorId": 1,
  "metricType": "TEMPERATURE",
  "value": 23.5,
  "timestamp": "2025-10-22T10:30:00"
}

# Response: 202 Accepted (empty body - fire and forget)

Use when: High-throughput IoT scenarios, bulk sensors, fire-and-forget.

Benefits:

✅ 4x better throughput (~2000 req/s vs ~500 req/s)
✅ Non-blocking for client
✅ Automatic backpressure handling

Trade-off: No generated ID in response (eventual consistency).

3. Batch Ingest

POST /api/v1/metrics/batch
Content-Type: application/json

[
  {"sensorId": 1, "metricType": "TEMPERATURE", "value": 23.5, "timestamp": "..."},
  {"sensorId": 1, "metricType": "HUMIDITY", "value": 65.0, "timestamp": "..."}
]

# Response: 201 Created (array of created metrics)

Query Endpoints

4. Query Aggregated Metrics

POST /api/v1/metrics/query

{
  "sensorIds": [1, 2],           # Optional: null = all sensors
  "metricTypes": ["TEMPERATURE", "HUMIDITY"],
  "statistic": "AVG",            # MIN | MAX | AVG | SUM
  "startDate": "2024-01-08T00:00:00",
  "endDate": "2024-01-15T23:59:59"
}

# Response: 200 OK
[
  {
    "metricType": "TEMPERATURE",
    "value": 22.3,
    "unit": "°C",
    "statistic": "AVG",
    "startDate": "2024-01-08T00:00:00",
    "endDate": "2024-01-15T23:59:59",
    "dataPointsCount": 1520
  }
]

Date Range Validation:

Min: 1 day, Max: 31 days (prevents expensive queries)
Default: Last 7 days
Multi-layer validation: Bean Validation + Service layer

Full API docs: http://localhost:8080/swagger-ui.html

Key Technical Decisions

1. Database-Level Aggregations

Decision: Execute aggregations in PostgreSQL, not in-memory.

Implementation:

@Query("""
    SELECT m.metricType,
           CASE WHEN :statistic = 'MIN' THEN MIN(m.value)
                WHEN :statistic = 'MAX' THEN MAX(m.value)
                WHEN :statistic = 'SUM' THEN SUM(m.value)
                ELSE AVG(m.value)
           END as result
    FROM MetricData m
    WHERE (:sensorIds IS NULL OR m.sensor.id IN :sensorIds)
      AND m.metricType IN :metricTypes
      AND m.timestamp BETWEEN :startDate AND :endDate
    GROUP BY m.metricType
""")
List<Object[]> calculateAggregatedStatistics(...);

Rationale:

✅ Leverages PostgreSQL's query optimizer
✅ Reduces data transfer (only aggregated results)
✅ Enables efficient index usage
✅ Scales horizontally with read replicas

Trade-off: Tighter coupling to SQL (mitigated with Repository abstraction)

2. BRIN Indexes for Time-Series Data

Decision: Use BRIN (Block Range Index) for timestamp column.

Comparison:

Index Type	Size (1M records)	Query Time	Insert Overhead
No Index	-	2500ms	None
B-Tree	21 MB	80ms	High
BRIN	24 KB	100ms	Low

Result: BRIN provides 25x speedup with 99% less storage vs B-tree.

Why it works: Time-series data is naturally ordered by timestamp, making BRIN optimal.

3. Async Processing with Thread Pool

Decision: Non-blocking ingestion with CompletableFuture and dedicated thread pool.

Configuration:

Thread Pool:
  Core Size: 10 threads (baseline)
  Max Size: 50 threads (burst capacity)
  Queue: 500 requests (buffer)
  Rejection: CallerRunsPolicy (backpressure)

Rationale:

✅ 4x throughput improvement (500 → 2000 req/s)
✅ Handles burst traffic gracefully
✅ Prevents system overload with backpressure
✅ Event-driven for extensibility

Trade-off: Response doesn't include generated ID (acceptable for IoT fire-and-forget).

4. MapStruct for DTO Mapping

Decision: Compile-time mapping with MapStruct vs runtime reflection.

Comparison:

Library	Performance	Type Safety	Decision
MapStruct	✅ Fast (compile-time)	✅ Compile errors	CHOSEN
ModelMapper	❌ Slow (reflection)	❌ Runtime errors	Rejected
Manual	✅ Fast	⚠️ Boilerplate	Rejected

Impact: 30% code reduction in service layer, zero reflection overhead.

5. TestContainers for Integration Tests

Decision: Real PostgreSQL via Docker vs in-memory H2.

Rationale:

✅ Tests actual BRIN indexes (H2 doesn't support)
✅ Validates PostgreSQL-specific SQL
✅ Catches production issues early
❌ Slower (28s vs 5s) - acceptable trade-off

Result: 0 production database bugs in testing phase.

Performance Benchmarks

Query Performance (1M records)

Query	Without Indexes	With Indexes	Improvement
Avg temp, 1 sensor, 7 days	2500ms	50ms	50x
Max humidity, 10 sensors, 1 month	5000ms	150ms	33x

Ingestion Throughput

Mode	Throughput	p50 Latency	p99 Latency
Sync	~500 req/s	10ms	50ms
Async	~2000 req/s	5ms	20ms
Batch (100)	~100 req/s	100ms	300ms

Query Throughput

Operation	Throughput	p50 Latency	p99 Latency
Query (1 week)	~500 req/s	20ms	100ms
Query (1 month)	~200 req/s	50ms	200ms

Test Environment: MacBook Pro M1, 16GB RAM, Docker Desktop

Project Structure

src/main/java/com/weathersensor/api/
├── domain/                   # Business logic (framework-independent)
│   ├── model/               # JPA entities
│   ├── repository/          # Data access interfaces
│   ├── specification/       # Dynamic query builders
│   └── event/               # Domain events
├── application/             # Use cases
│   ├── dto/                # Request/Response DTOs
│   ├── service/            # Business services
│   ├── mapper/             # MapStruct mappers
│   ├── validation/         # Custom validators
│   └── listener/           # Event listeners
├── infrastructure/          # External concerns
│   ├── config/             # Spring configuration (async, etc.)
│   ├── health/             # Custom health indicators
│   └── exception/          # Global error handling
└── web/                     # API layer
    └── controller/         # REST endpoints

src/main/resources/
└── db/migration/            # Flyway SQL scripts
    ├── V1__create_sensors_table.sql
    ├── V2__create_metric_data_table.sql
    └── V3__create_performance_indexes.sql

Testing Strategy

Test Pyramid

Test Pyramid:
        /\
       /E2E\      8 tests (25%) - TestContainers + real PostgreSQL
      /------\
     /        \
    /   Unit   \  24 tests (75%) - Mockito + isolated components
   /------------\

Total: 32 tests passing ✅
Coverage: 76%

Test Suites

Test Suite	Tests	Description
MetricQueryServiceTest	6 tests	Aggregation logic, date defaults, empty results
MetricIngestionServiceTest	7 tests	Sync/async ingestion, events, batch, errors
RateLimitInterceptorTest ✨	3 tests	Rate limiting logic, headers, 429 responses
Integration Tests	8 tests	End-to-end with real PostgreSQL
Validator Tests	8 tests	Custom validators, boundary conditions

New Tests in v1.2.0 ✨:

shouldAllowRequestWhenWithinLimit() - Verify rate limit allows requests
shouldBlockRequestWhenLimitExceeded() - Verify 429 response
shouldAllowWhenDisabled() - Verify bypass when disabled

Test Report

Run Tests:

./gradlew clean test
open build/reports/tests/test/index.html

Test Results Summary:

✅ 29/29 tests passing (100% success rate)
⏱️ Average execution time: 1.2s per test
🧪 7 new tests for async ingestion (all passing)
🔄 Concurrent async test validates thread pool behavior
📊 Event publishing verified with ArgumentCaptor
🐳 Integration tests use real PostgreSQL (TestContainers)

📸 Note: Test report screenshots can be generated by running tests and opening build/reports/tests/test/index.html in your browser. Key views to capture:

Overview page showing 29 tests passing

Classes breakdown (MetricQueryServiceTest, MetricIngestionServiceTest, etc.)

MetricIngestionServiceTest detail showing all 7 async tests

Test Report Screenshots:

Overview: 32 tests passing with 0 failures

Test classes: MetricQueryServiceTest (6), MetricIngestionServiceTest (7), Integration (8), Validators (8)

Development Workflow

Git Strategy: Feature branches merged to develop, then to main.

gitGraph
   commit id: "Initial setup"
   branch develop
   checkout develop

   branch feat/database
   commit id: "Flyway migrations"
   commit id: "DB schema + indexes"
   checkout develop
   merge feat/database

   branch feat/domain
   commit id: "JPA entities"
   commit id: "Repositories + specs"
   checkout develop
   merge feat/domain

   branch feat/application
   commit id: "Services + DTOs"
   commit id: "MapStruct mappers"
   checkout develop
   merge feat/application

   branch feat/web
   commit id: "Controllers + OpenAPI"
   checkout develop
   merge feat/web

   branch feat/testing
   commit id: "Unit tests"
   commit id: "Integration tests"
   checkout develop
   merge feat/testing

   branch feat/observability-and-async
   commit id: "Actuator + health checks"
   commit id: "Async ingestion + events"
   checkout develop
   merge feat/observability-and-async tag: "v1.1.0"

   checkout main
   merge develop

Installation

Prerequisites:

Java 17+
Docker & Docker Compose

Steps:

# 1. Start database
docker compose up -d

# 2. Run application
./gradlew bootRun

# 3. Access Swagger UI
open http://localhost:8080/swagger-ui.html

# 4. Check health
curl http://localhost:8080/actuator/health

# 5. View metrics
curl http://localhost:8080/actuator/metrics

Verify:

# Health check
curl http://localhost:8080/actuator/health
# {"status":"UP","components":{"database":{"status":"UP"},"sensors":{"status":"UP"}}}

# Prometheus metrics
curl http://localhost:8080/actuator/prometheus | grep metric_ingestion

Configuration

Profiles: dev, prod, test

# application-dev.yml
spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/weather_sensor_db
    username: postgres
    password: postgres
  jpa:
    show-sql: true # Debug SQL queries

management:
  endpoints:
    web:
      exposure:
        include: health,metrics,prometheus,info
  health:
    db:
      enabled: true

Production considerations:

Use environment variables for credentials
Enable connection pooling (HikariCP: 20 connections)
Monitor with Prometheus + Grafana
Configure thread pool based on workload
Set up alerts on health check failures

Monitoring & Observability

Prometheus Integration

Scrape Configuration (prometheus.yml):

scrape_configs:
  - job_name: "weather-sensor-api"
    metrics_path: "/actuator/prometheus"
    static_configs:
      - targets: ["localhost:8080"]

Grafana Dashboards

Recommended Metrics:

Request rate: rate(http_server_requests_seconds_count[1m])
Error rate: rate(metric_ingestion_errors_total[1m])
Query latency p99: histogram_quantile(0.99, metric_query_time_seconds_bucket)
Thread pool usage: executor_active_threads{name="metricIngestionExecutor"}
Health status: up{job="weather-sensor-api"}

⚠️ Known Limitations (v1.1.0)

This version prioritizes core functionality + production monitoring for the technical assessment.

❌ Not Yet Implemented

Authentication/Authorization (API is public)
Rate limiting / abuse protection
Pagination for large result sets
Distributed caching (Redis)
Horizontal scaling (single instance only)

Why: Optimize for correctness, observability, and performance first; defer advanced features to post-v1.1.

🚀 Future Enhancements

✅ Implemented in v1.1.0 (Quick Wins - Observability)

✅ Observability: Micrometer + Actuator + Prometheus
✅ Async ingestion: CompletableFuture + Thread Pool
✅ Event-driven architecture: Domain events + listeners
✅ Production metrics: Query/ingestion counters and timers
✅ Custom health checks: Database + Sensor connectivity

✅ Implemented in v1.2.0 (API Protection)

✅ Rate Limiting: Bucket4j token bucket (100 req/min per IP)
✅ Rate limit headers: X-RateLimit-Limit, X-RateLimit-Remaining
✅ 429 Too Many Requests: Proper HTTP status with retry-after
✅ Configurable limits: Via application.yml properties
✅ In-memory storage: ConcurrentHashMap for simplicity

🟢 Short-Term (Next Sprint - Not Implemented)

⏳ Distributed rate limiting with Redis (for horizontal scaling)
⏳ Per-API-key rate limiting (instead of per-IP)

Acceptance criteria: Secure endpoints, stable P99 latency, comprehensive dashboards.

🔵 Long-Term (Scale & Flexibility)

⚙️ Time-series optimization: TimescaleDB or PostgreSQL partitioning
⚙️ Event streaming with Kafka for decoupled ingestion
⚙️ Read replicas for horizontal read scaling
⚙️ JWT + RBAC (for operator/admin interfaces; sensors keep API keys)
⚙️ GraphQL API for client-driven, flexible queries
⚙️ WebSocket for real-time sensor data streaming

🧠 Notes

Designed to remain cloud/vendor-agnostic; maps cleanly to AWS (ECS/Fargate, RDS, ElastiCache, MSK, CloudWatch).
Security defaults off for assessment; enable via profile for production.

Troubleshooting

Port 5432 in use:

docker ps | grep postgres
docker stop <container_id>

Flyway migration fails:

./gradlew flywayClean  # CAUTION: drops tables
./gradlew flywayMigrate

TestContainers timeout:

docker pull postgres:15-alpine
./gradlew test --rerun-tasks

Thread pool exhausted:

# Check thread pool metrics
curl http://localhost:8080/actuator/metrics/executor.active
# Adjust AsyncConfig pool sizes if needed

Health check failing:

# Check detailed health info
curl http://localhost:8080/actuator/health | jq
# Look for specific component failures in the response

Technical Highlights

What makes this implementation senior-level:

✅ Strategic indexing: BRIN for time-series (not just default B-tree)
✅ Database-level aggregations: Leverages PostgreSQL optimizer
✅ Specification pattern: Avoids query method explosion
✅ Multi-layer validation: Defense in depth
✅ TestContainers: Real database in tests (catches index issues)
✅ MapStruct: Compile-time safety (no runtime surprises)
✅ Clean Architecture: Domain isolated from frameworks
✅ Async processing: High-throughput with CompletableFuture
✅ Event-driven: Domain events for extensibility
✅ Production monitoring: Actuator + Micrometer + Prometheus
✅ Custom health indicators: Proactive system monitoring
✅ Justified trade-offs: Every decision documented with rationale
✅ Comprehensive testing: 29 tests with async and concurrent scenarios
✅ Rate limiting: Token bucket algorithm for API protection ✨
✅ Graceful degradation: 429 responses with retry guidance ✨

Resources

API Documentation: http://localhost:8080/swagger-ui.html
Health Check: http://localhost:8080/actuator/health
Metrics: http://localhost:8080/actuator/metrics
Prometheus: http://localhost:8080/actuator/prometheus
Test Report: build/reports/tests/test/index.html

External Docs:

Author

Matheus Ferreira Amorim-Trindade

Name		Name	Last commit message	Last commit date
Latest commit History 37 Commits
docs/screenshots		docs/screenshots
gradle/wrapper		gradle/wrapper
src		src
.gitattributes		.gitattributes
.gitignore		.gitignore
README.md		README.md
Weather-Sensor-API.postman_collection.json		Weather-Sensor-API.postman_collection.json
build.gradle		build.gradle
docker-compose.yml		docker-compose.yml
gradlew		gradlew
gradlew.bat		gradlew.bat
settings.gradle		settings.gradle

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Weather Sensor Metrics API

📊 Project Management

Project Structure

Story Points Distribution

Jira Board Views

Task Tracking Example

Development Phases

Quick Start

Problem & Solution

Architecture

Tech Stack

🎯 New Features (v1.1.0)

Observability & Monitoring

⚡ Async Metric Ingestion

🛡️ Rate Limiting

Database Schema

API Endpoints

Ingestion Endpoints

1. Ingest Metric (Synchronous)

2. Ingest Metric (Asynchronous) NEW

3. Batch Ingest

Query Endpoints

4. Query Aggregated Metrics

Key Technical Decisions

1. Database-Level Aggregations

2. BRIN Indexes for Time-Series Data

3. Async Processing with Thread Pool

4. MapStruct for DTO Mapping

5. TestContainers for Integration Tests

Performance Benchmarks

Query Performance (1M records)

Ingestion Throughput

Query Throughput

Project Structure

Testing Strategy

Test Pyramid

Test Suites

Test Report

Development Workflow

Installation

Configuration

Monitoring & Observability

Prometheus Integration

Grafana Dashboards

⚠️ Known Limitations (v1.1.0)

❌ Not Yet Implemented

🚀 Future Enhancements

✅ Implemented in v1.1.0 (Quick Wins - Observability)

✅ Implemented in v1.2.0 (API Protection)

🟢 Short-Term (Next Sprint - Not Implemented)

🔵 Long-Term (Scale & Flexibility)

🧠 Notes

Troubleshooting

Technical Highlights

Resources

Author

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