Preparing for Laravel/PHP Backend Developer role at Optimise Everything

There are several approaches to API versioning in Laravel:

1. URI Versioning (Most Common):

Route::prefix('api/v1')->group(function () {
    Route::get('/sensors', [SensorController::class, 'index']);
});

2. Header Versioning:

Route::middleware('api.version:1')->group(function () {
    // routes
});

3. Query Parameter:

// api/sensors?version=1
public function handle($request, Closure $next)
{
    $version = $request->query('version', 1);
    // Logic here
}

For this IoT-focused platform, I'd recommend URI versioning as it's the most explicit and easiest to maintain when dealing with multiple building management systems and third-party integrations.

API Resources in Laravel are transformation layers that convert models into JSON responses. They're crucial for IoT data handling:

class SensorDataResource extends JsonResource
{
    public function toArray($request)
    {
        return [
            'id' => $this->id,
            'building_id' => $this->building_id,
            'temperature' => $this->temperature,
            'humidity' => $this->humidity,
            'timestamp' => $this->created_at->toIso8601String(),
            'meta' => [
                'unit' => 'celsius',
                'calibration_date' => $this->calibration_date
            ]
        ];
    }
}

Benefits particularly relevant to building management:

• Consistent data formatting across different sensor types
• Version-specific data transformations
• Efficient handling of nested relationships (building → floors → rooms → sensors)
• Control over data exposure

For a building management system handling IoT devices, rate limiting is crucial. Implementation:

1. Using Laravel's built-in throttle middleware:

Route::middleware('throttle:60,1')->group(function () {
    Route::get('/sensor-data', [SensorController::class, 'getData']);
});

2. Custom rate limiter for different client types:

RateLimiter::for('api', function (Request $request) {
    return Limit::perMinute(match ($request->user()->type) {
        'building' => 1000,    // Higher limit for building systems
        'dashboard' => 60,     // Standard limit for dashboard users
        default => 30
    });
});

3. Redis-based rate limiting for distributed systems:

RateLimiter::for('sensor-updates', function (Request $request) {
    return Limit::perMinute(300)->by($request->sensor_id);
});

For a building management system, I'd implement:

1. Laravel Sanctum (Primary Choice):

Route::middleware('auth:sanctum')->group(function () {
    Route::get('/building-metrics', [MetricsController::class, 'index']);
});

Pros:

• Perfect for SPA + mobile apps
• Supports multiple authentication guards
• Built-in CSRF protection Cons:
• Additional token management overhead

2. JWT Authentication: Pros:

• Stateless
• Ideal for microservices architecture
• Good for IoT device authentication Cons:
• Complex token management
• Need to handle token refresh

3. API Key Authentication (for IoT devices):

public function handle($request, $next)
{
    if (!$request->hasHeader('X-API-Key')) {
        return response()->json(['error' => 'Unauthorized'], 401);
    }
    return $next($request);
}

For this building management platform, I would implement:

1. OpenAPI/Swagger using L5-Swagger:

/**
 * @OA\Get(
 *     path="/api/building/{id}/sensors",
 *     summary="Get building sensor data",
 *     @OA\Parameter(name="id", in="path", required=true),
 *     @OA\Response(response="200", description="Success")
 * )
 */
public function getSensorData($id)
{
    // Implementation
}

2. Scribe for automated documentation:

php artisan scribe:generate

Best practices:

• Document all endpoints with examples
• Include authentication methods
• Provide real-world IoT data examples
• Version documentation alongside API
• Include error responses and codes

For a critical system like building management, robust error handling is essential:

1. Custom Exception Handler:

class Handler extends ExceptionHandler
{
    public function register()
    {
        $this->renderable(function (SensorConnectionException $e) {
            return response()->json([
                'error' => 'Sensor Connection Failed',
                'message' => $e->getMessage(),
                'building_id' => $e->getBuildingId(),
                'timestamp' => now()
            ], 503);
        });
    }
}

2. Validation Errors:

class SensorDataRequest extends FormRequest
{
    public function rules()
    {
        return [
            'temperature' => 'required|numeric|between:-50,100',
            'humidity' => 'required|numeric|between:0,100'
        ];
    }
}

3. Logging Integration:

Log::channel('iot')->error('Sensor failure', [
    'building_id' => $buildingId,
    'sensor_id' => $sensorId,
    'error' => $exception->getMessage()
]);

For handling large building management datasets:

1. Implement Pagination:

return SensorDataResource::collection(
    SensorData::paginate(100)
);

2. Eager Loading for Related Data:

$buildings = Building::with(['floors.rooms.sensors' => function($query) {
    $query->latest()->limit(100);
}])->get();

3. Chunking for Large Data Processing:

Building::chunk(100, function ($buildings) {
    foreach ($buildings as $building) {
        // Process building data
    }
});

4. Redis Caching for Frequently Accessed Data:

$sensorData = Cache::remember('building.sensors.'.$buildingId, 300, function () {
    return $this->sensorRepository->getLatestReadings();
});

5. API Resources with Conditional Attributes:

public function toArray($request)
{
    return [
        'id' => $this->id,
        'basic_metrics' => $this->when($request->includes('basic'), function() {
            return $this->getBasicMetrics();
        }),
        'detailed_analytics' => $this->when($request->includes('detailed'), function() {
            return $this->getDetailedAnalytics();
        })
    ];
}

For a building intelligence system handling multiple IoT sensors, I would implement:

1. Message Queue Architecture:

• Use RabbitMQ for message queuing to handle high-volume sensor data
• Implement separate queues for different types of sensors (temperature, air quality, energy consumption)

2. Data Processing Pipeline:

class SensorDataProcessor
{
    public function process(SensorData $data)
    {
        // Validate data
        $this->validator->validate($data);
        
        // Process based on sensor type
        match ($data->type) {
            'temperature' => $this->temperatureProcessor->handle($data),
            'air_quality' => $this->airQualityProcessor->handle($data),
            'energy' => $this->energyProcessor->handle($data),
        };
        
        // Store processed data
        $this->repository->store($data);
        
        // Trigger real-time updates
        event(new SensorDataProcessed($data));
    }
}

3. Scalable Storage:

• Time-series database for historical data
• Redis for real-time data caching

4. Load Balancing:

• Implement horizontal scaling using AWS Auto Scaling
• Use multiple worker processes for parallel processing

WebSockets are crucial for real-time bidirectional communication between IoT devices and the server. In Laravel, I would implement:

1. Laravel WebSockets or Pusher:

// WebSocket configuration
return [
    'default' => 'pusher',
    'connections' => [
        'pusher' => [
            'driver' => 'pusher',
            'key' => env('PUSHER_APP_KEY'),
            'secret' => env('PUSHER_APP_SECRET'),
            'app_id' => env('PUSHER_APP_ID'),
            'options' => [
                'cluster' => env('PUSHER_APP_CLUSTER'),
                'encrypted' => true
            ],
        ],
    ],
];

2. Event Broadcasting:

class SensorDataUpdated implements ShouldBroadcast
{
    public function broadcastOn()
    {
        return new PrivateChannel('building.'.$this->buildingId);
    }
}

3. Real-time Dashboard Updates:

• Implement client-side listeners for real-time updates
• Group channels by building/floor for efficient data distribution

For IoT sensor data validation, I would implement:

1. Custom Validation Rules:

class SensorDataValidator
{
    public function validate(array $data)
    {
        return Validator::make($data, [
            'device_id' => 'required|exists:devices,id',
            'type' => 'required|in:temperature,air_quality,energy',
            'value' => ['required', 'numeric', new WithinExpectedRange],
            'timestamp' => 'required|date',
            'building_id' => 'required|exists:buildings,id'
        ]);
    }
}

2. Custom Value Range Validation:

class WithinExpectedRange implements Rule
{
    public function passes($attribute, $value)
    {
        return match ($this->sensorType) {
            'temperature' => $value >= -30 && $value <= 50,
            'air_quality' => $value >= 0 && $value <= 500,
            'energy' => $value >= 0
        };
    }
}

3. Data Sanitization:

• Remove outliers
• Standardize units
• Handle missing values

For the building intelligence system, I would implement:

1. RabbitMQ Configuration:

class IoTEventPublisher
{
    public function publish(SensorData $data)
    {
        $exchange = 'building_sensors';
        $routingKey = "building.{$data->buildingId}.{$data->sensorType}";
        
        $this->channel->basic_publish(
            $msg,
            $exchange,
            $routingKey
        );
    }
}

2. Subscriber Implementation:

class SensorDataSubscriber
{
    public function subscribe(string $buildingId, string $sensorType)
    {
        $queue = "building_{$buildingId}_{$sensorType}";
        $this->channel->queue_declare($queue, false, true, false, false);
        
        $this->channel->basic_consume(
            $queue,
            '',
            false,
            true,
            false,
            false,
            [$this, 'processMessage']
        );
    }
}

3. Message Handling:

• Implement dead letter queues
• Set up message persistence
• Configure message TTL

To maintain data consistency with multiple IoT devices, I would:

1. Implement Optimistic Locking:

class SensorDataRepository
{
    public function store(SensorData $data)
    {
        DB::transaction(function () use ($data) {
            $record = SensorReading::lockForUpdate()
                ->where('device_id', $data->deviceId)
                ->where('timestamp', $data->timestamp)
                ->first();
                
            if ($record) {
                // Handle duplicate/conflict
                $this->handleConflict($record, $data);
            } else {
                // Store new reading
                $this->createNewReading($data);
            }
        });
    }
}

2. Use Queue Workers:

// horizon.php
'environments' => [
    'production' => [
        'sensor-data' => [
            'supervisor-1' => [
                'connection' => 'redis',
                'queue' => ['sensor-data'],
                'balance' => 'auto',
                'processes' => 10,
                'tries' => 3,
            ],
        ],
    ],
];

3. Implement Idempotency:

• Use unique message IDs
• Track processed messages
• Handle duplicate submissions

For IoT device security, I would implement:

1. Device Authentication:

class DeviceAuthenticationService
{
    public function authenticateDevice(string $deviceId, string $token)
    {
        $device = Device::where('id', $deviceId)
            ->where('api_token', hash('sha256', $token))
            ->first();
            
        if (!$device) {
            throw new UnauthorizedDeviceException();
        }
        
        return $device;
    }
}

2. JWT Implementation:

class DeviceTokenService
{
    public function generateToken(Device $device): string
    {
        return JWT::encode([
            'device_id' => $device->id,
            'building_id' => $device->building_id,
            'exp' => time() + (60 * 60 * 24)
        ], config('app.key'));
    }
}

3. Additional Security Measures:

• TLS/SSL encryption for all communications
• Rate limiting per device
• IP whitelisting
• Regular token rotation
• Audit logging

Laravel's query builder provides several optimization techniques:

1. Eager Loading (N+1 problem solution):

// Instead of
$devices = Device::all();
foreach ($devices as $device) {
    $device->sensors; // Separate query for each device

// Use
$devices = Device::with('sensors')->get();

2. Chunking for large datasets:

Device::chunk(1000, function ($devices) {
    foreach ($devices as $device) {
        // Process device data
    }
});

3. Query optimization techniques:

• Using proper indexing
• Selecting specific columns instead of SELECT *
• Using whereIn() for multiple conditions
• Implementing database views for complex queries
• Using raw queries when necessary for optimization

For complex IoT data queries, I would:

• Implement database partitioning for time-series data
• Use composite indexes for frequently queried columns
• Implement query caching for repeated queries

OpCache improves PHP performance by storing precompiled script bytecode in memory, eliminating the need for PHP to load and parse scripts on each request.

Key configuration for IoT application:

opcache.enable=1
opcache.memory_consumption=256
opcache.max_accelerated_files=10000
opcache.revalidate_freq=0
opcache.validate_timestamps=0
opcache.save_comments=0

For production environment:

1. Disable file timestamp validation in production
2. Allocate sufficient memory based on application size
3. Set appropriate number of max_accelerated_files
4. Implement warmup script for preloading frequently used classes

Monitoring OpCache stats through PHP:

$status = opcache_get_status();
$config = opcache_get_configuration();

For IoT data caching with Redis, I would implement:

1. Time-series data storage:

// Store sensor reading
Redis::setex("sensor:{$id}:reading", 3600, $value);

// Store multiple readings using Redis Hash
Redis::hMset("device:{$id}:readings", [
    'temperature' => $temp,
    'humidity' => $humidity
]);

2. Implement Redis Pub/Sub for real-time updates:

// Publisher
Redis::publish('sensor-updates', json_encode($sensorData));

// Subscriber
Redis::subscribe(['sensor-updates'], function ($message) {
    // Process real-time data
});

3. Cache strategies:

• Use Redis Sorted Sets for time-based queries
• Implement cache tags for related data
• Set appropriate TTL based on data freshness requirements
• Use Redis Lists for latest readings

4. Pipeline commands for bulk operations:

Redis::pipeline(function ($pipe) {
    foreach ($readings as $reading) {
        $pipe->zadd("device:{$id}:timeline", $timestamp, $reading);
    }
});

For IoT time-series data, I would implement:

1. Composite indexes for timestamp-based queries:

CREATE INDEX idx_device_timestamp ON readings (device_id, timestamp);

2. Partitioning strategy:

CREATE TABLE readings (
    id BIGINT,
    device_id INT,
    value FLOAT,
    timestamp TIMESTAMP
) PARTITION BY RANGE (UNIX_TIMESTAMP(timestamp));

CREATE PARTITION p_2024_01 VALUES LESS THAN (UNIX_TIMESTAMP('2024-02-01'));

3. Optimization techniques:

• Use DATETIME instead of TIMESTAMP for wider range
• Implement table partitioning by time ranges
• Create covering indexes for frequent queries
• Use clustered indexes effectively

4. Index maintenance:

// Artisan command for index maintenance
public function handle()
{
    DB::statement('ANALYZE TABLE readings');
    DB::statement('OPTIMIZE TABLE readings');
}

For horizontal scaling in AWS environment:

1. Stateless Application Design:

// Use distributed session storage
'session' => [
    'driver' => 'redis',
    'connection' => 'session',
],

// Configure queue for background jobs
'queue' => [
    'driver' => 'sqs',
    'key' => env('AWS_ACCESS_KEY_ID'),
    'secret' => env('AWS_SECRET_ACCESS_KEY'),
    'prefix' => env('SQS_PREFIX'),
    'queue' => env('SQS_QUEUE'),
    'region' => env('AWS_DEFAULT_REGION'),
],

2. Load Balancing:

• Implement AWS ELB for request distribution
• Use sticky sessions when necessary
• Configure health checks

3. Shared Resources:

• Use Amazon RDS for database
• Implement Amazon ElastiCache for Redis
• Use S3 for file storage

4. Cache Strategy:

// Implement distributed caching
Cache::tags(['device-readings'])->remember($key, $ttl, function () {
    return $this->computeExpensiveOperation();
});

For handling IoT data through queues:

1. Queue Configuration:

// Multiple queue workers for different priorities
'queues' => [
    'high' => 'critical-sensor-data',
    'default' => 'regular-updates',
    'low' => 'analytics-processing'
],

2. Job Batching for bulk processing:

Bus::batch([
    new ProcessSensorReadings($readings),
    new UpdateDeviceStatus($deviceId),
    new NotifyMonitoring($alertData)
])->dispatch();

3. Queue Workers:

# Run multiple queue workers
php artisan queue:work --queue=high,default,low --tries=3 --timeout=90

4. Monitoring and Error Handling:

• Implement Horizon for queue monitoring
• Use job middleware for rate limiting
• Implement proper error handling and retry logic
• Set up alerts for failed jobs

5. Performance Optimization:

• Use batch processing for multiple records
• Implement proper job chunking
• Configure optimal timeout values
• Use supervisor for worker management

For a building intelligence system handling IoT data, I would implement auto-scaling using:

1. AWS Auto Scaling Groups (ASG) configured with:
   • Minimum/maximum instance limits
   • Scale-out policies based on CPU utilization (>70%)
   • Custom CloudWatch metrics for IoT data processing load
2. Elastic Load Balancer (ELB) for distributing traffic
3. Launch Configuration including:
   • EC2 instance with PHP 8.3
   • Required Laravel dependencies
   • AWS Systems Manager for environment variables
4. Implementation steps:
   • Configure AMI with application code
   • Set up ELB health checks
   • Define scaling policies based on IoT data processing patterns
   • Implement session handling via Redis/DynamoDB

For Optimise Everything's building intelligence system, I would:

1. Use AWS IoT Core for device connectivity:
   • Set up MQTT topics for different sensor types
   • Implement device authentication using X.509 certificates
   • Create IoT Rules for routing sensor data
2. Laravel Integration:
   • Use AWS SDK for PHP to interact with IoT Core
   • Create Laravel jobs for processing IoT messages
   • Implement real-time data processing using Laravel's queue system
3. Data Flow:
   • IoT devices → IoT Core → SNS → SQS → Laravel Queue Workers
   • Store processed data in MySQL for analytics
   • Use Redis for caching real-time sensor readings

Given the remote team setup mentioned in the job description:

1. CI Pipeline (using AWS CodePipeline):
   • Source: GitHub/Bitbucket integration
   • Build:
     • Composer install
     • PHP Unit tests
     • Laravel static analysis
     • Code quality checks
2. CD Pipeline:
   • AWS CodeDeploy for deployment
   • Blue-green deployment strategy
   • Environment-specific configurations
3. Automation:
   • Automated database migrations
   • Cache clearing
   • Configuration caching
4. Monitoring:
   • CloudWatch alerts for deployment status
   • Integration with Slack for team notifications

For a building intelligence platform:

1. AWS CloudWatch:
   • Custom metrics for IoT device connectivity
   • Application performance monitoring
   • Resource utilization alerts
2. Logging Strategy:
   • Centralized logging using CloudWatch Logs
   • Log groups for different components:
     • API access logs
     • IoT device logs
     • Application errors
     • Performance metrics
3. Laravel Integration:
   • Custom logging channels
   • Context-aware log entries
   • Error tracking with proper stacktraces
4. Monitoring Dashboard:
   • Real-time system health
   • IoT device status
   • API response times
   • Resource utilization

For a system handling critical building data:

1. Database Backup Strategy:
   • Automated daily snapshots using AWS RDS
   • Point-in-time recovery enabled
   • Cross-region backup replication
2. Disaster Recovery Plan:
   • Multi-AZ deployment for high availability
   • Regular backup testing
   • Documented recovery procedures
3. Implementation:
   • Automated backup scripts
   • Monitoring of backup success/failure
   • Regular restore testing
4. Data Retention:
   • 30-day backup retention
   • Archival storage for historical data
   • Compliance with data protection regulations

For the IoT-focused platform:

1. Lambda Implementation:
   • Use Bref PHP framework for Laravel-Lambda integration
   • Create serverless.yml configuration
   • Define Lambda function handlers
2. Use Cases:
   • IoT data processing
   • Real-time analytics
   • Scheduled tasks
   • Image processing for building plans
3. Integration:
   • API Gateway for HTTP triggers
   • SQS for queue processing
   • S3 event triggers
4. Optimization:
   • Cold start optimization
   • Function size minimization
   • Proper timeout configuration
   • Memory allocation based on workload

In the context of building IoT infrastructure and building management systems, I would implement the Repository pattern as follows:

// Interface
interface DeviceRepositoryInterface {
    public function getAllDevices();
    public function getDeviceById($id);
    public function createDevice(array $data);
}

// Implementation
class EloquentDeviceRepository implements DeviceRepositoryInterface {
    protected $model;
    
    public function __construct(Device $model) {
        $this->model = $model;
    }
    
    public function getAllDevices() {
        return $this->model->with('sensors')->get();
    }
    // ... other methods
}

// Service Provider
class RepositoryServiceProvider extends ServiceProvider {
    public function register() {
        $this->app->bind(
            DeviceRepositoryInterface::class,
            EloquentDeviceRepository::class
        );
    }
}

Benefits:

1. Abstracts database logic from controllers
2. Easier to switch between different data sources (MySQL to MongoDB)
3. Simplifies unit testing through interfaces
4. Promotes clean, maintainable code

For IoT device monitoring, I'd implement the Observer pattern like this:

// Event
class DeviceStatusChanged {
    public $device;
    public $newStatus;
}

// Observer
class DeviceMonitor implements ShouldQueue {
    public function handle(DeviceStatusChanged $event) {
        // Process status change
        // Send notifications
        // Update building management system
    }
}

// Usage in Device model
class Device extends Model {
    protected $dispatchesEvents = [
        'status.changed' => DeviceStatusChanged::class
    ];
}

This implementation would be particularly useful for tracking building systems' status changes and energy consumption patterns, which is crucial for the company's Net Zero mission.

For the building intelligence platform, I would use Laravel's Service Container as follows:

// Bind interface to implementation
class AppServiceProvider extends ServiceProvider {
    public function register() {
        $this->app->bind(
            BuildingMetricsInterface::class,
            BuildingMetricsService::class
        );
    }
}

// Controller with dependency injection
class BuildingController extends Controller {
    private $metrics;
    
    public function __construct(BuildingMetricsInterface $metrics) {
        $this->metrics = $metrics;
    }
    
    public function getEnergyUsage(Building $building) {
        return $this->metrics->calculateEnergyUsage($building);
    }
}

This approach enables:

1. Loose coupling between components
2. Easier testing through mock injection
3. Flexible service implementation switching
4. Automatic dependency resolution

For the building management system, I'd implement CQRS like this:

// Command
class UpdateBuildingSettings {
    public $buildingId;
    public $settings;
}

// Command Handler
class UpdateBuildingSettingsHandler {
    public function handle(UpdateBuildingSettings $command) {
        // Update building settings in write database
    }
}

// Query
class GetBuildingEnergyReport {
    public $buildingId;
    public $dateRange;
}

// Query Handler
class GetBuildingEnergyReportHandler {
    public function handle(GetBuildingEnergyReport $query) {
        // Read from optimized read database
        // Return energy consumption report
    }
}

This separation is particularly beneficial for handling complex building data where write operations (sensor updates) and read operations (energy reports) have different performance requirements.

For the building intelligence system, I would organize the code following DDD principles:

src/
├── Domain/
│   ├── Building/
│   │   ├── Building.php
│   │   ├── BuildingRepository.php
│   │   └── ValueObjects/
│   ├── Device/
│   │   ├── Device.php
│   │   └── DeviceRepository.php
├── Application/
│   ├── Services/
│   │   └── EnergyOptimizationService.php
│   └── DTOs/
├── Infrastructure/
│   ├── Persistence/
│   └── ExternalServices/
└── Interfaces/
    ├── Api/
    └── Console/

Key aspects:

1. Domain layer contains business logic
2. Application layer orchestrates domain objects
3. Infrastructure handles external concerns
4. Clear boundaries between contexts

For processing different types of IoT device data:

// Strategy Interface
interface DataProcessingStrategy {
    public function processData(array $rawData): array;
}

// Concrete Strategies
class TemperatureSensorProcessor implements DataProcessingStrategy {
    public function processData(array $rawData): array {
        // Process temperature data
    }
}

class EnergyMeterProcessor implements DataProcessingStrategy {
    public function processData(array $rawData): array {
        // Process energy consumption data
    }
}

// Context
class DeviceDataProcessor {
    private $strategy;
    
    public function setStrategy(DataProcessingStrategy $strategy) {
        $this->strategy = $strategy;
    }
    
    public function process(array $data) {
        return $this->strategy->processData($data);
    }
}

This pattern is particularly valuable for handling various types of building sensors and meters, allowing for flexible processing of different data types while maintaining clean, maintainable code.