Preparing for Senior PHP Developer role at Skycop.com

For Skycop's flight compensation system, I would implement the Saga pattern to maintain data consistency across services. Here's how:

1. Event-Driven Approach:

• Use RabbitMQ (mentioned in requirements) for event publishing
• Implement compensating transactions for rollbacks
• Use eventual consistency model

2. Implementation Example:

class CompensationClaimSaga
{
    private MessageBusInterface $messageBus;
    
    public function processClaim(Claim $claim): void
    {
        try {
            // Start saga
            $this->messageBus->dispatch(new ValidateFlightDataCommand($claim));
            $this->messageBus->dispatch(new CalculateCompensationCommand($claim));
            $this->messageBus->dispatch(new ProcessPaymentCommand($claim));
        } catch (SagaException $e) {
            // Handle compensation logic
            $this->messageBus->dispatch(new RollbackClaimCommand($claim));
        }
    }
}

In the context of flight compensation processing:

Synchronous (REST):

• Use for immediate flight data validation
• Critical user-facing operations
• When immediate response is required

public function getFlightData(string $flightNumber): Response
{
    return $this->httpClient->request('GET', '/api/flights/' . $flightNumber);
}

Asynchronous (RabbitMQ):

• Long-running compensation calculations
• Batch processing of claims
• Email notifications

public function processCompensationClaim(Claim $claim): void
{
    $this->rabbitMqProducer->publish(
        'claims_processing',
        json_encode(['claim_id' => $claim->getId()])
    );
}

For Skycop's microservices architecture, I would implement:

1. Service Registry Pattern using Consul:

class ServiceRegistry
{
    public function register(string $serviceName, string $serviceUrl): void
    {
        $consul->registerService([
            'ID' => uniqid(),
            'Name' => $serviceName,
            'Address' => $serviceUrl,
            'Tags' => ['php', 'flight-compensation']
        ]);
    }
}

2. Client-Side Discovery:

• Load balancing
• Health checking
• Service instance caching

3. API Gateway:

• Route requests
• Handle authentication
• Request/response transformation

For Skycop's third-party API integrations, I would implement:

class CircuitBreaker
{
    private const THRESHOLD = 5;
    private const TIMEOUT = 60;
    
    private int $failures = 0;
    private ?float $lastFailureTime = null;
    
    public function execute(callable $operation)
    {
        if ($this->isOpen()) {
            throw new CircuitBreakerOpenException();
        }
        
        try {
            $result = $operation();
            $this->reset();
            return $result;
        } catch (Exception $e) {
            $this->recordFailure();
            throw $e;
        }
    }
    
    private function isOpen(): bool
    {
        if ($this->failures >= self::THRESHOLD) {
            if (time() - $this->lastFailureTime < self::TIMEOUT) {
                return true;
            }
            $this->reset();
        }
        return false;
    }
}

Usage with airline APIs:

$circuitBreaker->execute(function() {
    return $this->airlineApiClient->getFlightData($flightNumber);
});

For Skycop's compensation processing system:

1. Two-Phase Commit (2PC):

class DistributedTransaction
{
    public function process(Claim $claim): void
    {
        // Prepare phase
        $prepared = $this->prepareServices([
            'payment',
            'notification',
            'documentation'
        ]);
        
        if ($prepared) {
            // Commit phase
            $this->commitAll();
        } else {
            $this->rollbackAll();
        }
    }
}

2. Event Sourcing:

• Store all state changes as events
• Rebuild state from event stream
• Perfect for audit trails in compensation claims

3. CQRS Pattern:

• Separate read and write operations
• Optimize for specific use cases
• Handle eventual consistency

For Skycop's secure service communication:

1. JWT-based authentication:

class ServiceAuthenticator
{
    public function generateServiceToken(): string
    {
        return JWT::encode([
            'service_id' => 'claims_processor',
            'exp' => time() + 3600,
            'iss' => 'skycop_auth'
        ], $this->secretKey);
    }
    
    public function validateServiceToken(string $token): bool
    {
        try {
            $decoded = JWT::decode($token, $this->secretKey, ['HS256']);
            return $this->isValidService($decoded->service_id);
        } catch (Exception $e) {
            return false;
        }
    }
}

2. OAuth2 Client Credentials:

• Service-specific scopes
• Token rotation
• Centralized authentication server

For Skycop's microservices monitoring:

1. Metrics Collection:

class ServiceMetricsCollector
{
    public function recordMetrics(string $serviceName, array $metrics): void
    {
        $this->prometheus->gauge('skycop_service_metrics', $metrics, ['service' => $serviceName]);
    }
}

2. Distributed Tracing:

• Use OpenTelemetry
• Track request flow across services
• Measure processing times

3. Health Checks:

public function healthCheck(): array
{
    return [
        'service' => 'claims_processor',
        'status' => $this->isHealthy(),
        'dependencies' => [
            'database' => $this->checkDatabase(),
            'rabbitmq' => $this->checkMessageQueue(),
            'elasticsearch' => $this->checkSearch()
        ]
    ];
}

4. Log Aggregation:

• Centralized logging
• Error tracking
• Performance monitoring

For flight compensation data processing, I would implement several strategies:

1. Use generators/yield for iterating large datasets instead of loading everything into memory
2. Implement chunk processing for database queries:

Flight::query()->chunk(1000, function($flights) {
    foreach ($flights as $flight) {
        processCompensationClaim($flight);
    }
});

3. Clear object references and use unset() for large variables when no longer needed
4. Configure PHP's memory_limit appropriately for the application needs
5. Use streaming for large file processing instead of file_get_contents()
6. Implement cursor-based pagination for API responses

For Skycop's flight data analysis, I would:

1. Implement a queue-based system using RabbitMQ to manage batch jobs
2. Design a batch processor service:

class FlightDataBatchProcessor
{
    public function processBatch(array $flightData, int $batchSize = 1000)
    {
        return collect($flightData)
            ->chunk($batchSize)
            ->each(function ($batch) {
                $this->dispatchToQueue($batch);
            });
    }
}

3. Use Symfony Messenger for handling background jobs
4. Implement progress tracking and error handling mechanisms
5. Store batch processing results in Elasticsearch for quick retrieval
6. Provide retry mechanisms for failed batch operations

For airline API integration, I would:

1. Implement Token Bucket algorithm using Redis:

class ApiRateLimiter
{
    public function checkLimit(string $apiKey, int $limit, int $window): bool
    {
        $key = "rate_limit:{$apiKey}";
        $current = $this->redis->get($key) ?? 0;
        
        if ($current >= $limit) {
            return false;
        }
        
        $this->redis->incr($key);
        $this->redis->expire($key, $window);
        return true;
    }
}

2. Use circuit breaker pattern for API failure handling
3. Implement adaptive rate limiting based on API response times
4. Queue requests that exceed rate limits for later processing
5. Monitor API usage patterns and adjust limits dynamically

For Skycop's multi-source data aggregation:

1. Implement a data pipeline using microservices architecture
2. Use Elasticsearch for real-time aggregations:

$params = [
    'index' => 'flights',
    'body' => [
        'aggs' => [
            'by_airline' => [
                'terms' => ['field' => 'airline_id'],
                'aggs' => [
                    'avg_delay' => ['avg' => ['field' => 'delay_minutes']]
                ]
            ]
        ]
    ]
];

3. Implement materialized views for frequently accessed aggregations
4. Use Redis for caching intermediate aggregation results
5. Implement map-reduce patterns for complex aggregations
6. Design efficient database schemas with proper indexing

For parallel processing in Skycop's system:

1. Use Swoole for concurrent processing:

$pool = new Swoole\Process\Pool(4);
$pool->on('WorkerStart', function ($pool, $workerId) {
    $chunk = getDataChunk($workerId);
    processFlightData($chunk);
});
$pool->start();

2. Implement worker processes for different data processing tasks
3. Use RoadRunner for PHP application server with better concurrency
4. Implement job queues with multiple consumers
5. Use pcntl_fork() for native PHP process management when needed
6. Monitor process health and implement automatic recovery

In context of flight compensation systems:

1. Design optimized mapping for flight records:

$mapping = [
    'properties' => [
        'flight_number' => ['type' => 'keyword'],
        'departure_time' => ['type' => 'date'],
        'delay_duration' => ['type' => 'integer'],
        'passenger_details' => ['type' => 'nested']
    ]
];

2. Implement custom analyzers for better search relevance
3. Use bulk indexing for efficient data imports
4. Configure index sharding based on data volume
5. Implement search suggestions and autocomplete
6. Use scroll API for retrieving large result sets
7. Maintain index aliases for zero-downtime reindexing

PHP 8's attributes provide a more elegant and type-safe way to handle dependency injection in Symfony compared to traditional annotations. Key benefits include:

1. Native language support and better IDE integration
2. Type checking at compile time
3. Improved performance as they don't require parsing docblocks

Example:

use Symfony\Component\DependencyInjection\Attribute\Autowire;

class FlightCompensationService
{
    public function __construct(
        #[Autowire(service: 'flight.repository')]
        private FlightRepositoryInterface $repository,
        #[Autowire('%app.api_key%')]
        private string $apiKey
    ) {}
}

This is particularly relevant for Skycop's microservices architecture where clean dependency injection is crucial for maintainable code.

For Symfony, I would implement middleware (specifically for processing flight compensation requests) like this:

namespace App\EventSubscriber;

use Symfony\Component\EventDispatcher\EventSubscriberInterface;
use Symfony\Component\HttpKernel\Event\RequestEvent;
use Symfony\Component\HttpKernel\KernelEvents;

class FlightDataValidationMiddleware implements EventSubscriberInterface
{
    public static function getSubscribedEvents(): array
    {
        return [
            KernelEvents::REQUEST => ['validateFlightData', 20]
        ];
    }

    public function validateFlightData(RequestEvent $event): void
    {
        $request = $event->getRequest();
        // Implement flight data validation logic
    }
}

This middleware would be particularly useful for Skycop's need to process and validate flight data before handling compensation claims.

PHP's JIT (Just-In-Time) compilation, introduced in PHP 8.0, works by:

1. Converting PHP code to an intermediate bytecode
2. Converting frequently executed bytecode into machine code
3. Executing the machine code directly

JIT is most beneficial for:

• CPU-intensive tasks (like parsing large amounts of flight data)
• Long-running processes
• Complex mathematical calculations

For Skycop's use case, JIT would be particularly valuable when:

• Processing large datasets of flight information
• Performing complex compensation calculations
• Running data analysis tasks

However, for typical web requests, OpCache might be more beneficial due to lower warm-up time.

Service containers are crucial for managing dependencies and implementing IoC (Inversion of Control). In the context of Skycop's microservices architecture:

1. Dependency Management:

// services.yaml
services:
    App\Service\FlightCompensationCalculator:
        arguments:
            $repository: '@flight.repository'
            $cache: '@redis.cache'

2. Service Configuration:

• Automatic dependency injection
• Service lifecycle management
• Parameter management for different environments

3. Benefits:

• Loose coupling between components
• Easier unit testing through mock injection
• Consistent service configuration across microservices

This is particularly important for maintaining clean architecture in a large-scale system like Skycop's flight compensation platform.

In Symfony, I would implement an event system for flight compensation processing like this:

// Event
class CompensationCalculatedEvent
{
    public function __construct(
        private readonly string $flightNumber,
        private readonly float $amount
    ) {}
}

// Listener
#[AsEventListener(event: CompensationCalculatedEvent::class)]
class CompensationNotificationListener
{
    public function __invoke(CompensationCalculatedEvent $event): void
    {
        // Handle notification logic
    }
}

This pattern is especially useful for:

• Decoupling business logic
• Implementing async processing with RabbitMQ
• Creating audit trails
• Maintaining system extensibility

This aligns well with Skycop's need for processing large amounts of data and integrating with third-party services.

For Skycop's high-traffic compensation processing system, I would implement:

1. Caching Strategy:

• Redis for session storage
• Elasticsearch for search optimization
• Memcached for application-level caching

2. Database Optimization:

• Query optimization
• Proper indexing
• Read replicas for heavy queries

3. Application Level:

// Configure OPcache
opcache.enable=1
opcache.memory_consumption=256
opcache.max_accelerated_files=20000

// Implement response caching
#[Cache(expires: '1 hour')]
public function getFlightDetails(string $flightNumber): Response

4. Infrastructure:

• Load balancing
• Content Delivery Network (CDN)
• Horizontal scaling of microservices

These optimizations would be crucial for handling Skycop's tight deadlines and large data processing requirements.

For flight compensation data processing, I would implement the following optimizations:

1. Proper indexing strategy based on common query patterns (e.g., flight numbers, dates, claim status)
2. Using EXPLAIN to analyze and optimize query execution plans
3. Implementing pagination using cursor-based approach instead of OFFSET
4. Utilizing partitioning for historical flight data by date ranges
5. Using composite indexes for frequently combined conditions
6. Avoiding SELECT * and requesting only necessary columns
7. Using batch processing with chunked queries for large data operations
8. Implementing query caching with Redis/Memcache for frequently accessed data

For Skycop's system, I would implement:

1. L1: Application-level cache using APCu for local PHP objects
2. L2: Distributed cache using Redis/Memcache for:
   • Flight data lookup
   • User session data
   • API response caching
3. L3: Database query cache for complex calculations
4. Implementation details:
   • Cache tags for efficient invalidation
   • TTL based on data volatility
   • Write-through caching for critical data
   • Cache warming for predictable high-load periods

For Skycop's scale, I would implement:

1. Horizontal sharding based on:
   • Geographic regions for flight data
   • Time-based sharding for historical claims
2. Consistent hashing for shard selection
3. Implementation using:
   • Primary key ranges for even distribution
   • Separate read/write connections
   • Central metadata store for shard mapping
4. Consider using tools like Vitess or ProxySQL for shard management

In the context of flight compensation processing:

1. Primary indexing strategies:
   • Composite indexes for flight number + date combinations
   • Covering indexes for frequent lookup patterns
   • Partial indexes for specific claim statuses
2. Performance considerations:
   • Index selectivity analysis
   • Regular index usage monitoring
   • Periodic index maintenance
3. Using tools:
   • MySQL EXPLAIN for index effectiveness
   • Percona Toolkit for index analysis
   • Index impact monitoring with slow query log

Given Skycop's microservices architecture:

1. Event-driven invalidation:
   • Using RabbitMQ for broadcasting cache invalidation events
   • Implementing cache tags for granular invalidation
2. Versioning strategy:
   • Cache keys with version stamps
   • Atomic cache updates
3. Implementation approaches:
   • Cache-aside pattern with write-through
   • Pub/sub mechanism for real-time invalidation
   • Circuit breaker pattern for cache service failures

For high-performance requirements:

1. Using PHP-FPM with persistent connections
2. Implementing connection pooling through:
   • ProxySQL for MySQL connection management
   • Doctrine's connection pooling
3. Configuration optimization:
   • Pool size based on server resources
   • Connection timeout handling
   • Health checks for connection validity
4. Monitoring:
   • Connection pool metrics
   • Connection usage patterns
   • Performance impact analysis

For flight compensation processing, I would implement a robust retry mechanism using:

1. x-message-ttl header for retry delay
2. Dead Letter Exchange (DLE) configuration
3. Exponential backoff strategy

Example implementation:

class FlightCompensationProcessor
{
    private const MAX_RETRIES = 3;
    private const RETRY_DELAYS = [60000, 180000, 360000]; // in milliseconds

    public function processMessage(AMQPMessage $message)
    {
        try {
            // Process compensation claim
            $this->processCompensationClaim($message);
        } catch (ProcessingException $e) {
            $retryCount = $message->has('application_headers') 
                ? ($message->get('application_headers')->getNativeData()['retry_count'] ?? 0) 
                : 0;

            if ($retryCount < self::MAX_RETRIES) {
                $this->scheduleRetry($message, $retryCount);
            } else {
                $this->moveToDeadLetter($message);
            }
        }
    }

    private function scheduleRetry(AMQPMessage $message, int $retryCount): void
    {
        $headers = new AMQPTable([
            'retry_count' => $retryCount + 1,
            'x-message-ttl' => self::RETRY_DELAYS[$retryCount]
        ]);
        // Republish to retry queue with updated headers
    }
}

In the context of flight compensation processing, here are the relevant exchange types:

1. Direct Exchange:

• Used for routing specific compensation claim types to dedicated queues
• Example: routing claims by airline or compensation type

$channel->exchange_declare('compensation_exchange', 'direct');
$channel->queue_bind('delayed_flights', 'compensation_exchange', 'delayed');

2. Topic Exchange:

• Perfect for routing based on flight patterns
• Example: "EU.FLIGHT.DELAYED", "US.FLIGHT.CANCELLED"

$channel->exchange_declare('flight_patterns', 'topic');
$channel->queue_bind('eu_processors', 'flight_patterns', 'EU.#');

3. Fanout Exchange:

• Used for broadcasting updates to multiple processing services
• Example: system-wide updates or notifications

4. Headers Exchange:

• Used when routing based on multiple attributes
• Example: routing based on claim priority and type combination

For Skycop's compensation processing system, I would implement DLQ handling as follows:

1. Configure DLQ with policy:

$channel->exchange_declare('dlx_exchange', 'direct');
$channel->queue_declare('failed_compensations_dlq', false, true, false, false, false, new AMQPTable([
    'x-dead-letter-exchange' => 'dlx_exchange',
    'x-dead-letter-routing-key' => 'failed'
]));

2. Implement monitoring and alerting:

class DeadLetterMonitor
{
    public function processDeadLetter(AMQPMessage $message): void
    {
        $headers = $message->get('application_headers')->getNativeData();
        $this->logger->error('Compensation processing failed', [
            'original_queue' => $headers['x-first-death-queue'],
            'reason' => $headers['x-death'][0]['reason'],
            'time' => $headers['x-death'][0]['time']
        ]);
        
        // Notify support team if critical threshold reached
        if ($this->getDeadLetterCount() > self::ALERT_THRESHOLD) {
            $this->notifySupport();
        }
    }
}

3. Implement retry strategies for different failure types
4. Archive messages that cannot be processed after multiple attempts

For flight compensation claims, I would implement priority queuing as follows:

1. Define priority levels in queue declaration:

$channel->queue_declare('compensation_queue', false, true, false, false, false, new AMQPTable([
    'x-max-priority' => 10
]));

2. Implement priority assignment logic:

class CompensationPrioritizer
{
    private const PRIORITIES = [
        'VIP_CUSTOMER' => 10,
        'URGENT_CLAIM' => 8,
        'STANDARD_CLAIM' => 5,
        'BULK_PROCESSING' => 3
    ];

    public function publishClaim(CompensationClaim $claim): void
    {
        $priority = $this->calculatePriority($claim);
        
        $this->channel->basic_publish(
            new AMQPMessage($claim->toJson(), [
                'priority' => $priority,
                'delivery_mode' => AMQPMessage::DELIVERY_MODE_PERSISTENT
            ])
        );
    }

    private function calculatePriority(CompensationClaim $claim): int
    {
        // Priority calculation logic based on business rules
        return self::PRIORITIES[$claim->getType()] ?? self::PRIORITIES['STANDARD_CLAIM'];
    }
}

3. Use separate queues for different priority levels when needed
4. Implement priority boost for aging messages

For Skycop's critical compensation processing, I would implement the following reliability measures:

1. Enable persistent messages:

$message = new AMQPMessage(
    $data,
    [
        'delivery_mode' => AMQPMessage::DELIVERY_MODE_PERSISTENT,
        'message_id' => uniqid('claim_', true),
        'timestamp' => time()
    ]
);

2. Implement publisher confirms:

class ReliablePublisher
{
    public function publish(string $data): void
    {
        $this->channel->confirm_select();
        $this->channel->set_ack_handler(function (AMQPMessage $message) {
            $this->logger->info('Message confirmed', ['id' => $message->get('message_id')]);
        });
        
        $this->channel->basic_publish($message);
        $this->channel->wait_for_pending_acks();
    }
}

3. Configure consumer acknowledgments:

$channel->basic_qos(null, 1, null); // Process one message at a time
$channel->basic_consume('compensation_queue', '', false, false, false, false,
    function (AMQPMessage $message) {
        try {
            $this->processCompensation($message);
            $message->ack();
        } catch (Exception $e) {
            $message->nack(false, true); // Requeue the message
        }
    }
);

4. Implement clustering and high availability:

• Use RabbitMQ clusters
• Configure queue mirroring
• Implement application-level message deduplication