Preparing for Software Engineer I - Backend role at Booking.com

For Booking.com's scale, I would implement a multi-layer caching strategy:

1. Local Cache (L1):

• Use PHP's OPcache for bytecode caching
• Implement in-memory application cache using APCu

2. Distributed Cache (L2):

• Redis clusters for session data and frequently accessed hotel information
• Implement cache regions for different types of data (hotel info, user preferences, pricing)
• Use consistent hashing for cache key distribution

3. Cache Strategy:

• Write-through caching for critical booking data
• Cache-aside pattern for hotel information
• Implement TTL based on data volatility
• Use cache tags for efficient cache invalidation

4. Monitoring:

• Cache hit/miss ratio monitoring
• Cache eviction rate tracking
• Latency monitoring

The system would be containerized using Kubernetes for easy scaling and management, aligning with Booking.com's infrastructure requirements.

The CAP theorem states that a distributed system can only guarantee two of three properties: Consistency, Availability, and Partition tolerance.

In Booking.com's context:

1. Consistency:

• Critical for booking transactions and inventory
• Must ensure no double bookings
• Real-time price updates

2. Availability:

• System must handle millions of users
• Search functionality should always be available
• Non-critical features can be degraded

3. Partition Tolerance:

• Must handle network issues between data centers
• Essential for global distribution

Implementation Strategy:

• CP (Consistency/Partition Tolerance) for booking transactions
• AP (Availability/Partition Tolerance) for hotel searches and content
• Use eventual consistency where appropriate
• Implement SAGA pattern for distributed transactions

This aligns with Booking.com's need to handle large-scale operations while maintaining data integrity.

For Booking.com's scale, I would implement the following strategies:

1. Event-Driven Architecture:

• Use RabbitMQ/Redis for message queuing
• Implement event sourcing for booking changes
• Maintain change data capture (CDC)

2. Conflict Resolution:

• Vector clocks for version tracking
• Last-write-wins for non-critical updates
• Custom merge strategies for conflicting bookings

3. Consistency Patterns:

• Read-your-writes consistency for user actions
• Monotonic reads for price displays
• Version tracking for hotel information

4. Recovery Mechanisms:

• Background reconciliation jobs
• Anti-entropy protocols
• Automated conflict detection

5. Monitoring:

• Consistency lag metrics
• Conflict rate monitoring
• Reconciliation success rates

This approach ensures system reliability while maintaining high availability, crucial for a global booking platform.

For Booking.com's high-traffic environment, I would implement a multi-layer rate limiting approach:

1. Token Bucket Algorithm using Redis:

• Distributed rate counter per user/IP
• Sliding window implementation
• Configurable bucket sizes and refill rates

2. Implementation Layers:

• API Gateway level (first defense)
• Application level using Redis
• Database level protection

3. Rate Limit Categories:

• Search requests
• Booking attempts
• API calls per partner
• User-specific limits

4. Technical Implementation:

• Use Redis for distributed counter storage
• Implement fallback mechanisms
• Lua scripts for atomic operations
• Header-based rate limit information

5. Monitoring and Alerts:

• Rate limit breach notifications
• Usage pattern analysis
• Abuse detection

This aligns with the need to protect Booking.com's infrastructure while ensuring fair resource usage.

For Booking.com's microservices architecture, I would implement:

1. Saga Pattern:

• Choreography-based sagas for booking flow
• Orchestration-based sagas for complex workflows
• Compensation transactions for rollbacks

2. Two-Phase Commit (2PC):

• For critical booking transactions
• Prepare and commit phases
• Timeout handling and recovery

3. Event Sourcing:

• Store state changes as events
• Rebuild state from event log
• Event store for audit trails

4. CQRS Pattern:

• Separate read and write models
• Eventual consistency for reads
• Strong consistency for writes

5. Outbox Pattern:

• Reliable message publishing
• Transaction atomicity
• Message relay process

Implementation using:

• MySQL for transactional data
• Redis for distributed locking
• RabbitMQ for event communication
• Kubernetes for orchestration

This ensures reliable transaction handling across Booking.com's distributed system.

For Booking.com's microservices architecture, I would implement:

1. Service Registry:

• Use Kubernetes native service discovery
• Implement health checks
• Dynamic service registration

2. Discovery Patterns:

• Client-side discovery
• Server-side discovery
• Service mesh implementation

3. Technical Implementation:

• DNS-based service discovery
• Label-based service selection
• Load balancing integration

4. High Availability:

• Multiple registry instances
• Cache service information
• Fallback mechanisms

5. Security:

• Service-to-service authentication
• TLS communication
• Role-based access control

This aligns with Booking.com's use of Kubernetes and needs for reliable service communication.

For Booking.com's scale, I would implement:

1. Circuit Breaker Pattern:

• Implement using libraries like PHP-Circuit-Breaker
• Configure failure thresholds
• Gradual recovery mechanism

2. Fallback Strategies:

• Cache-based fallbacks
• Degraded functionality modes
• Alternative service paths

3. Failure Detection:

• Health check endpoints
• Timeout configurations
• Failure metrics collection

4. Recovery Mechanisms:

• Automatic retries with exponential backoff
• Request queuing
• Asynchronous recovery processes

5. Monitoring and Alerting:

• Real-time failure detection
• Error rate monitoring
• Service dependency mapping

This ensures system resilience and maintains service availability, crucial for Booking.com's global operations.

For Booking.com's scale, I would implement the following optimization strategies:

1. Analyze query execution plan using EXPLAIN to identify bottlenecks
2. Optimize indexes based on WHERE, JOIN, and ORDER BY clauses
3. Consider denormalization for frequently accessed data
4. Implement materialized views for complex aggregations
5. Use partitioning for large tables (e.g., by date for historical booking data)
6. Consider vertical partitioning to split rarely used columns
7. Implement query caching using Redis for frequently accessed data
8. Use LIMIT and pagination to handle large result sets
9. Consider using covering indexes for better performance

Sharding is particularly relevant for Booking.com's scale. It's a horizontal partitioning strategy where data is distributed across multiple database instances. For a travel booking system, I would implement sharding:

1. By geographic region (hotels/properties in different continents)
2. By time periods (historical vs. current bookings)
3. By customer segments (business vs. leisure travelers)

Key benefits:

• Improved query performance through parallel processing
• Better resource utilization
• Reduced impact of hardware failures
• Enhanced scalability for growing datasets

Implementation considerations:

• Consistent sharding key selection
• Cross-shard query handling
• Maintaining data consistency across shards

For a high-traffic platform like Booking.com, I would implement:

1. Master-Slave Replication:

• One master for writes
• Multiple read replicas for scaling read operations
• Automatic failover configuration

2. Semi-Synchronous Replication:

• Ensures at least one slave has received the transaction
• Balances data safety and performance

3. Monitoring and Maintenance:

• Replication lag monitoring
• Automated health checks
• Failover procedures

4. Load Balancing:

• Direct read queries to slaves
• Write operations to master only
• Implement connection pooling

Technology stack would include:

• MySQL Group Replication
• ProxySQL for load balancing
• Orchestrator for automated failover

For Booking.com's large-scale MySQL databases:

1. Analysis Phase:

• Profile most frequent queries
• Identify high-impact tables
• Analyze query patterns using slow query log

2. Index Design Principles:

• Create compound indexes matching query patterns
• Consider selectivity for index column order
• Implement covering indexes for frequently used queries
• Balance between read performance and write overhead

3. Specific Strategies:

• Index cardinality consideration
• Partial indexes for filtered queries
• Avoid redundant indexes
• Regular index usage analysis and cleanup

4. Monitoring:

• Track index hit ratios
• Monitor index size
• Analyze query performance improvements

For Booking.com's 24/7 operation requirements:

1. Preparation:

• Create backward-compatible changes
• Use online schema changes tools (e.g., gh-ost, pt-online-schema-change)
• Implement feature flags for new schema versions

2. Migration Process:

• Deploy new code that works with both old and new schema
• Gradually migrate data in small batches
• Maintain dual-write capability during transition
• Monitor replication lag and system performance

3. Rollback Plan:

• Maintain ability to revert changes
• Keep old schema until migration is complete
• Monitor error rates during migration

4. Verification:

• Compare data integrity
• Validate application functionality
• Monitor performance metrics

For Booking.com's critical reservation system:

ACID Properties:

1. Atomicity:

• Ensures complete booking transaction (room reservation, payment, confirmation)
• All operations succeed or all fail

2. Consistency:

• Maintains accurate inventory counts
• Ensures booking rules are enforced
• Prevents double bookings

3. Isolation:

• Handles concurrent bookings safely
• Prevents dirty reads of incomplete transactions
• Maintains accurate availability data

4. Durability:

• Guarantees completed bookings are permanent
• Survives system failures
• Maintains audit trail of transactions

Implementation using MySQL's InnoDB engine with proper transaction isolation levels.

For Booking.com's high-traffic environment:

1. Multi-Level Caching:

• L1: Application-level cache (in-memory)
• L2: Redis distributed cache
• L3: Database

2. Caching Strategies:

• Cache frequently accessed hotel data
• Store session data and user preferences
• Cache search results for popular destinations
• Implement rate limiting counters

3. Data Structures:

• Sorted sets for price ranges
• Hash maps for hotel details
• Lists for recent viewings
• Sets for feature flags

4. Cache Management:

• Implement TTL for volatile data
• Use cache-aside pattern
• Handle cache invalidation
• Monitor cache hit rates

5. High Availability:

• Redis cluster configuration
• Automatic failover
• Data persistence configuration

For Booking.com's scale, I would implement a hybrid approach:

1. Synchronous Communication:

• REST APIs for direct request-response patterns
• gRPC for performance-critical operations between services
• GraphQL for flexible data fetching

2. Asynchronous Communication:

• Event-driven architecture using RabbitMQ/Kafka for decoupled operations
• Message queues for handling booking operations that don't need immediate response
• Pub/Sub patterns for event broadcasting

3. Service Discovery:

• Kubernetes service discovery for internal routing
• API Gateway for external requests
• Service mesh (like Istio) for advanced routing and traffic management

The choice between these would depend on the specific use case, considering factors like latency requirements, consistency needs, and fault tolerance.

For a large-scale system like Booking.com, I would implement multiple authentication patterns:

1. JWT-based Authentication:

• Services receive and validate JWTs
• Claims contain service identity and permissions
• Short expiration times for security

2. Mutual TLS (mTLS):

• Each service has its own certificate
• Services authenticate each other bidirectionally
• Particularly useful within Kubernetes clusters

3. API Keys:

• For internal service authentication
• Rotated regularly
• Stored securely in Kubernetes secrets

4. OAuth 2.0 with Service Accounts:

• For third-party service integration
• Token-based authentication
• Scope-based access control

Additional security measures:

• Network policies
• Service mesh authentication
• Regular credential rotation

In Booking.com's context, I would implement circuit breakers as follows:

1. Circuit Breaker Implementation:

class CircuitBreaker {
    private $failureThreshold;
    private $resetTimeout;
    private $state = 'CLOSED';
    private $failures = 0;
    private $lastFailureTime;

    public function execute(callable $operation) {
        if ($this->isOpen()) {
            if ($this->shouldReset()) {
                $this->halfOpen();
            } else {
                throw new CircuitBreakerOpenException();
            }
        }

        try {
            $result = $operation();
            $this->reset();
            return $result;
        } catch (Exception $e) {
            $this->recordFailure();
            throw $e;
        }
    }
}

2. Integration Points:

• Implement in service clients
• Add monitoring and metrics
• Configure proper thresholds based on service SLAs

3. Fallback Mechanisms:

• Cache responses
• Default values
• Degraded functionality

For Booking.com's microservices architecture, I would implement:

1. OpenTelemetry Integration:

• Implement trace context propagation
• Use correlation IDs across services
• Add span attributes for business context

2. Instrumentation:

class BookingService {
    public function createBooking(array $data) {
        $span = $this->tracer->startSpan('create_booking');
        try {
            $span->setAttribute('hotel_id', $data['hotel_id']);
            $span->setAttribute('user_id', $data['user_id']);
            
            // Booking logic
            
            $span->setStatus(StatusCode::OK);
        } catch (Exception $e) {
            $span->setStatus(StatusCode::ERROR, $e->getMessage());
            throw $e;
        } finally {
            $span->end();
        }
    }
}

3. Monitoring Setup:

• Jaeger or Zipkin for trace visualization
• Custom sampling strategies
• Integration with existing monitoring tools

For Booking.com's booking system, I would implement:

1. Message Queue Structure:

class BookingEventPublisher {
    private $connection;
    
    public function publishBookingCreated(Booking $booking) {
        $event = [
            'type' => 'booking.created',
            'data' => $booking->toArray(),
            'timestamp' => time(),
            'trace_id' => $this->getTraceId()
        ];
        
        $this->connection->basic_publish($event, 
            'booking_exchange',
            'booking.created'
        );
    }
}

2. Event Types:

• Booking-related events
• Inventory updates
• Payment processing
• Notification triggers

3. Infrastructure:

• RabbitMQ for reliable message delivery
• Dead letter queues
• Message persistence
• High availability setup

4. Consumer Implementation:

• Idempotent processing
• Error handling
• Retry mechanisms

For Booking.com's reservation system, the saga pattern would be implemented as:

1. Choreography-based Saga:

class BookingCreationSaga {
    public function start(array $bookingData) {
        try {
            // Steps in the saga
            $this->checkInventory();
            $this->reserveRoom();
            $this->processPayment();
            $this->sendConfirmation();
        } catch (SagaStepException $e) {
            $this->compensate($e->getStep());
        }
    }
    
    private function compensate(string $failedStep) {
        // Compensation logic for each step
        switch($failedStep) {
            case 'payment':
                $this->releaseRoomReservation();
                break;
            // Other compensations
        }
    }
}

2. Key Components:

• Distributed transaction coordinator
• Compensation handlers
• State tracking
• Retry mechanisms

3. Implementation Considerations:

• Eventual consistency
• Idempotency
• Transaction monitoring
• Error handling and recovery

PHP 8.3's JIT (Just-In-Time) compilation improves performance by converting frequently executed PHP code into machine code at runtime. For Booking.com's high-load applications, this means:

1. Reduced CPU usage for computation-heavy operations
2. Faster execution of repetitive tasks (like processing large datasets)
3. Improved performance for long-running processes

Key benefits for travel platform operations:

• Better handling of complex pricing calculations
• Faster processing of booking algorithms
• Improved response times for search operations

The JIT compiler works alongside OPCache, providing up to 3x performance improvement for CPU-intensive tasks.

Dependency Injection (DI) is a design pattern that implements Inversion of Control (IoC) by injecting dependencies into a class rather than creating them internally. In a large-scale system like Booking.com, this is crucial because:

// Without DI
class BookingService {
    private $database;
    
    public function __construct() {
        $this->database = new Database(); // Tightly coupled
    }
}

// With DI
class BookingService {
    private DatabaseInterface $database;
    
    public function __construct(DatabaseInterface $database) {
        $this->database = $database; // Loosely coupled
    }
}

Benefits:

1. Easier unit testing through dependency mocking
2. Flexible switching between implementations
3. Better separation of concerns
4. Simplified maintenance and scalability

SOLID principles implementation in a booking system context:

1. Single Responsibility Principle:

class BookingValidator {
    public function validate(Booking $booking): bool {
        // Only handles booking validation logic
    }
}

2. Open/Closed Principle:

interface PaymentProcessorInterface {
    public function processPayment(Payment $payment): bool;
}

class StripeProcessor implements PaymentProcessorInterface {
    public function processPayment(Payment $payment): bool {
        // Stripe-specific implementation
    }
}

3. Liskov Substitution Principle:

abstract class Accommodation {
    abstract public function calculatePrice(): float;
}

class Hotel extends Accommodation {
    public function calculatePrice(): float {
        // Hotel-specific pricing
    }
}

4. Interface Segregation:

interface BookingCreatorInterface {
    public function create(BookingData $data): Booking;
}

interface BookingRetrieverInterface {
    public function retrieve(string $id): Booking;
}

5. Dependency Inversion:

class BookingService {
    private BookingRepositoryInterface $repository;
    
    public function __construct(BookingRepositoryInterface $repository) {
        $this->repository = $repository;
    }
}

For Booking.com's scale, I would implement these key design patterns:

1. Factory Pattern for booking creation:

class BookingFactory {
    public function createBooking(string $type): BookingInterface {
        return match($type) {
            'hotel' => new HotelBooking(),
            'apartment' => new ApartmentBooking(),
            default => throw new InvalidBookingTypeException()
        };
    }
}

2. Observer Pattern for booking events:

class BookingService implements SplSubject {
    private array $observers = [];
    
    public function attach(SplObserver $observer): void {
        $this->observers[] = $observer;
    }
    
    public function confirmBooking(Booking $booking): void {
        // Process booking
        $this->notify();
    }
}

3. Strategy Pattern for pricing:

interface PricingStrategy {
    public function calculatePrice(Booking $booking): float;
}

class SeasonalPricing implements PricingStrategy {
    public function calculatePrice(Booking $booking): float {
        // Season-specific calculations
    }
}

4. Repository Pattern for data access:

class BookingRepository {
    public function findAvailableRooms(
        DateTime $checkIn, 
        DateTime $checkOut
    ): array {
        // Database interaction
    }
}

For a travel booking microservice, I would implement comprehensive error handling:

1. Custom Exception Hierarchy:

abstract class BookingException extends Exception {}
class RoomNotAvailableException extends BookingException {}
class PaymentFailedException extends BookingException {}

try {
    $this->bookingService->create($bookingData);
} catch (RoomNotAvailableException $e) {
    $this->logger->error('Room booking failed', [
        'booking_id' => $bookingData->id,
        'error' => $e->getMessage()
    ]);
    throw new ApiException('Room not available', 409);
}

2. Global Exception Handler:

class ExceptionHandler {
    public function handle(Throwable $e): Response {
        if ($e instanceof ValidationException) {
            return new Response([
                'error' => 'Validation failed',
                'details' => $e->getErrors()
            ], 422);
        }
        // Handle other exceptions
    }
}

3. Logging and Monitoring:

• Integration with distributed tracing
• Error aggregation
• Alert systems for critical errors

Middleware implementation for a booking platform:

1. PSR-15 Compliant Middleware:

class AuthenticationMiddleware implements MiddlewareInterface {
    public function process(
        ServerRequestInterface $request, 
        RequestHandlerInterface $handler
    ): ResponseInterface {
        $token = $request->getHeader('Authorization')[0] ?? null;
        if (!$this->validateToken($token)) {
            return new JsonResponse(['error' => 'Unauthorized'], 401);
        }
        return $handler->handle($request);
    }
}

2. Middleware Pipeline:

$app->pipe(ErrorHandler::class);
$app->pipe(Authentication::class);
$app->pipe(RateLimiter::class);
$app->pipe(ApiLogger::class);

3. Specific Use Cases:

• Rate limiting for API endpoints
• Request/Response logging
• CORS handling
• Request validation
• Performance monitoring

For testing microservices with external dependencies, I would implement a multi-layered testing strategy:

1. Unit Tests:

• Use mocking frameworks (PHPUnit mocks) to simulate external service responses
• Focus on business logic isolation
• Test error handling and edge cases

2. Integration Tests:

• Implement test doubles (stubs) for external services
• Use tools like Testcontainers for database testing
• Test service boundaries and contracts

3. Contract Testing:

• Implement Consumer-Driven Contracts (CDC)
• Use tools like Pact for contract testing
• Ensure API compatibility

4. Component Tests:

• Use Docker containers to simulate external services
• Test complete service functionality
• Focus on service resilience

Given Booking.com's scale, I would also implement chaos testing to ensure service resilience when external dependencies fail.

For a distributed system like Booking.com's platform, I would implement integration tests following these principles:

1. Test Environment Setup:

• Use Docker Compose for local testing environment
• Implement service containerization with Kubernetes
• Set up dedicated test databases

2. Test Implementation:

• Focus on service interaction points
• Test message queue interactions (RabbitMQ)
• Verify database transactions across services

3. Test Data Management:

• Implement database seeding strategies
• Use fixtures for consistent test data
• Clean up test data after each run

4. Asynchronous Testing:

• Test event-driven communications
• Implement wait strategies for async operations
• Use message queue assertions

5. Monitoring and Logging:

• Implement distributed tracing
• Log aggregation for test debugging
• Performance metrics collection

For proper mocking in unit tests, especially in a PHP environment:

1. Dependency Injection:

• Use constructor injection for dependencies
• Implement interfaces for services
• Follow SOLID principles for testable code

2. Mocking Strategy:

public function testBookingCreation()
{
    // Create mock for external service
    $paymentService = $this->createMock(PaymentServiceInterface::class);
    
    // Set up expectations
    $paymentService->expects($this->once())
        ->method('processPayment')
        ->willReturn(true);
    
    $bookingService = new BookingService($paymentService);
    $result = $bookingService->createBooking($bookingData);
    
    $this->assertTrue($result);
}

3. Mock Types:

• Use Stubs for state verification
• Use Mocks for behavior verification
• Implement Spy objects when needed

4. Best Practices:

• Mock only direct dependencies
• Avoid mocking value objects
• Keep mocks simple and focused

For testing asynchronous operations, which are crucial in a travel booking platform:

1. Testing Strategy:

public function testAsyncBookingConfirmation()
{
    $queue = $this->createMock(QueueService::class);
    $queue->expects($this->once())
        ->method('publish')
        ->with(
            $this->equalTo('booking.confirmed'),
            $this->callback(function($data) {
                return isset($data['booking_id']);
            })
        );
        
    // Test async operation
    $result = $this->await($asyncOperation, 5000);
    $this->assertTrue($result->isComplete());
}

2. Tools and Techniques:

• Use PHP's native Fiber support (PHP 8.1+)
• Implement polling mechanisms
• Use event listeners for async assertions

3. Time Management:

• Set appropriate timeouts
• Use time-based assertions
• Handle race conditions

For E2E testing in Booking.com's microservices environment:

1. Testing Infrastructure:

• Use Kubernetes for test environment orchestration
• Implement service discovery
• Set up monitoring and logging

2. Test Implementation:

• Use tools like Behat or Codeception
• Implement BDD scenarios
• Focus on critical business flows

3. Data Management:

• Create isolated test data
• Implement test data cleanup
• Handle distributed transactions

4. Test Execution:

/**
 * @test
 * @group e2e
 */
public function completeBookingFlow()
{
    $this->browse(function ($browser) {
        $browser->visit('/hotels')
                ->select('location', 'Amsterdam')
                ->waitFor('.search-results')
                ->assertSee('Available Hotels')
                ->click('.book-button')
                ->waitForText('Booking Confirmed');
                
        // Verify database state
        $this->assertDatabaseHas('bookings', [
            'status' => 'confirmed',
            'location' => 'Amsterdam'
        ]);
    });
}

To achieve comprehensive test coverage in a complex application like Booking.com's platform:

1. Coverage Strategy:

• Aim for 80%+ code coverage
• Focus on critical business logic
• Implement risk-based testing

2. Test Types:

// Unit Test Example
public function testPriceCalculation()
{
    $calculator = new PriceCalculator();
    $price = $calculator->calculate([
        'base_price' => 100,
        'tax_rate' => 0.21,
        'discount' => 10
    ]);
    $this->assertEquals(109, $price);
}

// Integration Test Example
public function testBookingWorkflow()
{
    $booking = $this->bookingService->create($bookingData);
    $this->assertTrue($this->paymentService->verify($booking->payment_id));
    $this->assertEquals('confirmed', $booking->status);
}

3. Coverage Tools:

• Use PHPUnit's code coverage
• Implement SonarQube for quality metrics
• Set up CI/CD coverage checks

4. Documentation:

• Document test scenarios
• Maintain test documentation
• Track coverage metrics

For zero-downtime deployments at Booking.com's scale, I would implement:

1. Rolling Updates Strategy:

   • Configure deployment with RollingUpdate strategy
   • Set maxUnavailable and maxSurge parameters to control pod rotation
   • Use readiness probes to ensure new pods are ready before old ones are terminated

2. Blue-Green Deployment:

   • Maintain two identical environments (blue and green)
   • Deploy new version to inactive environment
   • Switch traffic using service selector updates
   • Particularly useful for Booking.com's high-traffic scenarios

3. Implementation example:

apiVersion: apps/v1
kind: Deployment
spec:
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0

For a security-critical system like Booking.com, I would implement:

1. Native Kubernetes Secrets:

   • Base64 encoded secret storage
   • Volume mounts or environment variables
   • RBAC for access control

2. External Secret Management:

   • HashiCorp Vault integration
   • AWS Secrets Manager
   • Sealed Secrets for GitOps

3. Security Best Practices:

apiVersion: v1
kind: Secret
metadata:
  name: booking-api-secrets
type: Opaque
data:
  API_KEY: <base64-encoded>
  DB_PASSWORD: <base64-encoded>

4. Regular rotation and monitoring of secrets access

For Booking.com's variable workload requirements:

1. Custom Metrics Setup:

   • Deploy Prometheus for metrics collection
   • Configure custom metrics adapter
   • Define relevant metrics (e.g., booking request queue length)

2. HPA Configuration:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: booking-service
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: booking-api
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: booking_queue_length
      target:
        type: AverageValue
        averageValue: 100

3. Implementation of metrics collection in application code using Prometheus client libraries

Based on Booking.com's tech stack, I would:

1. Pipeline Structure:

   • Use GitLab CI/Jenkins (mentioned in job description)
   • Implement multi-stage pipeline:

     stages:
       - code_quality
       - test
       - build
       - security_scan
       - deploy_staging
       - integration_test
       - deploy_production
     

2. Quality Gates:

   • Static code analysis
   • Unit/Integration tests
   • Security scanning
   • Performance testing

3. Deployment Strategy:

   • Kubernetes manifests management
   • Helm charts for package management
   • Canary deployments for risk mitigation

For Booking.com's MySQL-based system:

1. Migration Strategy:

   • Use flyway/liquibase for version control
   • Implement rollback capabilities
   • Execute migrations as Kubernetes Jobs

2. Implementation:

apiVersion: batch/v1
kind: Job
metadata:
  name: db-migration
spec:
  template:
    spec:
      containers:
      - name: db-migration
        image: flyway
        env:
        - name: DB_URL
          valueFrom:
            secretKeyRef:
              name: db-secrets
              key: url

3. Zero-downtime considerations:
   • Backward compatible changes
   • Progressive schema updates
   • Database replication lag monitoring

For Booking.com's microservices architecture:

1. Istio Implementation:

   • Deploy control plane components
   • Inject sidecars into application pods
   • Configure traffic management

2. Key Features:

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: booking-service
spec:
  hosts:
  - booking-service
  http:
  - route:
    - destination:
        host: booking-service
        subset: v1
      weight: 90
    - destination:
        host: booking-service
        subset: v2
      weight: 10

3. Service Mesh Benefits:
   • Traffic management
   • Security (mTLS)
   • Observability
   • Circuit breaking
   • Rate limiting