Preparing for Full Stack PHP Developer role at Vallum Associates

In Yii2, Dependency Injection is implemented through its DI Container (yii\di\Container). Key points:

1. Yii2's Implementation:

• Uses constructor injection primarily
• Configuration-based dependency resolution
• Supports interface binding

$container = new \yii\di\Container;
$container->set('PaymentGatewayInterface', 'StripeGateway');

2. Main differences from Laravel:

• Yii2 focuses on explicit configuration over automatic resolution
• Laravel's container offers more advanced features like contextual binding
• Laravel uses facades, while Yii2 relies more on direct dependency injection

Example from FinTech context:

class PaymentProcessor
{
    private PaymentGatewayInterface $gateway;
    
    public function __construct(PaymentGatewayInterface $gateway)
    {
        $this->gateway = $gateway;
    }
}

PHP 8.3 introduced readonly classes as an enhancement to readonly properties. In a FinTech context:

readonly class Transaction
{
    public function __construct(
        public string $id,
        public float $amount,
        public string $currency,
        public DateTime $timestamp
    ) {}
}

Benefits for domain models:

1. Guaranteed immutability for entire class
2. Prevents state mutations after instantiation
3. Thread-safe in concurrent environments
4. Perfect for value objects in financial systems
5. Reduces potential bugs in critical financial calculations

This is particularly relevant for the position's focus on "reliable, well-tested code" as mentioned in the job requirements.

Repository pattern implementation in Yii2 for a FinTech application:

interface TransactionRepositoryInterface
{
    public function findById(string $id): ?Transaction;
    public function save(Transaction $transaction): void;
}

class YiiTransactionRepository implements TransactionRepositoryInterface
{
    public function findById(string $id): ?Transaction
    {
        $model = TransactionRecord::findOne($id);
        return $model ? $this->mapToEntity($model) : null;
    }
}

Benefits:

1. Decouples business logic from data access
2. Facilitates unit testing through interface abstraction
3. Enables switching between different data sources
4. Centralizes data access logic
5. Makes it easier to implement caching strategies

This aligns with the job's requirement for "scalable software solutions" and "well-tested code."

Composition vs Inheritance in a FinTech context:

Inheritance example (problematic):

class BasePaymentProcessor
{
    protected function validateAmount() {}
    protected function processPayment() {}
}

class StripePaymentProcessor extends BasePaymentProcessor
{
    // Tightly coupled, hard to modify
}

Composition example (preferred):

class PaymentProcessor
{
    private PaymentValidatorInterface $validator;
    private PaymentGatewayInterface $gateway;
    
    public function __construct(
        PaymentValidatorInterface $validator,
        PaymentGatewayInterface $gateway
    ) {
        $this->validator = $validator;
        $this->gateway = $gateway;
    }
}

Benefits of Composition:

1. Flexible component replacement
2. Better testability
3. Reduced coupling
4. Easier to maintain and modify
5. Follows SOLID principles

This approach supports the job's requirement for "scalable software solutions" and "well-tested code."

Key differences:

Yii2 Active Record:

class Transaction extends \yii\db\ActiveRecord
{
    public static function tableName()
    {
        return '{{%transactions}}';
    }
}

DDD Approach:

class Transaction
{
    private TransactionId $id;
    private Money $amount;
    
    public function process(): void
    {
        // Pure domain logic
    }
}

class TransactionRepository
{
    public function save(Transaction $transaction): void
    {
        // Infrastructure concerns
    }
}

Main differences:

1. AR mixes persistence and domain logic; DDD separates them
2. AR is database-centric; DDD is domain-centric
3. AR objects are mutable; DDD prefers immutable objects
4. AR has direct database coupling; DDD uses abstraction layers
5. AR is simpler for CRUD; DDD better for complex business logic

This understanding is crucial for the position's requirement to "develop and maintain scalable software solutions."

Implementation for a FinTech payment system:

interface PaymentObserverInterface
{
    public function update(PaymentEvent $event): void;
}

class PaymentProcessor implements \SplSubject
{
    private \SplObjectStorage $observers;
    
    public function process(Payment $payment): void
    {
        // Process payment
        $event = new PaymentEvent($payment);
        $this->notify($event);
    }
}

class NotificationObserver implements PaymentObserverInterface
{
    public function update(PaymentEvent $event): void
    {
        // Send notifications
    }
}

class AuditLogObserver implements PaymentObserverInterface
{
    public function update(PaymentEvent $event): void
    {
        // Log for audit
    }
}

Benefits:

1. Decoupled event handling
2. Easy to add new observers
3. Maintains single responsibility
4. Scalable notification system
5. Facilitates audit trails

This pattern is particularly relevant for the position's FinTech focus and requirement for scalable solutions.

PHP's JIT (Just-In-Time) compilation:

Configuration:

opcache.jit=1235
opcache.jit_buffer_size=100M

Key points:

1. JIT compiles opcodes to machine code
2. Works alongside OpCache
3. Best for CPU-intensive operations
4. May not benefit I/O-heavy applications

When to enable in production:

1. CPU-intensive calculations (financial computations)
2. Long-running processes
3. Heavy algorithmic operations
4. When properly tested and benchmarked

When to avoid:

1. I/O-bound applications
2. Short-lived requests
3. Limited server resources
4. Without proper performance testing

This knowledge is particularly relevant for the position's focus on performance and scalability in a FinTech environment.

For a FinTech system using the mentioned stack (AWS, Fastify, Node.js), I would implement:

1. Synchronous Communication:

• RESTful APIs for direct requests using Fastify
• GraphQL for complex data aggregations
• AWS API Gateway for routing and security

2. Asynchronous Communication:

• Event-driven architecture using AWS SNS/SQS
• RabbitMQ for transaction queues
• Event sourcing for financial transactions

3. Implementation Details:

• Use JWT for service-to-service authentication
• Implement retry mechanisms with exponential backoff
• Add request/response logging using DataDog
• Error tracking with Sentry
• Standardized API contracts using OpenAPI/Swagger

This ensures reliable, traceable financial transactions with proper failure handling.

The Circuit Breaker pattern prevents cascade failures in distributed systems. In the context of the mentioned stack:

Implementation approach:

1. Using PHP libraries like Ganesha or Symfony's Circuit Breaker
2. States management:
   • CLOSED: Normal operation
   • OPEN: Stop calls to failing service
   • HALF-OPEN: Test if service recovered

Code example:

class PaymentCircuitBreaker {
    private $breaker;
    
    public function __construct() {
        $this->breaker = new CircuitBreaker(
            'payment_service',
            [
                'failureRateThreshold' => 50,
                'waitDurationInOpenState' => 30000,
                'ringBufferSizeInHalfOpenState' => 10,
            ]
        );
    }
    
    public function processPayment(Transaction $transaction) {
        return $this->breaker->call(
            fn() => $this->paymentService->process($transaction),
            fn(\Exception $e) => $this->handleFailure($e)
        );
    }
}

Monitor circuit states using DataDog for visibility into service health.

For FinTech applications, I would implement the Saga pattern with these components:

1. Choreography-based Saga:

class PaymentSaga {
    public function initiatePayment(Payment $payment) {
        try {
            // Start transaction
            $this->validateBalance();
            $this->processPayment();
            $this->updateLedger();
            $this->notifyUser();
        } catch (Exception $e) {
            // Compensating transactions
            $this->rollbackChanges();
            throw $e;
        }
    }
}

2. Implementation details:

• Use AWS Step Functions for orchestration
• RabbitMQ for reliable message delivery
• Event sourcing for transaction history
• Redis for distributed locking
• DataDog for transaction monitoring

3. Consistency guarantees:

• Two-phase commit for critical operations
• Eventual consistency for non-critical updates
• Idempotency keys for safe retries

Given the AWS-based infrastructure mentioned in the job description, I would implement:

1. AWS-native solution:

• AWS Cloud Map for service discovery
• ECS Service Discovery
• Route 53 DNS routing

2. Container-based approach:

• Docker container discovery via Docker Swarm
• Service registry pattern implementation
• Health checks integration with DataDog

3. Implementation example:

class ServiceRegistry {
    public function registerService(string $name, string $endpoint): void {
        $this->cloudMap->registerInstance([
            'ServiceId' => $name,
            'InstanceId' => uniqid(),
            'Attributes' => [
                'endpoint' => $endpoint,
                'health' => '/health'
            ]
        ]);
    }
}

4. Additional features:

• Cache service locations in Redis
• Implement circuit breakers for discovery requests
• Monitor service health with DataDog

For a FinTech application using the specified stack, I would implement event sourcing as follows:

1. Event Store:

class TransactionEventStore {
    public function append(Event $event): void {
        $serialized = $this->serialize($event);
        $this->store->append($event->aggregateId(), $serialized);
    }
    
    public function getEvents(string $aggregateId): array {
        return $this->store->getEvents($aggregateId);
    }
}

2. Components:

• Use AWS DynamoDB for event storage
• Implement CQRS pattern
• Event projections for read models
• Event handlers for side effects

3. Integration:

• RabbitMQ for event distribution
• Redis for caching current state
• DataDog for event monitoring
• Sentry for error tracking

4. Ensure:

• Event versioning
• Idempotency
• Audit trail
• Point-in-time reconstruction

In a FinTech context with the given technology stack, I would implement:

1. Consistency Strategies:

• Version vectors for conflict resolution
• Last-write-wins for non-critical data
• Custom merge functions for complex states

2. Implementation:

class ConsistencyManager {
    public function handleUpdate(Data $data): void {
        $this->redis->set(
            "data:{$data->getId()}",
            $data,
            ['version' => $data->getVersion()]
        );
        
        $this->eventBus->publish(new DataUpdatedEvent($data));
    }
}

3. Tools utilization:

• Redis for distributed caching
• AWS DynamoDB for consistent storage
• DataDog for monitoring consistency lag
• RabbitMQ for change propagation

4. Best practices:

• Implement retry mechanisms
• Use compensation transactions
• Monitor reconciliation processes
• Provide real-time consistency status

In a Yii2 application, I would implement multi-level caching using a combination of different cache storage systems:

1. First Level (In-Memory):

$cache = new yii\caching\ArrayCache();

2. Second Level (Redis):

$cache = new yii\redis\Cache([
    'redis' => [
        'hostname' => 'localhost',
        'port' => 6379,
        'database' => 0,
    ],
]);

3. Implement Chain Caching:

use yii\caching\ChainedDependency;

$cache = new yii\caching\ChainedCache([
    'caches' => [
        'array' => new yii\caching\ArrayCache(),
        'redis' => new yii\redis\Cache(),
    ],
]);

This setup aligns well with the project's tech stack, which includes Redis, and would be particularly effective for caching financial data that needs quick access but doesn't change frequently.

For REST API responses, especially in a FinTech application using Yii2 and React, I would implement these strategies:

1. HTTP Cache Headers:

Yii::$app->response->headers->set('Cache-Control', 'public, max-age=3600');
Yii::$app->response->headers->set('ETag', $etag);

2. Server-Side Caching with Redis:

public function behaviors()
{
    return [
        'httpCache' => [
            'class' => 'yii\filters\HttpCache',
            'cacheControlHeader' => 'public, max-age=3600',
            'etagSeed' => function ($action, $params) {
                return $this->getDataVersion();
            },
        ],
    ];
}

3. API Response Caching:

public function actionGetMarketData()
{
    $cacheKey = 'market_data_' . date('Y-m-d_H');
    $data = Yii::$app->cache->get($cacheKey);
    
    if ($data === false) {
        $data = $this->fetchMarketData();
        Yii::$app->cache->set($cacheKey, $data, 3600);
    }
    
    return $data;
}

This approach would work well with the project's Datadog and Sentry monitoring tools for tracking cache performance.

OpCache optimization is crucial for high-performance PHP applications:

1. Basic Configuration:

opcache.enable=1
opcache.memory_consumption=256
opcache.interned_strings_buffer=64
opcache.max_accelerated_files=100000
opcache.validate_timestamps=0
opcache.revalidate_freq=0
opcache.save_comments=1

2. JIT Configuration (PHP 8.3):

opcache.jit=1255
opcache.jit_buffer_size=100M

3. Monitoring Setup:

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

// Send metrics to Datadog
DatadogMetrics::gauge('opcache.memory_usage', $status['memory_usage']['used_memory']);

This configuration would be particularly effective in the AWS environment mentioned in the job description, optimizing performance for the Yii2 application.

For a FinTech application running on AWS with high load requirements:

1. Connection Management:

$redis = new Redis([
    'hostname' => getenv('REDIS_HOST'),
    'port' => getenv('REDIS_PORT'),
    'database' => 0,
    'retries' => 3,
    'retry_interval' => 100,
]);

2. Data Structure Optimization:

// Using Hash instead of individual keys
$redis->hMSet("user:1000", [
    'transactions' => json_encode($transactions),
    'balance' => $balance,
    'last_login' => time()
]);

3. Implementing Redis Cluster:

$options = [
    'cluster' => 'redis',
    'parameters' => [
        'password' => getenv('REDIS_PASSWORD'),
        'prefix' => 'fintech:',
    ]
];

4. Monitoring Integration:

// Integration with Datadog for monitoring
$redis->on('command', function($command) {
    Datadog::increment('redis.commands', 1, ['command' => $command]);
});

This setup would integrate well with the project's AWS infrastructure and monitoring tools (Datadog, Sentry).

For a distributed system using Yii2 and microservices:

1. Event-Based Invalidation:

class CacheInvalidationService
{
    public function invalidateCache($event)
    {
        $tags = $this->determineAffectedTags($event);
        $this->publishInvalidationMessage($tags);
    }
}

2. Message Queue Implementation (RabbitMQ):

public function publishInvalidationMessage($tags)
{
    $message = [
        'event' => 'cache_invalidation',
        'tags' => $tags,
        'timestamp' => microtime(true)
    ];
    
    $this->queue->publish('cache_events', json_encode($message));
}

3. Pattern-Based Invalidation:

public function invalidateByPattern($pattern)
{
    $keys = $this->redis->keys($pattern);
    foreach ($keys as $key) {
        $this->redis->del($key);
    }
}

This approach would work effectively with the project's distributed architecture and AWS infrastructure.

For testing asynchronous operations, I would implement a combination of approaches:

1. Using PHPUnit's built-in assertion methods with custom wait conditions:

public function testAsyncOperation()
{
    $promise = $this->asyncService->processPayment($paymentData);
    
    $result = \React\Async\await($promise);
    
    $this->assertInstanceOf(PaymentResult::class, $result);
}

2. Implementing test doubles for message queues (RabbitMQ mentioned in stack):

public function testQueueProcessing()
{
    $mockQueue = $this->createMock(QueueInterface::class);
    $mockQueue->expects($this->once())
        ->method('publish')
        ->with($this->equalTo($expectedPayload));
        
    $processor = new PaymentProcessor($mockQueue);
    $processor->processAsync($paymentData);
}

3. Using Datadog (mentioned in stack) for monitoring async operations in integration tests:

$datadogTracer->startSpan('async_operation');
// Test async operation
$datadogTracer->finishSpan();

For financial calculations, I emphasize precision and comprehensive test coverage:

1. Data Providers for multiple test scenarios:

/**
 * @dataProvider financialCalculationProvider
 */
public function testInterestCalculation($principal, $rate, $time, $expected)
{
    $calculator = new FinancialCalculator();
    $result = $calculator->calculateCompoundInterest($principal, $rate, $time);
    
    // Use bccomp for precise decimal comparison
    $this->assertEquals(0, bccomp($expected, $result, 8));
}

2. Boundary testing for edge cases:

public function testCalculationEdgeCases()
{
    $calculator = new FinancialCalculator();
    
    $this->expectException(InvalidArgumentException::class);
    $calculator->calculateInterest(-1000); // Negative amount
}

3. Integration with external validation services when necessary.

Given the project's microservices architecture, I would implement contract testing as follows:

1. Using Pact for consumer-driven contract testing:

class PaymentServiceContractTest extends TestCase
{
    public function testPaymentServiceContract()
    {
        $builder = new PactBuilder();
        $builder
            ->serviceProvider('payment-service')
            ->serviceConsumer('order-service')
            ->given('a valid payment request')
            ->uponReceiving('a payment processing request')
            ->withRequest('POST', '/api/payments')
            ->willRespondWith(200);
    }
}

2. Implementing OpenAPI/Swagger specifications for REST API validation:

/**
 * @OA\Post(
 *     path="/api/payments",
 *     @OA\Response(response="200", description="Payment processed")
 * )
 */
public function processPayment(Request $request)
{
    // Implementation
}

3. Using Fastify (mentioned in stack) for schema validation in tests.

For mocking external services, I employ several strategies:

1. Using PHPUnit's mock builder for service interfaces:

public function testExternalPaymentProcessor()
{
    $mockStripe = $this->createMock(StripeServiceInterface::class);
    $mockStripe->expects($this->once())
        ->method('processPayment')
        ->willReturn(new PaymentResponse(/* ... */));
        
    $paymentService = new PaymentService($mockStripe);
    $result = $paymentService->process($paymentData);
}

2. Implementing test doubles with specific behavior:

class MockAwsService implements AwsServiceInterface
{
    public function sendMessage(array $data): bool
    {
        return true; // Simulated successful response
    }
}

3. Using Docker (mentioned in stack) for isolated testing environments:

# docker-compose.test.yml
services:
  test-db:
    image: mysql:8.0
    environment:
      MYSQL_DATABASE: test_db

For E2E testing of payment processing, I would implement:

1. Using Cypress for frontend testing with React (mentioned in stack):

describe('Payment Flow', () => {
    it('processes payment successfully', () => {
        cy.intercept('POST', '/api/payments').as('paymentRequest');
        cy.get('[data-testid="payment-form"]').submit();
        cy.wait('@paymentRequest').its('response.status').should('eq', 200);
    });
});

2. Implementing complete flow testing with PHPUnit:

class PaymentFlowTest extends TestCase
{
    public function testCompletePaymentFlow()
    {
        // Setup test data
        $payment = $this->createPayment();
        
        // Process payment
        $result = $this->paymentService->process($payment);
        
        // Verify database state
        $this->assertDatabaseHas('transactions', [
            'status' => 'completed',
            'amount' => $payment->amount
        ]);
        
        // Verify events
        $this->assertEventDispatched(PaymentProcessedEvent::class);
    }
}

3. Using Sentry (mentioned in stack) for error tracking in E2E tests:

\Sentry\init(['dsn' => 'test-dsn']);
try {
    // Test execution
} catch (\Exception $e) {
    \Sentry\captureException($e);
    throw $e;
}

For a FinTech application, I would implement a flexible and scalable schema:

1. Base Transaction Table:

CREATE TABLE transactions (
    id BIGINT UNSIGNED AUTO_INCREMENT,
    transaction_type ENUM('payment', 'transfer', 'refund'),
    status VARCHAR(50),
    amount DECIMAL(20,4),
    currency VARCHAR(3),
    created_at TIMESTAMP,
    updated_at TIMESTAMP,
    metadata JSON,
    PRIMARY KEY (id)
);

2. Type-specific tables with foreign keys:

CREATE TABLE payments (
    transaction_id BIGINT UNSIGNED,
    payment_method VARCHAR(50),
    payment_provider VARCHAR(50),
    FOREIGN KEY (transaction_id) REFERENCES transactions(id)
);

This design follows:

• Single Responsibility Principle
• Easy extensibility for new transaction types
• JSON column for flexible metadata storage
• Strong referential integrity
• Audit trail support

For a FinTech reporting system, I would:

1. Implement proper indexing:

CREATE INDEX idx_trans_type_date ON transactions(transaction_type, created_at);
CREATE INDEX idx_amount_currency ON transactions(amount, currency);

2. Use materialized views for complex aggregations:

CREATE MATERIALIZED VIEW daily_transactions AS
SELECT 
    DATE(created_at) as date,
    transaction_type,
    currency,
    COUNT(*) as total_count,
    SUM(amount) as total_amount
FROM transactions
GROUP BY DATE(created_at), transaction_type, currency;

3. Implement query optimization techniques:

• EXPLAIN ANALYZE for query analysis
• Proper JOIN order
• Avoiding SELECT *
• Using appropriate data types
• Implementing partitioning for large tables

4. Leverage Redis for caching frequently accessed reports

For zero-downtime migrations in a FinTech environment:

1. Follow backward-compatible migration steps:

// Step 1: Add new column (deployable while running)
public function up()
{
    $this->addColumn('transactions', 'new_field', 'VARCHAR(255) NULL');
}

// Step 2: Copy data (background job)
// Step 3: Switch to new column
// Step 4: Remove old column (future migration)

2. Implementation strategy:

• Use tools like Bamboo (mentioned in stack) for orchestration
• Implement blue-green deployment
• Use database connection pooling
• Monitor replication lag
• Have rollback procedures ready

3. Best practices:

• Never modify existing columns directly
• Use temporary tables for large data migrations
• Implement feature flags for gradual rollout

For a high-load FinTech application using AWS (mentioned in stack):

1. Read/Write Splitting:

• Master for writes
• Multiple read replicas using AWS RDS
• Implement consistent hashing for read distribution

2. Sharding Strategy:

class TransactionRepository
{
    public function getShardConnection($transactionId)
    {
        return $this->shardManager->getConnection($transactionId % TOTAL_SHARDS);
    }
}

3. Caching Layer:

• Redis for hot data
• Implement write-through caching
• Cache invalidation strategy

4. Additional Considerations:

• AWS Auto Scaling for dynamic capacity
• Monitor with DataDog (mentioned in stack)
• Implement circuit breakers for database failover

For a FinTech system requiring event sourcing:

1. Events Table Structure:

CREATE TABLE events (
    id BIGINT UNSIGNED AUTO_INCREMENT,
    aggregate_id VARCHAR(36),
    event_type VARCHAR(100),
    event_data JSON,
    metadata JSON,
    created_at TIMESTAMP,
    version INT,
    PRIMARY KEY (id),
    INDEX idx_aggregate (aggregate_id, version)
);

2. PHP Implementation:

class EventStore
{
    public function appendToStream(string $aggregateId, Event $event): void
    {
        $this->db->insert('events', [
            'aggregate_id' => $aggregateId,
            'event_type' => get_class($event),
            'event_data' => json_encode($event->getData()),
            'version' => $this->getNextVersion($aggregateId)
        ]);
    }
}

3. Key Features:

• Immutable event log
• Versioning for optimistic locking
• JSON for flexible event data
• Projection tables for read models