Preparing for Node.js Developer role at we code Agency

Node.js uses an event-driven, non-blocking I/O model centered around the event loop. When an asynchronous operation is initiated, Node.js registers a callback and continues executing other code. The event loop continuously checks for completed operations and executes their callbacks. This allows Node.js to handle thousands of concurrent connections efficiently without creating threads for each one.

The libuv library manages a thread pool for certain operations that can't be made asynchronous at the OS level, like some file system operations or CPU-intensive tasks. However, JavaScript code still runs in a single thread, which means CPU-intensive JavaScript operations will block the event loop.

The main limitations of this approach become apparent with CPU-bound tasks. Since JavaScript runs in a single thread, long-running calculations or synchronous operations will block the entire application. Additionally, Node.js can't take full advantage of multi-core systems without using Worker Threads or the Cluster module, as the main event loop runs on a single core.

I recently worked on a service that needed to process large datasets with complex calculations. We chose Worker Threads over Cluster because the application required shared memory access and fine-grained control over thread creation and termination. Worker Threads allowed us to parallelize CPU-intensive work while maintaining a shared memory space for efficient data transfer.

With Cluster, you're essentially creating separate processes that don't share memory, making it more suitable for scaling entire HTTP servers. Worker Threads, on the other hand, are better for CPU-bound tasks where you need to parallelize specific operations within the same process. In our case, we needed to maintain a shared cache and coordinate work between threads, which would have been more complex and memory-intensive with Cluster.

The decision was also influenced by the nature of our workload. Since we were doing data processing rather than handling HTTP requests, Worker Threads provided better resource utilization and more precise control over the threading model. We could dynamically adjust the number of threads based on the workload and system resources.

To diagnose memory leaks in production, I start with monitoring tools like New Relic or Datadog to identify unusual memory growth patterns. Once detected, I use Node.js's built-in heap snapshot functionality through the heap-snapshot module to capture memory states at different intervals. These snapshots can be analyzed using Chrome DevTools to identify objects that aren't being properly garbage collected.

A common approach I've used is taking multiple heap snapshots: one at baseline, one after suspected memory leak operations, and one after garbage collection. Comparing these snapshots helps identify retained objects. I particularly look for growing arrays, event listeners that haven't been removed, and closures holding references to large objects.

For fixing leaks, I've found several common culprits: unbounded caches without TTL, event listeners not being properly removed, and promises that never resolve. I implement fixes like adding cache eviction policies, properly cleaning up event listeners, and ensuring all promises either resolve or reject. After implementing fixes, I validate with load testing tools like Artillery to ensure memory usage remains stable under load.

My approach to error handling in asynchronous operations follows a comprehensive strategy. First, I implement try-catch blocks within async functions and ensure all promises have proper .catch() handlers. I also set up global unhandledRejection and uncaughtException event handlers as a safety net, but these are mainly for logging and graceful shutdown rather than recovery.

For specific services, I implement custom error classes that extend Error to provide more context and maintain consistent error handling patterns. This helps with error tracking and debugging. I also use async boundary patterns where asynchronous operations are wrapped in higher-order functions that standardize error handling and logging.

In production applications, I combine this with structured logging using tools like Winston or Pino, ensuring errors include relevant context like request IDs and stack traces. For critical operations, I implement circuit breakers using libraries like Hystrix to prevent cascade failures in microservices architectures.

In a recent project, we noticed increasing latency in our API responses during peak loads. Using Node.js's built-in performance hooks and clinic.js, we identified event loop lag caused by synchronous operations in our authentication middleware. We used async_hooks to track async operations and found that database queries were being processed synchronously due to a misconfigured ORM.

I implemented several optimizations: moved CPU-intensive operations to Worker Threads, switched to connection pooling for database operations, and implemented caching for frequently accessed data. We used node --prof for CPU profiling and clinic doctor to visualize event loop performance before and after changes.

The most effective tool was node-clinic-bubbleprof, which helped us visualize async operations and identify bottlenecks in our promise chains. We also implemented better query batching using DataLoader pattern, which significantly reduced database round trips and improved event loop throughput.

For CPU-intensive tasks, I implement a multi-pronged approach based on the specific requirements. For calculations that can be parallelized, I create a Worker Thread pool where each worker handles a portion of the computation. I use a queue system like Bull to manage task distribution and handle retries, using Redis as the backend.

When dealing with real-time data processing, I often implement a streaming approach using Node.js streams with objectMode, breaking down large operations into smaller chunks that don't block the event loop. This is particularly effective when processing large datasets or performing ETL operations.

For cases where we need immediate response times, I've implemented job scheduling patterns where CPU-intensive tasks are offloaded to separate microservices running on dedicated hardware. This approach uses message queues (like RabbitMQ) for communication and maintains system responsiveness while handling heavy computations.

In my recent project, I designed a microservices architecture for an e-commerce platform handling 100K+ daily transactions. The system comprised 12 core services including inventory, ordering, payment processing, and user management. One key decision was implementing event-driven communication using RabbitMQ for asynchronous operations, while maintaining REST APIs for synchronous requests.

A major trade-off we faced was between data consistency and service autonomy. We implemented the Saga pattern for distributed transactions, particularly critical for order processing where we needed to coordinate inventory updates, payment processing, and order fulfillment. This increased complexity but ensured data consistency across services.

The architecture used Node.js with NestJS framework for most services, leveraging its dependency injection and modular structure. We implemented API gateways using Apollo GraphQL for frontend communication, which simplified client-side data fetching but required careful consideration of schema design and resolver implementation.

I approach data consistency in microservices using a combination of eventual consistency and event sourcing patterns. For example, in our payment processing system, we implement the outbox pattern where events are first written to a local database transaction, then published to a message queue (typically RabbitMQ or Apache Kafka) for other services to consume.

To handle temporary inconsistencies, we implement compensation transactions and retry mechanisms. Each service maintains its own PostgreSQL database, and we use MikroORM's unit of work pattern to ensure atomic operations within each service. For cross-service queries, we maintain materialized views that are updated through event subscriptions.

Critical to this approach is proper event versioning and careful consideration of event order. We use event-driven architectures with clear event schemas and versioning strategies, allowing services to evolve independently while maintaining backward compatibility.

For service discovery, I typically implement a combination of client-side discovery and server-side load balancing using tools like Consul for service registry and Nginx or HAProxy as reverse proxies. In Kubernetes environments, we leverage the built-in service discovery mechanisms and CoreDNS for internal routing.

Load balancing strategies vary based on service requirements. For stateless services, we use round-robin with health checks. For services with specific resource requirements, we implement weighted load balancing. We also use circuit breakers (typically implemented with libraries like Hystrix or our own Node.js implementation) to prevent cascade failures.

Monitoring is crucial - we use Prometheus for metrics collection and Grafana for visualization, helping us adjust load balancing parameters based on real-world performance data. This helps maintain optimal resource utilization across services.

I led the decomposition of a monolithic Node.js e-commerce application into microservices over a 6-month period. The first step was analyzing domain boundaries using event storming sessions with the team, which helped identify natural service boundaries. We started with the most independent components - the product catalog and user authentication services.

We used the strangler fig pattern, gradually moving functionality to new services while maintaining the monolith as the primary system. Each new service was built using NestJS and TypeScript, with its own PostgreSQL database. We implemented an API gateway using Apollo GraphQL to handle routing and data aggregation, which significantly simplified the transition for frontend clients.

The most challenging aspect was handling shared data. We implemented a data migration strategy where we first created read-only copies, then gradually moved write operations to the new services. We used feature flags to control traffic flow and maintained comprehensive monitoring using Datadog to catch any issues early.

I implement circuit breakers using a combination of the Opossum library for Node.js and custom middleware in NestJS applications. The typical configuration includes three states: closed (normal operation), open (failing fast), and half-open (testing recovery). For critical services, we set failure thresholds at around 50% of requests over a 10-second window.

The circuit breaker parameters are typically configured with a sliding window of 10 seconds, failure threshold of 5 errors, and a reset timeout of 30 seconds. However, these values are adjusted based on service characteristics and business requirements. For example, payment services have stricter thresholds (30% failure rate) compared to non-critical services.

We also implement fallback mechanisms for when circuits are open, such as serving cached data or degraded functionality. All circuit breaker events are logged and monitored through our observability stack (typically ELK or Datadog) to help identify patterns and adjust thresholds.

For handling distributed transactions, I implement the Saga pattern with both choreography and orchestration approaches depending on the use case. For example, in order processing, we use an orchestrator service that coordinates the entire transaction flow and handles compensation actions when failures occur.

Each step in the transaction is designed to be idempotent and has a corresponding compensation action. We use event sourcing to maintain a complete audit trail of all actions and their compensating events. The system uses RabbitMQ for reliable message delivery and implements the outbox pattern to ensure message publishing is atomic with local transactions.

Error handling includes automatic retries with exponential backoff for temporary failures, and manual intervention triggers for permanent failures. We maintain a transaction log service that tracks the state of all distributed transactions, making it easier to diagnose and recover from failures. This approach has helped us maintain data consistency while achieving 99.9% transaction reliability.

In my experience, the key to optimizing ORM query performance starts with proper eager loading strategies. I extensively use MikroORM's QueryBuilder to implement selective loading patterns, ensuring we only fetch the data we need. This prevents the N+1 query problem that often plagues ORM implementations.

I also implement strategic database indexing based on query patterns we observe in production. For frequently accessed relations, I create composite indexes and ensure our ORM queries are structured to utilize these indexes effectively. Another crucial strategy is implementing result caching at the ORM level, where we cache complex query results for configurable durations based on data volatility.

For large result sets, I implement pagination using cursor-based approaches rather than offset pagination, as this performs significantly better with PostgreSQL. I also regularly use the explain analyze command to understand query execution plans and optimize our ORM configurations accordingly.

I recently led a project where we needed to optimize a customer analytics system that was experiencing significant slowdown. The original schema had a single large table with numerous JSON columns, which was causing performance issues with our analytical queries.

My process began with analyzing query patterns using pg_stat_statements to identify the most resource-intensive operations. I then designed a new normalized schema that separated the JSON data into properly structured relational tables. We used materialized views for commonly accessed aggregate data and implemented partitioning on the timestamp column for historical data.

The migration process involved writing a careful transition plan using MikroORM migrations, including creating temporary tables for zero-downtime migration. We also implemented extensive testing in staging environments and monitored query performance improvements. The result was a 70% reduction in query execution time and significantly reduced database load.

In microservices environments, I follow a distributed transaction pattern for database migrations. Each service owns its database schema and is responsible for its own migrations. I use MikroORM's migration system with a versioning strategy that ensures backwards compatibility during deployments.

We maintain a clear migration strategy where breaking changes are implemented in multiple steps across services. First, we add new fields or tables while maintaining the old ones, then gradually transition the application code to use the new schema, and finally clean up the deprecated schema elements. This allows for zero-downtime deployments.

To coordinate migrations across services, we implement a migration orchestration service that tracks the state of all database schemas and ensures migrations are applied in the correct order. We also maintain comprehensive migration tests and rollback procedures for each change.

For distributed transactions across services, I implement the Saga pattern using either choreography or orchestration depending on the use case. In simpler scenarios, I use choreography where services publish events and react to other services' events. For more complex workflows, I implement an orchestrator service that manages the transaction flow.

I ensure idempotency in all transaction steps using unique transaction IDs and maintaining transaction states in a dedicated table. Each service implements compensating transactions that can roll back changes if any part of the distributed transaction fails. We use MikroORM's transaction API with custom hooks to integrate with our distributed transaction management system.

For monitoring and debugging, we implement distributed tracing using tools like OpenTelemetry to track transaction flows across services. This helps us identify bottlenecks and troubleshoot failed transactions effectively.

When designing PostgreSQL indexes, I first analyze the most frequent query patterns and their WHERE, ORDER BY, and JOIN conditions. I use pg_stat_statements to identify high-impact queries that would benefit most from indexing. For complex queries, I create composite indexes that match the exact query patterns.

I'm careful about index overhead, particularly write performance impact. I regularly monitor index usage with pg_stat_user_indexes to identify unused indexes that can be removed. For tables with heavy write loads, I consider partial indexes to reduce the index size and maintenance overhead.

I also implement specialized indexes like GiST for geometric data or GIN for full-text search when appropriate. For time-series data, I often use BRIN indexes as they provide good performance with minimal storage overhead.

In production environments, I implement comprehensive query monitoring using a combination of tools. We use pg_stat_statements to track query execution statistics and identify slow queries. I've set up automated alerts for queries exceeding certain execution time thresholds or those causing excessive database load.

I've implemented custom middleware in our Node.js application that logs all ORM queries with their execution times and related metadata. We use APM tools like New Relic or Datadog to correlate these queries with application performance metrics. This helps us identify problematic patterns in our ORM usage.

For optimization, I regularly review the generated SQL from MikroORM to ensure it's optimal. We maintain a query optimization workflow where identified problematic queries are analyzed, optimized, and tested in staging before deploying improvements to production. This includes reviewing eager loading strategies, query complexity, and proper use of indexes.

The decision between REST and GraphQL depends heavily on several key factors. First, I look at the data consumption patterns - if clients need highly flexible data fetching with multiple related resources, GraphQL typically provides better efficiency by reducing over-fetching and under-fetching. This was particularly relevant in a recent project where our mobile app needed varying data shapes across different screens.

I also consider the team's expertise and existing infrastructure. While GraphQL offers powerful capabilities, it requires additional tooling and expertise. With REST, we get excellent caching through HTTP, wide tooling support, and simpler implementation. Another crucial factor is real-time requirements - GraphQL subscriptions provide robust real-time capabilities, while REST would require additional WebSocket implementation.

Performance requirements play a major role too. For simple CRUD operations with predictable data shapes, REST often provides better performance due to its simplicity. However, for complex data requirements with multiple related resources, GraphQL can significantly reduce network overhead by allowing clients to specify exactly what they need in a single request.

In my experience, URI versioning has proven most effective for REST APIs, using patterns like /api/v1/resources. This approach offers clear visibility and makes it easy for clients to understand which version they're consuming. However, I always implement it alongside thorough documentation and deprecation notices to ensure smooth client transitions.

For handling breaking changes, I've found success with a dual-running strategy where we maintain both old and new versions for a deprecation period. We use monitoring to track usage patterns of different versions, allowing us to make informed decisions about when to sunset older versions. This approach helped us successfully migrate a large-scale e-commerce platform with minimal client disruption.

One particularly effective practice I've implemented is version sunset dates in API responses via custom headers, combined with automated notifications to clients still using deprecated versions. This proactive communication significantly reduces migration friction and helps maintain API hygiene.

For GraphQL authentication, I implement a multi-layer approach starting with JWT tokens handled through Apollo Server's context function. This allows us to validate authentication at the gateway level before queries even reach the resolvers. I've found this particularly effective when integrating with existing authentication systems while maintaining security standards.

For authorization, I implement a directive-based approach combined with field-level security. Using @auth directives allows us to declaratively specify permission requirements in the schema, making it easier to maintain and audit security requirements. I also implement role-based access control (RBAC) at the resolver level, using custom middleware to check permissions before executing queries.

In my recent projects, I've implemented context-aware authorization where permissions change based on not just the user's role but also the relationship to the requested resource. This was crucial for a multi-tenant system where users could have different permissions across different organizations.

In a recent project, we faced performance issues with deeply nested queries in a social media platform's feed system. Using Apollo Studio's performance metrics, we identified that certain queries were causing N+1 problems. I implemented DataLoader for batch loading and caching, which reduced our database queries by 70% for common operations.

We also utilized Apollo Server's APQ (Automatic Persisted Queries) to reduce query payload sizes and implemented field-level cost analysis to prevent resource-intensive queries. I wrote custom directives to implement query complexity calculations, allowing us to reject queries that would be too expensive to execute.

The most significant optimization came from implementing query planning and analyzing query patterns. Using Apollo's metrics and custom logging, we identified common query patterns and optimized our database schema and indexes accordingly. This included denormalization of frequently accessed data and strategic caching using Redis.

I implement a centralized error handling system using custom error classes that extend Apollo's ApolloError. Each error type (ValidationError, AuthenticationError, BusinessLogicError, etc.) has standardized fields including error code, user message, and internal details. This ensures consistent error formatting across all resolvers and services.

For monitoring and debugging, I integrate error tracking with our observability stack (typically ELK or DataDog) and implement error fingerprinting to group similar errors. We maintain an error catalog that maps internal error codes to user-friendly messages, supporting multiple languages through i18n.

In microservices architectures, I implement error boundary patterns where the GraphQL gateway normalizes errors from different services into a consistent format. This includes handling both GraphQL-specific errors and REST service errors when using Apollo Federation.

For rate limiting, I implement a token bucket algorithm at multiple levels. At the API gateway level, we use Redis to track request rates per client, considering both query complexity and frequency. I've created custom directives to assign complexity scores to different fields and operations, ensuring fair resource usage across clients.

Caching strategy involves multiple layers. At the client level, we use Apollo Client's cache policies and field-level cache control directives. Server-side, we implement Redis caching for frequently accessed data with careful consideration of invalidation patterns. For real-time data, we use partial cache invalidation triggered by relevant events.

Performance optimization includes implementing persisted queries to reduce query string transmission and analyzing query patterns to optimize cache strategies. In my last project, this approach reduced server load by 40% and improved response times by 60% for common queries.