Roadmap for backend from first principles

This video outlines an extensive, foundational curriculum designed to give a holistic understanding of backend engineering beyond CRUD APIs. The content spans fundamental theories, practical patterns, protocols, security, performance, and tooling necessary to build reliable, scalable, maintainable, and fault-tolerant backend systems. It emphasizes learning core underlying principles instead of being bound by language-specific ecosystems, preparing engineers for adaptability across technologies and real-world challenges.

1. Overview of Backend Engineering

Backend engineering covers building reliable, scalable, fault-tolerant, maintainable codebases and efficient systems, far more than just creating CRUD APIs. Many beginners struggle because:

• They learn within narrow scopes (college courses, bootcamps).
• Training often starts with specific languages or frameworks (Express, Spring Boot, Ruby on Rails), creating knowledge silos.
• Transferring skills between ecosystems can be difficult without an understanding of core systems.

The presenter commits to delivering a series of videos that explore backend foundations through concepts, protocols, and best practices rather than language syntax, enabling long-term mastery.

2. Request Lifecycle and HTTP Protocol

This segment covers the life cycle of a request from browser to backend server, emphasizing:

• Network routing, firewalls, and backend server invocation (e.g., AWS hosted servers).
• HTTP protocol fundamentals, including raw message structures and key HTTP headers (General, Request, Response, Security).
• HTTP methods: GET, POST, PUT, DELETE — their semantics and appropriate usage.
• Request differences such as simple vs. pre-flight CORS requests.
• HTTP response status codes and when to use common codes appropriately.
• HTTP caching techniques (ETags, max-age).
• Evolution of HTTP protocols: HTTP/1.1, HTTP/2, HTTP/3 — benefits and features.
• Content negotiation and persistent connections.
• Compression methods: gzip, deflate, Brotli.
• Security with SSL/TLS and HTTPS.

3. Routing and API Design

Key backend routing concepts include:

• Mapping URLs to server logic conditioned by HTTP methods.
• Route components: path parameters, query parameters.
• Route types: static, dynamic, nested, catch-all, regex-based routes.
• API versioning techniques and best practices for deprecating old versions.
• Route grouping to facilitate versioning, middleware sharing, and managing permissions.
• Route security and route matching performance optimizations.

4. Serialization and Deserialization

This involves converting data for network transfer and restoring it back, focusing on:

• Importance of serialization/deserialization for interoperability.
• Formats: text-based (JSON, XML) vs. binary (Protocol Buffers).
• Trade-offs between readability vs. performance (JSON easy to debug, protobuffers faster).
• JSON structure and common data types.
• Handling complex JSON cases like nested objects, collections.
• Common errors: missing/extra fields, null handling, date/time zones.
• Custom serialization implementation.
• Security risks such as injection attacks.
• Validation of JSON schemas before processing.
• Performance improvements via compression and minimizing unnecessary fields.

5. Authentication and Authorization

Security focus on controlling access:

• Different authentication types: stateful, stateless, basic auth, token-based.
• Key elements: sessions, JWT, cookies.
• Protocol deep dives: OAuth, OpenID Connect.
• API keys and multi-factor authentication.
• Cryptographic practices: salting, hashing.
• Access control models: ACL, RBAC, ReBAC.
• Security best practices: securing cookies, preventing CSRF, XSS, MITM.
• Monitoring auth events (audit logging).
• Consistency in error messages to prevent info leakage.
• Prevent timing attacks by not revealing execution time differences.

6. Validation and Transformation

Validation ensures data correctness before business logic:

• Types of validations:
  • Syntactic (format checks e.g., email validity).
  • Semantic (logic constraints e.g., DOB in the past).
  • Type validation (checking data types).
• Importance of server-side validation despite client validation.
• Transformation steps such as type casting (string to int), date format normalization.
• Normalization (e.g., lowercase emails, trim spaces).
• Sanitization to prevent injection attacks.
• Complex validation: relational (password vs confirm password), conditional, and chain validations.
• Error handling in validation with meaningful messages.
• Performance trade-offs with validation, optimizing by failing fast.

7. Middleware Architecture

Middleware are building blocks within request pipelines that add modular behavior:

• Execution flow: chaining, order importance.
• Common types:
  • Security headers.
  • CORS handling.
  • CSRF protection.
  • Rate limiting.
  • Authentication.
  • Logging and monitoring.
  • Error handling.
  • Compression.
  • Data parsing (JSON, forms, file uploads).
• Best practices: keep middleware lightweight, apply in correct order for security and performance.

8. Request Context and State Management

Request context stores metadata scoped to a single request:

• Components: HTTP method, URL, headers, query params, body, session info, user info.
• Use cases: authentication data propagation, rate limiting, tracing, logging.
• Best practices: lightweight to avoid memory overhead, cleaning context after request, preventing coupling.

9. Handlers, Controllers, and REST API Design

• MVC roles: controllers handle incoming requests, delegate to services.
• CRUD operations mapped to HTTP methods, common APIs with strict validation and error handling.
• Pagination, searching, sorting, and filtering best practices.
• Consistent response formatting, redacting sensitive fields.
• RESTful API principles: resource-based design, HTTP semantics.
• API versioning: URI, header, query string, media type.
• OpenAPI spec compliance for documentation and client communication.

10. Databases and Data Persistence

• Types: Relational vs Non-relational.
• Theoretical frameworks: ACID, CAP theorem.
• Database operations: querying, joins, schema design, indexing, migrations.
• Optimization: query tuning, caching, connection pooling.
• Data integrity: constraints, transactions, concurrency control.
• ORMs trade-offs.

11. Business Logic Layer (BLL)

• Middle layer for core domain logic separating presentation and data access.
• Components: services, domain models (entities like User, Order), business rules.
• Principles: separation of concerns, single responsibility, open/closed, dependency inversion.
• Error propagation from BLL to presentation layer.

12. Caching Strategies

• Purpose: improve response times and reduce DB/hard disk load.
• Types:
  • Memory caching.
  • Browser caching.
  • Database caching.
  • Client-side vs server-side caching.
• Strategies:
  • Cache aside, write-through, write-behind, read-through.
• Eviction policies: LRU, LFU, TTL, FIFO.
• Cache invalidation techniques.
• Hierarchical caching: combining fast small caches with slower large caches.
• Web app caching for static assets and API responses.
• Metrics: cache hit/miss ratio optimization.

13. Transactional Emails and Task Queuing

• Transactional emails anatomy: subject, preheader, body, CTA, footer, dynamic personalization.
• Task queuing use cases: email sending, image processing, heavy computations.
• Components: producer, queue, consumer, broker.
• Task dependencies: chain and parent-child relationships.
• Error handling with retries.
• Task prioritization and rate limiting.
• Scheduling use cases: backups, recurring notifications, maintenance.

14. Elasticsearch and Advanced Search

• Purpose: fast, scalable search with complex queries.
• Techniques: inverted index, term frequency–inverse document frequency (TF-IDF), shards, segments.
• Use cases: autocomplete, log analytics, social media searches.
• Querying: full-text search, relevance scoring, filtering, aggregation, fuzzy search.
• Tools: Kibana for visualization.
• Best practices: explicit field mappings, batch indexing, shard optimization.

15. Error Handling and Monitoring

• Types of errors: syntax, runtime, logical.
• Strategies: fail-safe, fail-fast, graceful degradation.
• Practices:
  • Early error catching.
  • Custom error types.
  • Logging with stack traces.
• Global error handlers for consistent API responses.
• User-facing error messages with actionable feedback.
• Monitoring tools: Sentry, ELK stack.
• Alerts management: email, Slack, threshold-based alerts.

16. Configuration Management

• Decoupling environment-specific configs from code.
• Use cases: different environments, secrets management, dynamic feature toggles.
• Types: static (DB credentials), dynamic (feature flags), sensitive (API keys).
• Sources: env files, static files (JSON/YAML), command-line flags.
• Best practices for secure, flexible config management.

17. Logging, Monitoring, and Observability

• Differentiating concepts:
  • Logging: recording events.
  • Tracing: following request paths.
  • Monitoring: health checks, metrics.
  • Observability: holistic understanding via logs, metrics, traces.
• Logging types: system, app, security.
• Log levels: DEBUG, INFO, WARN, ERROR, FATAL.
• Structured vs unstructured logs.
• Centralized logging, rotation, anonymization.
• Monitoring types: infrastructure, application performance, uptime.
• Tools: Prometheus, Grafana.
• Alert management and reducing alert fatigue.
• Security and compliance considerations around logs.

18. Graceful Shutdown

• Importance during server restarts, cloud scaling, microservices.
• Signal handling (SIGINT, SIGTERM).
• Steps:
  1. Capture termination signal.
  2. Stop accepting new requests.
  3. Complete inflight requests.
  4. Close open resources (DB connections, file handles).
  5. Exit safely.

19. Security Principles and Practices

• Common attacks: SQL injection, NoSQL injection, XSS, CSRF, broken authentication, insecure deserialization.
• Secure design:
  • Least privilege.
  • Defense in depth.
  • Fail-secure defaults.
  • Separation of duties.
  • Security by design.
• Defensive coding: input validation, sanitization, rate limiting.
• Content Security Policy (CSP), SameSite cookies.
• Ongoing event monitoring.

20. Performance and Scalability

• Metrics: response time, resource utilization.
• Bottleneck identification.
• Techniques:
  • Caching.
  • Database indexing, joins, lazy loading.
  • Batch processing.
  • Avoiding memory leaks.
  • Network overhead reduction (compression, payload size).
• Testing: profiling and performance tests.
• Best practices:
  • Write clear, modular, maintainable code.
  • Graceful degradation.
  • Offloading non-critical tasks (emails, logging) to background jobs.

21. Concurrency and Parallelism

• Difference:
  • Concurrency: managing multiple tasks simultaneously (especially IO-bound tasks).
  • Parallelism: executing multiple tasks literally at the same moment (CPU-bound tasks).
• Use case scenarios for each.

22. Object Storage and Large File Handling

• Use cases for object storage (e.g., AWS S3).
• Managing large files via chunking and streaming.
• Multipart uploads.

23. Real-Time Backends

• Technologies: WebSockets, Server-Sent Events (SSE), Pub/Sub architectures.
• Use cases for live updates, chat apps, notifications.

24. Testing and Code Quality

• Testing types: unit, integration, end-to-end, regression, load, security.
• Concepts: Test-Driven Development (TDD), automated testing in CI/CD.
• Code quality measures: linting, formatting.
• Metrics: cyclomatic complexity, maintainability index.
• 12-Factor app principles introduction.

25. OpenAPI Standards and API-First Development

• Importance of adhering to OpenAPI for consistency, automation, tooling.
• History: Swagger to OpenAPI transition, versions.
• Key document parts: paths, parameters, responses, security.
• Tools: Swagger UI, Postman.
• Best practices: avoid duplication, stick to specs.
• API-first development: define API spec before coding.

26. Webhooks

• Use cases: notifications, third-party integrations.
• Difference between APIs (polling) and webhooks (push).
• Components: webhook URL, event triggers, payload, HTTP method, response handling.
• Best practices: signature verification, HTTPS, retries, logging, testing.
• Examples: Stripe, GitHub, Slack webhooks.

27. DevOps for Backend Engineers

• Core concepts: CI, CD, continuous deployment.
• Practices: infrastructure as code, config management, version control.
• Tools: Docker for containers, Kubernetes for orchestration, CI/CD pipelines.
• Scaling: horizontal vs. vertical.
• Deployment strategies: red-green, rolling updates.

This curriculum is designed to be covered across ~30-40 videos, providing a systematic, deep dive into backend engineering essentials with a focus on both theory and practical implementations to build robust backend systems for modern applications.