What is application performance monitoring?

A good REST API is predictable, consistent, and self-documenting. Model resources as nouns, use HTTP methods for actions, and let status codes carry meaning rather than embedding errors in 200 responses. This guide covers application performance monitoring end to end — core concepts, best practices, concrete data, and a step-by-step approach you can apply right away.

What cache invalidation strategy should I use?

It depends on freshness needs. Time-based expiration (TTL) is simplest and works when slightly stale data is acceptable. For stronger consistency, invalidate or update the cache on writes, or use versioned cache keys. Choose per use case: a product price needs tighter invalidation than a rarely changing category list.

How many database indexes are too many?

There is no fixed number, but each index slows writes and consumes storage, so add only indexes that real queries use. Review query plans with EXPLAIN to confirm indexes are used, and periodically drop unused ones. If write performance degrades noticeably, you likely have redundant or over-specific indexes worth consolidating.

What is the difference between caching and a CDN?

Caching is the general technique of storing computed results to serve them faster, and it can live in memory, a database, or a service like Redis. A CDN is a specific caching layer of geographically distributed edge servers that cache content close to users, reducing latency for static assets and cacheable responses worldwide.

Should I start with microservices or a monolith?

Start with a well-structured monolith for most projects. It is simpler to build, test, and operate, and avoids distributed-system complexity early on. Extract microservices later only when you hit clear scaling, deployment, or team-ownership pressures. Premature microservices often add network overhead and operational burden without delivering real benefits.

Application Performance Monitoring

This is a practical, up-to-date guide to Application Performance Monitoring — what it is, why it matters in 2026, and how to apply it in real projects. It is written for developers and founders who want clear answers and proven best practices, not filler.

Whether you're just starting out or leveling up, treat this as a working reference you can return to. Every section is built to be skimmed, applied, and shared.

What Causes Technical Debt and How Do You Manage It?

Technical debt is the accumulated cost of shortcuts and decisions that made sense once but now slow the team down. Some debt is deliberate and strategic; some is the unintended result of changing requirements or rushed work.

Manage it like financial debt rather than ignoring it:

Make it visible by tracking it in the backlog, not in people's heads.
Pay down high-interest debt that slows frequent changes first.
Refactor opportunistically while touching nearby code.
Add tests before refactoring to lock in current behavior.

The goal is not zero debt, which is impractical, but keeping it at a level where the team can still move quickly and safely. Communicate the cost in business terms to justify the time.

How Should You Design a REST API?

A good REST API is predictable, consistent, and self-documenting. Model resources as nouns, use HTTP methods for actions, and let status codes carry meaning rather than embedding errors in 200 responses.

Principles that hold up well:

Use plural nouns: /users, /users/42/orders.
Map verbs to methods: GET reads, POST creates, PUT/PATCH update, DELETE removes.
Return correct status codes: 200, 201, 400, 401, 404, 409, 422, 500.
Support pagination, filtering, and sorting via query parameters.
Version the API and keep responses consistent in shape.

Make the API safe to evolve by adding fields without breaking clients and documenting deprecations. Idempotency for writes prevents duplicate effects when clients retry on flaky networks.

Why Is Observability Critical in Production?

You cannot fix what you cannot see. Observability is the ability to understand a system's internal state from its outputs, and it turns mysterious outages into diagnosable events.

It rests on three pillars:

Logs: structured, searchable records of discrete events.
Metrics: numeric time-series like latency, error rate, and throughput.
Traces: end-to-end request paths across services.

Track the signals that reflect user experience, often summarized as latency, traffic, errors, and saturation. Alert on symptoms users feel, not on every internal blip, to avoid alert fatigue. In distributed systems especially, distributed tracing is what makes it possible to pinpoint which service in a long call chain caused a slowdown or failure.

What Are the SOLID Principles?

SOLID is five object-oriented design principles that make code easier to extend and maintain. They guide where responsibilities and dependencies should live.

Single Responsibility: a class should have one reason to change.
Open/Closed: open for extension, closed for modification.
Liskov Substitution: subtypes must be usable wherever their base type is expected.
Interface Segregation: prefer many small interfaces over one fat one.
Dependency Inversion: depend on abstractions, not concrete implementations.

Applied with judgment, they reduce coupling and make changes local. Applied dogmatically, they cause over-engineering and needless indirection. Treat them as heuristics that point toward flexible designs, not rigid rules to satisfy in every class.

What Is the Difference Between a Monolith and Microservices?

A monolith deploys all functionality as a single unit, sharing one codebase, build, and process. Microservices split capabilities into independently deployable services that communicate over the network, each owning its data.

Monoliths are simpler to build, test, and debug early on, with no network calls between modules and easy transactions. Microservices offer independent scaling and deployment but add operational complexity: service discovery, distributed tracing, network failure handling, and eventual consistency.

Key decision factors:

Team size and whether teams can own services autonomously
Operational maturity (CI/CD, monitoring, on-call)
Whether different components genuinely need different scaling

Most teams should start with a well-structured modular monolith and extract services only when a clear boundary and need emerge.

Why Does Clean Code Matter?

Code is read far more often than it is written, so clarity directly affects how fast a team can ship and how often bugs slip through. Clean code lowers the cognitive load required to understand and safely change a system.

Practical habits that compound over time:

Use intention-revealing names; avoid abbreviations and mental mapping.
Keep functions small and focused on a single level of abstraction.
Prefer early returns over deep nesting.
Delete dead code instead of commenting it out.
Let tests document expected behavior.

Clean code is not about aesthetics. It is an economic decision that reduces the long-term cost of ownership and makes onboarding new contributors dramatically faster.