Tools

Thoughts on development tools, frameworks, and the technologies that shape how we build

Java & The JVM

Java remains one of the most pragmatic choices for building production systems at scale. Not the newest, not the trendiest—but proven. The JVM ecosystem offers stability, performance, and a mature toolchain that enterprise systems depend on.

Spring Framework

Spring Boot for rapid application development. Spring Security for authentication. Spring Data for persistence abstraction.

When to use: Enterprise applications, microservices, APIs

Trade-offs: Convention over configuration can obscure behavior

Sweet spot: Teams needing productivity + maintainability

Build Tools

Maven for convention, Gradle for flexibility. Both mature, both widely supported. Maven's XML verbosity vs Gradle's Groovy/Kotlin DSL flexibility.

Maven: Standard, predictable, extensive plugin ecosystem

Gradle: Flexible, faster builds, better multi-project support

Choice: Maven for simplicity, Gradle for complex builds

Java Philosophy

Verbosity as clarity: Explicit over implicit. More typing, less guessing.

Type safety: Catch errors at compile time. Refactoring with confidence.

Backward compatibility: Code from 2005 still runs. Stability matters.

Ecosystem maturity: Libraries for everything. Battle-tested in production.

Java Stack Experience

Spring Boot: Built enterprise APIs serving millions of requests daily. Transaction management, caching strategies, connection pooling at scale.

Kafka: Event-driven architectures with Spring Kafka. Reactive streams for high-throughput message processing. Idempotency and exactly-once semantics.

JPA/Hibernate: Complex domain models with relationships. Understanding N+1 queries, fetch strategies, L2 cache. When to use native queries vs JPQL.

Testing: JUnit 5, Mockito, TestContainers. Integration tests with real databases. Contract testing with Spring Cloud Contract.

React & Modern Frontend

React transformed frontend development by making UI a function of state. Component-based thinking, declarative rendering, unidirectional data flow. The ecosystem evolved from class components to hooks to server components—each shift revealing deeper patterns.

Next.js

React framework with opinions. App Router with server components. Static generation, server-side rendering, API routes. The pragmatic choice for production React apps.

Static export: This site (89.9 kB, zero server)

Server components: Less JavaScript to client

File-based routing: Intuitive, no config

TypeScript

Type safety for JavaScript. Catch errors before runtime. Refactoring with confidence. Self-documenting code through types.

Strict mode: Maximum safety, explicit nulls

Inference: Types without typing

Generics: Reusable type-safe components

React Patterns

Hooks over classes: useState, useEffect, custom hooks. Composition > inheritance.

Prop drilling vs context: Local state stays local. Context for truly global state.

Memoization: useMemo, useCallback, React.memo. Optimize only when needed.

Error boundaries: Graceful degradation. Don't let one component break the tree.

Frontend Stack Experience

Tailwind CSS: This site. Utility-first for rapid iteration. Custom design tokens without CSS bloat. Responsive design through breakpoint utilities.

State Management: Prop-based for simple apps. Context for app-level state. Zustand for complex state. Redux when team needs predictability.

Data Fetching: Server components when possible. React Query for client-side. SWR for real-time data. Understanding stale-while-revalidate patterns.

Performance: Code splitting, lazy loading, image optimization. Lighthouse scores matter. Bundle analysis with webpack-bundle-analyzer.

Template Engines & Server Rendering

Before React dominated, server-side template engines handled rendering. Jade (now Pug), Handlebars, EJS, Thymeleaf for Java. Different era, different trade-offs. Understanding this history informs architectural decisions today.

Jade/Pug

Whitespace-significant templates for Node.js. Clean syntax, mixins, includes. Popular in Express.js era before SPAs dominated.

Strengths: Minimal syntax, DRY with mixins

Trade-offs: Whitespace sensitivity, debugging difficulty

Legacy: Influenced modern template systems

Thymeleaf (Java)

Natural templates for Spring. Works with HTML, can be viewed in browser without server. Server-side rendering with Spring Boot.

Strengths: Designer-friendly, Spring integration

Use case: Traditional server-rendered apps

Modern alternative: HTMX for interactivity

"The pendulum swings: server rendering → client SPAs → server components. Each era discovers the trade-offs of the previous. Understanding history prevents repeating mistakes while enabling informed choices."

Databases & Persistence

Data outlives code. Choose persistence technology based on access patterns, consistency requirements, and operational complexity. No one database serves all needs.

PostgreSQL

ACID transactions. JSON support. Full-text search. Window functions. The pragmatic default.

Redis

In-memory speed. Pub/sub messaging. Session storage. Rate limiting. Cache invalidation hard.

MongoDB

Document flexibility. Schemaless (but schema-ful in practice). Good for rapid prototyping.

Database Patterns

Migration strategy: Version-controlled schema changes. Forward-only migrations.

Connection pooling: HikariCP for Java. pg-pool for Node. Size based on workload.

Indexing: B-tree for equality, GiST for ranges. Analyze query plans. EXPLAIN is your friend.

Transactions: ACID when needed. Eventual consistency when acceptable. Choose deliberately.

DevOps & Infrastructure

Code runs somewhere. Understanding deployment, monitoring, and operations separates experimental projects from production systems.

Docker & Kubernetes

Containerization for consistency. K8s for orchestration. Complex but powerful. Overkill for small projects, necessary for scale.

• Multi-stage builds for smaller images

• Health checks and readiness probes

• Resource limits and autoscaling

• Service mesh for observability

CI/CD

Automated testing and deployment. GitLab CI, GitHub Actions, Jenkins. Build once, deploy many times. Immutable artifacts.

• Automated tests on every commit

• Blue-green or canary deployments

• Rollback capability essential

• Deployment observability

Observability

Metrics: Prometheus, Grafana. CPU, memory, request rates, error rates.

Logging: Structured JSON logs. ELK stack or Loki. Correlation IDs for tracing.

Tracing: OpenTelemetry, Jaeger. Understanding distributed system flows.

Alerting: Alert on symptoms, not causes. Runbooks for on-call engineers.

Tool Selection Philosophy

Boring technology wins. Choose mature, well-documented tools with active communities. New doesn't mean better. Stability matters in production.

Understand trade-offs. Every tool optimizes for something and sacrifices something else. Know what you're trading. NoSQL isn't "better" than SQL— it's different trade-offs.

Team knowledge matters. The "best" tool your team can't maintain is worse than the "good enough" tool they understand. Optimize for team effectiveness, not resume-driven development.

Polyglot when justified. Multiple languages mean multiple build systems, deployment pipelines, monitoring strategies. Diversity has costs. Justify each addition.

"Tools are means, not ends. The goal is working software that serves users. Choose tools that enable the team to deliver value reliably. Everything else is distraction."