Java and Spring Boot
Spring Boot Docker Deployment: Complete Guide
Spring Boot Docker Deployment: Complete Guide requires decisions about multi-stage images, layered jars, non-root execution, runtime configuration, health checks and image scanning. This guide explains the architecture, delivery and production practices needed to achieve a small reproducible container image that starts predictably and exposes clear health signals.
Build a production container
Use a multi-stage build, a locked dependency tree and production-only runtime dependencies. Run as a non-root user, copy only required files and scan the resulting image rather than shipping compilers, caches and credentials.
Handle termination signals and expose a meaningful readiness check. Inject configuration and secrets at runtime, keep the image immutable and verify it in the same form that will be deployed.
Build a repeatable Spring Boot deployment
Create an immutable artifact or multi-stage container image, run as a non-root user and inject environment configuration at runtime. Separate liveness from readiness so traffic does not reach the service before dependencies and migrations are ready.
Automate deployment promotion, database migration and rollback. Use a secret manager, least-privilege service identity, centralized logs, metrics, backups and tested restoration in every production environment.
Secure Spring Boot with explicit authorization
Configure Spring Security with a deny-by-default filter chain and test public, authenticated and privileged routes. Validate token issuer, audience and expiry, then enforce resource ownership or method authorization instead of trusting roles sent by a client.
Define CORS precisely, decide whether CSRF applies to the authentication model, keep secrets outside source control and avoid exposing sensitive Actuator endpoints. Return safe errors and log security events without recording credentials or tokens.
Measure Spring Boot in production
Spring Boot Actuator and Micrometer can expose request latency, error rates, JVM behavior and custom business metrics. Add trace or request identifiers so a user-facing failure can be followed through controllers, database calls and external dependencies.
Set service-level targets before tuning. Profile CPU and allocations, inspect slow queries and load test with production-like data; cache or concurrency changes should respond to a measured bottleneck.
Transactions, JPA and PostgreSQL
Keep transactions short and aligned with business operations. Inspect the SQL generated by the ORM, avoid N+1 loading, page large results and use database constraints for invariants that must survive concurrent requests.
Add indexes from real query predicates and ordering, then confirm plans with EXPLAIN. Configure the connection pool against database capacity; increasing application instances must not create more connections than PostgreSQL can support.