Content Library

Java Memory Options You Need in Production

JVM memory tuning can be tricky. Teams increase -Xmx and assume the problem is solved. Then the app still hits OOM. Because maximum heap size is not the only thing that affects memory footprint. The JVM uses RAM for much more than heap: metaspace, thread stacks, JIT/code cache, direct buffers, and native allocations. That’s why your process can run out of memory while heap still looks “fine”. In this video, we break down how JVM memory actually works and how to control it with a minimal, production-safe set of flags. We cover heap sizing (-Xms, -Xmx), dynamic resizing, direct memory (-XX:MaxDirectMemorySize), and total RAM limits (-XX:MaxRAMPercentage) — especially in containerized environments like Docker and Kubernetes. We also explain GC choices such as G1, ZGC, and Shenandoah, when defaults are enough, and why GC logging (-Xlog:gc*) is mandatory before tuning. Finally, we show how to diagnose failures with heap dumps and OOM hooks. This is not about adding more flags. It’s about understanding what actually consumes memory — and making decisions you can justify in production.

Java Developer Roadmap 2026: From Basics to Production

Most Java roadmaps teach tools. This one teaches order — the only thing that actually gets you to production. You don’t need to learn everything. You need to learn the right things, in the right sequence. In this video, we break down a practical Java developer roadmap for 2026 — from syntax and OOP to Spring, databases, testing, and deployment. Structured into 8 levels, it shows how real engineers grow from fundamentals to production-ready systems. We cover what to learn and what to ignore: core Java, collections, streams, build tools, Git, SQL and JDBC before Hibernate, the Spring ecosystem, testing with JUnit, and deployment with Docker and CI/CD. You’ll also understand why most developers get stuck — jumping into frameworks too early, skipping SQL, or treating tools as knowledge. This roadmap gives you a clear path into real-world Java development — with priorities, trade-offs, and production context.

TOP-5 Lightweight Linux Distributions for Containers

In this video, we compare five lightweight Linux distributions commonly used as base images: Alpine, Alpaquita, Chiseled Ubuntu, RHEL UBI Micro, and Wolfi. There are no rankings or recommendations — just a structured look at how these distros differ so you can evaluate them in your own context.

jOOQ Deep Dive: CTE, MULTISET, and SQL Pipelines

Some backend developers reach the point where the ORM stops being helpful. Complex joins, nested result graphs, or CTE pipelines quickly push frameworks like Hibernate to their limits. And when that happens, teams often end up writing fragile raw SQL strings or fighting performance issues like the classic N+1 query problem. In this video, we build a healthcare scheduling application NeonCare using jOOQ, Spring Boot 4, and PostgreSQL, and show how to write production-grade SQL directly in Java while keeping full compile-time type safety.

Spring Developer Roadmap 2026: What You Need to Know

Spring Boot is powerful. But knowing the framework isn’t the same as understanding backend engineering. In this video, I walk through the roadmap I believe matters for a Spring developer in 2026. We start with data. That means real SQL — CTEs, window functions, normalization trade-offs — and understanding what ACID and BASE actually imply for system guarantees. Spring Data JPA is useful, but you still need to know what happens underneath. Then architecture: microservices vs modular monolith, serverless, CQRS, and when HTTP, gRPC, Kafka, or WebSockets make sense. Not as buzzwords — but as design choices with trade-offs. Security and infrastructure follow: OWASP Top 10, AuthN vs AuthZ, encryption in transit and at rest, Docker, Kubernetes, Infrastructure as Code, and observability with Micrometer, OpenTelemetry, and Grafana. This roadmap isn’t about mastering every tool. It’s about knowing what affects reliability in production.

Build Typed AI Agents in Java with Embabel

Most Java AI demos stop at prompt loops. That doesn't scale in production. In this video, we integrate Embabel into an existing Spring Boot application and build a multi-step, goal-driven agent for incident triage. Instead of manually orchestrating prompt → tool → prompt cycles, we define typed actions and let the agent plan across deterministic and LLM-powered steps. We parse structured input with Ollama, query MongoDB deterministically, classify risk using explicit thresholds, rank affected implants, generate a constrained root cause hypothesis, and produce a bounded containment plan. LLM handles reasoning. Java enforces rules. This is about controlled AI workflows on the JVM — not prompt glue code.

Spring Data MongoDB: From Repositories to Aggregations

Spring Data MongoDB breaks down fast once CRUD meets production—real queries, actual data volumes, analytics. What looks simple at first quickly turns into unreadable repository methods, overfetching, and slow queries. In this video, I walk through building a production-style Spring Boot application using Spring Data MongoDB — starting with basic setup and repositories, then moving into indexing, projections, custom queries, and aggregation pipelines. You'll see how MongoDB's document model changes data design compared to SQL, when embedding helps, and when it becomes a liability. We cover where repository method naming stops scaling, how to use @Query safely, when to switch to MongoTemplate, and how to reduce payload size with projections and DTOs. Finally, we implement real MongoDB aggregations to calculate analytics directly in the database and test everything against a real MongoDB instance using Testcontainers. This is not another MongoDB overview. It's a practical guide to actually using Spring Data MongoDB in production without fighting the database.

Backend Developer Roadmap 2026: What You Need to Know

Backend complexity keeps growing, and frameworks can't keep up. In 2026, knowing React or Django isn't enough. You need fundamentals that hold up when systems break, traffic spikes, or your architecture gets rewritten for the third time.I've been building production systems for 15 years. This roadmap covers three areas that separate people who know frameworks from people who can actually architect backend systems: data, architecture, and infrastructure. This is about how to think, not what tools to install.

The 2025 State of Container Security

JDBC Connection Pools in Microservices. Why They Break Down (and What to Do Instead)

In this livestream, Catherine is joined by Rogerio Robetti, the founder of Open J Proxy, to discuss why traditional JDBC connection pools break down when teams migrate to microservices, and what is a more efficient and reliable approach to organizing database access with microservice architecture.

Sizing JDBC Connection Pools for Real Production Load

Many production outages start with connection pool exhaustion. Your app waits seconds for connections while queries take milliseconds; yet, most teams run default settings that collapse under load. This video shows how to configure connection pools that survive real production traffic: sizing based on database limits and thread counts, setting timeouts that prevent cascading failures, and implementing an open source database proxy Open J Proxy for centralized connection management with virtual connection handles, client-side load balancing, and slow query segregation. For senior Java developers, DevOps engineers, and architects who need database performance that holds under pressure.

Bellsoft Hardened Images: Product Brief

JDBC vs ORM vs jOOQ: Choose the Right Java Database Tool

Still unsure what is the difference between JPA, Hibernate, JDBC, or jOOQ and when to use which? This video clarifies the entire Java database access stack with real, production-oriented examples. We start at the foundation, which is JDBC, a low-level API every other tool eventually relies on for database communication. Then, we go through the ORM concept, JPA as a specification of ORM, Hibernate as the implementation and extension of JPA, and Blaze Persistence as a powerful upgrade to JPA Criteria API. From there, we take a different path with jOOQ: a database-first, SQL-centric approach that provides type-safe queries and catches many SQL errors at compile time instead of runtime. You’ll see when raw JDBC makes sense for small, focused services, when Hibernate fits CRUD-heavy domains, and when jOOQ excels at complex reporting and analytics. We discuss real performance pitfalls such as N+1 queries and lazy loading, and show practical combination strategies like “JPA for CRUD, jOOQ for reports.” The goal is to equip you with clarity so that you can make informed architectural decisions based on domain complexity, query patterns, and long-term maintainability.

Hibernate: Ditch or Double Down? When ORM Isn't Enough

Every Java team debates Hibernate at some point: productivity champion or performance liability? Both are right. This video shows you when to rely on Hibernate's ORM magic and when to drop down to SQL. We walk through production scenarios: domain models with many-to-many relations where Hibernate excels, analytical reports with window functions where JDBC dominates, and hybrid architectures that use both in the same Spring Boot codebase. You'll see real code examples: the N+1 query trap that kills performance, complex window functions and anti-joins that Hibernate can't handle, equals/hashCode pitfalls with lazy loading, and practical two-level caching strategies. We also explore how Hibernate works under the hood—translating HQL to database-specific SQL dialects, managing sessions and transactions through JDBC, implementing JPA specifications. The strategic insight: modern applications need both ORM convenience for transactional business logic and SQL precision for data-intensive analytics. Use Hibernate for CRUD and relationship management. Use SQL where ORM abstractions leak or performance demands direct control.

Java in 2025: LTS Release, AI on JVM, Framework Modernization

Java in 2025 isn't about headline features, it's about how production systems changed under the hood. While release notes focus on individual JEPs, the real story is how the platform, frameworks, and tooling evolved to improve stability, performance, and long-term maintainability. In this video, we look at Java from a production perspective. What does Java 25 LTS mean for teams planning to upgrade? How are memory efficiency, startup time, and observability getting better? Why do changes like Scoped Values and AOT optimizations matter beyond benchmarks? We also cover the broader ecosystem: Spring Boot 4 and Framework 7, AI on the JVM with Spring AI and LangChain4j, Kotlin's growing role in backend systems, and tooling updates that make upgrades easier. Finally, we touch on container hardening and why runtime and supply-chain decisions matter just as much as language features.

I Solved Advent of Code 2025 in Kotlin: Here's How It Went

Every year, Advent of Code spawns thousands of solutions — but few engineers step back to see the bigger picture. This is a complete walkthrough of all 12 days from 2025, focused on engineering patterns rather than puzzle statements. We cover scalable techniques: interval math without brute force, dynamic programming, graph algorithms (JGraphT), geometry with Java AWT Polygon, and optimization problems that need constraint solvers like ojAlgo. You'll see how Java and Kotlin handle real constraints, how visualizations validate assumptions, and when to reach for libraries instead of writing everything from scratch. If you love puzzles, programming—or both—and maybe want to learn how to solve them on the JVM, this is for you.

Java 26 Preview: New JEPs and What They Mean for You

Java 26 is the next feature release that brings features for enhanced performance, security, and developer experience. This video discusses the upcoming JDK 26 release, highlighting ten JEPs including JEP 500. JEP 500 focuses on preparing developers for future restrictions on mutating final fields in Java, emphasizing their role in maintaining immutable state. This is crucial for robust programming and understanding the nuances of mutable vs immutable data, especially concerning an immutable class in java. We also touch upon the broader implications for functional programming in Java.

Liberica JDK: White Paper

Liberica Native Image Kit: White Paper

Liberica JDK Perfomance Edition: White Paper

Liberica JDK 6&7: White Paper

Alpaquita Linux: White Paper

Will AI Replace Developers? A Vibe Coding Reality Check 2025

Can AI replace software engineers? ChatGPT, Copilot, and LLM-powered vibe coding tools promise to automate development—but after testing them against 17 years of production experience, the answer is more nuanced than the hype suggests. Full project generation produces over-engineered code that's hard to refactor. AI assistants excel at boilerplate but fail at business logic. MCP servers solve hallucination problems but create context overload. Meanwhile, DevOps automation actually works. This breakdown separates AI capabilities from marketing promises—essential for teams integrating LLMs and copilots without compromising code quality or architectural decisions.

JRush | Container Essentials: Fast Builds, Secure Images, Zero Vulnerabilities

Web-conference for Java developers focused on hands-on strategies for building high-performance containers, eliminating CVEs, and detecting security issues before production.