Table of Contents Lost updatesDirty readsNon-repeatable readsPhantom readsHow to avoid these issuesIsolationRead concernWrite concern In a multi-threaded, distributed environment like MongoDB, when clients execute queries concurrently, operations interleave with one another if they are not isolated, whether those operations involve …
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Performance Best Practise No. 1: Optimize Database Operations
Table of Contents How GlassFish helps with improving database performanceConnection pool configurationJDBC batchingJakarta Persistence (JPA) batchingNext Steps Database operations are a very critical part of most applications in regards of performance. There are multiple reasons why database operations can significantly …
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Faster Java Warmup: CRaC versus ReadyNow
Table of Contents What happens at Java startupJava warmup phase DeoptimizationsSolutions Provided by AzulCoordinated Restore at Checkpoint (CRaC) ReadyNow Cloud Native CompilerCRaC Compared to ReadyNow Conclusion This is the first blog post in a series on faster Java application warmup with ReadyNow.Azul has developed …
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Java’s Structured Concurrency: Finally Finding Its Footing
Table of Contents What Actually Changed This TimeThe Core Concept Remains StrongThe headline change: static factory methodsJoiners: pick your success policyRolling your own JoinerBetter cancellation and deadlinesScoped values ride alongGuard-rails against misuseObservability improvementsSome more examples to try outFinal thoughts The …
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Brokk: AI for Large (Java) Codebases
Table of Contents Sidebar: Under the HoodRecommendationsWorking with GitSidebar: LLM ModelsThe Edit Loop There are two reasons that AI makes mistakes writing code: The LLM just isn’t smart enough to tackle the problem effectively, and it simply gets the answer …
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10 Best Practises For Jakarta EE Performance Optimization
Table of Contents Quick ComparisonSecrets of Performance Tuning Java on Kubernetes by Bruno BorgesNext Steps With this article, we start a series where we compiled 10 best practices for performance optimizations and suggestions how to implement them using Jakarta EE & Eclipse GlassFish. Enjoy …
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AI Test Generation: A Dev’s Guide Without Shooting Yourself in the Foot
Table of Contents So, AI Can Write Tests Now? Cool, But…How AI Learns to Code (And Why That’s a Problem for Tests)Problem #1: AI Tests Might Just Be WrongProblem #2: Testing the Code You Have, Not the Code You Need …
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How to send prompts in bulk with Spring AI and Java Virtual Threads
Table of Contents Here’s the flow:Virtual Threads for Massive ParallelismSpring AI Prompt CallProcessing in BatchesHandling Errors GracefullyProcess Results in BulkFull ImplementationStay curious! TL;DR: You’re building an AI-powered app that needs to send lots of prompts to OpenAI. Instead of sending …
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GenAI blood, sweat, and tears: Loading data to Pinecone
Table of Contents Getting startedIssue #1: APIs, SDKs, and rapid changeIssue #2: ConfigIssue #3: JSON formatIssue #4: Loading data to Pinecone1. Metadata keysAlternative embeddings – Book descriptionsWrapping up!Resources As someone who is pretty familiar with relational and graph databases, I …
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Local AI with Spring: Building Privacy-First Agents Using Ollama
Table of Contents IntroductionConfiguring OllamaSpring AI + Ollama: a perfect match!Setting up the projectEnough talk, show me the codeA quick detour on FastMCPConclusion Introduction Building local AI agents with Spring AI and Ollama has emerged as a game-changer for developers …
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Semantic Search with Spring Boot & Redis
Table of Contents Redis as a Vector DatabaseRedis OM SpringDatasetBuilding the Application0. GitHub Repository1. Add the required dependencies2. Define the Movie entity3. Repository Interface4. Search Service5. Movie Service for Data Loading5. Search Controller6. Application Bootstrap7. Sample RequestsWrapping upMore AI ResourcesStay …
