Optimize Java Performance with Stream-Based Queries

Recently, I was troubleshooting a Java application(Servlet) where a few SQL queries were taking unusually long to execute. At first, it looked like a database issue — but digging deeper, I noticed something interesting 👀 During those heavy query executions, the JVM memory usage was spiking, and GC (Garbage Collection) was running frequently, slowing things down even more. After some profiling and monitoring, we realized that the default heap settings were too low for our workload. So we adjusted our system configuration The result? ✅ SQL executions became much more stable ✅ Noticeable improvement in overall performance This reminded me how small JVM-level configurations can make a big difference — especially in data-heavy services. Performance tuning isn’t just about optimizing code or queries; sometimes, it’s about understanding how your application and JVM handle memory under load.   #SpringBoot #Java #PerformanceTuning #BackendDevelopment #MemoryManagement

Understanding @Modifying Annotation in Spring Data JPA By default, JPA queries are read-only. But what if you need to update or delete data using a custom query? That’s where the @Modifying annotation comes in. Key points: > Use @Modifying with @Query to execute INSERT, UPDATE, or DELETE operations. > Return type can be int or void. > Must be used inside a transaction (@Transactional), otherwise changes won’t persist. 📍Tip: Always be careful these queries skip entity-level lifecycle events and directly modify the database. #SpringBoot #JPA #Hibernate #Java

Over the past few week, I’ve been exploring how different Java-based data access frameworks — JDBC, Spring JDBC Template, JPA/Hibernate, MyBatis, jOOQ, and Spring Data JPA — perform across scalability, maintainability, and large dataset handling (think 400M+ records). This research started with a simple curiosity: ➡️ When should we use what? As projects scale, the right persistence strategy can make or break performance. So, I compiled a detailed, professionally structured comparison — covering advantages, disadvantages, architecture suitability, and best-fit scenarios for each technology. 📘 You can read/download the complete report here: 👉 [Attach PDF link or upload document] Would love to hear from other developers and architects — what’s your go-to stack for high-scale data handling in Java? #Java #SpringBoot #Hibernate #MyBatis #jOOQ #SoftwareEngineering #BackendDevelopment #SystemDesign #DatabaseEngineering #TechResearch #Programming

When converting between Java objects — such as entities, DTOs, or database objects — developers often rely on Jackson’s ObjectMapper for its convenience and flexibility. However, subtle differences in https://lnkd.in/dTgK9kKH

💡 Day 3 of my 𝗝𝗮𝘃𝗮 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘆 𝘀𝗲𝗿𝗶𝗲𝘀: 🧠 Question: You’re working on a Spring Boot application that interacts with a relational database. You notice that for every request, multiple queries are executed for fetching related entities - causing performance issues. 👉 What is the N+1 query problem in JPA/Hibernate, and how can you avoid it? ✅ Answer: The N+1 query problem happens when Hibernate fires: 1 query to fetch parent records N queries to fetch child records for each parent This results in N+1 queries, hurting performance. To avoid it: > Use fetch join 𝘚𝘌𝘓𝘌𝘊𝘛 𝘶 𝘍𝘙𝘖𝘔 𝘜𝘴𝘦𝘳 𝘶 𝘑𝘖𝘐𝘕 𝘍𝘌𝘛𝘊𝘏 𝘶.𝘰𝘳𝘥𝘦𝘳𝘴 > Use @EntityGraph to pre-fetch relationships > Enable batch fetching 𝘩𝘪𝘣𝘦𝘳𝘯𝘢𝘵𝘦.𝘥𝘦𝘧𝘢𝘶𝘭𝘵_𝘣𝘢𝘵𝘤𝘩_𝘧𝘦𝘵𝘤𝘩_𝘴𝘪𝘻𝘦=20 > Always monitor SQL logs to detect hidden N+1 issues. ✅ Fetch joins, entity graphs, and batch fetching = faster DB access & fewer queries ⚡ See you tomorrow for Day 4 👋 #Java #SpringBoot #Hibernate #JPA #Performance #BackendDeveloper #ContinuousLearning #QuestionOfTheDay

->>Persistence Layer in Java Applications The Persistence Layer is responsible for communicating with the database. In Java, we have multiple options to build it: ✅ JDBC ✅ Hibernate ORM ✅ Spring JDBC ✅ Spring ORM ✅ Spring Data JPA (latest & most popular) ✨ Why Spring Data JPA? No boilerplate code No need to write queries manually Built-in methods like: save() findById() findAll() count() deleteAll() Faster development & cleaner code .. Spring Data JPA has become the go-to choice for modern persistence layer development in Java-based applications. hashtag #SpringBoot #SpringDataJPA #JavaDeveloper #BackendDevelopment #PersistenceLayer #RESTAPI #Microservices #SpringSecurity #Hibernate #JPA #FullStackDeveloper #SoftwareEngineering #CleanCode #SpringFramework #DatabaseManagement #API #ObjectOrientedProgramming #SpringMVC #DevOpsIntegration #JavaProgramming #EnterpriseApplications #CloudNative #TechInterview #CodingInterview #AgileDevelopment #ContinuousIntegration

How to Create a Composite Primary Key in JPA Sometimes a single column isn’t enough to uniquely identify a record - that’s when we use a composite primary key. JPA gives two clean ways to implement it: @IdClass and @EmbeddedId. > The @Embeddable class holds the combined key fields. > The entity uses @EmbeddedId to mark the composite key. When to use: When two or more fields together uniquely identify a row When modeling many-to-many join tables When avoiding auto-generated IDs Composite keys keep your data consistent and enforce real-world uniqueness directly in the database. #SpringBoot #JPA #Hibernate #Java

⚡ FetchType.LAZY vs FetchType.EAGER in JPA When working with JPA/Hibernate, fetching strategies determine how related entities are loaded from the database. 1️⃣ FetchType.LAZY Loads the association only when you access it. Default for: @OneToMany, @ManyToMany. Pros: Saves memory, better performance for large collections. Cons: Can throw LazyInitializationException if accessed outside a session. Example: @OneToMany(mappedBy = "employee", fetch = FetchType.LAZY) private List<Address> addresses; addresses are loaded only when you call getAddresses(). 2️⃣ FetchType.EAGER Loads the association immediately with the parent entity. Default for: @OneToOne, @ManyToOne. Pros: Avoids lazy-loading issues. Cons: Can impact performance if associations are large. Example: @OneToOne(fetch = FetchType.EAGER) private Department department; department is loaded automatically whenever Employee is loaded. 💡 Tip: Use LAZY by default and fetch only what you need for better performance! #SpringBoot #Hibernate #Java #JPA #FetchType #JavaTips #Microservices CleanCode #TechTips #DeveloperLife #SoftwareEngineer #TechCommunity #CodeNewbie #100DaysOfCode #FullStackDeveloper #BackendDeveloper #Microservices #CareerInTech #LearnJava #Java8

🚀 Performance Tuning in Java: Small Tweaks, Big Impact Recently, I worked on optimizing a REST API built with Spring Boot + PostgreSQL that was taking ~1.3 seconds per call. After profiling and tuning, I brought it down to ~280 ms consistently. Here’s what made the difference 👇 ✅ Database Optimization: Added proper indexes and replaced SELECT * with specific column fetches. ✅ Pagination: Instead of loading 5K+ rows at once, implemented Pageable in Spring Data JPA. ✅ Caching: Used Spring Cache with Redis to store frequent read requests. ✅ Connection Pool Tuning: Adjusted HikariCP max connections and timeouts for our workload. ✅ DTO Projection: Reduced unnecessary entity-to-DTO mapping overhead. 💡 Lesson learned: 80% of performance issues come from data access and inefficient queries — not Java itself. Curious — what’s your go-to strategy when you see slow response times in a Spring Boot API? #Java #SpringBoot #PerformanceTuning #SoftwareEngineering #Microservices #BackendDevelopment