Preview Feature in Java 21 — The Vector API has been in preview since Java 16 (JEP 338). JEP 448 is the sixth preview iteration in Java 21. The API is stable and production-usable with --enable-preview; finalization is pending Project Valhalla value types. What Is SIMD and Why Does It Matter? Modern CPUs can perform the same arithmetic operation on multiple data values in a single instruction. This is called SIMD — Single Instruction, Multiple Data.

The Platform Thread Problem Every Java thread since Java 1.0 maps 1:1 to an OS thread. OS threads are heavy: Stack memory: 512 KB – 2 MB per thread (configurable with -Xss, default 512 KB on Linux) Context switch cost: ~1–10 μs per switch (kernel mode transition + cache invalidation) Hard limit: A 64-bit machine with 8 GB RAM can support roughly 8,000–16,000 OS threads before running out of stack memory This limit shapes how Java servers are built.

The Problem Kafka Solves Imagine an e-commerce platform. A customer places an order. What needs to happen next? Inventory must be reserved Payment must be charged A confirmation email must be sent The warehouse must be notified to pick and pack Analytics must record the sale Fraud detection must evaluate the transaction One request. Six downstream systems. In a traditional REST architecture, the Order Service calls each of those six services directly — synchronously, one after another.

What Is X.509 Authentication? X.509 authentication (also called mutual TLS or mTLS) uses digital certificates instead of passwords. The client presents a certificate during the TLS handshake. The server verifies the certificate against a trusted Certificate Authority (CA) and extracts the user identity from the certificate’s Common Name (CN) or Subject Alternative Name (SAN). sequenceDiagram participant Client as Client (with certificate) participant Server as Spring Boot Server Client->>Server: TLS ClientHello Server->>Client: TLS ServerHello + Server Certificate Server->>Client: CertificateRequest (ask for client cert) Client->>Server: Client Certificate + CertificateVerify Server->>Server: Verify against trusted CA Server->>Server: Extract CN from certificate: "

Introduction Real batch jobs often need more than one transformation step per item. You might validate first, then normalize, then enrich, then convert to the output type. CompositeItemProcessor chains multiple single-responsibility processors together. For I/O-bound enrichment steps, AsyncItemProcessor runs processing concurrently on a thread pool — giving you parallelism without rewriting your step as multi-threaded. CompositeItemProcessor CompositeItemProcessor chains a list of processors. The output of each processor becomes the input to the next.

Introduction The previous article covered the two most common writers: FlatFileItemWriter for files and JdbcBatchItemWriter for database inserts. This article covers advanced scenarios: Persisting JPA entities with JpaItemWriter Writing to multiple destinations simultaneously with CompositeItemWriter Routing items to different writers based on content with ClassifierCompositeItemWriter Building custom writers for REST APIs, message queues, and cloud storage Combining a writer with a post-step cleanup tasklet JpaItemWriter JpaItemWriter calls EntityManager.merge() on each item.

The read-process-write loop is at the heart of almost every Spring Batch job. Understanding exactly how it works — where transactions begin and end, what gets rolled back on failure, and how Spring Batch knows where to restart — makes everything else in the framework click into place. This article goes deep on chunk-oriented processing: the execution model, the three interfaces, the counters that track progress, and how to size chunks correctly.

Introduction So far every example has had a single step. Real batch jobs usually have multiple steps — validate input, import data, generate a report, send a notification, clean up temp files. Spring Batch’s JobBuilder DSL lets you compose these steps into flows with conditional branching, parallel execution, and decision logic. This article covers: Linear multi-step jobs Conditional step transitions using ExitStatus JobExecutionDecider for runtime branching Parallel flows with split Nested jobs with FlowStep The Flow abstraction for reusable step sequences Linear Multi-Step Job The simplest multi-step job runs steps in sequence.

“Spring Boot is magic” is something you hear a lot. Add spring-boot-starter-data-jpa and suddenly you have a working DataSource, a JpaTransactionManager, and a LocalContainerEntityManagerFactoryBean — without writing a single @Bean method. Understanding how this actually works turns the magic into a tool you can control, debug, and extend. The Entry Point: @EnableAutoConfiguration @SpringBootApplication is a shorthand for three annotations: @Configuration @EnableAutoConfiguration // this is the one that matters here @ComponentScan public class MyApplication { public static void main(String[] args) { SpringApplication.

Every application has a class of work that doesn’t fit the request-response model: process 2 million orders overnight, generate 500,000 monthly statements, migrate 10 years of legacy data before Monday morning. This work needs to be reliable, restartable after failures, and fast enough to finish in the available window. That’s what Spring Batch is built for. This article covers what Spring Batch is, when to use it, and how its architecture works.