DEV Community: Rajesh Mishra

Open source LLMs comparison LLaMA Mistral DeepSeek Gemma 2026 — Complete Guide 2026

Rajesh Mishra — Mon, 10 Aug 2026 09:06:39 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

Introduction In 2026 the landscape of open‑source large language models (LLMs) has matured dramatically, offering developers a rich palette of alternatives to proprietary offerings. This post focuses on four of the most talked‑about models released in the last few years— LLaMA , Mistral , DeepSeek , and Gemma —and provides a quick‑reference overview to help you decide which one fits your project best. Table of Contents Introduction Complete Guide: Comparing Open‑Source LLMs (LLaMA, Mistral, DeepSeek, Gemma 2026) 1. Quick Reference Table Model Release Year Parameters Architecture License Notable Features (2026) LLaMA 2 2023 (v2), 2024 (v2‑Chat) 7 B – 70 B Transformer, decoder‑only Meta‑OpenRAIL (non‑commercial) Fine‑tuned for instruction following, strong multilingual support, efficient inf

📖 Read the Full Tutorial

🔗 Open source LLMs comparison LLaMA Mistral DeepSeek Gemma 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Spring Security filter chain explained with custom filters — Complete Guide

Rajesh Mishra — Mon, 10 Aug 2026 08:26:31 +0000

Spring Security filter chain explained with custom filters — Complete Guide

A practical, in-depth guide to Spring Security filter chain explained with custom filters with examples.

INTRO

When you spin up a Spring Boot application and slap @EnableWebSecurity on a config class, you instantly get a wall of default filters protecting every endpoint. It feels great until you need to insert your own authentication step, log request metadata, or short‑circuit a request based on a custom header. The default filter order is opaque, and trying to “just add a bean” often results in ClassCastException or a silent no‑op because the filter never runs where you expect it to.

That friction isn’t just an annoyance—it leads to security bugs that hide in production. A mis‑ordered filter can let an unauthenticated request slip past your JWT validator, or it can cause a legitimate user to be logged out on every request. The real problem is a lack of visibility and control over the Spring Security filter chain. Understanding how the chain is built, how Spring decides the order, and how to inject custom filters at the right spot is essential for any engineer who wants reliable, maintainable security.

In the full guide we peel back the abstraction layer, show you the exact sequence of built‑in filters, and walk through three real‑world scenarios where a custom filter is the right tool. By the end you’ll be able to add, replace, or remove filters without breaking the chain, and you’ll have a checklist to avoid the most common pitfalls.

WHAT YOU'LL LEARN

The anatomy of the default filter chain – a step‑by‑step breakdown of each built‑in filter, why it exists, and where it sits in the order.
How Spring assembles the chain – the role of SecurityFilterChain, HttpSecurity, and the FilterRegistrationBean in the bootstrapping process.
Creating a custom filter – extending OncePerRequestFilter, handling doFilterInternal, and accessing the SecurityContext.
Strategic placement – using addFilterBefore, addFilterAfter, and addFilterAt to position your filter relative to UsernamePasswordAuthenticationFilter, BearerTokenAuthenticationFilter, etc.
Replacing a built‑in filter – when and how to swap out BasicAuthenticationFilter with a bespoke implementation without losing downstream processing.
Production‑ready tips – idempotent registration, avoiding duplicate execution, and profiling the chain with Spring Boot Actuator.

A SHORT CODE SNIPPET

@Configuration
public class SecurityConfig {

@Bean
SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
http
.addFilterBefore(new RequestIdFilter(), UsernamePasswordAuthenticationFilter.class)
.authorizeHttpRequests(auth -> auth.anyRequest().authenticated())
.oauth2ResourceServer(oauth -> oauth.jwt());
return http.build();
}
}

The snippet injects a RequestIdFilter before the username/password authentication step, guaranteeing that every request carries a traceable ID for downstream logging.

KEY TAKEAWAYS

The filter chain is a deterministic list, not a magic bean forest; knowing the exact order lets you reason about security flow.
Use the addFilter* family of methods on HttpSecurity to control placement; never rely on @Component scanning alone for custom filters.
Replacing a default filter requires explicitly disabling the original (http.removeFilter(...)) to prevent duplicate processing.
Profiling the final chain with http.getFilters() or Actuator’s httptrace endpoint reveals hidden ordering issues before they hit production.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Spring Security filter chain explained with custom filters — Complete Guide

latest Java multithreading interview questions 2026 — Complete Guide

Rajesh Mishra — Sun, 09 Aug 2026 07:52:57 +0000

latest Java multithreading interview questions 2026 — Complete Guide

A practical, in-depth guide to latest Java multithreading interview questions 2026 with examples.

INTRO

Java’s concurrency model has been stable for years, but the ecosystem around it is anything but static. With the rise of virtual threads in Project Loom, the deprecation of older APIs, and new memory‑model nuances introduced in recent JDK releases, interviewers are no longer satisfied with “what is synchronized?” They want to see that you understand the why behind the latest primitives, can reason about performance trade‑offs, and can spot subtle bugs that only appear under real‑world load.

If you’ve been slinging Thread objects and ExecutorService for the past decade, you’ll feel the pressure when a hiring manager asks, “How would you migrate a legacy thread‑pool to virtual threads?” or “What are the pitfalls of using ThreadLocal with structured concurrency?” Those questions expose a gap between legacy knowledge and production‑ready modern Java. This article sketches the most pressing interview topics for 2026 and points you to a full‑fledged guide that walks through each concept with code, common mistakes, and production tips.

WHAT YOU'LL LEARN

Virtual threads vs. platform threads: when to use each, performance implications, and how to refactor existing ExecutorService code.
Structured concurrency patterns: the new java.util.concurrent.Scope API, cancellation propagation, and why it matters for clean shutdowns.
Memory‑model updates: VarHandle vs. Atomic* classes, and how the JDK 22 changes affect visibility guarantees.
Advanced locking strategies: StampedLock, optimistic reads, and avoiding lock convoy.
Thread‑local pitfalls in modern runtimes: leakage with virtual threads, and safe alternatives.
Testing concurrency: using java.util.concurrent.Flow and ExecutorCompletionService to write deterministic unit tests.

A SHORT CODE SNIPPET

Below is a minimal example that swaps a classic FixedThreadPool for a virtual‑thread‑backed executor. Notice how the same Runnable logic runs unchanged, but the underlying thread creation cost drops dramatically.

import java.util.concurrent.*;

public class VirtualThreadDemo {
public static void main(String[] args) throws InterruptedException {
// Legacy executor (platform threads)
ExecutorService legacy = Executors.newFixedThreadPool(4);

// Modern executor using virtual threads
ExecutorService modern = Executors.newVirtualThreadPerTaskExecutor();

Runnable task = () -> {
System.out.println(
Thread.currentThread() + " handling " + Thread.currentThread().getName()
);
};

// Submit the same task to both executors
for (int i = 0; i < 8; i++) {
legacy.submit(task);
modern.submit(task);
}

legacy.shutdown();
modern.shutdown();
legacy.awaitTermination(1, TimeUnit.MINUTES);
modern.awaitTermination(1, TimeUnit.MINUTES);
}
}

Running this on JDK 22 shows the virtual‑thread executor spawning thousands of lightweight threads without exhausting OS resources—a clear talking point for any interview.

KEY TAKEAWAYS

Virtual threads are production‑ready: they solve scalability bottlenecks that traditional thread pools struggle with, but you still need to understand their interaction with blocking I/O.
Structured concurrency is no longer optional: it provides deterministic cleanup and error propagation, a must‑know for modern Java services.
Locking has evolved: StampedLock and VarHandle give you finer‑grained control and better performance than the classic synchronized block in many scenarios.
Testing concurrency is now systematic: the guide shows how to use ExecutorCompletionService and Flow to write repeatable, race‑free tests.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:
latest Java multithreading interview questions 2026 — Complete Guide

Running Phi-3 Mini locally on Mac for free step by step 2026 — Complete Guide 2026

Rajesh Mishra — Sat, 08 Aug 2026 08:27:09 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

Introduction In 2026 the Phi‑3 Mini model is one of the most capable open‑source LLMs you can run on a Mac without spending a dime. This guide gives you a quick, step‑by‑step overview of everything you need to get the model up and running locally—no cloud credits, no paid subscriptions, just your Mac (Intel or Apple Silicon) and a few free tools. Table of Contents Introduction Complete Guide 1. Prerequisites 2. Install Homebrew (if you don’t have it) 3. Install Python 3.11 and create a virtual environment 4. Upgrade pip and install core libraries Component Recommended Version (2026) Why It Matters macOS 14.x (Sonoma) or later Native support for Apple Silicon acceleration (Metal) and up‑to‑date system libraries. Python 3.12 Latest language features and binary wheels for torch and transforme

📖 Read the Full Tutorial

🔗 Running Phi-3 Mini locally on Mac for free step by step 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Terraform interview questions and answers for experienced 2026 — Complete Guide

Rajesh Mishra — Sat, 08 Aug 2026 07:46:55 +0000

Terraform interview questions and answers for experienced 2026 — Complete Guide

A practical, in-depth guide to Terraform interview questions and answers for experienced 2026 with examples.

INTRO

Hiring managers are no longer satisfied with candidates who can only spin up a single EC2 instance with a static terraform apply. Modern infrastructure teams demand engineers who understand the full lifecycle of IaC: state management, modular design, policy enforcement, and seamless integration with CI/CD pipelines. When you walk into a senior‑level interview, the questions will probe not just what you can do, but how you think about Terraform as a production‑grade tool.

That gap between “basic syntax” and “enterprise‑ready Terraform” is the real problem this guide tackles. It distills the most up‑to‑date concepts—state locking with Terraform Cloud, dynamic blocks for complex resources, and the new terraform validate hooks introduced in the 2025 release—into interview‑ready answers. By internalising these patterns, you’ll be able to articulate the why behind each feature, avoid common pitfalls, and demonstrate that you can design Terraform code that scales with a growing organization.

If you’ve already passed the junior round and now face a panel of senior architects, you need more than flashcards. You need a playbook that mirrors the day‑to‑day challenges you’ll encounter on the job. The following preview gives you a taste of that playbook and shows why the full guide is worth a deeper read.

WHAT YOU'LL LEARN

State strategies for multi‑team environments – when to use local, remote, and workspaces, and how to avoid lock contention in Terraform Cloud.
Advanced module patterns – building reusable, versioned modules with input validation, dynamic blocks, and for_each meta‑argument tricks.
Policy as code with Sentinel and OPA – writing policies that enforce tagging, cost limits, and compliance before any plan is applied.
Testing Terraform with Terratest and Checkov – integrating unit, integration, and security tests into your CI pipeline.
Handling drift and import – best practices for reconciling out‑of‑band changes and safely importing legacy resources.
Performance tuning for large state files – using -target, state splitting, and selective refresh to keep plans fast.

A SHORT CODE SNIPPET

// Example: Dynamic security group rules using a map variable
variable "ingress_rules" {
type = map(object({
from_port = number
to_port = number
protocol = string
cidr_blocks = list(string)
}))
default = {}
}

resource "aws_security_group" "example" {
name = "example-sg"

dynamic "ingress" {
for_each = var.ingress_rules
content {
from_port = ingress.value.from_port
to_port = ingress.value.to_port
protocol = ingress.value.protocol
cidr_blocks = ingress.value.cidr_blocks
}
}
}

KEY TAKEAWAYS

Mastering state backends and locking mechanisms is non‑negotiable for any senior Terraform role; interviewers will expect you to explain the trade‑offs of each backend.
Reusable modules should be versioned and validated with terraform validate and custom pre‑commit hooks to guarantee consistency across teams.
Policy‑as‑code is now a first‑class interview topic—be ready to write a simple Sentinel rule that blocks resources without required tags.
Real‑world Terraform code is tested just like application code; familiarity with Terratest or Checkov will set you apart from candidates who only run terraform plan.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Terraform interview questions and answers for experienced 2026 — Complete Guide

Terraform AWS EKS Kubernetes cluster setup tutorial — Complete Guide

Rajesh Mishra — Fri, 07 Aug 2026 08:12:42 +0000

Terraform AWS EKS Kubernetes cluster setup tutorial — Complete Guide

A practical, in-depth guide to Terraform AWS EKS Kubernetes cluster setup tutorial with examples.

INTRO

Running production workloads on Kubernetes is no longer a luxury; it’s a baseline expectation. Yet, the moment you try to spin up an Amazon EKS cluster manually, you quickly discover a maze of IAM roles, VPC networking, and version‑pinning headaches. The pain isn’t just in the initial creation—every change ripples through the control plane, and a single mis‑configured security group can bring down an entire service mesh.

Terraform promises declarative, repeatable infrastructure, but the EKS provider hides a lot of complexity behind its abstractions. If you’ve ever spent hours tweaking aws_auth ConfigMaps, wrestling with node group scaling, or fighting drift after a manual console edit, you know why a solid, end‑to‑end tutorial is essential. This article teases the full guide that walks you through a production‑ready EKS setup—no more copy‑paste, no more “it works on my machine” surprises.

WHAT YOU'LL LEARN

How to structure a modular Terraform repo for EKS, VPC, and IAM, keeping code DRY and testable.
The exact sequence of resources required to bootstrap a cluster, including the aws_eks_cluster, aws_eks_node_group, and the critical aws_auth ConfigMap.
Secrets management with AWS Secrets Manager and Kubernetes secrets integration, avoiding hard‑coded credentials.
Best‑practice networking: private subnets, NAT gateways, and security group rules that let pods talk to RDS without exposing them publicly.
Automated node‑group scaling policies using CloudWatch alarms and the aws_autoscaling_policy resource.
CI/CD pipeline snippets that run terraform plan/apply safely from GitHub Actions, with state locking via DynamoDB.

A SHORT CODE SNIPPET

// Minimal Java helper that generates a Terraform backend block
public class TerraformBackend {
public static String backendConfig(String bucket, String key, String region) {
return String.format(
"terraform {\n" +
" backend \"s3\" {\n" +
" bucket = \"%s\"\n" +
" key = \"%s\"\n" +
" region = \"%s\"\n" +
" encrypt = true\n" +
" dynamodb_table = \"tf-lock\"\n" +
" }\n" +
"}\n",
bucket, key, region
);
}

public static void main(String[] args) {
System.out.println(backendConfig("my-terraform-state", "eks/terraform.tfstate", "us-east-1"));
}
}

The snippet shows a tiny, self‑contained way to generate the Terraform backend configuration that locks state in an S3 bucket and DynamoDB table—exactly the foundation the full guide builds on.

KEY TAKEAWAYS

Modularity beats monolith – Separate VPC, IAM, and EKS modules to reuse across environments and reduce plan noise.
Never let drift happen – Keep the aws_auth ConfigMap under Terraform control; manual edits will be overwritten on the next apply.
Secure defaults are non‑negotiable – Deploy clusters in private subnets, enforce least‑privilege IAM roles, and rotate secrets via AWS Secrets Manager.
Automation is the safety net – Gate every terraform apply behind CI checks, state locking, and policy-as-code to prevent accidental production outages.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Terraform AWS EKS Kubernetes cluster setup tutorial — Complete Guide

LM Studio vs Ollama: Which Is the Better Choice for Local AI in 2026?

Rajesh Mishra — Thu, 06 Aug 2026 10:27:56 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR Both LM Studio and Ollama are leading platforms for running large language models locally in 2026, but they excel in different scenarios. Table of Contents TL;DR Introduction Quick‑start snippets (EnlighterJS) Feature snapshot Prerequisites 1. Hardware Requirements 2. Supported Operating Systems 3. GPU Drivers & CUDA Toolkit Core Concepts 1. Underlying Architecture Step‑by‑Step Setup 1. Prerequisites 2. Installing LM Studio Full Example Workflow Common Mistakes Production Tips 1. Scaling Strategies 2. Monitoring & Observability FAQ – LM Studio vs Ollama (Local AI 2026) Takeaways Quick‑Decision Flow Aspect LM Studio Ollama Primary Focus All‑in‑one GUI + CLI for model management, fine‑tuning, and inference. Lightweight, container‑based runtime optimized for speed and low‑memory footpri

📖 Read the Full Tutorial

🔗 LM Studio vs Ollama which is better for local AI 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Terraform backend configuration S3 and DynamoDB — Complete Guide

Rajesh Mishra — Thu, 06 Aug 2026 09:38:48 +0000

Terraform backend configuration S3 and DynamoDB — Complete Guide

A practical, in-depth guide to Terraform backend configuration S3 and DynamoDB with examples.

INTRO

When you start managing infrastructure with Terraform, the first thing you’ll notice is how easy it is to spin up resources locally. The flip side? State files live on your workstation, and every teammate who runs terraform apply ends up with their own copy. This quickly devolves into a nightmare: divergent state, accidental overwrites, and a painful “who deleted that resource?” investigation.

The real problem isn’t just losing a JSON file; it’s losing the single source of truth that guarantees your infrastructure stays consistent across a team, a CI pipeline, and disaster‑recovery scenarios. Using an S3 bucket for remote state storage coupled with a DynamoDB lock table is the de‑facto pattern that solves these issues in AWS‑centric environments. It gives you durability, versioning, and, most importantly, a reliable lock to prevent concurrent modifications.

But setting up the backend isn’t just “copy‑paste a snippet.” You need the right bucket policies, encryption, lifecycle rules, and a lock table that respects IAM boundaries. Miss a single attribute and you’ll either expose secrets or suffer from flaky state updates. The guide linked below walks through every nuance, from a minimal sandbox to a hardened production‑grade configuration.

WHAT YOU'LL LEARN

How to provision an S3 bucket and DynamoDB table solely with Terraform, keeping the bootstrap process self‑contained.
The exact IAM policies required for read/write access, encryption at rest, and versioning without over‑privileging.
Configuring the backend "s3" block to enable state locking, state file encryption, and automatic retries.
Common pitfalls such as bucket name collisions, missing dynamodb_table attributes, and how to debug lock contention.
Strategies for multi‑environment setups (dev, staging, prod) using workspaces or separate backends.
Production‑ready tips: lifecycle rules, server‑side encryption (SSE‑KMS), and disaster recovery of the state file.

A SHORT CODE SNIPPET

terraform {
backend "s3" {
bucket = "my-terraform-state-prod"
key = "infra/terraform.tfstate"
region = "us-east-1"
dynamodb_table = "tf-state-lock-prod"
encrypt = true
}
}

This minimal block tells Terraform to store its state in an S3 bucket, encrypt it, and use a DynamoDB table for locking. The full guide expands on each attribute and shows how to provision the bucket and table automatically.

KEY TAKEAWAYS

Remote state + locking = safety: S3 gives you durability and versioning; DynamoDB prevents race conditions.
Infrastructure‑as‑code the backend: Don’t create the bucket and lock table manually—let Terraform do it and keep everything version‑controlled.
Lock table design matters: Use a primary key of LockID and enable point‑in‑time recovery to survive accidental deletions.
Policy‑least‑privilege: Grant only s3:GetObject, s3:PutObject, and dynamodb:PutItem/DeleteItem to the role that runs Terraform.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Terraform backend configuration S3 and DynamoDB — Complete Guide

Semantic search with Java Spring Boot and vector embeddings — Complete Guide

Rajesh Mishra — Wed, 05 Aug 2026 07:55:55 +0000

Semantic search with Java Spring Boot and vector embeddings — Complete Guide

A practical, in-depth guide to Semantic search with Java Spring Boot and vector embeddings with examples.

INTRO

Traditional keyword‑based search works fine for exact matches, but it crumbles the moment users phrase their intent differently, misspell a word, or look for concepts rather than literal terms. In a product catalog or knowledge base, a user might type “budget‑friendly laptop for college” and expect results that include “affordable student notebook” – a scenario where classic inverted indexes return nothing useful. The gap isn’t just a UX annoyance; it translates into lost conversions, higher support tickets, and a perception that the platform is “dumb”.

Semantic search bridges that gap by turning text into dense vector embeddings that capture meaning, then performing nearest‑neighbor lookups instead of string matches. The challenge for Java teams is that most of the tooling and tutorials live in the Python ecosystem, leaving Spring Boot developers to reinvent the wheel or embed foreign services awkwardly. This article teases a practical, end‑to‑end approach that lets you stay inside the familiar Spring Boot stack while leveraging state‑of‑the‑art embedding models and vector databases.

WHAT YOU'LL LEARN

How to generate high‑quality text embeddings using OpenAI’s API (or an open‑source alternative) directly from a Spring service.
Setting up a lightweight vector store with Milvus (or PGVector) and wiring it into Spring Data repositories.
Building a REST endpoint that accepts a natural‑language query, converts it to a vector, and returns the top‑k semantically similar records.
Strategies for indexing, batching, and updating embeddings when source data changes.
Common pitfalls – from embedding drift to latency spikes – and how to mitigate them in production.
Deploy‑ready tips: connection pooling, async calls, and monitoring vector similarity scores.

A SHORT CODE SNIPPET

@Service
public class SemanticSearchService {

private final RestTemplate openAiClient;
private final VectorRepository vectorRepo; // Spring Data repository backed by Milvus

public SemanticSearchService(RestTemplate openAiClient,
VectorRepository vectorRepo) {
this.openAiClient = openAiClient;
this.vectorRepo = vectorRepo;
}

public List<SearchResult> search(String query, int k) {
// 1⃣ Convert query to embedding
double[] queryEmbedding = fetchEmbedding(query);

// 2⃣ Perform ANN search in Milvus
List<VectorEntity> nearest = vectorRepo.findTopKByEmbedding(queryEmbedding, k);

// 3⃣ Map back to domain objects
return nearest.stream()
.map(v -> new SearchResult(v.getId(), v.getScore()))
.toList();
}

private double[] fetchEmbedding(String text) {
var request = Map.of("model", "text-embedding-ada-002", "input", text);
var response = openAiClient.postForObject(
"https://api.openai.com/v1/embeddings",
request,
EmbeddingResponse.class);
return response.getData().get(0).getEmbedding();
}
}

The snippet shows the core loop: a user query is turned into a dense vector, then a nearest‑neighbor query runs against a vector store, and the results are returned as plain DTOs. The full guide expands this into a complete Spring Boot application with proper error handling, async processing, and a UI demo.

KEY TAKEAWAYS

Embedding‑first design: Store vectors alongside your primary entities; this decouples search from the relational schema and enables real‑time semantic relevance.
Choose the right vector DB: Milvus offers GPU‑accelerated ANN for massive datasets, while PGVector provides a low‑friction path for smaller workloads.
Refresh strategy matters: Re‑embedding on every write is costly; batch updates and incremental re‑indexing strike a practical balance.
Observability is non‑negotiable: Track latency of embedding calls and similarity scores to catch drift before it hurts user experience.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:
Semantic search with Java Spring Boot and vector embeddings — Complete Guide

Mistral 7B local installation and usage guide for developers 2026 — Complete Guide 2026

Rajesh Mishra — Tue, 04 Aug 2026 08:54:42 +0000

This is a summary of the full tutorial published on howtostartprogramming.in.

Introduction Welcome to the 2026 Mistral 7B local installation and usage guide for developers. This section gives you a quick snapshot of what the guide covers, why Mistral 7B remains a top choice for on‑premise LLM deployments, and the prerequisites you’ll need before diving into the step‑by‑step instructions. Table of Contents Introduction Quick start snippet Complete Guide 1. Overview 2. System Requirements 3. Environment Setup Aspect Details (2026) Model size 7 billion parameters, 4‑bit quantized ggml checkpoint (≈5 GB) Typical use‑cases Code completion, chat assistants, retrieval‑augmented generation, edge AI Supported OS Linux (x86_64, ARM64), macOS (Apple Silicon), Windows (WSL2) Hardware baseline CPU: 8‑core modern processor GPU (optional): NVIDIA RTX 3060 / AMD RX 6600 or better f

📖 Read the Full Tutorial

🔗 Mistral 7B local installation and usage guide for developers 2026 — Full Guide with Code Examples

The full article includes:

✅ Step-by-step code examples (copy-paste ready)
✅ Complete working project (Spring Boot / Java)
✅ Common mistakes + fixes
✅ Production tips and benchmarks
✅ FAQ section

Published on How to Start Programming — practical AI and Java tutorials for developers.

Java 17 and Java 21 interview questions new features 2026 — Complete Guide

Rajesh Mishra — Tue, 04 Aug 2026 07:54:53 +0000

Java 17 and Java 21 interview questions new features 2026 — Complete Guide

A practical, in-depth guide to Java 17 and Java 21 interview questions new features 2026 with examples.

INTRO

Hiring managers have stopped asking “what’s new in Java 8?” and are now demanding concrete knowledge of the language’s most recent releases. If you’re still polishing your answers with outdated features, you’ll look unprepared the moment a recruiter asks about sealed classes, pattern matching for switch, or the new record enhancements that landed after Java 17. The real problem isn’t just remembering a list of features—it’s being able to explain why they exist, how they affect code quality, and when to use them in a production setting.

In 2026 the Java ecosystem has settled around two LTS versions: Java 17 (the baseline for most enterprise stacks) and Java 21 (the newest LTS with a handful of game‑changing additions). Interviewers expect you to compare them, spot pitfalls, and write concise snippets on the spot. This teaser walks you through the most interview‑relevant changes, so you can stop guessing and start answering with confidence.

WHAT YOU'LL LEARN

The practical differences between Java 17 and Java 21 LTS, and which one to target for different job roles.
How sealed classes, pattern matching for switch, and record patterns simplify domain modeling and reduce boilerplate.
The new StringTemplate API and its impact on performance‑critical logging and SQL building.
Virtual threads (Project Loom) in Java 21: when they replace traditional thread pools and common pitfalls to avoid.
How the enhanced Foreign Function & Memory API (Incubator → Preview) lets you call native libraries without JNI.
Interview‑style coding exercises that combine these features, with explanations of the “why” behind each solution.

A SHORT CODE SNIPPET

// Java 21: sealed hierarchy with pattern matching in a switch expression
sealed interface Shape permits Circle, Rectangle {}

record Circle(double radius) implements Shape {}
record Rectangle(double width, double height) implements Shape {}

double area(Shape s) {
return switch (s) {
case Circle c -> Math.PI * c.radius() * c.radius();
case Rectangle r -> r.width() * r.height();
};
}

The snippet shows three interview‑ready concepts in under 15 lines: a sealed interface that restricts implementations, records that give you immutable data carriers for free, and a switch expression that pattern‑matches on those records. It’s the kind of answer that earns points for brevity, correctness, and modern Java style.

KEY TAKEAWAYS

Sealed types + pattern matching let you express exhaustive domain logic without instanceof chains, a frequent interview “trick question”.
Virtual threads in Java 21 dramatically lower the cost of concurrency, but you still need to understand when to fall back to platform threads (e.g., CPU‑bound workloads).
StringTemplate replaces cumbersome String.format calls, offering compile‑time safety for templated strings used in logging or SQL.
Foreign Function & Memory API is the future of native interop; knowing its basic usage signals that you’re ready for performance‑critical projects.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Java 17 and Java 21 interview questions new features 2026 — Complete Guide

Spring Batch tasklet vs chunk oriented processing — Complete Guide

Rajesh Mishra — Mon, 03 Aug 2026 08:11:16 +0000

Spring Batch tasklet vs chunk oriented processing — Complete Guide

A practical, in-depth guide to Spring Batch tasklet vs chunk oriented processing with examples.

INTRO

When you’re building a data‑intensive application—think nightly ETL jobs, large‑scale migrations, or periodic report generation—Spring Batch is often the first framework that comes to mind. It gives you the scaffolding to read, process, and write millions of records reliably. But the moment you open a Job definition you’re faced with a crucial design decision: should the step be a tasklet or a chunk‑oriented step?

Choosing the wrong model can bite you later. A tasklet that does everything in one execute call may look simple, yet it forces you to manage transaction boundaries, retries, and chunking logic manually. Conversely, a chunk‑oriented step gives you built‑in transaction management and restartability, but it introduces a bit more configuration overhead. The real problem isn’t “which one is easier?”—it’s “which one aligns with the data volume, failure semantics, and operational constraints of my job?”

In this teaser we’ll surface the trade‑offs that keep engineers up at night: memory consumption vs. throughput, fine‑grained error handling vs. coarse‑grained simplicity, and the hidden cost of custom retry logic. By the end you’ll have a mental checklist that tells you when to reach for a tasklet and when to let Spring Batch’s chunk engine do the heavy lifting.

WHAT YOU'LL LEARN

When to pick a tasklet – scenarios with single‑action jobs, external system calls, or non‑transactional work.
When chunk‑oriented processing shines – high‑volume reads, built‑in commit intervals, and automatic restart support.
How transaction boundaries differ between the two models and why that matters for data consistency.
Performance implications: memory footprint, I/O throughput, and how to tune chunk size for optimal speed.
Error handling patterns: retry, skip, and back‑off strategies in tasklet vs. chunk steps.
Real‑world migration example that walks through converting a monolithic tasklet into a scalable chunk step.

A SHORT CODE SNIPPET

Below is a minimal comparison: a tasklet that writes a single file versus a chunk step that reads from a CSV, processes each line, and writes to a database.

// Tasklet version – one‑off file creation
@Component
public class FileWritingTasklet implements Tasklet {
@Override
public RepeatStatus execute(StepContribution contribution,
ChunkContext chunkContext) throws Exception {
Files.writeString(Paths.get("output.txt"), "Hello, Spring Batch!");
return RepeatStatus.FINISHED;
}
}

// Chunk‑oriented version – read‑process‑write loop
@Configuration
public class CsvToDbJobConfig {

@Bean
public Job csvToDbJob(JobBuilderFactory jobs, Step step) {
return jobs.get("csvToDbJob")
.start(step)
.build();
}

@Bean
public Step step(StepBuilderFactory steps,
ItemReader<Person> reader,
ItemProcessor<Person, Person> processor,
ItemWriter<Person> writer) {
return steps.get("csvStep")
.<Person, Person>chunk(100) // commit every 100 records
.reader(reader)
.processor(processor)
.writer(writer)
.build();
}
}

The tasklet is a single method call; the chunk step declares a chunk(100) which tells Spring Batch to open a transaction, process up to 100 items, then commit. The rest of the guide dives into when that extra declarative power is worth the extra boilerplate.

KEY TAKEAWAYS

Tasklets are best for isolated, non‑transactional actions (e.g., kicking off a downstream service, cleaning up temp files).
Chunk processing gives you automatic transaction management, restartability, and scaling for any workload that can be split into discrete records.
Chunk size is a tuning knob, not a magic number; start small, measure, then increase until you hit I/O or memory limits.
Error handling diverges sharply: tasklets need explicit try/catch, while chunk steps can leverage Spring Batch’s built‑in skip, retry, and back‑off policies.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:

Spring Batch tasklet vs chunk oriented processing — Complete Guide