Java Spring Boot Microservices & System Design

JDK 21 Setup, javac Bytecode Inspection & Loop Dry-Run Lab

• Verify java -version & javac -version
• Inspect compiled bytecode with javap -c

Live Lab Sprint: 1D / 2D Arrays, String Constant Pool, StringBuilder, Regex & Stack vs Heap Memory

• 1D & 2D Array Traversals, Binary Search & Matrix Operations — verify: Process contiguous array elements in O(1) indexed access without ArrayIndexOutOfBounds bugs.
• String Constant Pool, Immutability & StringBuilder Loop Efficiency — verify: Prevent O(N^2) heap allocations when assembling reports, SQL builders, and CSV lines.
• Stack Frames vs Heap Objects & Pass-by-Value Reference Semantics — verify: Understand exactly why reassigning a parameter never mutates the caller’s reference variable.

Live Lab Sprint: Classes, Constructors, Encapsulation, Access Modifiers & Packages

• Constructor Overloading, Chaining (this()) & Static Members — verify: Initialize valid domain objects deterministically at construction time.
• Encapsulation & Package-Private / Private Visibility Guards — verify: Enforce balance and business-rule checks inside domain methods instead of public fields.
• Defensive Copying of Mutable Date / Collection Fields — verify: Prevent external callers from mutating internal object state through getter references.

Live Lab Sprint: Inheritance, super, Method Overriding, Runtime Polymorphism & Abstract Classes

• Method Overriding vs Overloading & Dynamic Method Dispatch — verify: Let the JVM invoke the exact runtime subtype implementation without brittle instanceof chains.
• Abstract Classes & Template Method Workflows — verify: Standardize multi-step validation -> execution -> audit skeletons across payment types.
• Preventing Fragile Base-Class Bugs via Composition Over Inheritance — verify: Delegate interchangeable behaviors cleanly instead of building deep rigid class hierarchies.

Live Lab Sprint: Interfaces, Default / Static Methods & All 5 SOLID Design Principles in Java

• Single Responsibility & Open/Closed Principles — verify: Add new UPI, Card, or NetBanking processors without modifying tested checkout code.
• Liskov Substitution & Interface Segregation Principles — verify: Keep interfaces focused and ensure every implementation honors its behavioral contract.
• Dependency Inversion Principle (Constructor-Injected Interfaces) — verify: Depend on pure Java interfaces so business logic runs in milliseconds with in-memory test fakes.

Live Lab Sprint: Checked vs Unchecked Exceptions, try-with-resources & Custom Business Exceptions

• Throwable Hierarchy: Error vs Checked Exception vs RuntimeException — verify: Distinguish unrecoverable JVM errors from recoverable I/O and domain rule violations.
• Deterministic Cleanup with AutoCloseable & try-with-resources — verify: Guarantee sockets, files, and JDBC statements close even when exceptions fly.
• Rich Domain Exceptions with Error Codes & Context Metadata — verify: Surface clear diagnostic context for logs and REST ProblemDetail translators.

Live Lab Sprint: Java Generics & Complete Collections Framework (List, Set, Queue, Deque, Map)

• ArrayList vs LinkedList vs ArrayDeque Memory & CPU Cache Locality — verify: Choose ArrayDeque for stacks/queues and ArrayList for fast cache-friendly iteration.
• HashMap Bucket Array, hashCode/equals Contract & Red-Black Treeify Threshold — verify: Guarantee O(1) average key lookups and avoid lost keys caused by mutable map keys.
• Comparable vs Comparator, TreeMap/TreeSet & PriorityQueue Top-K — verify: Maintain sorted orderings and priority scheduling cleanly in O(log N) time.

Live Lab Sprint: Relational Databases & SQL From Scratch: Normalization, JOINs, Aggregations & Indexes

• 1NF–3NF Schema Design, Primary/Foreign Keys & Check Constraints — verify: Enforce data integrity at the database engine level so bad data can never be stored.
• INNER / LEFT JOINs, GROUP BY / HAVING & Window Functions — verify: Generate accurate financial statements and customer cohort reports in pure SQL.
• ACID Transactions & B-Tree Indexing Fundamentals — verify: Understand commit/rollback atomicity and why unindexed foreign keys cause table locks.

Live Lab Sprint: JDBC From Scratch, HikariCP Connection Pooling, Maven Lifecycle & JUnit 5 TDD

• JDBC Connection, PreparedStatement Parameter Binding & Batch Execution — verify: Prevent SQL injection 100% and understand what Spring Data JPA/Hibernate executes under the hood.
• HikariCP Connection Pooling & Explicit Transaction Commit/Rollback — verify: Reuse TCP database connections efficiently across thousands of requests.
• Maven Dependencies, Build Lifecycles & JUnit 5 + AssertJ Assertions — verify: Automate compilation, packaging, and repeatable unit tests from Day 1.

Live Lab Sprint: Records, Sealed Interfaces & Pattern Matching for Switch

• Java Records for Concise Immutable Value Objects — verify: Eliminate boilerplate getters/equals/hashCode on DTOs and Kafka event payloads.
• Sealed Classes/Interfaces & Exhaustive Pattern Matching — verify: Let the javac compiler guarantee every domain result state is handled safely.
• Optional<T> Pipelines vs NullPointerException — verify: Express nullable lookups explicitly in repository and service return signatures.

Live Lab Sprint: Collections Internals, Bounded Generics & Custom Comparators

• HashMap Collision Handling, hashCode/equals Contracts & Load Factor — verify: Prevent O(N) hash bucket degradation and lost keys in in-memory caches.
• PECS Generics (? extends T / ? super T) — verify: Design flexible, type-safe utility libraries and domain event buses.
• Immutable Collections (List.of, Map.copyOf) — verify: Share read-only reference data safely across thousands of concurrent threads.

Live Lab Sprint: Functional Interfaces, Stream Collectors & BigDecimal Monetary Math

• Stream groupingBy, partitioningBy & Downstream Collectors — verify: Aggregate order and settlement reports cleanly without error-prone mutable loops.
• Exact Monetary Arithmetic with BigDecimal & RoundingMode.HALF_EVEN — verify: Eliminate IEEE-754 double rounding bugs in banking, tax, and invoice calculations.
• java.time (Instant, ZonedDateTime, Duration) Best Practices — verify: Store UTC timestamps cleanly and convert to IST/user timezones only at display boundaries.

Live Lab Sprint: Domain Exception Hierarchies, NIO.2 File Streams & Clean SOLID Boundaries

• Unchecked Domain Exceptions vs Checked I/O Recovery — verify: Keep service signatures clean while translating domain errors cleanly at API boundaries.
• try-with-resources AutoCloseable Discipline & Files.lines() Streaming — verify: Prevent file-descriptor and socket leaks on long-running Java enterprise servers.
• Dependency Inversion & Constructor-Driven Testability — verify: Decouple pure Java business policies from database and messaging frameworks.

Live Lab Sprint: JVM Heap Generations, Metaspace, G1GC/ZGC & Heap Dump Forensics

• Eden, Survivor, Old Gen & Object Promotion Lifecycle — verify: Understand why short-lived DTO allocations are cheap while static caches leak Old Gen RAM.
• Choosing G1GC vs Low-Pause ZGC & Reading GC Logs — verify: Keep p99 latency below 50ms on high-throughput Java microservices.
• Capturing & Analyzing Heap/Thread Dumps (jcmd, Eclipse MAT) — verify: Pinpoint exact memory-leaking classes and deadlocked threads in production incidents.

Live Lab Sprint: Java Memory Model, Locks, Atomic Operations & ConcurrentHashMap

• volatile Visibility, Happens-Before & AtomicLong/LongAdder — verify: Maintain accurate high-concurrency metrics and sequence counters lock-free.
• ConcurrentHashMap Compute Operations vs Synchronized Maps — verify: Avoid global lock bottlenecks when caching metadata across 64 CPU threads.
• ReentrantLock, ReadWriteLock & Deadlock Prevention — verify: Coordinate shared resources safely with bounded lock-acquisition timeouts.

Live Lab Sprint: CompletableFuture Pipelines, ExecutorPools & Structured Concurrency

• Dedicated Bounded ThreadPoolExecutor vs Common ForkJoinPool — verify: Prevent slow third-party HTTP calls from starving CPU worker threads.
• CompletableFuture.allOf, orTimeout & exceptionally — verify: Fetch profile, wallet, and recommendations in parallel within a 150ms budget.
• Java 21 StructuredTaskScope (Cancel-on-Failure) — verify: Automatically cancel sibling downstream calls the instant one mandatory call fails.

Live Lab Sprint: JMH Microbenchmarking, Lock-Free Queues & Thread-Pinning Diagnostics

• JMH Warmup/Fork Benchmarks vs Naive System.nanoTime() Myths — verify: Account for JIT C2 compilation and dead-code elimination when benchmarking hot paths.
• Replacing synchronized Blocks with ReentrantLock for Virtual Threads — verify: Prevent carrier OS threads from pinning during blocking JDBC/socket calls inside locks.
• Bounded BlockingQueue Backpressure & Load Shedding — verify: Reject excess burst load gracefully with HTTP 429/503 instead of crashing the JVM.

Live Lab Sprint: Spring IoC Container, Constructor Injection, Profiles & @ConfigurationProperties

• Constructor Injection & Bean Lifecycle Scopes — verify: Guarantee immutable dependencies and effortless Mockito unit testing without reflection hacks.
• Strongly Typed @ConfigurationProperties with @Validated — verify: Fail application startup immediately if required Kafka/DB/JWT env vars are invalid.
• Conditional Auto-Configuration & Multi-Environment Profiles — verify: Switch seamlessly between local Docker, staging, and AWS production configs.

Live Lab Sprint: RESTful Resource Design, Jakarta Bean Validation & RFC 9457 ProblemDetail

• Request/Response Record DTOs & Custom Jakarta Constraint Validators — verify: Never expose JPA entities directly over JSON — validate every field at controller entry.
• Global @RestControllerAdvice & RFC 9457 ProblemDetail — verify: Standardize 400, 404, 409, and 422 JSON error payloads across all microservices.
• Content Negotiation, ETag Conditional Caching & OpenAPI 3 Docs — verify: Reduce mobile bandwidth with 304 Not Modified responses and auto-generated Swagger UI.

Live Lab Sprint: Spring AOP Cross-Cutting Auditing, Actuator Health Probes & Graceful Shutdown

• Spring AOP @Around Aspects for Execution Timing & Audit Trails — verify: Measure service method latency and record compliance logs without polluting business logic.
• Spring Boot Actuator Liveness & Readiness Groups — verify: Let Kubernetes distinguish temporary DB slowdowns from hung JVM processes.
• Graceful Embedded Tomcat/Netty Shutdown — verify: Finish in-flight customer payments before terminating pods during rolling deploys.

Live Lab Sprint: Hibernate Persistence Context, Dirty Checking & Entity Lifecycle States

• First-Level Session Cache, Flush Modes & Automatic Dirty Checking — verify: Understand exact SQL UPDATE timing inside @Transactional service methods.
• FetchType.LAZY Everywhere by Default — verify: Prevent accidental cartesian joins loading 50 related tables on a simple findById().
• MappedSuperclass Audit Columns & UUIDv7 Time-Sorted Keys — verify: Generate k-sortable primary keys that avoid B-Tree index fragmentation at scale.

Live Lab Sprint: Solving N+1 Select Storms: @EntityGraph, JOIN FETCH & DTO Projections

• Detecting N+1 Queries in Integration Tests & SQL Logs — verify: Catch hidden lazy-loading loops before code reaches staging or production.
• Interface/Record DTO Projections for Read-Only Endpoints — verify: Skip Hibernate dirty-checking overhead completely on high-traffic dashboard reads.
• Dynamic Filtering with JPA Specifications & Keyset Pagination — verify: Support multi-criteria enterprise search without brittle string-concatenated SQL.

Live Lab Sprint: Optimistic (@Version) vs Pessimistic Locking & JDBC Batch Inserts

• Optimistic Concurrency Control with @Version & Retry Policies — verify: Detect lost updates on concurrent inventory/account edits without holding long DB locks.
• SELECT ... FOR UPDATE SKIP LOCKED Job Claiming — verify: Coordinate parallel microservice pods polling work tables without duplicate execution.
• Hibernate JDBC Batching & HikariCP Pool Sizing Formula — verify: Size DB connection pools scientifically ((core_count * 2) + spindle) to avoid lockups.

Live Lab Sprint: MVCC Internals, Transaction Isolation Levels & Deadlock Resolution

• PostgreSQL MVCC Snapshots, Write Skew & Phantom Reads — verify: Understand which concurrency anomalies each SQL isolation level prevents or allows.
• Consistent Lock Ordering to Eliminate Deadlocks — verify: Sort account IDs deterministically before locking multiple rows in transfer transactions.
• Spring @Transactional Propagation (REQUIRED vs REQUIRES_NEW) & Rollback Rules — verify: Ensure checked exceptions or nested audit logs behave exactly as intended.

Live Lab Sprint: B-Tree, GIN, Partial Indexes & EXPLAIN (ANALYZE, BUFFERS) Tuning

• Composite B-Tree Column Ordering & Index-Only Scans (INCLUDE) — verify: Satisfy hot queries entirely from RAM-cached index pages without heap fetches.
• GIN Indexes on JSONB Attributes & Partitioned Audit Tables — verify: Combine flexible metadata search with range-partitioned monthly archival.
• Interpreting Seq Scan vs Bitmap Heap Scan & Shared Buffers — verify: Identify missing indexes and stale planner statistics from EXPLAIN ANALYZE output.

Live Lab Sprint: Versioned Flyway Migrations & Expand-Contract Zero-Downtime Rollouts

• Immutable Versioned SQL Migrations (V1__, V2__) & Checksums — verify: Guarantee identical database schemas across dev, CI, staging, and production.
• Expand–Migrate–Contract Pattern for Column Renames/Drops — verify: Deploy schema changes without breaking live pods during rolling Kubernetes updates.
• Read Replicas & Read/Write Routing DataSources — verify: Offload heavy reporting queries away from the primary transactional writer node.

Live Lab Sprint: Spring Security 6 FilterChain Architecture & Password Hashing

• SecurityFilterChain Order, Stateless SessionPolicy & CSRF Strategy — verify: Secure REST microservices cleanly without server-side HttpSession memory bloat.
• Argon2id / BCrypt Password Encoding & Account Lockout Policies — verify: Defend credential stores against GPU cracking and credential-stuffing bots.
• Custom AuthenticationEntryPoint & AccessDeniedHandler — verify: Return structured 401/403 ProblemDetail JSON instead of default HTML login redirects.

Live Lab Sprint: OAuth2 Resource Servers, RSA/JWKS JWT Verification & Token Rotation

• RS256/ES256 Asymmetric Signing & Public JWKS Endpoint Caching — verify: Let only the Auth Service sign tokens while every microservice verifies them locally.
• Refresh Token Rotation & Reuse Detection — verify: Revoke the entire token family immediately if a stolen refresh token is replayed.
• Propagating Tenant & Scope Claims into SecurityContext — verify: Enforce strict multi-tenant row isolation inside service and repository queries.

Live Lab Sprint: Fine-Grained @PreAuthorize Method Security & Service-to-Service Auth

• Method Security Expressions (@PreAuthorize, @PostAuthorize) — verify: Verify resource ownership right on business methods, even when called outside HTTP.
• OAuth2 Client Credentials Grant for Internal Microservices — verify: Authenticate service-to-service calls without hardcoded static API keys.
• Audit Logging Security Principal & Sensitive Data Masking — verify: Satisfy SOC2/RBI audit trails by recording who approved every privileged action.

Live Lab Sprint: Domain-Driven Bounded Contexts & Database-Per-Service Decomposition

• Bounded Contexts, Ubiquitous Language & Context Mapping — verify: Ensure Order, Inventory, and Billing services own their schemas independently.
• Anti-Corruption Layers (ACL) Around Legacy External Systems — verify: Keep third-party SOAP/ERP quirks from leaking into clean internal domain models.
• Contract-First API Versioning & Consumer-Driven Contracts — verify: Verify producer changes never break downstream microservice consumers in CI.

Live Lab Sprint: Spring Cloud Gateway: Reactive Routing, Redis Rate Limiting & Auth Offload

• Non-Blocking Netty Route Predicates & Header Rewrite Filters — verify: Route /api/orders/** and /api/payments/** with sub-2ms gateway overhead.
• Distributed Token-Bucket Rate Limiting with Redis — verify: Protect backend clusters from noisy-neighbor tenants and traffic spikes.
• BFF (Backend-for-Frontend) Response Composition — verify: Tailor compact payloads for mobile apps vs rich dashboards for web consoles.

Live Lab Sprint: Declarative HTTP Clients (@HttpExchange), gRPC & Timeout Budgets

• Spring 6 Declarative @HttpExchange & RestClient — verify: Replace legacy RestTemplate with clean, typed HTTP client interfaces.
• High-Performance Internal RPC with Protocol Buffers & gRPC — verify: Cut serialization CPU and payload size by 60%+ on hot internal service hops.
• Deadline Propagation & Preventing Retry Amplification Storms — verify: Stop 3 retries × 3 hops from turning a minor slowdown into a 27× self-DDoS outage.

Live Lab Sprint: Circuit Breakers, Bulkhead Isolation & Exponential Jitter Retries

• Resilience4j CircuitBreaker State Machine (CLOSED -> OPEN -> HALF_OPEN) — verify: Fail fast in < 1ms when a payment partner is down instead of hanging threads for 30s.
• Semaphore & ThreadPool Bulkheads — verify: Isolate slow reporting dependencies so core checkout traffic always has reserved capacity.
• Bounded Retries with Exponential Backoff & Random Jitter — verify: Spread retry waves smoothly when a recovering service comes back online.

Live Lab Sprint: Multi-Tier Caching (Caffeine L1 + Redis L2) & Graceful Degradation

• Near-Cache Caffeine L1 + Distributed Redis L2 Topology — verify: Combine nanosecond in-JVM reads with cluster-wide shared cache coherence.
• Stale-If-Error Fallback Responses — verify: Serve last-known product metadata gracefully instead of showing a 500 error page.
• Deterministic Cache Key Versioning & Pub/Sub Invalidation — verify: Roll out DTO changes cleanly without deserialization crashes on cached entries.

Live Lab Sprint: Distributed Idempotency, ShedLock Scheduled Jobs & Chaos Fault Injection

• Database/Redis Backed Idempotency Filter for Mutating Endpoints — verify: Guarantee exactly-once business effect even under client/gateway network retries.
• Cluster-Safe Cron Coordination with ShedLock — verify: Ensure a nightly settlement job runs on exactly 1 pod out of 8 running replicas.
• Injecting Latency & Faults (Toxiproxy / Chaos Monkey) — verify: Prove your circuit breakers and alerts actually trigger before real outages strike.

Live Lab Sprint: Kafka Partitions, Consumer Groups, Acks=all & Schema Registry

• Partition Keys (orderId / accountId) for Strict Per-Entity Ordering — verify: Guarantee OrderCreated is always processed before OrderPaid or OrderCancelled.
• Producer Durability (acks=all, enable.idempotence=true, min.insync.replicas) — verify: Prevent lost events even if a Kafka broker crashes immediately after acknowledge.
• Avro / JSON Schema Evolution & Backward Compatibility — verify: Add new optional event fields without breaking deployed consumer groups.

Live Lab Sprint: Transactional Outbox Pattern, Debezium CDC & Idempotent Consumers

• Why Saving to DB + Calling kafkaTemplate.send() Directly Loses Data — verify: Understand the dual-write failure window when a pod crashes between commit and publish.
• Transactional Outbox Table + Relay / Debezium CDC — verify: Write business state and outbox event in one ACID SQL transaction for 100% delivery.
• Processed-Event Deduplication Table & Dead-Letter Topics (DLT) — verify: Make consumers safely idempotent under at-least-once delivery and isolate poison pills.

Live Lab Sprint: Distributed Sagas: Choreography vs Orchestration & Compensating Actions

• Orchestrated Saga State Machine for Multi-Step Checkout — verify: Coordinate ReserveStock -> ChargeCard -> ConfirmOrder with clear state visibility.
• Compensating Transactions (RefundPayment, ReleaseStock) — verify: Undo prior steps cleanly when a downstream step rejects an order.
• Semantic Locks (PENDING_APPROVAL) & Pivot Transactions — verify: Prevent users from double-withdrawing funds while a saga is in flight.

Live Lab Sprint: Distributed Tracing with OpenTelemetry across HTTP & Kafka Hops

• W3C traceparent Context Propagation over HTTP & Kafka Headers — verify: Correlate spans automatically across synchronous REST calls and async event consumers.
• Custom Business Spans & Database Query Span Tagging — verify: Pinpoint whether p99 latency comes from JVM serialization, SQL locks, or partner APIs.
• Tail-Based vs Head-Based Trace Sampling — verify: Capture 100% of error and slow traces without paying to store millions of healthy 5ms calls.

Live Lab Sprint: Micrometer Dimensional Metrics, Prometheus Histograms & Alerting SLOs

• Counters, Gauges, Timers & Histogram Buckets — verify: Track business KPIs (payments_succeeded_total) alongside HikariCP & Kafka lag metrics.
• Avoiding High-Cardinality Tag Explosions (Never Tag userId!) — verify: Keep Prometheus memory stable by bounding metric label cardinality strictly.
• Defining Actionable SLO Burn-Rate Alerts — verify: Page on-call engineers on customer-impacting error budgets rather than noisy CPU blips.

Live Lab Sprint: Structured JSON Log Aggregation (Loki/ELK) & Incident Runbooks

• Logback JSON Encoders with Automatic traceId/spanId Injection — verify: Jump directly from a Grafana latency spike to the exact exception stack trace.
• Masking PAN, Aadhaar, Tokens & Secrets at Logger Level — verify: Enforce zero-leak compliance across every microservice log stream.
• Post-Incident RCA Templates & Load-Shedding Playbooks — verify: Document root causes and permanently harden architecture after stress tests.

Live Lab Sprint: JUnit 5, Mockito, @WebMvcTest Slices & Testcontainers Real DB/Kafka Suites

• Fast Domain Unit Tests vs Narrow @WebMvcTest Controller Slices — verify: Verify validation, security annotations, and business math in sub-second test runs.
• @ServiceConnection Testcontainers for PostgreSQL & Apache Kafka — verify: Eliminate H2 SQL-dialect false positives by running Flyway + JPA against real Postgres 16.
• WireMock Stubs for Deterministic Third-Party Gateway Testing — verify: Simulate partner 429 rate limits, malformed JSON, and 5-second socket timeouts in CI.

Live Lab Sprint: Spring Boot Layered JARS, Distroless Containers & JVM Container Ergonomics

• Spring Boot Layered Jartools Extraction in Multi-Stage Dockerfiles — verify: Cache 80MB of Maven dependencies in lower layers so code rebuilds push only ~200KB.
• CDS / AOT Class Data Sharing & Virtual Thread Readiness — verify: Slash cold-start boot times by 40%+ during auto-scaling traffic bursts.
• Container-Aware Heap Sizing (-XX:MaxRAMPercentage) — verify: Prevent Linux OOMKilled (Exit Code 137) crashes inside memory-limited Kubernetes pods.

Live Lab Sprint: Kubernetes Deployments, HPA Autoscaling, Secrets & Zero-Downtime Rollouts

• Deployment Manifests, preStop Lifecycle Hooks & PodDisruptionBudgets — verify: Ensure load balancers stop sending traffic before Tomcat begins graceful shutdown.
• Startup, Liveness & Readiness Probes Mapped to Actuator — verify: Give slow-starting JVMs safe boot windows without premature kubelet restarts.
• HorizontalPodAutoscaler (CPU/Custom Lag Metrics) & Canary Releases — verify: Scale pods automatically during flash sales and verify new builds on 5% traffic first.

Guided Live Lab Checklist: Transactional Outbox Pattern with Spring Data JPA & Debezium/Polling Relay

• Hands-On Runnable Program (JAVA): Transactional Outbox Pattern with Spring Data JPA & Debezium/Polling Relay — Production Reference Implementatio... — Purpose: Practice writing and executing real Java Spring Boot Microservices & System Design production code instead of memorizing slides, verifying live inputs and outputs.
• Plain-English Mental Model & Architecture of Transactional Outbox Pattern with Spring Data JPA (Core Java Foundations, OOP & Collections, Java 21... — Purpose: Understand what real business & engineering problem Transactional Outbox Pattern with Spring Data JPA & Debezium/Polling Relay solves in Java Spring Boot Microservices & System Design systems bef...
• Step-by-Step Syntax, Configuration & Clean Code Pattern for Transactional Outbox Pattern with Spring Data JPA & Debezium/Polling Relay — Purpose: Learn exact Core Java Foundations, OOP & Collections, Java 21 LTS syntax, naming conventions, and modular structure used by senior developers in production.

Guided Live Lab Checklist: Idempotent Kafka Consumers, Dead-Letter Topics & Poison-Pill Recovery

• Hands-On Runnable Program (JAVA): Idempotent Kafka Consumers, Dead-Letter Topics & Poison-Pill Recovery — Production Reference Implementation — Purpose: Practice writing and executing real Java Spring Boot Microservices & System Design production code instead of memorizing slides, verifying live inputs and outputs.
• Plain-English Mental Model & Architecture of Idempotent Kafka Consumers (Core Java Foundations, OOP & Collections, Java 21 LTS) — Purpose: Understand what real business & engineering problem Idempotent Kafka Consumers, Dead-Letter Topics & Poison-Pill Recovery solves in Java Spring Boot Microservices & System Design systems before w...
• Step-by-Step Syntax, Configuration & Clean Code Pattern for Idempotent Kafka Consumers, Dead-Letter Topics & Poison-Pill Recovery — Purpose: Learn exact Core Java Foundations, OOP & Collections, Java 21 LTS syntax, naming conventions, and modular structure used by senior developers in production.

Guided Live Lab Checklist: Optimistic/Pessimistic JPA Locking & Redis Distributed Mutexes

• Hands-On Runnable Program (JAVA): Optimistic/Pessimistic JPA Locking & Redis Distributed Mutexes — Production Reference Implementation — Purpose: Practice writing and executing real Java Spring Boot Microservices & System Design production code instead of memorizing slides, verifying live inputs and outputs.
• Plain-English Mental Model & Architecture of Optimistic (Core Java Foundations, OOP & Collections, Java 21 LTS) — Purpose: Understand what real business & engineering problem Optimistic/Pessimistic JPA Locking & Redis Distributed Mutexes solves in Java Spring Boot Microservices & System Design systems before writing...
• Step-by-Step Syntax, Configuration & Clean Code Pattern for Optimistic/Pessimistic JPA Locking & Redis Distributed Mutexes — Purpose: Learn exact Core Java Foundations, OOP & Collections, Java 21 LTS syntax, naming conventions, and modular structure used by senior developers in production.

Guided Live Lab Checklist: JDK Flight Recorder (JFR), Heap Dump Analysis & GC Pause Tuning in Java 21

• Hands-On Runnable Program (JAVA): JDK Flight Recorder (JFR), Heap Dump Analysis & GC Pause Tuning in Java 21 — Production Reference Implementatio... — Purpose: Practice writing and executing real Java Spring Boot Microservices & System Design production code instead of memorizing slides, verifying live inputs and outputs.
• Plain-English Mental Model & Architecture of JDK Flight Recorder (JFR) (Core Java Foundations, OOP & Collections, Java 21 LTS) — Purpose: Understand what real business & engineering problem JDK Flight Recorder (JFR), Heap Dump Analysis & GC Pause Tuning in Java 21 solves in Java Spring Boot Microservices & System Design systems bef...
• Step-by-Step Syntax, Configuration & Clean Code Pattern for JDK Flight Recorder (JFR), Heap Dump Analysis & GC Pause Tuning in Java 21 — Purpose: Learn exact Core Java Foundations, OOP & Collections, Java 21 LTS syntax, naming conventions, and modular structure used by senior developers in production.

Guided Live Lab Checklist: Graceful Kubernetes Shutdown, Readiness/Liveness Probes & Testcontainers CI

• Hands-On Runnable Program (JAVA): Graceful Kubernetes Shutdown, Readiness/Liveness Probes & Testcontainers CI — Production Reference Implementati... — Purpose: Practice writing and executing real Java Spring Boot Microservices & System Design production code instead of memorizing slides, verifying live inputs and outputs.
• Plain-English Mental Model & Architecture of Graceful Kubernetes Shutdown (Core Java Foundations, OOP & Collections, Java 21 LTS) — Purpose: Understand what real business & engineering problem Graceful Kubernetes Shutdown, Readiness/Liveness Probes & Testcontainers CI solves in Java Spring Boot Microservices & System Design systems be...
• Step-by-Step Syntax, Configuration & Clean Code Pattern for Graceful Kubernetes Shutdown, Readiness/Liveness Probes & Testcontainers CI — Purpose: Learn exact Core Java Foundations, OOP & Collections, Java 21 LTS syntax, naming conventions, and modular structure used by senior developers in production.

Guided Live Lab Checklist: Enterprise Java Microservices System Design & Failure-Mode Walkthroughs

• Hands-On Runnable Program (JAVA): Enterprise Java Microservices System Design & Failure-Mode Walkthroughs — Production Reference Implementation — Purpose: Practice writing and executing real Java Spring Boot Microservices & System Design production code instead of memorizing slides, verifying live inputs and outputs.
• Plain-English Mental Model & Architecture of Enterprise Java Microservices System Design (Core Java Foundations, OOP & Collections, Java 21 LTS) — Purpose: Understand what real business & engineering problem Enterprise Java Microservices System Design & Failure-Mode Walkthroughs solves in Java Spring Boot Microservices & System Design systems before...
• Step-by-Step Syntax, Configuration & Clean Code Pattern for Enterprise Java Microservices System Design & Failure-Mode Walkthroughs — Purpose: Learn exact Core Java Foundations, OOP & Collections, Java 21 LTS syntax, naming conventions, and modular structure used by senior developers in production.