𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞

Microservices architecture breaks down applications into smaller, independent services.

Here's a rundown of the 𝟏𝟎 𝐤𝐞𝐲 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 in this architecture:

1. 𝐂𝐥𝐢𝐞𝐧𝐭
These are the end-users who interact with the application via different interfaces like web, mobile, or PC.

2. 𝐂𝐃𝐍 (Content Delivery Network)
CDNs deliver static content like images, stylesheets, and JavaScript files efficiently by caching them closer to the user's location, reducing load times.

3. 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫
It distributes incoming network traffic across multiple servers, ensuring no single server becomes a bottleneck and improving the application's availability and reliability.

4. 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲
An API Gateway acts as an entry point for all clients, handling tasks like request routing, composition, and protocol translation, which helps manage multiple microservices behind the scenes.

5. 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬
Each microservice is a small, independent service that performs a specific business function. They communicate with each other via APIs.

6. 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐁𝐫𝐨𝐤𝐞𝐫
A message broker facilitates communication between microservices by sending messages between them, ensuring they remain decoupled and can function independently.

7. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
Each microservice typically has its own database to ensure loose coupling. This can involve different databases for different microservices

8. 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐫
This component handles user authentication and authorization, ensuring secure access to services.

9. 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐑𝐞𝐠𝐢𝐬𝐭𝐫𝐲 𝐚𝐧𝐝 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲
This system keeps track of all microservices and their instances, allowing services to find and communicate with each other dynamically.

10. 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 (e.g., Zookeeper)
Tools like Zookeeper help manage and coordinate distributed services, ensuring they work together smoothly.

If you want to pass system design interviews,
learn these 15 heuristics:


1 Latency + Global → CDN
↳ Deliver data from edge servers to reduce latency

2 Read + Bottleneck → Cache
↳ Store frequent reads in cache to reduce database load

3 Write + Spike → Queue
↳ Buffer writes for asynchronous processing during traffic spikes

4 Distributed + Transaction → Saga
↳ Handle multi-service transactions with compensating steps

5 ACID + Relational → SQL
↳ Use a SQL database for strong consistency and transactional integrity

6 Flexible + Scale → NoSQL
↳ Use NoSQL for schema flexibility and horizontal scalability

7 SQL + Scale → Shard DB
↳ Federate and partition data across shards to scale effectively

8 Load + Growth → Scale Out
↳ Add servers to handle extra traffic

9 Traffic + Reliability → Load Balance
↳ Distribute requests evenly for performance

10 Core + Failure → Redundancy
↳ Replicate core services to avoid single points of failure

11 Durability + Faults → Replication
↳ Replicate data for availability and recovery

12 Requests + Spike → Throttle
↳ Limit excessive requests to prevent server overload

13 Load + Spike → Autoscale
↳ Add or remove server capacity automatically to handle changing load

14 Realtime + Updates → WebSockets
↳ Use WebSockets for live, bidirectional communication

15 Retry + Safety → Idempotent
↳ Make operations safe to retry without side effects using idempotency

Package Organization:
• Controller - REST API endpoints and request handling
• Service - Business logic and core functionality
• Repository - Data access layer with Spring Data JPA
• Model - Entity and DTO classes for data representation
• Config - Custom configurations (security, CORS, etc.)
• Exception - Centralized error handling and custom exceptions
• Security- JWT/OAuth2 authentication setup
• Utils- Reusable helper classes and validators

This layered approach ensures:
✅ Clear separation of responsibilities
✅ Easier testing and debugging
✅ Better code reusability
✅ Simplified team collaboration
✅ Enhanced maintainability

5 Best Java Interview Questions
1. Core Java - https://bit.ly/3p3dJrI
2. Multithreading - https://bit.ly/3bAABfk
3. Collections - https://bit.ly/3d2FHRG
4. OOP - https://bit.ly/3zLs0yu
5. Design Patterns - https://bit.ly/3JAE7Tn
6. Programming - https://bit.ly/3BQQ59K

 Core Java – Master the fundamentals (OOP, Collections, Streams, Generics, Exception Handling).
🔹 Java 25 (Latest LTS) – Learn new features like Virtual Threads (Project Loom), Pattern Matching, Records, Sealed Classes, Scoped Values & modern garbage collection improvements.
🔹 Advanced Java Concepts – JVM internals, concurrency, multithreading, classloading, and memory management.
🔹 Microservices & Messaging – Build resilient, distributed systems using Kafka, RabbitMQ, API Gateways, and Circuit Breakers.
🔹 Databases – Gain expertise in SQL (MySQL/Postgres), NoSQL (MongoDB/Cassandra), and ORM frameworks like Hibernate/JPA.
🔹 Frameworks (Spring Ecosystem) – Spring Boot, Spring MVC, Spring Security, Spring Data JPA, Spring Cloud.
🔹 Logging & Monitoring – Centralized logging with ELK, distributed tracing (Zipkin, Jaeger), and observability with Prometheus & Grafana.
🔹 Advanced Patterns & Architecture – Apply design patterns (Factory, Singleton, Builder, Observer) and system design best practices.

Core Principles

1. Every system is a trade-off -> you never get speed, cost, and simplicity all at once.
2. Latency compounds -> every millisecond added across layers adds up to user pain.
3. Scalability ≠ performance -> one handles growth, the other handles speed.
4. Read vs. write paths -> scaling each requires completely different strategies.
5. Design for change, not perfection -> requirements will shift.

Databases & Storage

6. Indexes are levers -> high-selectivity columns are worth indexing, low ones often aren’t.
7. Replication helps reads, partitioning helps writes -> don’t confuse the two.
8. Dual writes are a lie -> without coordination, you will see drift.
9. Event stores > queues (sometimes) -> better traceability, worse simplicity.
10. Cache invalidation is still the hardest problem -> freshness vs. performance is the eternal fight.

Reliability & Consistency

11. Idempotency saves you -> retries without it will hurt.
12. Fail fast, fail loud -> silent failures sink systems.
13. Eventual consistency is a feature -> not a bug, but only if the business allows it.
14. Conflict resolution in active-active is business logic, not infra.
15. Durability isn’t free -> syncing across regions has cost and latency.

Architecture Patterns

16. Microservices are an org structure, not a tech goal.
17. Monolith first, modular second, microservices last -> don’t jump too early.
18. Choreography scales, orchestration simplifies -> pick based on team maturity.
19. Serverless buys you focus, costs you control.
20. Queues don’t remove work, they smooth it.

Observability & Operations

21. Tracing > logging -> logs tell you “what”, traces tell you “why”.
22. Metrics rot without ownership -> measure what someone actually uses.
23. Retries without backoff = DDoS on yourself.
24. Dead-letter queues aren’t optional -> every system has poison messages.
25. Levers > knobs -> design quick kill switches and controls to contain blast radius.

Performance & Cost

26. Optimize the hot path, not the cold path.
27. Most bottlenecks live in the database, not the code.
28. Horizontal scaling beats vertical scaling ->until coordination kills it.
29. Warm caches mask bad queries -> measure against cold starts too.
30. The cheapest resource is disk, the most expensive is time.

People & Process

31. The best architecture dies without documentation.
32. System design reviews aren’t about diagrams, they’re about trade-offs.
33. Small PRs are for speed, big PRs are for context -> balance both.
34. A senior engineer’s role in design is asking the uncomfortable “what if”.
35. No design survives first contact with production -> but good ones bend instead of break.