API concepts that are discussed in interviews , don't skip these if you are a backend dev :
1. Idempotency: Know why PUT/DELETE are idempotent but POST isn’t. Helps avoid accidental double writes.
2. Pagination: Offset works until scale hits. Cursor/keyset is what you need for big data sets.
3. Versioning: URI vs Header vs Query param. There’s no one right way, just tradeoffs.
4. Rate limiting: Token bucket > naive fixed window if you care about fairness.
5. Error contracts: 400 (bad request) ≠ 422 (validation issue) ≠ 409 (conflict). Don’t just throw 500 everywhere.
6. Caching: ETag + Cache-Control save you from unnecessary DB load.
7. Security: JWTs expire for a reason. Don’t stuff user PII inside them.
8. N+1 Queries: Kill them early. Use batching or joins when returning nested resources.
9. Docs: OpenAPI/Swagger is not optional.
10. Consistency: Sometimes cached data is good enough.
1. Agreed, microservices aren't always the answer. But in complex, distributed orgs or teams with independently deployable units, they still solve real-world scale and autonomy issues when used with care.
2. IPC/shared memory & domain sockets : valid, especially for services colocated on the same node. In practice though, many teams trade off raw performance for cloud-native flexibility and deployment agility.
3. Presigned URLs & large packets : yes Leveraging these can drastically reduce load on internal services.
Common Java interview questions for beginners :
- Why Strings are immutable?
- Write your own immutable class
- shallow vs deep cloning
- How Can we write custom exception?
- Producer/Consumer problem using wait and notify
- noclasdeffound vs classnotfound
exception
Java multithreading interview questions for beginners -
- difference between synchronized blocks and the volatile keyword.
- Explain the concept of reentrant locks in Java
- difference between Thread.sleep() and Object.wait() methods
- What is a thread-local variable in Java?
AWS Learning (Day 1 to Day 4) 🔥🔥
Day 1 – What is AWS
https://t.co/CBcoqpkqdq
Day 2 – Core AWS Services
https://t.co/u0Wo4LvC6q
Day 3 – AWS Console Tour
https://t.co/Nrns7Z5qjm
Day 4 – S3 Basics
https://t.co/blqEBZIjWc
Day 5 – EC2 Basics (Coming tomorrow)
Check & Share to support!! 🔥🔥
@UndercoverKe@C_NyaKundiH If an AI-powered spacecraft can land on a moving asteroid, drill a hole, collect samples, send them back to Earth, and continue to other missions, what can’t AI do? All of the above are effortless for AI-powered robots.
Coding your own database server in C
In this series you will learn how to design data structures and how to think when designing and coding a fast in-memory database server. You will learn about Tcp network connections and parsing of Cli commands
Apache Kafka 4.0 was just released!
It bumps Kafka to 1.4 million lines of code.
What comes with this new release?
Here are the top 3 features you should know about 🔥
As a backend developer , expect some database questions as well in interviews.
Some common questions are :
- Difference between clustered and unclustered indexes
- what are materialized views
- Difference between procedure and functions
- Find employee with nth highest salary
As a Java developer, caching and performance are common interview topics. Make sure to read about these 20 key caching concepts before your interview 👇
1. Fundamentals of Caching: Types (In-Memory, Distributed, Persistent), Eviction Policies (LRU, LFU, FIFO, ARC)
2. Cache Invalidation Strategies: Write-Through, Write-Behind, Lazy Loading, TTL
3. Spring Boot Caching Basics (@ Cacheable, @ CachePut, @ CacheEvict) and Providers (Redis, Ehcache, Caffeine, Hazelcast)
4. Hibernate Caching Overview: First-Level vs. Second-Level Cache, Query Cache, and Their Use Cases
5. Handling Stale Data in Hibernate Cache Due to External DB Updates and Using Event Listeners for Cache Eviction
6. Redis Caching Basics: Key-Value Storage, Persistence (RDB, AOF), Expiry Policies
7. Redis Advanced Data Structures for Caching: Strings, Hashes, Lists, Sets, Sorted Sets
8. Redis Transactions and Pipelining: Optimizing Cache Writes
9. Redis Pub/Sub: Real-Time Messaging for Cache Invalidation Across Applications
10. Redis Cluster and Replication: Ensuring High Availability and Scalability
11. Redis Eviction Policies: LRU, LFU, Random, Noeviction Strategies
12. Redis LUA Scripting: Optimizing Batch Cache Operations
13. Redis Performance Optimization: Connection Pooling, Thread Tuning, Memory Optimization
14. Memcached vs. Redis: Key Differences and When to Use Each
15. Hazelcast and Apache Ignite: Distributed Caching, Near Cache, and In-Memory Data Grid Features
16. Cache Synchronization Across Microservices: Challenges and Solutions Using Redis
17. Implementing Cache Refresh Jobs to Handle Stale Data Automatically
18. Best Practices for Distributed Caching in Java: Avoiding Cache Stampede, Thundering Herd Problem
19. Optimizing Cache TTL: Balancing Performance and Data Freshness in Redis and Other Caches
20. Monitoring and Debugging Caches: Redis Insight, Micrometer, Prometheus, and Grafana