SDE2 at Amazon | Ex - Adobe, Morgan Stanley | GSoC17 | BITS

My personal website: https://ashishps.com
My newsletter: https://blog.algomaster.io/

Hi there! 👋 I'm Ashish.

I'm a software engineer with over 7 years of experience working in various domains like full stack, machine learning, backend and data engineering.

During my Software Engineering career, I have worked with multiple programming languages, libraries, frameworks and technologies such as Java, Python, TypeScript, React, Kafka, Kubernetes and AWS. 

I earned my B.E. in Computer Science back in 2017 and recently completed 11 years as a programmer. 
During the last semester of college, I participated in Google Summer of Code (GSoC) where I contributed to few open-source projects: FFMpeg and Netflix's VMAF library.

My interests range from distributed systems, big data to AI, and I'm always eager to explore new technological horizons.

On a personal level, I'm passionate about self-improvement and personal growth.
I believe that we can always learn and grow, both professionally and personally. 
I love reading books 📗 and listening to podcasts 🎙️
I actively create content on LinkedIn, X and Youtube to help others make a career in tech.

Stay in touch:
📩 Newsletter: https://blog.algomaster.io/
🎥 YouTube: https://www.youtube.com/@ashishps_1/
👨‍💻 Github: https://github.com/ashishps1
✍️ Medium: https://medium.com/@ashishps
🚀 X: https://twitter.com/ashishps_1

I hope you have a lovely day!

DISCLAIMER: All opinions shared by me are my own and based on my personal experience working as a Software Engineer.

Ashish Pratap Singh

Top 15 Design Patterns You Should Know:

1. 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧: https://lnkd.in/g3VrJz-k
2. 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐌𝐞𝐭𝐡𝐨𝐝: https://lnkd.in/gA6Uew8n
3. 𝐁𝐮𝐢𝐥𝐝𝐞𝐫: https://lnkd.in/gdTr2BBF
4. 𝐀𝐝𝐚𝐩𝐭𝐞𝐫: https://lnkd.in/g_yB_CZn
5. 𝐃𝐞𝐜𝐨𝐫𝐚𝐭𝐨𝐫: https://lnkd.in/g9zWv66w
6. 𝐅𝐚𝐜𝐚𝐝𝐞: https://lnkd.in/gHzPeaKG
7. 𝐏𝐫𝐨𝐱𝐲: https://lnkd.in/g2MF2hvS
8. 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐞: https://lnkd.in/gHwStDc3
9. 𝐎𝐛𝐬𝐞𝐫𝐯𝐞𝐫: https://lnkd.in/g4S_eGjy
10. 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: https://lnkd.in/gSjXJ3Cq
11. 𝐂𝐨𝐦𝐦𝐚𝐧𝐝: https://lnkd.in/gffxnxih
12. 𝐈𝐭𝐞𝐫𝐚𝐭𝐨𝐫: https://lnkd.in/g7F_PmD9
13. 𝐒𝐭𝐚𝐭𝐞: https://lnkd.in/gmfFnubm
14. 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞 𝐌𝐞𝐭𝐡𝐨𝐝: https://lnkd.in/gshGDpKE
15. 𝐂𝐡𝐚𝐢𝐧 𝐨𝐟 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: https://lnkd.in/gayT82-s

Which of these design patterns do you use most often in you work? Let me know in the comments.

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Founder @ AlgoMaster.io | YouTube (250k+) | Prev @ Amazon

10 microservices concepts developers should know:

1. 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲
A single entry point that routes client requests to the right backend service. It handles cross-cutting concerns like auth, rate limiting, and SSL termination so individual services don't have to.

2. 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲
When services scale dynamically, you can't hardcode addresses. Services register themselves with a service registry (like Consul or Eureka), and others discover them by name at runtime.

3. 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠
Distributes incoming traffic across multiple instances of a service to prevent any single instance from becoming a bottleneck. Can be client-side or server-side.

4. 𝐂𝐢𝐫𝐜𝐮𝐢𝐭 𝐁𝐫𝐞𝐚𝐤𝐞𝐫
Protects your system from cascading failures. When a downstream service keeps failing, the circuit "opens" and stops forwarding requests. After a timeout, it enters "half-open" to test if the service has recovered.

5. 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧
Instead of services calling each other directly, they publish events to a message queue (like Kafka or RabbitMQ). Other services consume those events asynchronously, decoupling producers from consumers.

6. 𝐂𝐐𝐑𝐒 (𝐂𝐨𝐦𝐦𝐚𝐧𝐝 𝐐𝐮𝐞𝐫𝐲 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐒𝐞𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧)
Separates write operations (Commands) from read operations (Queries) into different services and databases. Writes go to an optimized write store, reads come from an optimized read store.

7. 𝐒𝐚𝐠𝐚 𝐏𝐚𝐭𝐭𝐞𝐫𝐧
Handles distributed transactions across multiple services without a global lock. If a step fails, compensating transactions roll back the changes made by earlier steps.

8. 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐌𝐞𝐬𝐡
A dedicated infrastructure layer where a sidecar proxy sits next to every service and handles routing, retries, observability, and mTLS, without changing application code.

9. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐓𝐫𝐚𝐜𝐢𝐧𝐠
Assigns a unique Trace ID to each request that flows through multiple services. Each service creates a span, and all spans are stitched together to give you a full picture of the request lifecycle.

10. 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧
Packages an application along with its libraries and OS dependencies into a portable container (Docker). Ensures consistent behavior across development, staging, and production environments.

What else would you add to this list?

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👉 If you are preparing for Software Engineering interviews, checkout my website algomaster.io for comprehensive resources

10 things you can use PostgreSQL for:

1. 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞
The classic use case. PostgreSQL excels at managing structured data with tables, relationships, joins, constraints, and fully ACID-compliant transactions.

2. 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐒𝐭𝐨𝐫𝐞
PostgreSQL supports JSON and JSONB natively, so you can store and query semi-structured data with ease.

3. 𝐓𝐢𝐦𝐞-𝐒𝐞𝐫𝐢𝐞𝐬 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞
You can use native table partitioning for time-based data, or add TimescaleDB for features like automatic chunking, compression, and continuous aggregates.

4. 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞
Recursive CTEs allow you to model and traverse hierarchies and graph-like relationships directly in SQL. For more advanced graph workloads, the Apache AGE extension brings Cypher query support to PostgreSQL.

5. 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞
With PostGIS, PostgreSQL becomes a powerful geospatial database. You can store points, polygons, and other geometries, then run spatial queries like “find all restaurants within 5 km” with excellent performance.

6. 𝐅𝐮𝐥𝐥-𝐓𝐞𝐱𝐭 𝐒𝐞𝐚𝐫𝐜𝐡 𝐄𝐧𝐠𝐢𝐧𝐞
PostgreSQL includes built-in full-text search capabilities through tsvector and tsquery. You get indexing, ranking, stemming, and relevance-based search without needing a separate search engine for many use cases.

7. 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐐𝐮𝐞𝐮𝐞
PostgreSQL can also power lightweight messaging systems. LISTEN/NOTIFY enables pub/sub communication between connections, and SELECT ... FOR UPDATE SKIP LOCKED helps you build reliable job queues directly inside the database.

8. 𝐊𝐞𝐲-𝐕𝐚𝐥𝐮𝐞 𝐒𝐭𝐨𝐫𝐞
Using hstore or JSONB, PostgreSQL can serve as a key-value store as well. Both support indexing, which makes lookups fast. It can be a practical lightweight alternative to Redis for some workloads.

9. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞
With the pgvector extension, PostgreSQL can store embeddings and perform vector similarity search. You can create HNSW or IVFFlat indexes and run nearest-neighbor queries, making it a solid option for AI/ML applications.

10. 𝐂𝐫𝐨𝐧 𝐉𝐨𝐛 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞𝐫
With pg_cron, PostgreSQL can schedule recurring jobs directly from the database. This is useful for tasks like cleanup jobs, rollups, reporting, and maintenance workflows.

How have you used PostgreSQL in production? Let me know in the comments.

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👉 If you are preparing for Software Engineering interviews, checkout my website algomaster.io for comprehensive resources

Hello , I am a Computer Science Student From Babu Banarasi Das University, Lucknow 
Interested in Data Structures, Algorithms and web development.
Visit to my GitHub for Awesome projects.🙂

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