Aaj ke time me:
✔ Data bahut fast grow ho raha hai
✔ Users bahut zyada queries run kar rahe hain
✔ Reports real-time chahiye
Traditional database model is load ko handle karne me struggle karta hai.
Is problem ka modern solution hai:
👉 MPP – Massively Parallel Processing
Basic Idea – Sequential vs Parallel
Traditional Processing (Sequential)
Agar ek query me 5 tasks hain:
Task 1
Task 2
Task 3
Task 4
Task 5
Traditional system:
Ek ke baad ek execute karega.
Total time = sab tasks ka total time.
Slow.
MPP Processing (Parallel)
MPP system:
Sab tasks ek sath execute karega.
Task 1 → Node 1
Task 2 → Node 2
Task 3 → Node 3
Task 4 → Node 4
Task 5 → Node 5
Total time = slowest single task ka time.
Bahut fast
MPP Architecture Types
MPP ke 2 main architectures hote hain:
1️⃣ Shared Disk Architecture
✔ Compute nodes alag hote hain
✔ Storage central hota hai
Matlab:
Sab nodes ek hi disk se data read karte hain
But processing parallel hoti hai
Benefit:
✔ Faster than traditional
✔ Easier data management
Limitation:
Central disk bottleneck ban sakta hai.
2️⃣ Shared Nothing Architecture (Modern & Powerful)
Ye most advanced model hai.
Isme:
✔ Storage alag
✔ Compute alag
✔ Har node independent
Har node:
- Apna CPU
- Apni RAM
- Apni disk
- Apna OS
Kuch bhi share nahi karta.
Isliye bolte hain:
👉 Shared Nothing
Why Shared Nothing Is Powerful?
Imagine 3 nodes hain:
Node 1 → Data part 1
Node 2 → Data part 2
Node 3 → Data part 3
Query aayi:
System query ko break karega
Har node apne data par kaam karega
Final result combine hoga
Super scalable 🚀
Agar aur performance chahiye?
👉 Aur nodes add kar do.
Real-World Example
Suppose:
Sales table = 1 billion rows
Traditional system:
Single server → slow
MPP system:
10 nodes
Each process 100 million rows
Result = 10x faster
Kaun Use Karta Hai MPP?
Modern cloud data warehouses use MPP:
- Snowflake
- Azure Synapse
- Amazon Redshift
- Google BigQuery
Ye sab MPP architecture par based hain.
Simple Formula
Traditional = 1 brain working
MPP = Multiple brains working together
Zyada brains → Faster thinking