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MapReduce Kya Hai? (In Detail) MapReduce ek distributed framework hai jo bahut bade datasets (Petabytes of data) ko hazaron computers (cluster) par parallel mein process karne ke liye use hota hai. Iske do main hisse hote hain: Map aur Reduce. 1. MapReduce ka Working Mechanism (Phases) MapReduce ka kaam 5 main steps mein divide hota hai: Input Splitting: Hadoop HDFS se data leta hai aur use chote logical chunks mein todta hai jise "Input Splits" kehte hain. Har split par ek dedicated Mapper kaam karta hai. Mapping Phase: Yeh pehla programming phase hai. Mapper har record ko read karta hai aur use (Key, Value) pairs mein badal deta hai. Example: Agar sentence hai "Hadoop is good", toh mapper output dega (Hadoop, 1), (is, 1), (good, 1). Shuffling & Sorting: Yeh intermediate step hai. Mapper ka output directly Reducer ke paas nahi jata. System saari "Same Keys" ko ek saath group karta hai aur unhe sort karta hai taaki processing fast ho sake. Reducing Phase: Reducer ke paas grouped data aata hai. Yeh un values ko aggregate (sum/count/average) karta hai. Example: Agar Mapper ne (Apple, 1), (Apple, 1) bheja, toh Reducer use (Apple, 2) bana dega. Final Output: Final result ko HDFS (Hadoop Distributed File System) mein ek file ki tarah save kar diya jata hai. 2. MapReduce ke Main Components Hadoop cluster mein isse manage karne ke liye do main roles hote hain: JobTracker (Master): Yeh "Head Office" ki tarah hai. Iska kaam hai jobs ko schedule karna, resources manage karna, aur TaskTrackers par nazar rakhna. TaskTracker (Slave): Yeh "Worker" hai jo actual Map aur Reduce tasks ko execute karta hai aur Master ko progress report bhejta hai. 3. MapReduce ki Khasiyat (Features) Data Locality: MapReduce data ko code ke paas lane ki jagah, Code ko Data ke paas bhejta hai. Isse network bandwidth bachti hai aur speed badhti hai. Fault Tolerance: Agar processing ke beech koi machine fail ho jaye,