MapReduce algorithm and Mumbai’s Dabbawalas!!!!


It’s astonishing to see the effectiveness and the operation scale of Mumbai dubbawala delivery system.Mumbai dubbawala’s are basically your personalized DoorDash guys. They source freshly cooked lunch from your home and deliver right to your office so that you can enjoy home food.

Mumbai Dabbawala code
Mumbai Dabbawala code

Few things you should know about Dabbawala association:

  1. It started in British rule way back in 1880 when many Indian people worked under British dislike the food and they want it to be served to them from home.
  2. It got Six sigma certification(CMMi 6 level) which means 99.9999% successful delivery rate.This means there is a chance of 1 error in 60 million transaction
  3. Every day they doing 4,00,000 transaction of boxes in the radios of 60 KM in 3 hour.

Now what is MapReduce:
It was introduced by Google in 2004 and Hadoop is the famous open source tool that implements MapReduce Algorithm and it is the backbone of many larger data computations now.MapReduce basically a divide and con-quire algorithm which breaks down the problem in to small components and processing it in parallel to achieve effective computation on a larger data set. It is having two steps.
1. Map
2. Reduce
Map:

Map Reduce skeleton
Map Reduce skeleton

In the Map step the master node takes the input, partitions it up into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node.
Reduce:
In the Reduce step the master node then collects the answers to all the sub-problems and combines them in some way to form the output – the answer to the problem it was originally trying to solve.

Now do you wonder how dabbawalla functioning similar with MapReduce Algorithm?
Dabbawalla Map step :
Dabbawalla collects the boxes from individual homes of particular area ( Distributed Master node) and sort it out and partition in to groups based on the location and the buildings. Then they distribute in to nearest delivery point (distributed to workers node). Now at the delivery point (the workers point), if it is the destination they switch in to reduce step ( see below of Dabbawalla Reduce Step) else they perform same operation like partitioning and distributing to another delivery point ( next level of workers), which typically form the multilevel tree mode.

Dabbawalla Reduce Step:
Now if it is the destination point, the dabbawala’s collect all the boxes based on the partition and start to delivering it. If a particular building or street needs more boxes to be delivered, they allocate more workers ( dynamically scaling nodes based on the workload).

To read more about MapReduce, read here

Now you could say,

Dear Google, We implemented MapReduce 100 years back itself!!!!

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2 thoughts on “MapReduce algorithm and Mumbai’s Dabbawalas!!!!

  1. I was honored to get a call coming from a friend when he uncovered the important ideas shared on the site. Examining your blog post is a real brilliant experience. Thank you for thinking of readers like me, and I desire for you the best of achievements being a professional in this domain.

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