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HW 5: Map Reduce (Rust)

MapReduce is a programming model for scalable and highly parallelized data processing. It abstracts away the complexities of developing a fault tolerant distributed system by exposing a simple API where the user specifies two functions:

  • map: produces a set of key/value pairs from the input data
  • reduce: combines values corresponding to the same key

With these functions, tasks can be automatically parallelized and executed on a cluster. This paradigm is particularly powerful since it allows programmers with little background in distributed systems to write parallelizable code for a wide variety of real world tasks.

In this assignment, which is loosely based on a lab from MIT, you will be implementing your own fault tolerant MapReduce system in Rust. Specifically, you will be implementing a coordinator process that distributes tasks to worker processes that have already been implemented for you. You will also handle worker failure by implementing heartbeats and task redistribution. The system design you will be building is similar to that outlined in the MapReduce paper.

Note: In this assignment, we use the term “coordinator” rather than “master”.


This assignment is split up into three parts with distinct deadlines to help you space out the workload. Deadlines and grading details can be found on Ed.

gRPC lab

To help you get a basic idea of how the coordinator will communicate with workers, we have put together a lab that walks you through how gRPC works. gRPC is a modern open source Remote Procedure Call (RPC) framework.


You will be expected to complete the first part of the assignment (up to and including the Tasks section) by an earlier deadline. This checkpoint will be worth 25% of your grade on the assignment, so if you miss the checkpoint, you may get up to a 75% on the assignment if you pass all the tests by the final deadline.


The final component consists of the rest of the tasks (through Fault tolerance).

Getting started

We strongly recommend doing this assignment locally, but you can also do it from your VM. Begin by pulling the starter code:

cd ~/code/personal
git pull staff master
cd hw-map-reduce-rs

If you are doing the lab locally, you will need to install CMake by following the directions here. Make sure you install a relatively new version of CMake (at least 3.20.0) to ensure that you don’t run into any issues.

To check if CMake is installed correctly, run cmake --version and check if it outputs the version you expect.

If Rust is not installed, install it according to the directions here.