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Final Year Project

This is the code repository for my final year project - a query processing engine implemented over a distributed cluster of nodes. This project was developed between October 2022 and April 2023. Due to University requirements, the entire project is contained in this git repository, but each folder essentially acts as a standalone submodule.

The project consists of a frontend and domain specific language implemented in Python, and a backend implemented in Scala, which is all backed by a Cassandra database for persistent storage. The backend extensively uses gRPC and Akka Actors to provide thread-safe concurrent communication between workers in the cluster.

Contents

There are a number of folders, each relating to a separate part of the project. Details of each folder, as well as execution instructions, are included below:

Python Client

This module contains all code related to the client-side implementation of the framework. This includes the domain specific language and result parsing code.

A README file with further details of the contents of this folder can be found here.

Server

This module contains all code related to the server-side implementation of the framework The folder contains 3 projects: a core code project, and then implementations of the worker and orchestrator servers.

A README file with further details of the contents of this folder can be found here.

Protos

This folder contains protobuf definition files, which are used by both the python client, and the orchestrator and worker nodes.

Kubernetes

This folder contains .yaml files required for initialising the Kubernetes cluster.

A README file with further details of the contents of this folder can be found here.

Report

This folder contains the full project report, along with all source LaTeX files and images used for generating the report.

Execution

There are two main ways of executing this project:

  • Kubernetes: this is best for use in a production environment.
  • Local: this is best for use in testing, but is restricted to execution on a single machine.

Kubernetes Execution

Detailed instructions for setting up and using a Kubernetes cluster to execute the framework can be found here.

Local Execution

Running locally requires the following to be installed:

  • Docker Desktop
  • sbt, with Java 8 or 11.

Instructions to setup the cluster locally are included below:

  1. Start Cassandra on docker, with port 9042 exposed to the local machine using the following command:

    • docker run -d --name cassandra -p 9042:9042 cassandra

  2. Set the paths to each of the workers in line 10 of application.conf, and save the file. As this is running locally, each worker will be running on localhost, and each will have to bind to a different port.

  3. Run the orchestrator node using sbt run from the orchestrator directory: /server/orchestrator.

  4. Run each of the worker nodes in a separate terminal instance from the worker directory: /server/worker. Ensure the ports defined in application.conf match the ports the workers are created with.

    • For example, if a worker needs to be assigned to port 50051, use the command sbt "run 50051"

  5. Finally, in a separate terminal instance, start an interactive Python shell using python -i main.py from the python_client directory: /python_client/.

    • Connect to the orchestrator using ClusterManager("localhost")
    • Create a CassandraConnector to insert data using connector = CassandraConnector("localhost", 9042)

Other Notes

A list of dependencies that were used to complete this project are detailed in CONTRIBUTORS.md