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Review Assignment Due Date

Superjoin Hiring Assignment

Welcome to Superjoin's hiring assignment! 🚀

Objective

Build a solution that enables real-time synchronization of data between a Google Sheet and a specified database (e.g., MySQL, PostgreSQL). The solution should detect changes in the Google Sheet and update the database accordingly, and vice versa.

Problem Statement

Many businesses use Google Sheets for collaborative data management and databases for more robust and scalable data storage. However, keeping the data synchronised between Google Sheets and databases is often a manual and error-prone process. Your task is to develop a solution that automates this synchronisation, ensuring that changes in one are reflected in the other in real-time.

Requirements:

  1. Real-time Synchronisation
  • Implement a system that detects changes in Google Sheets and updates the database accordingly.
  • Similarly, detect changes in the database and update the Google Sheet.
  1. CRUD Operations
  • Ensure the system supports Create, Read, Update, and Delete operations for both Google Sheets and the database.
  • Maintain data consistency across both platforms.

Optional Challenges (This is not mandatory):

  1. Conflict Handling
  • Develop a strategy to handle conflicts that may arise when changes are made simultaneously in both Google Sheets and the database.
  • Provide options for conflict resolution (e.g., last write wins, user-defined rules).
  1. Scalability:
  • Ensure the solution can handle large datasets and high-frequency updates without performance degradation.
  • Optimize for scalability and efficiency.

Submission ⏰

The timeline for this submission is: Next 2 days

Some things you might want to take care of:

  • Make use of git and commit your steps!
  • Use good coding practices.
  • Write beautiful and readable code. Well-written code is nothing less than a work of art.
  • Use semantic variable naming.
  • Your code should be organized well in files and folders which is easy to figure out.
  • If there is something happening in your code that is not very intuitive, add some comments.
  • Add to this README at the bottom explaining your approach (brownie points 😋)
  • Use ChatGPT4o/o1/Github Co-pilot, anything that accelerates how you work 💪🏽.

Make sure you finish the assignment a little earlier than this so you have time to make any final changes.

Once you're done, make sure you record a video showing your project working. The video should NOT be longer than 120 seconds. While you record the video, tell us about your biggest blocker, and how you overcame it! Don't be shy, talk us through, we'd love that.

We have a checklist at the bottom of this README file, which you should update as your progress with your assignment. It will help us evaluate your project.

  • My code's working just fine! 🥳
  • I have recorded a video showing it working and embedded it in the README ▶️
  • I have tested all the normal working cases 😎
  • I have even solved some edge cases (brownie points) 💪
  • I added my very planned-out approach to the problem at the end of this README 📜

Got Questions❓

Feel free to check the discussions tab, you might get some help there. Check out that tab before reaching out to us. Also, did you know, the internet is a great place to explore? 😛

We're available at techhiring@superjoin.ai for all queries.

All the best ✨.

Developer's Section

Architectue of the application:

My application is built using Spring Boot and PostgreSQL, providing real-time synchronization between multiple pairs of database tables and Google Sheets. The system architecture includes the following key components:

  • Real-Time Synchronization:

  • Google Sheets Integration: I use Google Apps Script to send notifications to my backend whenever changes are made in the Google Sheets. This ensures immediate updates and synchronization with the database.

  • Database Integration: PostgreSQL functions and triggers are employed to notify the backend of any changes detected in the local database. This setup guarantees that updates in the database are promptly reflected in Google Sheets.

Dynamic Configuration:

  • Schema Management: The application dynamically constructs schemas for both the database tables and corresponding Google Sheets. This automatic configuration simplifies the setup process and ensures consistency between data sources.

  • CRUD Operations: My system supports full Create, Read, Update, and Delete (CRUD) operations for both Google Sheets and the PostgreSQL database. It seamlessly handles modifications and maintains synchronization across platforms.

  • Trigger and Function Management: The application dynamically creates necessary triggers and functions for each table, facilitating automated responses to data changes.

Deployment:

  • Local Database: The PostgreSQL database is currently hosted locally.

  • Remote Access: The Spring Boot application is exposed to the internet using ngrok, allowing external access and interaction with the application.

Edge Cases:

  • As mentioned above, my application can handle synchronisation between multiple pairs of database tables and Google Sheets.
  • The linking between tables in the database and Gooogle Sheets is persistent, i.e, the previous link between a Sheet and a Table remains connected even if the application has resatarted, while also being ready to create new links between another table and sheet on user demand.

Requirements for the Setup:

  • Java Development Kit (JDK):

    • Version 11 or newer, as Spring Boot typically supports recent LTS versions.

  • PostgreSQL

  • ngrok

    • once ngrok has been setup run the following command: 
      ngrok http 8080

Application-Setup:

  • Visit google marketplace > search for sheets > click on the google sheets api and click enable > click on credentails and add a service account > click on the service account and select keys > create a new key, download it and put the JSON file into the directory right outside the src directory in the project.
  • in the application.properties file enter your postgres database name in the url and your postgres username and password.
  • go to your postgres database and enter the following commands 
    • CREATE TABLE public.schema_link (
    id INTEGER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    link VARCHAR(255),
    table_name VARCHAR(255)
);

    • CREATE TABLE public.schema_registry (
    id INTEGER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    table_name VARCHAR(255),
    column_name VARCHAR(255),
    column_type VARCHAR(255),
    gsheetcol VARCHAR(10)
);

User-Setup:

  • The user has to make a Google Sheet and change access from "Restricted" to "Anyone with the link" and set permissions to "Editor"
  • When the user runs the application, they will be prompted with a question if they want to create a new table. If they do want to then they must type yes, else the application will still be running just listening to notifiations.
  • When they type yes, they will be asked to enter the sheet id. Then they must enter the google sheet's url
  • Then they will be prompted to enter the table name, primary key and column names of the table.
  • once the table is created on both the google sheets and the database, copy the App Script given in this repository.
  • In the google sheets window click on Extentions > App Script.
  • Then paste the Appscrpt there.
  • then click on the clock like button on the left
  • add a new trigger
    • set the function to "sendnotification" and "notify me immediately"
    • save the trigger
  • now the user can use his google sheets and database and synchronisation will start.

Future Scope:

To enhance the scalability and efficiency of the application, several improvements are planned:

  • Apache Kafka Integration:

    • High-Frequency Requests: Implementing Kafka will enable the application to handle high-frequency requests by storing all requests in Kafka topics. This approach will help prevent the loss of requests and ensure that no data is missed.
    • Topic Partitioning: By partitioning Kafka topics, the application will benefit from faster bulk data management, as partitions allow for parallel processing and better performance.

  • Conflict Management:

    • Semaphores vs. Locks: For managing conflicts when multiple users edit the same table, the application will use semaphores or locks. Given the design of the application, I would prefer semaphores for their simpler implementation.

  • Global Scaling:

    • YugaByte Database: To ensure global scalability, the application will leverage YugaByte’s database solution. The paid version of YugaByte supports global data replication, enabling seamless data distribution and high availability across different geographical locations.

These enhancements will provide robust handling of high-frequency data requests, efficient conflict management, and scalable data distribution, preparing the application for future growth and expanded use cases.

Video:

Watch the video