/Fetch-Intern-DataEngineer

Primary LanguagePython

General Information

This is the answer for the test of "Data Engineer"

How to run

Note

Please contact me if you have any questions about the code or the environment. I am happy to explain more details. I use Docker to minimize the chance there is an incompatibility in the environment, but Docker would have different behaviours on different platforms, which I can not fully predict and test. I spend a lot of efforts, and I do not want to lose the chance to get the job. Thank you!

Run with docker-compose

Requirements

  • docker
  • docker-compose

Tested Environment

  • Ubuntu 20.04 in WSL2

Steps

  1. Clone the repository
  2. Run docker-compose up
  3. You could see the output in the terminal
  4. To see the results you could run following bash scripts. You can interact with the database in the container that the python program runs.
    # You are in the host machine running the docker-compose now
    ssh root@localhost -p 4000 # password: passwd
    
    # You are in the container now
    PGPASSWORD=postgres psql -d postgres -U postgres -p 5432 -h postgres -c "SELECT * FROM user_logins"
  1. Use Ctrl+C to stop the whole docker-compose as well as the python program
    • It takes several seconds to wait for the program to clean up the queue and exit
    • Unfortunately, there is a long existing problem of docker compose that it will stop recording the logs after the program exits. Additional information during clean-up stage is ignored.

Run in the local machine

Requirements

  • Python 3.10
  • pip
  • awscli-local
  • awscli
  • psycopg2
  • cryptography

Steps

  1. Clone the repository
  2. Run with python3 main.py --local since the docker-compose version will redirect the network to the localstack container. You could see the output in the terminal
  3. Use Ctrl+C to stop the program
    • It takes several seconds to wait for the program to clean up the queue and exit
    • In a local environment, you can see the logs for cleaning after the Ctrl+C is pressed.
  4. To see the results you could run following bash scripts.
    PGPASSWORD=postgres psql -d postgres -U postgres -p 5432 -h localhost -c "SELECT * FROM user_logins"

Design Ideas

How will you read messages from the queue?

  • I create a thread to read the message
  • The thread execute the awslocal sqs receive-message command through bash using subprocess to read the message
  • It uses the network name localstack to connect to the localstack container in the docker-compose

What type of data structures should be used?

  • I use a Queue to store the un-resolved information
  • It is a first-in-first-out data structure, which will resolve the information in the order of the time they are inserted
  • Meanwhile, Queue in python is thread-safe, which is suitable for the multi-threading environment

How will you mask the PII data so that duplicate values can be identified?

  • I use the AES encryption algorithm with the same key to mask the PII data
  • It will generate the same encrypted data for the same PII data and different encrypted data for different PII data

What will be your strategy for connecting and writing to Postgres?

  • I use the psycopg2 library to connect to the database
  • And I could execute the SQL command through the cursor.execute() function

Where and how will your application run?

Please refer to the How to run section

Questions

How would you deploy this application in production?

As it is presented in the docker-compose.yml and the customized Dockerfile.awslocal. I would use docker-compose to deploy the application along with the database in production. The reason is that it is easy to use and maintain. It is also easy to scale up the application by adding more containers.

What other components would you want to add to make this production ready?

Python

  • Introduce more unit/integration tests
    • Sorry that I don't have enough time to find the way to show the tests through docker-compose and write the tests.
  • Use argparse to parse the arguments
    • argparse the information of the database, making it possible to insert into a different database without changing the code
  • Refactor the code to make it more readable
  • Introduce better and comprehensive error handling during different scenarios like the connection to the database fails
  • Introduce better approaches to store the un-resolved information (e.g. file) in the queue when exiting the program
  • Wrap the code into a class
  • Wrap the global variables into class/functions and remove global keyword
    • Replace the global variables with self.shutdown_flag = threading.Event() to make it thread-safe
  • Use a better crpyto library to mask the PII data

Dockerfile/Compose

  • Pass the arguments into the docker-compose and dockerfile instead of hard code them
  • Fix the version of the software (e.g. python) in case of the incompatibility of the new version

How can this application scale with a growing dataset.

  • Increase the number of threads or processes in the python program
  • Add more containers to the docker-compose to scale up the application
  • Utilize Kubernetes to include more physical machines to scale up the application
  • Introduce other technologies to make the application more scalable (e.g. Apache Spark, Apache Flink, etc.)

How can PII be recovered later on?

  • Use the same key to decrypt the PII data

What are the assumptions you made?

  • The general format of message will not change. The only thing that could change is the body of the message.
  • The generated message that is not in the standard format is useless and can be ignored
  • The version of application like a.b.c/a.b could be converted to an integer number a*pow(2,8) + b*pow(2,4) + c since the version number is not too large and this approach will fit the value into the integer type in the database