Data transformations with Scala

This is a collection of jobs that are supposed to transform data. These jobs are using Spark to process larger volumes of data and are supposed to run on a Spark cluster ( via spark-submit).

Gearing Up for the Pairing Session

✅ Goals

1. Get a working environment
   Either local (local, or using gitpod)
2. Get a high-level understanding of the code and test dataset structure
3. Have your preferred text editor or IDE setup and ready to go.

❌ Non-Goals

• solving the exercises / writing code

  ⚠️ The exercises will be given at the time of interview, and solved by pairing with the interviewer.

Pre-requisites

Please make sure you have the following installed

• Java 11
• Scala 2.12.16
• Sbt 1.7.x
• Apache Spark 3.3 with ability to run spark-submit

Local Setup Process

• Clone the repo
• Package the project with sbt package
• Ensure that you're able to run the tests with sbt test (some are ignored)
• Sample data is available in the src/test/resource/data directory

💡 If you don't manage to run the local setup or you have restrictions to install software in your laptop, use the gitpod one

Gitpod setup

Alternatively, you can setup the environment using

There's an initialize script setup that takes around 3 minutes to complete. Once you use paste this repository link in new Workspace, please wait until the packages are installed. After everything is setup, select Poetry's environment by clicking on thumbs up icon and navigate to Testing tab and hit refresh icon to discover tests.

Note that you can use gitpod's web interface or setup ssh to Gitpod so that you can use VS Code from local to remote to Gitpod

Remember to stop the vm and restart it just before the interview.

Verify setup

All of the following commands should be running successfully

Run all tests

sbt test

Run specific tests class

sbt "test:testOnly *MySuite"

Run style checks

sbt scalastyle

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STOP HERE: Do not code before the interview begins.

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STOP HERE: Do not code before the interview begins.

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Jobs

There are two applications in this repo: Word Count, and Citibike.

Currently these exist as skeletons, and have some initial test cases which are defined but ignored. For each application, please un-ignore the tests and implement the missing logic.

Wordcount

A NLP model is dependent on a specific input file. This job is supposed to preprocess a given text file to produce this input file for the NLP model (feature engineering). This job will count the occurrences of a word within the given text file (corpus).

There is a dump of the data lake for this under test/resources/data/words.txt with a text file.

Input

Simple *.txt file containing text.

Output

A single *.csv file containing data similar to:

"word","count"
"a","3"
"an","5"
...

Run the job

 spark-submit --master local --class thoughtworks.wordcount.WordCount \
    target/scala-2.12/tw-pipeline_2.12-0.1.0-SNAPSHOT.jar \
    "./src/main/resources/data/words.txt" \
    ./output

Citibike

For analytics purposes the BI department of a bike share company would like to present dashboards, displaying the distance each bike was driven. There is a *.csv file that contains historical data of previous bike rides. This input file needs to be processed in multiple steps. There is a pipeline running these jobs.

There is a dump of the datalake for this under /src/test/resources/data/citibike.csv with historical data.

Ingest

Reads a *.csv file and transforms it to parquet format. The column names will be sanitized (whitespaces replaced).

Input

Historical bike ride *.csv file:

"tripduration","starttime","stoptime","start station id","start station name","start station latitude",...
364,"2017-07-01 00:00:00","2017-07-01 00:06:05",539,"Metropolitan Ave & Bedford Ave",40.71534825,...
...

Output

*.parquet files containing the same content

"tripduration","starttime","stoptime","start_station_id","start_station_name","start_station_latitude",...
364,"2017-07-01 00:00:00","2017-07-01 00:06:05",539,"Metropolitan Ave & Bedford Ave",40.71534825,...
...

Run the job

spark-submit --master local --class thoughtworks.ingest.DailyDriver \
    target/scala-2.12/tw-pipeline_2.12-0.1.0-SNAPSHOT.jar \
    "./src/main/resources/data/citibike.csv" \
    "./output_int"

Distance calculation

This job takes bike trip information and calculates the "as the crow flies" distance traveled for each trip. It reads the previously ingested data parquet files.

Hint:

• For distance calculation, consider using Harvesine formula as an option.

Input

Historical bike ride *.parquet files

"tripduration",...
364,...
...

Outputs

*.parquet files containing historical data with distance column containing the calculated distance.

"tripduration",...,"distance"
364,...,1.34
...

Run the job

 spark-submit --master local --class thoughtworks.citibike.CitibikeTransformer \
    target/scala-2.12/tw-pipeline_2.12-0.1.0-SNAPSHOT.jar \
    "./output_int" \
    ./output