danielmorales9/bookings_code_challenge

Programming Assignment

We want to get a better understanding of the bookings that happen on our website. Specifically, how many bookings are cancellable, number of bookings per day, what are the popular destinations and what is the peak travel season. The data is available at a central location (assume any file system or database) in csv format and is updated periodically. You need to do the following tasks:

1. Design and build a data pipeline that will save and process this data to be able to answer the above questions.
2. Use the data to build a report that shows number of bookings per day. The output of the report should have two columns: a. date b. num_bookings
3. Create a report to show which bookings are free cancellable and which bookings are cancellable with a fee (cheap_cancellable) and until when. You are free to decide the structure of the report as you feel necessary

Data

1. bookings.csv : Data about bookings. eg. booking_id, booking_date, destination, source, travel_date, arrival_date
2. cancellation.csv : Information about which bookings are cancellable and till when. Columns : booking_id, cancellation_id (52 for free and 53 for cheap), enddate (till what date is the booking cancellable)

You are free to decide the storage location and format of the final report. Make assumptions wherever necessary. Please use Scala language and Spark framework (in local mode).

What do we look for?

1. The code that is scalable, easy to maintain and extensible. We want to keep as minimum manual work as possible
2. Tests wherever necessary
3. Readme to explain your assumptions, solution and how to run it
4. Bonus : Architecture diagram

The Solution

The solution provides a single entrypoint to both reporting and processing jobs by using a CLI. The application is conceived in such a way that each source of data (bookings and cancellations) can go through a first cleansing phase independently and later can be joined together in a single flat table to be able to be accessed for analytics purpose.

Cleansing Jobs

We provide two pre-processing jobs, namely, the job to cleanse the bookings and the cancellation data. The aim of the pre-processing jobs is to improve the data quality of the raw data as much as possible, in order to ensure reliable source data for any downstream jobs. Moreover, we don't only cleanse the source data by filtering out malformed records, we also provide, for each dataset, an error repository to track which record was filtered out from the original dataset. Having an error repository ensures that the quality of both the source data and the etl code is always high. However, the reader might think there is necessary manual work to be carried out in order to adjust the source data, on the long term, simple fixes can be automated either by incorporating such adjustments in the etl jobs or by creating ad-hoc scripts. Moreover, having such an error repository available via analytics tools by members of different departments, lays the foundation for a better communication between teams across the organization, but especially enables catching bugs earlier on in the software development cycle.

Therefore, each cleansing job produces a data and errors folder in each root directory, both partitioned by the date in which the batch was executed (assuming the batch was executed daily), hence extraction_date=yyyy-MM-dd.

The resulting error table will have the following schema (which is defined in the case class middleware.DataError):

• The rowKey (autogenerated value), which identifies the row within the source dataset.
• The stage of the pipeline where the record was found malformed.
• The fieldName or column name
• The fieldValue, the actual column value for the filtered out row
• The error, a human-readable message explaining the reason why the record was filtered out.

rowKey	stage	fieldName	fieldValue	error
100001	ValidateNotNullColumnStage	destination	1	Invalid Airport Code: 1
21450	ParseDateTimeStringStage	booking_date	-2147483648	Unable to parse DateTime string

This schema can be also extended to add the severity of the errors or additional information to ease any potential debugging efforts.

The rowKey could be either a monotonically increasing id, as in the example, a unique identifier in the dataset, such as the booking_id, or a hash of the entire row. We preferred the simplest among the strategies, mostly to demonstrate the potential of error tracking.

The Software Architecture

Even though, the processing steps are very simple and easy we want to aim for a readable and extensible software architecture. Thus, we built the pipeline as a sequence of composable transformation steps, where each is step is highly cohese ahd loosely coupled with the others. Each step carries out a simple computation step and is responsible for producing both a dataset of valid records and errors. The steps are then combined in order to form a full-blown pipeline, and can also be reused across different pipelines. This approach also makes it easier for the developer to test the code in isolation reducing also the overall execution of the tests, compared to having to test several e2e scenarios.

Modularity of the code is achieved by building several middleware components using the functional library cats. Each transformation step extends a DataStage, which has an apply method; it takes a DataFrame or Dataset as a parameter and returns a Writer which left argument is a Dataset of DataError. The writer class are then yield into the next DataStage and the Dataset of DataError are implicitly combined into a single dataset (see middleware.DataFrameOps).

Each ETL or Reporting Job that encapsulate such a pipeline follows a specific template (see jobs.JobTemplate) which contains the extract, transform, load methods. By structuring the jobs around this three simple methods, we reduce the amount of e2e tests to be written and executed. This is possible because most of the business logic is encapsulated in the transform method, which allows us to do integration testing by inject a DataFrame and checking the result of the method execution.

File Format

The cleansed data and the error repository are all stored as parquet files, while the result of the reports are stored in csv.

Building the artifact

Building the artifact is simple as:

sbt assembly

CLI Description

Here you can find an easy description of the cli.
To run successfully the entire steps within the project, you just need to copy and paste the commands written below the text Full command in your terminal and run.

spark-submit [...] --help

usage: entry_point [options] <command name>

[options]
 -h, --help  display help messages

[commands]
 job            List of all Jobs
 report         List of all Reports

Job Entrypoint

spark-submit [...] job --help

usage: [global options] job [options] <command name>
  jobs entrypoint

[global options]
 -h, --help  display help messages

[commands]
 bookings       Cleaning Bookings Data
 cancellation   Cleansing Cancellation Data
 joinBookings   Joining Data

Launching Bookings Job

spark-submit [...] job bookings --help

usage: bookings 
  Cleaning Bookings Data

[global options]
 -h, --help  display help messages
[options]
 -i, --input:[INPUTPATH]                 Input Path
 -o, --output:[OUTPUTPATH]               Output Path
 -m, --mode:[MODE]                       Mode
 -e, --extraction_date:[EXTRACTIONDATE]  Date of Extraction yyyy-MM-dd

Full command:

spark-submit \
  --class "de.holidaycheck.Main" \
  --master local[4] \
  target/scala-2.12/code_challenge-assembly-1.0.jar \
  job bookings \
    -i bookings.csv \
    -o bookings \
    -m overwrite \
    -e 2022-03-03

Launching Cancellation Job

spark-submit [...] job cancellation --help

usage: cancellation
Cleansing Cancellation Data

[global options]
-h, --help  display help messages

[options]
-i, --input:[INPUTPATH]                 Input Path
-o, --output:[OUTPUTPATH]               Output Path
-m, --mode:[MODE]                       Mode
-e, --extraction_date:[EXTRACTIONPATH]  Date of Extraction yyyy-MM-dd

Full Command:

spark-submit \
  --class "de.holidaycheck.Main" \
  --master local[4] \
  target/scala-2.12/code_challenge-assembly-1.0.jar \
  job cancellation \
    -i cancellation.csv \
    -o cancellation \
    -m overwrite \
    -e 2022-03-03

Launching Join Job

spark-submit [...] job joinBookings --help

usage: joinBookings 
  Joining Data

[global options]
 -h, --help  display help messages
 
[options]
 -b, --bookings:[BOOKINGSINPUTPATH]          Bookings Input Path
 -c, --cancellation:[CANCELLATIONINPUTPATH]  Cancellation Input Path
 -o, --output:[OUTPUTPATH]                   Output Path
 -m, --mode:[MODE]                           Mode
 -e, --extraction_date:[EXTRACTIONDATE]      Date of Extraction yyyy-MM-dd

Full Command:

spark-submit \
  --class "de.holidaycheck.Main" \
  --master local[4] \
  target/scala-2.12/code_challenge-assembly-1.0.jar \
  job joinBookings \
    -b bookings/data \
    -c cancellation/data \
    -o flatTable \
    -m overwrite \
    -e 2022-03-03

Reports Entrypoint

spark-submit [...] report --help

usage: [global options] report <command name>
List of all Reports

[global options]
-h, --help                 display help messages

[commands]
numBookingsPerDay              Number of Bookings per Day Report
cheapAndFreeCancellations      Cheap And Free Cancellations Report

Launching Number of Bookings per Day Report

spark-submit [...] report numBookingsPerDay --help

usage: numBookingsPerDay 
  Number of Bookings per Day Report

[global options]
 -h, --help                 display help messages
[options]
 -i, --input:[INPUTPATH]    Input Path
 -o, --output:[OUTPUTPATH]  Output Path
 -m, --mode:[MODE]          Mode

Full Command:

spark-submit \
  --class "de.holidaycheck.Main" \
  --master local[4] \
  target/scala-2.12/code_challenge-assembly-1.0.jar \
  report numBookingsPerDay \
    -i flatTable/data/extraction_date=2022-03-03 \
    -m overwrite \
    -o reports/numBookingsPerDay

Launching Cheap and Free Cancellations Report

spark-submit [...] report cheapAndFreeCancellations --help

usage: cheapAndFreeCancellations 
  Cheap And Free Cancellations Report

[global options]
 -h, --help                 display help messages
 
[options]
 -i, --input:[INPUTPATH]    Input Path
 -o, --output:[OUTPUTPATH]  Output Path
 -m, --mode:[MODE]          Mode

Full Command:

spark-submit \
  --class "de.holidaycheck.Main" \
  --master local[4] \
  target/scala-2.12/code_challenge-assembly-1.0.jar \
 report cheapAndFreeCancellations \
  -i flatTable/data/extraction_date=2022-03-03 \
  -o reports/cheapAndFree \
  -m overwrite

Future Work

• Introduce a Global Logging Level options for Spark Jobs and Reports
• E2E tests
• Refactoring potential in cli classes (duplicated code due to parameter validation)
• Use of reflection to instantiate jobs or reports by name