Batch File Booster Rocket for AWS

This package is a configurable Booster rocket for parallel batch file processing. First, it creates a source Object Storage (S3) and a Staging Object Storage(S3).

Every time a new File is uploaded to the Source S3 Bucket, a new Lambda function will be triggered that will:

• Split the source file in smaller chunks. The chunkSize is defined by the user as input parameter of the rocket (See example below).
• Persist the new formed chunk file in the Staging Bucket.
• Persist a new event (only containing fileUri and fileSize) in the Events Store for each chunk created.

Then, for every chunk file dropped in the Staging rocket a new Lambda function will be triggered that will:

• Read each chunk file line by line and persist a new event in the Events Store for each row.

This event is registered in the Booster application's store as a regular event. Then, you can create event handlers to perform any kind of processing.

You drop your file, and you implement your logic based on an event representing a line of the file.

Disclaimer: Currently the rocket supports CSV and jsonl files.

Usage

Install this package as a dependency in your Booster project.

npm install --save @boostercloud/rocket-batch-file-process-aws-infrastructure

In your Booster config file, pass a RocketDescriptor array to the AWS' Provider initializer configuring the batch file rocket:

import { Booster } from '@boostercloud/framework-core'
import { BoosterConfig } from '@boostercloud/framework-types'

Booster.configure('development', (config: BoosterConfig): void => {
  config.appName = 'test-app'
  config.providerPackage = '@boostercloud/framework-provider-aws'
  config.rockets = [
    {
      packageName: '@boostercloud/rocket-batch-file-process-aws-infrastructure',
      parameters: {
        bucketName: 'test-bucket',
        files: [
          {
            folderName: 'addresses',
            numberOfRecordsInParallel: '100',
          },
        ],
      },
    },
  ]
})

Based on this example let's also define the AddressAdded event and the AddressEntity entity in your Booster project

import { Event } from '@boostercloud/framework-core'
import { UUID } from '@boostercloud/framework-types'

@Event
export class AddressAdded {
  public constructor(
    readonly id: UUID,
    readonly firstName: string,
    readonly lastName: string,
    readonly address: string,
    readonly city: string,
    readonly state: string,
    readonly postalCode: string
  ) {}

  public entityID(): UUID {
    return this.id
  }
}

import { Entity, Reduces } from '@boostercloud/framework-core'
import { AddressAdded } from '../events/address-added'
import { UUID } from '@boostercloud/framework-types'

@Entity
export class AddressEntity {
  public constructor(
    public id: UUID,
    readonly firstName: string,
    readonly lastName: string,
    readonly address: string,
    readonly city: string,
    readonly state: string,
    readonly postalCode: string
  ) {}

  @Reduces(AddressAdded)
  public static reduceAddressAdded(event: AddressAdded, currentAddressEntity?: AddressEntity): AddressEntity {
    return new AddressEntity(
      event.id,
      event.firstName,
      event.lastName,
      event.address,
      event.city,
      event.state,
      event.postalCode
    )
  }
}

The attributes of the AddressAdded and AddressEntity classes are defined by the structure of the input file. This is an example of CSV file that will work with what we defined above:

entityId:id,eventTypeName:AddressAdded,entityTypeName:AddressEntity
id,firstName,lastName,address,city,state,postalCode
1a,John,Doe,120 jefferson st.,Riverside, NJ, 08075
2b,Jack,McGinnis,220 hobo Av.,Phila, PA,09119
3c,"John ""Da Man""",Repici,120 Jefferson St.,Riverside, NJ,08075
4d,Stephen,Tyler,"7452 Terrace ""At the Plaza"" road",SomeTown,SD, 91234
5e,,Blankman,,SomeTown, SD, 00298
6f,"Joan ""the bone"" Anne",Jet,"9th at Terrace plc",Desert City,CO,00123
7g,Juan,Perez,120 jota st.,NY,NY,00678

The first row instructs the rocket which fields should it generate for which events/entities.

The second row of the CSV file defines the headers of the schema and is a 1 to 1 match with the AddressAdded and AddressEntity attributes definition.

If we were to use jsonl files instead of csv, this would be an example

{"entityId":"id","eventTypeName":"AddressAdded","entityTypeName":"AddressEntity"}
{"id": "1a", "firstName": "John", "lastName": "Doe", "address": "120 jefferson st.", "city": "Riverside", "state": "NJ", "postalCode": "08075"}
{"id": "2b", "firstName": "Jack", "lastName": "McGinnis", "address": "220 hobo Av.", "city": "Phila", "state": "PA", "postalCode": "09119"}

The first line of the jsonl file is also used to specify some metadata.

As a result the event handler will look as following

import { EventHandler } from '@boostercloud/framework-core'
import { Register } from '@boostercloud/framework-types'
import { AddressAdded } from '../events/address-added'

@EventHandler(AddressAdded)
export class AddressEventHandler {
  public static async handle(event: AddressAdded, register: Register): Promise<void> {
    ...
  }
}