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Midgard

beam-kotlin

Because Beam ❤️ Kotlin, we created a new open source library called Midgard to :

  • Have more concise and expressive code
  • Remove Beam boilerplate code
  • Propose more Functional Programming style

This module is a Beam wrapper on Kotlin and proposes some extensions on PCollection DoFn and IO connectors

Behind the scene Kotlin extensions are used, the main advantage of this technic is adding behaviours and methods to an existing structure without affecting it.

Versions compatibility between Beam and Midgard

Midgard Beam
0.15.0 2.44.0
0.16.0 2.45.0
0.17.0 2.46.0
0.18.0 2.47.0
0.19.0 2.48.0
0.20.0 2.49.0
0.21.0 2.50.0
0.22.0 2.51.0
0.23.0 2.52.0
0.24.0 2.53.0
0.25.0 2.54.0
0.26.0 2.55.0
0.27.0 2.56.0
0.28.0 2.57.0

⚠️ The current Kotlin version used with Midgard is : 1.9.22

Installation of project

The project is hosted on Maven repository.
You can install it with all the build tools compatibles with Maven.

Example with Maven and Gradle :

Maven

<dependency>
    <groupId>fr.groupbees</groupId>
    <artifactId>midgard</artifactId>
    <version>0.28.0</version>
</dependency>

Gradle

implementation group: 'fr.groupbees', name: 'midgard', version: '0.28.0'

1- Extensions on PCollection

1-1 Usual Beam operators : map, flatMap and filter

Test data :

val psgPlayers = listOf(
    Player(firstName = "Kylian", lastName = "Mbappe", 24),
    Player(firstName = "Marco", lastName = "Verrati", 28)
)

val realPlayers = listOf(
    Player(firstName = "Karim", lastName = "Benzema", 35),
    Player(firstName = "Luca", lastName = "Modric", 39)
)

// Given.
val psgTeam = Team(name = "PSG", slogan = "Ici c'est Paris", psgPlayers)
val realTeam = Team(name = "REAL", slogan = "Hala Madrid", realPlayers)

Example of usual Beam pipeline with map, flatMap and filter operations :

val resultPlayers: PCollection<Player> = pipeline
    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .apply(
        "To Team with Slogan V2",
        MapElements
            .into(TypeDescriptor.of(Team::class.java))
            .via(SerializableFunction { it.copy(slogan = "${it.slogan} VERSION 2") })
    )
    .apply(
        "To Players",
        FlatMapElements
            .into(TypeDescriptor.of(Player::class.java))
            .via(SerializableFunction { it.players })
    )
    .apply("Filter age > 25", Filter.by(SerializableFunction { it.age > 25 }))

The same pipeline with Midgard library :

import fr.groupbees.midgard.*

val resultPlayersMidgard: PCollection<Player> = pipeline
    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .map("To Team with Slogan V2") { it.copy(slogan = "${it.slogan} VERSION 2") }
    .flatMap("To Players") { it.players }
    .filter("Filter age > 25") { it.age > 25 }

For each operator, there is its equivalent with Midgard :

  • MapElements -> map
  • FlatMapElements -> flatMap
  • Filter -> filter

To use extensions offered by Midgard, you have to add the following import in the code :

import fr.groupbees.midgard.*

Another big advantage of using Kotlin extensions, is the possibility to mix native methods of the PCollection with those specific to Midgard. The previous example contains :

Native method of the PCollection

    .apply("Create", Create.of(listOf(psgTeam, realTeam)))

Mixed with extensions and methods brought by Midgard :

    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .map("To Team with Slogan V2") { it.copy(slogan = "${it.slogan} VERSION 2") }
    .flatMap("To Players") { it.players }
    .filter("Filter age > 25") { it.age > 25 }

The map, flatMap and filter operators take as parameters :

  • The name and pipeline step
  • Lambda expression or the implementation of the function, to apply the needed operation
  • The Beam TypeDescriptor is deduced inside the operators

1-2 Operators interacting with Beam DoFn lifecycle

Beam allows interacting with the DoFn lifecycle (check DoFn lifecycle section) :

  • Setup
  • Start Bundle
  • Finish Bundle
  • Teardown

To be able to use these steps in the lifecycle, we have to create a class that extends DoFn and override the needed function with the associated annotation, example :

@Setup
public void setup() {
    // Setup action.
}

@StartBundle
public void startBundle() {
    // Start Bundle action.
}

@FinishBundle
public void finishBundle() {
    // Finish Bundle action.
}

@Teardown
public void teardown() {
    // Teardown action.
}

Midgard allows to propose map and flatMap operators and extensions while interacting with this lifecycle.

Example with a map and an interaction with all the lifecycle functions :

 val resultTeamMidgardMapLifeCycle: PCollection<Team> = pipeline
    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .mapFn(
        name = "To Team with Slogan V2",
        transform = { it.copy(slogan = "${it.slogan} VERSION 2") },
        setupAction = { println("Setup Action") },
        startBundleAction = { println("Start Bundle Action") },
        finishBundleAction = { println("Finish Bundle Action") },
        teardownAction = { println("Teardown Action") }
    )
  • name : the name of the current step
  • transform : current function containing the transformation logic
  • setupAction : action executed in the Setup DoFn lifecycle
  • startBundleAction : action executed in the Start Bundle DoFn lifecycle
  • finishBundleAction : action executed in the Finish Bundle DoFn lifecycle
  • teardownAction : action executed in the Teardown DoFn lifecycle

Example with flatMap :

 val resultPlayersMidgardFlatMapLifeCycle: PCollection<Player> = pipeline
    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .map("To Team with Slogan V2") { it.copy(slogan = "${it.slogan} VERSION 2") }
    .flatMapFn(
        name = "To Players",
        transform = { it.players },
        setupAction = { println("Setup Action") },
        startBundleAction = { println("Start Bundle Action") },
        finishBundleAction = { println("Finish Bundle Action") },
        teardownAction = { println("Teardown Action") }
    )

1-3 Operators interacting with Beam DoFn lifecycle and context

Sometimes we need to access to the current ProcessContext while applying the current transformation.
It's the case for example when we want to deal with side inputs.

Example with a map operation

// Simulate a side input for the slogan suffix.
val slogansSideInput: PCollectionView<String> = pipeline
    .apply("Read slogans", Create.of("VERSION 2"))
    .apply("Create as collection view", View.asSingleton())

val resultTeamMidgardMapContextLifeCycle: PCollection<Team> = pipeline
    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .mapFnWithContext(
        name = "To Team with Slogan V2",
        transform = { context -> toTeamWithSloganSuffixFromSideInput(slogansSideInput, context) },
        setupAction = { println("Setup Action") },
        sideInputs = listOf(slogansSideInput),
        startBundleAction = { println("Start Bundle Action") },
        finishBundleAction = { println("Finish Bundle Action") },
        teardownAction = { println("Teardown Action") }
    )

private fun toTeamWithSloganSuffixFromSideInput(
    sideInput: PCollectionView<String>,
    context: DoFn<Team, Team>.ProcessContext
): Team {
    val currentTeam: Team = context.element()
    val sloganSuffixSideInput: String = context.sideInput(sideInput)

    return currentTeam.copy(slogan = "${currentTeam.slogan} $sloganSuffixSideInput")
}

We simulated a side input with a slogan suffix as a PCollectionView.
The mapFnWithContext method gives access to the current DoFn ProcessContext and allows :

  • To retrieve the current String value inside the side input
  • To retrieve the current Team input element

The toTeamWithSloganSuffixFromSideInput method adds the side input suffix to the current slogan and returns a copy of the current object with the newly updated slogan field.

Example with flatMap operation

The principle is the same for flatMapFnWithContext, for the sake of simplicity we show an example without side input :

 val resultPlayersMidgardFlatMapContextLifeCycle: PCollection<Player> = pipeline
    .apply("Create", Create.of(listOf(psgTeam, realTeam)))
    .map("To Team with Slogan V2") { it.copy(slogan = "${it.slogan} VERSION 2") }
    .flatMapFnWithContext(
        name = "To Players",
        transform = { context -> context.element().players },
        setupAction = { println("Setup Action") },
        startBundleAction = { println("Start Bundle Action") },
        finishBundleAction = { println("Finish Bundle Action") },
        teardownAction = { println("Teardown Action") }
    )

In this example, the current input element in the context is directly returned.

Next steps and evolutions

The library could add in the future, extensions on native Beam IO like :

  • TextIO
  • BigQueryIO
  • ...