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If you’re a data scientist or data engineer, this might sound familiar while working on an ETL project:

  • Switching between multiple projects is a hassle
  • Debugging others’ code is a nightmare
  • Spending a lot of time solving non-business-related issues

SETL (Spark ETL, pronounced "settle") is a Scala framework that helps you structure your Spark ETL projects, modularize your data transformation logic and speed up your development.

Use SETL

In a new project

You can start working by cloning this template project.

In an existing project

<dependency>
  <groupId>com.jcdecaux.setl</groupId>
  <artifactId>setl_2.11</artifactId>
  <version>0.4.1</version>
</dependency>

To use the SNAPSHOT version, add Sonatype snapshot repository to your pom.xml

<repositories>
  <repository>
    <id>ossrh-snapshots</id>
    <url>https://oss.sonatype.org/content/repositories/snapshots/</url>
  </repository>
</repositories>

<dependencies>
  <dependency>
    <groupId>com.jcdecaux.setl</groupId>
    <artifactId>setl_2.11</artifactId>
    <version>0.4.2-SNAPSHOT</version>
  </dependency>
</dependencies>

Quick Start

Basic concept

With SETL, an ETL application could be represented by a Pipeline. A Pipeline contains multiple Stages. In each stage, we could find one or several Factories.

The class Factory[T] is an abstraction of a data transformation that will produce an object of type T. It has 4 methods (read, process, write and get) that should be implemented by the developer.

The class SparkRepository[T] is a data access layer abstraction. It could be used to read/write a Dataset[T] from/to a datastore. It should be defined in a configuration file. You can have as many SparkRepositories as you want.

The entry point of a SETL project is the object com.jcdecaux.setl.Setl, which will handle the pipeline and spark repository instantiation.

Show me some code

You can find the following tutorial code in the starter template of SETL. Go and clone it :)

Here we show a simple example of creating and saving a Dataset[TestObject]. The case class TestObject is defined as follows:

case class TestObject(partition1: Int, partition2: String, clustering1: String, value: Long)

Context initialization

Suppose that we want to save our output into src/main/resources/test_csv. We can create a configuration file local.conf in src/main/resources with the following content that defines the target datastore to save our dataset:

testObjectRepository {
  storage = "CSV"
  path = "src/main/resources/test_csv"
  inferSchema = "true"
  delimiter = ";"
  header = "true"
  saveMode = "Append"
}

In our App.scala file, we build Setl and register this data store:

val setl: Setl = Setl.builder()
  .withDefaultConfigLoader()
  .getOrCreate()

// Register a SparkRepository to context
setl.setSparkRepository[TestObject]("testObjectRepository")

Implementation of Factory

We will create our Dataset[TestObject] inside a Factory[Dataset[TestObject]]. A Factory[A] will always produce an object of type A, and it contains 4 abstract methods that you need to implement:

  • read
  • process
  • write
  • get
class MyFactory() extends Factory[Dataset[TestObject]] with HasSparkSession {
  
  import spark.implicits._
    
  // A repository is needed for writing data. It will be delivered by the pipeline
  @Delivery 
  private[this] val repo = SparkRepository[TestObject]

  private[this] var output = spark.emptyDataset[TestObject]

  override def read(): MyFactory.this.type = {
    // in our demo we don't need to read any data
    this
  }

  override def process(): MyFactory.this.type = {
    output = Seq(
      TestObject(1, "a", "A", 1L),
      TestObject(2, "b", "B", 2L)
    ).toDS()
    this
  }

  override def write(): MyFactory.this.type = {
    repo.save(output)  // use the repository to save the output
    this
  }

  override def get(): Dataset[TestObject] = output

}

Define the pipeline

To execute the factory, we should add it into a pipeline.

When we call setl.newPipeline(), Setl will instantiate a new Pipeline and configure all the registered repositories as inputs of the pipeline. Then we can call addStage to add our factory into the pipeline.

val pipeline = setl
  .newPipeline()
  .addStage[MyFactory]()

Run our pipeline

pipeline.describe().run()

The dataset will be saved into src/main/resources/test_csv

What's more?

As our MyFactory produces a Dataset[TestObject], it can be used by other factories of the same pipeline.

class AnotherFactory extends Factory[String] with HasSparkSession {

  import spark.implicits._

  @Delivery
  private[this] val outputOfMyFactory = spark.emptyDataset[TestObject]

  override def read(): AnotherFactory.this.type = this

  override def process(): AnotherFactory.this.type = this

  override def write(): AnotherFactory.this.type = {
    outputOfMyFactory.show()
    this
  }

  override def get(): String = "output"
}

Add this factory into the pipeline:

pipeline.addStage[AnotherFactory]()

Generate pipeline diagram (with v0.4.1+)

You can generate a Mermaid diagram by doing:

pipeline.showDiagram()

You will have some log like this:

--------- MERMAID DIAGRAM ---------
classDiagram
class MyFactory {
  <<Factory[Dataset[TestObject]]>>
  +SparkRepository[TestObject]
}

class DatasetTestObject {
  <<Dataset[TestObject]>>
  >partition1: Int
  >partition2: String
  >clustering1: String
  >value: Long
}

DatasetTestObject <|.. MyFactory : Output
class AnotherFactory {
  <<Factory[String]>>
  +Dataset[TestObject]
}

class StringFinal {
  <<String>>
  
}

StringFinal <|.. AnotherFactory : Output
class SparkRepositoryTestObjectExternal {
  <<SparkRepository[TestObject]>>
  
}

AnotherFactory <|-- DatasetTestObject : Input
MyFactory <|-- SparkRepositoryTestObjectExternal : Input

------- END OF MERMAID CODE -------

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Dependencies

SETL currently supports the following data source:

  • All file formats supported by Apache Spark (csv, json, parquet etc)
  • Excel
  • JDBC (you have to provide the jdbc driver)
  • Cassandra
  • DynamoDB

To read/write data from/to AWS S3 (or other storage services), you should include the corresponding hadoop library in your project.

For example

<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-aws</artifactId>
    <version>2.9.2</version>
</dependency>

You should also provide Scala and Spark in your pom file. SETL is tested against the following version of Spark:

Spark Version Scala Version Note
2.4 2.11 ✔️ Ok
2.4 2.12 ✔️ Ok
2.3 2.11 ⚠️ see known issues

Known issues

  • DynamoDBConnector doesn't work with Spark version 2.3
  • Compress annotation can only be used on Struct field or Array of Struct field with Spark 2.3

Documentation

Check our wiki

Contributing to SETL

Check our contributing guide