SETL examples

Lessons and exercises to get familiar with the wonderful SETL framework!

Chapters

1. Entry Point and configurations

Lesson

1.1. Entry point with basic configurations

The entry point is the first thing you need to learn to code with SETL. It is the starting point to run your ETL project.

val setl0: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

This is the minimum code needed to create a Setl object. It is the entry point of every SETL app. This will create a SparkSession, which is the entry point of any Spark job. Additionally, the withDefaultConfigLoader() method is used. This means that Setl will read the default ConfigLoader located in resources/application.conf, where setl.environment must be set. The ConfigLoader will then read the corresponding configuration file <app_env>.conf in the resources folder, where <app_env> is the value set for setl.environment.

resources/application.conf:
setl.environment = <app.env>

<app.env>.conf:

setl.config.spark {
   some.config.option = "some-value"
 }

The configuration file is where you can specify your SparkSession options, like when you create one in a basic Spark process. You must specify your SparkSession options under setl.config.spark.

1.2. Entry point with specific configurations

You can specify the configuration file that the default ConfigLoader should read. In the code below, instead of reading <app_env>.conf where <app_env> is defined in application.conf, it will read own_config_file.conf.

val setl1: Setl = Setl.builder()
    .withDefaultConfigLoader("own_config_file.conf")
    .getOrCreate()

resources/own_config_file.conf:

setl.config.spark {
   some.config.option = "some-other-value"
 }

You can also set your own ConfigLoader. In the code below, Setl will load local.conf from the setAppEnv() method. If no <app_env> is set, it will fetch the environment from the default ConfigLoader, located in resources/application.conf.

val configLoader: ConfigLoader = ConfigLoader.builder()
    .setAppEnv("local")
    .setAppName("Setl2_AppName")
    .setProperty("setl.config.spark.master", "local[*]")
    .setProperty("setl.config.spark.custom-key", "custom-value")
    .getOrCreate()
val setl2: Setl = Setl.builder()
    .setConfigLoader(configLoader)
    .getOrCreate()

You can also set your own SparkSession which will be used by Setl, with the setSparkSession() method. Please refer to the documentation or the source code of SETL.

1.3 Utilities

Helper methods

There are some quick methods that can be used to set your SparkSession configurations.

val setl3: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .setSparkMaster("local[*]") // set your master URL
    .setShufflePartitions(200) // spark setShufflePartitions
    .getOrCreate()

setSparkMaster() method set the spark.master property of the SparkSession in your Setl entry point
setShufflePartitions() method set the spark.sql.shuffle.partitions property of the SparkSession in your Setl entry point

SparkSession options

As mentioned earlier, the options you want to define in your SparkSession must be specified under setl.config.spark in your configuration file. However, you can change this path by using the setlSetlConfigPath() method:

val setl4: Setl = Setl.builder()
    .withDefaultConfigLoader("own_config_file.conf")
    .setSetlConfigPath("myApp")
    .getOrCreate()

resources/own_config_file.conf:

myApp.spark {
    some.config.option = "my-app-some-other-value"
}

Exercises

Nothing too crazy: try to build your own Setl object! Run your code and examine the logs to check about the options you specified. Make sure it loads the correct configuration file.

2. Extract

Lesson

SETL supports two types of data accessors: Connector and SparkRepository.

A Connector is a non-typed abstraction of data access layer (DAL). For simplicity, you can understand it to as a Spark DataFrame.
A SparkRepository is a typed abstraction data access layer (DAL). For simplicity, you can understand it as a Spark Dataset. For more information, please refer to the official documentation.

SETL supports multiple data format, such as CSV, JSON, Parquet, Excel, Cassandra, DynamoDB, JDBC or Delta.

To ingest data in the Setl object entry point, you first must register the data, using the setConnector() or the setSparkRepository[T] methods.

2.1 Registration with `Connector`

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

setl
    .setConnector("testObjectRepository", deliveryId = "id")

The first argument provided is a String that refers to an item in the specified configuration file. The second argument, deliveryId, must be specified for data ingestion. We will see in section 2.3 why it is necessary. Just think of it as an ID, and the only way for SETL to ingest a Connector is with its ID.

Note that deliveryId is not necessary for the registration but it is for the ingestion. However there is no much use if we only register the data. If you are a beginner in SETL, you should think as setting a Connector must always come with a deliveryId.

local.conf:

setl.config.spark {
  some.config.option = "some-value"
}

testObjectRepository {
  storage = "CSV"
  path = "src/main/resources/test_objects.csv"
  inferSchema = "true"
  delimiter = ","
  header = "true"
  saveMode = "Overwrite"
}

As you can see, testObjectRepository defines a configuration for data of type CSV. This data is in a file, located in src/main/resources/test_objects.csv. Other classic read or write options are configured.

In summary, to register a Connector, you need to:

Specify an item in your configuration file. This item must have a storage key, which represents the type of the data. Other keys might be mandatory depending on this type.
Register the data in your Setl object, using setConnector("<item>", deliveryId = "<id>").

2.2 Registration with `SparkRepository`

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

setl
    .setSparkRepository[TestObject]("testObjectRepository")

Like setConnector(), the argument provided is a String that refers to an item in the specified configuration file.

local.conf:

setl.config.spark {
  some.config.option = "some-value"
}

testObjectRepository {
  storage = "CSV"
  path = "src/main/resources/test_objects.csv"
  inferSchema = "true"
  delimiter = ","
  header = "true"
  saveMode = "Overwrite"
}

Notice that the above SparkRepository is set with the TestObject type. In this example, the data we want to register is a CSV file containing two columns: value1 of type String and value2 of type Int. That is why the TestObject class should be:

case class TestObject(value1: String,
                      value2: Int)

In summary, to register a SparkRepository, you need to:

Specify an item in your configuration file. This item must have a storage key, which represents the type of the data. Other keys might be mandatory depending on this type.
Create a class or a case class representing the object type of your data.
Register the data in your Setl object, using setSparkRepository[T]("<item>").

Connector or SparkRepository?

Sometimes, the data your are ingesting contain irrelevant information that you do not want to keep. For example, let's say that the CSV file you want to ingest contain 10 columns: value1, value2, value3 and 7 other columns you are not interested in.

It is possible to ingest these 3 columns only with a SparkRepository if you specify the correct object type of your data:
```
case class A(value1: T1,
             value2: T2,
             value3: T3)

setl
    .setSparkRepository[A]("itemInConfFile")
```
This is not possible with a Connector. If you register this CSV file with a Connector, all 10 columns will appear.
Annotations

@ColumnName

@ColumnName is an annotation used in a case class. When you want to rename some columns in your code for integrity but also keep the original name when writing the data, you can use this annotation.
```
case class A(@ColumnName("value_one") valueOne: T1,
             @ColumnName("value_two") valueTwo: T2)
```
As you probably know, Scala does not use snake_case but camelCase. If you register a SparkRepository of type [A] in your Setl object, and if you read it, the columns will be named as valueOne and valueTwo. The file you read will still keep their name, i.e value_one and value_two.
@CompoundKey

TODO
@Compress

TODO

2.3 Registration of multiple data sources

Most of the time, you will need to register multiple data sources.

2.3.1 Multiple `Connector`

Let's start with Connector. Note that it is perfectly possible to register multiple Connector, as said previously. However, there will be an issue during the ingestion. Setl has no way to differentiate one Connector from another. You will need to set what is called a deliveryId.

val setl1: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()
 
// /!\ This will work for data registration here but not for data ingestion later /!\
setl1
    .setConnector("testObjectRepository")
    .setConnector("pokeGradesRepository")
 
// Please get used to set a `deliveryId` when you register one or multiple `Connector`
setl1
    .setConnector("testObjectRepository", deliveryId = "testObject")
    .setConnector("pokeGradesRepository", deliveryId = "grades")

2.3.2 Multiple `SparkRepository`

Let's now look at how we can register multiple SparRepository. If the SparkRepository you register all have different type, there will be no issue during the ingestion. Indeed, Setl is capable of differentiating the upcoming data by inferring the object type.

val setl2: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl2
    .setSparkRepository[TestObject]("testObjectRepository")
    .setSparkRepository[Grade]("pokeGradesRepository")

However, if there are multiple SparkRepository with the same type, you must use a deliveryId for each of them. Otherwise, there will be an error during the data ingestion. This is the same reasoning as multiple Connector: there is no way to differentiate two SparkRepository of the same type.

val setl3: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

// /!\ This will work for data registration here but not for data ingestion later /!\
setl3
    .setSparkRepository[Grade]("pokeGradesRepository")
    .setSparkRepository[Grade]("digiGradesRepository")

// Please get used to set a `deliveryId` when you register multiple `SparkRepository` of same type
setl3
    .setSparkRepository[Grade]("pokeGradesRepository", deliveryId = "pokeGrades")
    .setSparkRepository[Grade]("digiGradesRepository", deliveryId = "digiGrades")

2.4 Data Ingestion

Before deep diving into data ingestion, we first must learn about how SETL organizes an ETL process. SETL uses Pipeline and Stage to organize workflows. A Pipeline is where the whole ETL process will be done. The registered data are ingested inside a Pipeline, and all transformations and restitution will be done inside it. A Pipeline is composed of multiple Stage. A Stage allows you to modularize your project. It can be constituted of multiple Factory. You can understand a Factory as a module of your ETL process. So in order to "see" the data ingestion, we have to create a Pipeline and add a Stage to it. As it may be a little bit theoretical, let's look at some examples.

App.scala:

val setl4: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl4
    .setConnector("testObjectRepository", deliveryId = "testObjectConnector")
    .setSparkRepository[TestObject]("testObjectRepository", deliveryId = "testObjectRepository")

setl4
    .newPipeline() // Creation of a `Pipeline`.
    .addStage[IngestionFactory]() // Add a `Stage` composed of one `Factory`: `IngestionFactory`.
    .run()

Before running the code, let's take a look at IngestionFactory.

class IngestionFactory extends Factory[DataFrame] with HasSparkSession {

    import spark.implicits._

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

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

    override def write(): IngestionFactory.this.type = this

    override def get(): DataFrame = spark.emptyDataFrame
}

This is a skeleton of a SETL Factory. A SETL Factory contains 4 main functions: read(), process(), write() and get(). These functions will be executed in this order. These 4 functions are the core of your ETL process. This is where you will write your classic Spark code of data transformation.

You can see that IngestionFactory is a child class of Factory[DataFrame]. This simply means that the output of this data transformation must be a DataFrame. IngestionFactory also has the trait HasSparkSession. It allows you to access the SparkSession easily. Usually, we use it simply to import spark.implicits.

Where is the ingestion?

class IngestionFactory extends Factory[DataFrame] with HasSparkSession {

    import spark.implicits._

    @Delivery(id = "testObjectConnector")
    val testObjectConnector: Connector = Connector.empty
    @Delivery(id = "testObjectRepository")
    val testObjectRepository: SparkRepository[TestObject] = SparkRepository[TestObject]
    
    var testObjectOne: DataFrame = spark.emptyDataFrame
    var testObjectTwo: Dataset[TestObject] = spark.emptyDataset[TestObject]

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

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

    override def write(): IngestionFactory.this.type = this

    override def get(): DataFrame = spark.emptyDataFrame
}

The structure of a SETL Factory starts with the @Delivery annotation. This annotation is the way SETL ingest the corresponding registered data. If you look at App.scala where this IngestionFactory is called, the associated Setl object has registered a Connector with id testObjectConnector and a SparkRepository with id testObjectRepository.

Note that it is not mandatory to use a deliveryId in this case, because there is only one Factory with TestObject as object type. You can try to remove the deliveryId when registering the SparkRepository and the id in the @Delivery annotation. The code will still run. Same can be said for the Connector.

With the @Delivery annotation, we retrieved a Connector and SparkRepository. The data has been correctly ingested, but these are data access layers. To process the data, we have to retrieve the DataFrame of the Connector and the Dataset of the SparkRepository. This is why we defined two var, one of type DataFrame and one of type Dataset[TestObject]. We will assign values to them during the read() function. These var are accessible from all the 4 core functions, and you will use them for your ETL process.

To retrieve the DataFrame of the Connector and the Dataset of the SparkRepository, we can use the read() function.

override def read(): IngestionFactory.this.type = {
    testObjectOne = testObjectConnector.read()
    testObjectTwo = testObjectRepository.findAll()

    this
}

The read() function is typically where you will do your data preprocessing. Usually, we will simply assign values to our variables. Occasionally, this is typically where you would want to do some filtering on your data.

To retrieve the DataFrame of a Connector, use the read() method.
To retrieve the Dataset of a SparkRepository, you can use the findAll() method, or the findBy() method. The latter allows you to do filtering based on Condition. More info here.

The registered data is then correctly ingested. It is now ready to be used during the process() function.

2.5 Additional resources

2.5.1 `AutoLoad`

In the previous IngestionFactory, we would set a val of type SparkRepository but also a var in which we assign the corresponding Dataset in the read() function. With autoLoad = true, we can skip the first step and directly declare a Dataset. The Dataset of the SparkRepository will be automatically assigned in it.

App.scala:

val setl5: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl5
    .setSparkRepository[TestObject]("testObjectRepository", deliveryId = "testObjectRepository")

setl5
    .newPipeline()
    .addStage[AutoLoadIngestionFactory]()
    .run()

AutoLoadIngestionFactory

class AutoLoadIngestionFactory extends Factory[DataFrame] with HasSparkSession {

  import spark.implicits._

  @Delivery(id = "testObjectRepository", autoLoad = true)
  val testObject: Dataset[TestObject] = spark.emptyDataset[TestObject]

  override def read(): AutoLoadIngestionFactory.this.type = {
    testObject.show(false)

    this
  }

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

  override def write(): AutoLoadIngestionFactory.this.type = this

  override def get(): DataFrame = spark.emptyDataFrame
}

Note that there is no way to use the findBy() method to filter the data, compared to the previous Factory. Also, autoLoad is available for SparkRepository only, and not for Connector.

2.5.2 Adding parameters to the `Pipeline`

If you want to set some primary type parameters, you can use the setInput[T]() method. Those inputs are directly set in the Pipeline, and there are no registrations like for Connector or SparkRepository.

App.scala:

val setl5: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl5
    .newPipeline()
    .setInput[Int](42)
    .setInput[String]("SETL", deliveryId = "ordered")
    .setInput[String]("LTES", deliveryId = "reversed")
    .setInput[Array[String]](Array("S", "E", "T", "L"))
    .addStage[AutoLoadIngestionFactory]()
    .run()

Inputs are retrieved in the same way Connector or SparkRepository are retrieved: the @Delivery annotation, and the deliveryId if necessary.

AutoLoadIngestionFactory.scala:

class AutoLoadIngestionFactory extends Factory[DataFrame] with HasSparkSession {

    import spark.implicits._

    @Delivery
    val integer: Int = 0
    @Delivery(id = "ordered")
    val firstString: String = ""
    @Delivery(id = "reversed")
    val secondString: String = ""
    @Delivery
    val stringArray: Array[String] = Array()

    override def read(): AutoLoadIngestionFactory.this.type = {
      // Showing that inputs work correctly
      println("integer: " + integer) // integer: 42
      println("ordered: " + firstString) // ordered: SETL
      println("reversed: " + secondString) // reversed: LTES
      println("array: " + stringArray.mkString(".")) // array: S.E.T.L

      this
    }

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

    override def write(): AutoLoadIngestionFactory.this.type = this

    override def get(): DataFrame = spark.emptyDataFrame
}

2.6 Summary

In summary, the extraction part of an ETL process translates to the following in a SETL project:

Create a configuration item representing the data you want to ingest in your configuration file.
Register the data in your Setl object by using the setConnector() or the setSparkRepository[]() method. Reminder: the mandatory parameter is the name of your object in your configuration file, and you might want to add a deliveryId.
Create a new Pipeline in your Setl object, then add a Stage with a Factory in which you want to process your data.
Create a SETL Factory, containing the 4 core functions: read(), process(), write() and get().
Retrieve your data using the @Delivery annotation.
Your data is ready to be processed.

2.7 Data format configuration cheat sheet

Cheat sheet can be found here.

Exercises

In these exercises, we are going to practice registering and ingesting different types of storage: a CSV file, a JSON file, a Parquet file, an Excel file, a table from DynamoDB, a table from Cassandra, a table from PostgreSQL and a table from Delta.

An App.scala is already prepared. We created a SETL entry point and use a configuration file located at src/main/resources/exercise/extract/extract.conf. In this file, a configuration object has been created for each storage type, but they are incomplete.

The goal here is to complete the configuration objects with the help of the documentation, register the data as Connector in a Pipeline and print them in a Factory after ingestion.

To verify registration and ingestion, we prepared CheckExtractFactory. To test your code, complete the ??? parts and uncomment the corresponding lines.

a) Ingesting a CSV file

The goal of this first exercise is to register and ingest a CSV file.

We are looking to read the CSV file located at src/main/resources/exercise/extract/paris-wi-fi-service.csv.

Complete the configuration object csvFile in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named csvFileConnector. It will read it into csvFile in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of CSV file registration and ingestion.

b) Ingesting a JSON file

The goal of this second exercise is to register and ingest a JSON file.

We are looking to read the JSON file located at src/main/resources/exercise/extract/paris-notable-trees.json.

Complete the configuration object jsonFile in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named jsonFileConnector. It will read it into parquetFile in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of JSON file registration and ingestion.

c) Ingesting a Parquet file

The goal of this third exercise is to register and ingest a Parquet file.

We are looking to read the Parquet file located at src/main/resources/exercise/extract/paris-public-toilet.parquet.

Complete the configuration object parquetFile in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named parquetFileConnector. It will read it into parquetFile in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of Parquet file registration and ingestion.

d) Ingesting an Excel file

The goal of this fourth exercise is to register and ingest an Excel file.

We are looking to read the Excel file located at src/main/resources/exercise/extract/paris-textile-containers.xlsx.

Complete the configuration object excelFile in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named excelFileConnector. It will read it into excelFile in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of Excel file registration and ingestion.

e) Ingesting data from DynamoDB

The goal of this fifth exercise is to register and ingest data from a table in DynamoDB.

To work on this exercise, we need to host a local DynamoDB server. To do that, we prepared a docker-compose.yml in the exercise-environment/ folder. Make sure you have Docker installed. In a terminal, change your directory to exercise-environment/ and execute docker-compose up. It will create a local DynamoDB server at http://localhost:8000. It will also create a table orders_table in the us-east-1 region, and populate it with some data.

Make sure you launch the Docker containers before starting this exercise.

We are looking to read the orders_table table from DynamoDB, located at the us-east-1 region.

Complete the configuration object dynamoDBData in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data. We already set the endpoint to be http://localhost:8000 so that the requests are pointing to your local DynamoDB instance.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named dynamoDBDataConnector. It will read it into dynamoDBData in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of DynamoDB data registration and ingestion.

f) Ingesting data from Cassandra

The goal of this sixth exercise is to register and ingest data from a table in Cassandra.

To work on this exercise, we need to host a local Cassandra server. To do that, we prepared a docker-compose.yml in the exercise-environment/ folder. Make sure you have Docker installed. In a terminal, change your directory to exercise-environment/ and execute docker-compose up. It will create a local Cassandra server at http://localhost:9042. It will also create a keyspace mykeyspace and a table profiles, and populate it with some data.

Make sure you launch the Docker containers before starting this exercise.

We are looking to read the profiles table from Cassandra, located at the mykeyspace keyspace.

Complete the configuration object cassandraDBData in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data. We already set the endpoint to be http://localhost:9042 so that the requests are pointing to your local Cassandra instance.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named cassandraDataConnector. It will read it into cassandraData in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of Cassandra data registration and ingestion.

g) Ingesting data from PostgreSQL

The goal of this seventh exercise is to register and ingest data from a table in PostgreSQL.

To work on this exercise, we need to host a local PostgreSQL server. To do that, we prepared a docker-compose.yml in the exercise-environment/ folder. Make sure you have Docker installed. In a terminal, change your directory to exercise-environment/ and execute docker-compose up. It will create a local PostgreSQL server at http://localhost:5432. It will also create a database postgres and a table products, and populate it with some data.

You will also need the PostgreSQL JDBC driver. As specified in the documentation, you must provide a JDBC driver when using JDBC storage type. o provide the PostgreSQL JDBC driver, head to https://jdbc.postgresql.org/download.html, download the driver, and make the JDBC library jar available to the project. If you are using IntelliJ IDEA, right click on the jar and click on Add as Library.

Make sure you launch the Docker containers before starting this exercise.

We are looking to read the products table from PostgreSQL, located at the postgres database.

Complete the configuration object jdbcDBData in src/main/resources/exercise/extract/extract.conf.
In App.scala, register a Connector with this data. Remember that the endpoint should be http://localhost:5432.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named jdbcDataConnector. It will read it into jdbcData in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of PostgreSQL data registration and ingestion.

h) Ingesting a local Delta table

The goal of this eighth exercise is to register and ingest a local Delta table.

We are looking to read the Delta table located at src/main/resources/exercise/extract/delta-table. This table contains two versions, and we will read those two versions.

Complete the two configuration objects deltaDataVersionZero and deltaDataVersionOne in src/main/resources/exercise/extract/extract.conf.
In App.scala, register one Connector with version zero data and one with version one data.
You may create your own Factory and implement the read() function to verify if you can ingest the data. If you are not sure how to do that yet, we already added a Factory to a Stage, which is added to the Pipeline. This CheckExtractFactory will ingest a Connector named deltaDataVersionZeroConnector and a Connector named deltaDataVersionOneConnector. It will read it into deltaDataVersionZero and deltaDataVersionOne respectively in the read() method, and verify the number of lines in this data. Uncomment the corresponding lines and complete the part on the @Delivery annotation before running your code to test your implementation of local Delta table registration and ingestion.

To challenge yourself, try to replace the different Connector with SparkRepository. Make use of what you have learned in the lesson!

3. Transform

Lesson

Transformations in SETL are the easiest part to learn. There is nothing new if you are used to write ETL jobs with Spark. This is where you will transfer the code you write with Spark into SETL.

3.1 `Factory`

After seeing what the read() function in a Factory looks like, let's have a look at the process() function that is executed right after.

class ProcessFactory extends Factory[DataFrame] with HasSparkSession {

    @Delivery(id = "testObject")
    val testObjectConnector: Connector = Connector.empty

    var testObject: DataFrame = spark.emptyDataFrame

    var result: DataFrame = spark.emptyDataFrame

    override def read(): ProcessFactory.this.type = {
      testObject = testObjectConnector.read()

      this
    }

    override def process(): ProcessFactory.this.type = {
      val testObjectDate = testObject.withColumn("date", lit("2020-11-20"))

      result = testObjectDate
        .withColumnRenamed("value1", "name")
        .withColumnRenamed("value2", "grade")

      this
    }

    override def write(): ProcessFactory.this.type = this

    override def get(): DataFrame = spark.emptyDataFrame
}

You should understand the first part of the code with the ingestion thanks to the @Delivery and the read() function. Here is declared a var result in which will be stored the result of the data transformations. It is declared globally so that it can be accessed later in the write() and get() functions. The data transformations are what is inside the process() function, and you must surely know what they do.

As it is previously said, there is nothing new to learn here: you just write your Spark functions to transform your data, and this is unrelated to SETL.

3.2 `Transformer`

You might not learn anything new for SETL for data transformations in itself, but SETL helps you to structure them. We will now take a look about SETL Transformer. You already know about Factory. A Factory can contain multiple Transformer. A Transformer is a piece of highly reusable code that represents one data transformation. Let's look at how it works.

class ProcessFactoryWithTransformer extends Factory[DataFrame] with HasSparkSession {

    @Delivery(id = "testObject")
    val testObjectConnector: Connector = Connector.empty

    var testObject: DataFrame = spark.emptyDataFrame

    var result: DataFrame = spark.emptyDataFrame

    override def read(): ProcessFactoryWithTransformer.this.type = {
        testObject = testObjectConnector.read()
  
        this
    }

    override def process(): ProcessFactoryWithTransformer.this.type = {
        val testObjectDate = new DateTransformer(testObject).transform().transformed
        result = new RenameTransformer(testObjectDate).transform().transformed
  
        this
    }

    override def write(): ProcessFactoryWithTransformer.this.type = this

    override def get(): DataFrame = spark.emptyDataFrame
}

If you compare this Factory with the previous ProcessFactory in the last section, it does the same job. However, the workflow is more structured. You can see that in the process() function, there is no Spark functions for data transformations. Instead, we used Transformer. The data transformation will be done in Transformer. This allows to make to code highly reusable and add a lot more structure to it. In the previous ProcessFactory, we can divide the job by two: the first process is adding a new column, and the second process is renaming the column.

First, we are calling the first Transformer by passing our input DataFrame. The transform() method is then called, and the result is retrieved with the transformed getter. The second data transformation is done with RenameTransformer, and the result is assigned to our result variable. Let's have a look at each Transformer.

A Transformer has two core methods:

transform() which is where the data transformation should happen.
transformed which is a getter to retrieve the result.

Typically, we will also declare a variable in which we will assign the result of the transformation. In this case, transformedData. The transformed getter returns this variable. This is why in ProcessingFactoryWithTransformer, the transform() method is called, before calling the transformed getter.

DateTransformer.scala:

class DateTransformer(testObject: DataFrame) extends Transformer[DataFrame] with HasSparkSession {
    private[this] var transformedData: DataFrame = spark.emptyDataFrame

    override def transformed: DataFrame = transformedData

    override def transform(): DateTransformer.this.type = {
      transformedData = testObject
          .withColumn("date", lit("2020-11-20"))

      this
    }
}

DateTransformer represents the first data transformation that is done in the ProcessFactory in the previous section: adding a new column.

RenameTransformer:

class RenameTransformer(testObjectDate: DataFrame) extends Transformer[DataFrame] with HasSparkSession {
    private[this] var transformedData: DataFrame = spark.emptyDataFrame

    override def transformed: DataFrame = transformedData

    override def transform(): RenameTransformer.this.type = {
      transformedData = testObjectDate
        .withColumnRenamed("value1", "name")
        .withColumnRenamed("value2", "grade")

      this
    }
}

RenameTransformer represents the second data transformation that is done in the ProcessFactory in the previous section: renaming the columns.

3.3 Summary

The classic data transformations happen in the process() function of your Factory. This is how you write your data transformations in SETL, given that you already did what is needed in the Extract part. You have two solutions:

Write all the data transformations with Spark functions in the process() function of your Factory. Remember to set a global variable to store the result so that it can be used in the next functions of the Factory.
Organize your workflow with Transformer. This is best for code reusability, readability, understanding and structuring. To use a Transformer, remember that you need to pass parameters, usually the DataFrame or the Dataset you want to transform, eventually some parameters. You need to add the transform() function which is where the core Spark functions should be called, and the transformed getter to retrieve the result.

Exercises

In this exercise, we are going to practice about how to structure a SETL project for transformation processes.

An App.scala is already prepared. We created a SETL entry point and use a configuration file located at src/main/resources/exercise/transform/transform.conf. In this file, configuration objects are already created. We will be working with pokeGrades.csv and digiGrades.csv, both files located at src/main/resources/. We are looking to looking to compute the mean score of each "poke" and then of each "digi".

We are going to extract the data: pokeGrades.csv and digiGrades.csv, from src/main/resources/. In App.scala, register these two as SparkRepository.
Next step is to complete the Factory. Head over to MeanGradeFactory to complete the part about data ingestion.
You should know a Factory has 4 core mandatory functions. Leave the write() and get() functions as they are. Use the read() function if necessary. Keep in mind about what autoLoad is.
In the process() function, we are going to compute the mean grade for pokeGrades and digiGrades data. To do that, we are going to create a Transformer, named MeanGradeTransformer. This Transformer takes a parameter of type Dataset[Grade] and outputs an object of type DataFrame. There should be two columns: one column name and one column grade for the mean grade.
In the process() function, we can now call the Transformer on each data, apply transformations and store the result in variables.
Lastly, we can merge the two results and verify the final DataFrame by printing it.

Follow the instructions in the code to achieve this exercise. If you'd like to challenge yourself, try to write a complete Pipeline by yourself, without the help of the prepared code files. For example, you can try to find the top-3 scores of each "poke" and each "digi".

4. Load

Lesson

The Load processes with SETL correspond to two key ideas: writing the output, or passing the output. Passing the output allows to pass the result of a Factory to another Factory, for example. The second Factory is then using the result of a previous Factory as an input.

4.1 Writing an output

In order to write data, you need to register a Connector or a SparkRepository. As you probably already know, if you want to write a DataFrame, register a Connector. If you want to write a Dataset, register a SparkRepository. Do not forget that you must create a configuration item in the configuration file. There, you can specify the path of your output.

App.scala:

val setl0: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl0
    .setConnector("testObjectRepository", deliveryId = "testObject")
    .setConnector("testObjectWriteRepository", deliveryId = "testObjectWrite")

setl0
    .newPipeline()
    .setInput[String]("2020-11-23", deliveryId = "date")
    .addStage[WriteFactory]()

local.conf:

testObjectRepository {
  storage = "CSV"
  path = "src/main/resources/test_objects.csv"
  inferSchema = "true"
  delimiter = ","
  header = "true"
  saveMode = "Overwrite"
}

testObjectWriteRepository {
  storage = "EXCEL"
  path = "src/main/resources/test_objects_write.xlsx"
  useHeader = "true"
  saveMode = "Overwrite"
}

WriteFactory.scala:

class WriteFactory extends Factory[DataFrame] with HasSparkSession {

    @Delivery(id = "date")
    val date: String = ""
    @Delivery(id = "testObject")
    val testObjectConnector: Connector = Connector.empty
    @Delivery(id = "testObjectWrite")
    val testObjectWriteConnector: Connector = Connector.empty

    var testObject: DataFrame = spark.emptyDataFrame

    var result: DataFrame = spark.emptyDataFrame

    override def read(): WriteFactory.this.type = {
        testObject = testObjectConnector.read()

        this
    }

    override def process(): WriteFactory.this.type = {
        result = testObject
            .withColumn("date", lit(date))

        this
    }
  
    override def write(): WriteFactory.this.type = {
        testObjectWriteConnector.write(result.coalesce(1))

        this
    }

    override def get(): DataFrame = spark.emptyDataFrame
}

Note that in the Deliveries, there is one with the ID testObjectWrite. It has been previously registered in the Pipeline. We are retrieving it, but using it as a way to write our output.

The write() function is the third executed function in a Factory, after read() and process(). The idea is to call the write() method of a Connector or a SparkRepository, and pass the result DataFrame or Dataset as argument. SETL will automatically read the configuration item; storage type, path and options, and write the result there.

The advantage of using SETL for the Load process is that it makes it easier for you because you can change everything you need in your configuration item. If you ever want to change the data storage, you only need to modify the value of the corresponding key. Same for the path, or other options.

In summary, to write an output in SETL, you need to:

Create a configuration item in your configuration file
Register the corresponding Connector or SparkRepository
Ingest it in your Factory with the @Delivery annotation
Use it in the write() function to write your output

4.2 Getting an output

As SETL is organized with Factory, it is possible to pass the result of a Factory to another. The result of a Factory can be of any type, it generally is a DataFrame or a Dataset.

4.2.1 Getting a `DataFrame`

We are now going to ingest data and make some transformations in FirstFactory, then use the result in SecondFactory. You can see in the Pipeline that FirstFactory is before SecondFactory.

App.scala:

val setl1: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl1.setConnector("testObjectRepository", deliveryId = "testObject")

setl1
    .newPipeline()
    .setInput[String]("2020-12-18", deliveryId = "date")
    .addStage[FirstFactory]()
    .addStage[SecondFactory]()
    .run()

FirstFactory.scala:

class FirstFactory extends Factory[DataFrame] with HasSparkSession {

    @Delivery(id = "date")
    val date: String = ""
    @Delivery(id = "testObject")
    val testObjectConnector: Connector = Connector.empty

    var testObject: DataFrame = spark.emptyDataFrame

    var result: DataFrame = spark.emptyDataFrame

    override def read(): FirstFactory.this.type = {
        testObject = testObjectConnector.read()

        this
    }

  override def process(): FirstFactory.this.type = {
    result = testObject
      .withColumn("date", lit(date))

    this
  }

  override def write(): FirstFactory.this.type = this

  override def get(): DataFrame = result
}

This FirstFactory is similar to the previous WriteFactory. Instead of writing the result, we are going to pass it in the get() function. The get() function is the fourth executed function in a Factory, after read(), process() and write(). In the above example, the output is simply returned.

Remember that the type of the output is defined at the start of the Factory, when specifying the parent class. In this case, the output is a DataFrame. This output is then injected in the Pipeline as a Deliverable. The other Factory can then ingest it.

SecondFactory.scala:

class SecondFactory extends Factory[DataFrame] with HasSparkSession {

    import spark.implicits._

    @Delivery(producer = classOf[FirstFactory])
    val firstFactoryResult: DataFrame = spark.emptyDataFrame

    var secondResult: DataFrame = spark.emptyDataFrame

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

    override def process(): SecondFactory.this.type = {
        secondResult = firstFactoryResult
            .withColumn("secondDate", $"date")

        secondResult.show(false)

        this
    }

    override def write(): SecondFactory.this.type = this

    override def get(): DataFrame = secondResult
}

In this SecondFactory, we want to retrieve the output produced by FirstFactory. Noticed that we used the producer argument in the @Delivery annotation. This is how SETL Pipeline retrieves the output of a Factory: the result of a Factory is injected into the Pipeline as a Deliverable, which can be ingested with the @Delivery annotation.

4.2.2 Getting a `Dataset`

In the previous Pipeline, we retrieved the result of FirstFactory to use it in SecondFactory. The result of FirstFactory was a DataFrame, and we needed to retrieve it in SecondFactory by using the producer argument in the @Delivery annotation. In the following Pipeline, we are going to produce a Dataset from FirstFactoryBis and use it in SecondFactoryBis.

App.scala:

val setl2: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .getOrCreate()

setl2
    .setConnector("testObjectRepository", deliveryId = "testObject")

setl2
    .newPipeline()
    .setInput[String]("2020-12-18", deliveryId = "date")
    .addStage[FirstFactoryBis]()
    .addStage[SecondFactoryBis]()
    .run()

FirstFactoryBis.scala:

class FirstFactoryBis extends Factory[Dataset[TestObject]] with HasSparkSession {

    import spark.implicits._

    @Delivery(id = "testObject")
    val testObjectConnector: Connector = Connector.empty

    var testObject: DataFrame = spark.emptyDataFrame

    var result: Dataset[TestObject] = spark.emptyDataset[TestObject]

    override def read(): FirstFactoryBis.this.type = {
      testObject = testObjectConnector.read()

      this
  }

    override def process(): FirstFactoryBis.this.type = {
        result = testObject
            .withColumn("value1", concat($"value1", lit("42")))
            .as[TestObject]

        this
    }

    override def write(): FirstFactoryBis.this.type = this

    override def get(): Dataset[TestObject] = result
}

Noticed that the FirstFactoryBis is a child class of Factory[Dataset[TestObject]], meaning that the output of it must be a Dataset[TestObject]. result is a variable of type Dataset[TestObject], and the get() function returns it. This Dataset is injected into the Pipeline.

SecondFactoryBis.scala:

class SecondFactoryBis extends Factory[DataFrame] with HasSparkSession {

    import spark.implicits._

    @Delivery(id = "date")
    val date: String = ""
    @Delivery
    val firstFactoryBisResult: Dataset[TestObject] = spark.emptyDataset

    var secondResult: DataFrame = spark.emptyDataFrame

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

    override def process(): SecondFactoryBis.this.type = {
        secondResult = firstFactoryBisResult
            .withColumn("secondDate", lit("date"))

        secondResult.show(false)

        this
    }

    override def write(): SecondFactoryBis.this.type = this

    override def get(): DataFrame = secondResult
}

The result of FirstFactoryBis is a Dataset[TestObject]. We used the @Delivery annotation to retrieve it. Compared to SecondFactory, we did not need to use the producer in the @Delivery annotation. This is because the Pipeline can infer on the data, and the only Dataset[TestObject] that it found is produced by FirstFactoryBis. So there is no need to specify it. This is the same mechanism that explains why a Connector needs a deliveryId to be retrieved, and not a SparkRepository[T] if there is only one of type T that is registered.

4.2.3 Summary

In summary, to use the output of a Factory in another one:

Check the type of the output.
Make sure that the Stage of the first Factory is before the Stage of the second Factory.
The second Factory must be a child class of Factory[T] where T is the type of the output of the first Factory.
Retrieve the output of the first Factory by using the @Delivery annotation. If it is a DataFrame, also use the producer argument.

Note: Although it is possible to retrieve the output of a Factory in another one, most of the time, we would prefer to save the output of the first Factory in a Connector or SparkRepository, and re-use the same Connector or SparkRepository in the second Factory to retrieve the output.

Exercises

In this exercise, we are going to practice about how to the Load processes with SETL, that is, how to pass the result of a Factory to another Factory, and how to write the result of a Factory.

An App.scala is already prepared. We created a SETL entry point and use a configuration file located at src/main/resources/exercise/load/load.conf. In this file, configuration objects are already created. We will be working with pokeGrades.csv and digiGrades.csv, both files located at src/main/resources/. We are going to find out how many exams there are per year. To do that, a first Factory will "compute" all the dates from the data, and pass this result to the second Factory. This second Factory ingest the result, extract the year of each date and count the number of exams per year.

We are going to extract the data: pokeGrades.csv and digiGrades.csv, from src/main/resources/. In App.scala, register these two as SparkRepository. Also register a Connector where to write your output. Remind that a configuration file have been provided.
Next step is to complete the two Factory. Head over to GetExamsDateFactory first.
Complete the part on the data ingestion by setting the Delivery. In GetExamsDateFactory, the goal is to get the different exam dates of pokeGrades.csv and digiGrades.csv. Use the read() function if necessary. In the process() function, concatenate both the "poke" and the "digi" data. Then, only keep the date column, as it is the only relevant column in this exercise. Leave the write() function as is, and complete the get() function by returning the result of your process.
Now, head over to ExamStatsFactory. This Factory will ingest the result of GetExamsDateFactory. As usual, the first step is to add the Delivery. Remember that to write an output, you also have to add a Connector or SparkRepository for the output, as it can define the storage type and the path. Also remember about producer. Go over to the lesson if you forgot about it.
Use the read() function if necessary. In the process() function, we are looking to compute the number of exams per year. Our input data is a DataFrame of 1 single column date. Replace the date column by extracting the year only: it is the first 4 characters of the date column. Then, count the number of date. Use the groupBy(), agg() and count() functions. In our data, each year is duplicated 10 times. Indeed, for each exam, there are always 10 "poke" or 10 "digi". As a consequence, we need to divide the count by 10.
Use the output Delivery you declared to save the result output in the write() function. Complete the get() function to return the result, even though it is not used. If you run the code, you should have the number of exams per year, located in src/main/resources/examsStats/.

Follow the instructions in the code to achieve this exercise. If you'd like to challenge yourself, try to write a complete Pipeline by yourself, without the help of the prepared code files. For example, you can try to save in a file the list of "poke" and "digi".

Note that these exercises are simply used to practise SETL and their structure may very well not be optimized for your production workflow. These are just simple illustrations of what you can usually do with the framework.

5. From local to production environment

Lesson

The difference between multiple development environment consists in the location of files/data we want to read and the location of files/data we want to write.

5.1 Changing the `path`

In order to see how SETL handles between local and production environment, we are going to set two Connector: one for local and one for prod.

App.scala:

val setl: Setl = Setl.builder()
    .withDefaultConfigLoader("storage.conf")
    .setSparkMaster("local[*]")
    .getOrCreate()

setl
    .setConnector("pokeGradesRepository", deliveryId = "pokeGradesRepository")
    .setConnector("pokeGradesRepositoryProd", deliveryId = "pokeGradesRepositoryProd")

setl
    .newPipeline()
    .addStage[ProductionFactory]()
    .run()

storage.conf:

setl.config.spark {
  spark.hadoop.fs.s3a.access.key = "dummyaccess" // Used to connect to AWS S3 prod environment
  spark.hadoop.fs.s3a.secret.key = "dummysecret" // Used to connect to AWS S3 prod environment
  spark.driver.bindAddress = "127.0.0.1"
}

pokeGradesRepository {
  storage = "CSV"
  path = "src/main/resources/pokeGrades.csv"
  inferSchema = "true"
  delimiter = ","
  header = "true"
  saveMode = "Overwrite"
}

pokeGradesRepositoryProd {
  storage = "CSV"
  path = "s3a://setl-examples/pokeGrades.csv"
  inferSchema = "true"
  delimiter = ","
  header = "true"
  saveMode = "Overwrite"
}

The difference between these two repositories is the path. The first object uses a local path, and the second uses a AWS S3 path, considered as a production environment. They are exactly the same file. When ingesting these files into a Factory, we can retrieve the same DataFrame. Thus, in SETL, it is possible to switch your development environment without looking at the code. You just need to make adjustments to the path of your configuration objects.

5.2 Generalize your configuration

Most of the time, you will have a lot of configuration objects for both input and output. Changing the path for all of these objects may not be efficient. Instead of having two configuration objects (pokeGradesRepository and pokeGradesRepositoryProd) like in the last section, you can simply declare one configuration object, and make it reusable.

local.conf:

setl.config.spark {
  some.config.option = "some-value"
}

root {
  path = "src/main/resources"
}

include "smartConf.conf" // /!\ important

prod.conf

setl.config.spark {
  spark.hadoop.fs.s3a.endpoint = "http://localhost:9090"
  spark.hadoop.fs.s3a.access.key = "dummyaccess"
  spark.hadoop.fs.s3a.secret.key = "dummysecret"
  spark.hadoop.fs.s3a.path.style.access = "true"
  spark.driver.bindAddress = "127.0.0.1"
}

root {
  path = "s3a://setl-examples"
}

include "smartConf.conf" // /!\ important

smartConf.conf:

smartPokeGradesRepository {
  storage = "CSV"
  path = ${root.path}"/pokeGrades.csv"
  inferSchema = "true"
  delimiter = ","
  header = "true"
  saveMode = "Overwrite"
}

If you look at smartConf.conf, notice the path key: it uses the root.path key. smartConf.conf is included in both in local.conf and prod.conf, which are the configuration files to be loaded. In local.conf, root.path is set to a value corresponding to a local path, and in prod.conf, it is set to a value corresponding to a prod path, which is a S3 path in this example. Let's now see how to switch development environment.

Note that in the Setl object below, we used the withDefaultConfigLoader() method. This means that application.conf will be loaded, and it retrieves the app.environment. app.environment is a VM option. By default, it is set to local in the pom.xml file. Depending on the app.environment, it will load the corresponding configuration file, i.e <app.environment>.conf.

App.scala:

val smartSetl: Setl = Setl.builder()
    .withDefaultConfigLoader()
    .setSparkMaster("local[*]")
    .getOrCreate()

smartSetl.setConnector("smartPokeGradesRepository", deliveryId = "smartPokeGradesRepository")
println(smartSetl.getConnector[Connector]("smartPokeGradesRepository").asInstanceOf[FileConnector].options.getPath)

Now, to see how easy it is to switch development environment with SETL, change the VM option -Dapp.environment by setting it to local or prod. If you run App.scala, you will see that the path will change according to the environment:

src/main/resources/pokeGrades.csv if -Dapp.environment=local
s3a://setl-examples/pokeGrades.csv if -Dapp.environment=prod

5.3 Summary

In summary, you can change your development environment by changing to path of your configuration objects. However, this can be obnoxious especially if you have a lot of input/output storage object. By writing a general configuration file, you simply need adjust the VM option to switch your development environment, and get the corresponding paths of your data.

Remind that SETL aims at simplifying the Extract and Load processes so that a Data Scientist can focus on his core job: data transformations. On top of that, it gives structure and allows more modularization of your code!

Name		Name	Last commit message	Last commit date
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exercise-environment		exercise-environment
src/main		src/main
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pom.xml		pom.xml

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SETL examples

Chapters

1. Entry Point and configurations

1.1. Entry point with basic configurations

1.2. Entry point with specific configurations

1.3 Utilities

Helper methods

SparkSession options

2. Extract

2.1 Registration with Connector

2.2 Registration with SparkRepository

2.3 Registration of multiple data sources

2.3.1 Multiple Connector

2.3.2 Multiple SparkRepository

2.4 Data Ingestion

2.5 Additional resources

2.5.1 AutoLoad

2.5.2 Adding parameters to the Pipeline

2.6 Summary

2.7 Data format configuration cheat sheet

3. Transform

3.1 Factory

3.2 Transformer

3.3 Summary

4. Load

4.1 Writing an output

4.2 Getting an output

4.2.1 Getting a DataFrame

4.2.2 Getting a Dataset

4.2.3 Summary

5. From local to production environment

5.1 Changing the path

5.2 Generalize your configuration

5.3 Summary

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2.1 Registration with `Connector`

2.2 Registration with `SparkRepository`

2.3.1 Multiple `Connector`

2.3.2 Multiple `SparkRepository`

2.5.1 `AutoLoad`

2.5.2 Adding parameters to the `Pipeline`

3.1 `Factory`

3.2 `Transformer`

4.2.1 Getting a `DataFrame`

4.2.2 Getting a `Dataset`

5.1 Changing the `path`