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setl

(★ 186)

A simple Spark-powered ETL framework that just works 🍺

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최종 버전 다운로드 (.zip)

- - bug_report.md
  - feature_request.md
- - release.yml
  - snapshot.yml
  - test.yml
- dependabot.yml
- stale.yml
- change-scala-version.sh
- deploy-release.sh
- deploy-snapshot.sh
- docker-compose.yml
- test.sh
- - configuration_example.md
  - Connector.md
  - ConnectorBuilder.md
  - CustomConnector.md
  - Repository.md
  - SparkRepositoryAdapter.md
  - SparkRepositoryBuilder.md
  - Structured-Streaming-Connector.md
- - logo_setl.png
  - logo_setl_1280_640.png
- - Compressor_Archiver.md
- _config.yml
- Annotations.md
- Architecture.md
- Condition.md
- Conf.md
- ConfigLoader.md
- Deliverable.md
- Factory.md
- index.md
- Logging.md
- Pipeline.md
- Quick-Start.md
- SchemaConverter.md
- Setl.md
- SparkRepository-caching.md
- SparkSessionBuilder.md
- Stage.md
- StructAnalyser.md
- Transformer.md
- vocabulary.md
.gitignore
CHANGELOG.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
pom.xml
README.md

// repository documentation

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![logo](https://raw.githubusercontent.com/SETL-Framework/setl/master/docs/img/logo_setl.png)
----------

[![build](https://github.com/SETL-Framework/setl/workflows/build/badge.svg?branch=master)](https://github.com/SETL-Framework/setl/actions)
[![codecov](https://codecov.io/gh/SETL-Framework/setl/branch/master/graph/badge.svg)](https://codecov.io/gh/SETL-Framework/setl)
[![Maven Central](https://img.shields.io/maven-central/v/io.github.setl-framework/setl_2.11.svg?label=Maven%20Central&color=blue)](https://mvnrepository.com/artifact/io.github.setl-framework/setl)
[![javadoc](https://javadoc.io/badge2/io.github.setl-framework/setl_2.12/javadoc.svg)](https://javadoc.io/doc/io.github.setl-framework/setl_2.12)
[![documentation](https://img.shields.io/badge/docs-passing-1f425f.svg)](https://setl-framework.github.io/setl/)

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** (pronounced "settle") is a Scala ETL framework powered by [Apache Spark](https://spark.apache.org/) 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](https://github.com/SETL-Framework/setl-template).

### In an existing project

```xml
<dependency>
  <groupId>io.github.setl-framework</groupId>
  <artifactId>setl_2.12</artifactId>
  <version>1.0.0-RC2</version>
</dependency>
```

To use the SNAPSHOT version, add Sonatype snapshot repository to your `pom.xml`
```xml
<repositories>
  <repository>
    <id>ossrh-snapshots</id>
    <url>https://s01.oss.sonatype.org/content/repositories/snapshots/</url>
  </repository>
</repositories>

<dependencies>
  <dependency>
    <groupId>io.github.setl-framework</groupId>
    <artifactId>setl_2.12</artifactId>
    <version>1.0.0-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 `io.github.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](https://github.com/SETL-Framework/setl-template). 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:

```scala
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:

```txt
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:
```scala  
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

```scala
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.

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

#### Run our pipeline

```scala
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.

```scala
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:

```scala
pipeline.addStage[AnotherFactory]()
```

### Custom Connector

You can implement you own data source connector by implementing the `ConnectorInterface`

```scala
class CustomConnector extends ConnectorInterface with CanDrop {
  override def setConf(conf: Conf): Unit = null

override def read(): DataFrame = {
    import spark.implicits._
    Seq(1, 2, 3).toDF("id")
  }

override def write(t: DataFrame, suffix: Option[String]): Unit = logDebug("Write with suffix")

override def write(t: DataFrame): Unit = logDebug("Write")

/**
   * Drop the entire table.
   */
  override def drop(): Unit = logDebug("drop")
}
```

To use it, just set the storage to **OTHER** and provide the class reference of your connector:

```txt
myConnector {
  storage = "OTHER"
  class = "com.example.CustomConnector"  // class reference of your connector 
}
```

### Generate pipeline diagram

You can generate a [Mermaid diagram](https://mermaid-js.github.io/mermaid/#/) by doing:
```scala
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 -------

You can copy the previous code to a markdown viewer that supports Mermaid.

Or you can try the live editor: https://mermaid-js.github.io/mermaid-live-editor/#/edit/eyJjb2RlIjoiY2xhc3NEaWFncmFtXG5jbGFzcyBNeUZhY3Rvcnkge1xuICA8PEZhY3RvcnlbRGF0YXNldFtUZXN0T2JqZWN0XV0-PlxuICArU3BhcmtSZXBvc2l0b3J5W1Rlc3RPYmplY3RdXG59XG5cbmNsYXNzIERhdGFzZXRUZXN0T2JqZWN0IHtcbiAgPDxEYXRhc2V0W1Rlc3RPYmplY3RdPj5cbiAgPnBhcnRpdGlvbjE6IEludFxuICA-cGFydGl0aW9uMjogU3RyaW5nXG4gID5jbHVzdGVyaW5nMTogU3RyaW5nXG4gID52YWx1ZTogTG9uZ1xufVxuXG5EYXRhc2V0VGVzdE9iamVjdCA8fC4uIE15RmFjdG9yeSA6IE91dHB1dFxuY2xhc3MgQW5vdGhlckZhY3Rvcnkge1xuICA8PEZhY3RvcnlbU3RyaW5nXT4-XG4gICtEYXRhc2V0W1Rlc3RPYmplY3RdXG59XG5cbmNsYXNzIFN0cmluZ0ZpbmFsIHtcbiAgPDxTdHJpbmc-PlxuICBcbn1cblxuU3RyaW5nRmluYWwgPHwuLiBBbm90aGVyRmFjdG9yeSA6IE91dHB1dFxuY2xhc3MgU3BhcmtSZXBvc2l0b3J5VGVzdE9iamVjdEV4dGVybmFsIHtcbiAgPDxTcGFya1JlcG9zaXRvcnlbVGVzdE9iamVjdF0-PlxuICBcbn1cblxuQW5vdGhlckZhY3RvcnkgPHwtLSBEYXRhc2V0VGVzdE9iamVjdCA6IElucHV0XG5NeUZhY3RvcnkgPHwtLSBTcGFya1JlcG9zaXRvcnlUZXN0T2JqZWN0RXh0ZXJuYWwgOiBJbnB1dFxuIiwibWVybWFpZCI6eyJ0aGVtZSI6ImRlZmF1bHQifX0=

```

You can either copy the code into a Markdown viewer or just copy the link into your browser ([link](https://mermaid-js.github.io/mermaid-live-editor/#/edit/eyJjb2RlIjoiY2xhc3NEaWFncmFtXG5jbGFzcyBNeUZhY3Rvcnkge1xuICA8PEZhY3RvcnlbRGF0YXNldFtUZXN0T2JqZWN0XV0-PlxuICArU3BhcmtSZXBvc2l0b3J5W1Rlc3RPYmplY3RdXG59XG5cbmNsYXNzIERhdGFzZXRUZXN0T2JqZWN0IHtcbiAgPDxEYXRhc2V0W1Rlc3RPYmplY3RdPj5cbiAgPnBhcnRpdGlvbjE6IEludFxuICA-cGFydGl0aW9uMjogU3RyaW5nXG4gID5jbHVzdGVyaW5nMTogU3RyaW5nXG4gID52YWx1ZTogTG9uZ1xufVxuXG5EYXRhc2V0VGVzdE9iamVjdCA8fC4uIE15RmFjdG9yeSA6IE91dHB1dFxuY2xhc3MgQW5vdGhlckZhY3Rvcnkge1xuICA8PEZhY3RvcnlbU3RyaW5nXT4-XG4gICtEYXRhc2V0W1Rlc3RPYmplY3RdXG59XG5cbmNsYXNzIFN0cmluZ0ZpbmFsIHtcbiAgPDxTdHJpbmc-PlxuICBcbn1cblxuU3RyaW5nRmluYWwgPHwuLiBBbm90aGVyRmFjdG9yeSA6IE91dHB1dFxuY2xhc3MgU3BhcmtSZXBvc2l0b3J5VGVzdE9iamVjdEV4dGVybmFsIHtcbiAgPDxTcGFya1JlcG9zaXRvcnlbVGVzdE9iamVjdF0-PlxuICBcbn1cblxuQW5vdGhlckZhY3RvcnkgPHwtLSBEYXRhc2V0VGVzdE9iamVjdCA6IElucHV0XG5NeUZhY3RvcnkgPHwtLSBTcGFya1JlcG9zaXRvcnlUZXN0T2JqZWN0RXh0ZXJuYWwgOiBJbnB1dFxuIiwibWVybWFpZCI6eyJ0aGVtZSI6ImRlZmF1bHQifX0=)) 🍻

### App Configuration

The configuration system of SETL allows users to execute their Spark application in different execution environments, by
using environment-specific configurations.

In `src/main/resources` directory, you should have at least two configuration files named `application.conf`
and `local.conf`
(take a look at this [example](https://github.com/SETL-Framework/setl-template/tree/master/src/main/resources)). These
are what you need if you only want to run your application in one single environment.

You can also create other configurations (for example `dev.conf` and `prod.conf`), in which environment-specific
parameters can be defined.

##### application.conf

This configuration file should contain universal configurations that could be used regardless the execution environment.

##### env.conf (e.g. local.conf, dev.conf)

These files should contain environment-specific parameters. By default, `local.conf` will be used.

##### How to use the configuration

Imagine the case we have two environments, a local development environment and a remote production environment. Our application
needs a repository for saving and loading data. In this use case, let's prepare `application.conf`, `local.conf`, `prod.conf`
and `storage.conf`

```hocon
# application.conf
setl.environment = ${app.environment}
setl.config {
  spark.app.name = "my_application"
  # and other general spark configurations  
}
```

```hocon
# local.conf
include "application.conf"

setl.config {
  spark.default.parallelism = "200"
  spark.sql.shuffle.partitions = "200"
  # and other local spark configurations  
}

app.root.dir = "/some/local/path"

include "storage.conf"
```

```hocon
# prod.conf
setl.config {
  spark.default.parallelism = "1000"
  spark.sql.shuffle.partitions = "1000"
  # and other production spark configurations  
}

app.root.dir = "/some/remote/path"

include "storage.conf"
```

```hocon
# storage.conf
myRepository {
  storage = "CSV"
  path = ${app.root.dir}  // this path will depend on the execution environment
  inferSchema = "true"
  delimiter = ";"
  header = "true"
  saveMode = "Append"
}
```

To compile with local configuration, with maven, just run:
```shell
mvn compile
```

To compile with production configuration, pass the jvm property `app.environment`.
```shell
mvn compile -Dapp.environment=prod
```

Make sure that your resources directory has filtering enabled:
```xml
<resources>
    <resource>
        <directory>src/main/resources</directory>
        <filtering>true</filtering>
    </resource>
</resources>
```

## Dependencies

**SETL** currently supports the following data source. You won't need to provide these libraries in your project (except the JDBC driver):
  - All file formats supported by Apache Spark (csv, json, parquet etc)
  - Delta
  - Excel ([crealytics/spark-excel](https://github.com/crealytics/spark-excel))
  - Cassandra ([datastax/spark-cassandra-connector](https://github.com/datastax/spark-cassandra-connector))
  - DynamoDB ([audienceproject/spark-dynamodb](https://github.com/audienceproject/spark-dynamodb))
  - JDBC (you have to provide the jdbc driver)

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                         |
| ------------- | -------------  | -----------------------------|
|     3.0       |        2.12    | :heavy_check_mark: Ok        |
|     2.4       |        2.12    | :heavy_check_mark: Ok        |
|     2.4       |        2.11    | :warning: see *known issues* |
|     2.3       |        2.11    | :warning: see *known issues* |

## Known issues

### Spark 2.4 with Scala 2.11

When using `setl_2.11-1.x.x` with Spark 2.4 and Scala 2.11, you may need to include manually these following dependencies to override the default version:
```xml
<dependency>
    <groupId>com.audienceproject</groupId>
    <artifactId>spark-dynamodb_2.11</artifactId>
    <version>1.0.4</version>
</dependency>
<dependency>
    <groupId>io.delta</groupId>
    <artifactId>delta-core_2.11</artifactId>
    <version>0.7.0</version>
</dependency>
<dependency>
    <groupId>com.datastax.spark</groupId>
    <artifactId>spark-cassandra-connector_2.11</artifactId>
    <version>2.5.1</version>
</dependency>
```

### Spark 2.3 with Scala 2.11

- `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

## Test Coverage

[![coverage.svg](https://codecov.io/gh/SETL-Framework/setl/branch/master/graphs/sunburst.svg)](https://codecov.io/gh/SETL-Framework/setl)

## Documentation

[https://setl-framework.github.io/setl/](https://setl-framework.github.io/setl/)

## Contributing to SETL

[Check our contributing guide](https://github.com/SETL-Framework/setl/blob/master/CONTRIBUTING.md)