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https://github.com/Kotlin/kotlin-spark-api

This projects gives Kotlin bindings and several extensions for Apache Spark. We are looking to have this as a part of Apache Spark 3.x
https://github.com/Kotlin/kotlin-spark-api
bigdata kotlin nullability scala spark
Last synced: 3 months ago
JSON representation
This projects gives Kotlin bindings and several extensions for Apache Spark. We are looking to have this as a part of Apache Spark 3.x
Host: GitHub
URL: https://github.com/Kotlin/kotlin-spark-api
Owner: Kotlin
License: apache-2.0
Created: 2020-06-01T11:07:43.000Z (about 4 years ago)
Default Branch: release
Last Pushed: 2024-04-07T13:51:13.000Z (3 months ago)
Last Synced: 2024-04-07T14:41:55.669Z (3 months ago)
Topics: bigdata, kotlin, nullability, scala, spark
Language: Kotlin
Homepage:
Size: 4.42 MB
Stars: 436
Watchers: 18
Forks: 34
Open Issues: 17
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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awesome-spark - Kotlin for Apache Spark - commit/Kotlin/kotlin-spark-api.svg"> - Kotlin API bindings and extensions. (Packages / Language Bindings)
README

        # Kotlin for Apache® Spark™

[![Kotlin Stable](https://kotl.in/badges/stable.svg)](https://kotlinlang.org/docs/components-stability.html)

[![JetBrains official project](http://jb.gg/badges/official.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)

[![Maven Central](https://img.shields.io/maven-central/v/org.jetbrains.kotlinx.spark/kotlin-spark-api_3.3.2_2.13.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:"org.jetbrains.kotlinx.spark"%20AND%20a:"kotlin-spark-api_3.3.2_2.13")

[![Join the chat at https://gitter.im/JetBrains/kotlin-spark-api](https://badges.gitter.im/JetBrains/kotlin-spark-api.svg)](https://gitter.im/JetBrains/kotlin-spark-api?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

Your next API to work with  [Apache Spark](https://spark.apache.org/). 

This project adds a missing layer of compatibility between [Kotlin](https://kotlinlang.org/) and [Apache Spark](https://spark.apache.org/).

It allows Kotlin developers to use familiar language features such as data classes, and lambda expressions as simple expressions in curly braces or method references. 

We have opened a Spark Project Improvement Proposal: [Kotlin support for Apache Spark](http://issues.apache.org/jira/browse/SPARK-32530) to work with the community towards getting Kotlin support as a first-class citizen in Apache Spark. We encourage you to voice your opinions and participate in the discussion.

## Table of Contents

- [Supported versions of Apache Spark](#supported-versions-of-apache-spark)

- [Releases](#releases)

- [How to configure Kotlin for Apache Spark in your project](#how-to-configure-kotlin-for-apache-spark-in-your-project)

- [Kotlin for Apache Spark features](#kotlin-for-apache-spark-features)

    - [Creating a SparkSession in Kotlin](#creating-a-sparksession-in-kotlin)

    - [Creating a Dataset in Kotlin](#creating-a-dataset-in-kotlin)

    - [Null safety](#null-safety)

    - [withSpark function](#withspark-function)

    - [withCached function](#withcached-function)

    - [toList and toArray](#tolist-and-toarray-methods)

    - [Column infix/operator functions](#column-infixoperator-functions)

    - [Overload Resolution Ambiguity](#overload-resolution-ambiguity)

    - [Tuples](#tuples)

    - [Streaming](#streaming)

    - [User Defined Functions](#user-defined-functions)

- [Examples](#examples)

- [Reporting issues/Support](#reporting-issues--support)

- [Code of Conduct](#code-of-conduct)

- [License](#license)

## Supported versions of Apache Spark

| Apache Spark | Scala |       Kotlin for Apache Spark       |

|:------------:|:-----:|:-----------------------------------:|

|    3.3.2     | 2.13  | kotlin-spark-api_3.3.2_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.3.2_2.12:VERSION |

|    3.3.1     | 2.13  | kotlin-spark-api_3.3.1_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.3.1_2.12:VERSION |

|    3.3.0     | 2.13  | kotlin-spark-api_3.3.0_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.3.0_2.12:VERSION |

|    3.2.3     | 2.13  | kotlin-spark-api_3.2.3_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.2.3_2.12:VERSION |

|    3.2.2     | 2.13  | kotlin-spark-api_3.2.2_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.2.2_2.12:VERSION |

|    3.2.1     | 2.13  | kotlin-spark-api_3.2.1_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.2.1_2.12:VERSION |

|    3.2.0     | 2.13  | kotlin-spark-api_3.2.0_2.13:VERSION |

|              | 2.12  | kotlin-spark-api_3.2.0_2.12:VERSION |

|    3.1.3     | 2.12  | kotlin-spark-api_3.1.3_2.12:VERSION |

|    3.1.2     | 2.12  | kotlin-spark-api_3.1.2_2.12:VERSION |

|    3.1.1     | 2.12  | kotlin-spark-api_3.1.1_2.12:VERSION |

|    3.1.0     | 2.12  | kotlin-spark-api_3.1.0_2.12:VERSION |

|    3.0.3     | 2.12  | kotlin-spark-api_3.0.3_2.12:VERSION |

|    3.0.2     | 2.12  | kotlin-spark-api_3.0.2_2.12:VERSION |

|    3.0.1     | 2.12  | kotlin-spark-api_3.0.1_2.12:VERSION |

|    3.0.0     | 2.12  | kotlin-spark-api_3.0.0_2.12:VERSION |

## Deprecated versions

| Apache Spark | Scala |     Kotlin for Apache Spark     |

|:------------:|:-----:|:-------------------------------:|

|    2.4.1+    | 2.12  | kotlin-spark-api-2.4_2.12:1.0.2 |

|    2.4.1+    | 2.11  | kotlin-spark-api-2.4_2.11:1.0.2 |

## Releases

The list of Kotlin for Apache Spark releases is available [here](https://github.com/JetBrains/kotlin-spark-api/releases/).

The Kotlin for Spark artifacts adhere to the following convention:

`[name]_[Apache Spark version]_[Scala core version]:[Kotlin for Apache Spark API version]` 

The only exception to this is `scala-tuples-in-kotlin_[Scala core version]:[Kotlin for Apache Spark API version]`, which is 

independent of Spark.

[![Maven Central](https://img.shields.io/maven-central/v/org.jetbrains.kotlinx.spark/kotlin-spark-api_3.3.2_2.13.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:"org.jetbrains.kotlinx.spark"%20AND%20a:"kotlin-spark-api_3.3.2_2.13")

## How to configure Kotlin for Apache Spark in your project

You can add Kotlin for Apache Spark as a dependency to your project: `Maven`, `Gradle`, `SBT`, and `leinengen` are supported.

 

Here's an example `pom.xml`:

```xml

  org.jetbrains.kotlinx.spark

  kotlin-spark-api_3.3.2_2.13

  ${kotlin-spark-api.version}

    org.apache.spark

    spark-sql_2.13

    ${spark.version}

```

Note that you must match the version of the Kotlin for Apache Spark API to the Spark- and Scala version of your project.

You can find a complete example with `pom.xml` and `build.gradle` in the [Quick Start Guide](https://github.com/JetBrains/kotlin-spark-api/wiki/Quick-Start-Guide).

If you want to try a development version. You can use the versions published to [GH Packages](https://github.com/orgs/Kotlin/packages?tab=packages&q=kotlin-spark-api_3.3.0_2.13).

They typically have the same version as the release version, but with a `-SNAPSHOT` suffix. See the [GitHub Docs](https://docs.github.com/en/packages/learn-github-packages/installing-a-package)

for more information.

Once you have configured the dependency, you only need to add the following import to your Kotlin file: 

```kotlin

import org.jetbrains.kotlinx.spark.api.*

```   

### Jupyter

The Kotlin Spark API also supports Kotlin Jupyter notebooks.

To it, simply add

```jupyterpython

%use spark

```

to the top of your notebook. This will get the latest version of the API, together with the latest version of Spark.

To define a certain version of Spark or the API itself, simply add it like this:

```jupyterpython

%use spark(spark=3.3.2, scala=2.13, v=1.2.4)

```

Inside the notebook a Spark session will be initiated automatically. This can be accessed via the `spark` value.

`sc: JavaSparkContext` can also be accessed directly. The API operates pretty similarly.

There is also support for HTML rendering of Datasets and simple (Java)RDDs.

Check out the [example](examples/src/main/kotlin/org/jetbrains/kotlinx/spark/examples/JupyterExample.ipynb) as well.

To use Spark Streaming abilities, instead use

```jupyterpython

%use spark-streaming

```

This does not start a Spark session right away, meaning you can call `withSparkStreaming(batchDuration) {}` 

in whichever cell you want.

Check out the [example](examples/src/main/kotlin/org/jetbrains/kotlinx/spark/examples/streaming/JupyterStreamingExample.ipynb).

NOTE: You need `kotlin-jupyter-kernel` to be at least version 0.11.0.83 for the Kotlin Spark API to work. Also, if the 

`%use spark` magic does not output "Spark session has been started...", and `%use spark-streaming` doesn't work at all, 

add `%useLatestDescriptors` above it.

For more information, check the [wiki](https://github.com/JetBrains/kotlin-spark-api/wiki/Jupyter).

## Kotlin for Apache Spark features

### Creating a SparkSession in Kotlin

```kotlin

val spark = SparkSession

        .builder()

        .master("local[2]")

        .appName("Simple Application").orCreate

```

This is not needed when running the Kotlin Spark API from a Jupyter notebook.

### Creating a Dataset in Kotlin

```kotlin

spark.dsOf("a" to 1, "b" to 2)

```

The example above produces `Dataset>`. While Kotlin Pairs and Triples are supported, Scala Tuples are 

recommended for better support.

 

### Null safety

There are several aliases in API, like `leftJoin`, `rightJoin` etc. These are null-safe by design. 

For example, `leftJoin` is aware of nullability and returns `Dataset>`.

Note that we are forcing `RIGHT` to be nullable for you as a developer to be able to handle this situation. 

`NullPointerException`s are hard to debug in Spark, and we're doing our best to make them as rare as possible.

In Spark, you might also come across Scala-native `Option<*>` or Java-compatible `Optional<*>` classes.

We provide `getOrNull()` and `getOrElse()` functions for these to use Kotlin's null safety for good.

Similarly, you can also create `Option<*>`s and `Optional<*>`s like `T?.toOptional()` if a Spark function requires it.

### withSpark function

We provide you with useful function `withSpark`, which accepts everything that may be needed to run Spark — properties, name, master location and so on. It also accepts a block of code to execute inside Spark context.

After work block ends, `spark.stop()` is called automatically.

Do not use this when running the Kotlin Spark API from a Jupyter notebook.

```kotlin

withSpark {

    dsOf(1, 2)

        .map { it X it } // creates Tuple2

        .show()

}

```

`dsOf` is just one more way to create `Dataset` (`Dataset`) from varargs.

### withCached function

It can easily happen that we need to fork our computation to several paths. To compute things only once we should call `cache`

method. However, it becomes difficult to control when we're using cached `Dataset` and when not.

It is also easy to forget to unpersist cached data, which can break things unexpectedly or take up more memory

than intended.

To solve these problems we've added `withCached` function

```kotlin

withSpark {

    dsOf(1, 2, 3, 4, 5)

        .map { tupleOf(it, it + 2) }

        .withCached {

            showDS()

  

            filter { it._1 % 2 == 0 }.showDS()

        }

        .map { tupleOf(it._1, it._2, (it._1 + it._2) * 2) }

        .show()

}

```

Here we're showing cached `Dataset` for debugging purposes then filtering it. 

The `filter` method returns filtered `Dataset` and then the cached `Dataset` is being unpersisted, so we have more memory t

o call the `map` method and collect the resulting `Dataset`.

### toList and toArray methods

For more idiomatic Kotlin code we've added `toList` and `toArray` methods in this API. You can still use the `collect` method as in Scala API, however the result should be casted to `Array`.

  This is because `collect` returns a Scala array, which is not the same as Java/Kotlin one.

### Column infix/operator functions

Similar to the Scala API for `Columns`, many of the operator functions could be ported over.

For example:

```kotlin

dataset.select( col("colA") + 5 )

dataset.select( col("colA") / col("colB") )

dataset.where( col("colA") `===` 6 )

// or alternatively

dataset.where( col("colA") eq 6)

```

To read more, check the [wiki](https://github.com/JetBrains/kotlin-spark-api/wiki/Column-functions).

### Overload resolution ambiguity

We had to implement the functions `reduceGroups` and `reduce` for Kotlin separately as `reduceGroupsK` and `reduceK` respectively, because otherwise it caused resolution ambiguity between Kotlin, Scala and Java APIs, which was quite hard to solve.

We have a special example of work with this function in the [Groups example](https://github.com/JetBrains/kotlin-spark-api/blob/main/examples/src/main/kotlin/org/jetbrains/kotlinx/spark/examples/Group.kt).

### Tuples

Inspired by [ScalaTuplesInKotlin](https://github.com/Jolanrensen/ScalaTuplesInKotlin), the API introduces a lot of helper- extension functions

to make working with Scala Tuples a breeze in your Kotlin Spark projects. While working with data classes is encouraged,

for pair-like Datasets / RDDs / DStreams Scala Tuples are recommended, both for the useful helper functions, as well as Spark performance.

To enable these features

simply add

```kotlin

import org.jetbrains.kotlinx.spark.api.tuples.*

```

to the start of your file.

Tuple creation can be done in the following manners:

```kotlin

val a: Tuple2 = tupleOf(1, 2L)

val b: Tuple3 = t("test", 1.0, 2)

val c: Tuple3 = 5f X "aaa" X 1

```

To read more about tuples and all the added functions, refer to the [wiki](https://github.com/JetBrains/kotlin-spark-api/wiki/Tuples).

### Streaming

A popular Spark extension is [Spark Streaming](https://spark.apache.org/docs/latest/streaming-programming-guide.html). 

Of course the Kotlin Spark API also introduces a more Kotlin-esque approach to write your streaming programs.

There are examples for use with a checkpoint, Kafka and SQL in the [examples module](examples/src/main/kotlin/org/jetbrains/kotlinx/spark/examples/streaming).

We shall also provide a quick example below:

```kotlin

// Automatically provides ssc: JavaStreamingContext which starts and awaits termination or timeout

withSparkStreaming(batchDuration = Durations.seconds(1), timeout = 10_000) { // this: KSparkStreamingSession

    // create input stream for, for instance, Netcat: `$ nc -lk 9999`

    val lines: JavaReceiverInputDStream = ssc.socketTextStream("localhost", 9999)

  

    // split input stream on space

    val words: JavaDStream = lines.flatMap { it.split(" ").iterator() }

    // perform action on each formed RDD in the stream

    words.foreachRDD { rdd: JavaRDD, _: Time ->

      

          // to convert the JavaRDD to a Dataset, we need a spark session using the RDD context

          withSpark(rdd) { // this: KSparkSession

            val dataframe: Dataset = rdd.map { TestRow(word = it) }.toDS()

            dataframe

                .groupByKey { it.word }

                .count()

                .show()

            // +-----+--------+

            // |  key|count(1)|

            // +-----+--------+

            // |hello|       1|

            // |   is|       1|

            // |    a|       1|

            // | this|       1|

            // | test|       3|

            // +-----+--------+

        }

    }

}

```

For more information, check the [wiki](https://github.com/JetBrains/kotlin-spark-api/wiki/Streaming).

### User Defined Functions

Spark has a way to call functions from SQL using so-called [UDFs](https://spark.apache.org/docs/latest/sql-ref-functions-udf-scalar.html).

Using the Scala/Java API from Kotlin is not that obvious, so we decided to add special UDF support for Kotlin.

This support grew into a typesafe, name-safe, and feature-rich solution for which we will give an example:

```kotlin

// example of creation/naming, and registering of a simple UDF

val plusOne by udf { x: Int -> x + 1 }

plusOne.register()

spark.sql("SELECT plusOne(5)").show()

// +----------+

// |plusOne(5)|

// +----------+

// |         6|

// +----------+

// directly registering

udf.register("plusTwo") { x: Double -> x + 2.0 }

spark.sql("SELECT plusTwo(2.0d)").show()

// +------------+

// |plusTwo(2.0)|

// +------------+

// |         4.0|

// +------------+

// dataset select

val result: Dataset = myDs.select(

  plusOne(col(MyType::age))

)

```

We support:

  - a notation close to Spark's

  - smart naming (with reflection)

  - creation from function references

  - typed column operations

  - UDAF support and functional creation

  - (Unique!) simple vararg UDF support

For more, check the [extensive examples](examples/src/main/kotlin/org/jetbrains/kotlinx/spark/examples/UDFs.kt).

Also, check out the [wiki](https://github.com/Kotlin/kotlin-spark-api/wiki/UDF).

## Examples

For more, check out [examples](examples/src/main/kotlin/org/jetbrains/kotlinx/spark/examples) module.

To get up and running quickly, check out this [tutorial](https://github.com/JetBrains/kotlin-spark-api/wiki/Quick-Start-Guide). 

## Reporting issues / support

Please use [GitHub issues](https://github.com/JetBrains/kotlin-spark-api/issues) for filing feature requests and bug reports.

You are also welcome to join [kotlin-spark channel](https://kotlinlang.slack.com/archives/C015B9ZRGJF) in the Kotlin Slack.

## Contribution guide

Contributions are more than welcome! Pull requests can be created for the [main](https://github.com/Kotlin/kotlin-spark-api/tree/main) branch

and will be considered as soon as possible. Be sure to add the necessary tests for any new feature you add. The [main](https://github.com/Kotlin/kotlin-spark-api/tree/main)

branch always aims to target the latest available [Apache Spark version](https://spark.apache.org/downloads.html).

Note that we use [Java Comment Preprocessor](https://github.com/raydac/java-comment-preprocessor) to build the library

for all different supported versions of Apache Spark and Scala. 

The current values of these versions can be edited in `gradle.properties` and should always be the latest versions for commits.

For testing, all versions need a pass for the request to be accepted.

We use GitHub Actions to test and deploy the library for all versions, but locally you can also use the `gradlew_all_versions` file.

Of the [main](https://github.com/Kotlin/kotlin-spark-api/tree/main) branch, development versions of the library are published to 

[GitHub Packages](https://github.com/orgs/Kotlin/packages?tab=packages&q=kotlin-spark-api). This way, new features can be

tested quickly without having to wait for a full release. 

For full releases, the [release](https://github.com/Kotlin/kotlin-spark-api/tree/release) branch is updated.

## Code of Conduct

This project and the corresponding community is governed by the [JetBrains Open Source and Community Code of Conduct](https://confluence.jetbrains.com/display/ALL/JetBrains+Open+Source+and+Community+Code+of+Conduct). Please make sure you read it. 

## License

Kotlin for Apache Spark is licensed under the [Apache 2.0 License](LICENSE).