JetBrains/ kotlin-spark-api

(1.0.0-preview2) about 1 month ago

Kotlin

Scala

bigdata

Apache Spark

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JetBrains

176 Stars

16 Forks

Apache License 2.0

152 Commits

11 Opened issues

Description

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

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Kotlin for Apache® Spark™

Name: kotlin-spark-api
Brand: kotlin-spark-api
SKU: //JetBrains/kotlin-spark-api
Rating: 2.19 (176 reviews)

Your next API to work with Apache Spark.

This project adds a missing layer of compatibility between Kotlin and Apache Spark. 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 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.

Supported versions of Apache Spark

| Apache Spark | Scala | Kotlin for Apache Spark | |:------------:|:-----------:|:------------:| | 3.0.0+ | 2.12 | kotlin-spark-api-3.0.0:1.0.0-preview2 | | 2.4.1+ | 2.12 | kotlin-spark-api-2.42.12:1.0.0-preview2 | | 2.4.1+ | 2.11 | kotlin-spark-api-2.42.11:1.0.0-preview2 |

Releases

The list of Kotlin for Apache Spark releases is available here. The Kotlin for Spark artifacts adhere to the following convention:

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

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

  org.jetbrains.kotlinx.spark
  kotlin-spark-api-3.0.0
  ${kotlin-spark-api.version}


    org.apache.spark
    spark-sql_2.12
    ${spark.version}

Note that

core

is being compiled against Scala version

2.12

.
You can find a complete example with

pom.xml

and

build.gradle

in the Quick Start Guide.

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.*

Kotlin for Apache Spark features

Creating a SparkSession in Kotlin

val spark = SparkSession
        .builder()
        .master("local[2]")
        .appName("Simple Application").orCreate

Creating a Dataset in Kotlin

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

The example above produces

Dataset>

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 doing our best to make them as rare as possible.

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.

withSpark {
    dsOf(1, 2)
            .map { it to it }
            .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

withSpark {
    dsOf(1, 2, 3, 4, 5)
            .map { it to (it + 2) }
            .withCached {
                showDS()

            filter { it.first % 2 == 0 }.showDS()
        }
        .map { c(it.first, it.second, (it.first + it.second) * 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.

Examples

For more, check out examples module. To get up and running quickly, check out this tutorial.

Reporting issues/Support

Please use GitHub issues for filing feature requests and bug reports. You are also welcome to join kotlin-spark channel in the Kotlin Slack.

Code of Conduct

This project and the corresponding community is governed by the 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.