renaming jupyter integration

Author: Jolanrensen

README.md

@@ -80,12 +80,12 @@ The Kotlin Spark API also supports Kotlin Jupyter notebooks.
 To it, simply add
 
 ```jupyterpython
-%use kotlin-spark-api
+%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 kotlin-spark-api(spark=3.2, v=1.1.0)
+%use spark(spark=3.2, v=1.1.0)
 ```
 
 Inside the notebook a Spark session will be initiated automatically. This can be accessed via the `spark` value.
@@ -95,7 +95,7 @@ There is also support for HTML rendering of Datasets and simple (Java)RDDs.
 
 To use Spark Streaming abilities, instead use
 ```jupyterpython
-%use kotlin-spark-api-streaming
+%use spark-streaming
 ```
 This does not start a Spark session right away, meaning you can call `withSparkStreaming(batchDuration) {}` 
 in whichever cell you want.

jupyter/src/main/kotlin/org/jetbrains/kotlinx/spark/api/jupyter/SparkIntegration.kt

@@ -24,7 +24,7 @@ import org.intellij.lang.annotations.Language
 import org.jetbrains.kotlinx.jupyter.api.KotlinKernelHost
 
 /**
- * %use kotlin-spark-api
+ * %use spark
  */
 @Suppress("UNUSED_VARIABLE", "LocalVariableName")
 @OptIn(ExperimentalStdlibApi::class)
@@ -49,7 +49,7 @@ internal class SparkIntegration : Integration() {
                     org.apache.spark.api.java.JavaSparkContext(spark.sparkContext) 
                 }""".trimIndent(),
             """
-                println("Spark session has been started and is running. No `withSpark { }` necessary, you can access `spark` and `sc` directly. To use Spark streaming, use `%use kotlin-spark-api-streaming` instead.")""".trimIndent(),
+                println("Spark session has been started and is running. No `withSpark { }` necessary, you can access `spark` and `sc` directly. To use Spark streaming, use `%use spark-streaming` instead.")""".trimIndent(),
             """
                 inline fun <reified T> List<T>.toDS(): Dataset<T> = toDS(spark)""".trimIndent(),
             """

jupyter/src/main/kotlin/org/jetbrains/kotlinx/spark/api/jupyter/SparkStreamingIntegration.kt

@@ -42,7 +42,7 @@ import scala.collection.Iterable as ScalaIterable
 import scala.collection.Iterator as ScalaIterator
 
 /**
- * %use kotlin-spark-api-streaming
+ * %use spark-streaming
  */
 @Suppress("UNUSED_VARIABLE", "LocalVariableName")
 @OptIn(ExperimentalStdlibApi::class)
@@ -54,7 +54,7 @@ internal class SparkStreamingIntegration : Integration() {
         @Language("kts")
         val _1 = listOf(
             """
-                println("To start a spark streaming session, simply use `withSparkStreaming { }` inside a cell. To use Spark normally, use `withSpark { }` in a cell, or use `%use kotlin-spark-api` to start a Spark session for the whole notebook.")""".trimIndent(),
+                println("To start a spark streaming session, simply use `withSparkStreaming { }` inside a cell. To use Spark normally, use `withSpark { }` in a cell, or use `%use spark` to start a Spark session for the whole notebook.")""".trimIndent(),
         ).map(::execute)
     }
 }