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Experimental Spark features


The package sub-command offers to package Scala CLI projects as JARs ready to be passed to spark-submit, and optimized for it.

//> using dep org.apache.spark::spark-sql:3.0.3
//> using scala 2.12.15

import org.apache.spark._
import org.apache.spark.sql._

object SparkJob {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("Test job")
import spark.implicits._
def sc = spark.sparkContext
val accum = sc.longAccumulator
sc.parallelize(1 to 10).foreach(x => accum.add(x))
println("Result: " + accum.value)
scala-cli --power package --spark SparkJob.scala -o spark-job.jar
Compiling project (Scala 2.12.15, JVM)
Compiled project (Scala 2.12.15, JVM)
Wrote spark-job.jar
spark-submit spark-job.jar

Result: 55

Running Spark jobs

The run sub-command can run Spark jobs, when passed --spark:

scala-cli run --spark SparkJob.scala # same example as above

Note that this requires either

  • spark-submit to be in available in PATH
  • SPARK_HOME to be set in the environment

Running Spark jobs in a standalone way

The run sub-command can not only run Spark jobs, but it can also work without a Spark distribution. For that to work, it downloads Spark JARs, and calls the main class of spark-submit itself via these JARs:

scala-cli run --spark-standalone SparkJob.scala # same example as above

Running Hadoop jobs

The run sub-command can run Hadoop jobs, by calling the hadoop jar command under-the-hood:

//> using dep org.apache.hadoop:hadoop-client-api:3.3.3

// from

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {

public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
context.write(word, one);

public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
context.write(key, result);

public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
scala-cli run --hadoop