The document shows commands to submit a Spark job called "demo" to a Spark job server. It uploads a JAR file containing the job, creates a context with resources, then submits the job, returning a job ID. It contains the Scala code for the WordCountExample job which counts words in a string passed as a config parameter. It validates the input and runs the job to count words and return the results.

5. curl --data-binary @my-spark-job-allinone.jar jobserver:8090/jars/demo
curl -d "" jobserver:8090/contexts/agg?num-cpu-cores=2&memory-per-node=2048m
curl -d "input.string = A lazy dog jumped mean dog"
'jobserver:8090/jobs?appName=demo&context=agg&classPath=spark.jobserver.WordCountExample'
{
"status": "STARTED",
"result": {
"jobId": "a3e0e19d-f10f-4b78-9eb3-e65d63853a74",
"context": "agg"
}
}

6. object WordCountExample extends SparkJob {
override def validate(sc: SparkContext, config: Config): SparkJobValidation = {
Try(config.getString(“input.string”))
.map(x => SparkJobValid)
.getOrElse(SparkJobInvalid(“No input.string”))
}
override def runJob(sc: SparkContext, config: Config): Any = {
val dd = sc.parallelize(config.getString(“input.string”).split(" ").toSeq)
dd.map((_, 1)).reduceByKey(_ + _).collect().toMap
}
}

7. this.namedRdds.update("french_dictionary", frenchDictionaryRDD)
val rdd = this.namedRdds.get[(String, String)]("french_dictionary").get
val NamedRDD(frenchDictionaryRDD, _ ,_) =
namedObjects.get[NamedRDD[(String, String)]]("rdd:french_dictionary").get
val NamedDataFrame(frenchDictionaryDF, _, _) =
namedObjects.get[NamedDataFrame]("df:some df").get