ERROR: java.lang.IllegalStateException: User did not initialize spark context - scala

Question

Scala version: 2.11.12
Spark version: 2.4.0
emr-5.23.0
Get the following when running the below command to create an Amazon EMR cluster
spark-submit --class etl.SparkDataProcessor --master yarn --deploy-mode cluster --conf spark.yarn.appMasterEnv.ETL_NAME=foo --conf spark.yarn.appMasterEnv.ETL_SPARK_MASTER=yarn --conf spark.yarn.appMasterEnv.ETL_AWS_ACCESS_KEY_ID=123 --conf spark.yarn.appMasterEnv.ETL_AWS_SECRET_ACCESS_KEY=abc MY-Tool.jar
Exception
ERROR ApplicationMaster: Uncaught exception:
java.lang.IllegalStateException: User did not initialize spark context!
at org.apache.spark.deploy.yarn.ApplicationMaster.runDriver(ApplicationMaster.scala:485)
at org.apache.spark.deploy.yarn.ApplicationMaster.org$apache$spark$deploy$yarn$ApplicationMaster$$runImpl(ApplicationMaster.scala:305)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$1.apply$mcV$sp(ApplicationMaster.scala:245)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$1.apply(ApplicationMaster.scala:245)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$run$1.apply(ApplicationMaster.scala:245)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$3.run(ApplicationMaster.scala:773)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1844)
at org.apache.spark.deploy.yarn.ApplicationMaster.doAsUser(ApplicationMaster.scala:772)
at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:244)
at org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:797)
at org.apache.spark.deploy.yarn.ApplicationMaster.main(ApplicationMaster.scala)
How I create my spark session (where sparkMaster = yarn)
lazy val spark: SparkSession = {
val logger: Logger = Logger.getLogger("etl");
val sparkAppName = EnvConfig.ETL_NAME
val sparkMaster = EnvConfig.ETL_SPARK_MASTER
val sparkInstance = SparkSession
.builder()
.appName(sparkAppName)
.master(sparkMaster)
.getOrCreate()
val hadoopConf = sparkInstance.sparkContext.hadoopConfiguration
hadoopConf.set("fs.s3.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
hadoopConf.set("fs.s3a.access.key", EnvConfig.ETL_AWS_ACCESS_KEY_ID)
hadoopConf.set("fs.s3a.secret.key", EnvConfig.ETL_AWS_SECRET_ACCESS_KEY)
logger.info("Created My SparkSession")
logger.info(s"Spark Application Name: $sparkAppName")
logger.info(s"Spark Master: $sparkMaster")
sparkInstance
}
UPDATE:
I determined that due to the application logic, in certain cases, we did not initialize the spark session. Because of this, it seems that when the cluster terminates, it also tries to do something with the session (perhaps close it) and is thus failing. Now that I have figured out this issue, the application runs but never actually completes. Currently, it seems to be hanging in a particular part involving spark when running in cluster mode:
val data: DataFrame = spark.read
.option("header", "true")
.option("inferSchema", "true")
.csv(s"s3://$csvPath/$fileKey")
.toDF()
20/03/16 18:38:35 INFO Client: Application report for application_1584324418613_0031 (state: RUNNING)

Answer 1

AFAIK EnvConfig.ETL_AWS_ACCESS_KEY_ID and ETL_AWS_SECRET_ACCESS_KEY are not getting populated due to which sparksession cant be instanciated with null or empty values . try to print and debug the values.
also reading the properties from --conf spark.xxx
should be like this example. I hope you are following this...
spark.sparkContext.getConf.getOption("spark. ETL_AWS_ACCESS_KEY_ID")
once you check that, this example way should work...
/**
* Hadoop-AWS Configuration
*/
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3a.proxy.host", proxyHost)
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3a.proxy.port", proxyPort)
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3a.aws.credentials.provider", "com.amazonaws.auth.DefaultAWSCredentialsProviderChain")
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3.impl", "org.apache.hadoop.fs.s3native.NativeS3FileSystem")
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3a.server-side-encryption-algorithm", "AES256")
sparkSession.sparkContext.hadoopConfiguration.set("fs.s3n.server-side-encryption-algorithm", "AES256")
sparkSession.sparkContext.hadoopConfiguration.set("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem
another thing is, use
--master yarn or --master local[*] you can use instead of
-conf spark.yarn.appMasterEnv.ETL_SPARK_MASTER=yarn
UPDATE :
--conf spark.driver.port=20002 may solve this issue. where 20002 is orbitary port.. seems like its waiting for the particular port for some time and its retrying for some time and its failing with the exception you got.
I got this idea by walking through the Sparks application master code from here
and comment This a bit hacky, but we need to wait until the spark.driver.port property has been set by the Thread executing the user class.
you can try this and let me know.
Further reading : Apache Spark : How to change the port the Spark driver listens to

Answer 2

In my case (after resolving the application issues), I needed to include core AND task node types when deploying in cluster mode.