Has any one been able to connect Spark Job Server to HDInsight - azure-hdinsight

Question

HDinsight used to support Spark Job Server https://github.com/Huachao/azure-content/blob/master/articles/hdinsight/hdinsight-apache-spark-job-server.md but it is no longer supported does anyone still connect to HDInsight via SJS, thanks, I am trying to connect by embedding SJS in an identical spark distribution and running it as a service while pointing to HDinsigt cluster via configurations
spark {
master = yarn
# Default # of CPUs for jobs to use for Spark standalone cluster
job-number-cpus = 4
jobserver {
port = 8090
context-per-jvm = true
daorootdir = "/tmp/spark-jobserver"
binarydao {
class = spark.jobserver.io.HdfsBinaryDAO
}
metadatadao {
class = spark.jobserver.io.MetaDataSqlDAO
}
sqldao {
# Slick database driver, full classpath
slick-driver = slick.jdbc.MySQLProfile
# JDBC driver, full classpath
jdbc-driver = com.mysql.jdbc.Driver
jdbc {
url = "jdbc:mysql://x.x.x.x/spark_jobserver"
user = "xxxxx"
password = "xxxx"
}
dbcp {
maxactive = 20
maxidle = 10
initialsize = 10
}
}
}
flyway.locations="db/mysql/migration"
# predefined Spark contexts
contexts {
my-low-latency-context {
num-cpu-cores = 1 # Number of cores to allocate. Required.
memory-per-node = 512m # Executor memory per node, -Xmx style eg 512m, 1G, etc.
}
# define additional contexts here
}
# universal context configuration. These settings can be overridden, see README.md
context-settings {
# choose a port that is free on your system and also the 16 (depends on max retries for submitting the job) next portnumbers should be free
spark.driver.port = 32456 # important
# defines the place where your spark-assembly jar is located in your hdfs
spark.yarn.jar = "hdfs://hadoopHDFSCluster/spark_jars/spark-assembly-1.6.0-hadoop2.6.0.jar" # important
# defines the YARN queue the job is submitted to
#spark.yarn.queue = root.myYarnQueue
num-cpu-cores = 2 # Number of cores to allocate. Required.
memory-per-node = 512m # Executor memory per node, -Xmx style eg 512m, #1G, etc.
# in case spark distribution should be accessed from HDFS (as opposed to being installed on every mesos slave)
# spark.executor.uri = "hdfs://namenode:8020/apps/spark/spark.tgz"
# uris of jars to be loaded into the classpath for this context. Uris is a string list, or a string separated by commas ','
# dependent-jar-uris = ["file:///some/path/present/in/each/mesos/slave/somepackage.jar"]
# If you wish to pass any settings directly to the sparkConf as-is, add them here in passthrough,
# such as hadoop connection settings that don't use the "spark." prefix
passthrough {
#es.nodes = "192.1.1.1"
}
}
# This needs to match SPARK_HOME for cluster SparkContexts to be created successfully
home = "/usr/hdp/current/spark2-client"
}