The Answer to your questions is basically understanding the difference of a Spark Application and a Spark Session.
Spark application can be used:
- for a single batch job
- an interactive session with multiple jobs
- a long-lived server continually satisfying requests
- A Spark job can consist of more than just a single map and reduce.
- A Spark Application can consist of more than one session
A SparkSession on the other hand is associated to a Spark Application:
- Generally, a session is an interaction between two or more entities.
- in Spark 2.0 you can use SparkSession
- A SparkSession can be created without creating SparkConf, SparkContext or SQLContext, (they’re encapsulated within the SparkSession)
Global temporary views are introduced in Spark 2.1.0 release. This feature is useful when you want to share data among different sessions and keep alive until your application ends.Please see a shot sample I wrote to illustrate the use for createTempView
and createGlobalTempView
object NewSessionApp {
def main(args: Array[String]): Unit = {
val logFile = "data/README.md" // Should be some file on your system
val spark = SparkSession.
builder.
appName("Simple Application").
master("local").
getOrCreate()
val logData = spark.read.textFile(logFile).cache()
logData.createGlobalTempView("logdata")
spark.range(1).createTempView("foo")
// within the same session the foo table exists
println("""spark.catalog.tableExists("foo") = """ + spark.catalog.tableExists("foo"))
//spark.catalog.tableExists("foo") = true
// for a new session the foo table does not exists
val newSpark = spark.newSession
println("""newSpark.catalog.tableExists("foo") = """ + newSpark.catalog.tableExists("foo"))
//newSpark.catalog.tableExists("foo") = false
//both session can access the logdata table
spark.sql("SELECT * FROM global_temp.logdata").show()
newSpark.sql("SELECT * FROM global_temp.logdata").show()
spark.stop()
}
}
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