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From "Dongjoon Hyun (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-15466) Make `SparkSession` as the entry point to programming with RDD too
Date Sat, 21 May 2016 21:36:12 GMT
Dongjoon Hyun created SPARK-15466:
-------------------------------------

             Summary: Make `SparkSession` as the entry point to programming with RDD too
                 Key: SPARK-15466
                 URL: https://issues.apache.org/jira/browse/SPARK-15466
             Project: Spark
          Issue Type: Improvement
          Components: Examples, SQL
            Reporter: Dongjoon Hyun


`SparkSession` greatly reduces the number of concepts which Spark users must know. Currently,
`SparkSession` is defined as the entry point to programming Spark with the Dataset and DataFrame
API. And, we can easily get `RDD` by calling `Dataset.rdd` or `DataFrame.rdd`, too.

However, many usages (including examples) are observed to extract `SparkSession.sparkContext`
and keep it as own variable to call `parallelize`.

If `SparkSession` supports RDD seamlessly too, it would be great for usability. We can do
this by simply adding `parallelize` API.

** Example **
{code:title=SparkPi.scala|borderStyle=solid}
 object SparkPi {
   def main(args: Array[String]) {
     val spark = SparkSession
       .builder
       .appName("Spark Pi")
       .getOrCreate()
-    val sc = spark.sparkContext
     val slices = if (args.length > 0) args(0).toInt else 2
     val n = math.min(100000L * slices, Int.MaxValue).toInt // avoid overflow
-    val count = sc.parallelize(1 until n, slices).map { i =>
+    val count = spark.parallelize(1 until n, slices).map { i =>
     val count = spark.parallelize(1 until n, slices).map { i =>
       val x = random * 2 - 1
       val y = random * 2 - 1
       if (x*x + y*y < 1) 1 else 0
     }.reduce(_ + _)
     println("Pi is roughly " + 4.0 * count / n)
     spark.stop()
   }
 }
{code}

{code:title=pi.py|borderStyle=solid}
 spark = SparkSession\
   .builder\
   .appName("PythonPi")\
   .getOrCreate()

- sc = spark._sc
-
 partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
 n = 100000 * partitions

 def f(_):
   x = random() * 2 - 1
   y = random() * 2 - 1
   return 1 if x ** 2 + y ** 2 < 1 else 0

-count = sc.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
 count = spark.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
 print("Pi is roughly %f" % (4.0 * count / n))

 spark.stop()
{code}



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