[SPARK-15031][EXAMPLE] Use SparkSession in examples

## What changes were proposed in this pull request?
Use `SparkSession` according to [SPARK-15031](https://issues.apache.org/jira/browse/SPARK-15031)

`MLLLIB` is not recommended to use now, so examples in `MLLIB` are ignored in this PR.
`StreamingContext` can not be directly obtained from `SparkSession`, so example in `Streaming` are ignored too.

cc andrewor14

## How was this patch tested?
manual tests with spark-submit

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #13164 from zhengruifeng/use_sparksession_ii.

examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java

@@ -17,11 +17,10 @@
 
 package org.apache.spark.examples;
 
-import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.sql.SparkSession;
 
 import java.io.Serializable;
 import java.util.Arrays;
@@ -122,9 +121,12 @@ public final class JavaHdfsLR {
 
     showWarning();
 
-    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
-    JavaSparkContext sc = new JavaSparkContext(sparkConf);
-    JavaRDD<String> lines = sc.textFile(args[0]);
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaHdfsLR")
+      .getOrCreate();
+
+    JavaRDD<String> lines = spark.read().text(args[0]).javaRDD();
     JavaRDD<DataPoint> points = lines.map(new ParsePoint()).cache();
     int ITERATIONS = Integer.parseInt(args[1]);
 
@@ -152,6 +154,6 @@ public final class JavaHdfsLR {
 
     System.out.print("Final w: ");
     printWeights(w);
-    sc.stop();
+    spark.stop();
   }
 }

examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java

@@ -20,12 +20,13 @@ package org.apache.spark.examples;
 import com.google.common.collect.Lists;
 import scala.Tuple2;
 import scala.Tuple3;
-import org.apache.spark.SparkConf;
+
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.sql.SparkSession;
 
 import java.io.Serializable;
 import java.util.List;
@@ -99,9 +100,12 @@ public final class JavaLogQuery {
   }
 
   public static void main(String[] args) {
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaLogQuery")
+      .getOrCreate();
 
-    SparkConf sparkConf = new SparkConf().setAppName("JavaLogQuery");
-    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
+    JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
 
     JavaRDD<String> dataSet = (args.length == 1) ? jsc.textFile(args[0]) : jsc.parallelize(exampleApacheLogs);
 
@@ -123,6 +127,6 @@ public final class JavaLogQuery {
     for (Tuple2<?,?> t : output) {
       System.out.println(t._1() + "\t" + t._2());
     }
-    jsc.stop();
+    spark.stop();
   }
 }

examples/src/main/java/org/apache/spark/examples/JavaPageRank.java

@@ -26,14 +26,13 @@ import scala.Tuple2;
 
 import com.google.common.collect.Iterables;
 
-import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFlatMapFunction;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.sql.SparkSession;
 
 /**
  * Computes the PageRank of URLs from an input file. Input file should
@@ -73,15 +72,17 @@ public final class JavaPageRank {
 
     showWarning();
 
-    SparkConf sparkConf = new SparkConf().setAppName("JavaPageRank");
-    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaPageRank")
+      .getOrCreate();
 
     // Loads in input file. It should be in format of:
     //     URL         neighbor URL
     //     URL         neighbor URL
     //     URL         neighbor URL
     //     ...
-    JavaRDD<String> lines = ctx.textFile(args[0], 1);
+    JavaRDD<String> lines = spark.read().text(args[0]).javaRDD();
 
     // Loads all URLs from input file and initialize their neighbors.
     JavaPairRDD<String, Iterable<String>> links = lines.mapToPair(
@@ -132,6 +133,6 @@ public final class JavaPageRank {
         System.out.println(tuple._1() + " has rank: " + tuple._2() + ".");
     }
 
-    ctx.stop();
+    spark.stop();
   }
 }

examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java

@@ -17,11 +17,11 @@
 
 package org.apache.spark.examples;
 
-import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.sql.SparkSession;
 
 import java.util.ArrayList;
 import java.util.List;
@@ -33,8 +33,12 @@ import java.util.List;
 public final class JavaSparkPi {
 
   public static void main(String[] args) throws Exception {
-    SparkConf sparkConf = new SparkConf().setAppName("JavaSparkPi");
-    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaSparkPi")
+      .getOrCreate();
+
+    JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
 
     int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
     int n = 100000 * slices;
@@ -61,6 +65,6 @@ public final class JavaSparkPi {
 
     System.out.println("Pi is roughly " + 4.0 * count / n);
 
-    jsc.stop();
+    spark.stop();
   }
 }

examples/src/main/java/org/apache/spark/examples/JavaStatusTrackerDemo.java

@@ -17,13 +17,14 @@
 
 package org.apache.spark.examples;
 
-import org.apache.spark.SparkConf;
 import org.apache.spark.SparkJobInfo;
 import org.apache.spark.SparkStageInfo;
 import org.apache.spark.api.java.JavaFutureAction;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
+import org.apache.spark.sql.SparkSession;
+
 
 import java.util.Arrays;
 import java.util.List;
@@ -44,11 +45,15 @@ public final class JavaStatusTrackerDemo {
   }
 
   public static void main(String[] args) throws Exception {
-    SparkConf sparkConf = new SparkConf().setAppName(APP_NAME);
-    final JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    SparkSession spark = SparkSession
+      .builder()
+      .appName(APP_NAME)
+      .getOrCreate();
+
+    final JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
 
     // Example of implementing a progress reporter for a simple job.
-    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 5).map(
+    JavaRDD<Integer> rdd = jsc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 5).map(
         new IdentityWithDelay<Integer>());
     JavaFutureAction<List<Integer>> jobFuture = rdd.collectAsync();
     while (!jobFuture.isDone()) {
@@ -58,13 +63,13 @@ public final class JavaStatusTrackerDemo {
         continue;
       }
       int currentJobId = jobIds.get(jobIds.size() - 1);
-      SparkJobInfo jobInfo = sc.statusTracker().getJobInfo(currentJobId);
-      SparkStageInfo stageInfo = sc.statusTracker().getStageInfo(jobInfo.stageIds()[0]);
+      SparkJobInfo jobInfo = jsc.statusTracker().getJobInfo(currentJobId);
+      SparkStageInfo stageInfo = jsc.statusTracker().getStageInfo(jobInfo.stageIds()[0]);
       System.out.println(stageInfo.numTasks() + " tasks total: " + stageInfo.numActiveTasks() +
           " active, " + stageInfo.numCompletedTasks() + " complete");
     }
 
     System.out.println("Job results are: " + jobFuture.get());
-    sc.stop();
+    spark.stop();
   }
 }

examples/src/main/java/org/apache/spark/examples/JavaTC.java

@@ -25,10 +25,10 @@ import java.util.Set;
 
 import scala.Tuple2;
 
-import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.sql.SparkSession;
 
 /**
  * Transitive closure on a graph, implemented in Java.
@@ -64,10 +64,15 @@ public final class JavaTC {
   }
 
   public static void main(String[] args) {
-    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
-    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaTC")
+      .getOrCreate();
+
+    JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
+
     Integer slices = (args.length > 0) ? Integer.parseInt(args[0]): 2;
-    JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(generateGraph(), slices).cache();
+    JavaPairRDD<Integer, Integer> tc = jsc.parallelizePairs(generateGraph(), slices).cache();
 
     // Linear transitive closure: each round grows paths by one edge,
     // by joining the graph's edges with the already-discovered paths.
@@ -94,6 +99,6 @@ public final class JavaTC {
     } while (nextCount != oldCount);
 
     System.out.println("TC has " + tc.count() + " edges.");
-    sc.stop();
+    spark.stop();
   }
 }

examples/src/main/java/org/apache/spark/examples/JavaWordCount.java

@@ -18,13 +18,13 @@
 package org.apache.spark.examples;
 
 import scala.Tuple2;
-import org.apache.spark.SparkConf;
+
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.sql.SparkSession;
 
 import java.util.Arrays;
 import java.util.Iterator;
@@ -41,9 +41,12 @@ public final class JavaWordCount {
       System.exit(1);
     }
 
-    SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");
-    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
-    JavaRDD<String> lines = ctx.textFile(args[0], 1);
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaWordCount")
+      .getOrCreate();
+
+    JavaRDD<String> lines = spark.read().text(args[0]).javaRDD();
 
     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
@@ -72,6 +75,6 @@ public final class JavaWordCount {
     for (Tuple2<?,?> tuple : output) {
       System.out.println(tuple._1() + ": " + tuple._2());
     }
-    ctx.stop();
+    spark.stop();
   }
 }

examples/src/main/python/als.py

@@ -28,7 +28,7 @@ import sys
 import numpy as np
 from numpy.random import rand
 from numpy import matrix
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 LAMBDA = 0.01   # regularization
 np.random.seed(42)
@@ -62,7 +62,13 @@ if __name__ == "__main__":
       example. Please use pyspark.ml.recommendation.ALS for more
       conventional use.""", file=sys.stderr)
 
-    sc = SparkContext(appName="PythonALS")
+    spark = SparkSession\
+        .builder\
+        .appName("PythonALS")\
+        .getOrCreate()
+
+    sc = spark._sc
+
     M = int(sys.argv[1]) if len(sys.argv) > 1 else 100
     U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
     F = int(sys.argv[3]) if len(sys.argv) > 3 else 10
@@ -99,4 +105,4 @@ if __name__ == "__main__":
         print("Iteration %d:" % i)
         print("\nRMSE: %5.4f\n" % error)
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/avro_inputformat.py

@@ -19,8 +19,8 @@ from __future__ import print_function
 
 import sys
 
-from pyspark import SparkContext
 from functools import reduce
+from pyspark.sql import SparkSession
 
 """
 Read data file users.avro in local Spark distro:
@@ -64,7 +64,13 @@ if __name__ == "__main__":
         exit(-1)
 
     path = sys.argv[1]
-    sc = SparkContext(appName="AvroKeyInputFormat")
+
+    spark = SparkSession\
+        .builder\
+        .appName("AvroKeyInputFormat")\
+        .getOrCreate()
+
+    sc = spark._sc
 
     conf = None
     if len(sys.argv) == 3:
@@ -82,4 +88,4 @@ if __name__ == "__main__":
     for k in output:
         print(k)
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/kmeans.py

@@ -27,7 +27,7 @@ from __future__ import print_function
 import sys
 
 import numpy as np
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 
 def parseVector(line):
@@ -55,8 +55,12 @@ if __name__ == "__main__":
        as an example! Please refer to examples/src/main/python/ml/kmeans_example.py for an
        example on how to use ML's KMeans implementation.""", file=sys.stderr)
 
-    sc = SparkContext(appName="PythonKMeans")
-    lines = sc.textFile(sys.argv[1])
+    spark = SparkSession\
+        .builder\
+        .appName("PythonKMeans")\
+        .getOrCreate()
+
+    lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])
     data = lines.map(parseVector).cache()
     K = int(sys.argv[2])
     convergeDist = float(sys.argv[3])
@@ -79,4 +83,4 @@ if __name__ == "__main__":
 
     print("Final centers: " + str(kPoints))
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/logistic_regression.py

@@ -27,7 +27,7 @@ from __future__ import print_function
 import sys
 
 import numpy as np
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 
 D = 10  # Number of dimensions
@@ -55,8 +55,13 @@ if __name__ == "__main__":
       Please refer to examples/src/main/python/ml/logistic_regression_with_elastic_net.py
       to see how ML's implementation is used.""", file=sys.stderr)
 
-    sc = SparkContext(appName="PythonLR")
-    points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache()
+    spark = SparkSession\
+        .builder\
+        .appName("PythonLR")\
+        .getOrCreate()
+
+    points = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])\
+        .mapPartitions(readPointBatch).cache()
     iterations = int(sys.argv[2])
 
     # Initialize w to a random value
@@ -80,4 +85,4 @@ if __name__ == "__main__":
 
     print("Final w: " + str(w))
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/pagerank.py

@@ -25,7 +25,7 @@ import re
 import sys
 from operator import add
 
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 
 def computeContribs(urls, rank):
@@ -51,14 +51,17 @@ if __name__ == "__main__":
           file=sys.stderr)
 
     # Initialize the spark context.
-    sc = SparkContext(appName="PythonPageRank")
+    spark = SparkSession\
+        .builder\
+        .appName("PythonPageRank")\
+        .getOrCreate()
 
     # Loads in input file. It should be in format of:
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     ...
-    lines = sc.textFile(sys.argv[1], 1)
+    lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])
 
     # Loads all URLs from input file and initialize their neighbors.
     links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
@@ -79,4 +82,4 @@ if __name__ == "__main__":
     for (link, rank) in ranks.collect():
         print("%s has rank: %s." % (link, rank))
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/parquet_inputformat.py

@@ -18,7 +18,7 @@ from __future__ import print_function
 
 import sys
 
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 """
 Read data file users.parquet in local Spark distro:
@@ -47,7 +47,13 @@ if __name__ == "__main__":
         exit(-1)
 
     path = sys.argv[1]
-    sc = SparkContext(appName="ParquetInputFormat")
+
+    spark = SparkSession\
+        .builder\
+        .appName("ParquetInputFormat")\
+        .getOrCreate()
+
+    sc = spark._sc
 
     parquet_rdd = sc.newAPIHadoopFile(
         path,
@@ -59,4 +65,4 @@ if __name__ == "__main__":
     for k in output:
         print(k)
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/pi.py

@@ -20,14 +20,20 @@ import sys
 from random import random
 from operator import add
 
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 
 if __name__ == "__main__":
     """
         Usage: pi [partitions]
     """
-    sc = SparkContext(appName="PythonPi")
+    spark = SparkSession\
+        .builder\
+        .appName("PythonPi")\
+        .getOrCreate()
+
+    sc = spark._sc
+
     partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     n = 100000 * partitions
 
@@ -39,4 +45,4 @@ if __name__ == "__main__":
     count = sc.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
     print("Pi is roughly %f" % (4.0 * count / n))
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/sort.py

@@ -19,15 +19,20 @@ from __future__ import print_function
 
 import sys
 
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 
 if __name__ == "__main__":
     if len(sys.argv) != 2:
         print("Usage: sort <file>", file=sys.stderr)
         exit(-1)
-    sc = SparkContext(appName="PythonSort")
-    lines = sc.textFile(sys.argv[1], 1)
+
+    spark = SparkSession\
+        .builder\
+        .appName("PythonSort")\
+        .getOrCreate()
+
+    lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])
     sortedCount = lines.flatMap(lambda x: x.split(' ')) \
         .map(lambda x: (int(x), 1)) \
         .sortByKey()
@@ -37,4 +42,4 @@ if __name__ == "__main__":
     for (num, unitcount) in output:
         print(num)
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/transitive_closure.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 import sys
 from random import Random
 
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 numEdges = 200
 numVertices = 100
@@ -41,7 +41,13 @@ if __name__ == "__main__":
     """
     Usage: transitive_closure [partitions]
     """
-    sc = SparkContext(appName="PythonTransitiveClosure")
+    spark = SparkSession\
+        .builder\
+        .appName("PythonTransitiveClosure")\
+        .getOrCreate()
+
+    sc = spark._sc
+
     partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     tc = sc.parallelize(generateGraph(), partitions).cache()
 
@@ -67,4 +73,4 @@ if __name__ == "__main__":
 
     print("TC has %i edges" % tc.count())
 
-    sc.stop()
+    spark.stop()

examples/src/main/python/wordcount.py

@@ -20,15 +20,20 @@ from __future__ import print_function
 import sys
 from operator import add
 
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 
 
 if __name__ == "__main__":
     if len(sys.argv) != 2:
         print("Usage: wordcount <file>", file=sys.stderr)
         exit(-1)
-    sc = SparkContext(appName="PythonWordCount")
-    lines = sc.textFile(sys.argv[1], 1)
+
+    spark = SparkSession\
+        .builder\
+        .appName("PythonWordCount")\
+        .getOrCreate()
+
+    lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])
     counts = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (x, 1)) \
                   .reduceByKey(add)
@@ -36,4 +41,4 @@ if __name__ == "__main__":
     for (word, count) in output:
         print("%s: %i" % (word, count))
 
-    sc.stop()
+    spark.stop()

examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala

@@ -18,7 +18,8 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.SparkSession
 
 /**
  * Usage: BroadcastTest [slices] [numElem] [blockSize]
@@ -28,9 +29,16 @@ object BroadcastTest {
 
     val blockSize = if (args.length > 2) args(2) else "4096"
 
-    val sparkConf = new SparkConf().setAppName("Broadcast Test")
+    val sparkConf = new SparkConf()
       .set("spark.broadcast.blockSize", blockSize)
-    val sc = new SparkContext(sparkConf)
+
+    val spark = SparkSession
+      .builder
+      .config(sparkConf)
+      .appName("Broadcast Test")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
 
     val slices = if (args.length > 0) args(0).toInt else 2
     val num = if (args.length > 1) args(1).toInt else 1000000
@@ -48,7 +56,7 @@ object BroadcastTest {
       println("Iteration %d took %.0f milliseconds".format(i, (System.nanoTime - startTime) / 1E6))
     }
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/DFSReadWriteTest.scala

@@ -22,7 +22,7 @@ import java.io.File
 
 import scala.io.Source._
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * Simple test for reading and writing to a distributed
@@ -101,11 +101,14 @@ object DFSReadWriteTest {
     val fileContents = readFile(localFilePath.toString())
     val localWordCount = runLocalWordCount(fileContents)
 
-    println("Creating SparkConf")
-    val conf = new SparkConf().setAppName("DFS Read Write Test")
+    println("Creating SparkSession")
+    val spark = SparkSession
+      .builder
+      .appName("DFS Read Write Test")
+      .getOrCreate()
 
     println("Creating SparkContext")
-    val sc = new SparkContext(conf)
+    val sc = spark.sparkContext
 
     println("Writing local file to DFS")
     val dfsFilename = dfsDirPath + "/dfs_read_write_test"
@@ -124,7 +127,7 @@ object DFSReadWriteTest {
       .values
       .sum
 
-    sc.stop()
+    spark.stop()
 
     if (localWordCount == dfsWordCount) {
       println(s"Success! Local Word Count ($localWordCount) " +

examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala

@@ -17,18 +17,22 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 object ExceptionHandlingTest {
   def main(args: Array[String]) {
-    val sparkConf = new SparkConf().setAppName("ExceptionHandlingTest")
-    val sc = new SparkContext(sparkConf)
+    val spark = SparkSession
+      .builder
+      .appName("ExceptionHandlingTest")
+      .getOrCreate()
+    val sc = spark.sparkContext
+
     sc.parallelize(0 until sc.defaultParallelism).foreach { i =>
       if (math.random > 0.75) {
         throw new Exception("Testing exception handling")
       }
     }
 
-    sc.stop()
+    spark.stop()
   }
 }

examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala

@@ -20,20 +20,24 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * Usage: GroupByTest [numMappers] [numKVPairs] [KeySize] [numReducers]
  */
 object GroupByTest {
   def main(args: Array[String]) {
-    val sparkConf = new SparkConf().setAppName("GroupBy Test")
+    val spark = SparkSession
+      .builder
+      .appName("GroupBy Test")
+      .getOrCreate()
+
     var numMappers = if (args.length > 0) args(0).toInt else 2
     var numKVPairs = if (args.length > 1) args(1).toInt else 1000
     var valSize = if (args.length > 2) args(2).toInt else 1000
     var numReducers = if (args.length > 3) args(3).toInt else numMappers
 
-    val sc = new SparkContext(sparkConf)
+    val sc = spark.sparkContext
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
@@ -50,7 +54,7 @@ object GroupByTest {
 
     println(pairs1.groupByKey(numReducers).count())
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala

@@ -18,7 +18,7 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import org.apache.spark._
+import org.apache.spark.sql.SparkSession
 
 
 object HdfsTest {
@@ -29,9 +29,11 @@ object HdfsTest {
       System.err.println("Usage: HdfsTest <file>")
       System.exit(1)
     }
-    val sparkConf = new SparkConf().setAppName("HdfsTest")
-    val sc = new SparkContext(sparkConf)
-    val file = sc.textFile(args(0))
+    val spark = SparkSession
+      .builder
+      .appName("HdfsTest")
+      .getOrCreate()
+    val file = spark.read.text(args(0)).rdd
     val mapped = file.map(s => s.length).cache()
     for (iter <- 1 to 10) {
       val start = System.currentTimeMillis()
@@ -39,7 +41,7 @@ object HdfsTest {
       val end = System.currentTimeMillis()
       println("Iteration " + iter + " took " + (end-start) + " ms")
     }
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala

@@ -18,8 +18,9 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.SparkSession
+
 
 /**
  * Usage: MultiBroadcastTest [slices] [numElem]
@@ -27,8 +28,12 @@ import org.apache.spark.rdd.RDD
 object MultiBroadcastTest {
   def main(args: Array[String]) {
 
-    val sparkConf = new SparkConf().setAppName("Multi-Broadcast Test")
-    val sc = new SparkContext(sparkConf)
+    val spark = SparkSession
+      .builder
+      .appName("Multi-Broadcast Test")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
 
     val slices = if (args.length > 0) args(0).toInt else 2
     val num = if (args.length > 1) args(1).toInt else 1000000
@@ -51,7 +56,7 @@ object MultiBroadcastTest {
     // Collect the small RDD so we can print the observed sizes locally.
     observedSizes.collect().foreach(i => println(i))
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala

@@ -20,23 +20,26 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * Usage: SimpleSkewedGroupByTest [numMappers] [numKVPairs] [valSize] [numReducers] [ratio]
  */
 object SimpleSkewedGroupByTest {
   def main(args: Array[String]) {
+    val spark = SparkSession
+      .builder
+      .appName("SimpleSkewedGroupByTest")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
 
-    val sparkConf = new SparkConf().setAppName("SimpleSkewedGroupByTest")
     var numMappers = if (args.length > 0) args(0).toInt else 2
     var numKVPairs = if (args.length > 1) args(1).toInt else 1000
     var valSize = if (args.length > 2) args(2).toInt else 1000
     var numReducers = if (args.length > 3) args(3).toInt else numMappers
     var ratio = if (args.length > 4) args(4).toInt else 5.0
 
-    val sc = new SparkContext(sparkConf)
-
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
       var result = new Array[(Int, Array[Byte])](numKVPairs)
@@ -64,7 +67,7 @@ object SimpleSkewedGroupByTest {
     //                           .map{case (k,v) => (k, v.size)}
     //                           .collectAsMap)
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala

@@ -20,20 +20,25 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * Usage: GroupByTest [numMappers] [numKVPairs] [KeySize] [numReducers]
  */
 object SkewedGroupByTest {
   def main(args: Array[String]) {
-    val sparkConf = new SparkConf().setAppName("GroupBy Test")
+    val spark = SparkSession
+      .builder
+      .appName("GroupBy Test")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
+
     var numMappers = if (args.length > 0) args(0).toInt else 2
     var numKVPairs = if (args.length > 1) args(1).toInt else 1000
     var valSize = if (args.length > 2) args(2).toInt else 1000
     var numReducers = if (args.length > 3) args(3).toInt else numMappers
 
-    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
@@ -54,7 +59,7 @@ object SkewedGroupByTest {
 
     println(pairs1.groupByKey(numReducers).count())
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SparkALS.scala

@@ -20,7 +20,7 @@ package org.apache.spark.examples
 
 import org.apache.commons.math3.linear._
 
-import org.apache.spark._
+import org.apache.spark.sql.SparkSession
 
 /**
  * Alternating least squares matrix factorization.
@@ -108,8 +108,12 @@ object SparkALS {
 
     println(s"Running with M=$M, U=$U, F=$F, iters=$ITERATIONS")
 
-    val sparkConf = new SparkConf().setAppName("SparkALS")
-    val sc = new SparkContext(sparkConf)
+    val spark = SparkSession
+      .builder
+      .appName("SparkALS")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
 
     val R = generateR()
 
@@ -135,7 +139,7 @@ object SparkALS {
       println()
     }
 
-    sc.stop()
+    spark.stop()
   }
 
   private def randomVector(n: Int): RealVector =

examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala

@@ -23,9 +23,8 @@ import java.util.Random
 import scala.math.exp
 
 import breeze.linalg.{DenseVector, Vector}
-import org.apache.hadoop.conf.Configuration
 
-import org.apache.spark._
+import org.apache.spark.sql.SparkSession
 
 /**
  * Logistic regression based classification.
@@ -67,11 +66,14 @@ object SparkHdfsLR {
 
     showWarning()
 
-    val sparkConf = new SparkConf().setAppName("SparkHdfsLR")
+    val spark = SparkSession
+      .builder
+      .appName("SparkHdfsLR")
+      .getOrCreate()
+
     val inputPath = args(0)
-    val conf = new Configuration()
-    val sc = new SparkContext(sparkConf)
-    val lines = sc.textFile(inputPath)
+    val lines = spark.read.text(inputPath).rdd
+
     val points = lines.map(parsePoint).cache()
     val ITERATIONS = args(1).toInt
 
@@ -88,7 +90,7 @@ object SparkHdfsLR {
     }
 
     println("Final w: " + w)
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala

@@ -20,7 +20,7 @@ package org.apache.spark.examples
 
 import breeze.linalg.{squaredDistance, DenseVector, Vector}
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * K-means clustering.
@@ -66,14 +66,17 @@ object SparkKMeans {
 
     showWarning()
 
-    val sparkConf = new SparkConf().setAppName("SparkKMeans")
-    val sc = new SparkContext(sparkConf)
-    val lines = sc.textFile(args(0))
+    val spark = SparkSession
+      .builder
+      .appName("SparkKMeans")
+      .getOrCreate()
+
+    val lines = spark.read.text(args(0)).rdd
     val data = lines.map(parseVector _).cache()
     val K = args(1).toInt
     val convergeDist = args(2).toDouble
 
-    val kPoints = data.takeSample(withReplacement = false, K, 42).toArray
+    val kPoints = data.takeSample(withReplacement = false, K, 42)
     var tempDist = 1.0
 
     while(tempDist > convergeDist) {
@@ -97,7 +100,7 @@ object SparkKMeans {
 
     println("Final centers:")
     kPoints.foreach(println)
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SparkLR.scala

@@ -24,7 +24,7 @@ import scala.math.exp
 
 import breeze.linalg.{DenseVector, Vector}
 
-import org.apache.spark._
+import org.apache.spark.sql.SparkSession
 
 /**
  * Logistic regression based classification.
@@ -63,8 +63,13 @@ object SparkLR {
 
     showWarning()
 
-    val sparkConf = new SparkConf().setAppName("SparkLR")
-    val sc = new SparkContext(sparkConf)
+    val spark = SparkSession
+      .builder
+      .appName("SparkLR")
+      .getOrCreate()
+
+    val sc = spark.sparkContext
+
     val numSlices = if (args.length > 0) args(0).toInt else 2
     val points = sc.parallelize(generateData, numSlices).cache()
 
@@ -82,7 +87,7 @@ object SparkLR {
 
     println("Final w: " + w)
 
-    sc.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala

@@ -18,7 +18,7 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * Computes the PageRank of URLs from an input file. Input file should
@@ -50,10 +50,13 @@ object SparkPageRank {
 
     showWarning()
 
-    val sparkConf = new SparkConf().setAppName("PageRank")
+    val spark = SparkSession
+      .builder
+      .appName("SparkPageRank")
+      .getOrCreate()
+
     val iters = if (args.length > 1) args(1).toInt else 10
-    val ctx = new SparkContext(sparkConf)
-    val lines = ctx.textFile(args(0), 1)
+    val lines = spark.read.text(args(0)).rdd
     val links = lines.map{ s =>
       val parts = s.split("\\s+")
       (parts(0), parts(1))
@@ -71,7 +74,7 @@ object SparkPageRank {
     val output = ranks.collect()
     output.foreach(tup => println(tup._1 + " has rank: " + tup._2 + "."))
 
-    ctx.stop()
+    spark.stop()
   }
 }
 // scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/SparkPi.scala

@@ -20,16 +20,19 @@ package org.apache.spark.examples
 
 import scala.math.random
 
-import org.apache.spark._
+import org.apache.spark.sql.SparkSession
 
 /** Computes an approximation to pi */
 object SparkPi {
   def main(args: Array[String]) {
-    val conf = new SparkConf().setAppName("Spark Pi")
-    val spark = new SparkContext(conf)
+    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 = spark.parallelize(1 until n, slices).map { i =>
+    val count = sc.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

examples/src/main/scala/org/apache/spark/examples/SparkTC.scala

@@ -21,7 +21,7 @@ package org.apache.spark.examples
 import scala.collection.mutable
 import scala.util.Random
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.sql.SparkSession
 
 /**
  * Transitive closure on a graph.
@@ -42,10 +42,13 @@ object SparkTC {
   }
 
   def main(args: Array[String]) {
-    val sparkConf = new SparkConf().setAppName("SparkTC")
-    val spark = new SparkContext(sparkConf)
+    val spark = SparkSession
+      .builder
+      .appName("SparkTC")
+      .getOrCreate()
+    val sc = spark.sparkContext
     val slices = if (args.length > 0) args(0).toInt else 2
-    var tc = spark.parallelize(generateGraph, slices).cache()
+    var tc = sc.parallelize(generateGraph, slices).cache()
 
     // Linear transitive closure: each round grows paths by one edge,
     // by joining the graph's edges with the already-discovered paths.

examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala

@@ -22,7 +22,7 @@ import java.io.File
 
 import com.google.common.io.{ByteStreams, Files}
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkConf
 import org.apache.spark.sql._
 
 object HiveFromSpark {

sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java

@@ -168,8 +168,8 @@ public class JavaDataFrameSuite {
     Assert.assertEquals(
       new StructField("d", new ArrayType(DataTypes.StringType, true), true, Metadata.empty()),
       schema.apply("d"));
-    Assert.assertEquals(new StructField("e", DataTypes.createDecimalType(38,0), true, Metadata.empty()),
-      schema.apply("e"));
+    Assert.assertEquals(new StructField("e", DataTypes.createDecimalType(38,0), true,
+      Metadata.empty()), schema.apply("e"));
     Row first = df.select("a", "b", "c", "d", "e").first();
     Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0);
     // Now Java lists and maps are converted to Scala Seq's and Map's. Once we get a Seq below,
@@ -189,7 +189,7 @@ public class JavaDataFrameSuite {
     for (int i = 0; i < d.length(); i++) {
       Assert.assertEquals(bean.getD().get(i), d.apply(i));
     }
-      // Java.math.BigInteger is equavient to Spark Decimal(38,0)
+    // Java.math.BigInteger is equavient to Spark Decimal(38,0)
     Assert.assertEquals(new BigDecimal(bean.getE()), first.getDecimal(4));
   }