make sure there is at most one spark context inside the same jvm

Author: mengxr

mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala

@@ -21,15 +21,23 @@ import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
 import org.apache.spark.mllib.util.LocalSparkContext
-import org.apache.spark.sql.SchemaRDD
+import org.apache.spark.sql.{SQLContext, SchemaRDD}
 
 class LogisticRegressionSuite extends FunSuite with LocalSparkContext {
 
-  import sqlContext._
+  @transient var sqlContext: SQLContext = _
+  @transient var dataset: SchemaRDD = _
 
-  val dataset: SchemaRDD = sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2)
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sqlContext = new SQLContext(sc)
+    dataset = sqlContext.createSchemaRDD(
+      sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2))
+  }
 
   test("logistic regression") {
+    val sqlContext = this.sqlContext
+    import sqlContext._
     val lr = new LogisticRegression
     val model = lr.fit(dataset)
     model.transform(dataset)
@@ -38,6 +46,8 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext {
   }
 
   test("logistic regression with setters") {
+    val sqlContext = this.sqlContext
+    import sqlContext._
     val lr = new LogisticRegression()
       .setMaxIter(10)
       .setRegParam(1.0)
@@ -48,6 +58,8 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext {
   }
 
   test("logistic regression fit and transform with varargs") {
+    val sqlContext = this.sqlContext
+    import sqlContext._
     val lr = new LogisticRegression
     val model = lr.fit(dataset, lr.maxIter -> 10, lr.regParam -> 1.0)
     model.transform(dataset, model.threshold -> 0.8, model.scoreCol -> "probability")

mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala

@@ -23,13 +23,18 @@ import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
 import org.apache.spark.mllib.util.LocalSparkContext
-import org.apache.spark.sql.SchemaRDD
+import org.apache.spark.sql.{SQLContext, SchemaRDD}
 
 class CrossValidatorSuite extends FunSuite with LocalSparkContext {
 
-  import sqlContext._
+  @transient var dataset: SchemaRDD = _
 
-  val dataset: SchemaRDD = sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2)
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    val sqlContext = new SQLContext(sc)
+    dataset = sqlContext.createSchemaRDD(
+      sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2))
+  }
 
   test("cross validation with logistic regression") {
     val lr = new LogisticRegression

mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala

@@ -17,17 +17,26 @@
 
 package org.apache.spark.mllib.util
 
-import org.scalatest.{BeforeAndAfterAll, Suite}
+import org.scalatest.Suite
+import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.SparkContext
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.{SparkConf, SparkContext}
 
 trait LocalSparkContext extends BeforeAndAfterAll { self: Suite =>
-  @transient val sc = new SparkContext("local", "test")
-  @transient lazy val sqlContext = new SQLContext(sc)
+  @transient var sc: SparkContext = _
+
+  override def beforeAll() {
+    super.beforeAll()
+    val conf = new SparkConf()
+      .setMaster("local[2]")
+      .setAppName("MLlibUnitTest")
+    sc = new SparkContext(conf)
+  }
 
   override def afterAll() {
-    sc.stop()
+    if (sc != null) {
+      sc.stop()
+    }
     super.afterAll()
   }
 }