[SPARK-15171][SQL] Remove the references to deprecated method dataset.registerTempTable

## What changes were proposed in this pull request?

Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.

## How was this patch tested?

This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #13098 from clockfly/spark-15171-remove-deprecation.

3 files changed, 8 insertions, 8 deletions

diff --git a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java
index 46c26e8b92..a81a36d1b1 100644
--- a/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/JavaPipelineSuite.java
@@ -68,7 +68,7 @@ public class JavaPipelineSuite {
     Pipeline pipeline = new Pipeline()
       .setStages(new PipelineStage[]{scaler, lr});
     PipelineModel model = pipeline.fit(dataset);
-    model.transform(dataset).registerTempTable("prediction");
+    model.transform(dataset).createOrReplaceTempView("prediction");
     Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction");
     predictions.collectAsList();
   }
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 98abca221c..b8da04c26a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -54,7 +54,7 @@ public class JavaLogisticRegressionSuite implements Serializable {
     List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
     datasetRDD = jsc.parallelize(points, 2);
     dataset = spark.createDataFrame(datasetRDD, LabeledPoint.class);
-    dataset.registerTempTable("dataset");
+    dataset.createOrReplaceTempView("dataset");
   }
 
   @After
@@ -68,7 +68,7 @@ public class JavaLogisticRegressionSuite implements Serializable {
     LogisticRegression lr = new LogisticRegression();
     Assert.assertEquals(lr.getLabelCol(), "label");
     LogisticRegressionModel model = lr.fit(dataset);
-    model.transform(dataset).registerTempTable("prediction");
+    model.transform(dataset).createOrReplaceTempView("prediction");
     Dataset<Row> predictions = spark.sql("SELECT label, probability, prediction FROM prediction");
     predictions.collectAsList();
     // Check defaults
@@ -97,14 +97,14 @@ public class JavaLogisticRegressionSuite implements Serializable {
 
     // Modify model params, and check that the params worked.
     model.setThreshold(1.0);
-    model.transform(dataset).registerTempTable("predAllZero");
+    model.transform(dataset).createOrReplaceTempView("predAllZero");
     Dataset<Row> predAllZero = spark.sql("SELECT prediction, myProbability FROM predAllZero");
     for (Row r : predAllZero.collectAsList()) {
       Assert.assertEquals(0.0, r.getDouble(0), eps);
     }
     // Call transform with params, and check that the params worked.
     model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
-      .registerTempTable("predNotAllZero");
+      .createOrReplaceTempView("predNotAllZero");
     Dataset<Row> predNotAllZero = spark.sql("SELECT prediction, myProb FROM predNotAllZero");
     boolean foundNonZero = false;
     for (Row r : predNotAllZero.collectAsList()) {
@@ -130,7 +130,7 @@ public class JavaLogisticRegressionSuite implements Serializable {
     LogisticRegressionModel model = lr.fit(dataset);
     Assert.assertEquals(2, model.numClasses());
 
-    model.transform(dataset).registerTempTable("transformed");
+    model.transform(dataset).createOrReplaceTempView("transformed");
     Dataset<Row> trans1 = spark.sql("SELECT rawPrediction, probability FROM transformed");
     for (Row row : trans1.collectAsList()) {
       Vector raw = (Vector) row.get(0);
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
index d3ef5f6fca..126aa6298f 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
@@ -50,7 +50,7 @@ public class JavaLinearRegressionSuite implements Serializable {
     List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
     datasetRDD = jsc.parallelize(points, 2);
     dataset = spark.createDataFrame(datasetRDD, LabeledPoint.class);
-    dataset.registerTempTable("dataset");
+    dataset.createOrReplaceTempView("dataset");
   }
 
   @After
@@ -65,7 +65,7 @@ public class JavaLinearRegressionSuite implements Serializable {
     assertEquals("label", lr.getLabelCol());
     assertEquals("auto", lr.getSolver());
     LinearRegressionModel model = lr.fit(dataset);
-    model.transform(dataset).registerTempTable("prediction");
+    model.transform(dataset).createOrReplaceTempView("prediction");
     Dataset<Row> predictions = spark.sql("SELECT label, prediction FROM prediction");
     predictions.collect();
     // Check defaults