diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
index d554377975b1b7e66921014feef07a508a1c459a..0a6e9c2a1f93c218462d0e6f76b23de7acbdd6d6 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
@@ -58,7 +58,7 @@ public class JavaBinarizerExample {
.setThreshold(0.5);
Dataset<Row> binarizedDataFrame = binarizer.transform(continuousDataFrame);
Dataset<Row> binarizedFeatures = binarizedDataFrame.select("binarized_feature");
- for (Row r : binarizedFeatures.collectRows()) {
+ for (Row r : binarizedFeatures.collectAsList()) {
Double binarized_value = r.getDouble(0);
System.out.println(binarized_value);
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java
index 90bc94c45bbf938d55b44454573781382994b2b7..07edeb3e521c30b4533294b24a75228f86c85612 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java
@@ -117,7 +117,7 @@ public class JavaCrossValidatorExample {
// Make predictions on test documents. cvModel uses the best model found (lrModel).
Dataset<Row> predictions = cvModel.transform(test);
- for (Row r: predictions.select("id", "text", "probability", "prediction").collectRows()) {
+ for (Row r: predictions.select("id", "text", "probability", "prediction").collectAsList()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index e8ae100d685294ba7ec7d7245015f00924088c51..8a10dd48aa72fa4bbc56a73d944d8d144df944e1 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -85,7 +85,7 @@ public class JavaDeveloperApiExample {
// Make predictions on test documents. cvModel uses the best model found (lrModel).
Dataset<Row> results = model.transform(test);
double sumPredictions = 0;
- for (Row r : results.select("features", "label", "prediction").collectRows()) {
+ for (Row r : results.select("features", "label", "prediction").collectAsList()) {
sumPredictions += r.getDouble(2);
}
if (sumPredictions != 0.0) {
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
index f13698ae5e07ed1cf7fc9d3aa2500f5895af12e0..604b193dd489bad916c30079d3de081561ad4170 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
@@ -100,7 +100,8 @@ public class JavaEstimatorTransformerParamExample {
// Note that model2.transform() outputs a 'myProbability' column instead of the usual
// 'probability' column since we renamed the lr.probabilityCol parameter previously.
Dataset<Row> results = model2.transform(test);
- for (Row r : results.select("features", "label", "myProbability", "prediction").collectRows()) {
+ Dataset<Row> rows = results.select("features", "label", "myProbability", "prediction");
+ for (Row r: rows.collectAsList()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
index e394605db70ea001b39b9ffe75f90ab66cd47e7e..c4122d1247a94f5a4deb1929732f9a8accc6f9af 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java
@@ -111,7 +111,7 @@ public class JavaModelSelectionViaCrossValidationExample {
// Make predictions on test documents. cvModel uses the best model found (lrModel).
Dataset<Row> predictions = cvModel.transform(test);
- for (Row r : predictions.select("id", "text", "probability", "prediction").collectRows()) {
+ for (Row r : predictions.select("id", "text", "probability", "prediction").collectAsList()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java
index 0305f737ca94ca7ad234099b2e2fd7714be6febc..608bd802856558b98760e588264f4c79036c5c78 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java
@@ -60,7 +60,7 @@ public class JavaNGramExample {
Dataset<Row> ngramDataFrame = ngramTransformer.transform(wordDataFrame);
- for (Row r : ngramDataFrame.select("ngrams", "label").takeRows(3)) {
+ for (Row r : ngramDataFrame.select("ngrams", "label").takeAsList(3)) {
java.util.List<String> ngrams = r.getList(0);
for (String ngram : ngrams) System.out.print(ngram + " --- ");
System.out.println();
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
index 6ae418d564d1fecffe02bda1200bb6223a9ec05b..305420f208b79ad5e8e82c13d876bd8abbd07982 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java
@@ -80,7 +80,7 @@ public class JavaPipelineExample {
// Make predictions on test documents.
Dataset<Row> predictions = model.transform(test);
- for (Row r : predictions.select("id", "text", "probability", "prediction").collectRows()) {
+ for (Row r : predictions.select("id", "text", "probability", "prediction").collectAsList()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
index 5a4064c604301965523d0ba0b3bd142f3b9922e9..48fc3c8acb0c0d8be4d5abba0af53f52b90670be 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java
@@ -23,6 +23,7 @@ import org.apache.spark.sql.SQLContext;
// $example on$
import java.util.Arrays;
+import java.util.List;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.feature.PolynomialExpansion;
@@ -61,8 +62,8 @@ public class JavaPolynomialExpansionExample {
Dataset<Row> df = jsql.createDataFrame(data, schema);
Dataset<Row> polyDF = polyExpansion.transform(df);
- Row[] row = polyDF.select("polyFeatures").takeRows(3);
- for (Row r : row) {
+ List<Row> rows = polyDF.select("polyFeatures").takeAsList(3);
+ for (Row r : rows) {
System.out.println(r.get(0));
}
// $example off$
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
index 52bb4ec050376ef289a72fa73de20e087610194e..cb911ef5ef5869930553b21cfb6235de547260ae 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
@@ -103,7 +103,8 @@ public class JavaSimpleParamsExample {
// Note that model2.transform() outputs a 'myProbability' column instead of the usual
// 'probability' column since we renamed the lr.probabilityCol parameter previously.
Dataset<Row> results = model2.transform(test);
- for (Row r: results.select("features", "label", "myProbability", "prediction").collectRows()) {
+ Dataset<Row> rows = results.select("features", "label", "myProbability", "prediction");
+ for (Row r: rows.collectAsList()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
index 9bd543c44f98330cebaf6fee547617cd7ee89326..a18a60f4481666947433df54d4564c9bda7efe2a 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java
@@ -84,7 +84,7 @@ public class JavaSimpleTextClassificationPipeline {
// Make predictions on test documents.
Dataset<Row> predictions = model.transform(test);
- for (Row r: predictions.select("id", "text", "probability", "prediction").collectRows()) {
+ for (Row r: predictions.select("id", "text", "probability", "prediction").collectAsList()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
index fd1ce424bf8c441672566800053e1c191ab87211..37a3d0d84dae2c22a674d1be2925c5edb876f3d7 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java
@@ -66,7 +66,7 @@ public class JavaTfIdfExample {
IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features");
IDFModel idfModel = idf.fit(featurizedData);
Dataset<Row> rescaledData = idfModel.transform(featurizedData);
- for (Row r : rescaledData.select("features", "label").takeRows(3)) {
+ for (Row r : rescaledData.select("features", "label").takeAsList(3)) {
Vector features = r.getAs(0);
Double label = r.getDouble(1);
System.out.println(features);
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
index a2f8c436e32f6f44f4ec76670d52927a659dd1d9..9225fe2262f57105f5067249b8b0319bef1cdd2a 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java
@@ -59,7 +59,7 @@ public class JavaTokenizerExample {
Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
Dataset<Row> wordsDataFrame = tokenizer.transform(sentenceDataFrame);
- for (Row r : wordsDataFrame.select("words", "label").takeRows(3)) {
+ for (Row r : wordsDataFrame.select("words", "label").takeAsList(3)) {
java.util.List<String> words = r.getList(0);
for (String word : words) System.out.print(word + " ");
System.out.println();
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java
index 2dce8c2168c2df5a9beb1cca04e398c9afddb83e..c5bb1eaaa34461055bc8d0dc1745d56249110b14 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java
@@ -59,7 +59,7 @@ public class JavaWord2VecExample {
.setMinCount(0);
Word2VecModel model = word2Vec.fit(documentDF);
Dataset<Row> result = model.transform(documentDF);
- for (Row r : result.select("result").takeRows(3)) {
+ for (Row r : result.select("result").takeAsList(3)) {
System.out.println(r);
}
// $example off$
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
index b87605ebfd6a3d29ae40c4952af1b4aacf03fcc6..e2da11183b93f1ccf30fc3826dd7d63493cdb817 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
@@ -78,7 +78,7 @@ public class JavaVectorSlicerSuite {
Dataset<Row> output = vectorSlicer.transform(dataset);
- for (Row r : output.select("userFeatures", "features").takeRows(2)) {
+ for (Row r : output.select("userFeatures", "features").takeAsList(2)) {
Vector features = r.getAs(1);
Assert.assertEquals(features.size(), 2);
}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index f1791e6943bb72c3205746de2da5edcc7b9318d7..1ea7db0388689b4f5c4fd08b307599234ec54ef4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1762,10 +1762,6 @@ class Dataset[T] private[sql](
*/
def take(n: Int): Array[T] = head(n)
- def takeRows(n: Int): Array[Row] = withTypedCallback("takeRows", limit(n)) { ds =>
- ds.collectRows(needCallback = false)
- }
-
/**
* Returns the first `n` rows in the [[DataFrame]] as a list.
*
@@ -1790,8 +1786,6 @@ class Dataset[T] private[sql](
*/
def collect(): Array[T] = collect(needCallback = true)
- def collectRows(): Array[Row] = collectRows(needCallback = true)
-
/**
* Returns a Java list that contains all of [[Row]]s in this [[DataFrame]].
*
@@ -1820,18 +1814,6 @@ class Dataset[T] private[sql](
}
}
- private def collectRows(needCallback: Boolean): Array[Row] = {
- def execute(): Array[Row] = withNewExecutionId {
- queryExecution.executedPlan.executeCollectPublic()
- }
-
- if (needCallback) {
- withCallback("collect", toDF())(_ => execute())
- } else {
- execute()
- }
- }
-
/**
* Returns the number of rows in the [[DataFrame]].
* @group action
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index 42af813bc1cd3256f82864516c83acd7f74fd83e..ae9c8cc1ba9ff574eae99d822b2881be0ac1e6f6 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -109,13 +109,13 @@ public class JavaApplySchemaSuite implements Serializable {
Dataset<Row> df = sqlContext.createDataFrame(rowRDD, schema);
df.registerTempTable("people");
- Row[] actual = sqlContext.sql("SELECT * FROM people").collectRows();
+ List<Row> actual = sqlContext.sql("SELECT * FROM people").collectAsList();
List<Row> expected = new ArrayList<>(2);
expected.add(RowFactory.create("Michael", 29));
expected.add(RowFactory.create("Yin", 28));
- Assert.assertEquals(expected, Arrays.asList(actual));
+ Assert.assertEquals(expected, actual);
}
@Test
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 47cc74dbc1f28216b8746e84a7c375f501c85aa9..42554720edae5195143b4c55271499e6cf149bd7 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -19,6 +19,7 @@ package test.org.apache.spark.sql;
import java.io.Serializable;
import java.util.Arrays;
+import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import java.util.Map;
@@ -65,7 +66,7 @@ public class JavaDataFrameSuite {
@Test
public void testExecution() {
Dataset<Row> df = context.table("testData").filter("key = 1");
- Assert.assertEquals(1, df.select("key").collectRows()[0].get(0));
+ Assert.assertEquals(1, df.select("key").collectAsList().get(0).get(0));
}
@Test
@@ -208,8 +209,8 @@ public class JavaDataFrameSuite {
StructType schema = createStructType(Arrays.asList(createStructField("i", IntegerType, true)));
List<Row> rows = Arrays.asList(RowFactory.create(0));
Dataset<Row> df = context.createDataFrame(rows, schema);
- Row[] result = df.collectRows();
- Assert.assertEquals(1, result.length);
+ List<Row> result = df.collectAsList();
+ Assert.assertEquals(1, result.size());
}
@Test
@@ -241,8 +242,8 @@ public class JavaDataFrameSuite {
Assert.assertEquals("a_b", columnNames[0]);
Assert.assertEquals("2", columnNames[1]);
Assert.assertEquals("1", columnNames[2]);
- Row[] rows = crosstab.collectRows();
- Arrays.sort(rows, crosstabRowComparator);
+ List<Row> rows = crosstab.collectAsList();
+ Collections.sort(rows, crosstabRowComparator);
Integer count = 1;
for (Row row : rows) {
Assert.assertEquals(row.get(0).toString(), count.toString());
@@ -257,7 +258,7 @@ public class JavaDataFrameSuite {
Dataset<Row> df = context.table("testData2");
String[] cols = {"a"};
Dataset<Row> results = df.stat().freqItems(cols, 0.2);
- Assert.assertTrue(results.collectRows()[0].getSeq(0).contains(1));
+ Assert.assertTrue(results.collectAsList().get(0).getSeq(0).contains(1));
}
@Test
@@ -278,27 +279,27 @@ public class JavaDataFrameSuite {
public void testSampleBy() {
Dataset<Row> df = context.range(0, 100, 1, 2).select(col("id").mod(3).as("key"));
Dataset<Row> sampled = df.stat().<Integer>sampleBy("key", ImmutableMap.of(0, 0.1, 1, 0.2), 0L);
- Row[] actual = sampled.groupBy("key").count().orderBy("key").collectRows();
- Assert.assertEquals(0, actual[0].getLong(0));
- Assert.assertTrue(0 <= actual[0].getLong(1) && actual[0].getLong(1) <= 8);
- Assert.assertEquals(1, actual[1].getLong(0));
- Assert.assertTrue(2 <= actual[1].getLong(1) && actual[1].getLong(1) <= 13);
+ List<Row> actual = sampled.groupBy("key").count().orderBy("key").collectAsList();
+ Assert.assertEquals(0, actual.get(0).getLong(0));
+ Assert.assertTrue(0 <= actual.get(0).getLong(1) && actual.get(0).getLong(1) <= 8);
+ Assert.assertEquals(1, actual.get(1).getLong(0));
+ Assert.assertTrue(2 <= actual.get(1).getLong(1) && actual.get(1).getLong(1) <= 13);
}
@Test
public void pivot() {
Dataset<Row> df = context.table("courseSales");
- Row[] actual = df.groupBy("year")
+ List<Row> actual = df.groupBy("year")
.pivot("course", Arrays.<Object>asList("dotNET", "Java"))
- .agg(sum("earnings")).orderBy("year").collectRows();
+ .agg(sum("earnings")).orderBy("year").collectAsList();
- Assert.assertEquals(2012, actual[0].getInt(0));
- Assert.assertEquals(15000.0, actual[0].getDouble(1), 0.01);
- Assert.assertEquals(20000.0, actual[0].getDouble(2), 0.01);
+ Assert.assertEquals(2012, actual.get(0).getInt(0));
+ Assert.assertEquals(15000.0, actual.get(0).getDouble(1), 0.01);
+ Assert.assertEquals(20000.0, actual.get(0).getDouble(2), 0.01);
- Assert.assertEquals(2013, actual[1].getInt(0));
- Assert.assertEquals(48000.0, actual[1].getDouble(1), 0.01);
- Assert.assertEquals(30000.0, actual[1].getDouble(2), 0.01);
+ Assert.assertEquals(2013, actual.get(1).getInt(0));
+ Assert.assertEquals(48000.0, actual.get(1).getDouble(1), 0.01);
+ Assert.assertEquals(30000.0, actual.get(1).getDouble(2), 0.01);
}
@Test