[SPARK-5097][SQL] DataFrame

examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java

@@ -33,7 +33,7 @@
 import org.apache.spark.ml.tuning.CrossValidator;
 import org.apache.spark.ml.tuning.CrossValidatorModel;
 import org.apache.spark.ml.tuning.ParamGridBuilder;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
@@ -71,7 +71,7 @@ public static void main(String[] args) {
       new LabeledDocument(9L, "a e c l", 0.0),
       new LabeledDocument(10L, "spark compile", 1.0),
       new LabeledDocument(11L, "hadoop software", 0.0));
-    SchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
+    DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
 
     // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
     Tokenizer tokenizer = new Tokenizer()
@@ -112,11 +112,11 @@ public static void main(String[] args) {
       new Document(5L, "l m n"),
       new Document(6L, "mapreduce spark"),
       new Document(7L, "apache hadoop"));
-    SchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
+    DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
 
     // Make predictions on test documents. cvModel uses the best model found (lrModel).
-    cvModel.transform(test).registerAsTable("prediction");
-    SchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
+    cvModel.transform(test).registerTempTable("prediction");
+    DataFrame predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
     for (Row r: predictions.collect()) {
       System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2)
           + ", prediction=" + r.get(3));

examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java

@@ -28,7 +28,7 @@
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
@@ -48,13 +48,13 @@ public static void main(String[] args) {
 
     // Prepare training data.
     // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
-    // into SchemaRDDs, where it uses the bean metadata to infer the schema.
+    // into DataFrames, where it uses the bean metadata to infer the schema.
     List<LabeledPoint> localTraining = Lists.newArrayList(
       new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
       new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
       new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
       new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
-    SchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class);
+    DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledPoint.class);
 
     // Create a LogisticRegression instance.  This instance is an Estimator.
     LogisticRegression lr = new LogisticRegression();
@@ -94,14 +94,14 @@ public static void main(String[] args) {
         new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
         new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
         new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
-    SchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class);
+    DataFrame test = jsql.applySchema(jsc.parallelize(localTest), LabeledPoint.class);
 
     // Make predictions on test documents using the Transformer.transform() method.
     // LogisticRegression.transform will only use the 'features' column.
     // Note that model2.transform() outputs a 'probability' column instead of the usual 'score'
     // column since we renamed the lr.scoreCol parameter previously.
-    model2.transform(test).registerAsTable("results");
-    SchemaRDD results =
+    model2.transform(test).registerTempTable("results");
+    DataFrame results =
         jsql.sql("SELECT features, label, probability, prediction FROM results");
     for (Row r: results.collect()) {
       System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)

examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleTextClassificationPipeline.java

@@ -29,7 +29,7 @@
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.feature.HashingTF;
 import org.apache.spark.ml.feature.Tokenizer;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
@@ -54,7 +54,7 @@ public static void main(String[] args) {
       new LabeledDocument(1L, "b d", 0.0),
       new LabeledDocument(2L, "spark f g h", 1.0),
       new LabeledDocument(3L, "hadoop mapreduce", 0.0));
-    SchemaRDD training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
+    DataFrame training = jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
 
     // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
     Tokenizer tokenizer = new Tokenizer()
@@ -79,11 +79,11 @@ public static void main(String[] args) {
       new Document(5L, "l m n"),
       new Document(6L, "mapreduce spark"),
       new Document(7L, "apache hadoop"));
-    SchemaRDD test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
+    DataFrame test = jsql.applySchema(jsc.parallelize(localTest), Document.class);
 
     // Make predictions on test documents.
-    model.transform(test).registerAsTable("prediction");
-    SchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
+    model.transform(test).registerTempTable("prediction");
+    DataFrame predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
     for (Row r: predictions.collect()) {
       System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> score=" + r.get(2)
           + ", prediction=" + r.get(3));

examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java

@@ -26,9 +26,9 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
 
-import org.apache.spark.sql.SQLContext;
-import org.apache.spark.sql.SchemaRDD;
+import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
 
 public class JavaSparkSQL {
   public static class Person implements Serializable {
@@ -74,13 +74,13 @@ public Person call(String line) {
       });
 
     // Apply a schema to an RDD of Java Beans and register it as a table.
-    SchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class);
+    DataFrame schemaPeople = sqlCtx.applySchema(people, Person.class);
     schemaPeople.registerTempTable("people");
 
     // SQL can be run over RDDs that have been registered as tables.
-    SchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
+    DataFrame teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
 
-    // The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
+    // The results of SQL queries are DataFrames and support all the normal RDD operations.
     // The columns of a row in the result can be accessed by ordinal.
     List<String> teenagerNames = teenagers.toJavaRDD().map(new Function<Row, String>() {
       @Override
@@ -93,17 +93,17 @@ public String call(Row row) {
     }
 
     System.out.println("=== Data source: Parquet File ===");
-    // JavaSchemaRDDs can be saved as parquet files, maintaining the schema information.
+    // DataFrames can be saved as parquet files, maintaining the schema information.
     schemaPeople.saveAsParquetFile("people.parquet");
 
     // Read in the parquet file created above.
     // Parquet files are self-describing so the schema is preserved.
-    // The result of loading a parquet file is also a JavaSchemaRDD.
-    SchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
+    // The result of loading a parquet file is also a DataFrame.
+    DataFrame parquetFile = sqlCtx.parquetFile("people.parquet");
 
     //Parquet files can also be registered as tables and then used in SQL statements.
     parquetFile.registerTempTable("parquetFile");
-    SchemaRDD teenagers2 =
+    DataFrame teenagers2 =
       sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
     teenagerNames = teenagers2.toJavaRDD().map(new Function<Row, String>() {
       @Override
@@ -119,8 +119,8 @@ public String call(Row row) {
     // A JSON dataset is pointed by path.
     // The path can be either a single text file or a directory storing text files.
     String path = "examples/src/main/resources/people.json";
-    // Create a JavaSchemaRDD from the file(s) pointed by path
-    SchemaRDD peopleFromJsonFile = sqlCtx.jsonFile(path);
+    // Create a DataFrame from the file(s) pointed by path
+    DataFrame peopleFromJsonFile = sqlCtx.jsonFile(path);
 
     // Because the schema of a JSON dataset is automatically inferred, to write queries,
     // it is better to take a look at what is the schema.
@@ -130,13 +130,13 @@ public String call(Row row) {
     //  |-- age: IntegerType
     //  |-- name: StringType
 
-    // Register this JavaSchemaRDD as a table.
+    // Register this DataFrame as a table.
     peopleFromJsonFile.registerTempTable("people");
 
     // SQL statements can be run by using the sql methods provided by sqlCtx.
-    SchemaRDD teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
+    DataFrame teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
 
-    // The results of SQL queries are JavaSchemaRDDs and support all the normal RDD operations.
+    // The results of SQL queries are DataFrame and support all the normal RDD operations.
     // The columns of a row in the result can be accessed by ordinal.
     teenagerNames = teenagers3.toJavaRDD().map(new Function<Row, String>() {
       @Override
@@ -146,14 +146,14 @@ public String call(Row row) {
       System.out.println(name);
     }
 
-    // Alternatively, a JavaSchemaRDD can be created for a JSON dataset represented by
+    // Alternatively, a DataFrame can be created for a JSON dataset represented by
     // a RDD[String] storing one JSON object per string.
     List<String> jsonData = Arrays.asList(
           "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
     JavaRDD<String> anotherPeopleRDD = ctx.parallelize(jsonData);
-    SchemaRDD peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD.rdd());
+    DataFrame peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD.rdd());
 
-    // Take a look at the schema of this new JavaSchemaRDD.
+    // Take a look at the schema of this new DataFrame.
     peopleFromJsonRDD.printSchema();
     // The schema of anotherPeople is ...
     // root
@@ -164,7 +164,7 @@ public String call(Row row) {
 
     peopleFromJsonRDD.registerTempTable("people2");
 
-    SchemaRDD peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
+    DataFrame peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
     List<String> nameAndCity = peopleWithCity.toJavaRDD().map(new Function<Row, String>() {
       @Override
       public String call(Row row) {

examples/src/main/python/mllib/dataset_example.py

@@ -16,7 +16,7 @@
 #
 
 """
-An example of how to use SchemaRDD as a dataset for ML. Run with::
+An example of how to use DataFrame as a dataset for ML. Run with::
     bin/spark-submit examples/src/main/python/mllib/dataset_example.py
 """
 

examples/src/main/python/sql.py

@@ -30,26 +30,26 @@
     some_rdd = sc.parallelize([Row(name="John", age=19),
                               Row(name="Smith", age=23),
                               Row(name="Sarah", age=18)])
-    # Infer schema from the first row, create a SchemaRDD and print the schema
-    some_schemardd = sqlContext.inferSchema(some_rdd)
-    some_schemardd.printSchema()
+    # Infer schema from the first row, create a DataFrame and print the schema
+    some_df = sqlContext.inferSchema(some_rdd)
+    some_df.printSchema()
 
     # Another RDD is created from a list of tuples
     another_rdd = sc.parallelize([("John", 19), ("Smith", 23), ("Sarah", 18)])
     # Schema with two fields - person_name and person_age
     schema = StructType([StructField("person_name", StringType(), False),
                         StructField("person_age", IntegerType(), False)])
-    # Create a SchemaRDD by applying the schema to the RDD and print the schema
-    another_schemardd = sqlContext.applySchema(another_rdd, schema)
-    another_schemardd.printSchema()
+    # Create a DataFrame by applying the schema to the RDD and print the schema
+    another_df = sqlContext.applySchema(another_rdd, schema)
+    another_df.printSchema()
     # root
     #  |-- age: integer (nullable = true)
     #  |-- name: string (nullable = true)
 
     # A JSON dataset is pointed to by path.
     # The path can be either a single text file or a directory storing text files.
     path = os.path.join(os.environ['SPARK_HOME'], "examples/src/main/resources/people.json")
-    # Create a SchemaRDD from the file(s) pointed to by path
+    # Create a DataFrame from the file(s) pointed to by path
     people = sqlContext.jsonFile(path)
     # root
     #  |-- person_name: string (nullable = false)
@@ -61,7 +61,7 @@
     #  |-- age: IntegerType
     #  |-- name: StringType
 
-    # Register this SchemaRDD as a table.
+    # Register this DataFrame as a table.
     people.registerAsTable("people")
 
     # SQL statements can be run by using the sql methods provided by sqlContext

examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala

@@ -18,7 +18,6 @@
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
@@ -101,7 +100,7 @@ object CrossValidatorExample {
 
     // Make predictions on test documents. cvModel uses the best model found (lrModel).
     cvModel.transform(test)
-      .select('id, 'text, 'score, 'prediction)
+      .select("id", "text", "score", "prediction")
       .collect()
       .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) =>
       println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction)

examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala

@@ -143,7 +143,7 @@ object MovieLensALS {
 
     // Evaluate the model.
     // TODO: Create an evaluator to compute RMSE.
-    val mse = predictions.select('rating, 'prediction)
+    val mse = predictions.select("rating", "prediction").rdd
       .flatMap { case Row(rating: Float, prediction: Float) =>
         val err = rating.toDouble - prediction
         val err2 = err * err

examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala

@@ -18,7 +18,6 @@
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -42,7 +41,7 @@ object SimpleParamsExample {
 
     // Prepare training data.
     // We use LabeledPoint, which is a case class.  Spark SQL can convert RDDs of Java Beans
-    // into SchemaRDDs, where it uses the bean metadata to infer the schema.
+    // into DataFrames, where it uses the bean metadata to infer the schema.
     val training = sparkContext.parallelize(Seq(
       LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
       LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
@@ -92,7 +91,7 @@ object SimpleParamsExample {
     // Note that model2.transform() outputs a 'probability' column instead of the usual 'score'
     // column since we renamed the lr.scoreCol parameter previously.
     model2.transform(test)
-      .select('features, 'label, 'probability, 'prediction)
+      .select("features", "label", "probability", "prediction")
       .collect()
       .foreach { case Row(features: Vector, label: Double, prob: Double, prediction: Double) =>
         println("(" + features + ", " + label + ") -> prob=" + prob + ", prediction=" + prediction)

examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala

@@ -20,7 +20,6 @@ package org.apache.spark.examples.ml
 import scala.beans.BeanInfo
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
@@ -80,7 +79,7 @@ object SimpleTextClassificationPipeline {
 
     // Make predictions on test documents.
     model.transform(test)
-      .select('id, 'text, 'score, 'prediction)
+      .select("id", "text", "score", "prediction")
       .collect()
       .foreach { case Row(id: Long, text: String, score: Double, prediction: Double) =>
         println("(" + id + ", " + text + ") --> score=" + score + ", prediction=" + prediction)