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[SPARK-13817][BUILD][SQL] Re-enable MiMA and removes object DataFrame

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## What changes were proposed in this pull request?

PR #11443 temporarily disabled MiMA check, this PR re-enables it.

One extra change is that `object DataFrame` is also removed. The only purpose of introducing `object DataFrame` was to use it as an internal factory for creating `Dataset[Row]`. By replacing this internal factory with `Dataset.newDataFrame`, both `DataFrame` and `DataFrame$` are entirely removed from the API, so that we can simply put a `MissingClassProblem` filter in `MimaExcludes.scala` for most DataFrame API  changes.

## How was this patch tested?

Tested by MiMA check triggered by Jenkins.

Author: Cheng Lian <lian@databricks.com>

Closes #11656 from liancheng/re-enable-mima.
This commit is contained in:
Cheng Lian 2016-03-11 22:17:50 +08:00
parent 07f1c54477
commit 6d37e1eb90
33 changed files with 78 additions and 69 deletions
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3
dev/run-tests.py
View file

@ -573,8 +573,7 @@ def main():
# backwards compatibility checks
if build_tool == "sbt":
# Note: compatibility tests only supported in sbt for now
# TODO Temporarily disable MiMA check for DF-to-DS migration prototyping
# detect_binary_inop_with_mima()
detect_binary_inop_with_mima()
# Since we did not build assembly/assembly before running dev/mima, we need to
# do it here because the tests still rely on it; see SPARK-13294 for details.
build_spark_assembly_sbt(hadoop_version)

1
examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java
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@ -26,7 +26,6 @@ import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.regression.AFTSurvivalRegression;
import org.apache.spark.ml.regression.AFTSurvivalRegressionModel;
import org.apache.spark.mllib.linalg.*;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java
View file

@ -31,7 +31,6 @@ import org.apache.spark.api.java.function.Function;
import org.apache.spark.ml.evaluation.RegressionEvaluator;
import org.apache.spark.ml.recommendation.ALS;
import org.apache.spark.ml.recommendation.ALSModel;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.types.DataTypes;
// $example off$

1
examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java
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@ -27,7 +27,6 @@ import java.util.Arrays;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.feature.Binarizer;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.DataTypes;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java
View file

@ -33,7 +33,6 @@ import org.apache.spark.ml.param.ParamMap;
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.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
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@ -33,7 +33,6 @@ import org.apache.spark.mllib.linalg.BLAS;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java
View file

@ -29,7 +29,6 @@ import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// $example off$

1
examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
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@ -32,7 +32,6 @@ import org.apache.spark.ml.clustering.KMeans;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.VectorUDT;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java
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@ -28,7 +28,6 @@ import org.apache.spark.ml.clustering.LDAModel;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.VectorUDT;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
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@ -29,7 +29,6 @@ import org.apache.spark.ml.util.MetadataUtils;
import org.apache.spark.mllib.evaluation.MulticlassMetrics;
import org.apache.spark.mllib.linalg.Matrix;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

1
examples/src/main/java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java
View file

@ -24,7 +24,6 @@ import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.feature.SQLTransformer;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;

22
project/MimaExcludes.scala
View file

@ -296,6 +296,28 @@ object MimaExcludes {
// SPARK-12073: backpressure rate controller consumes events preferentially from lagging partitions
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.kafka.KafkaTestUtils.createTopic"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.kafka.DirectKafkaInputDStream.maxMessagesPerPartition")
) ++ Seq(
// [SPARK-13244][SQL] Migrates DataFrame to Dataset
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.sql.DataFrameHolder.apply"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.DataFrameHolder.toDF"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.DataFrameHolder.toDF"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.sql.DataFrameHolder.copy"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.DataFrameHolder.copy$default$1"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.DataFrameHolder.df$1"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.sql.DataFrameHolder.this"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.SQLContext.tables"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.SQLContext.tables"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.SQLContext.sql"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.SQLContext.baseRelationToDataFrame"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.SQLContext.table"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.sql.DataFrame.apply"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.DataFrame"),
ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.DataFrame$"),
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.evaluation.MultilabelMetrics.this"),
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.predictions"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.predictions")
)
case v if v.startsWith("1.6") =>
Seq(

16
sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
View file

@ -48,18 +48,16 @@ import org.apache.spark.sql.types._
import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.Utils
private[sql] object DataFrame {
def apply(sqlContext: SQLContext, logicalPlan: LogicalPlan): DataFrame = {
val qe = sqlContext.executePlan(logicalPlan)
qe.assertAnalyzed()
new Dataset[Row](sqlContext, logicalPlan, RowEncoder(qe.analyzed.schema))
}
}
private[sql] object Dataset {
def apply[T: Encoder](sqlContext: SQLContext, logicalPlan: LogicalPlan): Dataset[T] = {
new Dataset(sqlContext, logicalPlan, implicitly[Encoder[T]])
}
def newDataFrame(sqlContext: SQLContext, logicalPlan: LogicalPlan): DataFrame = {
val qe = sqlContext.executePlan(logicalPlan)
qe.assertAnalyzed()
new Dataset[Row](sqlContext, logicalPlan, RowEncoder(qe.analyzed.schema))
}
}
/**
@ -2129,7 +2127,7 @@ class Dataset[T] private[sql](
/** A convenient function to wrap a logical plan and produce a DataFrame. */
@inline private def withPlan(logicalPlan: => LogicalPlan): DataFrame = {
DataFrame(sqlContext, logicalPlan)
Dataset.newDataFrame(sqlContext, logicalPlan)
}
/** A convenient function to wrap a logical plan and produce a DataFrame. */

8
sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
View file

@ -128,7 +128,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
userSpecifiedSchema = userSpecifiedSchema,
className = source,
options = extraOptions.toMap)
DataFrame(sqlContext, LogicalRelation(dataSource.resolveRelation()))
Dataset.newDataFrame(sqlContext, LogicalRelation(dataSource.resolveRelation()))
}
/**
@ -175,7 +175,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
userSpecifiedSchema = userSpecifiedSchema,
className = source,
options = extraOptions.toMap)
DataFrame(sqlContext, StreamingRelation(dataSource.createSource()))
Dataset.newDataFrame(sqlContext, StreamingRelation(dataSource.createSource()))
}
/**
@ -345,7 +345,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
InferSchema.infer(jsonRDD, sqlContext.conf.columnNameOfCorruptRecord, parsedOptions)
}
DataFrame(
Dataset.newDataFrame(
sqlContext,
LogicalRDD(
schema.toAttributes,
@ -393,7 +393,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
* @since 1.4.0
*/
def table(tableName: String): DataFrame = {
DataFrame(sqlContext,
Dataset.newDataFrame(sqlContext,
sqlContext.catalog.lookupRelation(sqlContext.sqlParser.parseTableIdentifier(tableName)))
}

8
sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
View file

@ -55,17 +55,17 @@ class GroupedData protected[sql](
groupType match {
case GroupedData.GroupByType =>
DataFrame(
Dataset.newDataFrame(
df.sqlContext, Aggregate(groupingExprs, aliasedAgg, df.logicalPlan))
case GroupedData.RollupType =>
DataFrame(
Dataset.newDataFrame(
df.sqlContext, Aggregate(Seq(Rollup(groupingExprs)), aliasedAgg, df.logicalPlan))
case GroupedData.CubeType =>
DataFrame(
Dataset.newDataFrame(
df.sqlContext, Aggregate(Seq(Cube(groupingExprs)), aliasedAgg, df.logicalPlan))
case GroupedData.PivotType(pivotCol, values) =>
val aliasedGrps = groupingExprs.map(alias)
DataFrame(
Dataset.newDataFrame(
df.sqlContext, Pivot(aliasedGrps, pivotCol, values, aggExprs, df.logicalPlan))
}
}

2
sql/core/src/main/scala/org/apache/spark/sql/GroupedDataset.scala
View file

@ -64,7 +64,7 @@ class GroupedDataset[K, V] private[sql](
private def groupedData =
new GroupedData(
DataFrame(sqlContext, logicalPlan), groupingAttributes, GroupedData.GroupByType)
Dataset.newDataFrame(sqlContext, logicalPlan), groupingAttributes, GroupedData.GroupByType)
/**
* Returns a new [[GroupedDataset]] where the type of the key has been mapped to the specified

28
sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
View file

@ -374,7 +374,7 @@ class SQLContext private[sql](
val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType]
val attributeSeq = schema.toAttributes
val rowRDD = RDDConversions.productToRowRdd(rdd, schema.map(_.dataType))
DataFrame(self, LogicalRDD(attributeSeq, rowRDD)(self))
Dataset.newDataFrame(self, LogicalRDD(attributeSeq, rowRDD)(self))
}
/**
@ -389,7 +389,7 @@ class SQLContext private[sql](
SQLContext.setActive(self)
val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType]
val attributeSeq = schema.toAttributes
DataFrame(self, LocalRelation.fromProduct(attributeSeq, data))
Dataset.newDataFrame(self, LocalRelation.fromProduct(attributeSeq, data))
}
/**
@ -399,7 +399,7 @@ class SQLContext private[sql](
* @since 1.3.0
*/
def baseRelationToDataFrame(baseRelation: BaseRelation): DataFrame = {
DataFrame(this, LogicalRelation(baseRelation))
Dataset.newDataFrame(this, LogicalRelation(baseRelation))
}
/**
@ -454,7 +454,7 @@ class SQLContext private[sql](
rowRDD.map{r: Row => InternalRow.fromSeq(r.toSeq)}
}
val logicalPlan = LogicalRDD(schema.toAttributes, catalystRows)(self)
DataFrame(this, logicalPlan)
Dataset.newDataFrame(this, logicalPlan)
}
@ -489,7 +489,7 @@ class SQLContext private[sql](
// TODO: use MutableProjection when rowRDD is another DataFrame and the applied
// schema differs from the existing schema on any field data type.
val logicalPlan = LogicalRDD(schema.toAttributes, catalystRows)(self)
DataFrame(this, logicalPlan)
Dataset.newDataFrame(this, logicalPlan)
}
/**
@ -517,7 +517,7 @@ class SQLContext private[sql](
*/
@DeveloperApi
def createDataFrame(rows: java.util.List[Row], schema: StructType): DataFrame = {
DataFrame(self, LocalRelation.fromExternalRows(schema.toAttributes, rows.asScala))
Dataset.newDataFrame(self, LocalRelation.fromExternalRows(schema.toAttributes, rows.asScala))
}
/**
@ -536,7 +536,7 @@ class SQLContext private[sql](
val localBeanInfo = Introspector.getBeanInfo(Utils.classForName(className))
SQLContext.beansToRows(iter, localBeanInfo, attributeSeq)
}
DataFrame(this, LogicalRDD(attributeSeq, rowRdd)(this))
Dataset.newDataFrame(this, LogicalRDD(attributeSeq, rowRdd)(this))
}
/**
@ -564,7 +564,7 @@ class SQLContext private[sql](
val className = beanClass.getName
val beanInfo = Introspector.getBeanInfo(beanClass)
val rows = SQLContext.beansToRows(data.asScala.iterator, beanInfo, attrSeq)
DataFrame(self, LocalRelation(attrSeq, rows.toSeq))
Dataset.newDataFrame(self, LocalRelation(attrSeq, rows.toSeq))
}
/**
@ -770,7 +770,7 @@ class SQLContext private[sql](
*/
@Experimental
def range(start: Long, end: Long, step: Long, numPartitions: Int): DataFrame = {
DataFrame(this, Range(start, end, step, numPartitions))
Dataset.newDataFrame(this, Range(start, end, step, numPartitions))
}
/**
@ -781,7 +781,7 @@ class SQLContext private[sql](
* @since 1.3.0
*/
def sql(sqlText: String): DataFrame = {
DataFrame(this, parseSql(sqlText))
Dataset.newDataFrame(this, parseSql(sqlText))
}
/**
@ -795,7 +795,7 @@ class SQLContext private[sql](
}
private def table(tableIdent: TableIdentifier): DataFrame = {
DataFrame(this, catalog.lookupRelation(tableIdent))
Dataset.newDataFrame(this, catalog.lookupRelation(tableIdent))
}
/**
@ -807,7 +807,7 @@ class SQLContext private[sql](
* @since 1.3.0
*/
def tables(): DataFrame = {
DataFrame(this, ShowTablesCommand(None))
Dataset.newDataFrame(this, ShowTablesCommand(None))
}
/**
@ -819,7 +819,7 @@ class SQLContext private[sql](
* @since 1.3.0
*/
def tables(databaseName: String): DataFrame = {
DataFrame(this, ShowTablesCommand(Some(databaseName)))
Dataset.newDataFrame(this, ShowTablesCommand(Some(databaseName)))
}
/**
@ -886,7 +886,7 @@ class SQLContext private[sql](
schema: StructType): DataFrame = {
val rowRdd = rdd.map(r => python.EvaluatePython.fromJava(r, schema).asInstanceOf[InternalRow])
DataFrame(this, LogicalRDD(schema.toAttributes, rowRdd)(self))
Dataset.newDataFrame(this, LogicalRDD(schema.toAttributes, rowRdd)(self))
}
/**

4
sql/core/src/main/scala/org/apache/spark/sql/execution/command/commands.scala
View file

@ -21,7 +21,7 @@ import java.util.NoSuchElementException
import org.apache.spark.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row, SQLContext}
import org.apache.spark.sql.{Dataset, Row, SQLContext}
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.errors.TreeNodeException
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
@ -252,7 +252,7 @@ case class CacheTableCommand(
override def run(sqlContext: SQLContext): Seq[Row] = {
plan.foreach { logicalPlan =>
sqlContext.registerDataFrameAsTable(DataFrame(sqlContext, logicalPlan), tableName)
sqlContext.registerDataFrameAsTable(Dataset.newDataFrame(sqlContext, logicalPlan), tableName)
}
sqlContext.cacheTable(tableName)

4
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
View file

@ -27,7 +27,7 @@ import org.apache.hadoop.fs.Path
import org.apache.spark.Logging
import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.sql.{AnalysisException, DataFrame, SaveMode, SQLContext}
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
import org.apache.spark.sql.execution.streaming.{FileStreamSource, Sink, Source}
import org.apache.spark.sql.sources._
@ -154,7 +154,7 @@ case class DataSource(
}
def dataFrameBuilder(files: Array[String]): DataFrame = {
DataFrame(
Dataset.newDataFrame(
sqlContext,
LogicalRelation(
DataSource(

2
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
View file

@ -34,7 +34,7 @@ private[sql] case class InsertIntoDataSource(
override def run(sqlContext: SQLContext): Seq[Row] = {
val relation = logicalRelation.relation.asInstanceOf[InsertableRelation]
val data = DataFrame(sqlContext, query)
val data = Dataset.newDataFrame(sqlContext, query)
// Apply the schema of the existing table to the new data.
val df = sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema)
relation.insert(df, overwrite)

2
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
View file

@ -114,7 +114,7 @@ private[sql] case class InsertIntoHadoopFsRelation(
val partitionSet = AttributeSet(partitionColumns)
val dataColumns = query.output.filterNot(partitionSet.contains)
val queryExecution = DataFrame(sqlContext, query).queryExecution
val queryExecution = Dataset.newDataFrame(sqlContext, query).queryExecution
SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
val relation =
WriteRelation(

10
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
View file

@ -17,7 +17,7 @@
package org.apache.spark.sql.execution.datasources
import org.apache.spark.sql.{DataFrame, Row, SaveMode, SQLContext}
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
import org.apache.spark.sql.catalyst.plans.logical
@ -100,7 +100,7 @@ case class CreateTempTableUsing(
options = options)
sqlContext.catalog.registerTable(
tableIdent,
DataFrame(sqlContext, LogicalRelation(dataSource.resolveRelation())).logicalPlan)
Dataset.newDataFrame(sqlContext, LogicalRelation(dataSource.resolveRelation())).logicalPlan)
Seq.empty[Row]
}
@ -115,7 +115,7 @@ case class CreateTempTableUsingAsSelect(
query: LogicalPlan) extends RunnableCommand {
override def run(sqlContext: SQLContext): Seq[Row] = {
val df = DataFrame(sqlContext, query)
val df = Dataset.newDataFrame(sqlContext, query)
val dataSource = DataSource(
sqlContext,
className = provider,
@ -125,7 +125,7 @@ case class CreateTempTableUsingAsSelect(
val result = dataSource.write(mode, df)
sqlContext.catalog.registerTable(
tableIdent,
DataFrame(sqlContext, LogicalRelation(result)).logicalPlan)
Dataset.newDataFrame(sqlContext, LogicalRelation(result)).logicalPlan)
Seq.empty[Row]
}
@ -146,7 +146,7 @@ case class RefreshTable(tableIdent: TableIdentifier)
if (isCached) {
// Create a data frame to represent the table.
// TODO: Use uncacheTable once it supports database name.
val df = DataFrame(sqlContext, logicalPlan)
val df = Dataset.newDataFrame(sqlContext, logicalPlan)
// Uncache the logicalPlan.
sqlContext.cacheManager.tryUncacheQuery(df, blocking = true)
// Cache it again.

4
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
View file

@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.stat
import scala.collection.mutable.{Map => MutableMap}
import org.apache.spark.Logging
import org.apache.spark.sql.{Column, DataFrame, Row}
import org.apache.spark.sql.{Column, DataFrame, Dataset, Row}
import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
import org.apache.spark.sql.types._
@ -121,6 +121,6 @@ private[sql] object FrequentItems extends Logging {
StructField(v._1 + "_freqItems", ArrayType(v._2, false))
}
val schema = StructType(outputCols).toAttributes
DataFrame(df.sqlContext, LocalRelation.fromExternalRows(schema, Seq(resultRow)))
Dataset.newDataFrame(df.sqlContext, LocalRelation.fromExternalRows(schema, Seq(resultRow)))
}
}

4
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
View file

@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.stat
import scala.collection.mutable.ArrayBuffer
import org.apache.spark.Logging
import org.apache.spark.sql.{Column, DataFrame, Row}
import org.apache.spark.sql.{Column, DataFrame, Dataset, Row}
import org.apache.spark.sql.catalyst.expressions.{Cast, GenericMutableRow}
import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
import org.apache.spark.sql.functions._
@ -454,6 +454,6 @@ private[sql] object StatFunctions extends Logging {
}
val schema = StructType(StructField(tableName, StringType) +: headerNames)
DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)).na.fill(0.0)
Dataset.newDataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table)).na.fill(0.0)
}
}

2
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala
View file

@ -211,7 +211,7 @@ class StreamExecution(
// Construct the batch and send it to the sink.
val batchOffset = streamProgress.toCompositeOffset(sources)
val nextBatch = new Batch(batchOffset, DataFrame(sqlContext, newPlan))
val nextBatch = new Batch(batchOffset, Dataset.newDataFrame(sqlContext, newPlan))
sink.addBatch(nextBatch)
}

2
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala
View file

@ -59,7 +59,7 @@ case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
}
def toDF()(implicit sqlContext: SQLContext): DataFrame = {
DataFrame(sqlContext, logicalPlan)
Dataset.newDataFrame(sqlContext, logicalPlan)
}
def addData(data: A*): Offset = {

2
sql/core/src/main/scala/org/apache/spark/sql/functions.scala
View file

@ -936,7 +936,7 @@ object functions extends LegacyFunctions {
* @since 1.5.0
*/
def broadcast(df: DataFrame): DataFrame = {
DataFrame(df.sqlContext, BroadcastHint(df.logicalPlan))
Dataset.newDataFrame(df.sqlContext, BroadcastHint(df.logicalPlan))
}
/**

2
sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
View file

@ -933,7 +933,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
assert(e2.getMessage.contains("Inserting into an RDD-based table is not allowed."))
// error case: insert into an OneRowRelation
DataFrame(sqlContext, OneRowRelation).registerTempTable("one_row")
Dataset.newDataFrame(sqlContext, OneRowRelation).registerTempTable("one_row")
val e3 = intercept[AnalysisException] {
insertion.write.insertInto("one_row")
}

2
sql/core/src/test/scala/org/apache/spark/sql/StreamTest.scala
View file

@ -65,7 +65,7 @@ import org.apache.spark.sql.execution.streaming._
trait StreamTest extends QueryTest with Timeouts {
implicit class RichSource(s: Source) {
def toDF(): DataFrame = DataFrame(sqlContext, StreamingRelation(s))
def toDF(): DataFrame = Dataset.newDataFrame(sqlContext, StreamingRelation(s))
def toDS[A: Encoder](): Dataset[A] = Dataset(sqlContext, StreamingRelation(s))
}

2
sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
View file

@ -219,7 +219,7 @@ private[sql] trait SQLTestUtils
* way to construct [[DataFrame]] directly out of local data without relying on implicits.
*/
protected implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
DataFrame(sqlContext, plan)
Dataset.newDataFrame(sqlContext, plan)
}
/**

2
sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
View file

@ -246,7 +246,7 @@ case class CreateMetastoreDataSourceAsSelect(
createMetastoreTable = true
}
val data = DataFrame(hiveContext, query)
val data = Dataset.newDataFrame(hiveContext, query)
val df = existingSchema match {
// If we are inserting into an existing table, just use the existing schema.
case Some(s) => sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, s)

4
sql/hive/src/test/scala/org/apache/spark/sql/hive/SQLBuilderTest.scala
View file

@ -19,7 +19,7 @@ package org.apache.spark.sql.hive
import scala.util.control.NonFatal
import org.apache.spark.sql.{DataFrame, QueryTest}
import org.apache.spark.sql.{DataFrame, Dataset, QueryTest}
import org.apache.spark.sql.catalyst.expressions.Expression
import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
import org.apache.spark.sql.hive.test.TestHiveSingleton
@ -63,7 +63,7 @@ abstract class SQLBuilderTest extends QueryTest with TestHiveSingleton {
""".stripMargin)
}
checkAnswer(sqlContext.sql(generatedSQL), DataFrame(sqlContext, plan))
checkAnswer(sqlContext.sql(generatedSQL), Dataset.newDataFrame(sqlContext, plan))
}
protected def checkSQL(df: DataFrame, expectedSQL: String): Unit = {

2
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
View file

@ -968,7 +968,7 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
// Create a new df to make sure its physical operator picks up
// spark.sql.TungstenAggregate.testFallbackStartsAt.
// todo: remove it?
val newActual = DataFrame(sqlContext, actual.logicalPlan)
val newActual = Dataset.newDataFrame(sqlContext, actual.logicalPlan)
QueryTest.checkAnswer(newActual, expectedAnswer) match {
case Some(errorMessage) =>