org.apache.spark.sql.Dataset.scala Maven / Gradle / Ivy
The newest version!
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql
import java.io.{ByteArrayOutputStream, CharArrayWriter, DataOutputStream}
import java.util
import scala.collection.mutable.{ArrayBuffer, HashSet}
import scala.jdk.CollectionConverters._
import scala.reflect.ClassTag
import scala.reflect.runtime.universe.TypeTag
import scala.util.control.NonFatal
import org.apache.commons.lang3.StringUtils
import org.apache.commons.text.StringEscapeUtils
import org.apache.spark.TaskContext
import org.apache.spark.annotation.{DeveloperApi, Stable, Unstable}
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.api.java.function._
import org.apache.spark.api.python.{PythonRDD, SerDeUtil}
import org.apache.spark.api.r.RRDD
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.resource.ResourceProfile
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, QueryPlanningTracker, ScalaReflection, TableIdentifier}
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.catalog.HiveTableRelation
import org.apache.spark.sql.catalyst.encoders._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.json.{JacksonGenerator, JSONOptions}
import org.apache.spark.sql.catalyst.parser.{ParseException, ParserUtils}
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.trees.{TreeNodeTag, TreePattern}
import org.apache.spark.sql.catalyst.types.DataTypeUtils.toAttributes
import org.apache.spark.sql.catalyst.util.{CharVarcharUtils, IntervalUtils}
import org.apache.spark.sql.catalyst.util.TypeUtils.toSQLId
import org.apache.spark.sql.errors.{QueryCompilationErrors, QueryExecutionErrors}
import org.apache.spark.sql.execution._
import org.apache.spark.sql.execution.aggregate.TypedAggregateExpression
import org.apache.spark.sql.execution.arrow.{ArrowBatchStreamWriter, ArrowConverters}
import org.apache.spark.sql.execution.command._
import org.apache.spark.sql.execution.datasources.LogicalRelation
import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, DataSourceV2ScanRelation, FileTable}
import org.apache.spark.sql.execution.python.EvaluatePython
import org.apache.spark.sql.execution.stat.StatFunctions
import org.apache.spark.sql.internal.{DataFrameWriterImpl, DataFrameWriterV2Impl, MergeIntoWriterImpl, SQLConf, ToScalaUDF}
import org.apache.spark.sql.internal.ExpressionUtils.column
import org.apache.spark.sql.internal.TypedAggUtils.withInputType
import org.apache.spark.sql.streaming.DataStreamWriter
import org.apache.spark.sql.types._
import org.apache.spark.sql.util.SchemaUtils
import org.apache.spark.storage.StorageLevel
import org.apache.spark.unsafe.array.ByteArrayMethods
import org.apache.spark.util.ArrayImplicits._
import org.apache.spark.util.Utils
private[sql] object Dataset {
val curId = new java.util.concurrent.atomic.AtomicLong()
val DATASET_ID_KEY = "__dataset_id"
val COL_POS_KEY = "__col_position"
val DATASET_ID_TAG = TreeNodeTag[HashSet[Long]]("dataset_id")
def apply[T: Encoder](sparkSession: SparkSession, logicalPlan: LogicalPlan): Dataset[T] = {
val dataset = new Dataset(sparkSession, logicalPlan, implicitly[Encoder[T]])
// Eagerly bind the encoder so we verify that the encoder matches the underlying
// schema. The user will get an error if this is not the case.
// optimization: it is guaranteed that [[InternalRow]] can be converted to [[Row]] so
// do not do this check in that case. this check can be expensive since it requires running
// the whole [[Analyzer]] to resolve the deserializer
if (dataset.exprEnc.clsTag.runtimeClass != classOf[Row]) {
dataset.resolvedEnc
}
dataset
}
def ofRows(sparkSession: SparkSession, logicalPlan: LogicalPlan): DataFrame =
sparkSession.withActive {
val qe = sparkSession.sessionState.executePlan(logicalPlan)
qe.assertAnalyzed()
new Dataset[Row](qe, ExpressionEncoder(qe.analyzed.schema))
}
def ofRows(
sparkSession: SparkSession,
logicalPlan: LogicalPlan,
shuffleCleanupMode: ShuffleCleanupMode): DataFrame =
sparkSession.withActive {
val qe = new QueryExecution(
sparkSession, logicalPlan, shuffleCleanupMode = shuffleCleanupMode)
qe.assertAnalyzed()
new Dataset[Row](qe, ExpressionEncoder(qe.analyzed.schema))
}
/** A variant of ofRows that allows passing in a tracker so we can track query parsing time. */
def ofRows(
sparkSession: SparkSession,
logicalPlan: LogicalPlan,
tracker: QueryPlanningTracker,
shuffleCleanupMode: ShuffleCleanupMode = DoNotCleanup)
: DataFrame = sparkSession.withActive {
val qe = new QueryExecution(
sparkSession, logicalPlan, tracker, shuffleCleanupMode = shuffleCleanupMode)
qe.assertAnalyzed()
new Dataset[Row](qe, ExpressionEncoder(qe.analyzed.schema))
}
}
/**
* A Dataset is a strongly typed collection of domain-specific objects that can be transformed
* in parallel using functional or relational operations. Each Dataset also has an untyped view
* called a `DataFrame`, which is a Dataset of [[Row]].
*
* Operations available on Datasets are divided into transformations and actions. Transformations
* are the ones that produce new Datasets, and actions are the ones that trigger computation and
* return results. Example transformations include map, filter, select, and aggregate (`groupBy`).
* Example actions count, show, or writing data out to file systems.
*
* Datasets are "lazy", i.e. computations are only triggered when an action is invoked. Internally,
* a Dataset represents a logical plan that describes the computation required to produce the data.
* When an action is invoked, Spark's query optimizer optimizes the logical plan and generates a
* physical plan for efficient execution in a parallel and distributed manner. To explore the
* logical plan as well as optimized physical plan, use the `explain` function.
*
* To efficiently support domain-specific objects, an [[Encoder]] is required. The encoder maps
* the domain specific type `T` to Spark's internal type system. For example, given a class `Person`
* with two fields, `name` (string) and `age` (int), an encoder is used to tell Spark to generate
* code at runtime to serialize the `Person` object into a binary structure. This binary structure
* often has much lower memory footprint as well as are optimized for efficiency in data processing
* (e.g. in a columnar format). To understand the internal binary representation for data, use the
* `schema` function.
*
* There are typically two ways to create a Dataset. The most common way is by pointing Spark
* to some files on storage systems, using the `read` function available on a `SparkSession`.
* {{{
* val people = spark.read.parquet("...").as[Person] // Scala
* Dataset people = spark.read().parquet("...").as(Encoders.bean(Person.class)); // Java
* }}}
*
* Datasets can also be created through transformations available on existing Datasets. For example,
* the following creates a new Dataset by applying a filter on the existing one:
* {{{
* val names = people.map(_.name) // in Scala; names is a Dataset[String]
* Dataset names = people.map(
* (MapFunction) p -> p.name, Encoders.STRING()); // Java
* }}}
*
* Dataset operations can also be untyped, through various domain-specific-language (DSL)
* functions defined in: Dataset (this class), [[Column]], and [[functions]]. These operations
* are very similar to the operations available in the data frame abstraction in R or Python.
*
* To select a column from the Dataset, use `apply` method in Scala and `col` in Java.
* {{{
* val ageCol = people("age") // in Scala
* Column ageCol = people.col("age"); // in Java
* }}}
*
* Note that the [[Column]] type can also be manipulated through its various functions.
* {{{
* // The following creates a new column that increases everybody's age by 10.
* people("age") + 10 // in Scala
* people.col("age").plus(10); // in Java
* }}}
*
* A more concrete example in Scala:
* {{{
* // To create Dataset[Row] using SparkSession
* val people = spark.read.parquet("...")
* val department = spark.read.parquet("...")
*
* people.filter("age > 30")
* .join(department, people("deptId") === department("id"))
* .groupBy(department("name"), people("gender"))
* .agg(avg(people("salary")), max(people("age")))
* }}}
*
* and in Java:
* {{{
* // To create Dataset using SparkSession
* Dataset people = spark.read().parquet("...");
* Dataset department = spark.read().parquet("...");
*
* people.filter(people.col("age").gt(30))
* .join(department, people.col("deptId").equalTo(department.col("id")))
* .groupBy(department.col("name"), people.col("gender"))
* .agg(avg(people.col("salary")), max(people.col("age")));
* }}}
*
* @groupname basic Basic Dataset functions
* @groupname action Actions
* @groupname untypedrel Untyped transformations
* @groupname typedrel Typed transformations
*
* @since 1.6.0
*/
@Stable
class Dataset[T] private[sql](
@DeveloperApi @Unstable @transient val queryExecution: QueryExecution,
@DeveloperApi @Unstable @transient val encoder: Encoder[T])
extends api.Dataset[T, Dataset] {
type RGD = RelationalGroupedDataset
@transient lazy val sparkSession: SparkSession = {
if (queryExecution == null || queryExecution.sparkSession == null) {
throw QueryExecutionErrors.transformationsAndActionsNotInvokedByDriverError()
}
queryExecution.sparkSession
}
import sparkSession.RichColumn
// A globally unique id of this Dataset.
private[sql] val id = Dataset.curId.getAndIncrement()
queryExecution.assertAnalyzed()
// Note for Spark contributors: if adding or updating any action in `Dataset`, please make sure
// you wrap it with `withNewExecutionId` if this actions doesn't call other action.
def this(sparkSession: SparkSession, logicalPlan: LogicalPlan, encoder: Encoder[T]) = {
this(sparkSession.sessionState.executePlan(logicalPlan), encoder)
}
def this(sqlContext: SQLContext, logicalPlan: LogicalPlan, encoder: Encoder[T]) = {
this(sqlContext.sparkSession, logicalPlan, encoder)
}
@transient private[sql] val logicalPlan: LogicalPlan = {
val plan = queryExecution.commandExecuted
if (sparkSession.conf.get(SQLConf.FAIL_AMBIGUOUS_SELF_JOIN_ENABLED)) {
val dsIds = plan.getTagValue(Dataset.DATASET_ID_TAG).getOrElse(new HashSet[Long])
dsIds.add(id)
plan.setTagValue(Dataset.DATASET_ID_TAG, dsIds)
}
plan
}
/**
* Currently [[ExpressionEncoder]] is the only implementation of [[Encoder]], here we turn the
* passed in encoder to [[ExpressionEncoder]] explicitly, and mark it implicit so that we can use
* it when constructing new Dataset objects that have the same object type (that will be
* possibly resolved to a different schema).
*/
private[sql] implicit val exprEnc: ExpressionEncoder[T] = encoderFor(encoder)
// The resolved `ExpressionEncoder` which can be used to turn rows to objects of type T, after
// collecting rows to the driver side.
private lazy val resolvedEnc = {
exprEnc.resolveAndBind(logicalPlan.output, sparkSession.sessionState.analyzer)
}
private implicit def classTag: ClassTag[T] = exprEnc.clsTag
// sqlContext must be val because a stable identifier is expected when you import implicits
@transient lazy val sqlContext: SQLContext = sparkSession.sqlContext
private[sql] def resolve(colName: String): NamedExpression = {
val resolver = sparkSession.sessionState.analyzer.resolver
queryExecution.analyzed.resolveQuoted(colName, resolver)
.getOrElse(throw QueryCompilationErrors.unresolvedColumnError(colName, schema.fieldNames))
}
private[sql] def numericColumns: Seq[Expression] = {
schema.fields.filter(_.dataType.isInstanceOf[NumericType]).map { n =>
queryExecution.analyzed.resolveQuoted(n.name, sparkSession.sessionState.analyzer.resolver).get
}.toImmutableArraySeq
}
/**
* Get rows represented in Sequence by specific truncate and vertical requirement.
*
* @param numRows Number of rows to return
* @param truncate If set to more than 0, truncates strings to `truncate` characters and
* all cells will be aligned right.
*/
private[sql] def getRows(
numRows: Int,
truncate: Int): Seq[Seq[String]] = {
val newDf = commandResultOptimized.toDF()
val castCols = newDf.logicalPlan.output.map { col =>
column(ToPrettyString(col))
}
val data = newDf.select(castCols: _*).take(numRows + 1)
// For array values, replace Seq and Array with square brackets
// For cells that are beyond `truncate` characters, replace it with the
// first `truncate-3` and "..."
(schema.fieldNames
.map(SchemaUtils.escapeMetaCharacters).toImmutableArraySeq +: data.map { row =>
row.toSeq.map { cell =>
assert(cell != null, "ToPrettyString is not nullable and should not return null value")
// Escapes meta-characters not to break the `showString` format
val str = SchemaUtils.escapeMetaCharacters(cell.toString)
if (truncate > 0 && str.length > truncate) {
// do not show ellipses for strings shorter than 4 characters.
if (truncate < 4) str.substring(0, truncate)
else str.substring(0, truncate - 3) + "..."
} else {
str
}
}: Seq[String]
}).toImmutableArraySeq
}
/**
* Compose the string representing rows for output
*
* @param _numRows Number of rows to show
* @param truncate If set to more than 0, truncates strings to `truncate` characters and
* all cells will be aligned right.
* @param vertical If set to true, prints output rows vertically (one line per column value).
*/
private[sql] def showString(
_numRows: Int,
truncate: Int = 20,
vertical: Boolean = false): String = {
val numRows = _numRows.max(0).min(ByteArrayMethods.MAX_ROUNDED_ARRAY_LENGTH - 1)
// Get rows represented by Seq[Seq[String]], we may get one more line if it has more data.
val tmpRows = getRows(numRows, truncate)
val hasMoreData = tmpRows.length - 1 > numRows
val rows = tmpRows.take(numRows + 1)
val sb = new StringBuilder
val numCols = schema.fieldNames.length
// We set a minimum column width at '3'
val minimumColWidth = 3
if (!vertical) {
// Initialise the width of each column to a minimum value
val colWidths = Array.fill(numCols)(minimumColWidth)
// Compute the width of each column
for (row <- rows) {
for ((cell, i) <- row.zipWithIndex) {
colWidths(i) = math.max(colWidths(i), Utils.stringHalfWidth(cell))
}
}
val paddedRows = rows.map { row =>
row.zipWithIndex.map { case (cell, i) =>
if (truncate > 0) {
StringUtils.leftPad(cell, colWidths(i) - Utils.stringHalfWidth(cell) + cell.length)
} else {
StringUtils.rightPad(cell, colWidths(i) - Utils.stringHalfWidth(cell) + cell.length)
}
}
}
// Create SeparateLine
val sep: String = colWidths.map("-" * _).addString(sb, "+", "+", "+\n").toString()
// column names
paddedRows.head.addString(sb, "|", "|", "|\n")
sb.append(sep)
// data
paddedRows.tail.foreach(_.addString(sb, "|", "|", "|\n"))
sb.append(sep)
} else {
// Extended display mode enabled
val fieldNames = rows.head
val dataRows = rows.tail
// Compute the width of field name and data columns
val fieldNameColWidth = fieldNames.foldLeft(minimumColWidth) { case (curMax, fieldName) =>
math.max(curMax, Utils.stringHalfWidth(fieldName))
}
val dataColWidth = dataRows.foldLeft(minimumColWidth) { case (curMax, row) =>
math.max(curMax, row.map(cell => Utils.stringHalfWidth(cell)).max)
}
dataRows.zipWithIndex.foreach { case (row, i) =>
// "+ 5" in size means a character length except for padded names and data
val rowHeader = StringUtils.rightPad(
s"-RECORD $i", fieldNameColWidth + dataColWidth + 5, "-")
sb.append(rowHeader).append("\n")
row.zipWithIndex.map { case (cell, j) =>
val fieldName = StringUtils.rightPad(fieldNames(j),
fieldNameColWidth - Utils.stringHalfWidth(fieldNames(j)) + fieldNames(j).length)
val data = StringUtils.rightPad(cell,
dataColWidth - Utils.stringHalfWidth(cell) + cell.length)
s" $fieldName | $data "
}.addString(sb, "", "\n", "\n")
}
}
// Print a footer
if (vertical && rows.tail.isEmpty) {
// In a vertical mode, print an empty row set explicitly
sb.append("(0 rows)\n")
} else if (hasMoreData) {
// For Data that has more than "numRows" records
val rowsString = if (numRows == 1) "row" else "rows"
sb.append(s"only showing top $numRows $rowsString\n")
}
sb.toString()
}
/**
* Compose the HTML representing rows for output
*
* @param _numRows Number of rows to show
* @param truncate If set to more than 0, truncates strings to `truncate` characters and
* all cells will be aligned right.
*/
private[sql] def htmlString(
_numRows: Int,
truncate: Int = 20): String = {
val numRows = _numRows.max(0).min(ByteArrayMethods.MAX_ROUNDED_ARRAY_LENGTH - 1)
// Get rows represented by Seq[Seq[String]], we may get one more line if it has more data.
val tmpRows = getRows(numRows, truncate)
val hasMoreData = tmpRows.length - 1 > numRows
val rows = tmpRows.take(numRows + 1)
val sb = new StringBuilder
sb.append("\n")
sb.append(rows.head.map(StringEscapeUtils.escapeHtml4)
.mkString("", " ", " ", " ", "
\n")
if (hasMoreData) {
sb.append(s"only showing top $numRows ${if (numRows == 1) "row" else "rows"}\n")
}
sb.toString()
}
override def toString: String = {
try {
val builder = new StringBuilder
val fields = schema.take(2).map { f =>
s"${f.name}: ${f.dataType.simpleString(2)}"
}
builder.append("[")
builder.append(fields.mkString(", "))
if (schema.length > 2) {
if (schema.length - fields.size == 1) {
builder.append(" ... 1 more field")
} else {
builder.append(" ... " + (schema.length - 2) + " more fields")
}
}
builder.append("]").toString()
} catch {
case NonFatal(e) =>
s"Invalid tree; ${e.getMessage}:\n$queryExecution"
}
}
/** @inheritdoc */
// This is declared with parentheses to prevent the Scala compiler from treating
// `ds.toDF("1")` as invoking this toDF and then apply on the returned DataFrame.
def toDF(): DataFrame = new Dataset[Row](queryExecution, ExpressionEncoder(schema))
/** @inheritdoc */
def as[U : Encoder]: Dataset[U] = Dataset[U](sparkSession, logicalPlan)
/** @inheritdoc */
def to(schema: StructType): DataFrame = withPlan {
val replaced = CharVarcharUtils.failIfHasCharVarchar(schema).asInstanceOf[StructType]
Project.matchSchema(logicalPlan, replaced, sparkSession.sessionState.conf)
}
/** @inheritdoc */
@scala.annotation.varargs
def toDF(colNames: String*): DataFrame = {
require(schema.size == colNames.size,
"The number of columns doesn't match.\n" +
s"Old column names (${schema.size}): " + schema.fields.map(_.name).mkString(", ") + "\n" +
s"New column names (${colNames.size}): " + colNames.mkString(", "))
val newCols = logicalPlan.output.zip(colNames).map { case (oldAttribute, newName) =>
column(oldAttribute).as(newName)
}
select(newCols : _*)
}
/** @inheritdoc */
def schema: StructType = sparkSession.withActive {
queryExecution.analyzed.schema
}
/** @inheritdoc */
def explain(mode: String): Unit = sparkSession.withActive {
// Because temporary views are resolved during analysis when we create a Dataset, and
// `ExplainCommand` analyzes input query plan and resolves temporary views again. Using
// `ExplainCommand` here will probably output different query plans, compared to the results
// of evaluation of the Dataset. So just output QueryExecution's query plans here.
// scalastyle:off println
println(queryExecution.explainString(ExplainMode.fromString(mode)))
// scalastyle:on println
}
/** @inheritdoc */
def isLocal: Boolean = logicalPlan.isInstanceOf[LocalRelation] ||
logicalPlan.isInstanceOf[CommandResult]
/** @inheritdoc */
def isEmpty: Boolean = withAction("isEmpty",
commandResultOptimized.select().limit(1).queryExecution) { plan =>
plan.executeTake(1).isEmpty
}
/** @inheritdoc */
def isStreaming: Boolean = logicalPlan.isStreaming
/** @inheritdoc */
protected[sql] def checkpoint(eager: Boolean, reliableCheckpoint: Boolean): Dataset[T] = {
val actionName = if (reliableCheckpoint) "checkpoint" else "localCheckpoint"
withAction(actionName, queryExecution) { physicalPlan =>
val internalRdd = physicalPlan.execute().map(_.copy())
if (reliableCheckpoint) {
internalRdd.checkpoint()
} else {
internalRdd.localCheckpoint()
}
if (eager) {
internalRdd.doCheckpoint()
}
withTypedPlan[T] {
LogicalRDD.fromDataset(rdd = internalRdd, originDataset = this, isStreaming = isStreaming)
}
}
}
/** @inheritdoc */
// We only accept an existing column name, not a derived column here as a watermark that is
// defined on a derived column cannot referenced elsewhere in the plan.
def withWatermark(eventTime: String, delayThreshold: String): Dataset[T] = withTypedPlan {
val parsedDelay = IntervalUtils.fromIntervalString(delayThreshold)
require(!IntervalUtils.isNegative(parsedDelay),
s"delay threshold ($delayThreshold) should not be negative.")
EliminateEventTimeWatermark(
EventTimeWatermark(UnresolvedAttribute(eventTime), parsedDelay, logicalPlan))
}
/** @inheritdoc */
// scalastyle:off println
def show(numRows: Int, truncate: Boolean): Unit = if (truncate) {
println(showString(numRows, truncate = 20))
} else {
println(showString(numRows, truncate = 0))
}
/** @inheritdoc */
// scalastyle:off println
def show(numRows: Int, truncate: Int, vertical: Boolean): Unit =
println(showString(numRows, truncate, vertical))
// scalastyle:on println
/** @inheritdoc */
def na: DataFrameNaFunctions = new DataFrameNaFunctions(toDF())
/** @inheritdoc */
def stat: DataFrameStatFunctions = new DataFrameStatFunctions(toDF())
/** @inheritdoc */
def join(right: Dataset[_]): DataFrame = withPlan {
Join(logicalPlan, right.logicalPlan, joinType = Inner, None, JoinHint.NONE)
}
/** @inheritdoc */
def join(right: Dataset[_], usingColumns: Seq[String], joinType: String): DataFrame = {
// Analyze the self join. The assumption is that the analyzer will disambiguate left vs right
// by creating a new instance for one of the branch.
val joined = sparkSession.sessionState.executePlan(
Join(logicalPlan, right.logicalPlan, joinType = JoinType(joinType), None, JoinHint.NONE))
.analyzed.asInstanceOf[Join]
withPlan {
Join(
joined.left,
joined.right,
UsingJoin(JoinType(joinType), usingColumns.toIndexedSeq),
None,
JoinHint.NONE)
}
}
/**
* find the trivially true predicates and automatically resolves them to both sides.
*/
private def resolveSelfJoinCondition(
right: Dataset[_],
joinExprs: Option[Column],
joinType: String): Join = {
// Note that in this function, we introduce a hack in the case of self-join to automatically
// resolve ambiguous join conditions into ones that might make sense [SPARK-6231].
// Consider this case: df.join(df, df("key") === df("key"))
// Since df("key") === df("key") is a trivially true condition, this actually becomes a
// cartesian join. However, most likely users expect to perform a self join using "key".
// With that assumption, this hack turns the trivially true condition into equality on join
// keys that are resolved to both sides.
// Trigger analysis so in the case of self-join, the analyzer will clone the plan.
// After the cloning, left and right side will have distinct expression ids.
val plan = withPlan(
Join(logicalPlan, right.logicalPlan,
JoinType(joinType), joinExprs.map(_.expr), JoinHint.NONE))
.queryExecution.analyzed.asInstanceOf[Join]
// If auto self join alias is disabled, return the plan.
if (!sparkSession.sessionState.conf.dataFrameSelfJoinAutoResolveAmbiguity) {
return plan
}
// If left/right have no output set intersection, return the plan.
val lanalyzed = this.queryExecution.analyzed
val ranalyzed = right.queryExecution.analyzed
if (lanalyzed.outputSet.intersect(ranalyzed.outputSet).isEmpty) {
return plan
}
// Otherwise, find the trivially true predicates and automatically resolves them to both sides.
// By the time we get here, since we have already run analysis, all attributes should've been
// resolved and become AttributeReference.
JoinWith.resolveSelfJoinCondition(sparkSession.sessionState.analyzer.resolver, plan)
}
/** @inheritdoc */
def join(right: Dataset[_], joinExprs: Column, joinType: String): DataFrame = {
withPlan {
resolveSelfJoinCondition(right, Some(joinExprs), joinType)
}
}
/** @inheritdoc */
def crossJoin(right: Dataset[_]): DataFrame = withPlan {
Join(logicalPlan, right.logicalPlan, joinType = Cross, None, JoinHint.NONE)
}
/** @inheritdoc */
def joinWith[U](other: Dataset[U], condition: Column, joinType: String): Dataset[(T, U)] = {
// Creates a Join node and resolve it first, to get join condition resolved, self-join resolved,
// etc.
val joined = sparkSession.sessionState.executePlan(
Join(
this.logicalPlan,
other.logicalPlan,
JoinType(joinType),
Some(condition.expr),
JoinHint.NONE)).analyzed.asInstanceOf[Join]
implicit val tuple2Encoder: Encoder[(T, U)] =
ExpressionEncoder
.tuple(Seq(this.exprEnc, other.exprEnc), useNullSafeDeserializer = true)
.asInstanceOf[Encoder[(T, U)]]
withTypedPlan(JoinWith.typedJoinWith(
joined,
sparkSession.sessionState.conf.dataFrameSelfJoinAutoResolveAmbiguity,
sparkSession.sessionState.analyzer.resolver,
this.exprEnc.isSerializedAsStructForTopLevel,
other.exprEnc.isSerializedAsStructForTopLevel))
}
// TODO(SPARK-22947): Fix the DataFrame API.
private[sql] def joinAsOf(
other: Dataset[_],
leftAsOf: Column,
rightAsOf: Column,
usingColumns: Seq[String],
joinType: String,
tolerance: Column,
allowExactMatches: Boolean,
direction: String): DataFrame = {
val joinConditions = usingColumns.map { name =>
this(name) === other(name)
}
val joinCondition = joinConditions.reduceOption(_ && _).orNull
joinAsOf(other, leftAsOf, rightAsOf, joinCondition, joinType,
tolerance, allowExactMatches, direction)
}
// TODO(SPARK-22947): Fix the DataFrame API.
private[sql] def joinAsOf(
other: Dataset[_],
leftAsOf: Column,
rightAsOf: Column,
joinExprs: Column,
joinType: String,
tolerance: Column,
allowExactMatches: Boolean,
direction: String): DataFrame = {
val joined = resolveSelfJoinCondition(other, Option(joinExprs), joinType)
val leftAsOfExpr = leftAsOf.expr.transformUp {
case a: AttributeReference if logicalPlan.outputSet.contains(a) =>
val index = logicalPlan.output.indexWhere(_.exprId == a.exprId)
joined.left.output(index)
}
val rightAsOfExpr = rightAsOf.expr.transformUp {
case a: AttributeReference if other.logicalPlan.outputSet.contains(a) =>
val index = other.logicalPlan.output.indexWhere(_.exprId == a.exprId)
joined.right.output(index)
}
withPlan {
AsOfJoin(
joined.left, joined.right,
leftAsOfExpr, rightAsOfExpr,
joined.condition,
joined.joinType,
Option(tolerance).map(_.expr),
allowExactMatches,
AsOfJoinDirection(direction)
)
}
}
/** @inheritdoc */
@scala.annotation.varargs
def hint(name: String, parameters: Any*): Dataset[T] = withTypedPlan {
val exprs = parameters.map {
case c: Column => c.expr
case s: Symbol => Column(s.name).expr
case e: Expression => e
case literal => Literal(literal)
}
UnresolvedHint(name, exprs, logicalPlan)
}
/** @inheritdoc */
def col(colName: String): Column = colName match {
case "*" =>
column(ResolvedStar(queryExecution.analyzed.output))
case _ =>
if (sparkSession.sessionState.conf.supportQuotedRegexColumnName) {
colRegex(colName)
} else {
column(addDataFrameIdToCol(resolve(colName)))
}
}
/** @inheritdoc */
def metadataColumn(colName: String): Column =
column(queryExecution.analyzed.getMetadataAttributeByName(colName))
// Attach the dataset id and column position to the column reference, so that we can detect
// ambiguous self-join correctly. See the rule `DetectAmbiguousSelfJoin`.
// This must be called before we return a `Column` that contains `AttributeReference`.
// Note that, the metadata added here are only available in the analyzer, as the analyzer rule
// `DetectAmbiguousSelfJoin` will remove it.
private def addDataFrameIdToCol(expr: NamedExpression): NamedExpression = {
val newExpr = expr transform {
case a: AttributeReference
if sparkSession.conf.get(SQLConf.FAIL_AMBIGUOUS_SELF_JOIN_ENABLED) =>
val metadata = new MetadataBuilder()
.withMetadata(a.metadata)
.putLong(Dataset.DATASET_ID_KEY, id)
.putLong(Dataset.COL_POS_KEY, logicalPlan.output.indexWhere(a.semanticEquals))
.build()
a.withMetadata(metadata)
}
newExpr.asInstanceOf[NamedExpression]
}
/** @inheritdoc */
def colRegex(colName: String): Column = {
val caseSensitive = sparkSession.sessionState.conf.caseSensitiveAnalysis
colName match {
case ParserUtils.escapedIdentifier(columnNameRegex) =>
column(UnresolvedRegex(columnNameRegex, None, caseSensitive))
case ParserUtils.qualifiedEscapedIdentifier(nameParts, columnNameRegex) =>
column(UnresolvedRegex(columnNameRegex, Some(nameParts), caseSensitive))
case _ =>
column(addDataFrameIdToCol(resolve(colName)))
}
}
/** @inheritdoc */
def as(alias: String): Dataset[T] = withTypedPlan {
SubqueryAlias(alias, logicalPlan)
}
/** @inheritdoc */
@scala.annotation.varargs
def select(cols: Column*): DataFrame = withPlan {
val untypedCols = cols.map {
case typedCol: TypedColumn[_, _] =>
// Checks if a `TypedColumn` has been inserted with
// specific input type and schema by `withInputType`.
val needInputType = typedCol.expr.exists {
case ta: TypedAggregateExpression if ta.inputDeserializer.isEmpty => true
case _ => false
}
if (!needInputType) {
typedCol
} else {
throw QueryCompilationErrors.cannotPassTypedColumnInUntypedSelectError(typedCol.toString)
}
case other => other
}
Project(untypedCols.map(_.named), logicalPlan)
}
/** @inheritdoc */
def select[U1](c1: TypedColumn[T, U1]): Dataset[U1] = {
implicit val encoder: ExpressionEncoder[U1] = encoderFor(c1.encoder)
val tc1 = withInputType(c1.named, exprEnc, logicalPlan.output)
val project = Project(tc1 :: Nil, logicalPlan)
if (!encoder.isSerializedAsStructForTopLevel) {
new Dataset[U1](sparkSession, project, encoder)
} else {
// Flattens inner fields of U1
new Dataset[Tuple1[U1]](sparkSession, project, ExpressionEncoder.tuple(encoder)).map(_._1)
}
}
/** @inheritdoc */
protected def selectUntyped(columns: TypedColumn[_, _]*): Dataset[_] = {
val encoders = columns.map(c => encoderFor(c.encoder))
val namedColumns = columns.map(c => withInputType(c.named, exprEnc, logicalPlan.output))
val execution = new QueryExecution(sparkSession, Project(namedColumns, logicalPlan))
new Dataset(execution, ExpressionEncoder.tuple(encoders))
}
/** @inheritdoc */
def filter(condition: Column): Dataset[T] = withTypedPlan {
Filter(condition.expr, logicalPlan)
}
/**
* Groups the Dataset using the specified columns, so we can run aggregation on them. See
* [[RelationalGroupedDataset]] for all the available aggregate functions.
*
* {{{
* // Compute the average for all numeric columns grouped by department.
* ds.groupBy($"department").avg()
*
* // Compute the max age and average salary, grouped by department and gender.
* ds.groupBy($"department", $"gender").agg(Map(
* "salary" -> "avg",
* "age" -> "max"
* ))
* }}}
*
* @group untypedrel
* @since 2.0.0
*/
@scala.annotation.varargs
def groupBy(cols: Column*): RelationalGroupedDataset = {
RelationalGroupedDataset(toDF(), cols.map(_.expr), RelationalGroupedDataset.GroupByType)
}
/** @inheritdoc */
@scala.annotation.varargs
def rollup(cols: Column*): RelationalGroupedDataset = {
RelationalGroupedDataset(toDF(), cols.map(_.expr), RelationalGroupedDataset.RollupType)
}
/** @inheritdoc */
@scala.annotation.varargs
def cube(cols: Column*): RelationalGroupedDataset = {
RelationalGroupedDataset(toDF(), cols.map(_.expr), RelationalGroupedDataset.CubeType)
}
/** @inheritdoc */
@scala.annotation.varargs
def groupingSets(groupingSets: Seq[Seq[Column]], cols: Column*): RelationalGroupedDataset = {
RelationalGroupedDataset(
toDF(),
cols.map(_.expr),
RelationalGroupedDataset.GroupingSetsType(groupingSets.map(_.map(_.expr))))
}
/** @inheritdoc */
def reduce(func: (T, T) => T): T = withNewRDDExecutionId("reduce") {
rdd.reduce(func)
}
/**
* (Scala-specific)
* Returns a [[KeyValueGroupedDataset]] where the data is grouped by the given key `func`.
*
* @group typedrel
* @since 2.0.0
*/
def groupByKey[K: Encoder](func: T => K): KeyValueGroupedDataset[K, T] = {
val withGroupingKey = AppendColumns(func, logicalPlan)
val executed = sparkSession.sessionState.executePlan(withGroupingKey)
new KeyValueGroupedDataset(
encoderFor[K],
encoderFor[T],
executed,
logicalPlan.output,
withGroupingKey.newColumns)
}
/**
* (Java-specific)
* Returns a [[KeyValueGroupedDataset]] where the data is grouped by the given key `func`.
*
* @group typedrel
* @since 2.0.0
*/
def groupByKey[K](func: MapFunction[T, K], encoder: Encoder[K]): KeyValueGroupedDataset[K, T] =
groupByKey(ToScalaUDF(func))(encoder)
/** @inheritdoc */
def unpivot(
ids: Array[Column],
values: Array[Column],
variableColumnName: String,
valueColumnName: String): DataFrame = withPlan {
Unpivot(
Some(ids.map(_.named).toImmutableArraySeq),
Some(values.map(v => Seq(v.named)).toImmutableArraySeq),
None,
variableColumnName,
Seq(valueColumnName),
logicalPlan
)
}
/** @inheritdoc */
def unpivot(
ids: Array[Column],
variableColumnName: String,
valueColumnName: String): DataFrame = withPlan {
Unpivot(
Some(ids.map(_.named).toImmutableArraySeq),
None,
None,
variableColumnName,
Seq(valueColumnName),
logicalPlan
)
}
/**
* Called from Python as Seq[Column] are easier to create via py4j than Array[Column].
* We use Array[Column] for unpivot rather than Seq[Column] as those are Java-friendly.
*/
private[sql] def unpivotWithSeq(
ids: Seq[Column],
values: Seq[Column],
variableColumnName: String,
valueColumnName: String): DataFrame =
unpivot(ids.toArray, values.toArray, variableColumnName, valueColumnName)
/**
* Called from Python as Seq[Column] are easier to create via py4j than Array[Column].
* We use Array[Column] for unpivot rather than Seq[Column] as those are Java-friendly.
*/
private[sql] def unpivotWithSeq(
ids: Seq[Column],
variableColumnName: String,
valueColumnName: String): DataFrame =
unpivot(ids.toArray, variableColumnName, valueColumnName)
/** @inheritdoc */
def transpose(indexColumn: Column): DataFrame = withPlan {
UnresolvedTranspose(
Seq(indexColumn.named),
logicalPlan
)
}
/** @inheritdoc */
def transpose(): DataFrame = withPlan {
UnresolvedTranspose(
Seq.empty,
logicalPlan
)
}
/** @inheritdoc */
@scala.annotation.varargs
def observe(name: String, expr: Column, exprs: Column*): Dataset[T] = withTypedPlan {
CollectMetrics(name, (expr +: exprs).map(_.named), logicalPlan, id)
}
/** @inheritdoc */
@scala.annotation.varargs
def observe(observation: Observation, expr: Column, exprs: Column*): Dataset[T] = {
sparkSession.observationManager.register(observation, this)
observe(observation.name, expr, exprs: _*)
}
/** @inheritdoc */
def limit(n: Int): Dataset[T] = withTypedPlan {
Limit(Literal(n), logicalPlan)
}
/** @inheritdoc */
def offset(n: Int): Dataset[T] = withTypedPlan {
Offset(Literal(n), logicalPlan)
}
// This breaks caching, but it's usually ok because it addresses a very specific use case:
// using union to union many files or partitions.
private def combineUnions(plan: LogicalPlan): LogicalPlan = {
plan.transformDownWithPruning(_.containsPattern(TreePattern.UNION)) {
case Distinct(u: Union) =>
Distinct(flattenUnion(u, isUnionDistinct = true))
// Only handle distinct-like 'Deduplicate', where the keys == output
case Deduplicate(keys: Seq[Attribute], u: Union) if AttributeSet(keys) == u.outputSet =>
Deduplicate(keys, flattenUnion(u, isUnionDistinct = true))
case u: Union =>
flattenUnion(u, isUnionDistinct = false)
}
}
private def flattenUnion(u: Union, isUnionDistinct: Boolean): Union = {
var changed = false
// We only need to look at the direct children of Union, as the nested adjacent Unions should
// have been combined already by previous `Dataset#union` transformations.
val newChildren = u.children.flatMap {
case Distinct(Union(children, byName, allowMissingCol))
if isUnionDistinct && byName == u.byName && allowMissingCol == u.allowMissingCol =>
changed = true
children
// Only handle distinct-like 'Deduplicate', where the keys == output
case Deduplicate(keys: Seq[Attribute], child @ Union(children, byName, allowMissingCol))
if AttributeSet(keys) == child.outputSet && isUnionDistinct && byName == u.byName &&
allowMissingCol == u.allowMissingCol =>
changed = true
children
case Union(children, byName, allowMissingCol)
if !isUnionDistinct && byName == u.byName && allowMissingCol == u.allowMissingCol =>
changed = true
children
case other =>
Seq(other)
}
if (changed) {
val newUnion = Union(newChildren)
newUnion.copyTagsFrom(u)
newUnion
} else {
u
}
}
/** @inheritdoc */
def union(other: Dataset[T]): Dataset[T] = withSetOperator {
combineUnions(Union(logicalPlan, other.logicalPlan))
}
/** @inheritdoc */
def unionByName(other: Dataset[T], allowMissingColumns: Boolean): Dataset[T] = {
withSetOperator {
// We need to resolve the by-name Union first, as the underlying Unions are already resolved
// and we can only combine adjacent Unions if they are all resolved.
val resolvedUnion = sparkSession.sessionState.executePlan(
Union(logicalPlan :: other.logicalPlan :: Nil, byName = true, allowMissingColumns))
combineUnions(resolvedUnion.analyzed)
}
}
/** @inheritdoc */
def intersect(other: Dataset[T]): Dataset[T] = withSetOperator {
Intersect(logicalPlan, other.logicalPlan, isAll = false)
}
/** @inheritdoc */
def intersectAll(other: Dataset[T]): Dataset[T] = withSetOperator {
Intersect(logicalPlan, other.logicalPlan, isAll = true)
}
/** @inheritdoc */
def except(other: Dataset[T]): Dataset[T] = withSetOperator {
Except(logicalPlan, other.logicalPlan, isAll = false)
}
/** @inheritdoc */
def exceptAll(other: Dataset[T]): Dataset[T] = withSetOperator {
Except(logicalPlan, other.logicalPlan, isAll = true)
}
/** @inheritdoc */
def sample(withReplacement: Boolean, fraction: Double, seed: Long): Dataset[T] = {
withTypedPlan {
Sample(0.0, fraction, withReplacement, seed, logicalPlan)
}
}
/** @inheritdoc */
def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]] = {
require(weights.forall(_ >= 0),
s"Weights must be nonnegative, but got ${weights.mkString("[", ",", "]")}")
require(weights.sum > 0,
s"Sum of weights must be positive, but got ${weights.mkString("[", ",", "]")}")
// It is possible that the underlying dataframe doesn't guarantee the ordering of rows in its
// constituent partitions each time a split is materialized which could result in
// overlapping splits. To prevent this, we explicitly sort each input partition to make the
// ordering deterministic. Note that MapTypes cannot be sorted and are explicitly pruned out
// from the sort order.
val sortOrder = logicalPlan.output
.filter(attr => RowOrdering.isOrderable(attr.dataType))
.map(SortOrder(_, Ascending))
val plan = if (sortOrder.nonEmpty) {
Sort(sortOrder, global = false, logicalPlan)
} else {
// SPARK-12662: If sort order is empty, we materialize the dataset to guarantee determinism
cache()
logicalPlan
}
val sum = weights.sum
val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _)
normalizedCumWeights.sliding(2).map { x =>
new Dataset[T](
sparkSession, Sample(x(0), x(1), withReplacement = false, seed, plan), encoder)
}.toArray
}
/** @inheritdoc */
override def randomSplit(weights: Array[Double]): Array[Dataset[T]] =
randomSplit(weights, Utils.random.nextLong())
/**
* Randomly splits this Dataset with the provided weights. Provided for the Python Api.
*
* @param weights weights for splits, will be normalized if they don't sum to 1.
* @param seed Seed for sampling.
*/
private[spark] def randomSplit(weights: List[Double], seed: Long): Array[Dataset[T]] = {
randomSplit(weights.toArray, seed)
}
/** @inheritdoc */
override def randomSplitAsList(weights: Array[Double], seed: Long): util.List[Dataset[T]] =
util.Arrays.asList(randomSplit(weights, seed): _*)
/** @inheritdoc */
@deprecated("use flatMap() or select() with functions.explode() instead", "2.0.0")
def explode[A <: Product : TypeTag](input: Column*)(f: Row => IterableOnce[A]): DataFrame = {
val elementSchema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType]
val convert = CatalystTypeConverters.createToCatalystConverter(elementSchema)
val rowFunction =
f.andThen(_.map(convert(_).asInstanceOf[InternalRow]))
val generator = UserDefinedGenerator(elementSchema, rowFunction, input.map(_.expr))
withPlan {
Generate(generator, unrequiredChildIndex = Nil, outer = false,
qualifier = None, generatorOutput = Nil, logicalPlan)
}
}
/** @inheritdoc */
@deprecated("use flatMap() or select() with functions.explode() instead", "2.0.0")
def explode[A, B : TypeTag](inputColumn: String, outputColumn: String)(f: A => IterableOnce[B])
: DataFrame = {
val dataType = ScalaReflection.schemaFor[B].dataType
val attributes = AttributeReference(outputColumn, dataType)() :: Nil
// TODO handle the metadata?
val elementSchema = attributes.toStructType
def rowFunction(row: Row): IterableOnce[InternalRow] = {
val convert = CatalystTypeConverters.createToCatalystConverter(dataType)
f(row(0).asInstanceOf[A]).map(o => InternalRow(convert(o)))
}
val generator = UserDefinedGenerator(elementSchema, rowFunction, apply(inputColumn).expr :: Nil)
withPlan {
Generate(generator, unrequiredChildIndex = Nil, outer = false,
qualifier = None, generatorOutput = Nil, logicalPlan)
}
}
/** @inheritdoc */
protected[spark] def withColumns(colNames: Seq[String], cols: Seq[Column]): DataFrame = {
require(colNames.size == cols.size,
s"The size of column names: ${colNames.size} isn't equal to " +
s"the size of columns: ${cols.size}")
SchemaUtils.checkColumnNameDuplication(
colNames,
sparkSession.sessionState.conf.caseSensitiveAnalysis)
val resolver = sparkSession.sessionState.analyzer.resolver
val output = queryExecution.analyzed.output
val columnSeq = colNames.zip(cols)
val replacedAndExistingColumns = output.map { field =>
columnSeq.find { case (colName, _) =>
resolver(field.name, colName)
} match {
case Some((colName: String, col: Column)) => col.as(colName)
case _ => column(field)
}
}
val newColumns = columnSeq.filter { case (colName, col) =>
!output.exists(f => resolver(f.name, colName))
}.map { case (colName, col) => col.as(colName) }
select(replacedAndExistingColumns ++ newColumns : _*)
}
/** @inheritdoc */
private[spark] def withColumns(
colNames: Seq[String],
cols: Seq[Column],
metadata: Seq[Metadata]): DataFrame = {
require(colNames.size == metadata.size,
s"The size of column names: ${colNames.size} isn't equal to " +
s"the size of metadata elements: ${metadata.size}")
val newCols = colNames.zip(cols).zip(metadata).map { case ((colName, col), metadata) =>
col.as(colName, metadata)
}
withColumns(colNames, newCols)
}
/** @inheritdoc */
private[spark] def withColumn(colName: String, col: Column, metadata: Metadata): DataFrame =
withColumns(Seq(colName), Seq(col), Seq(metadata))
protected[spark] def withColumnsRenamed(
colNames: Seq[String],
newColNames: Seq[String]): DataFrame = {
require(colNames.size == newColNames.size,
s"The size of existing column names: ${colNames.size} isn't equal to " +
s"the size of new column names: ${newColNames.size}")
val resolver = sparkSession.sessionState.analyzer.resolver
val output: Seq[NamedExpression] = queryExecution.analyzed.output
var shouldRename = false
val projectList = colNames.zip(newColNames).foldLeft(output) {
case (attrs, (existingName, newName)) =>
attrs.map(attr =>
if (resolver(attr.name, existingName)) {
shouldRename = true
Alias(attr, newName)()
} else {
attr
}
)
}
if (shouldRename) {
withPlan(Project(projectList, logicalPlan))
} else {
toDF()
}
}
/** @inheritdoc */
def withMetadata(columnName: String, metadata: Metadata): DataFrame = {
withColumn(columnName, col(columnName), metadata)
}
/** @inheritdoc */
@scala.annotation.varargs
def drop(colNames: String*): DataFrame = {
val resolver = sparkSession.sessionState.analyzer.resolver
val allColumns = queryExecution.analyzed.output
val remainingCols = allColumns.filter { attribute =>
colNames.forall(n => !resolver(attribute.name, n))
}.map(attribute => column(attribute))
if (remainingCols.size == allColumns.size) {
toDF()
} else {
this.select(remainingCols: _*)
}
}
/** @inheritdoc */
@scala.annotation.varargs
def drop(col: Column, cols: Column*): DataFrame = withPlan {
DataFrameDropColumns((col +: cols).map(_.expr), logicalPlan)
}
/** @inheritdoc */
def dropDuplicates(): Dataset[T] = dropDuplicates(this.columns)
/** @inheritdoc */
def dropDuplicates(colNames: Seq[String]): Dataset[T] = withTypedPlan {
val groupCols = groupColsFromDropDuplicates(colNames)
Deduplicate(groupCols, logicalPlan)
}
/** @inheritdoc */
def dropDuplicatesWithinWatermark(): Dataset[T] = {
dropDuplicatesWithinWatermark(this.columns)
}
/** @inheritdoc */
def dropDuplicatesWithinWatermark(colNames: Seq[String]): Dataset[T] = withTypedPlan {
val groupCols = groupColsFromDropDuplicates(colNames)
// UnsupportedOperationChecker will fail the query if this is called with batch Dataset.
DeduplicateWithinWatermark(groupCols, logicalPlan)
}
private def groupColsFromDropDuplicates(colNames: Seq[String]): Seq[Attribute] = {
val resolver = sparkSession.sessionState.analyzer.resolver
val allColumns = queryExecution.analyzed.output
// SPARK-31990: We must keep `toSet.toSeq` here because of the backward compatibility issue
// (the Streaming's state store depends on the `groupCols` order).
colNames.toSet.toSeq.flatMap { (colName: String) =>
// It is possibly there are more than one columns with the same name,
// so we call filter instead of find.
val cols = allColumns.filter(col => resolver(col.name, colName))
if (cols.isEmpty) {
throw QueryCompilationErrors.cannotResolveColumnNameAmongAttributesError(
colName, schema.fieldNames.mkString(", "))
}
cols
}
}
/** @inheritdoc */
@scala.annotation.varargs
def summary(statistics: String*): DataFrame = StatFunctions.summary(this, statistics)
/** @inheritdoc */
@scala.annotation.varargs
override def describe(cols: String*): DataFrame = {
val selected = if (cols.isEmpty) this else select(cols.head, cols.tail: _*)
selected.summary("count", "mean", "stddev", "min", "max")
}
/** @inheritdoc */
def head(n: Int): Array[T] = withAction("head", limit(n).queryExecution)(collectFromPlan)
/** @inheritdoc */
def filter(func: T => Boolean): Dataset[T] = {
withTypedPlan(TypedFilter(func, logicalPlan))
}
/** @inheritdoc */
def filter(func: FilterFunction[T]): Dataset[T] = {
withTypedPlan(TypedFilter(func, logicalPlan))
}
/** @inheritdoc */
def map[U : Encoder](func: T => U): Dataset[U] = {
withTypedPlan(MapElements[T, U](func, logicalPlan))
}
/** @inheritdoc */
def map[U](func: MapFunction[T, U], encoder: Encoder[U]): Dataset[U] = {
implicit val uEnc: Encoder[U] = encoder
withTypedPlan(MapElements[T, U](func, logicalPlan))
}
/** @inheritdoc */
def mapPartitions[U : Encoder](func: Iterator[T] => Iterator[U]): Dataset[U] = {
new Dataset[U](
sparkSession,
MapPartitions[T, U](func, logicalPlan),
implicitly[Encoder[U]])
}
/**
* Returns a new `DataFrame` that contains the result of applying a serialized R function
* `func` to each partition.
*/
private[sql] def mapPartitionsInR(
func: Array[Byte],
packageNames: Array[Byte],
broadcastVars: Array[Broadcast[Object]],
schema: StructType): DataFrame = {
val rowEncoder = encoder.asInstanceOf[ExpressionEncoder[Row]]
Dataset.ofRows(
sparkSession,
MapPartitionsInR(func, packageNames, broadcastVars, schema, rowEncoder, logicalPlan))
}
/**
* Applies a Scalar iterator Pandas UDF to each partition. The user-defined function
* defines a transformation: `iter(pandas.DataFrame)` -> `iter(pandas.DataFrame)`.
* Each partition is each iterator consisting of DataFrames as batches.
*
* This function uses Apache Arrow as serialization format between Java executors and Python
* workers.
*/
private[sql] def mapInPandas(
funcCol: Column,
isBarrier: Boolean = false,
profile: ResourceProfile = null): DataFrame = {
val func = funcCol.expr
Dataset.ofRows(
sparkSession,
MapInPandas(
func,
toAttributes(func.dataType.asInstanceOf[StructType]),
logicalPlan,
isBarrier,
Option(profile)))
}
/**
* Applies a function to each partition in Arrow format. The user-defined function
* defines a transformation: `iter(pyarrow.RecordBatch)` -> `iter(pyarrow.RecordBatch)`.
* Each partition is each iterator consisting of `pyarrow.RecordBatch`s as batches.
*/
private[sql] def mapInArrow(
funcCol: Column,
isBarrier: Boolean = false,
profile: ResourceProfile = null): DataFrame = {
val func = funcCol.expr
Dataset.ofRows(
sparkSession,
MapInArrow(
func,
toAttributes(func.dataType.asInstanceOf[StructType]),
logicalPlan,
isBarrier,
Option(profile)))
}
/** @inheritdoc */
def foreachPartition(f: Iterator[T] => Unit): Unit = withNewRDDExecutionId("foreachPartition") {
rdd.foreachPartition(f)
}
/** @inheritdoc */
def tail(n: Int): Array[T] = withAction(
"tail", withTypedPlan(Tail(Literal(n), logicalPlan)).queryExecution)(collectFromPlan)
/** @inheritdoc */
def collect(): Array[T] = withAction("collect", queryExecution)(collectFromPlan)
/** @inheritdoc */
def collectAsList(): java.util.List[T] = withAction("collectAsList", queryExecution) { plan =>
val values = collectFromPlan(plan)
java.util.Arrays.asList(values : _*)
}
/** @inheritdoc */
def toLocalIterator(): java.util.Iterator[T] = {
withAction("toLocalIterator", queryExecution) { plan =>
val fromRow = resolvedEnc.createDeserializer()
plan.executeToIterator().map(fromRow).asJava
}
}
/** @inheritdoc */
def count(): Long = withAction("count", groupBy().count().queryExecution) { plan =>
plan.executeCollect().head.getLong(0)
}
/** @inheritdoc */
def repartition(numPartitions: Int): Dataset[T] = withTypedPlan {
Repartition(numPartitions, shuffle = true, logicalPlan)
}
protected def repartitionByExpression(
numPartitions: Option[Int],
partitionExprs: Seq[Column]): Dataset[T] = {
// The underlying `LogicalPlan` operator special-cases all-`SortOrder` arguments.
// However, we don't want to complicate the semantics of this API method.
// Instead, let's give users a friendly error message, pointing them to the new method.
val sortOrders = partitionExprs.filter(_.expr.isInstanceOf[SortOrder])
if (sortOrders.nonEmpty) throw new IllegalArgumentException(
s"""Invalid partitionExprs specified: $sortOrders
|For range partitioning use repartitionByRange(...) instead.
""".stripMargin)
withTypedPlan {
RepartitionByExpression(partitionExprs.map(_.expr), logicalPlan, numPartitions)
}
}
protected def repartitionByRange(
numPartitions: Option[Int],
partitionExprs: Seq[Column]): Dataset[T] = {
require(partitionExprs.nonEmpty, "At least one partition-by expression must be specified.")
val sortOrder: Seq[SortOrder] = partitionExprs.map(_.expr match {
case expr: SortOrder => expr
case expr: Expression => SortOrder(expr, Ascending)
})
withTypedPlan {
RepartitionByExpression(sortOrder, logicalPlan, numPartitions)
}
}
/** @inheritdoc */
def coalesce(numPartitions: Int): Dataset[T] = withTypedPlan {
Repartition(numPartitions, shuffle = false, logicalPlan)
}
/** @inheritdoc */
def persist(): this.type = persist(sparkSession.sessionState.conf.defaultCacheStorageLevel)
/** @inheritdoc */
override def cache(): this.type = persist()
/** @inheritdoc */
def persist(newLevel: StorageLevel): this.type = {
sparkSession.sharedState.cacheManager.cacheQuery(this, None, newLevel)
this
}
/** @inheritdoc */
def storageLevel: StorageLevel = {
sparkSession.sharedState.cacheManager.lookupCachedData(this).map { cachedData =>
cachedData.cachedRepresentation.cacheBuilder.storageLevel
}.getOrElse(StorageLevel.NONE)
}
/** @inheritdoc */
def unpersist(blocking: Boolean): this.type = {
sparkSession.sharedState.cacheManager.uncacheQuery(this, cascade = false, blocking)
this
}
/** @inheritdoc */
override def unpersist(): this.type = unpersist(blocking = false)
// Represents the `QueryExecution` used to produce the content of the Dataset as an `RDD`.
@transient private lazy val rddQueryExecution: QueryExecution = {
val deserialized = CatalystSerde.deserialize[T](logicalPlan)
sparkSession.sessionState.executePlan(deserialized)
}
/**
* Represents the content of the Dataset as an `RDD` of `T`.
*
* @group basic
* @since 1.6.0
*/
lazy val rdd: RDD[T] = {
val objectType = exprEnc.deserializer.dataType
rddQueryExecution.toRdd.mapPartitions { rows =>
rows.map(_.get(0, objectType).asInstanceOf[T])
}
}
/**
* Returns the content of the Dataset as a `JavaRDD` of `T`s.
* @group basic
* @since 1.6.0
*/
def toJavaRDD: JavaRDD[T] = rdd.toJavaRDD()
/**
* Returns the content of the Dataset as a `JavaRDD` of `T`s.
* @group basic
* @since 1.6.0
*/
def javaRDD: JavaRDD[T] = toJavaRDD
protected def createTempView(
viewName: String,
replace: Boolean,
global: Boolean): Unit = sparkSession.withActive {
val viewType = if (global) GlobalTempView else LocalTempView
val identifier = try {
sparkSession.sessionState.sqlParser.parseMultipartIdentifier(viewName)
} catch {
case _: ParseException => throw QueryCompilationErrors.invalidViewNameError(viewName)
}
if (!SQLConf.get.allowsTempViewCreationWithMultipleNameparts && identifier.size > 1) {
// Temporary view names should NOT contain database prefix like "database.table"
throw new AnalysisException(
errorClass = "TEMP_VIEW_NAME_TOO_MANY_NAME_PARTS",
messageParameters = Map("actualName" -> viewName))
}
withPlan {
CreateViewCommand(
name = TableIdentifier(identifier.last),
userSpecifiedColumns = Nil,
comment = None,
properties = Map.empty,
originalText = None,
plan = logicalPlan,
allowExisting = false,
replace = replace,
viewType = viewType,
isAnalyzed = true)
}
}
/** @inheritdoc */
def write: DataFrameWriter[T] = {
if (isStreaming) {
logicalPlan.failAnalysis(
errorClass = "CALL_ON_STREAMING_DATASET_UNSUPPORTED",
messageParameters = Map("methodName" -> toSQLId("write")))
}
new DataFrameWriterImpl[T](this)
}
/** @inheritdoc */
def writeTo(table: String): DataFrameWriterV2[T] = {
// TODO: streaming could be adapted to use this interface
if (isStreaming) {
logicalPlan.failAnalysis(
errorClass = "CALL_ON_STREAMING_DATASET_UNSUPPORTED",
messageParameters = Map("methodName" -> toSQLId("writeTo")))
}
new DataFrameWriterV2Impl[T](table, this)
}
/**
* Merges a set of updates, insertions, and deletions based on a source table into
* a target table.
*
* Scala Examples:
* {{{
* spark.table("source")
* .mergeInto("target", $"source.id" === $"target.id")
* .whenMatched($"salary" === 100)
* .delete()
* .whenNotMatched()
* .insertAll()
* .whenNotMatchedBySource($"salary" === 100)
* .update(Map(
* "salary" -> lit(200)
* ))
* .merge()
* }}}
*
* @group basic
* @since 4.0.0
*/
def mergeInto(table: String, condition: Column): MergeIntoWriter[T] = {
if (isStreaming) {
logicalPlan.failAnalysis(
errorClass = "CALL_ON_STREAMING_DATASET_UNSUPPORTED",
messageParameters = Map("methodName" -> toSQLId("mergeInto")))
}
new MergeIntoWriterImpl[T](table, this, condition)
}
/**
* Interface for saving the content of the streaming Dataset out into external storage.
*
* @group basic
* @since 2.0.0
*/
def writeStream: DataStreamWriter[T] = {
if (!isStreaming) {
logicalPlan.failAnalysis(
errorClass = "WRITE_STREAM_NOT_ALLOWED",
messageParameters = Map.empty)
}
new DataStreamWriter[T](this)
}
/** @inheritdoc */
override def toJSON: Dataset[String] = {
val rowSchema = exprEnc.schema
val sessionLocalTimeZone = sparkSession.sessionState.conf.sessionLocalTimeZone
mapPartitions { iter =>
val writer = new CharArrayWriter()
// create the Generator without separator inserted between 2 records
val gen = new JacksonGenerator(rowSchema, writer,
new JSONOptions(Map.empty[String, String], sessionLocalTimeZone))
new Iterator[String] {
private val toRow = exprEnc.createSerializer()
override def hasNext: Boolean = iter.hasNext
override def next(): String = {
gen.write(toRow(iter.next()))
gen.flush()
val json = writer.toString
if (hasNext) {
writer.reset()
} else {
gen.close()
}
json
}
}
} (Encoders.STRING)
}
/** @inheritdoc */
def inputFiles: Array[String] = {
val files: Seq[String] = queryExecution.optimizedPlan.collect {
case LogicalRelation(fsBasedRelation: FileRelation, _, _, _) =>
fsBasedRelation.inputFiles
case fr: FileRelation =>
fr.inputFiles
case r: HiveTableRelation =>
r.tableMeta.storage.locationUri.map(_.toString).toArray
case DataSourceV2ScanRelation(DataSourceV2Relation(table: FileTable, _, _, _, _),
_, _, _, _) =>
table.fileIndex.inputFiles
}.flatten
files.toSet.toArray
}
/** @inheritdoc */
@DeveloperApi
def sameSemantics(other: Dataset[T]): Boolean = {
queryExecution.analyzed.sameResult(other.queryExecution.analyzed)
}
/** @inheritdoc */
@DeveloperApi
def semanticHash(): Int = {
queryExecution.analyzed.semanticHash()
}
////////////////////////////////////////////////////////////////////////////
// Return type overrides to make sure we return the implementation instead
// of the interface. This is done for a couple of reasons:
// - Retain the old signatures for binary compatibility;
// - Java compatibility . The java compiler uses the byte code signatures,
// and those would point to api.Dataset being returned instead of Dataset.
// This causes issues when the java code tries to materialize results, or
// tries to use functionality that is implementation specfic.
// - Scala method resolution runs into problems when the ambiguous methods are
// scattered across the interface and implementation. `drop` and `select`
// suffered from this.
////////////////////////////////////////////////////////////////////////////
/** @inheritdoc */
override def drop(colName: String): DataFrame = super.drop(colName)
/** @inheritdoc */
override def drop(col: Column): DataFrame = super.drop(col)
/** @inheritdoc */
override def join(right: Dataset[_], usingColumn: String): DataFrame =
super.join(right, usingColumn)
/** @inheritdoc */
override def join(right: Dataset[_], usingColumns: Array[String]): DataFrame =
super.join(right, usingColumns)
/** @inheritdoc */
override def join(right: Dataset[_], usingColumns: Seq[String]): DataFrame =
super.join(right, usingColumns)
/** @inheritdoc */
override def join(right: Dataset[_], usingColumn: String, joinType: String): DataFrame =
super.join(right, usingColumn, joinType)
/** @inheritdoc */
override def join(
right: Dataset[_],
usingColumns: Array[String],
joinType: String): DataFrame =
super.join(right, usingColumns, joinType)
/** @inheritdoc */
override def join(right: Dataset[_], joinExprs: Column): DataFrame =
super.join(right, joinExprs)
/** @inheritdoc */
@scala.annotation.varargs
override def select(col: String, cols: String*): DataFrame = super.select(col, cols: _*)
/** @inheritdoc */
override def select[U1, U2](c1: TypedColumn[T, U1], c2: TypedColumn[T, U2]): Dataset[(U1, U2)] =
super.select(c1, c2)
/** @inheritdoc */
override def select[U1, U2, U3](
c1: TypedColumn[T, U1],
c2: TypedColumn[T, U2],
c3: TypedColumn[T, U3]): Dataset[(U1, U2, U3)] = super.select(c1, c2, c3)
/** @inheritdoc */
override def select[U1, U2, U3, U4](
c1: TypedColumn[T, U1],
c2: TypedColumn[T, U2],
c3: TypedColumn[T, U3],
c4: TypedColumn[T, U4]): Dataset[(U1, U2, U3, U4)] = super.select(c1, c2, c3, c4)
/** @inheritdoc */
override def select[U1, U2, U3, U4, U5](
c1: TypedColumn[T, U1],
c2: TypedColumn[T, U2],
c3: TypedColumn[T, U3],
c4: TypedColumn[T, U4],
c5: TypedColumn[T, U5]): Dataset[(U1, U2, U3, U4, U5)] = super.select(c1, c2, c3, c4, c5)
override def melt(
ids: Array[Column],
values: Array[Column],
variableColumnName: String,
valueColumnName: String): DataFrame =
super.melt(ids, values, variableColumnName, valueColumnName)
/** @inheritdoc */
override def melt(
ids: Array[Column],
variableColumnName: String,
valueColumnName: String): DataFrame =
super.melt(ids, variableColumnName, valueColumnName)
/** @inheritdoc */
override def withColumn(colName: String, col: Column): DataFrame =
super.withColumn(colName, col)
/** @inheritdoc */
override def withColumns(colsMap: Map[String, Column]): DataFrame =
super.withColumns(colsMap)
/** @inheritdoc */
override def withColumns(colsMap: util.Map[String, Column]): DataFrame =
super.withColumns(colsMap)
/** @inheritdoc */
override def withColumnRenamed(existingName: String, newName: String): DataFrame =
super.withColumnRenamed(existingName, newName)
/** @inheritdoc */
override def withColumnsRenamed(colsMap: Map[String, String]): DataFrame =
super.withColumnsRenamed(colsMap)
/** @inheritdoc */
override def withColumnsRenamed(colsMap: util.Map[String, String]): DataFrame =
super.withColumnsRenamed(colsMap)
/** @inheritdoc */
override def checkpoint(): Dataset[T] = super.checkpoint()
/** @inheritdoc */
override def checkpoint(eager: Boolean): Dataset[T] = super.checkpoint(eager)
/** @inheritdoc */
override def localCheckpoint(): Dataset[T] = super.localCheckpoint()
/** @inheritdoc */
override def localCheckpoint(eager: Boolean): Dataset[T] = super.localCheckpoint(eager)
/** @inheritdoc */
override def joinWith[U](other: Dataset[U], condition: Column): Dataset[(T, U)] =
super.joinWith(other, condition)
/** @inheritdoc */
@scala.annotation.varargs
override def sortWithinPartitions(sortCol: String, sortCols: String*): Dataset[T] =
super.sortWithinPartitions(sortCol, sortCols: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def sortWithinPartitions(sortExprs: Column*): Dataset[T] =
super.sortWithinPartitions(sortExprs: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def sort(sortCol: String, sortCols: String*): Dataset[T] =
super.sort(sortCol, sortCols: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def sort(sortExprs: Column*): Dataset[T] = super.sort(sortExprs: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def orderBy(sortCol: String, sortCols: String*): Dataset[T] =
super.orderBy(sortCol, sortCols: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def orderBy(sortExprs: Column*): Dataset[T] = super.orderBy(sortExprs: _*)
/** @inheritdoc */
override def as(alias: Symbol): Dataset[T] = super.as(alias)
/** @inheritdoc */
override def alias(alias: String): Dataset[T] = super.alias(alias)
/** @inheritdoc */
override def alias(alias: Symbol): Dataset[T] = super.alias(alias)
/** @inheritdoc */
@scala.annotation.varargs
override def selectExpr(exprs: String*): DataFrame = super.selectExpr(exprs: _*)
/** @inheritdoc */
override def filter(conditionExpr: String): Dataset[T] = super.filter(conditionExpr)
/** @inheritdoc */
override def where(condition: Column): Dataset[T] = super.where(condition)
/** @inheritdoc */
override def where(conditionExpr: String): Dataset[T] = super.where(conditionExpr)
/** @inheritdoc */
override def unionAll(other: Dataset[T]): Dataset[T] = super.unionAll(other)
/** @inheritdoc */
override def unionByName(other: Dataset[T]): Dataset[T] = super.unionByName(other)
/** @inheritdoc */
override def sample(fraction: Double, seed: Long): Dataset[T] = super.sample(fraction, seed)
/** @inheritdoc */
override def sample(fraction: Double): Dataset[T] = super.sample(fraction)
/** @inheritdoc */
override def sample(withReplacement: Boolean, fraction: Double): Dataset[T] =
super.sample(withReplacement, fraction)
/** @inheritdoc */
override def dropDuplicates(colNames: Array[String]): Dataset[T] = super.dropDuplicates(colNames)
/** @inheritdoc */
@scala.annotation.varargs
override def dropDuplicates(col1: String, cols: String*): Dataset[T] =
super.dropDuplicates(col1, cols: _*)
/** @inheritdoc */
override def dropDuplicatesWithinWatermark(colNames: Array[String]): Dataset[T] =
super.dropDuplicatesWithinWatermark(colNames)
/** @inheritdoc */
@scala.annotation.varargs
override def dropDuplicatesWithinWatermark(col1: String, cols: String*): Dataset[T] =
super.dropDuplicatesWithinWatermark(col1, cols: _*)
/** @inheritdoc */
override def mapPartitions[U](f: MapPartitionsFunction[T, U], encoder: Encoder[U]): Dataset[U] =
super.mapPartitions(f, encoder)
/** @inheritdoc */
override def flatMap[U: Encoder](func: T => IterableOnce[U]): Dataset[U] = super.flatMap(func)
/** @inheritdoc */
override def flatMap[U](f: FlatMapFunction[T, U], encoder: Encoder[U]): Dataset[U] =
super.flatMap(f, encoder)
/** @inheritdoc */
override def foreachPartition(func: ForeachPartitionFunction[T]): Unit =
super.foreachPartition(func)
/** @inheritdoc */
@scala.annotation.varargs
override def repartition(numPartitions: Int, partitionExprs: Column*): Dataset[T] =
super.repartition(numPartitions, partitionExprs: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def repartition(partitionExprs: Column*): Dataset[T] =
super.repartition(partitionExprs: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def repartitionByRange(numPartitions: Int, partitionExprs: Column*): Dataset[T] =
super.repartitionByRange(numPartitions, partitionExprs: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def repartitionByRange(partitionExprs: Column*): Dataset[T] =
super.repartitionByRange(partitionExprs: _*)
/** @inheritdoc */
override def distinct(): Dataset[T] = super.distinct()
/** @inheritdoc */
@scala.annotation.varargs
override def groupBy(col1: String, cols: String*): RelationalGroupedDataset =
super.groupBy(col1, cols: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def rollup(col1: String, cols: String*): RelationalGroupedDataset =
super.rollup(col1, cols: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def cube(col1: String, cols: String*): RelationalGroupedDataset =
super.cube(col1, cols: _*)
/** @inheritdoc */
override def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame =
super.agg(aggExpr, aggExprs: _*)
/** @inheritdoc */
override def agg(exprs: Map[String, String]): DataFrame = super.agg(exprs)
/** @inheritdoc */
override def agg(exprs: java.util.Map[String, String]): DataFrame = super.agg(exprs)
/** @inheritdoc */
@scala.annotation.varargs
override def agg(expr: Column, exprs: Column*): DataFrame = super.agg(expr, exprs: _*)
////////////////////////////////////////////////////////////////////////////
// For Python API
////////////////////////////////////////////////////////////////////////////
/**
* Converts a JavaRDD to a PythonRDD.
*/
private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
val structType = schema // capture it for closure
val rdd = queryExecution.toRdd.map(EvaluatePython.toJava(_, structType))
EvaluatePython.javaToPython(rdd)
}
private[sql] def collectToPython(): Array[Any] = {
EvaluatePython.registerPicklers()
withAction("collectToPython", queryExecution) { plan =>
val toJava: (Any) => Any = EvaluatePython.toJava(_, schema)
val iter: Iterator[Array[Byte]] = new SerDeUtil.AutoBatchedPickler(
plan.executeCollect().iterator.map(toJava))
PythonRDD.serveIterator(iter, "serve-DataFrame")
}
}
private[sql] def tailToPython(n: Int): Array[Any] = {
EvaluatePython.registerPicklers()
withAction("tailToPython", queryExecution) { plan =>
val toJava: (Any) => Any = EvaluatePython.toJava(_, schema)
val iter: Iterator[Array[Byte]] = new SerDeUtil.AutoBatchedPickler(
plan.executeTail(n).iterator.map(toJava))
PythonRDD.serveIterator(iter, "serve-DataFrame")
}
}
private[sql] def getRowsToPython(
_numRows: Int,
truncate: Int): Array[Any] = {
EvaluatePython.registerPicklers()
val numRows = _numRows.max(0).min(ByteArrayMethods.MAX_ROUNDED_ARRAY_LENGTH - 1)
val rows = getRows(numRows, truncate).map(_.toArray).toArray
val toJava: (Any) => Any = EvaluatePython.toJava(_, ArrayType(ArrayType(StringType)))
val iter: Iterator[Array[Byte]] = new SerDeUtil.AutoBatchedPickler(
rows.iterator.map(toJava))
PythonRDD.serveIterator(iter, "serve-GetRows")
}
/**
* Collect a Dataset as Arrow batches and serve stream to SparkR. It sends
* arrow batches in an ordered manner with buffering. This is inevitable
* due to missing R API that reads batches from socket directly. See ARROW-4512.
* Eventually, this code should be deduplicated by `collectAsArrowToPython`.
*/
private[sql] def collectAsArrowToR(): Array[Any] = {
val timeZoneId = sparkSession.sessionState.conf.sessionLocalTimeZone
RRDD.serveToStream("serve-Arrow") { outputStream =>
withAction("collectAsArrowToR", queryExecution) { plan =>
val buffer = new ByteArrayOutputStream()
val out = new DataOutputStream(outputStream)
val batchWriter =
new ArrowBatchStreamWriter(schema, buffer, timeZoneId, errorOnDuplicatedFieldNames = true)
val arrowBatchRdd = toArrowBatchRdd(plan)
val numPartitions = arrowBatchRdd.partitions.length
// Store collection results for worst case of 1 to N-1 partitions
val results = new Array[Array[Array[Byte]]](Math.max(0, numPartitions - 1))
var lastIndex = -1 // index of last partition written
// Handler to eagerly write partitions to Python in order
def handlePartitionBatches(index: Int, arrowBatches: Array[Array[Byte]]): Unit = {
// If result is from next partition in order
if (index - 1 == lastIndex) {
batchWriter.writeBatches(arrowBatches.iterator)
lastIndex += 1
// Write stored partitions that come next in order
while (lastIndex < results.length && results(lastIndex) != null) {
batchWriter.writeBatches(results(lastIndex).iterator)
results(lastIndex) = null
lastIndex += 1
}
// After last batch, end the stream
if (lastIndex == results.length) {
batchWriter.end()
val batches = buffer.toByteArray
out.writeInt(batches.length)
out.write(batches)
}
} else {
// Store partitions received out of order
results(index - 1) = arrowBatches
}
}
sparkSession.sparkContext.runJob(
arrowBatchRdd,
(ctx: TaskContext, it: Iterator[Array[Byte]]) => it.toArray,
0 until numPartitions,
handlePartitionBatches)
}
}
}
/**
* Collect a Dataset as Arrow batches and serve stream to PySpark. It sends
* arrow batches in an un-ordered manner without buffering, and then batch order
* information at the end. The batches should be reordered at Python side.
*/
private[sql] def collectAsArrowToPython: Array[Any] = {
val timeZoneId = sparkSession.sessionState.conf.sessionLocalTimeZone
val errorOnDuplicatedFieldNames =
sparkSession.sessionState.conf.pandasStructHandlingMode == "legacy"
PythonRDD.serveToStream("serve-Arrow") { outputStream =>
withAction("collectAsArrowToPython", queryExecution) { plan =>
val out = new DataOutputStream(outputStream)
val batchWriter =
new ArrowBatchStreamWriter(schema, out, timeZoneId, errorOnDuplicatedFieldNames)
// Batches ordered by (index of partition, batch index in that partition) tuple
val batchOrder = ArrayBuffer.empty[(Int, Int)]
// Handler to eagerly write batches to Python as they arrive, un-ordered
val handlePartitionBatches = (index: Int, arrowBatches: Array[Array[Byte]]) =>
if (arrowBatches.nonEmpty) {
// Write all batches (can be more than 1) in the partition, store the batch order tuple
batchWriter.writeBatches(arrowBatches.iterator)
arrowBatches.indices.foreach {
partitionBatchIndex => batchOrder.append((index, partitionBatchIndex))
}
}
Utils.tryWithSafeFinally {
val arrowBatchRdd = toArrowBatchRdd(plan)
sparkSession.sparkContext.runJob(
arrowBatchRdd,
(it: Iterator[Array[Byte]]) => it.toArray,
handlePartitionBatches)
} {
// After processing all partitions, end the batch stream
batchWriter.end()
// Write batch order indices
out.writeInt(batchOrder.length)
// Sort by (index of partition, batch index in that partition) tuple to get the
// overall_batch_index from 0 to N-1 batches, which can be used to put the
// transferred batches in the correct order
batchOrder.zipWithIndex.sortBy(_._1).foreach { case (_, overallBatchIndex) =>
out.writeInt(overallBatchIndex)
}
}
}
}
}
private[sql] def toPythonIterator(prefetchPartitions: Boolean = false): Array[Any] = {
withNewExecutionId {
PythonRDD.toLocalIteratorAndServe(javaToPython.rdd, prefetchPartitions)
}
}
////////////////////////////////////////////////////////////////////////////
// Private Helpers
////////////////////////////////////////////////////////////////////////////
/**
* Wrap a Dataset action to track all Spark jobs in the body so that we can connect them with
* an execution.
*/
private def withNewExecutionId[U](body: => U): U = {
SQLExecution.withNewExecutionId(queryExecution)(body)
}
/**
* Wrap an action of the Dataset's RDD to track all Spark jobs in the body so that we can connect
* them with an execution. Before performing the action, the metrics of the executed plan will be
* reset.
*/
private def withNewRDDExecutionId[U](name: String)(body: => U): U = {
SQLExecution.withNewExecutionId(rddQueryExecution, Some(name)) {
rddQueryExecution.executedPlan.resetMetrics()
body
}
}
/**
* Wrap a Dataset action to track the QueryExecution and time cost, then report to the
* user-registered callback functions, and also to convert asserts/NPE to
* the internal error exception.
*/
private def withAction[U](name: String, qe: QueryExecution)(action: SparkPlan => U) = {
SQLExecution.withNewExecutionId(qe, Some(name)) {
QueryExecution.withInternalError(s"""The "$name" action failed.""") {
qe.executedPlan.resetMetrics()
action(qe.executedPlan)
}
}
}
/**
* Collect all elements from a spark plan.
*/
private def collectFromPlan(plan: SparkPlan): Array[T] = {
val fromRow = resolvedEnc.createDeserializer()
plan.executeCollect().map(fromRow)
}
protected def sortInternal(global: Boolean, sortExprs: Seq[Column]): Dataset[T] = {
val sortOrder: Seq[SortOrder] = sortExprs.map { col =>
col.expr match {
case expr: SortOrder =>
expr
case expr: Expression =>
SortOrder(expr, Ascending)
}
}
withTypedPlan {
Sort(sortOrder, global = global, logicalPlan)
}
}
/** A convenient function to wrap a logical plan and produce a DataFrame. */
@inline private def withPlan(logicalPlan: LogicalPlan): DataFrame = {
Dataset.ofRows(sparkSession, logicalPlan)
}
/** A convenient function to wrap a logical plan and produce a Dataset. */
@inline private def withTypedPlan[U : Encoder](logicalPlan: LogicalPlan): Dataset[U] = {
Dataset(sparkSession, logicalPlan)
}
/** A convenient function to wrap a set based logical plan and produce a Dataset. */
@inline private def withSetOperator[U : Encoder](logicalPlan: LogicalPlan): Dataset[U] = {
if (classTag.runtimeClass.isAssignableFrom(classOf[Row])) {
// Set operators widen types (change the schema), so we cannot reuse the row encoder.
Dataset.ofRows(sparkSession, logicalPlan).asInstanceOf[Dataset[U]]
} else {
Dataset(sparkSession, logicalPlan)
}
}
/** Returns a optimized plan for CommandResult, convert to `LocalRelation`. */
private def commandResultOptimized: Dataset[T] = {
logicalPlan match {
case c: CommandResult =>
// Convert to `LocalRelation` and let `ConvertToLocalRelation` do the casting locally to
// avoid triggering a job
Dataset(sparkSession, LocalRelation(c.output, c.rows))
case _ => this
}
}
/** Convert to an RDD of serialized ArrowRecordBatches. */
private[sql] def toArrowBatchRdd(plan: SparkPlan): RDD[Array[Byte]] = {
val schemaCaptured = this.schema
val maxRecordsPerBatch = sparkSession.sessionState.conf.arrowMaxRecordsPerBatch
val timeZoneId = sparkSession.sessionState.conf.sessionLocalTimeZone
val errorOnDuplicatedFieldNames =
sparkSession.sessionState.conf.pandasStructHandlingMode == "legacy"
plan.execute().mapPartitionsInternal { iter =>
val context = TaskContext.get()
ArrowConverters.toBatchIterator(
iter, schemaCaptured, maxRecordsPerBatch, timeZoneId, errorOnDuplicatedFieldNames, context)
}
}
// This is only used in tests, for now.
private[sql] def toArrowBatchRdd: RDD[Array[Byte]] = {
toArrowBatchRdd(queryExecution.executedPlan)
}
}