HeartSaVioR commented on code in PR #47133:
URL: https://github.com/apache/spark/pull/47133#discussion_r1713781596
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/pythonLogicalOperators.scala:
##########
@@ -161,6 +161,41 @@ case class FlatMapGroupsInPandasWithState(
newChild: LogicalPlan): FlatMapGroupsInPandasWithState = copy(child =
newChild)
}
+object TransformWithStateInPandas {
+ def apply(functionExpr: Expression,
Review Comment:
nit: move the param to the next line
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/pythonLogicalOperators.scala:
##########
@@ -161,6 +161,41 @@ case class FlatMapGroupsInPandasWithState(
newChild: LogicalPlan): FlatMapGroupsInPandasWithState = copy(child =
newChild)
}
+object TransformWithStateInPandas {
Review Comment:
Any reason we can't just use the generated constructor of case class? params
here are exactly the same with constructor param in case class.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/pythonLogicalOperators.scala:
##########
@@ -161,6 +161,41 @@ case class FlatMapGroupsInPandasWithState(
newChild: LogicalPlan): FlatMapGroupsInPandasWithState = copy(child =
newChild)
}
+object TransformWithStateInPandas {
+ def apply(functionExpr: Expression,
+ groupingAttributes: Seq[Attribute],
+ outputAttrs: Seq[Attribute],
+ outputMode: OutputMode,
+ timeMode: TimeMode,
+ child: LogicalPlan): TransformWithStateInPandas = {
+
+ new TransformWithStateInPandas(
+ functionExpr,
+ groupingAttributes,
+ outputAttrs,
+ outputMode,
+ timeMode,
+ child = child
+ )
+ }
+}
+
+case class TransformWithStateInPandas(
Review Comment:
nit: shall we add a short description as class doc while we are here?
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/pythonLogicalOperators.scala:
##########
@@ -161,6 +161,41 @@ case class FlatMapGroupsInPandasWithState(
newChild: LogicalPlan): FlatMapGroupsInPandasWithState = copy(child =
newChild)
}
+object TransformWithStateInPandas {
+ def apply(functionExpr: Expression,
+ groupingAttributes: Seq[Attribute],
+ outputAttrs: Seq[Attribute],
+ outputMode: OutputMode,
+ timeMode: TimeMode,
+ child: LogicalPlan): TransformWithStateInPandas = {
+
+ new TransformWithStateInPandas(
Review Comment:
nit: omit `new` from case class instantiation
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/TransformWithStateInPandasExec.scala:
##########
@@ -0,0 +1,161 @@
+/*
+ * 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.execution.python
+
+import scala.concurrent.duration.NANOSECONDS
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.JobArtifactSet
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute,
Expression, PythonUDF, SortOrder}
+import org.apache.spark.sql.catalyst.plans.physical.Distribution
+import org.apache.spark.sql.catalyst.types.DataTypeUtils
+import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+import org.apache.spark.sql.execution.python.PandasGroupUtils.{executePython,
groupAndProject, resolveArgOffsets}
+import org.apache.spark.sql.execution.streaming.{StatefulOperatorPartitioning,
StatefulOperatorStateInfo, StatefulProcessorHandleImpl, StateStoreWriter,
WatermarkSupport}
+import
org.apache.spark.sql.execution.streaming.state.{NoPrefixKeyStateEncoderSpec,
StateSchemaValidationResult, StateStore, StateStoreOps}
+import org.apache.spark.sql.streaming.{OutputMode, TimeMode}
+import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
+import org.apache.spark.util.CompletionIterator
+
+/**
+ * Physical operator for executing
+ * [[org.apache.spark.sql.catalyst.plans.logical.TransformWithStateInPandas]]
+ * @param functionExpr function called on each group
+ * @param groupingAttributes used to group the data
+ * @param output used to define the output rows
+ * @param outputMode defines the output mode for the statefulProcessor
+ * @param timeMode The time mode semantics of the stateful processor for
timers and TTL.
+ * @param stateInfo Used to identify the state store for a given operator.
+ * @param batchTimestampMs processing timestamp of the current batch.
+ * @param eventTimeWatermarkForLateEvents event time watermark for filtering
late events
+ * @param eventTimeWatermarkForEviction event time watermark for state eviction
+ * @param child the physical plan for the underlying data
+ */
+case class TransformWithStateInPandasExec(
+ functionExpr: Expression,
+ groupingAttributes: Seq[Attribute],
+ output: Seq[Attribute],
+ outputMode: OutputMode,
+ timeMode: TimeMode,
+ stateInfo: Option[StatefulOperatorStateInfo],
+ batchTimestampMs: Option[Long],
+ eventTimeWatermarkForLateEvents: Option[Long],
+ eventTimeWatermarkForEviction: Option[Long],
+ child: SparkPlan) extends UnaryExecNode with StateStoreWriter with
WatermarkSupport {
+
+ private val pythonUDF = functionExpr.asInstanceOf[PythonUDF]
+ private val pythonFunction = pythonUDF.func
+ private val chainedFunc =
+ Seq((ChainedPythonFunctions(Seq(pythonFunction)), pythonUDF.resultId.id))
+
+ private val sessionLocalTimeZone = conf.sessionLocalTimeZone
+ private val pythonRunnerConf = ArrowPythonRunner.getPythonRunnerConfMap(conf)
+ private[this] val jobArtifactUUID =
JobArtifactSet.getCurrentJobArtifactState.map(_.uuid)
+
+ private val groupingKeyStructFields = groupingAttributes
+ .map(a => StructField(a.name, a.dataType, a.nullable))
+ private val groupingKeySchema = StructType(groupingKeyStructFields)
+ private val groupingKeyExprEncoder = ExpressionEncoder(groupingKeySchema)
+ .resolveAndBind().asInstanceOf[ExpressionEncoder[Any]]
+
+ /** The keys that may have a watermark attribute. */
+ override def keyExpressions: Seq[Attribute] = groupingAttributes
+
+ protected val schemaForKeyRow: StructType = new StructType().add("key",
BinaryType)
Review Comment:
Maybe better to leave a code comment to remind that each state variable has
its own schema and this is just dummy one.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/TransformWithStateInPandasPythonRunner.scala:
##########
@@ -0,0 +1,167 @@
+/*
+ * 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.execution.python
+
+import java.io.DataOutputStream
+import java.net.ServerSocket
+
+import scala.concurrent.ExecutionContext
+
+import org.apache.arrow.vector.VectorSchemaRoot
+import org.apache.arrow.vector.ipc.ArrowStreamWriter
+
+import org.apache.spark.TaskContext
+import org.apache.spark.api.python.{BasePythonRunner, ChainedPythonFunctions,
PythonRDD}
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.arrow
+import org.apache.spark.sql.execution.arrow.ArrowWriter
+import org.apache.spark.sql.execution.metric.SQLMetric
+import
org.apache.spark.sql.execution.python.TransformWithStateInPandasPythonRunner.{InType,
OutType}
+import org.apache.spark.sql.execution.streaming.StatefulProcessorHandleImpl
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.vectorized.ColumnarBatch
+import org.apache.spark.util.ThreadUtils
+
+/**
+ * Python runner implementation for TransformWithStateInPandas.
+ */
+class TransformWithStateInPandasPythonRunner(
+ funcs: Seq[(ChainedPythonFunctions, Long)],
+ evalType: Int,
+ argOffsets: Array[Array[Int]],
+ _schema: StructType,
+ processorHandle: StatefulProcessorHandleImpl,
+ _timeZoneId: String,
+ initialWorkerConf: Map[String, String],
+ override val pythonMetrics: Map[String, SQLMetric],
+ jobArtifactUUID: Option[String],
+ groupingKeySchema: StructType)
+ extends BasePythonRunner[InType, OutType](funcs.map(_._1), evalType,
argOffsets, jobArtifactUUID)
+ with PythonArrowInput[InType]
Review Comment:
nit: same indentation with extends
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/TransformWithStateInPandasExec.scala:
##########
@@ -0,0 +1,161 @@
+/*
+ * 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.execution.python
+
+import scala.concurrent.duration.NANOSECONDS
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.JobArtifactSet
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute,
Expression, PythonUDF, SortOrder}
+import org.apache.spark.sql.catalyst.plans.physical.Distribution
+import org.apache.spark.sql.catalyst.types.DataTypeUtils
+import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+import org.apache.spark.sql.execution.python.PandasGroupUtils.{executePython,
groupAndProject, resolveArgOffsets}
+import org.apache.spark.sql.execution.streaming.{StatefulOperatorPartitioning,
StatefulOperatorStateInfo, StatefulProcessorHandleImpl, StateStoreWriter,
WatermarkSupport}
+import
org.apache.spark.sql.execution.streaming.state.{NoPrefixKeyStateEncoderSpec,
StateSchemaValidationResult, StateStore, StateStoreOps}
+import org.apache.spark.sql.streaming.{OutputMode, TimeMode}
+import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
+import org.apache.spark.util.CompletionIterator
+
+/**
+ * Physical operator for executing
+ * [[org.apache.spark.sql.catalyst.plans.logical.TransformWithStateInPandas]]
+ * @param functionExpr function called on each group
+ * @param groupingAttributes used to group the data
+ * @param output used to define the output rows
+ * @param outputMode defines the output mode for the statefulProcessor
+ * @param timeMode The time mode semantics of the stateful processor for
timers and TTL.
+ * @param stateInfo Used to identify the state store for a given operator.
+ * @param batchTimestampMs processing timestamp of the current batch.
+ * @param eventTimeWatermarkForLateEvents event time watermark for filtering
late events
+ * @param eventTimeWatermarkForEviction event time watermark for state eviction
+ * @param child the physical plan for the underlying data
+ */
+case class TransformWithStateInPandasExec(
+ functionExpr: Expression,
+ groupingAttributes: Seq[Attribute],
+ output: Seq[Attribute],
+ outputMode: OutputMode,
+ timeMode: TimeMode,
+ stateInfo: Option[StatefulOperatorStateInfo],
+ batchTimestampMs: Option[Long],
+ eventTimeWatermarkForLateEvents: Option[Long],
+ eventTimeWatermarkForEviction: Option[Long],
+ child: SparkPlan) extends UnaryExecNode with StateStoreWriter with
WatermarkSupport {
+
+ private val pythonUDF = functionExpr.asInstanceOf[PythonUDF]
+ private val pythonFunction = pythonUDF.func
+ private val chainedFunc =
+ Seq((ChainedPythonFunctions(Seq(pythonFunction)), pythonUDF.resultId.id))
+
+ private val sessionLocalTimeZone = conf.sessionLocalTimeZone
+ private val pythonRunnerConf = ArrowPythonRunner.getPythonRunnerConfMap(conf)
+ private[this] val jobArtifactUUID =
JobArtifactSet.getCurrentJobArtifactState.map(_.uuid)
+
+ private val groupingKeyStructFields = groupingAttributes
+ .map(a => StructField(a.name, a.dataType, a.nullable))
+ private val groupingKeySchema = StructType(groupingKeyStructFields)
+ private val groupingKeyExprEncoder = ExpressionEncoder(groupingKeySchema)
+ .resolveAndBind().asInstanceOf[ExpressionEncoder[Any]]
+
+ /** The keys that may have a watermark attribute. */
+ override def keyExpressions: Seq[Attribute] = groupingAttributes
+
+ protected val schemaForKeyRow: StructType = new StructType().add("key",
BinaryType)
+
+ protected val schemaForValueRow: StructType = new StructType().add("value",
BinaryType)
+
+ override def requiredChildDistribution: Seq[Distribution] = {
+ StatefulOperatorPartitioning.getCompatibleDistribution(groupingAttributes,
+ getStateInfo, conf) ::
+ Nil
+ }
+
+ override def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq(
+ groupingAttributes.map(SortOrder(_, Ascending)))
+
+ override def validateAndMaybeEvolveStateSchema(
+ hadoopConf: Configuration,
+ batchId: Long,
+ stateSchemaVersion: Int): List[StateSchemaValidationResult] = {
+ List.empty
Review Comment:
What is the plan with this? Does this need to follow the same path of Scala
version of transformWithState and it's just pending other PR's change? If then
let's file a JIRA ticket and add TODO comment here. Otherwise please leave a
comment about why we don't need to handle anything here unlike Scala version.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/TransformWithStateInPandasExec.scala:
##########
@@ -0,0 +1,161 @@
+/*
+ * 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.execution.python
+
+import scala.concurrent.duration.NANOSECONDS
+
+import org.apache.hadoop.conf.Configuration
+
+import org.apache.spark.JobArtifactSet
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute,
Expression, PythonUDF, SortOrder}
+import org.apache.spark.sql.catalyst.plans.physical.Distribution
+import org.apache.spark.sql.catalyst.types.DataTypeUtils
+import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+import org.apache.spark.sql.execution.python.PandasGroupUtils.{executePython,
groupAndProject, resolveArgOffsets}
+import org.apache.spark.sql.execution.streaming.{StatefulOperatorPartitioning,
StatefulOperatorStateInfo, StatefulProcessorHandleImpl, StateStoreWriter,
WatermarkSupport}
+import
org.apache.spark.sql.execution.streaming.state.{NoPrefixKeyStateEncoderSpec,
StateSchemaValidationResult, StateStore, StateStoreOps}
+import org.apache.spark.sql.streaming.{OutputMode, TimeMode}
+import org.apache.spark.sql.types.{BinaryType, StructField, StructType}
+import org.apache.spark.util.CompletionIterator
+
+/**
+ * Physical operator for executing
+ * [[org.apache.spark.sql.catalyst.plans.logical.TransformWithStateInPandas]]
+ * @param functionExpr function called on each group
+ * @param groupingAttributes used to group the data
+ * @param output used to define the output rows
+ * @param outputMode defines the output mode for the statefulProcessor
+ * @param timeMode The time mode semantics of the stateful processor for
timers and TTL.
+ * @param stateInfo Used to identify the state store for a given operator.
+ * @param batchTimestampMs processing timestamp of the current batch.
+ * @param eventTimeWatermarkForLateEvents event time watermark for filtering
late events
+ * @param eventTimeWatermarkForEviction event time watermark for state eviction
+ * @param child the physical plan for the underlying data
+ */
+case class TransformWithStateInPandasExec(
+ functionExpr: Expression,
+ groupingAttributes: Seq[Attribute],
+ output: Seq[Attribute],
+ outputMode: OutputMode,
+ timeMode: TimeMode,
+ stateInfo: Option[StatefulOperatorStateInfo],
+ batchTimestampMs: Option[Long],
+ eventTimeWatermarkForLateEvents: Option[Long],
+ eventTimeWatermarkForEviction: Option[Long],
+ child: SparkPlan) extends UnaryExecNode with StateStoreWriter with
WatermarkSupport {
+
+ private val pythonUDF = functionExpr.asInstanceOf[PythonUDF]
+ private val pythonFunction = pythonUDF.func
+ private val chainedFunc =
+ Seq((ChainedPythonFunctions(Seq(pythonFunction)), pythonUDF.resultId.id))
+
+ private val sessionLocalTimeZone = conf.sessionLocalTimeZone
+ private val pythonRunnerConf = ArrowPythonRunner.getPythonRunnerConfMap(conf)
+ private[this] val jobArtifactUUID =
JobArtifactSet.getCurrentJobArtifactState.map(_.uuid)
+
+ private val groupingKeyStructFields = groupingAttributes
+ .map(a => StructField(a.name, a.dataType, a.nullable))
+ private val groupingKeySchema = StructType(groupingKeyStructFields)
+ private val groupingKeyExprEncoder = ExpressionEncoder(groupingKeySchema)
+ .resolveAndBind().asInstanceOf[ExpressionEncoder[Any]]
+
+ /** The keys that may have a watermark attribute. */
+ override def keyExpressions: Seq[Attribute] = groupingAttributes
+
+ protected val schemaForKeyRow: StructType = new StructType().add("key",
BinaryType)
+
+ protected val schemaForValueRow: StructType = new StructType().add("value",
BinaryType)
+
+ override def requiredChildDistribution: Seq[Distribution] = {
+ StatefulOperatorPartitioning.getCompatibleDistribution(groupingAttributes,
+ getStateInfo, conf) ::
+ Nil
+ }
+
+ override def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq(
+ groupingAttributes.map(SortOrder(_, Ascending)))
+
+ override def validateAndMaybeEvolveStateSchema(
+ hadoopConf: Configuration,
+ batchId: Long,
+ stateSchemaVersion: Int): List[StateSchemaValidationResult] = {
+ List.empty
+ }
+
+ /**
+ * Produces the result of the query as an `RDD[InternalRow]`
+ */
+ override protected def doExecute(): RDD[InternalRow] = {
+ metrics
+
+ val (dedupAttributes, argOffsets) = resolveArgOffsets(child.output,
groupingAttributes)
+
+ child.execute().mapPartitionsWithStateStore[InternalRow](
+ getStateInfo,
+ schemaForKeyRow,
+ schemaForValueRow,
+ NoPrefixKeyStateEncoderSpec(schemaForKeyRow),
+ session.sqlContext.sessionState,
+ Some(session.sqlContext.streams.stateStoreCoordinator),
+ useColumnFamilies = true,
+ useMultipleValuesPerKey = true
+ ) {
+ case (store: StateStore, dataIterator: Iterator[InternalRow]) =>
+ val allUpdatesTimeMs = longMetric("allUpdatesTimeMs")
+ val commitTimeMs = longMetric("commitTimeMs")
+ val currentTimeNs = System.nanoTime
+ val updatesStartTimeNs = currentTimeNs
+
+ val data = groupAndProject(dataIterator, groupingAttributes,
child.output, dedupAttributes)
+
+ val processorHandle = new StatefulProcessorHandleImpl(store,
getStateInfo.queryRunId,
+ groupingKeyExprEncoder, timeMode)
+ val runner = new TransformWithStateInPandasPythonRunner(
+ chainedFunc,
+ PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_UDF,
+ Array(argOffsets),
+ DataTypeUtils.fromAttributes(dedupAttributes),
+ processorHandle,
+ sessionLocalTimeZone,
+ pythonRunnerConf,
+ pythonMetrics,
+ jobArtifactUUID,
+ groupingKeySchema
+ )
+
+ val outputIterator = executePython(data, output, runner)
+
+ CompletionIterator[InternalRow, Iterator[InternalRow]](outputIterator,
{
Review Comment:
Where we count `numOutputRows` in this node?
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