sahnib commented on code in PR #45023:
URL: https://github.com/apache/spark/pull/45023#discussion_r1516471532
##########
python/pyspark/sql/datasource.py:
##########
@@ -298,6 +320,133 @@ def read(self, partition: InputPartition) ->
Iterator[Union[Tuple, Row]]:
...
+class DataSourceStreamReader(ABC):
+ """
+ A base class for streaming data source readers. Data source stream readers
are responsible
+ for outputting data from a streaming data source.
+
+ .. versionadded: 4.0.0
+ """
+
+ def initialOffset(self) -> dict:
Review Comment:
Should we make this abstract to force user to implement it?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonStreamingSourceRunner.scala:
##########
@@ -0,0 +1,208 @@
+/*
+ * 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.{BufferedInputStream, BufferedOutputStream, DataInputStream,
DataOutputStream}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.jdk.CollectionConverters._
+
+import org.apache.spark.SparkEnv
+import org.apache.spark.api.python.{PythonFunction, PythonWorker,
PythonWorkerFactory, PythonWorkerUtils, SpecialLengths}
+import org.apache.spark.internal.Logging
+import org.apache.spark.internal.config.BUFFER_SIZE
+import org.apache.spark.internal.config.Python.{PYTHON_AUTH_SOCKET_TIMEOUT,
PYTHON_USE_DAEMON}
+import org.apache.spark.sql.errors.{QueryCompilationErrors,
QueryExecutionErrors}
+import org.apache.spark.sql.types.StructType
+
+object PythonStreamingSourceRunner {
+ // When the python process for python_streaming_source_runner receives one
of the
+ // integers below, it will invoke the corresponding function of StreamReader
instance.
+ val INITIAL_OFFSET_FUNC_ID = 884
+ val LATEST_OFFSET_FUNC_ID = 885
+ val PARTITIONS_FUNC_ID = 886
+ val COMMIT_FUNC_ID = 887
+}
+
+/**
+ * This class is a proxy to invoke methods in Python DataSourceStreamReader
from JVM.
+ * A runner spawns a python worker process. In the main function, set up
communication
+ * between JVM and python process through socket and create a
DataSourceStreamReader instance.
+ * In an infinite loop, the python worker process poll information(function
name and parameters)
+ * from the socket, invoke the corresponding method of StreamReader and send
return value to JVM.
+ */
+class PythonStreamingSourceRunner(
+ func: PythonFunction,
+ outputSchema: StructType) extends Logging {
+ val workerModule = "pyspark.sql.streaming.python_streaming_source_runner"
+
+ private val conf = SparkEnv.get.conf
+ private val bufferSize: Int = conf.get(BUFFER_SIZE)
+ private val authSocketTimeout = conf.get(PYTHON_AUTH_SOCKET_TIMEOUT)
+
+ private val envVars: java.util.Map[String, String] = func.envVars
+ private val pythonExec: String = func.pythonExec
+ private var pythonWorker: Option[PythonWorker] = None
+ private var pythonWorkerFactory: Option[PythonWorkerFactory] = None
+ private val pythonVer: String = func.pythonVer
+
+ private var dataOut: DataOutputStream = null
+ private var dataIn: DataInputStream = null
+
+ import PythonStreamingSourceRunner._
+
+ /**
+ * Initializes the Python worker for running the streaming source.
+ */
+ def init(): Unit = {
+ logInfo(s"Initializing Python runner pythonExec: $pythonExec")
+ val env = SparkEnv.get
+
+ val localdir = env.blockManager.diskBlockManager.localDirs.map(f =>
f.getPath()).mkString(",")
+ envVars.put("SPARK_LOCAL_DIRS", localdir)
+
+ envVars.put("SPARK_AUTH_SOCKET_TIMEOUT", authSocketTimeout.toString)
+ envVars.put("SPARK_BUFFER_SIZE", bufferSize.toString)
+
+ val prevConf = conf.get(PYTHON_USE_DAEMON)
+ conf.set(PYTHON_USE_DAEMON, false)
+ try {
+ val workerFactory =
+ new PythonWorkerFactory(pythonExec, workerModule,
envVars.asScala.toMap)
+ val (worker: PythonWorker, _) =
workerFactory.createSimpleWorker(blockingMode = true)
+ pythonWorker = Some(worker)
+ pythonWorkerFactory = Some(workerFactory)
+ } finally {
+ conf.set(PYTHON_USE_DAEMON, prevConf)
+ }
+
+ val stream = new BufferedOutputStream(
+ pythonWorker.get.channel.socket().getOutputStream, bufferSize)
+ dataOut = new DataOutputStream(stream)
+
+ PythonWorkerUtils.writePythonVersion(pythonVer, dataOut)
+
+ val pythonIncludes = func.pythonIncludes.asScala.toSet
+ PythonWorkerUtils.writeSparkFiles(Some("streaming_job"), pythonIncludes,
dataOut)
+
+ // Send the user function to python process
+ PythonWorkerUtils.writePythonFunction(func, dataOut)
+
+ // Send output schema
+ PythonWorkerUtils.writeUTF(outputSchema.json, dataOut)
+
+ dataOut.flush()
+
+ dataIn = new DataInputStream(
+ new
BufferedInputStream(pythonWorker.get.channel.socket().getInputStream,
bufferSize))
+
+ val initStatus = dataIn.readInt()
+ if (initStatus == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryCompilationErrors.pythonDataSourceError(
+ action = "plan", tpe = "initialize source", msg = msg)
+ }
+ }
+
+ /**
+ * Invokes latestOffset() function of the stream reader and receive the
return value.
+ */
+ def latestOffset(): String = {
+ dataOut.writeInt(LATEST_OFFSET_FUNC_ID)
+ dataOut.flush()
+ val len = dataIn.readInt()
+ if (len == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "latestOffset", msg)
+ }
+ PythonWorkerUtils.readUTF(len, dataIn)
+ }
+
+ /**
+ * Invokes initialOffset() function of the stream reader and receive the
return value.
+ */
+ def initialOffset(): String = {
+ dataOut.writeInt(INITIAL_OFFSET_FUNC_ID)
+ dataOut.flush()
+ val len = dataIn.readInt()
+ if (len == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "initialOffset", msg)
+ }
+ PythonWorkerUtils.readUTF(len, dataIn)
+ }
+
+ /**
+ * Invokes partitions(start, end) function of the stream reader and receive
the return value.
+ */
+ def partitions(start: String, end: String): Array[Array[Byte]] = {
+ dataOut.writeInt(PARTITIONS_FUNC_ID)
+ PythonWorkerUtils.writeUTF(start, dataOut)
+ PythonWorkerUtils.writeUTF(end, dataOut)
+ dataOut.flush()
+ // Receive the list of partitions, if any.
+ val pickledPartitions = ArrayBuffer.empty[Array[Byte]]
+ val numPartitions = dataIn.readInt()
+ if (numPartitions == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "planPartitions", msg)
+ }
+ for (_ <- 0 until numPartitions) {
+ val pickledPartition: Array[Byte] = PythonWorkerUtils.readBytes(dataIn)
+ pickledPartitions.append(pickledPartition)
+ }
+ pickledPartitions.toArray
+ }
+
+ /**
+ * Invokes commit(end) function of the stream reader and receive the return
value.
+ */
+ def commit(end: String): Unit = {
+ dataOut.writeInt(COMMIT_FUNC_ID)
+ PythonWorkerUtils.writeUTF(end, dataOut)
+ dataOut.flush()
+ val status = dataIn.readInt()
+ if (status == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "commitSource", msg)
+ }
+ }
+
+ /**
+ * Stop the python worker process and invoke stop() on stream reader.
+ */
+ def stop(): Unit = {
+ logInfo(s"Stopping streaming runner for module: $workerModule.")
+ try {
+ pythonWorkerFactory.foreach { factory =>
+ pythonWorker.foreach { worker =>
+ factory.stopWorker(worker)
+ factory.stop()
+ }
+ }
+ } catch {
+ case e: Exception =>
+ logError("Exception when trying to kill worker", e)
+ }
Review Comment:
Is it safe to swallow this?
##########
python/pyspark/sql/datasource.py:
##########
@@ -298,6 +320,133 @@ def read(self, partition: InputPartition) ->
Iterator[Union[Tuple, Row]]:
...
+class DataSourceStreamReader(ABC):
+ """
+ A base class for streaming data source readers. Data source stream readers
are responsible
+ for outputting data from a streaming data source.
+
+ .. versionadded: 4.0.0
+ """
+
+ def initialOffset(self) -> dict:
+ """
+ Return the initial offset of the streaming data source.
+ A new streaming query starts reading data from the initial offset.
+ If Spark is restarting an existing query, it will restart from the
check-pointed offset
+ rather than the initial one.
+
+ Returns
+ -------
+ dict
+ A dict or recursive dict whose key and value are primitive types,
which includes
+ Integer, String and Boolean.
+
+ Examples
+ --------
+ >>> def initialOffset(self):
+ ... return {"parititon-1": {"index": 3, "closed": True},
"partition-2": {"index": 5}}
+ """
+
+ ...
+ raise PySparkNotImplementedError(
+ error_class="NOT_IMPLEMENTED",
+ message_parameters={"feature": "initialOffset"},
+ )
+
+ def latestOffset(self) -> dict:
+ """
+ Returns the most recent offset available.
+
+ Returns
+ -------
+ dict
+ A dict or recursive dict whose key and value are primitive types,
which includes
+ Integer, String and Boolean.
+
+ Examples
+ --------
+ >>> def latestOffset(self):
+ ... return {"parititon-1": {"index": 3, "closed": True},
"partition-2": {"index": 5}}
+ """
+ ...
+ raise PySparkNotImplementedError(
+ error_class="NOT_IMPLEMENTED",
+ message_parameters={"feature": "latestOffset"},
+ )
+
+ def partitions(self, start: dict, end: dict) -> Sequence[InputPartition]:
+ """
+ Returns a list of InputPartition given the start and end offsets.
Each InputPartition
+ represents a data split that can be processed by one Spark task.
+
+ Parameters
+ ----------
+ start : dict
+ The start offset of the microbatch to plan partitioning.
+ end : dict
+ The end offset of the microbatch to plan partitioning.
+
+ Returns
+ -------
+ Sequence[InputPartition]
+ A sequence of partitions for this data source. Each partition value
+ must be an instance of `InputPartition` or a subclass of it.
+ """
+ ...
+ raise PySparkNotImplementedError(
+ error_class="NOT_IMPLEMENTED",
+ message_parameters={"feature": "partitions"},
+ )
+
+ @abstractmethod
+ def read(self, partition: InputPartition) -> Iterator[Union[Tuple, Row]]:
Review Comment:
I am not completely familiar with internals of Unions in Python, however ..
We do not expect values in the Iterator to differ in type, i.e., either the
entire output is Iterator[Tuple] or Iterator[Row]. Should the return type be
then - `Union[Iterator{Tuple], Iterator[Row]]`?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonStreamingSourceRunner.scala:
##########
@@ -0,0 +1,208 @@
+/*
+ * 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.{BufferedInputStream, BufferedOutputStream, DataInputStream,
DataOutputStream}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.jdk.CollectionConverters._
+
+import org.apache.spark.SparkEnv
+import org.apache.spark.api.python.{PythonFunction, PythonWorker,
PythonWorkerFactory, PythonWorkerUtils, SpecialLengths}
+import org.apache.spark.internal.Logging
+import org.apache.spark.internal.config.BUFFER_SIZE
+import org.apache.spark.internal.config.Python.{PYTHON_AUTH_SOCKET_TIMEOUT,
PYTHON_USE_DAEMON}
+import org.apache.spark.sql.errors.{QueryCompilationErrors,
QueryExecutionErrors}
+import org.apache.spark.sql.types.StructType
+
+object PythonStreamingSourceRunner {
+ // When the python process for python_streaming_source_runner receives one
of the
+ // integers below, it will invoke the corresponding function of StreamReader
instance.
+ val INITIAL_OFFSET_FUNC_ID = 884
+ val LATEST_OFFSET_FUNC_ID = 885
+ val PARTITIONS_FUNC_ID = 886
+ val COMMIT_FUNC_ID = 887
+}
+
+/**
+ * This class is a proxy to invoke methods in Python DataSourceStreamReader
from JVM.
+ * A runner spawns a python worker process. In the main function, set up
communication
+ * between JVM and python process through socket and create a
DataSourceStreamReader instance.
+ * In an infinite loop, the python worker process poll information(function
name and parameters)
+ * from the socket, invoke the corresponding method of StreamReader and send
return value to JVM.
+ */
+class PythonStreamingSourceRunner(
+ func: PythonFunction,
+ outputSchema: StructType) extends Logging {
+ val workerModule = "pyspark.sql.streaming.python_streaming_source_runner"
+
+ private val conf = SparkEnv.get.conf
+ private val bufferSize: Int = conf.get(BUFFER_SIZE)
+ private val authSocketTimeout = conf.get(PYTHON_AUTH_SOCKET_TIMEOUT)
+
+ private val envVars: java.util.Map[String, String] = func.envVars
+ private val pythonExec: String = func.pythonExec
+ private var pythonWorker: Option[PythonWorker] = None
+ private var pythonWorkerFactory: Option[PythonWorkerFactory] = None
+ private val pythonVer: String = func.pythonVer
+
+ private var dataOut: DataOutputStream = null
+ private var dataIn: DataInputStream = null
+
+ import PythonStreamingSourceRunner._
+
+ /**
+ * Initializes the Python worker for running the streaming source.
+ */
+ def init(): Unit = {
+ logInfo(s"Initializing Python runner pythonExec: $pythonExec")
+ val env = SparkEnv.get
+
+ val localdir = env.blockManager.diskBlockManager.localDirs.map(f =>
f.getPath()).mkString(",")
+ envVars.put("SPARK_LOCAL_DIRS", localdir)
+
+ envVars.put("SPARK_AUTH_SOCKET_TIMEOUT", authSocketTimeout.toString)
+ envVars.put("SPARK_BUFFER_SIZE", bufferSize.toString)
+
+ val prevConf = conf.get(PYTHON_USE_DAEMON)
+ conf.set(PYTHON_USE_DAEMON, false)
+ try {
+ val workerFactory =
+ new PythonWorkerFactory(pythonExec, workerModule,
envVars.asScala.toMap)
+ val (worker: PythonWorker, _) =
workerFactory.createSimpleWorker(blockingMode = true)
+ pythonWorker = Some(worker)
+ pythonWorkerFactory = Some(workerFactory)
+ } finally {
+ conf.set(PYTHON_USE_DAEMON, prevConf)
+ }
+
+ val stream = new BufferedOutputStream(
+ pythonWorker.get.channel.socket().getOutputStream, bufferSize)
+ dataOut = new DataOutputStream(stream)
+
+ PythonWorkerUtils.writePythonVersion(pythonVer, dataOut)
+
+ val pythonIncludes = func.pythonIncludes.asScala.toSet
+ PythonWorkerUtils.writeSparkFiles(Some("streaming_job"), pythonIncludes,
dataOut)
+
+ // Send the user function to python process
+ PythonWorkerUtils.writePythonFunction(func, dataOut)
+
+ // Send output schema
+ PythonWorkerUtils.writeUTF(outputSchema.json, dataOut)
+
+ dataOut.flush()
+
+ dataIn = new DataInputStream(
+ new
BufferedInputStream(pythonWorker.get.channel.socket().getInputStream,
bufferSize))
+
+ val initStatus = dataIn.readInt()
+ if (initStatus == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryCompilationErrors.pythonDataSourceError(
+ action = "plan", tpe = "initialize source", msg = msg)
+ }
+ }
+
+ /**
+ * Invokes latestOffset() function of the stream reader and receive the
return value.
+ */
+ def latestOffset(): String = {
+ dataOut.writeInt(LATEST_OFFSET_FUNC_ID)
+ dataOut.flush()
+ val len = dataIn.readInt()
+ if (len == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "latestOffset", msg)
+ }
+ PythonWorkerUtils.readUTF(len, dataIn)
+ }
+
+ /**
+ * Invokes initialOffset() function of the stream reader and receive the
return value.
+ */
+ def initialOffset(): String = {
+ dataOut.writeInt(INITIAL_OFFSET_FUNC_ID)
+ dataOut.flush()
+ val len = dataIn.readInt()
+ if (len == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "initialOffset", msg)
+ }
+ PythonWorkerUtils.readUTF(len, dataIn)
+ }
+
+ /**
+ * Invokes partitions(start, end) function of the stream reader and receive
the return value.
+ */
+ def partitions(start: String, end: String): Array[Array[Byte]] = {
+ dataOut.writeInt(PARTITIONS_FUNC_ID)
+ PythonWorkerUtils.writeUTF(start, dataOut)
+ PythonWorkerUtils.writeUTF(end, dataOut)
+ dataOut.flush()
+ // Receive the list of partitions, if any.
+ val pickledPartitions = ArrayBuffer.empty[Array[Byte]]
+ val numPartitions = dataIn.readInt()
+ if (numPartitions == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "planPartitions", msg)
+ }
+ for (_ <- 0 until numPartitions) {
+ val pickledPartition: Array[Byte] = PythonWorkerUtils.readBytes(dataIn)
+ pickledPartitions.append(pickledPartition)
+ }
+ pickledPartitions.toArray
+ }
+
+ /**
+ * Invokes commit(end) function of the stream reader and receive the return
value.
+ */
+ def commit(end: String): Unit = {
+ dataOut.writeInt(COMMIT_FUNC_ID)
+ PythonWorkerUtils.writeUTF(end, dataOut)
+ dataOut.flush()
+ val status = dataIn.readInt()
+ if (status == SpecialLengths.PYTHON_EXCEPTION_THROWN) {
+ val msg = PythonWorkerUtils.readUTF(dataIn)
+ throw QueryExecutionErrors.pythonStreamingDataSourceRuntimeError(
+ action = "commitSource", msg)
+ }
+ }
+
+ /**
+ * Stop the python worker process and invoke stop() on stream reader.
+ */
+ def stop(): Unit = {
+ logInfo(s"Stopping streaming runner for module: $workerModule.")
+ try {
+ pythonWorkerFactory.foreach { factory =>
+ pythonWorker.foreach { worker =>
+ factory.stopWorker(worker)
+ factory.stop()
Review Comment:
We should have a call to Stop function on datasource here, right?
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonStreamingSourceRunner.scala:
##########
@@ -0,0 +1,209 @@
+/*
+ * 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.{BufferedInputStream, BufferedOutputStream, DataInputStream,
DataOutputStream}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.jdk.CollectionConverters._
+
+import org.apache.spark.SparkEnv
+import org.apache.spark.api.python.{PythonFunction, PythonWorker,
PythonWorkerFactory, PythonWorkerUtils, SpecialLengths}
+import org.apache.spark.internal.Logging
+import org.apache.spark.internal.config.BUFFER_SIZE
+import org.apache.spark.internal.config.Python.{PYTHON_AUTH_SOCKET_TIMEOUT,
PYTHON_USE_DAEMON}
+import org.apache.spark.sql.errors.{QueryCompilationErrors,
QueryExecutionErrors}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.StructType
+
+object PythonStreamingSourceRunner {
+ val initialOffsetFuncId = 884
+ val latestOffsetFuncId = 885
+ val partitionsFuncId = 886
+ val commitFuncId = 887
+}
+
+/**
+ * This class is a proxy to invoke methods in Python DataSourceStreamReader
from JVM.
+ * A runner spawns a python worker process. In the main function, set up
communication
+ * between JVM and python process through socket and create a
DataSourceStreamReader instance.
+ * In an infinite loop, the python worker process poll information(function
name and parameters)
+ * from the socket, invoke the corresponding method of StreamReader and send
return value to JVM.
+ */
+class PythonStreamingSourceRunner(
+ func: PythonFunction,
+ outputSchema: StructType) extends Logging {
+ val workerModule = "pyspark.sql.streaming.python_streaming_source_runner"
+
+ private val conf = SparkEnv.get.conf
+ protected val bufferSize: Int = conf.get(BUFFER_SIZE)
+ protected val authSocketTimeout = conf.get(PYTHON_AUTH_SOCKET_TIMEOUT)
+
+ private val envVars: java.util.Map[String, String] = func.envVars
+ private val pythonExec: String = func.pythonExec
+ private var pythonWorker: Option[PythonWorker] = None
+ private var pythonWorkerFactory: Option[PythonWorkerFactory] = None
+ protected val pythonVer: String = func.pythonVer
+
+ private var dataOut: DataOutputStream = null
+ private var dataIn: DataInputStream = null
+
+ import PythonStreamingSourceRunner._
+
+ /**
+ * Initializes the Python worker for running the streaming source.
+ */
+ def init(): Unit = {
+ logInfo(s"Initializing Python runner pythonExec: $pythonExec")
+ val env = SparkEnv.get
+
+ val localdir = env.blockManager.diskBlockManager.localDirs.map(f =>
f.getPath()).mkString(",")
+ envVars.put("SPARK_LOCAL_DIRS", localdir)
+
+ envVars.put("SPARK_AUTH_SOCKET_TIMEOUT", authSocketTimeout.toString)
+ envVars.put("SPARK_BUFFER_SIZE", bufferSize.toString)
+
+ val prevConf = conf.get(PYTHON_USE_DAEMON)
Review Comment:
I think if a task launches a PythonUDFWorker at this time, we would not use
the daemon? @chaoqin-li1123 Rather than setting the config, can we pass a
parameter to PythonWorkerFactory to explicitly not use the daemon? That would
prevent us from modifying the config.
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