rangadi commented on code in PR #42035:
URL: https://github.com/apache/spark/pull/42035#discussion_r1265804405
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/StreamingForeachBatchHelper.scala:
##########
@@ -57,13 +63,63 @@ object StreamingForeachBatchHelper extends Logging {
* Handles setting up Scala remote session and other Spark Connect
environment and then runs the
* provided foreachBatch function `fn`.
*
- * HACK ALERT: This version does not atually set up Spark connect. Directly
passes the
- * DataFrame, so the user code actually runs with legacy DataFrame.
+ * HACK ALERT: This version does not actually set up Spark Connect session.
Directly passes the
+ * DataFrame, so the user code actually runs with legacy DataFrame and
session..
*/
def scalaForeachBatchWrapper(
fn: ForeachBatchFnType,
sessionHolder: SessionHolder): ForeachBatchFnType = {
// TODO: Set up Spark Connect session. Do we actually need this for the
first version?
- dataFrameCachingWrapper(fn, sessionHolder)
+ dataFrameCachingWrapper(
+ (args: FnArgsWithId) => {
+ assert(sessionHolder.session == args.df.sparkSession) // XXX
+ fn(args.df, args.batchId) // dfId is not used, see hack comment above.
+ },
+ sessionHolder)
+ }
+
+ def pythonForeachBatchWrapper(
+ pythonFn: SimplePythonFunction,
+ sessionHolder: SessionHolder): ForeachBatchFnType = {
+
+ val port = SparkConnectService.localPort
+ val connectUrl = s"sc://localhost:$port/;user_id=${sessionHolder.userId}"
+ val runner = StreamingPythonRunner(pythonFn, connectUrl)
+ val (dataOut, dataIn) = runner.init(sessionHolder.sessionId)
+
+ val foreachBatchRunnerFn: FnArgsWithId => Unit = (args: FnArgsWithId) => {
+
+ // TODO: Set userId
+ // TODO: Auth credentials
Review Comment:
Added tickets.
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/StreamingForeachBatchHelper.scala:
##########
@@ -57,13 +63,59 @@ object StreamingForeachBatchHelper extends Logging {
* Handles setting up Scala remote session and other Spark Connect
environment and then runs the
* provided foreachBatch function `fn`.
*
- * HACK ALERT: This version does not atually set up Spark connect. Directly
passes the
- * DataFrame, so the user code actually runs with legacy DataFrame.
+ * HACK ALERT: This version does not actually set up Spark Connect session.
Directly passes the
+ * DataFrame, so the user code actually runs with legacy DataFrame and
session..
*/
def scalaForeachBatchWrapper(
fn: ForeachBatchFnType,
sessionHolder: SessionHolder): ForeachBatchFnType = {
// TODO: Set up Spark Connect session. Do we actually need this for the
first version?
- dataFrameCachingWrapper(fn, sessionHolder)
+ dataFrameCachingWrapper(
+ (args: FnArgsWithId) => {
+ fn(args.df, args.batchId) // dfId is not used, see hack comment above.
+ },
+ sessionHolder)
}
+
+ def pythonForeachBatchWrapper(
Review Comment:
Done.
##########
core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala:
##########
@@ -110,6 +110,57 @@ private[spark] class PythonWorkerFactory(pythonExec:
String, envVars: Map[String
}
}
+ def createStreamingWorker(): (Socket, Option[Int]) = {
Review Comment:
Drastically simplified this by reusing existing `createSimpleWorker()`.
Added brief documentation.
##########
core/src/main/scala/org/apache/spark/api/python/StreamingPythonRunner.scala:
##########
@@ -0,0 +1,86 @@
+/*
+ * 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.api.python
+
+import java.io.{BufferedInputStream, BufferedOutputStream, DataInputStream,
DataOutputStream}
+import java.net.Socket
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.SparkEnv
+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}
+
+
+private[spark] object StreamingPythonRunner {
+ def apply(func: PythonFunction, connectUrl: String): StreamingPythonRunner =
{
+ new StreamingPythonRunner(func, connectUrl)
+ }
+}
+
+private[spark] class StreamingPythonRunner(func: PythonFunction, connectUrl:
String)
+ extends Logging {
+ 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
+ protected val pythonVer: String = func.pythonVer
+
+ def init(sessionId: String): (DataOutputStream, DataInputStream) = {
Review Comment:
Done.
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