[SPARK-12511] [PYSPARK] [STREAMING] Make sure PythonDStream.registerSerializer is called only once

There is an issue that Py4J's PythonProxyHandler.finalize blocks forever. (py4j/py4j#184)

Py4j will create a PythonProxyHandler in Java for "transformer_serializer" when calling "registerSerializer". If we call "registerSerializer" twice, the second PythonProxyHandler will override the first one, then the first one will be GCed and trigger "PythonProxyHandler.finalize". To avoid that, we should not call"registerSerializer" more than once, so that "PythonProxyHandler" in Java side won't be GCed.

Author: Shixiong Zhu <[email protected]>

Closes #10514 from zsxwing/SPARK-12511.

Author: zsxwing

python/pyspark/streaming/context.py

@@ -98,8 +98,28 @@ def _ensure_initialized(cls):
 
         # register serializer for TransformFunction
         # it happens before creating SparkContext when loading from checkpointing
-        cls._transformerSerializer = TransformFunctionSerializer(
-            SparkContext._active_spark_context, CloudPickleSerializer(), gw)
+        if cls._transformerSerializer is None:
+            transformer_serializer = TransformFunctionSerializer()
+            transformer_serializer.init(
+                SparkContext._active_spark_context, CloudPickleSerializer(), gw)
+            # SPARK-12511 streaming driver with checkpointing unable to finalize leading to OOM
+            # There is an issue that Py4J's PythonProxyHandler.finalize blocks forever.
+            # (https://github.com/bartdag/py4j/pull/184)
+            #
+            # Py4j will create a PythonProxyHandler in Java for "transformer_serializer" when
+            # calling "registerSerializer". If we call "registerSerializer" twice, the second
+            # PythonProxyHandler will override the first one, then the first one will be GCed and
+            # trigger "PythonProxyHandler.finalize". To avoid that, we should not call
+            # "registerSerializer" more than once, so that "PythonProxyHandler" in Java side won't
+            # be GCed.
+            #
+            # TODO Once Py4J fixes this issue, we should upgrade Py4j to the latest version.
+            transformer_serializer.gateway.jvm.PythonDStream.registerSerializer(
+                transformer_serializer)
+            cls._transformerSerializer = transformer_serializer
+        else:
+            cls._transformerSerializer.init(
+                SparkContext._active_spark_context, CloudPickleSerializer(), gw)
 
     @classmethod
     def getOrCreate(cls, checkpointPath, setupFunc):
@@ -116,16 +136,13 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         gw = SparkContext._gateway
 
         # Check whether valid checkpoint information exists in the given path
-        if gw.jvm.CheckpointReader.read(checkpointPath).isEmpty():
+        ssc_option = gw.jvm.StreamingContextPythonHelper().tryRecoverFromCheckpoint(checkpointPath)
+        if ssc_option.isEmpty():
             ssc = setupFunc()
             ssc.checkpoint(checkpointPath)
             return ssc
 
-        try:
-            jssc = gw.jvm.JavaStreamingContext(checkpointPath)
-        except Exception:
-            print("failed to load StreamingContext from checkpoint", file=sys.stderr)
-            raise
+        jssc = gw.jvm.JavaStreamingContext(ssc_option.get())
 
         # If there is already an active instance of Python SparkContext use it, or create a new one
         if not SparkContext._active_spark_context:

python/pyspark/streaming/util.py

@@ -89,11 +89,10 @@ class TransformFunctionSerializer(object):
     it uses this class to invoke Python, which returns the serialized function
     as a byte array.
     """
-    def __init__(self, ctx, serializer, gateway=None):
+    def init(self, ctx, serializer, gateway=None):
         self.ctx = ctx
         self.serializer = serializer
         self.gateway = gateway or self.ctx._gateway
-        self.gateway.jvm.PythonDStream.registerSerializer(self)
         self.failure = None
 
     def dumps(self, id):

streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala

@@ -902,3 +902,15 @@ object StreamingContext extends Logging {
     result
   }
 }
+
+private class StreamingContextPythonHelper {
+
+  /**
+   * This is a private method only for Python to implement `getOrCreate`.
+   */
+  def tryRecoverFromCheckpoint(checkpointPath: String): Option[StreamingContext] = {
+    val checkpointOption = CheckpointReader.read(
+      checkpointPath, new SparkConf(), SparkHadoopUtil.get.conf, false)
+    checkpointOption.map(new StreamingContext(null, _, null))
+  }
+}