dianfu commented on a change in pull request #12370:
URL: https://github.com/apache/flink/pull/12370#discussion_r433644752
##########
File path:
flink-table/flink-table-planner/src/main/scala/org/apache/flink/table/plan/nodes/CommonPythonBase.scala
##########
@@ -150,6 +152,64 @@ trait CommonPythonBase {
}
realEnv
}
+
+ private def isPythonWorkerUsingManagedMemory(config: Configuration): Boolean
= {
+ val clazz = loadClass("org.apache.flink.python.PythonOptions")
+ config.getBoolean(clazz.getField("USE_MANAGED_MEMORY").get(null)
+ .asInstanceOf[ConfigOption[java.lang.Boolean]])
+ }
+
+ private def getPythonWorkerMemory(config: Configuration): MemorySize = {
+ val clazz = loadClass("org.apache.flink.python.PythonOptions")
+ val pythonFrameworkMemorySize = MemorySize.parse(
+ config.getString(
+ clazz.getField("PYTHON_FRAMEWORK_MEMORY_SIZE").get(null)
+ .asInstanceOf[ConfigOption[String]]))
+ val pythonBufferMemorySize = MemorySize.parse(
+ config.getString(
+ clazz.getField("PYTHON_DATA_BUFFER_MEMORY_SIZE").get(null)
+ .asInstanceOf[ConfigOption[String]]))
+ pythonFrameworkMemorySize.add(pythonBufferMemorySize)
+ }
+
+ private def checkPythonWorkerMemory(
+ config: Configuration, env: StreamExecutionEnvironment = null): Unit = {
+ if (!isPythonWorkerUsingManagedMemory(config)) {
+ val taskOffHeapMemory =
config.get(TaskManagerOptions.TASK_OFF_HEAP_MEMORY)
+ val requiredPythonWorkerOffHeapMemory = getPythonWorkerMemory(config)
+ if (taskOffHeapMemory.compareTo(requiredPythonWorkerOffHeapMemory) < 0) {
+ throw new TableException(String.format("The configured Task Off-Heap
Memory %s is less " +
+ "than the least required Python worker Memory %s. The Task Off-Heap
Memory can be " +
+ "configured using the configuration key
'taskmanager.memory.task.off-heap.size'.",
+ taskOffHeapMemory, requiredPythonWorkerOffHeapMemory))
+ }
+ } else if (env != null && isRocksDbUsingManagedMemory(env)) {
+ throw new TableException("Currently it doesn't support to use Managed
Memory for both " +
+ "RocksDB state backend and Python worker at the same time. You can
either configure " +
+ "RocksDB state backend to use Task Off-Heap Memory via the
configuration key " +
+ "'state.backend.rocksdb.memory.managed' or configure Python worker to
use " +
+ "Task Off-Heap Memory via the configuration key " +
+ "'python.fn-execution.memory.managed'.")
+ }
+ }
+
+ private def isRocksDbUsingManagedMemory(env: StreamExecutionEnvironment):
Boolean = {
Review comment:
Do you mean the duplicate methods between the old planner and the blink
planner? If so, I'm afraid it's currently not possible as there is way to share
code between them. Although it allows to share the code technically by moving
the shared code to flink-table-common, I think it's not a good idea to do that
for this case.
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