def __init__(self, jprogress: JavaObject) -> None:
from pyspark import SparkContext
self._jprogress: JavaObject = jprogress
self._id: uuid.UUID = uuid.UUID(jprogress.id().toString())
self._runId: uuid.UUID = uuid.UUID(jprogress.runId().toString())
self._name: Optional[str] = jprogress.name()
self._timestamp: str = jprogress.timestamp()
self._batchId: int = jprogress.batchId()
self._batchDuration: int = jprogress.batchDuration()
self._durationMs: Dict[str, int] = dict(jprogress.durationMs())
self._eventTime: Dict[str, str] = dict(jprogress.eventTime())
self._stateOperators: List[StateOperatorProgress] = [
StateOperatorProgress(js) for js in jprogress.stateOperators()
]
self._sources: List[SourceProgress] = [
SourceProgress(js) for js in jprogress.sources()
]
self._sink: SinkProgress = SinkProgress(jprogress.sink())
# TODO(SPARK-38760): Write a test with DataFrame.observe API implementation.
self._observedMetrics: Dict[str, Row] = {
k: pickle.loads(
SparkContext._jvm.PythonSQLUtils.toPyRow(
jr) # type: ignore[union-attr]
)
for k, jr in dict(jprogress.observedMetrics()).items()
}
def __init__(self, jevent: JavaObject) -> None:
self._id: uuid.UUID = uuid.UUID(jevent.id().toString())
self._runId: uuid.UUID = uuid.UUID(jevent.runId().toString())
self._name: Optional[str] = jevent.name()
self._timestamp: str = jevent.timestamp()
