Source code for pyflink.datastream.checkpointing_mode

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from enum import Enum

from pyflink.java_gateway import get_gateway

__all__ = ['CheckpointingMode']


class CheckpointingMode(Enum):
    """
    The checkpointing mode defines what consistency guarantees the system gives in the presence of
    failures.

    When checkpointing is activated, the data streams are replayed such that lost parts of the
    processing are repeated. For stateful operations and functions, the checkpointing mode defines
    whether the system draws checkpoints such that a recovery behaves as if the operators/functions
    see each record "exactly once" (:data:`CheckpointingMode.EXACTLY_ONCE`), or whether the
    checkpoints are drawn in a simpler fashion that typically encounters some duplicates upon
    recovery (:data:`CheckpointingMode.AT_LEAST_ONCE`)

    :data:`EXACTLY_ONCE`:

    Sets the checkpointing mode to "exactly once". This mode means that the system will
    checkpoint the operator and user function state in such a way that, upon recovery,
    every record will be reflected exactly once in the operator state.

    For example, if a user function counts the number of elements in a stream,
    this number will consistently be equal to the number of actual elements in the stream,
    regardless of failures and recovery.

    Note that this does not mean that each record flows through the streaming data flow
    only once. It means that upon recovery, the state of operators/functions is restored such
    that the resumed data streams pick up exactly at after the last modification to the state.

    Note that this mode does not guarantee exactly-once behavior in the interaction with
    external systems (only state in Flink's operators and user functions). The reason for that
    is that a certain level of "collaboration" is required between two systems to achieve
    exactly-once guarantees. However, for certain systems, connectors can be written that
    facilitate this collaboration.

    This mode sustains high throughput. Depending on the data flow graph and operations,
    this mode may increase the record latency, because operators need to align their input
    streams, in order to create a consistent snapshot point. The latency increase for simple
    dataflows (no repartitioning) is negligible. For simple dataflows with repartitioning, the
    average latency remains small, but the slowest records typically have an increased latency.

    :data:`AT_LEAST_ONCE`:

    Sets the checkpointing mode to "at least once". This mode means that the system will
    checkpoint the operator and user function state in a simpler way. Upon failure and recovery,
    some records may be reflected multiple times in the operator state.

    For example, if a user function counts the number of elements in a stream,
    this number will equal to, or larger, than the actual number of elements in the stream,
    in the presence of failure and recovery.

    This mode has minimal impact on latency and may be preferable in very-low latency
    scenarios, where a sustained very-low latency (such as few milliseconds) is needed,
    and where occasional duplicate messages (on recovery) do not matter.
    """

    EXACTLY_ONCE = 0
    AT_LEAST_ONCE = 1

    @staticmethod
    def _from_j_checkpointing_mode(j_checkpointing_mode) -> 'CheckpointingMode':
        return CheckpointingMode[j_checkpointing_mode.name()]

    def _to_j_checkpointing_mode(self):
        gateway = get_gateway()
        JCheckpointingMode = \
            gateway.jvm.org.apache.flink.streaming.api.CheckpointingMode
        return getattr(JCheckpointingMode, self.name)