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from enum import Enum

from pyflink.java_gateway import get_gateway

__all__ = ['TimeCharacteristic']


class TimeCharacteristic(Enum):
    """
    The time characteristic defines how the system determines time for time-dependent
    order and operations that depend on time (such as time windows).

    :data:`ProcessingTime`:

    Processing time for operators means that the operator uses the system clock of the machine
    to determine the current time of the data stream. Processing-time windows trigger based
    on wall-clock time and include whatever elements happen to have arrived at the operator at
    that point in time.

    Using processing time for window operations results in general in quite non-deterministic
    results, because the contents of the windows depends on the speed in which elements arrive.
    It is, however, the cheapest method of forming windows and the method that introduces the
    least latency.

    :data:`IngestionTime`:

    Ingestion time means that the time of each individual element in the stream is determined
    when the element enters the Flink streaming data flow. Operations like windows group the
    elements based on that time, meaning that processing speed within the streaming dataflow
    does not affect windowing, but only the speed at which sources receive elements.

    Ingestion time is often a good compromise between processing time and event time.
    It does not need any special manual form of watermark generation, and events are typically
    not too much out-or-order when they arrive at operators; in fact, out-of-orderness can
    only be introduced by streaming shuffles or split/join/union operations. The fact that
    elements are not very much out-of-order means that the latency increase is moderate,
    compared to event time.

    :data:`EventTime`:

    Event time means that the time of each individual element in the stream (also called event)
    is determined by the event's individual custom timestamp. These timestamps either exist in
    the elements from before they entered the Flink streaming dataflow, or are user-assigned at
    the sources. The big implication of this is that it allows for elements to arrive in the
    sources and in all operators out of order, meaning that elements with earlier timestamps may
    arrive after elements with later timestamps.

    Operators that window or order data with respect to event time must buffer data until they
    can be sure that all timestamps for a certain time interval have been received. This is
    handled by the so called "time watermarks".

    Operations based on event time are very predictable - the result of windowing operations
    is typically identical no matter when the window is executed and how fast the streams
    operate. At the same time, the buffering and tracking of event time is also costlier than
    operating with processing time, and typically also introduces more latency. The amount of
    extra cost depends mostly on how much out of order the elements arrive, i.e., how long the
    time span between the arrival of early and late elements is. With respect to the
    "time watermarks", this means that the cost typically depends on how early or late the
    watermarks can be generated for their timestamp.

    In relation to :data:`IngestionTime`, the event time is similar, but refers the the
    event's original time, rather than the time assigned at the data source. Practically, that
    means that event time has generally more meaning, but also that it takes longer to determine
    that all elements for a certain time have arrived.
    """

    ProcessingTime = 0
    IngestionTime = 1
    EventTime = 2

    @staticmethod
    def _from_j_time_characteristic(j_time_characteristic) -> 'TimeCharacteristic':
        return TimeCharacteristic[j_time_characteristic.name()]

    def _to_j_time_characteristic(self):
        gateway = get_gateway()
        JTimeCharacteristic = gateway.jvm.org.apache.flink.streaming.api.TimeCharacteristic
        return getattr(JTimeCharacteristic, self.name)