airflow/models/taskinstance.py
def next_retry_datetime(self):
        """
        Get datetime of the next retry if the task instance fails. For exponential
        backoff, retry_delay is used as base and will be converted to seconds.
        """
        delay = self.task.retry_delay
        if self.task.retry_exponential_backoff:
            min_backoff = int(delay.total_seconds() * (2 ** (self.try_number - 2)))
            # deterministic per task instance
            hash = int(hashlib.sha1("{}#{}#{}#{}".format(self.dag_id,
                                                         self.task_id,
                                                         self.execution_date,
                                                         self.try_number)
                                    .encode('utf-8')).hexdigest(), 16)
            # between 0.5 * delay * (2^retry_number) and 1.0 * delay * (2^retry_number)
            modded_hash = min_backoff + hash % min_backoff
            # timedelta has a maximum representable value. The exponentiation
            # here means this value can be exceeded after a certain number
            # of tries (around 50 if the initial delay is 1s, even fewer if
            # the delay is larger). Cap the value here before creating a
            # timedelta object so the operation doesn't fail.
            delay_backoff_in_seconds = min(
                modded_hash,
                timedelta.max.total_seconds() - 1
            )
            delay = timedelta(seconds=delay_backoff_in_seconds)
            if self.task.max_retry_delay:
                delay = min(self.task.max_retry_delay, delay)
        return self.end_date + delay
def next_retry_datetime(self):
        """
        Get datetime of the next retry if the task instance fails. For exponential
        backoff, retry_delay is used as base and will be converted to seconds.
        """
        delay = self.task.retry_delay
        if self.task.retry_exponential_backoff:
            min_backoff = int(delay.total_seconds() * (2 ** (self.try_number - 2)))
            # deterministic per task instance
            hash = int(hashlib.sha1("{}#{}#{}#{}".format(self.dag_id,
                                                         self.task_id,
                                                         self.execution_date,
                                                         self.try_number)
                                    .encode('utf-8')).hexdigest(), 16)
            # between 0.5 * delay * (2^retry_number) and 1.0 * delay * (2^retry_number)
            modded_hash = min_backoff + hash % min_backoff
            # timedelta has a maximum representable value. The exponentiation
            # here means this value can be exceeded after a certain number
            # of tries (around 50 if the initial delay is 1s, even fewer if
            # the delay is larger). Cap the value here before creating a
            # timedelta object so the operation doesn't fail.
            delay_backoff_in_seconds = min(
                modded_hash,
                timedelta.max.total_seconds() - 1
            )
            delay = timedelta(seconds=delay_backoff_in_seconds)
            if self.task.max_retry_delay:
                delay = min(self.task.max_retry_delay, delay)
        return self.end_date + delay
Get datetime of the next retry if the task instance fails. For exponential
        backoff, retry_delay is used as base and will be converted to seconds.