text: # MIT License
  #
  # Copyright (c) 2018 Evgeny Medvedev, evge.medvedev@gmail.com
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  import logging
  import time
  
  from requests.exceptions import Timeout as RequestsTimeout, HTTPError, TooManyRedirects
  
  from blockchainetl_common.executors.bounded_executor import BoundedExecutor
  from blockchainetl_common.executors.fail_safe_executor import FailSafeExecutor
  from blockchainetl_common.progress_logger import ProgressLogger
  from blockchainetl_common.utils import dynamic_batch_iterator
  
  from blockchainetl_common.executors.retriable_value_error import RetriableValueError
  
  RETRY_EXCEPTIONS = (ConnectionError, HTTPError, RequestsTimeout, TooManyRedirects, OSError, RetriableValueError)
  
  BATCH_CHANGE_COOLDOWN_PERIOD_SECONDS = 2 * 60
  
  
  # Executes the given work in batches, reducing the batch size exponentially in case of errors.
  class BatchWorkExecutor:
      def __init__(self, starting_batch_size, max_workers, retry_exceptions=RETRY_EXCEPTIONS, max_retries=5):
          self.batch_size = starting_batch_size
          self.max_batch_size = starting_batch_size
          self.latest_batch_size_change_time = None
          self.max_workers = max_workers
          # Using bounded executor prevents unlimited queue growth
          # and allows monitoring in-progress futures and failing fast in case of errors.
          self.executor = FailSafeExecutor(BoundedExecutor(1, self.max_workers))
          self.retry_exceptions = retry_exceptions
          self.max_retries = max_retries
          self.progress_logger = ProgressLogger()
          self.logger = logging.getLogger('BatchWorkExecutor')
  
      def execute(self, work_iterable, work_handler, total_items=None):
          self.progress_logger.start(total_items=total_items)
          for batch in dynamic_batch_iterator(work_iterable, lambda: self.batch_size):
              self.executor.submit(self._fail_safe_execute, work_handler, batch)
  
      def _fail_safe_execute(self, work_handler, batch):
          try:
              work_handler(batch)
              self._try_increase_batch_size(len(batch))
          except self.retry_exceptions:
              self.logger.exception('An exception occurred while executing work_handler.')
              self._try_decrease_batch_size(len(batch))
              self.logger.info('The batch of size {} will be retried one item at a time.'.format(len(batch)))
              for item in batch:
                  execute_with_retries(work_handler, [item],
                                       max_retries=self.max_retries, retry_exceptions=self.retry_exceptions)
  
          self.progress_logger.track(len(batch))
  
      # Some acceptable race conditions are possible
      def _try_decrease_batch_size(self, current_batch_size):
          batch_size = self.batch_size
          if batch_size == current_batch_size and batch_size > 1:
              new_batch_size = int(current_batch_size / 2)
              self.logger.info('Reducing batch size to {}.'.format(new_batch_size))
              self.batch_size = new_batch_size
              self.latest_batch_size_change_time = time.time()
  
      def _try_increase_batch_size(self, current_batch_size):
          if current_batch_size * 2 <= self.max_batch_size:
              current_time = time.time()
              latest_batch_size_change_time = self.latest_batch_size_change_time
              seconds_since_last_change = current_time - latest_batch_size_change_time \
                  if latest_batch_size_change_time is not None else 0
              if seconds_since_last_change > BATCH_CHANGE_COOLDOWN_PERIOD_SECONDS:
                  new_batch_size = current_batch_size * 2
                  self.logger.info('Increasing batch size to {}.'.format(new_batch_size))
                  self.batch_size = new_batch_size
                  self.latest_batch_size_change_time = current_time
  
      def shutdown(self):
          self.executor.shutdown()
          self.progress_logger.finish()
  
  
  def execute_with_retries(func, *args, max_retries=5, retry_exceptions=RETRY_EXCEPTIONS, sleep_seconds=1):
      for i in range(max_retries):
          try:
              return func(*args)
          except retry_exceptions:
              logging.exception('An exception occurred while executing execute_with_retries. Retry #{}'.format(i))
              if i < max_retries - 1:
                  logging.info('The request will be retried after {} seconds. Retry #{}'.format(sleep_seconds, i))
                  time.sleep(sleep_seconds)
                  continue
              else:
                  raise
  