appengine-mapreduce 达到内存限制

However, as I run this on Google App Engine, I encountered this error message in the logs section:

在服务 130 个请求后，超过了 180.56 MB 的软私有内存限制在处理这个请求时，发现处理这个请求的进程使用了​​太多内存并被终止.这可能会导致一个新进程被用于对您的应用程序的下一个请求.如果您经常看到此消息，则您的应用程序可能存在内存泄漏.

def time_count_map(data):
  """Time count map function."""
  (entry, text_fn) = data
  text = text_fn()

  try:
    q = text.split('
')
    for m in q:
        reader = csv.reader([m.replace('', '')], skipinitialspace=True)
        for s in reader:
            """Calculate time elapsed"""
            sdw = s[1]
            start_date = time.strptime(sdw,"%m/%d/%y %I:%M:%S%p")
            edw = s[2]
            end_date = time.strptime(edw,"%m/%d/%y %I:%M:%S%p")
            time_difference = time.mktime(end_date) - time.mktime(start_date)
            yield (s[0], time_difference)
  except IndexError, e:
    logging.debug(e)


def time_count_reduce(key, values):
  """Time count reduce function."""
  time = 0.0
  for subtime in values:
    time += float(subtime)
    realtime = int(time)
  yield "%s: %d
" % (key, realtime)

谁能建议我如何更好地优化我的代码?谢谢！！

class TimeCountPipeline(base_handler.PipelineBase):
  """A pipeline to run Time count demo.

  Args:
    blobkey: blobkey to process as string. Should be a zip archive with
      text files inside.
  """

  def run(self, filekey, blobkey):
    logging.debug("filename is %s" % filekey)
    output = yield mapreduce_pipeline.MapreducePipeline(
        "time_count",
        "main.time_count_map",
        "main.time_count_reduce",
        "mapreduce.input_readers.BlobstoreZipInputReader",
        "mapreduce.output_writers.BlobstoreOutputWriter",
        mapper_params={
            "blob_key": blobkey,
        },
        reducer_params={
            "mime_type": "text/plain",
        },
        shards=32)
    yield StoreOutput("TimeCount", filekey, output)

mapreduce:
- name: Make messages lowercase
  params:
  - name: done_callback
    value: /done
  mapper:
    handler: main.lower_case_posts
    input_reader: mapreduce.input_readers.DatastoreInputReader
    params:
    - name: entity_kind
      default: main.Post
    - name: processing_rate
      default: 100
    - name: shard_count
      default: 4
- name: Make messages upper case
  params:
  - name: done_callback
    value: /done
  mapper:
    handler: main.upper_case_posts
    input_reader: mapreduce.input_readers.DatastoreInputReader
    params:
    - name: entity_kind
      default: main.Post
    - name: processing_rate
      default: 100
    - name: shard_count
      default: 4

def time_count_map(data):
    """Time count map function."""
    text = data[1]

    try:
        reader = csv.reader([text.replace('', '')], skipinitialspace=True)
        for s in reader:
            """Calculate time elapsed"""
            sdw = s[1]
            start_date = time.strptime(sdw,"%m/%d/%y %I:%M:%S%p")
            edw = s[2]
            end_date = time.strptime(edw,"%m/%d/%y %I:%M:%S%p")
            time_difference = time.mktime(end_date) - time.mktime(start_date)
            yield (s[0], time_difference)
    except IndexError, e:
        logging.debug(e)