call.py 文件源码

def build(self):
            end_time = timeit.default_timer()
            end_mem = memory_profiler.memory_usage()[0]

            sys.stdout = self._previous_stdout

            self._log.write("END RequestId: {r}\n".format(
                r=self._context.aws_request_id))

            duration_in_millis = int(math.ceil(1000 * (end_time - self._start_time)))
            # The memory overhead of setting up the AWS Lambda environment
            # (when actually run in AWS) is roughly 14 MB
            max_memory_used_in_mb = (end_mem - self._start_mem) / 1048576 + 14

            self._log.write(
                "REPORT RequestId: {r}\tDuration: {d} ms\t"
                "Max Memory Used: {m} MB\n"
                .format(r=self._context.aws_request_id,
                        d=duration_in_millis,
                        m=max_memory_used_in_mb))

            log = self._log.getvalue()
            return LambdaCallSummary(duration_in_millis, max_memory_used_in_mb, log)