# # Copyright 2013 Intel Corp. # # Author: Yunhong Jiang # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import abc import six from stevedore import extension class TransformerExtensionManager(extension.ExtensionManager): def __init__(self, namespace): super(TransformerExtensionManager, self).__init__( namespace=namespace, invoke_on_load=False, invoke_args=(), invoke_kwds={} ) self.by_name = dict((e.name, e) for e in self.extensions) def get_ext(self, name): return self.by_name[name] @six.add_metaclass(abc.ABCMeta) class TransformerBase(object): """Base class for plugins that transform the sample.""" def __init__(self, **kwargs): """Setup transformer. Each time a transformed is involved in a pipeline, a new transformer instance is created and chained into the pipeline. i.e. transformer instance is per pipeline. This helps if transformer need keep some cache and per-pipeline information. :param kwargs: The parameters that are defined in pipeline config file. """ super(TransformerBase, self).__init__() @abc.abstractmethod def handle_sample(self, context, sample): """Transform a sample. :param context: Passed from the data collector. :param sample: A sample. """ def flush(self, context): """Flush samples cached previously. :param context: Passed from the data collector. """ return []