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# Copyright 2015-2016 ARM Limited
#
# 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.
#
"""Aggregators are responsible for aggregating information
for further analysis. These aggregations can produce
both scalars and vectors and each aggregator implementation
is expected to handle its "aggregation" mechanism.
"""
from trappy.utils import listify
from trappy.stats.Indexer import MultiTriggerIndexer
from abc import ABCMeta, abstractmethod
class AbstractAggregator(object):
"""Abstract class for all aggregators
:param indexer: Indexer is passed on by the Child class
for handling indices during correlation
:type indexer: :mod:`trappy.stats.Indexer.Indexer`
:param aggfunc: Function that accepts a pandas.Series and
process it for aggregation.
:type aggfunc: function
"""
__metaclass__ = ABCMeta
# The current implementation needs the index to
# be unified across data frames to account for
# variable sampling across data frames
def __init__(self, indexer, aggfunc=None):
self._result = {}
self._aggregated = False
self._aggfunc = aggfunc
self.indexer = indexer
def _add_result(self, pivot, series):
"""Add the result for the given pivot and trace
:param pivot: The pivot for which the result is being generated
:type pivot(hashable)
:param series: series to be added to result
:type series: :mod:`pandas.Series`
"""
if pivot not in self._result:
self._result[pivot] = self.indexer.series()
for idx in series.index:
self._result[pivot][idx] = series[idx]
@abstractmethod
def aggregate(self, trace_idx, **kwargs):
"""Abstract Method for aggregating data for various
pivots.
:param trace_idx: Index of the trace to be aggregated
:type trace_idx: int
:return: The aggregated result
"""
raise NotImplementedError("Method Not Implemented")
class MultiTriggerAggregator(AbstractAggregator):
"""This aggregator accepts a list of triggers and each trigger has
a value associated with it.
"""
def __init__(self, triggers, topology, aggfunc=None):
"""
:param triggers: trappy.stat.Trigger): A list or a singular trigger object
:type triggers: :mod:`trappy.stat.Trigger.Trigger`
:param topology (trappy.stat.Topology): A topology object for aggregation
levels
:type topology: :mod:`trappy.stat.Topology`
:param aggfunc: A function to be applied on each series being aggregated.
For each topology node, a series will be generated and this
will be processed by the aggfunc
:type aggfunc: function
"""
self._triggers = triggers
self.topology = topology
super(
MultiTriggerAggregator,
self).__init__(MultiTriggerIndexer(triggers), aggfunc)
def aggregate(self, **kwargs):
"""
Aggregate implementation that aggregates
triggers for a given topological level. All the arguments passed to
it are forwarded to the aggregator function except level (if present)
:return: A scalar or a vector aggregated result. Each group in the
level produces an element in the result list with a one to one
index correspondence
::
groups["level"] = [[1,2], [3,4]]
result = [result_1, result_2]
"""
level = kwargs.pop("level", "all")
# This function is a hot spot in the code. It is
# worth considering a memoize decorator to cache
# the function. The memoization can also be
# maintained by the aggregator object. This will
# help the code scale efficeintly
level_groups = self.topology.get_level(level)
result = []
if not self._aggregated:
self._aggregate_base()
for group in level_groups:
group = listify(group)
if self._aggfunc is not None:
level_res = self._aggfunc(self._result[group[0]], **kwargs)
else:
level_res = self._result[group[0]]
for node in group[1:]:
if self._aggfunc is not None:
node_res = self._aggfunc(self._result[node], **kwargs)
else:
node_res = self._result[node]
level_res += node_res
result.append(level_res)
return result
def _aggregate_base(self):
"""A memoized function to generate the base series
for each node in the flattened topology
::
topo["level_1"] = [[1, 2], [3, 4]]
This function will generate the fundamental
aggregations for all nodes 1, 2, 3, 4 and
store the result in _agg_result
"""
for trigger in self._triggers:
for node in self.topology.flatten():
result_series = trigger.generate(node)
self._add_result(node, result_series)
self._aggregated = True
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