monasca-analytics/main/ingestor/cloud.py

70 lines
2.2 KiB
Python

#!/usr/bin/env python
# Copyright (c) 2016 Hewlett Packard Enterprise Development Company, L.P.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not used 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 json
import logging
import numpy as np
import schema
from main.ingestor import base
logger = logging.getLogger(__name__)
class CloudIngestor(base.BaseIngestor):
"""Data ingestor for Cloud"""
def __init__(self, _id, _config):
super(CloudIngestor, self).__init__(_id=_id, _config=_config)
@staticmethod
def validate_config(_config):
return schema.Schema({
"module": schema.And(basestring,
lambda i: not any(c.isspace() for c in i))
}).validate(_config)
def map_dstream(self, dstream):
feature_list = list(self._features)
return dstream.map(
lambda rdd_entry: CloudIngestor._process_data(rdd_entry,
feature_list))
@staticmethod
def get_default_config():
return {"module": CloudIngestor.__name__}
# TODO: With the new model, this can now be method, and the lambda
# can be removed.
@staticmethod
def _process_data(rdd_entry, feature_list):
json_value = json.loads(rdd_entry)
return CloudIngestor._parse_and_vectorize(json_value, feature_list)
@staticmethod
def _parse_and_vectorize(json_value, feature_list):
values = {
"support_1": 0
}
for feature in feature_list:
values[feature] = 0
for e in json_value["events"]:
if e["id"] in values:
values[e["id"]] += 1
res = [values[f] for f in feature_list]
return np.array(res)