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py payload task into learn/predict + removing grafana_data_provider from py begin

pull/1/head
Coin de Gamma 6 years ago
parent
commit
5c0ad89b61
  1. 33
      analytics/analytic_unit_worker.py
  2. 229
      server/src/services/metrics_service.ts

33
analytics/analytic_unit_worker.py

@ -11,21 +11,18 @@ logger = logging.getLogger('WORKER')
class AnalyticUnitWorker(object):
models_cache = {}
detectors_cache = {}
# TODO: get task as an object built from json
async def do_task(self, task):
try:
type = task['type']
analytic_unit_id = task['analyticUnitId']
payload = task['payload']
if type == "PREDICT":
last_prediction_time = task['lastPredictionTime']
pattern = task['pattern']
result = await self.do_predict(analytic_unit_id, last_prediction_time, pattern)
result = await self.do_predict(analytic_unit_id, payload)
elif type == "LEARN":
segments = task['segments']
pattern = task['pattern']
result = await self.do_learn(analytic_unit_id, segments, pattern)
result = await self.do_learn(analytic_unit_id, payload)
else:
result = {
'status': "FAILED",
@ -44,7 +41,10 @@ class AnalyticUnitWorker(object):
}
return result
async def do_learn(self, analytic_unit_id, segments, pattern):
async def do_learn(self, analytic_unit_id, payload):
pattern = payload['pattern']
segments = payload['segments']
model = self.get_model(analytic_unit_id, pattern)
model.synchronize_data()
last_prediction_time = await model.learn(segments)
@ -64,7 +64,10 @@ class AnalyticUnitWorker(object):
result['task'] = 'LEARN'
return result
async def do_predict(self, analytic_unit_id, last_prediction_time, pattern):
async def do_predict(self, analytic_unit_id, payload):
pattern = payload['pattern']
last_prediction_time = payload['lastPredictionTime']
model = self.get_model(analytic_unit_id, pattern)
model.synchronize_data()
segments, last_prediction_time = await model.predict(last_prediction_time)
@ -76,11 +79,11 @@ class AnalyticUnitWorker(object):
'lastPredictionTime': last_prediction_time
}
def get_model(self, analytic_unit_id, pattern_type):
if analytic_unit_id not in self.models_cache:
def get_detector(self, analytic_unit_id, pattern_type):
if analytic_unit_id not in self.detectors_cache:
if pattern_type == 'GENERAL':
model = detectors.GeneralDetector(analytic_unit_id)
detector = detectors.GeneralDetector(analytic_unit_id)
else:
model = detectors.PatternDetector(analytic_unit_id, pattern_type)
self.models_cache[analytic_unit_id] = model
return self.models_cache[analytic_unit_id]
detector = detectors.PatternDetector(analytic_unit_id, pattern_type)
self.detectors_cache[analytic_unit_id] = detector
return self.detectors_cache[analytic_unit_id]

229
server/src/services/metrics_service.ts

@ -0,0 +1,229 @@
// import pandas as pd
// import os, re
// import numpy as np
// from urllib.parse import urlencode, urlparse
// import urllib.request
// import json
// from time import time
// from config import HASTIC_API_KEY
// MS_IN_WEEK = 604800000
// class GrafanaDataProvider:
// chunk_size = 50000
// def __init__(self, datasource, target, data_filename):
// self.datasource = datasource
// self.target = target
// self.data_filename = data_filename
// self.last_time = None
// self.total_size = 0
// self.last_chunk_index = 0
// self.chunk_last_times = {}
// self.__init_chunks()
// self.synchronize()
// def get_dataframe(self, after_time=None):
// result = pd.DataFrame()
// for chunk_index, last_chunk_time in self.chunk_last_times.items():
// if after_time is None or after_time <= last_chunk_time:
// chunk = self.__load_chunk(chunk_index)
// if after_time is not None:
// chunk = chunk[chunk['timestamp'] >= after_time]
// result = pd.concat([result, chunk])
// return result
// def get_upper_bound(self, after_time):
// for chunk_index, last_chunk_time in self.chunk_last_times.items():
// if after_time < last_chunk_time:
// chunk = self.__load_chunk(chunk_index)
// chunk = chunk[chunk['timestamp'] >= after_time]
// return chunk.index[0]
// return self.size()
// def size(self):
// return self.total_size
// def get_data_range(self, start_index, stop_index=None):
// return self.__get_data(start_index, stop_index)
// def transform_anomalies(self, anomalies):
// result = []
// if len(anomalies) == 0:
// return result
// dataframe = self.get_dataframe(None)
// for anomaly in anomalies:
// start_time = pd.to_datetime(anomaly['start'] - 1, unit='ms')
// finish_time = pd.to_datetime(anomaly['finish'] + 1, unit='ms')
// current_index = (dataframe['timestamp'] >= start_time) & (dataframe['timestamp'] <= finish_time)
// anomaly_frame = dataframe[current_index]
// if anomaly_frame.empty:
// continue
// cur_anomaly = {
// 'start': anomaly_frame.index[0],
// 'finish': anomaly_frame.index[len(anomaly_frame) - 1],
// 'labeled': anomaly['labeled']
// }
// result.append(cur_anomaly)
// return result
// def inverse_transform_indexes(self, indexes):
// if len(indexes) == 0:
// return []
// dataframe = self.get_data_range(indexes[0][0], indexes[-1][1] + 1)
// return [(dataframe['timestamp'][i1], dataframe['timestamp'][i2]) for (i1, i2) in indexes]
// def synchronize(self):
// append_dataframe = self.load_from_db(self.last_time)
// self.__append_data(append_dataframe)
// def custom_query(self, after_time, before_time = None):
// if self.datasource['type'] == 'influxdb':
// query = self.datasource['params']['q']
// if after_time is not None:
// if before_time is not None:
// timeFilter = 'time >= %s AND time <= %s' % (after_time, before_time)
// else:
// timeFilter = 'time >= "%s"' % (str(after_time))
// else:
// timeFilter = 'time > 0ms'
// query = re.sub(r'(?:time >.+?)(GROUP.+)*$', timeFilter + r' \1', query)
// return query
// else:
// raise 'Datasource type ' + self.datasource['type'] + ' is not supported yet'
// def load_from_db(self, after_time=None):
// result = self.__load_data_chunks(after_time)
// if result == None or len(result['values']) == 0:
// dataframe = pd.DataFrame([])
// else:
// dataframe = pd.DataFrame(result['values'], columns = result['columns'])
// cols = dataframe.columns.tolist()
// cols.remove('time')
// cols = ['time'] + cols
// dataframe = dataframe[cols]
// dataframe['time'] = pd.to_datetime(dataframe['time'], unit='ms')
// dataframe = dataframe.dropna(axis=0, how='any')
// return dataframe
// def __load_data_chunks(self, after_time = None):
// params = self.datasource['params']
// if after_time == None:
// res = {
// 'columns': [],
// 'values': []
// }
// after_time = int(time() * 1000 - MS_IN_WEEK)
// before_time = int(time() * 1000)
// while True:
// params['q'] = self.custom_query(str(after_time) + 'ms', str(before_time) + 'ms')
// serie = self.__query_grafana(params)
// if serie != None:
// res['columns'] = serie['columns']
// res['values'] += serie['values']
// after_time -= MS_IN_WEEK
// before_time -= MS_IN_WEEK
// else:
// return res
// else:
// params['q'] = self.custom_query(str(after_time))
// return self.__query_grafana(params)
// def __query_grafana(self, params):
// headers = { 'Authorization': 'Bearer ' + HASTIC_API_KEY }
// url = self.datasource['origin'] + '/' + self.datasource['url'] + '?' + urlencode(params)
// req = urllib.request.Request(url, headers=headers)
// with urllib.request.urlopen(req) as resp:
// res = json.loads(resp.read().decode('utf-8'))['results'][0]
// if 'series' in res:
// return res['series'][0]
// else:
// return None
// def __init_chunks(self):
// chunk_index = 0
// self.last_chunk_index = 0
// while True:
// filename = self.data_filename
// if chunk_index > 0:
// filename += "." + str(chunk_index)
// if os.path.exists(filename):
// self.last_chunk_index = chunk_index
// chunk = self.__load_chunk(chunk_index)
// chunk_last_time = chunk.iloc[len(chunk) - 1]['timestamp']
// self.chunk_last_times[chunk_index] = chunk_last_time
// self.last_time = chunk_last_time
// else:
// break
// chunk_index += 1
// self.total_size = self.last_chunk_index * self.chunk_size
// last_chunk = self.__load_chunk(self.last_chunk_index)
// self.total_size += len(last_chunk)
// def __load_chunk(self, index):
// filename = self.data_filename
// if index > 0:
// filename += "." + str(index)
// if os.path.exists(filename):
// chunk = pd.read_csv(filename, parse_dates=[0])
// frame_index = np.arange(index * self.chunk_size, index * self.chunk_size + len(chunk))
// chunk = chunk.set_index(frame_index)
// return chunk.rename(columns={chunk.columns[0]: "timestamp", chunk.columns[1]: "value"})
// return pd.DataFrame()
// def __save_chunk(self, index, dataframe):
// filename = self.data_filename
// if index > 0:
// filename += "." + str(index)
// chunk_last_time = dataframe.iloc[len(dataframe) - 1]['time']
// self.chunk_last_times[index] = chunk_last_time
// if os.path.exists(filename):
// dataframe.to_csv(filename, mode='a', index=False, header=False)
// else:
// dataframe.to_csv(filename, mode='w', index=False, header=True)
// def __append_data(self, dataframe):
// while len(dataframe) > 0:
// chunk = self.__load_chunk(self.last_chunk_index)
// rows_count = min(self.chunk_size - len(chunk), len(dataframe))
// rows = dataframe.iloc[0:rows_count]
// if len(rows) > 0:
// self.__save_chunk(self.last_chunk_index, rows)
// self.total_size += rows_count
// self.last_time = rows.iloc[-1]['time']
// dataframe = dataframe[rows_count:]
// if len(dataframe) > 0:
// self.last_chunk_index += 1
// def __get_data(self, start_index, stop_index):
// result = pd.DataFrame()
// start_chunk = start_index // self.chunk_size
// finish_chunk = self.last_chunk_index
// if stop_index is not None:
// finish_chunk = stop_index // self.chunk_size
// for chunk_num in range(start_chunk, finish_chunk + 1):
// chunk = self.__load_chunk(chunk_num)
// if stop_index is not None and chunk_num == finish_chunk:
// chunk = chunk[:stop_index % self.chunk_size]
// if chunk_num == start_chunk:
// chunk = chunk[start_index % self.chunk_size:]
// result = pd.concat([result, chunk])
// return result
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