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

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]

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