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