You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
225 lines
8.4 KiB
225 lines
8.4 KiB
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 |
|
|
|
MS_IN_WEEK = 604800000 |
|
|
|
class DataProvider: |
|
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] |
|
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 ' + os.environ['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
|
|
|