132-proxy-db-queries-through-grafana

server/src/services/alerts.ts

@@ -52,7 +52,7 @@ async function alertsTick() {
 
 const alertTimeout = 60000; // ms
 const activeAlerts = new Set<string>();
-setTimeout(alertsTick, 5000);
+// setTimeout(alertsTick, 5000);
 
 
 export { getAlertsAnomalies, saveAlertsAnomalies }

server/src/services/analytics.ts

@@ -15,7 +15,6 @@ const learnWorker = spawn('python3', ['worker.py'], { cwd: ANALYTICS_PATH })
 learnWorker.stdout.pipe(split())
   .pipe(
     mapSync(function(line){
-      console.log(line)
       onMessage(line)
     })
   );
@@ -26,6 +25,7 @@ const taskMap = {};
 let nextTaskId = 0;
 
 function onMessage(data) {
+  console.log(`worker stdout: ${data}`);
   let response = JSON.parse(data);
   let taskId = response.__task_id;
   // let anomalyName = response.anomaly_name;

src/anomaly_model.py

@@ -4,6 +4,7 @@ from data_preprocessor import data_preprocessor
 import json
 import pandas as pd
 import logging
+from urllib.parse import urlparse
 
 datasource_folder = "datasources/"
 dataset_folder = "datasets/"
@@ -26,22 +27,22 @@ class AnomalyModel:
         self.anomaly_name = anomaly_name
         self.load_anomaly_config()
 
-        datasource = self.anomaly_config['metric']['datasource']
+        parsedUrl = urlparse(self.anomaly_config['panelUrl'])
+        origin = parsedUrl.scheme + '://' + parsedUrl.netloc
+
+        datasource = self.anomaly_config['datasource']
+        datasource['origin'] = origin
         metric_name = self.anomaly_config['metric']['targets'][0]
 
-        dbconfig_filename = os.path.join(datasource_folder, datasource + ".json")
         target_filename = os.path.join(metrics_folder, metric_name + ".json")
 
         dataset_filename = os.path.join(dataset_folder, metric_name + ".csv")
         augmented_path = os.path.join(dataset_folder, metric_name + "_augmented.csv")
 
-        with open(dbconfig_filename, 'r') as config_file:
-            dbconfig = json.load(config_file)
-
         with open(target_filename, 'r') as file:
             target = json.load(file)
 
-        self.data_prov = DataProvider(dbconfig, target, dataset_filename)
+        self.data_prov = DataProvider(datasource, target, dataset_filename)
         self.preprocessor = data_preprocessor(self.data_prov, augmented_path)
         self.model = None
 
@@ -96,8 +97,6 @@ class AnomalyModel:
         start_index = self.data_prov.get_upper_bound(last_prediction_time)
         stop_index = self.data_prov.size()
 
-        # last_prediction_time = pd.to_datetime(last_prediction_time, unit='ms')
-        # dataframe = dataframe[dataframe['timestamp'] > last_prediction_time]
         last_prediction_time = int(last_prediction_time.timestamp() * 1000)
 
         predicted_anomalies = []

src/data_preprocessor.py

@@ -64,7 +64,6 @@ class data_preprocessor:
         start_frame = start_index
         stop_frame = stop_index
         augmented = self.__get_data(start_frame, stop_frame)
-
         if len(anomalies) > 0:
             anomalies_indexes = self.transform_anomalies(anomalies)
             augmented = augmented.drop(anomalies_indexes)
@@ -84,8 +83,8 @@ class data_preprocessor:
                 anomaly_index = current_index
 
         rows = dataframe[anomaly_index]
-        # indexes = np.floor_divide(rows.index, self.frame_size)
-        indexes = np.unique(rows.index)
+        indexes = np.floor_divide(rows.index, self.frame_size)
+        # indexes = np.unique(rows.index)
         return indexes
 
     def inverse_transform_anomalies(self, prediction):

src/data_provider.py

@@ -1,14 +1,18 @@
-from influxdb import InfluxDBClient
 import pandas as pd
-import os.path
+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, dbconfig, target, data_filename):
-        self.dbconfig = dbconfig
+    def __init__(self, datasource, target, data_filename):
+        self.datasource = datasource
         self.target = target
         self.data_filename = data_filename
         self.last_time = None
@@ -69,79 +73,80 @@ class DataProvider:
         return [(dataframe['timestamp'][i1], dataframe['timestamp'][i2]) for (i1, i2) in indexes]
 
     def synchronize(self):
-        # last_time = None
-        # if len(self.dataframe/) > 0:
-        #     last_time = self.dataframe['time'][len(self.dataframe)-1]
         append_dataframe = self.load_from_db(self.last_time)
         self.__append_data(append_dataframe)
-        # append_dataframe
-        # append_dataframe.to_csv(self.data_filename, mode='a', index=False, header=False)
-        # self.dataframe = pd.concat([self.dataframe, append_dataframe], ignore_index=True)
-
-    # def load(self):
-    #     if os.path.exists(self.data_filename):
-    #         self.dataframe = pd.read_csv(self.data_filename, parse_dates=[0])
-    #         self.synchronize()
-    #     else:
-    #         append_dataframe = self.load_from_db()
-    #         self.__append_data(append_dataframe)
-    #         #self.dataframe.to_csv(self.data_filename, index=False, header=True)
-
-    def custom_query(self, after_time):
-        query = self.target["query"]
-        timeFilter = "TRUE"
+
+    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:
-            timeFilter = "time > '%s'" % (str(after_time))
-        query = query.replace("$timeFilter", timeFilter)
+                if before_time is not None:
+                    timeFilter = 'time >= %s AND time <= %s' % (after_time, before_time)
+                else:
+                    timeFilter = 'time >= "%s"' % (str(after_time))
+            else:
+                timeFilter = 'time > 0ms'
+            query = re.sub(r'(?:time >.+?)(GROUP.+)*$', 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):
-        """Instantiate a connection to the InfluxDB."""
-        host = self.dbconfig['host']
-        port = self.dbconfig['port']
-        user = self.dbconfig['user']
-        password = self.dbconfig['password']
-        dbname = self.dbconfig['dbname']
-
-        client = InfluxDBClient(host, port, user, password, dbname)
-        # query = 'select k0, k1, k2 from vals;'
-
-        measurement = self.target['measurement']
-        select = self.target['select']
-        tags = self.target['tags']
-
-        if "query" in self.target:
-            query = self.custom_query(after_time)
+        result = self.__load_data_chunks(after_time)
+        if result == None or len(result['values']) == 0:
+            dataframe = pd.DataFrame([])
         else:
-            select_values = select[0][0]['params']
-            escaped_select_values = ["\"" + value + "\"" for value in select_values]
-
-            conditions_entries = []
-            if len(tags) > 0:
-                for tag in tags:
-                    conditions_entries.append("(\"" + tag['key'] + "\"" + tag['operator'] + "'" + tag['value'] + "')")
-            if after_time:
-                conditions_entries.append("time > '%s'" % (str(after_time)))
-
-            condition = ""
-            if len(conditions_entries) > 0:
-                condition = " where " + " AND ".join(conditions_entries)
-
-            query = "select %s from \"%s\"%s;" % (",".join(escaped_select_values), measurement, condition)
-
-        result = client.query(query, chunked=True, chunk_size=10000)
-        dataframe = pd.DataFrame(result.get_points())
-        if len(dataframe) > 0:
+            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'])
+            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) + 'ms')
+
+            return self.__query_grafana(params)
+
+    def __query_grafana(self, params):
+        
+        headers = { 'Authorization': 'Bearer ' + os.environ['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

src/learn.py

@@ -1,52 +0,0 @@
-#!/usr/bin/env python
-import csv
-import os
-from worker import worker
-
-
-def enqueue_task():
-    tasks_file = "tasks.csv"
-    tasks = []
-    with open(tasks_file) as csvfile:
-        rdr = csv.reader(csvfile, delimiter=',')
-        tasks = list(rdr)
-    if len(tasks) == 0:
-        return None
-    res = tasks[0][0]
-    tasks = tasks[1:]
-    with open(tasks_file, "w+") as csvfile:
-        writer = csv.writer(csvfile)
-        writer.writerows(tasks)
-    return res
-
-
-def set_lock(value):
-    lock_file = "learn.lock"
-    exists = os.path.exists(lock_file)
-    if exists == value:
-        return False
-
-    if value:
-        open(lock_file, "w+")
-    else:
-        os.remove(lock_file)
-    return True
-
-
-if __name__ == "__main__":
-    if not set_lock(True):
-        print("learn locked")
-        exit(0)
-
-    w = worker()
-    while True:
-        task = enqueue_task()
-        if task is None:
-            break
-
-        w.start()
-        w.add_task({"type": "learn", "anomaly_name": task})
-        w.add_task({"type": "predict", "anomaly_name": task})
-        w.stop()
-
-    set_lock(False)

src/predict.py

@@ -1,83 +0,0 @@
-import argparse
-import csv
-import time
-import datetime
-import pandas as pd
-import matplotlib.pyplot as plt
-
-from influxdb import InfluxDBClient
-from sklearn import svm
-import numpy as np
-import math
-import pickle
-
-
-host = "209.205.120.226"
-port = 8086
-datasetFile = "/tmp/dataset.csv"
-anomaliesFile = "anomalies.csv"
-predictedAnomaliesFile = "predicted_anomalies.csv"
-modelFilename = 'finalized_model.sav'
-
-
-def readAnomalies():
-    anomalies = []
-
-    with open(anomaliesFile) as csvfile:
-        rdr = csv.reader(csvfile, delimiter=',')
-        for row in rdr:
-            anomaly = (int(row[0]), int(row[1]))
-            anomalies.append(anomaly)
-
-    return anomalies
-
-
-"""Instantiate a connection to the InfluxDB."""
-user = ''
-password = ''
-dbname = 'accelerometer'
-query = 'select k0, k1, k2 from vals limit 10000;'
-
-
-client = InfluxDBClient(host, port, user, password, dbname)
-
-def predict(host=host, port=port):
-
-    result = client.query(query)
-    df = pd.DataFrame(result['vals'], columns=['time', 'k0', 'k1', 'k2'])
-
-    basedAnomalies = readAnomalies()
-
-    df2 = df.rolling(200, win_type='triang').sum()
-    df2['time'] = pd.to_datetime(df2['time'])
-    df2 = df2[np.isfinite(df2['k0'])]
-
-    print(len(df2))
-
-
-    anomalies = []
-    last_anomaly = (-1, -1)
-    with open(modelFilename, 'rb') as fid:
-        clf = pickle.load(fid)
-        prediction = clf.predict(df2[['k0', 'k1', 'k2']])
-        print(len(prediction))
-        #print(prediction)
-        for i in range(len(prediction)):
-            if prediction[i] > 0.:
-                t = df2['time'][i + 199].timestamp()
-                t = ((t + 0 * 3600) * 1000)
-                if t < basedAnomalies[len(basedAnomalies) - 1][1]:
-                    continue
-                if t < last_anomaly[1] + 1000:
-                    last_anomaly = (last_anomaly[0], t)
-                else:
-                    if last_anomaly[1] != -1:
-                        anomalies.append(last_anomaly)
-                    last_anomaly = (t, t)
-
-    with open(predictedAnomaliesFile, "w") as file:
-        for anomaly in anomalies:
-            file.write(str(int(anomaly[0])) + "," + str(int(anomaly[1])) + "\n")
-
-predict()
-