Threshold detector #324 (#330)

analytics/analytics/analytic_unit_manager.py

@@ -15,8 +15,13 @@ WORKERS_EXECUTORS = 20
 AnalyticUnitId = str
 
 
-def get_detector_by_type(analytic_unit_type) -> detectors.Detector:
+def get_detector_by_type(detector_type: str, analytic_unit_type: str) -> detectors.Detector:
-    return detectors.PatternDetector(analytic_unit_type)
+    if detector_type == 'pattern':
+        return detectors.PatternDetector(analytic_unit_type)
+    elif detector_type == 'threshold':
+        return detectors.ThresholdDetector()
+
+    raise ValueError('Unknown detector type "%s"' % detector_type)
 
 def prepare_data(data: list):
     """
@@ -40,11 +45,16 @@ class AnalyticUnitManager:
         self.analytic_workers: Dict[AnalyticUnitId, AnalyticUnitWorker] = dict()
         self.workers_executor = ThreadPoolExecutor(max_workers=WORKERS_EXECUTORS)
 
-    def __ensure_worker(self, analytic_unit_id: AnalyticUnitId, analytic_unit_type) -> AnalyticUnitWorker:
+    def __ensure_worker(
+        self,
+        analytic_unit_id: AnalyticUnitId,
+        detector_type: str,
+        analytic_unit_type: str
+    ) -> AnalyticUnitWorker:
         if analytic_unit_id in self.analytic_workers:
             # TODO: check that type is the same
             return self.analytic_workers[analytic_unit_id]
-        detector = get_detector_by_type(analytic_unit_type)
+        detector = get_detector_by_type(detector_type, analytic_unit_type)
         worker = AnalyticUnitWorker(analytic_unit_id, detector, self.workers_executor)
         self.analytic_workers[analytic_unit_id] = worker
         return worker
@@ -61,12 +71,17 @@ class AnalyticUnitManager:
             return
 
         payload = task['payload']
-        worker = self.__ensure_worker(analytic_unit_id, payload['pattern'])
+        worker = self.__ensure_worker(analytic_unit_id, payload['detector'], payload['analyticUnitType'])
         data = prepare_data(payload['data'])
         if task['type'] == 'PUSH':
             return await worker.recieve_data(data, payload['cache'])
         elif task['type'] == 'LEARN':
-            return await worker.do_train(payload['segments'], data, payload['cache'])
+            if 'segments' in payload:
+                return await worker.do_train(payload['segments'], data, payload['cache'])
+            elif 'threshold' in payload:
+                return await worker.do_train(payload['threshold'], data, payload['cache'])
+            else:
+                raise ValueError('No segments or threshold in LEARN payload')
         elif task['type'] == 'DETECT':
             return await worker.do_detect(data, payload['cache'])
 

analytics/analytics/analytic_unit_worker.py

@@ -2,7 +2,7 @@ import config
 import detectors
 import logging
 import pandas as pd
-from typing import Optional
+from typing import Optional, Union
 from models import ModelCache
 from concurrent.futures import Executor, CancelledError
 import asyncio
@@ -19,10 +19,10 @@ class AnalyticUnitWorker:
         self._training_feature: asyncio.Future = None
 
     async def do_train(
-        self, segments: list, data: pd.DataFrame, cache: Optional[ModelCache]
+        self, payload: Union[list, dict], data: pd.DataFrame, cache: Optional[ModelCache]
     ) -> ModelCache:
         self._training_feature = asyncio.get_event_loop().run_in_executor(
-            self._executor, self._detector.train, data, segments, cache
+            self._executor, self._detector.train, data, payload, cache
         )
         try:
             new_cache: ModelCache = await self._training_feature

analytics/analytics/detectors/__init__.py

@@ -1,2 +1,3 @@
 from detectors.detector import Detector
 from detectors.pattern_detector import PatternDetector
+from detectors.threshold_detector import ThresholdDetector

analytics/analytics/detectors/detector.py

@@ -1,13 +1,13 @@
 from models import ModelCache
 from abc import ABC, abstractmethod
 from pandas import DataFrame
-from typing import Optional
+from typing import Optional, Union
 
 
 class Detector(ABC):
 
     @abstractmethod
-    def train(self, dataframe: DataFrame, segments: list, cache: Optional[ModelCache]) -> ModelCache:
+    def train(self, dataframe: DataFrame, payload: Union[list, dict], cache: Optional[ModelCache]) -> ModelCache:
         """
             Should be thread-safe to other detectors' train method
         """

analytics/analytics/detectors/threshold_detector.py

@@ -0,0 +1,60 @@
+import logging
+
+import pandas as pd
+from typing import Optional
+
+from detectors import Detector
+from models import ModelCache
+
+
+logger = logging.getLogger('THRESHOLD_DETECTOR')
+
+
+class ThresholdDetector(Detector):
+
+    def __init__(self):
+        pass
+
+    def train(self, dataframe: pd.DataFrame, threshold: dict, cache: Optional[ModelCache]) -> ModelCache:
+        return {
+            'cache': {
+                'value': threshold['value'],
+                'condition': threshold['condition']
+            }
+        }
+
+    def detect(self, dataframe: pd.DataFrame, cache: Optional[ModelCache]) -> dict:
+        value = cache['value']
+        condition = cache['condition']
+
+        last_entry = dataframe.iloc[-1]
+        last_value = last_entry['value']
+        # TODO: convert from nanoseconds to millisecond in a better way: not by dividing by 10^6
+        last_time = last_entry['timestamp'].value / 1000000
+
+        segment = (last_time, last_time)
+        segments = []
+        if condition == '>':
+            if last_value > value:
+                segments.append(segment)
+        elif condition == '>=':
+            if last_value >= value:
+                segments.append(segment)
+        elif condition == '=':
+            if last_value == value:
+                segments.append(segment)
+        elif condition == '<=':
+            if last_value <= value:
+                segments.append(segment)
+        elif condition == '<':
+            if last_value < value:
+                segments.append(segment)
+
+        return {
+            'cache': cache,
+            'segments': segments,
+            'lastDetectionTime': last_time
+        }
+
+    def recieve_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[dict]:
+        return self.detect(self.bucket.data, cache)

server/src/config.ts

@@ -15,6 +15,7 @@ export const ANALYTIC_UNITS_DATABASE_PATH = path.join(DATA_PATH, 'analytic_units
 export const SEGMENTS_DATABASE_PATH = path.join(DATA_PATH, 'segments.db');
 export const ANALYTIC_UNIT_CACHES_DATABASE_PATH = path.join(DATA_PATH, 'analytic_unit_caches.db');
 export const PANELS_DATABASE_PATH = path.join(DATA_PATH, 'panels.db');
+export const THRESHOLD_DATABASE_PATH = path.join(DATA_PATH, 'treshold.db');
 
 
 export const HASTIC_PORT = getConfigField('HASTIC_PORT', '8000');

server/src/controllers/analytics_controller.ts

@@ -2,6 +2,7 @@ import { AnalyticsMessageMethod, AnalyticsMessage } from '../models/analytics_me
 import { AnalyticsTask, AnalyticsTaskType, AnalyticsTaskId } from '../models/analytics_task_model';
 import * as AnalyticUnitCache from '../models/analytic_unit_cache_model';
 import * as Segment from '../models/segment_model';
+import * as Threshold from '../models/threshold_model';
 import * as AnalyticUnit from '../models/analytic_unit_model';
 import { AnalyticsService } from '../services/analytics_service';
 import { sendWebhook } from '../services/notification_service';
@@ -13,6 +14,7 @@ import { queryByMetric } from 'grafana-datasource-kit';
 
 import * as _ from 'lodash';
 
+const SECONDS_IN_MINUTE = 60;
 
 type TaskResult = any;
 type DetectionResult = any;
@@ -87,6 +89,37 @@ async function runTask(task: AnalyticsTask): Promise<TaskResult> {
   });
 }
 
+async function query(analyticUnit: AnalyticUnit.AnalyticUnit, detector: AnalyticUnit.DetectorType) {
+  let range;
+  if(detector === AnalyticUnit.DetectorType.PATTERN) {
+    const segments = await Segment.findMany(analyticUnit.id, { labeled: true });
+    if(segments.length === 0) {
+      throw new Error('Need at least 1 labeled segment');
+    }
+
+    range = getQueryRangeForLearningBySegments(segments);
+  } else if(detector === AnalyticUnit.DetectorType.THRESHOLD) {
+    const now = Date.now();
+    range = {
+      from: now - 5 * SECONDS_IN_MINUTE,
+      to: now
+    };
+  }
+  console.debug(`query time range: from ${new Date(range.from)} to ${new Date(range.to)}`);
+  const queryResult = await queryByMetric(
+    analyticUnit.metric,
+    analyticUnit.panelUrl,
+    range.from,
+    range.to,
+    HASTIC_API_KEY
+  );
+  const data = queryResult.values;
+  if(data.length === 0) {
+    throw new Error('Empty data to detect on');
+  }
+  return data;
+}
+
 /**
  * Finds range for selecting subset for learning
  * @param segments labeled segments
@@ -112,25 +145,9 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId) {
 
     let analyticUnit = await AnalyticUnit.findById(id);
     if(analyticUnit.status === AnalyticUnit.AnalyticUnitStatus.LEARNING) {
-      throw new Error('Can`t starn learning when it`s already started [' + id + ']');
+      throw new Error('Can`t start learning when it`s already started [' + id + ']');
     }
 
-    let segments = await Segment.findMany(id, { labeled: true });
-    if(segments.length === 0) {
-      throw new Error('Need at least 1 labeled segment');
-    }
-
-    let segmentObjs = segments.map(s => s.toObject());
-
-    let { from, to } = getQueryRangeForLearningBySegments(segments);
-    console.debug(`query time range: from ${new Date(from)} to ${new Date(to)}`);
-    let queryResult = await queryByMetric(analyticUnit.metric, analyticUnit.panelUrl, from, to, HASTIC_API_KEY);
-    let data = queryResult.values;
-    if(data.length === 0) {
-      throw new Error('Empty data to learn on');
-    }
-
-    let pattern = analyticUnit.type;
     let oldCache = await AnalyticUnitCache.findById(id);
     if(oldCache !== null) {
       oldCache = oldCache.data;
@@ -138,12 +155,31 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId) {
       await AnalyticUnitCache.create(id);
     }
 
-    let deletedSegments = await Segment.findMany(id, { deleted: true });
+    let analyticUnitType = analyticUnit.type;
-    let deletedSegmentsObjs = deletedSegments.map(s => s.toObject());
+    let detector = AnalyticUnit.getDetectorByType(analyticUnitType);
-    segmentObjs = _.concat(segmentObjs, deletedSegmentsObjs);
+    let taskPayload: any = { detector, analyticUnitType, cache: oldCache };
+
+    if(detector === AnalyticUnit.DetectorType.PATTERN) {
+      let segments = await Segment.findMany(id, { labeled: true });
+      if(segments.length === 0) {
+        throw new Error('Need at least 1 labeled segment');
+      }
+
+      let segmentObjs = segments.map(s => s.toObject());
+
+      let deletedSegments = await Segment.findMany(id, { deleted: true });
+      let deletedSegmentsObjs = deletedSegments.map(s => s.toObject());
+      segmentObjs = _.concat(segmentObjs, deletedSegmentsObjs);
+      taskPayload.segments = segmentObjs;
+    } else if(detector === AnalyticUnit.DetectorType.THRESHOLD) {
+      const threshold = await Threshold.findOne(id);
+      taskPayload.threshold = threshold;
+    }
+
+    taskPayload.data = await query(analyticUnit, detector);
 
     let task = new AnalyticsTask(
-      id, AnalyticsTaskType.LEARN, { pattern, segments: segmentObjs, data, cache: oldCache }
+      id, AnalyticsTaskType.LEARN, taskPayload
     );
     AnalyticUnit.setStatus(id, AnalyticUnit.AnalyticUnitStatus.LEARNING);
     console.debug(`run task, id:${id}`);
@@ -165,20 +201,10 @@ export async function runDetect(id: AnalyticUnit.AnalyticUnitId) {
   try {
     let unit = await AnalyticUnit.findById(id);
     previousLastDetectionTime = unit.lastDetectionTime;
-    let pattern = unit.type;
+    let analyticUnitType = unit.type;
+    let detector = AnalyticUnit.getDetectorByType(analyticUnitType);
 
-    let segments = await Segment.findMany(id, { labeled: true });
+    const data = await query(unit, detector);
-    if(segments.length === 0) {
-      throw new Error('Need at least 1 labeled segment');
-    }
-
-    let { from, to } = getQueryRangeForLearningBySegments(segments);
-    console.debug(`query time range: from ${new Date(from)} to ${new Date(to)}`);
-    let queryResult = await queryByMetric(unit.metric, unit.panelUrl, from, to, HASTIC_API_KEY);
-    let data = queryResult.values;
-    if(data.length === 0) {
-      throw new Error('Empty data to detect on');
-    }
 
     let oldCache = await AnalyticUnitCache.findById(id);
     if(oldCache !== null) {
@@ -189,7 +215,7 @@ export async function runDetect(id: AnalyticUnit.AnalyticUnitId) {
     let task = new AnalyticsTask(
       id,
       AnalyticsTaskType.DETECT,
-      { pattern, lastDetectionTime: unit.lastDetectionTime, data, cache: oldCache }
+      { detector, analyticUnitType, lastDetectionTime: unit.lastDetectionTime, data, cache: oldCache }
     );
     console.debug(`run task, id:${id}`);
     let result = await runTask(task);
@@ -264,18 +290,16 @@ async function processDetectionResult(analyticUnitId: AnalyticUnit.AnalyticUnitI
     segment => new Segment.Segment(analyticUnitId, segment.from, segment.to, false, false)
   );
   const analyticUnit = await AnalyticUnit.findById(analyticUnitId);
-  if(analyticUnit.alert) {
+  if (!_.isEmpty(segments) && analyticUnit.alert) {
-    if(!_.isEmpty(segments)) {
+    try {
-      try {
+      sendWebhook(analyticUnit.name, _.last(segments));
-        sendWebhook(analyticUnit.name, _.last(segments));
+    } catch(err) {
-      } catch(err) {
+      console.error(`Error while sending webhook: ${err.message}`);
-        console.error(`Error while sending webhook: ${err.message}`);
-      }
     }
   }
   return {
     lastDetectionTime: detectionResult.lastDetectionTime,
-    segments: segments,
+    segments,
     cache: detectionResult.cache
   };
 
@@ -319,8 +343,23 @@ export async function updateSegments(
   ]);
   removed = removed.map(s => s._id);
 
+  runFirstLearning(id);
+  return { addedIds, removed };
+}
+
+export async function updateThreshold(
+  id: AnalyticUnit.AnalyticUnitId,
+  value: number,
+  condition: Threshold.Condition
+) {
+  await Threshold.updateThreshold(id, value, condition);
+
+  runFirstLearning(id);
+}
+
+async function runFirstLearning(id: AnalyticUnit.AnalyticUnitId) {
   // TODO: move setting status somehow "inside" learning
   await AnalyticUnit.setStatus(id, AnalyticUnit.AnalyticUnitStatus.PENDING);
-  runLearning(id).then(() => runDetect(id));
+  runLearning(id)
-  return { addedIds, removed };
+    .then(() => runDetect(id));
 }

server/src/index.ts

@@ -1,6 +1,7 @@
 import { router as analyticUnitsRouter } from './routes/analytic_units_router';
 import { router as segmentsRouter } from './routes/segments_router';
 import { router as panelRouter } from './routes/panel_router';
+import { router as thresholdRouter } from './routes/threshold_router';
 
 import * as AnalyticsController from './controllers/analytics_controller';
 
@@ -38,6 +39,7 @@ var rootRouter = new Router();
 rootRouter.use('/analyticUnits', analyticUnitsRouter.routes(), analyticUnitsRouter.allowedMethods());
 rootRouter.use('/segments', segmentsRouter.routes(), segmentsRouter.allowedMethods());
 rootRouter.use('/panel', panelRouter.routes(), panelRouter.allowedMethods());
+rootRouter.use('/threshold', thresholdRouter.routes(), thresholdRouter.allowedMethods());
 
 rootRouter.get('/', async (ctx) => {
   ctx.response.body = {

server/src/models/analytic_unit_model.ts

@@ -2,6 +2,7 @@ import { Collection, makeDBQ } from '../services/data_service';
 
 import { Metric } from 'grafana-datasource-kit';
 
+import * as _ from 'lodash';
 
 let db = makeDBQ(Collection.ANALYTIC_UNITS);
 
@@ -37,6 +38,11 @@ export const ANALYTIC_UNIT_TYPES = {
   ]
 };
 
+export enum DetectorType {
+  PATTERN = 'pattern',
+  THRESHOLD = 'threshold'
+};
+
 export type AnalyticUnitId = string;
 export enum AnalyticUnitStatus {
   READY = 'READY',
@@ -167,3 +173,17 @@ export async function setDetectionTime(id: AnalyticUnitId, lastDetectionTime: nu
 export async function setAlert(id: AnalyticUnitId, alert: boolean) {
   return db.updateOne(id, { alert });
 }
+
+export function getDetectorByType(analyticUnitType: string): DetectorType {
+  let detector;
+  _.forOwn(ANALYTIC_UNIT_TYPES, (types, detectorType) => {
+    if(_.find(types, { value: analyticUnitType }) !== undefined) {
+      detector = detectorType;
+    }
+  });
+
+  if(detector === undefined) {
+    throw new Error(`Can't find detector for analytic unit of type "${analyticUnitType}"`);
+  }
+  return detector;
+}

server/src/models/threshold_model.ts

@@ -0,0 +1,70 @@
+import { AnalyticUnitId } from './analytic_unit_model';
+
+import { Collection, makeDBQ } from '../services/data_service';
+
+import * as _ from 'lodash';
+
+let db = makeDBQ(Collection.THRESHOLD);
+
+
+export enum Condition {
+  ABOVE = '>',
+  ABOVE_OR_EQUAL = '>=',
+  EQUAL = '=',
+  LESS_OR_EQUAL = '<=',
+  LESS = '<'
+};
+
+export class Threshold {
+  constructor(
+    public id: AnalyticUnitId,
+    public value: number,
+    public condition: Condition
+  ) {
+    if(id === undefined) {
+      throw new Error('id is undefined');
+    }
+    if(value === undefined) {
+      throw new Error('condition is undefined');
+    }
+    if(condition === undefined) {
+      throw new Error('condition is undefined');
+    }
+  }
+
+  public toObject() {
+    return {
+      _id: this.id,
+      value: this.value,
+      condition: this.condition
+    };
+  }
+
+  static fromObject(obj: any): Threshold {
+    if(obj === undefined) {
+      throw new Error('obj is undefined');
+    }
+    return new Threshold(obj._id, +obj.value, obj.condition);
+  }
+}
+
+export async function findOne(id: AnalyticUnitId): Promise<Threshold | null> {
+  const query: any = { id };
+
+  const threshold = await db.findOne(query);
+  if(threshold === null) {
+    return null;
+  }
+  return Threshold.fromObject(threshold);
+}
+
+export async function updateThreshold(id: AnalyticUnitId, value: number, condition: Condition) {
+  if(findOne(id) === null) {
+    return db.insertOne({ id, value, condition });
+  }
+  return db.updateOne({ id }, { value, condition });
+}
+
+export async function removeThreshold(id: AnalyticUnitId) {
+  return db.removeOne(id);
+}

server/src/routes/threshold_router.ts

@@ -0,0 +1,59 @@
+import * as AnalyticsController from '../controllers/analytics_controller';
+
+import { AnalyticUnitId } from '../models/analytic_unit_model';
+import * as Threshold from '../models/threshold_model';
+
+import * as Router from 'koa-router';
+import * as _ from 'lodash';
+
+
+async function getThresholds(ctx: Router.IRouterContext) {
+  try {
+    const ids: AnalyticUnitId = ctx.request.query.ids;
+    if(ids === undefined || _.isEmpty(ids)) {
+      throw new Error('analyticUnitIds (ids) are missing');
+    }
+
+    const thresholds = await Promise.all(
+      _.map(ids, id => Threshold.findOne(id))
+    );
+
+    ctx.response.body = { thresholds };
+  } catch(e) {
+    console.error(e);
+    ctx.response.status = 500;
+    ctx.response.body = {
+      code: 500,
+      message: `GET /threshold error: ${e.message}`
+    };
+  }
+}
+
+async function updateThreshold(ctx: Router.IRouterContext) {
+  try {
+    const {
+      id, value, condition
+    } = ctx.request.body as {
+      id: AnalyticUnitId, value: number, condition: Threshold.Condition
+    };
+
+    await AnalyticsController.updateThreshold(id, value, condition);
+
+    ctx.response.body = {
+      code: 200,
+      message: 'Success'
+    };
+  } catch(e) {
+    console.error(e);
+    ctx.response.status = 500;
+    ctx.response.body = {
+      code: 500,
+      message: `PATCH /threshold error: ${e.message}`
+    };
+  }
+}
+
+export const router = new Router();
+
+router.get('/', getThresholds);
+router.patch('/', updateThreshold);

server/src/services/data_service.ts

@@ -4,7 +4,7 @@ import * as nedb from 'nedb';
 import * as fs from 'fs';
 
 
-export enum Collection { ANALYTIC_UNITS, SEGMENTS, ANALYTIC_UNIT_CACHES, PANELS };
+export enum Collection { ANALYTIC_UNITS, SEGMENTS, ANALYTIC_UNIT_CACHES, PANELS, THRESHOLD };
 
 
 /**
@@ -212,3 +212,4 @@ db.set(Collection.ANALYTIC_UNITS, new nedb({ filename: config.ANALYTIC_UNITS_DAT
 db.set(Collection.SEGMENTS, new nedb({ filename: config.SEGMENTS_DATABASE_PATH, autoload: true }));
 db.set(Collection.ANALYTIC_UNIT_CACHES, new nedb({ filename: config.ANALYTIC_UNIT_CACHES_DATABASE_PATH, autoload: true }));
 db.set(Collection.PANELS, new nedb({ filename: config.PANELS_DATABASE_PATH, autoload: true }));
+db.set(Collection.THRESHOLD, new nedb({ filename: config.THRESHOLD_DATABASE_PATH, autoload: true }));