def find_alerts(timelyMetric, analyticConfig, notebook=False): df = timelyMetric.getDataFrame() graphDF = TimelyMetric.pivot(df, timelyMetric.metric, groupByColumn=analyticConfig.groupByColumn) if analyticConfig.excludeColRegex is not None: graphDF = graphDF.select(lambda x : not (re.search(analyticConfig.excludeColRegex, x)), axis=1) if analyticConfig.includeColRegex is not None: graphDF = graphDF.select(lambda x : re.search(analyticConfig.includeColRegex, x), axis=1) if analyticConfig.sample is not None: graphDF = TimelyMetric.resample(graphDF, analyticConfig.sample, how=analyticConfig.how) graphDF_avg = pandas.DataFrame(graphDF, copy=True) combined = pandas.DataFrame() now = Timestamp.now() seriesConfig = {} for i in graphDF_avg.columns: col = str(i) any_conditions_values = False result_values = np.ones(graphDF[col].shape, bool) if analyticConfig.orCondition: result_values = np.zeros(graphDF[col].shape, bool) any_conditions_average = False result_average = np.ones(graphDF_avg[col].shape, bool) if analyticConfig.orCondition: result_average = np.zeros(graphDF_avg[col].shape, bool) if analyticConfig.min_threshold is not None: currCondition = graphDF[col].astype(float) < analyticConfig.min_threshold result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.max_threshold is not None: currCondition = graphDF[col].astype(float) > analyticConfig.max_threshold result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.rolling_average_samples is not None: graphDF_avg = TimelyMetric.rolling_average(graphDF_avg, col, rolling_average=analyticConfig.rolling_average_samples) if analyticConfig.alert_percentage is not None: if analyticConfig.alert_percentage > 0: multiple = 1.0 + (float(abs(analyticConfig.alert_percentage)) / float(100)) currCondition = graphDF[col].astype(float) > (graphDF_avg[col].astype(float) * multiple) result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.alert_percentage < 0: multiple = 1.0 - (float(abs(analyticConfig.alert_percentage)) / float(100)) if multiple > 0: currCondition = graphDF[col].astype(float) < (graphDF_avg[col].astype(float) * multiple) result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.average_min_threshold is not None: currCondition = graphDF_avg[col].astype(float) < analyticConfig.average_min_threshold result_average = addCondition(analyticConfig.orCondition, result_average, currCondition) any_conditions_average = True if analyticConfig.average_max_threshold is not None: currCondition = graphDF_avg[col].astype(float) > analyticConfig.average_max_threshold result_average = addCondition(analyticConfig.orCondition, result_average, currCondition) any_conditions_average = True # if orCondition is AND and no exceptional conditions have been found, then result_values will be all True if any_conditions_values == False: result_values = np.zeros(graphDF[col].shape, bool) exceptional_values = graphDF.loc[result_values, col] # if orCondition is AND and no exceptional conditions have been found, then result_average will be all True if any_conditions_average == False: result_average = np.zeros(graphDF_avg[col].shape, bool) exceptional_average = graphDF_avg.loc[result_average, col] # only evaluate the last analyticConfig.last_alert_minutes if set if analyticConfig.last_alert_minutes is not None: recentEnoughBegin = now - timedelta(minutes=analyticConfig.last_alert_minutes) exceptional_values = exceptional_values.ix[recentEnoughBegin:now] exceptional_average = exceptional_average.ix[recentEnoughBegin:now] # only keep alerts that are in consecutive periods of length analyticConfig.min_alert_minutes exceptional_values = keepConsecutiveAlerts(graphDF, exceptional_values, analyticConfig.min_alert_minutes) exceptional_average = keepConsecutiveAlerts(graphDF_avg, exceptional_average, analyticConfig.min_alert_minutes) anyValueExceptions = exceptional_values.size > 0 anyAverageExceptions = exceptional_average.size > 0 if (analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and anyValueExceptions): combined[col] = graphDF[col] if analyticConfig.rolling_average_samples is not None: if (analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and (anyAverageExceptions or analyticConfig.alert_percentage is not None)): combined[col+'_avg'] = graphDF_avg[col] if ((analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and anyValueExceptions)): combined[col + '_warn'] = exceptional_values.dropna() seriesConfig[col + '_warn'] = { "mode" : "markers", "marker" : { "symbol" : "hash-open", "color" : "red" } } if ((analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and anyAverageExceptions)): combined[col + '_avg_warn'] = exceptional_average.dropna() seriesConfig[col + '_avg_warn'] = { "mode" : "markers", "marker" : { "symbol" : "hash-open", "color" : "red" } } timelyAlert = None if not combined.empty: alertAnalyticConfig = TimelyAnalyticConfiguration(analyticConfig) if alertAnalyticConfig.groupByColumn is None: alertAnalyticConfig.groupByColumn = timelyMetric.metric + "_obs" combined = TimelyMetric.unpivot(combined, timelyMetric.metric, groupByColumn=alertAnalyticConfig.groupByColumn) combined = combined.sort_index() combined['date'] = combined.index.values combined = combined.sort_values(['date', analyticConfig.groupByColumn]) combined = combined.drop(['date'], 1) combined = combined.dropna() combined = DataOperations.ensureMinSeriesLength(combined, alertAnalyticConfig.groupByColumn) message = DataOperations.getTitle(timelyMetric, analyticConfig, separator=', ') timelyAlert = TimelyAlert(timelyMetric, combined, message, seriesConfig, alertAnalyticConfig, notebook) return timelyAlert
def find_alerts(timelyMetric, analyticConfig, notebook=False): if (analyticConfig.counter == True): convertCounterToRate(timelyMetric, analyticConfig) df = timelyMetric.getDataFrame() graphDF = TimelyMetric.pivot(df, timelyMetric.metric, groupByColumn=analyticConfig.groupByColumn) if analyticConfig.excludeColRegex is not None: graphDF = graphDF.select(lambda x : not (re.search(analyticConfig.excludeColRegex, x)), axis=1) if analyticConfig.includeColRegex is not None: graphDF = graphDF.select(lambda x : re.search(analyticConfig.includeColRegex, x), axis=1) if analyticConfig.sample is not None: graphDF = TimelyMetric.resample(graphDF, analyticConfig.sample, how=analyticConfig.how) graphDF_avg = pandas.DataFrame(graphDF, copy=True) combined = pandas.DataFrame() seriesConfig = {} for i in graphDF_avg.columns: col = str(i) any_conditions_values = False result_values = np.ones(graphDF[col].shape, bool) if analyticConfig.orCondition: result_values = np.zeros(graphDF[col].shape, bool) any_conditions_average = False result_average = np.ones(graphDF_avg[col].shape, bool) if analyticConfig.orCondition: result_average = np.zeros(graphDF_avg[col].shape, bool) if analyticConfig.min_threshold is not None: currCondition = graphDF[col].astype(float) < analyticConfig.min_threshold result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.max_threshold is not None: currCondition = graphDF[col].astype(float) > analyticConfig.max_threshold result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.rolling_average_samples is not None: graphDF_avg = TimelyMetric.rolling_average(graphDF_avg, col, rolling_average=analyticConfig.rolling_average_samples) if analyticConfig.min_threshold_percentage is not None: if analyticConfig.min_threshold_percentage >= 0: multiple = 1.0 + (float(abs(analyticConfig.min_threshold_percentage)) / float(100)) else: multiple = 1.0 - (float(abs(analyticConfig.min_threshold_percentage)) / float(100)) currCondition = graphDF[col].astype(float) < (graphDF_avg[col].astype(float) * multiple) result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.max_threshold_percentage is not None: if analyticConfig.max_threshold_percentage >= 0: multiple = 1.0 + (float(abs(analyticConfig.max_threshold_percentage)) / float(100)) else: multiple = 1.0 - (float(abs(analyticConfig.max_threshold_percentage)) / float(100)) currCondition = graphDF[col].astype(float) > (graphDF_avg[col].astype(float) * multiple) result_values = addCondition(analyticConfig.orCondition, result_values, currCondition) any_conditions_values = True if analyticConfig.average_min_threshold is not None: currCondition = graphDF_avg[col].astype(float) < analyticConfig.average_min_threshold result_average = addCondition(analyticConfig.orCondition, result_average, currCondition) any_conditions_average = True if analyticConfig.average_max_threshold is not None: currCondition = graphDF_avg[col].astype(float) > analyticConfig.average_max_threshold result_average = addCondition(analyticConfig.orCondition, result_average, currCondition) any_conditions_average = True # if orCondition is AND and no exceptional conditions have been found, then result_values will be all True if any_conditions_values == False: result_values = np.zeros(graphDF[col].shape, bool) exceptional_values = graphDF.loc[result_values, col] # if orCondition is AND and no exceptional conditions have been found, then result_average will be all True if any_conditions_average == False: result_average = np.zeros(graphDF_avg[col].shape, bool) exceptional_average = graphDF_avg.loc[result_average, col] # only keep alerts that are in consecutive periods of length analyticConfig.min_alert_minutes exceptional_values = keepConsecutiveAlerts(graphDF, exceptional_values, analyticConfig.min_alert_minutes) exceptional_average = keepConsecutiveAlerts(graphDF_avg, exceptional_average, analyticConfig.min_alert_minutes) # only evaluate the last analyticConfig.last_alert_minutes if set if analyticConfig.last_alert_minutes is not None: end = datetime.fromtimestamp(timelyMetric.timeDateRange.getEndMs() / 1000.00, DataOperations.utc) recentEnoughBegin = end - timedelta(minutes=analyticConfig.last_alert_minutes) exceptional_values = exceptional_values.ix[recentEnoughBegin:end] exceptional_average = exceptional_average.ix[recentEnoughBegin:end] anyValueExceptions = exceptional_values.size > 0 anyAverageExceptions = exceptional_average.size > 0 if (analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and anyValueExceptions): combined[col] = graphDF[col] if analyticConfig.rolling_average_samples is not None: if (analyticConfig.display.lower() == "all"): combined[col + '_avg'] = graphDF_avg[col] else: if (anyAverageExceptions): combined[col + '_avg'] = graphDF_avg[col] if (anyValueExceptions and (analyticConfig.min_threshold_percentage is not None or analyticConfig.max_threshold_percentage is not None)): combined[col + '_avg'] = graphDF_avg[col] if ((analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and anyValueExceptions)): combined[col + '_warn'] = exceptional_values.dropna() seriesConfig[col + '_warn'] = { "mode" : "markers", "marker" : { "symbol" : "hash-open", "color" : "red" } } if ((analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and anyAverageExceptions)): combined[col + '_avg_warn'] = exceptional_average.dropna() seriesConfig[col + '_avg_warn'] = { "mode" : "markers", "marker" : { "symbol" : "hash-open", "color" : "red" } } timelyAlert = None if not combined.empty: alertAnalyticConfig = TimelyAnalyticConfiguration(analyticConfig) if alertAnalyticConfig.groupByColumn is None: alertAnalyticConfig.groupByColumn = timelyMetric.metric + "_obs" combined = TimelyMetric.unpivot(combined, timelyMetric.metric, groupByColumn=alertAnalyticConfig.groupByColumn) combined = combined.sort_index() combined['date'] = combined.index.values combined = combined.sort_values(['date', alertAnalyticConfig.groupByColumn]) combined = combined.drop(['date'], 1) combined = combined.dropna() combined = DataOperations.ensureMinSeriesLength(combined, alertAnalyticConfig.groupByColumn) message = DataOperations.getTitle(timelyMetric, analyticConfig, separator=', ') timelyAlert = TimelyAlert(timelyMetric, combined, message, seriesConfig, alertAnalyticConfig, notebook) return timelyAlert
def find_alerts(timelyMetric, analyticConfig, notebook=False): if analyticConfig.groupByColumn is None: return df = timelyMetric.getDataFrame() graphDF = TimelyMetric.pivot(df, timelyMetric.metric, groupByColumn=analyticConfig.groupByColumn) if analyticConfig.sample is not None: graphDF = TimelyMetric.resample(graphDF, analyticConfig.sample, how=analyticConfig.how) if analyticConfig.excludeColRegex is not None: graphDF = graphDF.select( lambda x: not (re.search(analyticConfig.excludeColRegex, x)), axis=1) if analyticConfig.includeColRegex is not None: graphDF = graphDF.select( lambda x: re.search(analyticConfig.includeColRegex, x), axis=1) graphDF_avg = pandas.DataFrame(graphDF, copy=True) combined = pandas.DataFrame() seriesConfig = {} for i in graphDF_avg.columns: col = str(i) anyConditions = False result = np.ones(graphDF[col].shape, bool) if analyticConfig.orCondition: result = np.zeros(graphDF[col].shape, bool) if analyticConfig.min_threshold is not None: currCondition = graphDF[col].astype( float) < analyticConfig.min_threshold result = addCondition(analyticConfig.orCondition, result, currCondition) anyConditions = True if analyticConfig.max_threshold is not None: currCondition = graphDF[col].astype( float) > analyticConfig.max_threshold result = addCondition(analyticConfig.orCondition, result, currCondition) anyConditions = True graphDF_avg = TimelyMetric.rolling_average( graphDF_avg, str(i), rolling_average=analyticConfig.rolling_average) if (analyticConfig.alert_percentage is not None) and (analyticConfig.rolling_average is not None): if analyticConfig.alert_percentage > 0: multiple = 1.0 + (float(abs(analyticConfig.alert_percentage)) / float(100)) currCondition = graphDF[col].astype(float) > ( graphDF_avg[col].astype(float) * multiple) result = addCondition(analyticConfig.orCondition, result, currCondition) anyConditions = True if analyticConfig.alert_percentage < 0: multiple = 1.0 - (float(abs(analyticConfig.alert_percentage)) / float(100)) if multiple > 0: currCondition = graphDF[col].astype(float) < ( graphDF_avg[col].astype(float) * multiple) result = addCondition(analyticConfig.orCondition, result, currCondition) anyConditions = True if anyConditions == False: result = np.zeros(graphDF[col].shape, bool) exceptional = graphDF.loc[result, col] if (analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and exceptional.size > 0): combined[col] = graphDF[col] if ((analyticConfig.rolling_average is not None) and ((analyticConfig.display.lower() == "all") or (analyticConfig.display.lower() == "alerts" and exceptional.size > 0 and analyticConfig.alert_percentage is not None))): combined[col + '_avg'] = graphDF_avg[col] if (exceptional.size > 0): combined[col + '_warn'] = exceptional.dropna() seriesConfig[col + '_warn'] = { "mode": "markers", "marker": { "symbol": "hash-open", "color": "red" } } timelyAlert = None if not combined.empty: combined = TimelyMetric.unpivot( combined, timelyMetric.metric, groupByColumn=analyticConfig.groupByColumn) combined = combined.sort_index() combined['date'] = combined.index.values combined = combined.sort_values(['date', analyticConfig.groupByColumn]) combined = combined.drop(['date'], 1) combined = combined.dropna() combined = DataOperations.ensureMinSeriesLength( combined, analyticConfig.groupByColumn) timelyAlert = TimelyAlert(timelyMetric, combined, seriesConfig, analyticConfig, notebook) return timelyAlert