Table of Contents
5 Funções de tendência
As funções de tendência, ao contrário das funções de histórico, usam dados de tendência para cálculos.
As tendências armazenam valores agregados por hora. As funções de tendência usam essas médias horárias e, portanto, são úteis para análises de longo prazo.
Os resultados da função de tendência são armazenados em cache para que várias chamadas para a mesma função com os mesmos parâmetros busquem informações do banco de dados apenas uma vez. O cache da função de tendência é controlado pelo parâmetro do servidor TrendCacheSize.
As triggers que fazem referência a funções de tendência somente são avaliados uma vez no menor período de tempo na expressão. Por exemplo, uma trigger como
será avaliado uma vez por dia. Se a trigger contiver funções de tendência e histórico (ou baseadas em tempo),
é calculado de acordo com os princípios usuais.
Todas as funções listadas aqui são suportadas em:
Algumas notas gerais sobre parâmetros de função:
- Os parâmetros da função são separados por uma vírgula
- Parâmetros de funções opcionais (ou partes de parâmetros) são indicados por
<>
- Os parâmetros específicos da função são descritos com cada função
- Os parâmetros
/host/keyetime period:time shiftnunca devem ser citados
Parâmetros comuns
/host/keyé um primeiro parâmetro obrigatório comum
time period:time shifté um segundo parâmetro comum, onde:
- período de tempo - o período de tempo (mínimo '1h'), definido como <N><unidade de tempo> onde
N- o número de unidades de tempo,unidade de tempo- h (hora), d (dia), w (semana), M (mês) ou y (ano).
- time shift - o deslocamento do período de tempo (veja exemplos de função)
Trend functions
| FUNCTION | |||
|---|---|---|---|
| Description | Function-specific parameters | Comments | |
| baselinedev (/host/key,data period:time shift,season_unit,num_seasons) | |||
| Returns the number of deviations (by stddevpop algorithm) between the last data period and the same data periods in preceding seasons. | data period - the data gathering period within a season, defined as <N><time unit> whereN - number of time unitstime unit - h (hour), d (day), w (week), M (month) or y (year), must be equal to or less than season Time shift - the time period offset (see examples) season_unit - duration of one season (h, d, w, M, y), cannot be smaller than data period num_seasons - number of seasons to evaluate |
Examples: => baselinedev(/host/key,1d:now/d,"M",6) → calculating the number of standard deviations (population) between the previous day and the same day in the previous 6 months. If the date doesn't exist in a previous month, the last day of the month will be used (Jul,31 will be analysed against Jan,31, Feb, 28,... June, 30). => baselinedev(/host/key,1h:now/h,"d",10) → calculating the number of standard deviations (population) between the previous hour and the same hours over the period of ten days before yesterday. |
|
| baselinewma (/host/key,data period:time shift,season_unit,num_seasons) | |||
| Calculates the baseline by averaging data from the same timeframe in multiple equal time periods ('seasons') using the weighted moving average algorithm. | data period - the data gathering period within a season, defined as <N><time unit> whereN - number of time unitstime unit - h (hour), d (day), w (week), M (month) or y (year), must be equal to or less than season Time shift - the time period offset, defines the end of data gathering time frame in seasons (see examples) season_unit - duration of one season (h, d, w, M, y), cannot be smaller than data period num_seasons - number of seasons to evaluate |
Examples: => baselinewma(/host/key,1h:now/h,"d",3) → calculating baseline based on the last full hour within a 3-day period that ended yesterday. If "now" is Monday 13:30, the data for 12:00-12:59 on Friday, Saturday, and Sunday will be analyzed. => baselinemwa(/host/key,2h:now/h,"d",3) → calculating baseline based on the last two hours within a 3-day period that ended yesterday. If "now" is Monday 13:30, the data for 10:00-11:59 on Friday, Saturday, and Sunday will be analyzed. => baselinewma(/host/key,1d:now/d,"M",4) → calculating baseline based on the last days of previous 4 months, excluding the last full month. If today is September 1st, the data for July 31st, June 30th, May 31st, April 30th will be analyzed. |
|
| trendavg (/host/key,time period:time shift) | |||
| Average of trend values within the defined time period. | See common parameters. | Examples: => trendavg(/host/key,1h:now/h) → average for the previous hour (e.g. 12:00-13:00) => trendavg(/host/key,1h:now/h-1h) → average for two hours ago (11:00-12:00) => trendavg(/host/key,1h:now/h-2h) → average for three hours ago (10:00-11:00) => trendavg(/host/key,1M:now/M-1y) → average for the previous month a year ago |
|
| trendcount (/host/key,time period:time shift) | |||
| Number of successfully retrieved trend values within the defined time period. | See common parameters. | Examples: => trendcount(/host/key,1h:now/h) → count for the previous hour (e.g. 12:00-13:00) => trendcount(/host/key,1h:now/h-1h) → count for two hours ago (11:00-12:00) => trendcount(/host/key,1h:now/h-2h) → count for three hours ago (10:00-11:00) => trendcount(/host/key,1M:now/M-1y) → count for the previous month a year ago |
|
| trendmax (/host/key,time period:time shift) | |||
| The maximum in trend values within the defined time period. | See common parameters. | Examples: => trendmax(/host/key,1h:now/h) → maximum for the previous hour (e.g. 12:00-13:00) => trendmax(/host/key,1h:now/h) - trendmin(/host/key,1h:now/h) → calculate the difference between the maximum and minimum values (trend delta) for the previous hour (12:00-13:00) => trendmax(/host/key,1h:now/h-1h) → maximum for two hours ago (11:00-12:00) => trendmax(/host/key,1h:now/h-2h) → maximum for three hours ago (10:00-11:00) => trendmax(/host/key,1M:now/M-1y) → maximum for the previous month a year ago |
|
| trendmin (/host/key,time period:time shift) | |||
| The minimum in trend values within the defined time period. | See common parameters. | Examples: => trendmin(/host/key,1h:now/h) → minimum for the previous hour (e.g. 12:00-13:00) => trendmin(/host/key,1h:now/h) - trendmin(/host/key,1h:now/h) → calculate the difference between the maximum and minimum values (trend delta) for the previous hour (12:00-13:00) => trendmin(/host/key,1h:now/h-1h) → minimum for two hours ago (11:00-12:00) => trendmin(/host/key,1h:now/h-2h) → minimum for three hours ago (10:00-11:00) => trendmin(/host/key,1M:now/M-1y) → minimum for the previous month a year ago |
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| trendstl (/host/key,eval period:time shift,detection period,season,<deviations>,<devalg>,<s_window>) | |||
Returns the rate of anomalies during the detection period - a decimal value between 0 and 1 that is ((the number of anomaly values)/(total number of values)). |
eval period - the time period that must be decomposed (minimum '1h'), defined as <N><time unit> whereN - number of time unitstime unit - h (hour), d (day), w (week), M (month) or y (year).Time shift - the time period offset (see examples) detection period - the time period before the end of eval period for which anomalies are calculated (minimum '1h', cannot be longer than eval period), defined as <N><time unit> where N - number of time unitstime unit - h (hour), d (day), w (week).season - the shortest time period where a repeating pattern ("season") is expected (minimum '2h', cannot be longer than eval period, number of entries in the eval period must be greater than the two times of the resulting frequency (season/h)), defined as <N><time unit> where N - number of time unitstime unit - h (hour), d (day), w (week).deviations - the number of deviations (calculated by devalg) to count as anomaly (can be decimal), (must be greater than or equal to 1, default is 3) devalg (must be double-quoted) - deviation algorithm, can be stddevpop, stddevsamp or mad (default) s_window - the span (in lags) of the loess window for seasonal extraction (default is 10 * number of entries in eval period + 1) |
Examples: => trendstl(/host/key,100h:now/h,10h,2h) → analyse the last 100 hours of trend data, find the anomaly rate for the last 10 hours of that period, expecting the periodicity to be 2h, the remainder series values of the evaluation period are considered anomalies if they reach the value of 3 deviations of the MAD of that remainder series => trendstl(/host/key,100h:now/h-10h,100h,2h,2.1,"mad") → analyse the period of 100 hours of trend data, up to 10 hours ago, find the anomaly rate for that entire period expecting the periodicity to be 2h, the remainder series values of the evaluation period are considered anomalies if they reach the value of 2,1 deviations of the MAD of that remainder series => trendstl(/host/key,100d:now/d-1d,10d,1d,4,,10) → analyse 100 days of trend data up to a day ago, find the anomaly rate for the period of last 10d of that period, expecting the periodicity to be 1d, the remainder series values of the evaluation period are considered anomalies if they reach the value of 4 deviations of the MAD of that remainder series, overriding the default span of the loess window for seasonal extraction of "10 * number of entries in eval period + 1" with the span of 10 lags => trendstl(/host/key,1M:now/M-1y,1d,2h,,"stddevsamp") → analyse the previous month a year ago, find the anomaly rate of the last day of that period expecting the periodicity to be 2h, the remainder series values of the evaluation period are considered anomalies if they reach the value of 3 deviation of the sample standard deviation of that remainder series |
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| trendsum (/host/key,time period:time shift) | |||
| Sum of trend values within the defined time period. | See common parameters. | Examples: => trendsum(/host/key,1h:now/h) → sum for the previous hour (e.g. 12:00-13:00) => trendsum(/host/key,1h:now/h-1h) → sum for two hours ago (11:00-12:00) => trendsum(/host/key,1h:now/h-2h) → sum for three hours ago (10:00-11:00) => trendsum(/host/key,1M:now/M-1y) → sum for the previous month a year ago |
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