May be higher or lower on average but you’re only interested in theĪn example use case is for comparing different round trip time Useful to compare different series where the values in each series Offsets a metric or wildcard seriesList by subtracting the minimum You want to compare it to the time of the datapoint, to render an age Useful when you have another series where the value is a timestamp, and Returns datapoints where the value equals the timestamp of the datapoint. Data fromīootstrapInterval (one week by default) previous to the series is used to bootstrap the initial forecast. Performs a Holt-Winters forecast using the series as input data. holtWintersForecast ( seriesList, bootstrapInterval='7d', seasonality='1d' ) ¶ Upper and lower bands with the predicted forecast deviations. Performs a Holt-Winters forecast using the series as input data and plots holtWintersConfidenceBands ( seriesList, delta=3, bootstrapInterval='7d', seasonality='1d' ) ¶ holtWintersConfidenceArea ( seriesList, delta=3, bootstrapInterval='7d', seasonality='1d' ) ¶Īrea between the upper and lower bands of the predicted forecast deviations. Positive or negative deviation of the series data from the forecast. Performs a Holt-Winters forecast using the series as input data and plots the holtWintersAberration ( seriesList, delta=3, bootstrapInterval='7d', seasonality='1d' ) ¶ Or coarse-grained records) and handles rarely-occurring events (so that a similar graph results from using either fine-grained This function is like summarize(),Įxcept that it compensates automatically for different time scales It calculates hits per some larger interval This function assumes the values in each time series represent hitcount ( seriesList, intervalString, alignToInterval=False ) ¶Įstimate hit counts from a list of time series. This is an alias for highest with aggregation max. &target=highestMax(server*.instance*.threads.busy,5)ĭraws the top 5 servers who have had the most busy threads during the time If desired these series can be filtered out by piping the result through Names like asPercent(someSeries,MISSING) or asPercent(MISSING,someTotalSeries) and all When using nodes, any series or totals that can’t be matched will create output series with &target=asPercent(Server*.cpu.*.jiffies, None, 0) # cpu stats for each server as a percentage of its total ![]() # Server01 cpu stats as a percentage of its total &target=sumSeries(-, Server*.connections.attempted, 0)
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