com.netflix.atlas.core.algorithm
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Represents the current state for an online algorithm. Can be used with OnlineAlgorithm.apply
to create a new instance with the same state. This state object is reliably serializable with helpers such as the atlas-json
library so state can be persisted.
Represents the current state for an online algorithm. Can be used with OnlineAlgorithm.apply
to create a new instance with the same state. This state object is reliably serializable with helpers such as the atlas-json
library so state can be persisted.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalsclass Objecttrait Matchableclass AnyShow all
Base trait for online algorithms used on time series.
Base trait for online algorithms used on time series.
Attributes
- Companion
- object
- Supertypes
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class Objecttrait Matchableclass Any
- Known subtypes
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class OnlineDelayclass OnlineDerivativeclass OnlineDesclass OnlineIgnoreNclass OnlineIntegralclass OnlineRollingCountclass OnlineRollingMaxclass OnlineRollingMeanclass OnlineRollingMinclass OnlineRollingSumclass OnlineSlidingDesclass OnlineTrendclass PipelineShow all
Attributes
- Companion
- trait
- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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OnlineAlgorithm.type
Delays the values by the window size. This is similar to the :offset
operator except that it can be applied to any input line instead of just changing the time window fetched with a DataExpr. Short delays can be useful for alerting to detect changes in slightly shifted trend lines.
Delays the values by the window size. This is similar to the :offset
operator except that it can be applied to any input line instead of just changing the time window fetched with a DataExpr. Short delays can be useful for alerting to detect changes in slightly shifted trend lines.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineDelay.type
Computes the rate of change per step of the input time series.
Computes the rate of change per step of the input time series.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineDerivative.type
Helper to compute DES value iteratively for a set of numbers.
Helper to compute DES value iteratively for a set of numbers.
Value parameters
- alpha
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Data smoothing factor.
- beta
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Trend smoothing factor.
- training
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Number of samples to record before emitting predicted values.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Ignore the first N datapoints that are passed in. This is typically used to achieve an initial alignment to step boundaries when using a deterministic sliding window approach like SDES.
Ignore the first N datapoints that are passed in. This is typically used to achieve an initial alignment to step boundaries when using a deterministic sliding window approach like SDES.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineIgnoreN.type
Sum the values across the evaluation context. This is typically used to approximate the distinct number of events that occurred. If the input is non-negative, then each datapoint for the output line will represent the area under the input line from the start of the graph to the time for that datapoint.
Sum the values across the evaluation context. This is typically used to approximate the distinct number of events that occurred. If the input is non-negative, then each datapoint for the output line will represent the area under the input line from the start of the graph to the time for that datapoint.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineIntegral.type
Keeps track of the number of true values within a given window. A value is considered as true if it is not near zero. This is frequently used to check for a number of occurrences before triggering an alert.
Keeps track of the number of true values within a given window. A value is considered as true if it is not near zero. This is frequently used to check for a number of occurrences before triggering an alert.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineRollingCount.type
Keeps track of the maximum value within a given window. This is typically used as a way to get a smooth upper bound line that closely tracks a noisy input.
Keeps track of the maximum value within a given window. This is typically used as a way to get a smooth upper bound line that closely tracks a noisy input.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineRollingMax.type
Mean of the values within a moving window of the input. The denominator is the number of values (non-NaN entries) in the rolling buffer.
Mean of the values within a moving window of the input. The denominator is the number of values (non-NaN entries) in the rolling buffer.
Value parameters
- buf
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Rolling buffer to keep track of the input for a given window.
- minNumValues
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Minimum number of values that must be present within the buffer for an average to be emitted. If there are not enough non-NaN values, then NaN will be emitted.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineRollingMean.type
Keeps track of the minimum value within a given window. This is typically used as a way to get a smooth lower bound line that closely tracks a noisy input.
Keeps track of the minimum value within a given window. This is typically used as a way to get a smooth lower bound line that closely tracks a noisy input.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineRollingMin.type
Sum of the values within a moving window of the input.
Sum of the values within a moving window of the input.
Value parameters
- buf
-
Rolling buffer to keep track of the input for a given window.
Attributes
- Companion
- object
- Supertypes
-
trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineRollingSum.type
Alternate between two DES functions after each training period. This provides a deterministic estimate within a bounded amount of time.
Alternate between two DES functions after each training period. This provides a deterministic estimate within a bounded amount of time.
Value parameters
- alpha
-
Data smoothing factor.
- beta
-
Trend smoothing factor.
- training
-
Number of samples to record before emitting predicted values.
Attributes
- Companion
- object
- Supertypes
-
trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineSlidingDes.type
Keeps track of the number of true values within a given window. A value is considered as true if it is not near zero. This is frequently used to check for a number of occurrences before triggering an alert.
Keeps track of the number of true values within a given window. A value is considered as true if it is not near zero. This is frequently used to check for a number of occurrences before triggering an alert.
Attributes
- Companion
- object
- Supertypes
-
trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Attributes
- Companion
- class
- Supertypes
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trait Producttrait Mirrorclass Objecttrait Matchableclass Any
- Self type
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OnlineTrend.type
Push a value through a sequence of online algorithms and return the result.
Push a value through a sequence of online algorithms and return the result.
Attributes
- Companion
- object
- Supertypes
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trait Serializabletrait Producttrait Equalstrait OnlineAlgorithmclass Objecttrait Matchableclass AnyShow all
Buffer for tracking the last N values of a time series.
Buffer for tracking the last N values of a time series.
Value parameters
- start
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Starting position within the array. This is typically only used when restoring from state captured from another buffer.
- values
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Underlying array that is used to store the values. As new data is added it will roll through the array and overwrite old values that are now out of the window. The window size is the size of the array.
Attributes
- Companion
- object
- Supertypes
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class Objecttrait Matchableclass Any
Attributes
- Companion
- class
- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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RollingBuffer.type