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atlas-core/com.netflix.atlas.core/com.netflix.atlas.core.algorithm

com.netflix.atlas.core.algorithm

package com.netflix.atlas.core.algorithm

Members list

Type members

Classlikes

case class AlgoState(algorithm: String, settings: Map[String, Any])

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.

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object AlgoState

Helper functions to make it easier to create state objects.

Helper functions to make it easier to create state objects.

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trait OnlineAlgorithm

Base trait for online algorithms used on time series.

Base trait for online algorithms used on time series.

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Known subtypes
class OnlineDelay
class OnlineDes
class OnlineTrend
class Pipeline
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object OnlineAlgorithm

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case class OnlineDelay(buf: RollingBuffer) extends OnlineAlgorithm

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.

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object OnlineDelay

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case class OnlineDerivative(var prev: Double) extends OnlineAlgorithm

Computes the rate of change per step of the input time series.

Computes the rate of change per step of the input time series.

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object OnlineDerivative

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case class OnlineDes(training: Int, alpha: Double, beta: Double) extends OnlineAlgorithm

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

Data smoothing factor.

beta

Trend smoothing factor.

training

Number of samples to record before emitting predicted values.

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object OnlineDes

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case class OnlineIgnoreN(n: Int) extends OnlineAlgorithm

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.

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object OnlineIgnoreN

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case class OnlineIntegral(var value: Double) extends OnlineAlgorithm

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.

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object OnlineIntegral

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case class OnlineRollingCount(buf: RollingBuffer) extends OnlineAlgorithm

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.

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object OnlineRollingCount

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case class OnlineRollingMax(buf: RollingBuffer) extends OnlineAlgorithm

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.

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object OnlineRollingMax

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case class OnlineRollingMean(buf: RollingBuffer, minNumValues: Int) extends OnlineAlgorithm

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

Rolling buffer to keep track of the input for a given window.

minNumValues

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.

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object OnlineRollingMean

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case class OnlineRollingMin(buf: RollingBuffer) extends OnlineAlgorithm

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.

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object OnlineRollingMin

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case class OnlineRollingSum(buf: RollingBuffer) extends OnlineAlgorithm

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.

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object OnlineRollingSum

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case class OnlineSlidingDes(training: Int, alpha: Double, beta: Double, des1: OnlineDes, des2: OnlineDes) extends OnlineAlgorithm

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.

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object OnlineSlidingDes

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case class OnlineTrend(buf: RollingBuffer) extends OnlineAlgorithm

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.

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object OnlineTrend

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case class Pipeline(stages: List[OnlineAlgorithm]) extends OnlineAlgorithm

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.

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object Pipeline

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class RollingBuffer(val values: Array[Double], start: Int)

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

Starting position within the array. This is typically only used when restoring from state captured from another buffer.

values

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.

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object RollingBuffer

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