Add |
Calculates an outcome by adding the specified value to the data points in the
dataset. |

Average |
Calculates the arithmetic means of all currently selected metrics. |

Bottom |
Shows only the lowest specified number of values of the metric
dataset. |

Chi-square |
Shows how well a statistical model fits the metric dataset. |

Count |
Shows the count of data points within the metric dataset. |

Decompose |
Separates out components of predictive models. You can decompose and request
both the min and the max to get the lower and upper bounds of a predictive
model. |

Divide |
Calculates an outcome by dividing the data points in the dataset by a
specified value. |

Envelope |
Shows the minimum and maximum values of the metric dataset. |

Filter |
Produces a new series with values calculated using the given aggregation
function over a sliding time window of the given duration. A sliding 15-minute
average would use the Filter transform with the Average
aggregation function and a duration of 15 minutes. Supported aggregation functions: |

Fit |
Generates a prediction model that can be used by the model-based
trigger. |

Fractiles |
Returns a new series with values representing the given percentiles of the
underlying data. For example, to query for the 90th and 99th percentile response
times, supply an array of [0.9,0.99]. |

Interpolate |
Constructs new data points a specified duration to calculate an
outcome. |

Label |
Enables you to set a label for your transform. |

Last |
Returns the last defined value in the period window. |

Log |
Calculates the natural logarithm of all values in the dataset. |

Max |
Shows the largest value at each point in time for the metric dataset. |

Median |
Shows the median of the metric dataset. The median separates the higher
values of the metric dataset from the lower values. |

Min |
Shows the smallest value at each point in time for the metric
dataset. |

Multiply |
Calculates an outcome by multiplying the data points in the dataset by a
specified value. |

Partition |
Produces a new series with values calculated using the given aggregation
function over a fixed time frame of a given duration. Specify the
Base (a timestamp) to align the partition window.Supported aggregation functions: |

Predict |
Compares predicted time-series data generated by the prediction model
selected in the MetricBase Models table (mb_model) to real data. The predicted and
real data can be graphed. Prediction triggers are based on the predicted values as
well as thresholds. Thresholds are values above and below the predicted value.
Real data that falls outside of those thresholds execute prediction triggers. |

Put |
Copies a time-series metric into a different MetricBase time-series metric,
for example, `copyData('targetMetric').put()` . |

Resample |
Expands or contracts the data to fit the given period. When you extend the
period, the aggregation function is used to combine the data to fit the new
period. When you shorten the period, the existing data is propagated to the
underlying periods.Supported aggregation functions: |

Standard Deviation |
Calculates the standard deviation across the underlying data. Used to
quantify the variation or dispersion of a set of data values in the metric
dataset. |

Subtract |
Calculates an outcome by subtracting the specified value from the data points
in the dataset. |

Sum |
Calculates the sum of the data points within the metric dataset. See Sum
transform for more information. |

Top |
Shows only the highest specified number of values of the metric
dataset. |