2.169.0

class Stats

LanguageType name
.NETAmazon.CDK.AWS.CloudWatch.Stats
Gogithub.com/aws/aws-cdk-go/awscdk/v2/awscloudwatch#Stats
Javasoftware.amazon.awscdk.services.cloudwatch.Stats
Pythonaws_cdk.aws_cloudwatch.Stats
TypeScript (source)aws-cdk-lib » aws_cloudwatch » Stats

Factory functions for standard statistics strings.

Example

declare const dashboard: cloudwatch.Dashboard;
declare const executionCountMetric: cloudwatch.Metric;
declare const errorCountMetric: cloudwatch.Metric;

dashboard.addWidgets(new cloudwatch.GraphWidget({
  title: "Executions vs error rate",

  left: [executionCountMetric],

  right: [errorCountMetric.with({
    statistic: cloudwatch.Stats.AVERAGE,
    label: "Error rate",
    color: cloudwatch.Color.GREEN,
  })]
}));

Initializer

new Stats()

Properties

NameTypeDescription
static AVERAGEstringThe value of Sum / SampleCount during the specified period.
static IQMstringInterquartile mean (IQM) is the trimmed mean of the interquartile range, or the middle 50% of values.
static MAXIMUMstringThe highest value observed during the specified period.
static MINIMUMstringThe lowest value observed during the specified period.
static SAMPLE_COUNTstringThe count (number) of data points used for the statistical calculation.
static SUMstringAll values submitted for the matching metric added together.

static AVERAGE

Type: string

The value of Sum / SampleCount during the specified period.

static IQM

Type: string

Interquartile mean (IQM) is the trimmed mean of the interquartile range, or the middle 50% of values.

It is equivalent to trimmedMean(25, 75).

static MAXIMUM

Type: string

The highest value observed during the specified period.

You can use this value to determine high volumes of activity for your application.

static MINIMUM

Type: string

The lowest value observed during the specified period.

You can use this value to determine low volumes of activity for your application.

static SAMPLE_COUNT

Type: string

The count (number) of data points used for the statistical calculation.

static SUM

Type: string

All values submitted for the matching metric added together.

This statistic can be useful for determining the total volume of a metric.

Methods

NameDescription
static p(percentile)A shorter alias for percentile().
static percentile(percentile)Percentile indicates the relative standing of a value in a dataset.
static percentileRank(v1, v2?)Percentile rank (PR) is the percentage of values that meet a fixed threshold.
static pr(v1, v2?)Shorter alias for percentileRank().
static tc(p1, p2?)Shorter alias for trimmedCount().
static tm(p1, p2?)A shorter alias for trimmedMean().
static trimmedCount(p1, p2?)Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.
static trimmedMean(p1, p2?)Trimmed mean (TM) is the mean of all values that are between two specified boundaries.
static trimmedSum(p1, p2?)Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.
static ts(p1, p2?)Shorter alias for trimmedSum().
static winsorizedMean(p1, p2?)Winsorized mean (WM) is similar to trimmed mean.
static wm(p1, p2?)A shorter alias for winsorizedMean().

static p(percentile)

public static p(percentile: number): string

Parameters

  • percentile number

Returns

  • string

A shorter alias for percentile().

static percentile(percentile)

public static percentile(percentile: number): string

Parameters

  • percentile number

Returns

  • string

Percentile indicates the relative standing of a value in a dataset.

Percentiles help you get a better understanding of the distribution of your metric data.

For example, p(90) is the 90th percentile and means that 90% of the data within the period is lower than this value and 10% of the data is higher than this value.

static percentileRank(v1, v2?)

public static percentileRank(v1: number, v2?: number): string

Parameters

  • v1 number
  • v2 number

Returns

  • string

Percentile rank (PR) is the percentage of values that meet a fixed threshold.

  • If two numbers are given, they define the lower and upper bounds in absolute values, respectively.
  • If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, percentileRank(300) returns the percentage of data points that have a value of 300 or less. percentileRank(100, 2000) returns the percentage of data points that have a value between 100 and 2000.

static pr(v1, v2?)

public static pr(v1: number, v2?: number): string

Parameters

  • v1 number
  • v2 number

Returns

  • string

Shorter alias for percentileRank().

static tc(p1, p2?)

public static tc(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

Shorter alias for trimmedCount().

static tm(p1, p2?)

public static tm(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

A shorter alias for trimmedMean().

static trimmedCount(p1, p2?)

public static trimmedCount(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.

  • If two numbers are given, they define the lower and upper bounds in percentages, respectively.
  • If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, tc(90) returns the number of data points not including any data points that fall in the highest 10% of the values. tc(10, 90) returns the number of data points not including any data points that fall in the lowest 10% of the values and the highest 90% of the values.

static trimmedMean(p1, p2?)

public static trimmedMean(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

Trimmed mean (TM) is the mean of all values that are between two specified boundaries.

Values outside of the boundaries are ignored when the mean is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers are percentages.

  • If two numbers are given, they define the lower and upper bounds in percentages, respectively.
  • If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, tm(90) calculates the average after removing the 10% of data points with the highest values; tm(10, 90) calculates the average after removing the 10% with the lowest and 10% with the highest values.

static trimmedSum(p1, p2?)

public static trimmedSum(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.

It is equivalent to (Trimmed Mean) * (Trimmed count).

  • If two numbers are given, they define the lower and upper bounds in percentages, respectively.
  • If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, ts(90) returns the sum of the data points not including any data points that fall in the highest 10% of the values. ts(10, 90) returns the sum of the data points not including any data points that fall in the lowest 10% of the values and the highest 90% of the values.

static ts(p1, p2?)

public static ts(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

Shorter alias for trimmedSum().

static winsorizedMean(p1, p2?)

public static winsorizedMean(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

Winsorized mean (WM) is similar to trimmed mean.

However, with winsorized mean, the values that are outside the boundary are not ignored, but instead are considered to be equal to the value at the edge of the appropriate boundary. After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.

  • If two numbers are given, they define the lower and upper bounds in percentages, respectively.
  • If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, tm(90) calculates the average after removing the 10% of data points with the highest values; tm(10, 90) calculates the average after removing the 10% with the lowest and 10% with the highest values.

For example, wm(90) calculates the average while treating the 10% of the highest values to be equal to the value at the 90th percentile. wm(10, 90) calculates the average while treaing the bottom 10% and the top 10% of values to be equal to the boundary values.

static wm(p1, p2?)

public static wm(p1: number, p2?: number): string

Parameters

  • p1 number
  • p2 number

Returns

  • string

A shorter alias for winsorizedMean().

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