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CDK Constructs for AWS CloudWatch

Project description

Amazon CloudWatch Construct Library


Stability: Stable


Metric objects represent a metric that is emitted by AWS services or your own application, such as CPUUsage, FailureCount or Bandwidth.

Metric objects can be constructed directly or are exposed by resources as attributes. Resources that expose metrics will have functions that look like metricXxx() which will return a Metric object, initialized with defaults that make sense.

For example, lambda.Function objects have the fn.metricErrors() method, which represents the amount of errors reported by that Lambda function:

# Example may have issues. See
errors = fn.metric_errors()


To graph or alarm on metrics you must aggregate them first, using a function like Average or a percentile function like P99. By default, most Metric objects returned by CDK libraries will be configured as Average over 300 seconds (5 minutes). The exception is if the metric represents a count of discrete events, such as failures. In that case, the Metric object will be configured as Sum over 300 seconds, i.e. it represents the number of times that event occurred over the time period.

If you want to change the default aggregation of the Metric object (for example, the function or the period), you can do so by passing additional parameters to the metric function call:

# Example may have issues. See
minute_error_rate = fn.metric_errors(
    label="Lambda failure rate"

This function also allows changing the metric label or color (which will be useful when embedding them in graphs, see below).

Rates versus Sums

The reason for using Sum to count discrete events is that some events are emitted as either 0 or 1 (for example Errors for a Lambda) and some are only emitted as 1 (for example NumberOfMessagesPublished for an SNS topic).

In case 0-metrics are emitted, it makes sense to take the Average of this metric: the result will be the fraction of errors over all executions.

If 0-metrics are not emitted, the Average will always be equal to 1, and not be very useful.

In order to simplify the mental model of Metric objects, we default to aggregating using Sum, which will be the same for both metrics types. If you happen to know the Metric you want to alarm on makes sense as a rate (Average) you can always choose to change the statistic.


Alarms can be created on metrics in one of two ways. Either create an Alarm object, passing the Metric object to set the alarm on:

# Example may have issues. See
Alarm(self, "Alarm",

Alternatively, you can call metric.createAlarm():

# Example may have issues. See
fn.metric_errors().create_alarm(self, "Alarm",

The most important properties to set while creating an Alarms are:

  • threshold: the value to compare the metric against.
  • comparisonOperator: the comparison operation to use, defaults to metric >= threshold.
  • evaluationPeriods: how many consecutive periods the metric has to be breaching the the threshold for the alarm to trigger.


Dashboards are set of Widgets stored server-side which can be accessed quickly from the AWS console. Available widgets are graphs of a metric over time, the current value of a metric, or a static piece of Markdown which explains what the graphs mean.

The following widgets are available:

  • GraphWidget -- shows any number of metrics on both the left and right vertical axes.
  • AlarmWidget -- shows the graph and alarm line for a single alarm.
  • SingleValueWidget -- shows the current value of a set of metrics.
  • TextWidget -- shows some static Markdown.

Graph widget

A graph widget can display any number of metrics on either the left or right vertical axis:

# Example may have issues. See
    title="Executions vs error rate",


        label="Error rate",

Alarm widget

An alarm widget shows the graph and the alarm line of a single alarm:

# Example may have issues. See

Single value widget

A single-value widget shows the latest value of a set of metrics (as opposed to a graph of the value over time):

# Example may have issues. See
    metrics=[visitor_count, purchase_count]

Text widget

A text widget shows an arbitrary piece of MarkDown. Use this to add explanations to your dashboard:

# Example may have issues. See
    markdown="# Key Performance Indicators"

Dashboard Layout

The widgets on a dashboard are visually laid out in a grid that is 24 columns wide. Normally you specify X and Y coordinates for the widgets on a Dashboard, but because this is inconvenient to do manually, the library contains a simple layout system to help you lay out your dashboards the way you want them to.

Widgets have a width and height property, and they will be automatically laid out either horizontally or vertically stacked to fill out the available space.

Widgets are added to a Dashboard by calling add(widget1, widget2, ...). Widgets given in the same call will be laid out horizontally. Widgets given in different calls will be laid out vertically. To make more complex layouts, you can use the following widgets to pack widgets together in different ways:

  • Column: stack two or more widgets vertically.
  • Row: lay out two or more widgets horizontally.
  • Spacer: take up empty space

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