Amazon CloudWatch Construct Library¶--- ![Stability: Stable](https://img.shields.io/badge/stability-Stable-success.svg?style=for-the-badge) ---
Metric objects represent a metric that is emitted by AWS services or your own
application, such as
Metric objects can be constructed directly or are exposed by resources as
attributes. Resources that expose metrics will have functions that look
metricXxx() which will return a Metric object, initialized with defaults
that make sense.
lambda.Function objects have the
fn.metricErrors() method, which
represents the amount of errors reported by that Lambda function:
# Example automatically generated. See https://github.com/aws/jsii/issues/826 errors = fn.metric_errors()
To graph or alarm on metrics you must aggregate them first, using a function
Average or a percentile function like
P99. By default, most Metric objects
returned by CDK libraries will be configured as
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
300 seconds, i.e. it represents the number of times that event occurred over the
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 automatically generated. See https://github.com/aws/jsii/issues/826 minute_error_rate = fn.metric_errors( statistic="avg", period=Duration.minutes(1), 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
Sumto count discrete events is that some events are emitted as either
Errorsfor a Lambda) and some are only emitted as
NumberOfMessagesPublishedfor an SNS topic).
0-metrics are emitted, it makes sense to take the
Averageof this metric: the result will be the fraction of errors over all executions.
0-metrics are not emitted, the
Averagewill always be equal to
1, and not be very useful.
In order to simplify the mental model of
Metricobjects, 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
object, passing the
Metric object to set the alarm on:
# Example automatically generated. See https://github.com/aws/jsii/issues/826 Alarm(self, "Alarm", metric=fn.metric_errors(), threshold=100, evaluation_periods=2 )
Alternatively, you can call
# Example automatically generated. See https://github.com/aws/jsii/issues/826 fn.metric_errors().create_alarm(self, "Alarm", threshold=100, evaluation_periods=2 )
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.
A graph widget can display any number of metrics on either the
right vertical axis:
# Example automatically generated. See https://github.com/aws/jsii/issues/826 dashboard.add_widgets(GraphWidget( title="Executions vs error rate", left=[execution_count_metric], right=[error_count_metric.with( statistic="average", label="Error rate", color="00FF00" )] ))
An alarm widget shows the graph and the alarm line of a single alarm:
# Example automatically generated. See https://github.com/aws/jsii/issues/826 dashboard.add_widgets(AlarmWidget( title="Errors", alarm=error_alarm ))
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 automatically generated. See https://github.com/aws/jsii/issues/826 dashboard.add_widgets(SingleValueWidget( metrics=[visitor_count, purchase_count] ))
A text widget shows an arbitrary piece of MarkDown. Use this to add explanations to your dashboard:
# Example automatically generated. See https://github.com/aws/jsii/issues/826 dashboard.add_widgets(TextWidget( markdown="# Key Performance Indicators" ))
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
height property, and they will be automatically
laid out either horizontally or vertically stacked to fill out the available
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