Amazon CloudWatch Construct Library
Metric objects
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:
# fn: lambda.Function
errors = fn.metric_errors()
Metric
objects can be account and region aware. You can specify account
and region
as properties of the metric, or use the metric.attachTo(Construct)
method. metric.attachTo()
will automatically copy the region
and account
fields of the Construct
, which can come from anywhere in the Construct tree.
You can also instantiate Metric
objects to reference any
published metric
that’s not exposed using a convenience method on the CDK construct.
For example:
hosted_zone = route53.HostedZone(self, "MyHostedZone", zone_name="example.org")
metric = cloudwatch.Metric(
namespace="AWS/Route53",
metric_name="DNSQueries",
dimensions_map={
"HostedZoneId": hosted_zone.hosted_zone_id
}
)
Instantiating a new Metric object
If you want to reference a metric that is not yet exposed by an existing construct,
you can instantiate a Metric
object to represent it. For example:
metric = cloudwatch.Metric(
namespace="MyNamespace",
metric_name="MyMetric",
dimensions_map={
"ProcessingStep": "Download"
}
)
Metric Math
Math expressions are supported by instantiating the MathExpression
class.
For example, a math expression that sums two other metrics looks like this:
# fn: lambda.Function
all_problems = cloudwatch.MathExpression(
expression="errors + throttles",
using_metrics={
"errors": fn.metric_errors(),
"throttles": fn.metric_throttles()
}
)
You can use MathExpression
objects like any other metric, including using
them in other math expressions:
# fn: lambda.Function
# all_problems: cloudwatch.MathExpression
problem_percentage = cloudwatch.MathExpression(
expression="(problems / invocations) * 100",
using_metrics={
"problems": all_problems,
"invocations": fn.metric_invocations()
}
)
Search Expressions
Math expressions also support search expressions. For example, the following search expression returns all CPUUtilization metrics that it finds, with the graph showing the Average statistic with an aggregation period of 5 minutes:
cpu_utilization = cloudwatch.MathExpression(
expression="SEARCH('{AWS/EC2,InstanceId} MetricName=\"CPUUtilization\"', 'Average', 300)",
# Specifying '' as the label suppresses the default behavior
# of using the expression as metric label. This is especially appropriate
# when using expressions that return multiple time series (like SEARCH()
# or METRICS()), to show the labels of the retrieved metrics only.
label=""
)
Cross-account and cross-region search expressions are also supported. Use
the searchAccount
and searchRegion
properties to specify the account
and/or region to evaluate the search expression against.
Aggregation
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:
# fn: lambda.Function
minute_error_rate = fn.metric_errors(
statistic=cloudwatch.Stats.AVERAGE,
period=Duration.minutes(1),
label="Lambda failure rate"
)
The statistic
field accepts a string
; the cloudwatch.Stats
object has a
number of predefined factory functions that help you constructs strings that are
appropriate for CloudWatch. The metricErrors
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 either0
or1
(for exampleErrors
for a Lambda) and some are only emitted as1
(for exampleNumberOfMessagesPublished
for an SNS topic).In case
0
-metrics are emitted, it makes sense to take theAverage
of this metric: the result will be the fraction of errors over all executions.If
0
-metrics are not emitted, theAverage
will always be equal to1
, and not be very useful.In order to simplify the mental model of
Metric
objects, we default to aggregating usingSum
, 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.
Available Aggregation Statistics
For your metrics aggregation, you can use the following statistics:
Statistic | Short format | Long format | Factory name |
---|---|---|---|
SampleCount (n) | ❌ | ❌ | Stats.SAMPLE_COUNT |
Average (avg) | ❌ | ❌ | Stats.AVERAGE |
Sum | ❌ | ❌ | Stats.SUM |
Minimum (min) | ❌ | ❌ | Stats.MINIMUM |
Maximum (max) | ❌ | ❌ | Stats.MAXIMUM |
Interquartile mean (IQM) | ❌ | ❌ | Stats.IQM |
Percentile (p) | p99 |
❌ | Stats.p(99) |
Winsorized mean (WM) | wm99 = WM(:99%) |
WM(x:y) \| WM(x%:y%) \| WM(x%:) \| WM(:y%) |
Stats.wm(10, 90) |
Trimmed count (TC) | tc99 = TC(:99%) |
TC(x:y) \| TC(x%:y%) \| TC(x%:) \| TC(:y%) |
Stats.tc(10, 90) |
Trimmed sum (TS) | ts99 = TS(:99%) |
TS(x:y) \| TS(x%:y%) \| TS(x%:) \| TS(:y%) |
Stats.ts(10, 90) |
Percentile rank (PR) | ❌ | PR(x:y) \| PR(x:) \| PR(:y) |
Stats.pr(10, 5000) |
The most common values are provided in the cloudwatch.Stats
class. You can provide any string if your statistic is not in the class.
Read more at CloudWatch statistics definitions.
# hosted_zone: route53.HostedZone
cloudwatch.Metric(
namespace="AWS/Route53",
metric_name="DNSQueries",
dimensions_map={
"HostedZoneId": hosted_zone.hosted_zone_id
},
statistic=cloudwatch.Stats.SAMPLE_COUNT,
period=Duration.minutes(5)
)
cloudwatch.Metric(
namespace="AWS/Route53",
metric_name="DNSQueries",
dimensions_map={
"HostedZoneId": hosted_zone.hosted_zone_id
},
statistic=cloudwatch.Stats.p(99),
period=Duration.minutes(5)
)
cloudwatch.Metric(
namespace="AWS/Route53",
metric_name="DNSQueries",
dimensions_map={
"HostedZoneId": hosted_zone.hosted_zone_id
},
statistic="TS(7.5%:90%)",
period=Duration.minutes(5)
)
Labels
Metric labels are displayed in the legend of graphs that include the metrics.
You can use dynamic labels to show summary information about the displayed time series in the legend. For example, if you use:
# fn: lambda.Function
minute_error_rate = fn.metric_errors(
statistic=cloudwatch.Stats.SUM,
period=Duration.hours(1),
# Show the maximum hourly error count in the legend
label="[max: ${MAX}] Lambda failure rate"
)
As the metric label, the maximum value in the visible range will be shown next to the time series name in the graph’s legend.
If the metric is a math expression producing more than one time series, the maximum will be individually calculated and shown for each time series produce by the math expression.
Alarms
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:
# fn: lambda.Function
cloudwatch.Alarm(self, "Alarm",
metric=fn.metric_errors(),
threshold=100,
evaluation_periods=2
)
Alternatively, you can call metric.createAlarm()
:
# fn: lambda.Function
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 tometric >= threshold
.evaluationPeriods
: how many consecutive periods the metric has to be breaching the threshold for the alarm to trigger.
To create a cross-account alarm, make sure you have enabled cross-account functionality in CloudWatch. Then, set the account
property in the Metric
object either manually or via the metric.attachTo()
method.
Please note that it is not possible to:
Create a cross-Account alarm that has
evaluateLowSampleCountPercentile: "ignore"
. The reason is that the only way to pass an AccountID is to use theMetrics
field of the Alarm resource. If we use theMetrics
field, the CloudWatch event that is used to evaluate the Alarm doesn’t have aSampleCount
field anymore (”When CloudWatch evaluates alarms, periods are aggregated into single data points”). The result is that the Alarm cannot evaluate at all.Create a cross-Region alarm (source).
Alarm Actions
To add actions to an alarm, use the integration classes from the
aws-cdk-lib/aws-cloudwatch-actions
package. For example, to post a message to
an SNS topic when an alarm breaches, do the following:
import aws_cdk.aws_cloudwatch_actions as cw_actions
# alarm: cloudwatch.Alarm
topic = sns.Topic(self, "Topic")
alarm.add_alarm_action(cw_actions.SnsAction(topic))
Notification formats
Alarms can be created in one of two “formats”:
With “top-level parameters” (these are the classic style of CloudWatch Alarms).
With a list of metrics specifications (these are the modern style of CloudWatch Alarms).
For backwards compatibility, CDK will try to create classic, top-level CloudWatch alarms as much as possible, unless you are using features that cannot be expressed in that format. Features that require the new-style alarm format are:
Metric math
Cross-account metrics
Labels
The difference between these two does not impact the functionality of the alarm in any way, except that the format of the notifications the Alarm generates is different between them. This affects both the notifications sent out over SNS, as well as the EventBridge events generated by this Alarm. If you are writing code to consume these notifications, be sure to handle both formats.
Composite Alarms
Composite Alarms can be created from existing Alarm resources.
# alarm1: cloudwatch.Alarm
# alarm2: cloudwatch.Alarm
# alarm3: cloudwatch.Alarm
# alarm4: cloudwatch.Alarm
alarm_rule = cloudwatch.AlarmRule.any_of(
cloudwatch.AlarmRule.all_of(
cloudwatch.AlarmRule.any_of(alarm1,
cloudwatch.AlarmRule.from_alarm(alarm2, cloudwatch.AlarmState.OK), alarm3),
cloudwatch.AlarmRule.not(cloudwatch.AlarmRule.from_alarm(alarm4, cloudwatch.AlarmState.INSUFFICIENT_DATA))),
cloudwatch.AlarmRule.from_boolean(False))
cloudwatch.CompositeAlarm(self, "MyAwesomeCompositeAlarm",
alarm_rule=alarm_rule
)
Actions Suppressor
If you want to disable actions of a Composite Alarm based on a certain condition, you can use Actions Suppression.
# alarm1: cloudwatch.Alarm
# alarm2: cloudwatch.Alarm
# on_alarm_action: cloudwatch.IAlarmAction
# on_ok_action: cloudwatch.IAlarmAction
# actions_suppressor: cloudwatch.Alarm
alarm_rule = cloudwatch.AlarmRule.any_of(alarm1, alarm2)
my_composite_alarm = cloudwatch.CompositeAlarm(self, "MyAwesomeCompositeAlarm",
alarm_rule=alarm_rule,
actions_suppressor=actions_suppressor
)
my_composite_alarm.add_alarm_action(on_alarm_action)
my_composite_alarm.add_ok_action(on_ok_action)
In the provided example, if actionsSuppressor
is in ALARM
state, onAlarmAction
won’t be triggered even if myCompositeAlarm
goes into ALARM
state.
Similar, if actionsSuppressor
is in ALARM
state and myCompositeAlarm
goes from ALARM
into OK
state, onOkAction
won’t be triggered.
A note on units
In CloudWatch, Metrics datums are emitted with units, such as seconds
or
bytes
. When Metric
objects are given a unit
attribute, it will be used to
filter the stream of metric datums for datums emitted using the same unit
attribute.
In particular, the unit
field is not used to rescale datums or alarm threshold
values (for example, it cannot be used to specify an alarm threshold in
Megabytes if the metric stream is being emitted as bytes).
You almost certainly don’t want to specify the unit
property when creating
Metric
objects (which will retrieve all datums regardless of their unit),
unless you have very specific requirements. Note that in any case, CloudWatch
only supports filtering by unit
for Alarms, not in Dashboard graphs.
Please see the following GitHub issue for a discussion on real unit calculations in CDK: https://github.com/aws/aws-cdk/issues/5595
Dashboards
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.AlarmStatusWidget
– shows the status of your alarms in a grid view.
Graph widget
A graph widget can display any number of metrics on either the left
or
right
vertical axis:
# dashboard: cloudwatch.Dashboard
# execution_count_metric: cloudwatch.Metric
# error_count_metric: cloudwatch.Metric
dashboard.add_widgets(cloudwatch.GraphWidget(
title="Executions vs error rate",
left=[execution_count_metric],
right=[error_count_metric.with(
statistic=cloudwatch.Stats.AVERAGE,
label="Error rate",
color=cloudwatch.Color.GREEN
)]
))
Using the methods addLeftMetric()
and addRightMetric()
you can add metrics to a graph widget later on.
Graph widgets can also display annotations attached to the left or right y-axis or the x-axis.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.GraphWidget(
# ...
left_annotations=[cloudwatch.HorizontalAnnotation(value=1800, label=Duration.minutes(30).to_human_string(), color=cloudwatch.Color.RED), cloudwatch.HorizontalAnnotation(value=3600, label="1 hour", color="#2ca02c")
],
vertical_annotations=[cloudwatch.VerticalAnnotation(date="2022-10-19T00:00:00Z", label="Deployment", color=cloudwatch.Color.RED)
]
))
The graph legend can be adjusted from the default position at bottom of the widget.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.GraphWidget(
# ...
legend_position=cloudwatch.LegendPosition.RIGHT
))
The graph can publish live data within the last minute that has not been fully aggregated.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.GraphWidget(
# ...
live_data=True
))
The graph view can be changed from default ‘timeSeries’ to ‘bar’ or ‘pie’.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.GraphWidget(
# ...
view=cloudwatch.GraphWidgetView.BAR
))
The start
and end
properties can be used to specify the time range for each graph widget independently from those of the dashboard.
The parameters can be specified at GraphWidget
, GaugeWidget
, and SingleValueWidget
.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.GraphWidget(
# ...
start="-P7D",
end="2018-12-17T06:00:00.000Z"
))
Table Widget
A TableWidget
can display any number of metrics in tabular form.
# dashboard: cloudwatch.Dashboard
# execution_count_metric: cloudwatch.Metric
dashboard.add_widgets(cloudwatch.TableWidget(
title="Executions",
metrics=[execution_count_metric]
))
The layout
property can be used to invert the rows and columns of the table.
The default cloudwatch.TableLayout.HORIZONTAL
means that metrics are shown in rows and datapoints in columns.
cloudwatch.TableLayout.VERTICAL
means that metrics are shown in columns and datapoints in rows.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TableWidget(
# ...
layout=cloudwatch.TableLayout.VERTICAL
))
The summary
property allows customizing the table to show summary columns (columns
sub property),
whether to make the summary columns sticky remaining in view while scrolling (sticky
sub property),
and to optionally only present summary columns (hideNonSummaryColumns
sub property).
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TableWidget(
# ...
summary=cloudwatch.TableSummaryProps(
columns=[cloudwatch.TableSummaryColumn.AVERAGE],
hide_non_summary_columns=True,
sticky=True
)
))
The thresholds
property can be used to highlight cells with a color when the datapoint value falls within the threshold.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TableWidget(
# ...
thresholds=[
cloudwatch.TableThreshold.above(1000, cloudwatch.Color.RED),
cloudwatch.TableThreshold.between(500, 1000, cloudwatch.Color.ORANGE),
cloudwatch.TableThreshold.below(500, cloudwatch.Color.GREEN)
]
))
The showUnitsInLabel
property can be used to display what unit is associated with a metric in the label column.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TableWidget(
# ...
show_units_in_label=True
))
The fullPrecision
property can be used to show as many digits as can fit in a cell, before rounding.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TableWidget(
# ...
full_precision=True
))
Gauge widget
Gauge graph requires the max and min value of the left Y axis, if no value is informed the limits will be from 0 to 100.
# dashboard: cloudwatch.Dashboard
# error_alarm: cloudwatch.Alarm
# gauge_metric: cloudwatch.Metric
dashboard.add_widgets(cloudwatch.GaugeWidget(
metrics=[gauge_metric],
left_yAxis=cloudwatch.YAxisProps(
min=0,
max=1000
)
))
Alarm widget
An alarm widget shows the graph and the alarm line of a single alarm:
# dashboard: cloudwatch.Dashboard
# error_alarm: cloudwatch.Alarm
dashboard.add_widgets(cloudwatch.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):
# dashboard: cloudwatch.Dashboard
# visitor_count: cloudwatch.Metric
# purchase_count: cloudwatch.Metric
dashboard.add_widgets(cloudwatch.SingleValueWidget(
metrics=[visitor_count, purchase_count]
))
Show as many digits as can fit, before rounding.
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.SingleValueWidget(
metrics=[],
full_precision=True
))
Sparkline allows you to glance the trend of a metric by displaying a simplified linegraph below the value. You can’t use sparkline: true
together with setPeriodToTimeRange: true
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.SingleValueWidget(
metrics=[],
sparkline=True
))
Period allows you to set the default period for the widget:
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.SingleValueWidget(
metrics=[],
period=Duration.minutes(15)
))
Text widget
A text widget shows an arbitrary piece of MarkDown. Use this to add explanations to your dashboard:
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TextWidget(
markdown="# Key Performance Indicators"
))
Optionally set the TextWidget background to be transparent
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.TextWidget(
markdown="# Key Performance Indicators",
background=cloudwatch.TextWidgetBackground.TRANSPARENT
))
Alarm Status widget
An alarm status widget displays instantly the status of any type of alarms and gives the ability to aggregate one or more alarms together in a small surface.
# dashboard: cloudwatch.Dashboard
# error_alarm: cloudwatch.Alarm
dashboard.add_widgets(
cloudwatch.AlarmStatusWidget(
alarms=[error_alarm]
))
An alarm status widget only showing firing alarms, sorted by state and timestamp:
# dashboard: cloudwatch.Dashboard
# error_alarm: cloudwatch.Alarm
dashboard.add_widgets(cloudwatch.AlarmStatusWidget(
title="Errors",
alarms=[error_alarm],
sort_by=cloudwatch.AlarmStatusWidgetSortBy.STATE_UPDATED_TIMESTAMP,
states=[cloudwatch.AlarmState.ALARM]
))
Query results widget
A LogQueryWidget
shows the results of a query from Logs Insights:
# dashboard: cloudwatch.Dashboard
dashboard.add_widgets(cloudwatch.LogQueryWidget(
log_group_names=["my-log-group"],
view=cloudwatch.LogQueryVisualizationType.TABLE,
# The lines will be automatically combined using '\n|'.
query_lines=["fields @message", "filter @message like /Error/"
]
))
Custom widget
A CustomWidget
shows the result of an AWS Lambda function:
# dashboard: cloudwatch.Dashboard
# Import or create a lambda function
fn = lambda_.Function.from_function_arn(dashboard, "Function", "arn:aws:lambda:us-east-1:123456789012:function:MyFn")
dashboard.add_widgets(cloudwatch.CustomWidget(
function_arn=fn.function_arn,
title="My lambda baked widget"
))
You can learn more about custom widgets in the Amazon Cloudwatch User Guide.
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
Column widget
A column widget contains other widgets and they will be laid out in a vertical column. Widgets will be put one after another in order.
# widget_a: cloudwatch.IWidget
# widget_b: cloudwatch.IWidget
cloudwatch.Column(widget_a, widget_b)
You can add a widget after object instantiation with the method
addWidget()
. Each new widget will be put at the bottom of the column.
Row widget
A row widget contains other widgets and they will be laid out in a horizontal row. Widgets will be put one after another in order. If the total width of the row exceeds the max width of the grid of 24 columns, the row will wrap automatically and adapt its height.
# widget_a: cloudwatch.IWidget
# widget_b: cloudwatch.IWidget
cloudwatch.Row(widget_a, widget_b)
You can add a widget after object instantiation with the method
addWidget()
.
Interval duration for dashboard
Interval duration for metrics in dashboard. You can specify defaultInterval
with
the relative time(eg. 7 days) as Duration.days(7)
.
import aws_cdk.aws_cloudwatch as cw
dashboard = cw.Dashboard(self, "Dash",
default_interval=Duration.days(7)
)
Here, the dashboard would show the metrics for the last 7 days.
Dashboard variables
Dashboard variables are a convenient way to create flexible dashboards that display different content depending on the value of an input field within a dashboard. They create a dashboard on which it’s possible to quickly switch between different Lambda functions, Amazon EC2 instances, etc.
You can learn more about Dashboard variables in the Amazon Cloudwatch User Guide
There are two types of dashboard variables available: a property variable and a pattern variable.
Property variables can change any JSON property in the JSON source of a dashboard like
region
. It can also change the dimension name for a metric.Pattern variables use a regular expression pattern to change all or part of a JSON property.
A use case of a property variable is a dashboard with the ability to toggle the region
property to see the same dashboard in different regions:
import aws_cdk.aws_cloudwatch as cw
dashboard = cw.Dashboard(self, "Dash",
default_interval=Duration.days(7),
variables=[cw.DashboardVariable(
id="region",
type=cw.VariableType.PROPERTY,
label="Region",
input_type=cw.VariableInputType.RADIO,
value="region",
values=cw.Values.from_values(cw.VariableValue(label="IAD", value="us-east-1"), label="DUB", value="us-west-2"),
default_value=cw.DefaultValue.value("us-east-1"),
visible=True
)]
)
This example shows how to change region
everywhere, assuming the current dashboard is showing region us-east-1
already, by using pattern variable
import aws_cdk.aws_cloudwatch as cw
dashboard = cw.Dashboard(self, "Dash",
default_interval=Duration.days(7),
variables=[cw.DashboardVariable(
id="region2",
type=cw.VariableType.PATTERN,
label="RegionPattern",
input_type=cw.VariableInputType.INPUT,
value="us-east-1",
default_value=cw.DefaultValue.value("us-east-1"),
visible=True
)]
)
The following example generates a Lambda function variable, with a radio button for each function. Functions are discovered by a metric query search.
The values
with cw.Values.fromSearchComponents
indicates that the values will be populated from FunctionName
values retrieved from the search expression {AWS/Lambda,FunctionName} MetricName=\"Duration\"
.
The defaultValue
with cw.DefaultValue.FIRST
indicates that the default value will be the first value returned from the search.
import aws_cdk.aws_cloudwatch as cw
dashboard = cw.Dashboard(self, "Dash",
default_interval=Duration.days(7),
variables=[cw.DashboardVariable(
id="functionName",
type=cw.VariableType.PATTERN,
label="Function",
input_type=cw.VariableInputType.RADIO,
value="originalFuncNameInDashboard",
# equivalent to cw.Values.fromSearch('{AWS/Lambda,FunctionName} MetricName=\"Duration\"', 'FunctionName')
values=cw.Values.from_search_components(
namespace="AWS/Lambda",
dimensions=["FunctionName"],
metric_name="Duration",
populate_from="FunctionName"
),
default_value=cw.DefaultValue.FIRST,
visible=True
)]
)
You can add a variable after object instantiation with the method dashboard.addVariable()
.