- Resource: AlertPolicy
- Documentation
- Link
- Condition
- MetricThreshold
- Aggregation
- Aligner
- Reducer
- ForecastOptions
- ComparisonType
- Trigger
- EvaluationMissingData
- MetricAbsence
- LogMatch
- MonitoringQueryLanguageCondition
- PrometheusQueryLanguageCondition
- ConditionCombinerType
- AlertStrategy
- NotificationRateLimit
- NotificationPrompt
- NotificationChannelStrategy
- Severity
- Methods
Resource: AlertPolicy
A description of the conditions under which some aspect of your system is considered to be "unhealthy" and the ways to notify people or services about this state. For an overview of alerting policies, see Introduction to Alerting.
JSON representation |
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{ "name": string, "displayName": string, "documentation": { object ( |
Fields | |
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name |
Identifier. Required if the policy exists. The resource name for this policy. The format is:
|
displayName |
A short name or phrase used to identify the policy in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple policies in the same project. The name is limited to 512 Unicode characters. The convention for the displayName of a PrometheusQueryLanguageCondition is "{rule group name}/{alert name}", where the {rule group name} and {alert name} should be taken from the corresponding Prometheus configuration file. This convention is not enforced. In any case the displayName is not a unique key of the AlertPolicy. |
documentation |
Documentation that is included with notifications and incidents related to this policy. Best practice is for the documentation to include information to help responders understand, mitigate, escalate, and correct the underlying problems detected by the alerting policy. Notification channels that have limited capacity might not show this documentation. |
userLabels |
User-supplied key/value data to be used for organizing and identifying the The field can contain up to 64 entries. Each key and value is limited to 63 Unicode characters or 128 bytes, whichever is smaller. Labels and values can contain only lowercase letters, numerals, underscores, and dashes. Keys must begin with a letter. Note that Prometheus {alert name} is a valid Prometheus label names, whereas Prometheus {rule group} is an unrestricted UTF-8 string. This means that they cannot be stored as-is in user labels, because they may contain characters that are not allowed in user-label values. |
conditions[] |
A list of conditions for the policy. The conditions are combined by AND or OR according to the |
combiner |
How to combine the results of multiple conditions to determine if an incident should be opened. If |
enabled |
Whether or not the policy is enabled. On write, the default interpretation if unset is that the policy is enabled. On read, clients should not make any assumption about the state if it has not been populated. The field should always be populated on List and Get operations, unless a field projection has been specified that strips it out. |
validity |
Read-only description of how the alerting policy is invalid. This field is only set when the alerting policy is invalid. An invalid alerting policy will not generate incidents. |
notificationChannels[] |
Identifies the notification channels to which notifications should be sent when incidents are opened or closed or when new violations occur on an already opened incident. Each element of this array corresponds to the
|
creationRecord |
A read-only record of the creation of the alerting policy. If provided in a call to create or update, this field will be ignored. |
mutationRecord |
A read-only record of the most recent change to the alerting policy. If provided in a call to create or update, this field will be ignored. |
alertStrategy |
Control over how this alerting policy's notification channels are notified. |
severity |
Optional. The severity of an alerting policy indicates how important incidents generated by that policy are. The severity level will be displayed on the Incident detail page and in notifications. |
Documentation
Documentation that is included in the notifications and incidents pertaining to this policy.
JSON representation |
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{
"content": string,
"mimeType": string,
"subject": string,
"links": [
{
object ( |
Fields | |
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content |
The body of the documentation, interpreted according to |
mimeType |
The format of the |
subject |
Optional. The subject line of the notification. The subject line may not exceed 10,240 bytes. In notifications generated by this policy, the contents of the subject line after variable expansion will be truncated to 255 bytes or shorter at the latest UTF-8 character boundary. The 255-byte limit is recommended by this thread. It is both the limit imposed by some third-party ticketing products and it is common to define textual fields in databases as VARCHAR(255). The contents of the subject line can be templatized by using variables. If this field is missing or empty, a default subject line will be generated. |
links[] |
Optional. Links to content such as playbooks, repositories, and other resources. This field can contain up to 3 entries. |
Link
Links to content such as playbooks, repositories, and other resources.
JSON representation |
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{ "displayName": string, "url": string } |
Fields | |
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displayName |
A short display name for the link. The display name must not be empty or exceed 63 characters. Example: "playbook". |
url |
The url of a webpage. A url can be templatized by using variables in the path or the query parameters. The total length of a URL should not exceed 2083 characters before and after variable expansion. Example: "https://my_domain.com/playbook?name=${resource.name}" |
Condition
A condition is a true/false test that determines when an alerting policy should open an incident. If a condition evaluates to true, it signifies that something is wrong.
JSON representation |
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{ "name": string, "displayName": string, // Union field |
Fields | |
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name |
Required if the condition exists. The unique resource name for this condition. Its format is:
When calling the When calling the Best practice is to preserve |
displayName |
A short name or phrase used to identify the condition in dashboards, notifications, and incidents. To avoid confusion, don't use the same display name for multiple conditions in the same policy. |
Union field condition . Only one of the following condition types will be specified. condition can be only one of the following: |
|
conditionThreshold |
A condition that compares a time series against a threshold. |
conditionAbsent |
A condition that checks that a time series continues to receive new data points. |
conditionMatchedLog |
A condition that checks for log messages matching given constraints. If set, no other conditions can be present. |
conditionMonitoringQueryLanguage |
A condition that uses the Monitoring Query Language to define alerts. |
conditionPrometheusQueryLanguage |
A condition that uses the Prometheus query language to define alerts. |
MetricThreshold
A condition type that compares a collection of time series against a threshold.
JSON representation |
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{ "filter": string, "aggregations": [ { object ( |
Fields | |
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filter |
Required. A filter that identifies which time series should be compared with the threshold. The filter is similar to the one that is specified in the |
aggregations[] |
Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified. This field is similar to the one in the |
denominatorFilter |
A filter that identifies a time series that should be used as the denominator of a ratio that will be compared with the threshold. If a The filter must specify the metric type and optionally may contain restrictions on resource type, resource labels, and metric labels. This field may not exceed 2048 Unicode characters in length. |
denominatorAggregations[] |
Specifies the alignment of data points in individual time series selected by When computing ratios, the |
forecastOptions |
When this field is present, the |
comparison |
The comparison to apply between the time series (indicated by Only |
thresholdValue |
A value against which to compare the time series. |
duration |
The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the |
trigger |
The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by |
evaluationMissingData |
A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. To use this control, the value of the |
Aggregation
Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set is aligned to the same time interval boundaries, then the set of time series is optionally reduced in number.
Alignment consists of applying the perSeriesAligner
operation to each time series after its data has been divided into regular alignmentPeriod
time intervals. This process takes all of the data points in an alignment period, applies a mathematical transformation such as averaging, minimum, maximum, delta, etc., and converts them into a single data point per period.
Reduction is when the aligned and transformed time series can optionally be combined, reducing the number of time series through similar mathematical transformations. Reduction involves applying a crossSeriesReducer
to all the time series, optionally sorting the time series into subsets with groupByFields
, and applying the reducer to each subset.
The raw time series data can contain a huge amount of information from multiple sources. Alignment and reduction transforms this mass of data into a more manageable and representative collection of data, for example "the 95% latency across the average of all tasks in a cluster". This representative data can be more easily graphed and comprehended, and the individual time series data is still available for later drilldown. For more details, see Filtering and aggregation.
JSON representation |
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{ "alignmentPeriod": string, "perSeriesAligner": enum ( |
Fields | |
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alignmentPeriod |
The The value must be at least 60 seconds. If a per-series aligner other than The maximum value of the |
perSeriesAligner |
An Not all alignment operations may be applied to all time series. The valid choices depend on the Time series data must be aligned in order to perform cross-time series reduction. If |
crossSeriesReducer |
The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series. Not all reducer operations can be applied to all time series. The valid choices depend on the Time series data must first be aligned (see |
groupByFields[] |
The set of fields to preserve when |
Aligner
The Aligner
specifies the operation that will be applied to the data points in each alignment period in a time series. Except for ALIGN_NONE
, which specifies that no operation be applied, each alignment operation replaces the set of data values in each alignment period with a single value: the result of applying the operation to the data values. An aligned time series has a single data value at the end of each alignmentPeriod
.
An alignment operation can change the data type of the values, too. For example, if you apply a counting operation to boolean values, the data valueType
in the original time series is BOOLEAN
, but the valueType
in the aligned result is INT64
.
Enums | |
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ALIGN_NONE |
No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The valueType of the result is the same as the valueType of the input. |
ALIGN_DELTA |
Align and convert to This alignment is valid for |
ALIGN_RATE |
Align and convert to a rate. The result is computed as This aligner is valid for If, by "rate", you mean "percentage change", see the |
ALIGN_INTERPOLATE |
Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The valueType of the aligned result is the same as the valueType of the input. |
ALIGN_NEXT_OLDER |
Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The valueType of the aligned result is the same as the valueType of the input. |
ALIGN_MIN |
Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The valueType of the aligned result is the same as the valueType of the input. |
ALIGN_MAX |
Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The valueType of the aligned result is the same as the valueType of the input. |
ALIGN_MEAN |
Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The valueType of the aligned result is DOUBLE . |
ALIGN_COUNT |
Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The valueType of the aligned result is INT64 . |
ALIGN_SUM |
Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The valueType of the aligned result is the same as the valueType of the input. |
ALIGN_STDDEV |
Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The valueType of the output is DOUBLE . |
ALIGN_COUNT_TRUE |
Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The valueType of the output is INT64 . |
ALIGN_COUNT_FALSE |
Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The valueType of the output is INT64 . |
ALIGN_FRACTION_TRUE |
Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range [0.0, 1.0] and has valueType DOUBLE . |
ALIGN_PERCENTILE_99 |
Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with valueType DOUBLE . |
ALIGN_PERCENTILE_95 |
Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with valueType DOUBLE . |
ALIGN_PERCENTILE_50 |
Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with valueType DOUBLE . |
ALIGN_PERCENTILE_05 |
Align the time series by using percentile aggregation. The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with valueType DOUBLE . |
ALIGN_PERCENT_CHANGE |
Align and convert to a percentage change. This aligner is valid for If the values of A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are |
Reducer
A Reducer operation describes how to aggregate data points from multiple time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.
Enums | |
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REDUCE_NONE |
No cross-time series reduction. The output of the Aligner is returned. |
REDUCE_MEAN |
Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The valueType of the output is DOUBLE . |
REDUCE_MIN |
Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The valueType of the output is the same as the valueType of the input. |
REDUCE_MAX |
Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The valueType of the output is the same as the valueType of the input. |
REDUCE_SUM |
Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The valueType of the output is the same as the valueType of the input. |
REDUCE_STDDEV |
Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The valueType of the output is DOUBLE . |
REDUCE_COUNT |
Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string valueType . The valueType of the output is INT64 . |
REDUCE_COUNT_TRUE |
Reduce by computing the number of True -valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean valueType . The valueType of the output is INT64 . |
REDUCE_COUNT_FALSE |
Reduce by computing the number of False -valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean valueType . The valueType of the output is INT64 . |
REDUCE_FRACTION_TRUE |
Reduce by computing the ratio of the number of True -valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean valueType . The output value is in the range [0.0, 1.0] and has valueType DOUBLE . |
REDUCE_PERCENTILE_99 |
Reduce by computing the 99th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE . |
REDUCE_PERCENTILE_95 |
Reduce by computing the 95th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE . |
REDUCE_PERCENTILE_50 |
Reduce by computing the 50th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE . |
REDUCE_PERCENTILE_05 |
Reduce by computing the 5th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE . |
ForecastOptions
Options used when forecasting the time series and testing the predicted value against the threshold.
JSON representation |
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{ "forecastHorizon": string } |
Fields | |
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forecastHorizon |
Required. The length of time into the future to forecast whether a time series will violate the threshold. If the predicted value is found to violate the threshold, and the violation is observed in all forecasts made for the configured |
ComparisonType
Specifies an ordering relationship on two arguments, called left
and right
.
Enums | |
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COMPARISON_UNSPECIFIED |
No ordering relationship is specified. |
COMPARISON_GT |
True if the left argument is greater than the right argument. |
COMPARISON_GE |
True if the left argument is greater than or equal to the right argument. |
COMPARISON_LT |
True if the left argument is less than the right argument. |
COMPARISON_LE |
True if the left argument is less than or equal to the right argument. |
COMPARISON_EQ |
True if the left argument is equal to the right argument. |
COMPARISON_NE |
True if the left argument is not equal to the right argument. |
Trigger
Specifies how many time series must fail a predicate to trigger a condition. If not specified, then a {count: 1}
trigger is used.
JSON representation |
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{ // Union field |
Fields | |
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Union field type . A type of trigger. type can be only one of the following: |
|
count |
The absolute number of time series that must fail the predicate for the condition to be triggered. |
percent |
The percentage of time series that must fail the predicate for the condition to be triggered. |
EvaluationMissingData
A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. This control doesn't affect metric-absence policies.
Enums | |
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EVALUATION_MISSING_DATA_UNSPECIFIED |
An unspecified evaluation missing data option. Equivalent to EVALUATION_MISSING_DATA_NO_OP. |
EVALUATION_MISSING_DATA_INACTIVE |
If there is no data to evaluate the condition, then evaluate the condition as false. |
EVALUATION_MISSING_DATA_ACTIVE |
If there is no data to evaluate the condition, then evaluate the condition as true. |
EVALUATION_MISSING_DATA_NO_OP |
Do not evaluate the condition to any value if there is no data. |
MetricAbsence
A condition type that checks that monitored resources are reporting data. The configuration defines a metric and a set of monitored resources. The predicate is considered in violation when a time series for the specified metric of a monitored resource does not include any data in the specified duration
.
JSON representation |
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{ "filter": string, "aggregations": [ { object ( |
Fields | |
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filter |
Required. A filter that identifies which time series should be compared with the threshold. The filter is similar to the one that is specified in the |
aggregations[] |
Specifies the alignment of data points in individual time series as well as how to combine the retrieved time series together (such as when aggregating multiple streams on each resource to a single stream for each resource or when aggregating streams across all members of a group of resources). Multiple aggregations are applied in the order specified. This field is similar to the one in the |
duration |
The amount of time that a time series must fail to report new data to be considered failing. The minimum value of this field is 120 seconds. Larger values that are a multiple of a minute--for example, 240 or 300 seconds--are supported. If an invalid value is given, an error will be returned. The |
trigger |
The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by |
LogMatch
A condition type that checks whether a log message in the scoping project satisfies the given filter. Logs from other projects in the metrics scope are not evaluated.
JSON representation |
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{ "filter": string, "labelExtractors": { string: string, ... } } |
Fields | |
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filter |
Required. A logs-based filter. See Advanced Logs Queries for how this filter should be constructed. |
labelExtractors |
Optional. A map from a label key to an extractor expression, which is used to extract the value for this label key. Each entry in this map is a specification for how data should be extracted from log entries that match Please see the documentation on logs-based metric |
MonitoringQueryLanguageCondition
A condition type that allows alerting policies to be defined using Monitoring Query Language.
JSON representation |
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{ "query": string, "duration": string, "trigger": { object ( |
Fields | |
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query |
Monitoring Query Language query that outputs a boolean stream. |
duration |
The amount of time that a time series must violate the threshold to be considered failing. Currently, only values that are a multiple of a minute--e.g., 0, 60, 120, or 300 seconds--are supported. If an invalid value is given, an error will be returned. When choosing a duration, it is useful to keep in mind the frequency of the underlying time series data (which may also be affected by any alignments specified in the |
trigger |
The number/percent of time series for which the comparison must hold in order for the condition to trigger. If unspecified, then the condition will trigger if the comparison is true for any of the time series that have been identified by |
evaluationMissingData |
A condition control that determines how metric-threshold conditions are evaluated when data stops arriving. |
PrometheusQueryLanguageCondition
A condition type that allows alerting policies to be defined using Prometheus Query Language (PromQL).
The PrometheusQueryLanguageCondition message contains information from a Prometheus alerting rule and its associated rule group.
A Prometheus alerting rule is described here. The semantics of a Prometheus alerting rule is described here.
A Prometheus rule group is described here. The semantics of a Prometheus rule group is described here.
Because Cloud Alerting has no representation of a Prometheus rule group resource, we must embed the information of the parent rule group inside each of the conditions that refer to it. We must also update the contents of all Prometheus alerts in case the information of their rule group changes.
The PrometheusQueryLanguageCondition protocol buffer combines the information of the corresponding rule group and alerting rule. The structure of the PrometheusQueryLanguageCondition protocol buffer does NOT mimic the structure of the Prometheus rule group and alerting rule YAML declarations. The PrometheusQueryLanguageCondition protocol buffer may change in the future to support future rule group and/or alerting rule features. There are no new such features at the present time (2023-06-26).
JSON representation |
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{ "query": string, "duration": string, "evaluationInterval": string, "labels": { string: string, ... }, "ruleGroup": string, "alertRule": string } |
Fields | |
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query |
Required. The PromQL expression to evaluate. Every evaluation cycle this expression is evaluated at the current time, and all resultant time series become pending/firing alerts. This field must not be empty. |
duration |
Optional. Alerts are considered firing once their PromQL expression was evaluated to be "true" for this long. Alerts whose PromQL expression was not evaluated to be "true" for long enough are considered pending. Must be a non-negative duration or missing. This field is optional. Its default value is zero. |
evaluationInterval |
Optional. How often this rule should be evaluated. Must be a positive multiple of 30 seconds or missing. This field is optional. Its default value is 30 seconds. If this PrometheusQueryLanguageCondition was generated from a Prometheus alerting rule, then this value should be taken from the enclosing rule group. |
labels |
Optional. Labels to add to or overwrite in the PromQL query result. Label names must be valid. Label values can be templatized by using variables. The only available variable names are the names of the labels in the PromQL result, including "__name__" and "value". "labels" may be empty. |
ruleGroup |
Optional. The rule group name of this alert in the corresponding Prometheus configuration file. Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future. This field is optional. If this field is not empty, then it must contain a valid UTF-8 string. This field may not exceed 2048 Unicode characters in length. |
alertRule |
Optional. The alerting rule name of this alert in the corresponding Prometheus configuration file. Some external tools may require this field to be populated correctly in order to refer to the original Prometheus configuration file. The rule group name and the alert name are necessary to update the relevant AlertPolicies in case the definition of the rule group changes in the future. This field is optional. If this field is not empty, then it must be a valid Prometheus label name. This field may not exceed 2048 Unicode characters in length. |
ConditionCombinerType
Operators for combining conditions.
Enums | |
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COMBINE_UNSPECIFIED |
An unspecified combiner. |
AND |
Combine conditions using the logical AND operator. An incident is created only if all the conditions are met simultaneously. This combiner is satisfied if all conditions are met, even if they are met on completely different resources. |
OR |
Combine conditions using the logical OR operator. An incident is created if any of the listed conditions is met. |
AND_WITH_MATCHING_RESOURCE |
Combine conditions using logical AND operator, but unlike the regular AND option, an incident is created only if all conditions are met simultaneously on at least one resource. |
AlertStrategy
Control over how the notification channels in notificationChannels
are notified when this alert fires.
JSON representation |
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{ "notificationRateLimit": { object ( |
Fields | |
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notificationRateLimit |
Required for log-based alerting policies, i.e. policies with a This limit is not implemented for alerting policies that do not have a LogMatch condition. |
notificationPrompts[] |
For log-based alert policies, the notification prompts is always [OPENED]. For non log-based alert policies, the notification prompts can be [OPENED] or [OPENED, CLOSED]. |
autoClose |
If an alerting policy that was active has no data for this long, any open incidents will close |
notificationChannelStrategy[] |
Control how notifications will be sent out, on a per-channel basis. |
NotificationRateLimit
Control over the rate of notifications sent to this alerting policy's notification channels.
JSON representation |
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{ "period": string } |
Fields | |
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period |
Not more than one notification per |
NotificationPrompt
Control when notifications will be sent out.
Enums | |
---|---|
NOTIFICATION_PROMPT_UNSPECIFIED |
No strategy specified. Treated as error. |
OPENED |
Notify when an incident is opened. |
CLOSED |
Notify when an incident is closed. |
NotificationChannelStrategy
Control over how the notification channels in notificationChannels
are notified when this alert fires, on a per-channel basis.
JSON representation |
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{ "notificationChannelNames": [ string ], "renotifyInterval": string } |
Fields | |
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notificationChannelNames[] |
The full REST resource name for the notification channels that these settings apply to. Each of these correspond to the name field in one of the NotificationChannel objects referenced in the notificationChannels field of this AlertPolicy. The format is:
|
renotifyInterval |
The frequency at which to send reminder notifications for open incidents. |
Severity
An enumeration of possible severity level for an alerting policy.
Enums | |
---|---|
SEVERITY_UNSPECIFIED |
No severity is specified. This is the default value. |
CRITICAL |
This is the highest severity level. Use this if the problem could cause significant damage or downtime. |
ERROR |
This is the medium severity level. Use this if the problem could cause minor damage or downtime. |
WARNING |
This is the lowest severity level. Use this if the problem is not causing any damage or downtime, but could potentially lead to a problem in the future. |
Methods |
|
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|
Creates a new alerting policy. |
|
Deletes an alerting policy. |
|
Gets a single alerting policy. |
|
Lists the existing alerting policies for the workspace. |
|
Updates an alerting policy. |