- Resource: AlertPolicy
- Documentation
- Condition
- MetricThreshold
- Aggregation
- Aligner
- Reducer
- ComparisonType
- Trigger
- MetricAbsence
- ConditionCombinerType
- Status
- MutationRecord
- 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 alert policies, see Introduction to Alerting.
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{ "name": string, "displayName": string, "documentation": { object ( |
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name |
Required if the policy exists. The resource name for this policy. The syntax is:
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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. |
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. An object containing a list of |
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. |
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 alert policy is invalid. OK if the alert policy is valid. If not OK, the alert 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
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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. |
Documentation
A content string and a MIME type that describes the content string's format.
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{ "content": string, "mimeType": string } |
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content |
The text of the documentation, interpreted according to |
mimeType |
The format of the |
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.
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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 syntax is:
When calling the When calling the Best practice is to preserve |
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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. |
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Union field condition . Only one of the following condition types will be specified. condition can be only one of the following: |
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conditionThreshold |
A condition that compares a time series against a threshold. |
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conditionAbsent |
A condition that checks that a time series continues to receive new data points. |
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 |
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 resrouces). 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 |
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 |
Aggregation
Describes how to combine multiple time series to provide different views of the data. Aggregation consists of an alignment step on individual time series (alignmentPeriod
and perSeriesAligner
) followed by an optional reduction step of the data across the aligned time series (crossSeriesReducer
and groupByFields
). For more details, see Aggregation.
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{ "alignmentPeriod": string, "perSeriesAligner": enum ( |
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alignmentPeriod |
The alignment period for per- |
perSeriesAligner |
The approach to be used to align individual time series. Not all alignment functions may be applied to all time series, depending on the metric type and value type of the original time series. Alignment may change the metric type or the value type of the time series. Time series data must be aligned in order to perform cross-time series reduction. If |
crossSeriesReducer |
The approach to be used to combine time series. Not all reducer functions may be applied to all time series, depending on the metric type and the value type of the original time series. Reduction may change the metric type of value type of the time series. Time series data must be aligned in order to perform cross-time series reduction. If |
groupByFields[] |
The set of fields to preserve when |
Aligner
The Aligner describes how to bring the data points in a single time series into temporal alignment.
Enums | |
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ALIGN_NONE |
No alignment. Raw data is returned. Not valid if cross-time series reduction is requested. The value type of the result is the same as the value type of the input. |
ALIGN_DELTA |
Align and convert to delta metric type. This alignment is valid for cumulative metrics and delta metrics. Aligning an existing delta metric to a delta metric requires that the alignment period be increased. The value type of the result is the same as the value type of the input. One can think of this aligner as a rate but without time units; that is, the output is conceptually (second_point - first_point). |
ALIGN_RATE |
Align and convert to a rate. This alignment is valid for cumulative metrics and delta metrics with numeric values. The output is a gauge metric with value type One can think of this aligner as conceptually providing the slope of the line that passes through the value at the start and end of the window. In other words, this is conceptually ((y1 - y0)/(t1 - t0)), and the output unit is one that has a "/time" dimension. If, by rate, you are looking for percentage change, see the |
ALIGN_INTERPOLATE |
Align by interpolating between adjacent points around the period boundary. This alignment is valid for gauge metrics with numeric values. The value type of the result is the same as the value type of the input. |
ALIGN_NEXT_OLDER |
Align by shifting the oldest data point before the period boundary to the boundary. This alignment is valid for gauge metrics. The value type of the result is the same as the value type of the input. |
ALIGN_MIN |
Align time series via aggregation. The resulting data point in the alignment period is the minimum of all data points in the period. This alignment is valid for gauge and delta metrics with numeric values. The value type of the result is the same as the value type of the input. |
ALIGN_MAX |
Align time series via aggregation. The resulting data point in the alignment period is the maximum of all data points in the period. This alignment is valid for gauge and delta metrics with numeric values. The value type of the result is the same as the value type of the input. |
ALIGN_MEAN |
Align time series via aggregation. The resulting data point in the alignment period is the average or arithmetic mean of all data points in the period. This alignment is valid for gauge and delta metrics with numeric values. The value type of the output is DOUBLE . |
ALIGN_COUNT |
Align time series via aggregation. The resulting data point in the alignment period is the count of all data points in the period. This alignment is valid for gauge and delta metrics with numeric or Boolean values. The value type of the output is INT64 . |
ALIGN_SUM |
Align time series via aggregation. The resulting data point in the alignment period is the sum of all data points in the period. This alignment is valid for gauge and delta metrics with numeric and distribution values. The value type of the output is the same as the value type of the input. |
ALIGN_STDDEV |
Align time series via aggregation. The resulting data point in the alignment period is the standard deviation of all data points in the period. This alignment is valid for gauge and delta metrics with numeric values. The value type of the output is DOUBLE . |
ALIGN_COUNT_TRUE |
Align time series via aggregation. The resulting data point in the alignment period is the count of True-valued data points in the period. This alignment is valid for gauge metrics with Boolean values. The value type of the output is INT64 . |
ALIGN_COUNT_FALSE |
Align time series via aggregation. The resulting data point in the alignment period is the count of False-valued data points in the period. This alignment is valid for gauge metrics with Boolean values. The value type of the output is INT64 . |
ALIGN_FRACTION_TRUE |
Align time series via aggregation. The resulting data point in the alignment period is the fraction of True-valued data points in the period. This alignment is valid for gauge metrics with Boolean values. The output value is in the range [0, 1] and has value type DOUBLE . |
ALIGN_PERCENTILE_99 |
Align time series via aggregation. The resulting data point in the alignment period is the 99th percentile of all data points in the period. This alignment is valid for gauge and delta metrics with distribution values. The output is a gauge metric with value type DOUBLE . |
ALIGN_PERCENTILE_95 |
Align time series via aggregation. The resulting data point in the alignment period is the 95th percentile of all data points in the period. This alignment is valid for gauge and delta metrics with distribution values. The output is a gauge metric with value type DOUBLE . |
ALIGN_PERCENTILE_50 |
Align time series via aggregation. The resulting data point in the alignment period is the 50th percentile of all data points in the period. This alignment is valid for gauge and delta metrics with distribution values. The output is a gauge metric with value type DOUBLE . |
ALIGN_PERCENTILE_05 |
Align time series via aggregation. The resulting data point in the alignment period is the 5th percentile of all data points in the period. This alignment is valid for gauge and delta metrics with distribution values. The output is a gauge metric with value type DOUBLE . |
ALIGN_PERCENT_CHANGE |
Align and convert to a percentage change. This alignment is valid for gauge and delta metrics with numeric values. This alignment conceptually computes the equivalent of "((current - previous)/previous)*100" where previous value is determined based on the alignmentPeriod. In the event that previous is 0 the calculated value is infinity with the exception that if both (current - previous) and previous are 0 the calculated value is 0. A 10 minute moving mean is computed at each point of the time window prior to the above calculation to smooth the metric and prevent false positives from very short lived spikes. Only applicable for data that is >= 0. Any values < 0 are treated as no data. While delta metrics are accepted by this alignment special care should be taken that the values for the metric will always be positive. The output is a gauge metric with value type DOUBLE . |
Reducer
A Reducer describes how to aggregate data points from multiple time series into a single 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 across time series for each alignment period. This reducer is valid for delta and gauge metrics with numeric or distribution values. The value type of the output is DOUBLE . |
REDUCE_MIN |
Reduce by computing the minimum across time series for each alignment period. This reducer is valid for delta and gauge metrics with numeric values. The value type of the output is the same as the value type of the input. |
REDUCE_MAX |
Reduce by computing the maximum across time series for each alignment period. This reducer is valid for delta and gauge metrics with numeric values. The value type of the output is the same as the value type 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 value type of the output is the same as the value type 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 value type of the output is DOUBLE . |
REDUCE_COUNT |
Reduce by computing the count 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 value type. The value type of the output is INT64 . |
REDUCE_COUNT_TRUE |
Reduce by computing the count of True-valued data points across time series for each alignment period. This reducer is valid for delta and gauge metrics of Boolean value type. The value type of the output is INT64 . |
REDUCE_COUNT_FALSE |
Reduce by computing the count of False-valued data points across time series for each alignment period. This reducer is valid for delta and gauge metrics of Boolean value type. The value type of the output is INT64 . |
REDUCE_FRACTION_TRUE |
Reduce by computing the fraction of True-valued data points across time series for each alignment period. This reducer is valid for delta and gauge metrics of Boolean value type. The output value is in the range [0, 1] and has value type DOUBLE . |
REDUCE_PERCENTILE_99 |
Reduce by computing 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 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 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 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 |
ComparisonType
Specifies an ordering relationship on two arguments, here called left and right.
Enums | |
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COMPARISON_UNSPECIFIED |
No ordering relationship is specified. |
COMPARISON_GT |
The left argument is greater than the right argument. |
COMPARISON_GE |
The left argument is greater than or equal to the right argument. |
COMPARISON_LT |
The left argument is less than the right argument. |
COMPARISON_LE |
The left argument is less than or equal to the right argument. |
COMPARISON_EQ |
The left argument is equal to the right argument. |
COMPARISON_NE |
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.
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{ // Union field |
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Union field type . A type of trigger. type can be only one of the following: |
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count |
The absolute number of time series that must fail the predicate for the condition to be triggered. |
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percent |
The percentage of time series that must fail the predicate for the condition to be triggered. |
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
.
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{ "filter": string, "aggregations": [ { object ( |
Fields | |
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filter |
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 resrouces). 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. Currently, only values that are a multiple of a minute--e.g. 60, 120, 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 |
ConditionCombinerType
Operators for combining conditions.
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COMBINE_UNSPECIFIED |
An unspecified combiner. |
AND |
Combine conditions using the logical AND operator. An incident is created only if all 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. |
Status
The Status
type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status
message contains three pieces of data: error code, error message, and error details.
You can find out more about this error model and how to work with it in the API Design Guide.
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{ "code": number, "message": string, "details": [ { "@type": string, field1: ..., ... } ] } |
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code |
The status code, which should be an enum value of |
message |
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the |
details[] |
A list of messages that carry the error details. There is a common set of message types for APIs to use. An object containing fields of an arbitrary type. An additional field |
MutationRecord
Describes a change made to a configuration.
JSON representation | |
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{ "mutateTime": string, "mutatedBy": string } |
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mutateTime |
When the change occurred. A timestamp in RFC3339 UTC "Zulu" format, accurate to nanoseconds. Example: |
mutatedBy |
The email address of the user making the change. |
Methods |
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Creates a new alerting policy. |
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Deletes an alerting policy. |
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Gets a single alerting policy. |
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Lists the existing alerting policies for the project. |
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Updates an alerting policy. |