REST Resource: projects.alertPolicies

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.

JSON representation 
{
  "name": string,
  "displayName": string,
  "documentation": {
    object (Documentation)
  },
  "userLabels": {
    string: string,
    ...
  },
  "conditions": [
    {
      object (Condition)
    }
  ],
  "combiner": enum (ConditionCombinerType),
  "enabled": boolean,
  "notificationChannels": [
    string
  ],
  "creationRecord": {
    object (MutationRecord)
  },
  "mutationRecord": {
    object (MutationRecord)
  }
}
Fields
name

string

Required if the policy exists. The resource name for this policy. The syntax is:

projects/[PROJECT_ID]/alertPolicies/[ALERT_POLICY_ID]

[ALERT_POLICY_ID] is assigned by Stackdriver Monitoring when the policy is created. When calling the alertPolicies.create method, do not include the name field in the alerting policy passed as part of the request.
displayName

string

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

object (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

map (key: string, value: string)

User-supplied key/value data to be used for organizing and identifying the AlertPolicy objects.

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 "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
conditions[]

object (Condition)

A list of conditions for the policy. The conditions are combined by AND or OR according to the combiner field. If the combined conditions evaluate to true, then an incident is created. A policy can have from one to six conditions.

combiner

enum (ConditionCombinerType)

How to combine the results of multiple conditions to determine if an incident should be opened.

enabled

boolean

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.

notificationChannels[]

string

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 name field in each of the NotificationChannel objects that are returned from the notificationChannels.list method. The syntax of the entries in this field is:

projects/[PROJECT_ID]/notificationChannels/[CHANNEL_ID]

creationRecord

object (MutationRecord)

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

object (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.

JSON representation 
{
  "content": string,
  "mimeType": string
}
Fields
content

string

The text of the documentation, interpreted according to mimeType. The content may not exceed 8,192 Unicode characters and may not exceed more than 10,240 bytes when encoded in UTF-8 format, whichever is smaller.

mimeType

string

The format of the content field. Presently, only the value "text/markdown" is supported. See Markdown for more information.

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 
{
  "name": string,
  "displayName": string,

  // Union field condition can be only one of the following:
  "conditionThreshold": {
    object (MetricThreshold)
  },
  "conditionAbsent": {
    object (MetricAbsence)
  }
  // End of list of possible types for union field condition.
}
Fields
name

string

Required if the condition exists. The unique resource name for this condition. Its syntax is:

projects/[PROJECT_ID]/alertPolicies/[POLICY_ID]/conditions/[CONDITION_ID]

[CONDITION_ID] is assigned by Stackdriver Monitoring when the condition is created as part of a new or updated alerting policy.

When calling the alertPolicies.create method, do not include the name field in the conditions of the requested alerting policy. Stackdriver Monitoring creates the condition identifiers and includes them in the new policy.

When calling the alertPolicies.update method to update a policy, including a condition name causes the existing condition to be updated. Conditions without names are added to the updated policy. Existing conditions are deleted if they are not updated.

Best practice is to preserve [CONDITION_ID] if you make only small changes, such as those to condition thresholds, durations, or trigger values. Otherwise, treat the change as a new condition and let the existing condition be deleted.
displayName

string

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

object (MetricThreshold)

A condition that compares a time series against a threshold.

conditionAbsent

object (MetricAbsence)

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 
{
  "filter": string,
  "aggregations": [
    {
      object (Aggregation)
    }
  ],
  "denominatorFilter": string,
  "denominatorAggregations": [
    {
      object (Aggregation)
    }
  ],
  "comparison": enum (ComparisonType),
  "thresholdValue": number,
  "duration": string,
  "trigger": {
    object (Trigger)
  }
}
Fields
filter

string

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 MetricService.ListTimeSeries request (that call is useful to verify the time series that will be retrieved / processed) and 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.
aggregations[]

object (Aggregation)

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 MetricService.ListTimeSeries request. It is advisable to use the timeSeries.list method when debugging this field.
denominatorFilter

string

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 denominatorFilter is specified, the time series specified by the filter field will be used as the numerator.

The filter is similar to the one that is specified in the MetricService.ListTimeSeries request (that call is useful to verify the time series that will be retrieved / processed) and 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[]

object (Aggregation)

Specifies the alignment of data points in individual time series selected by denominatorFilter 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).

When computing ratios, the aggregations and denominatorAggregations fields must use the same alignment period and produce time series that have the same periodicity and labels.

This field is similar to the one in the MetricService.ListTimeSeries request. It is advisable to use the timeSeries.list method when debugging this field.
comparison

enum (ComparisonType)

The comparison to apply between the time series (indicated by filter and aggregation) and the threshold (indicated by thresholdValue). The comparison is applied on each time series, with the time series on the left-hand side and the threshold on the right-hand side.

Only COMPARISON_LT and COMPARISON_GT are supported currently.
thresholdValue

number

A value against which to compare the time series.

duration

string (Duration format)

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 aggregations field); a good duration is long enough so that a single outlier does not generate spurious alerts, but short enough that unhealthy states are detected and alerted on quickly.

trigger

object (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 filter and aggregations, or by the ratio, if denominatorFilter and denominatorAggregations are specified.

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.

JSON representation 
{
  "alignmentPeriod": string,
  "perSeriesAligner": enum (Aligner),
  "crossSeriesReducer": enum (Reducer),
  "groupByFields": [
    string
  ]
}
Fields
alignmentPeriod

string (Duration format)

The alignment period for per-time series alignment. If present, alignmentPeriod must be at least 60 seconds. After per-time series alignment, each time series will contain data points only on the period boundaries. If perSeriesAligner is not specified or equals ALIGN_NONE, then this field is ignored. If perSeriesAligner is specified and does not equal ALIGN_NONE, then this field must be defined; otherwise an error is returned.

perSeriesAligner

enum (Aligner)

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 is specified, then perSeriesAligner must be specified and not equal ALIGN_NONE and alignmentPeriod must be specified; otherwise, an error is returned.
crossSeriesReducer

enum (Reducer)

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 crossSeriesReducer is specified, then perSeriesAligner must be specified and not equal ALIGN_NONE and alignmentPeriod must be specified; otherwise, an error is returned.
groupByFields[]

string

The set of fields to preserve when crossSeriesReducer is specified. The groupByFields determine how the time series are partitioned into subsets prior to applying the aggregation function. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The crossSeriesReducer is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains resource.type. Fields not specified in groupByFields are aggregated away. If groupByFields is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If crossSeriesReducer is not defined, this field is ignored.

Aligner

The Aligner describes how to bring the data points in a single time series into temporal alignment.

Enums
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 DOUBLE.

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_PERCENT_CHANGE aligner option.
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
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
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.

JSON representation 
{

  // Union field type can be only one of the following:
  "count": number,
  "percent": number
  // End of list of possible types for union field type.
}
Fields
Union field type. A type of trigger. type can be only one of the following:
count

number

The absolute number of time series that must fail the predicate for the condition to be triggered.

percent

number

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.

JSON representation 
{
  "filter": string,
  "aggregations": [
    {
      object (Aggregation)
    }
  ],
  "duration": string,
  "trigger": {
    object (Trigger)
  }
}
Fields
filter

string

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 MetricService.ListTimeSeries request (that call is useful to verify the time series that will be retrieved / processed) and 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.
aggregations[]

object (Aggregation)

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 MetricService.ListTimeSeries request. It is advisable to use the timeSeries.list method when debugging this field.
duration

string (Duration format)

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 Duration.nanos field is ignored.

trigger

object (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 filter and aggregations.

ConditionCombinerType

Operators for combining conditions.

Enums
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.

MutationRecord

Describes a change made to a configuration.

JSON representation 
{
  "mutateTime": string,
  "mutatedBy": string
}
Fields
mutateTime

string (Timestamp format)

When the change occurred.

A timestamp in RFC3339 UTC "Zulu" format, accurate to nanoseconds. Example: "2014-10-02T15:01:23.045123456Z".
mutatedBy

string

The email address of the user making the change.

Methods

create

 Creates a new alerting policy.

delete

 Deletes an alerting policy.

get

 Gets a single alerting policy.

list

 Lists the existing alerting policies for the project.

patch

 Updates an alerting policy.
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