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,
  "validity": {
    object (Status)
  },
  "notificationChannels": [
    string
  ],
  "creationRecord": {
    object (MutationRecord)
  },
  "mutationRecord": {
    object (MutationRecord)
  }
}
Fields
name

string

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

projects/[PROJECT_ID_OR_NUMBER]/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. If conditionTimeSeriesQueryLanguage is present, it must be the only condition.
combiner

enum (ConditionCombinerType)

How to combine the results of multiple conditions to determine if an incident should be opened. If conditionTimeSeriesQueryLanguage is present, this must be COMBINE_UNSPECIFIED.
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.
validity

object (Status)

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[]

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 format of the entries in this field is:

projects/[PROJECT_ID_OR_NUMBER]/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)
  },
  "conditionMonitoringQueryLanguage": {
    object (MonitoringQueryLanguageCondition)
  }
  // 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 format is:

projects/[PROJECT_ID_OR_NUMBER]/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.
conditionMonitoringQueryLanguage

object (MonitoringQueryLanguageCondition)

A condition that uses the Monitoring Query Language to define alerts.

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

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 timeSeries.list request (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must 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 timeSeries.list 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 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.
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 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 
{
  "alignmentPeriod": string,
  "perSeriesAligner": enum (Aligner),
  "crossSeriesReducer": enum (Reducer),
  "groupByFields": [
    string
  ]
}
Fields
alignmentPeriod

string (Duration format)

The alignmentPeriod specifies a time interval, in seconds, that is used to divide the data in all the time series into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.

The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.

The maximum value of the alignmentPeriod is 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.
perSeriesAligner

enum (Aligner)

An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignmentPeriod to be mathematically grouped together, resulting in a single data point for each alignmentPeriod with end timestamp at the end of the period.

Not all alignment operations may be applied to all time series. The valid choices depend on the metricKind and valueType of the original time series. Alignment can change the metricKind or the valueType 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 to ALIGN_NONE and alignmentPeriod must be specified; otherwise, an error is returned.
crossSeriesReducer

enum (Reducer)

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 metricKind and the valueType of the original time series. Reduction can yield a time series with a different metricKind or valueType than the input time series.

Time series data must first be aligned (see perSeriesAligner) in order to perform cross-time series reduction. If crossSeriesReducer is specified, then perSeriesAligner must be specified, and must not be ALIGN_NONE. An alignmentPeriod must also 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 operation. 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 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
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 DELTA. The output is delta = y1 - y0.

This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The valueType of the aligned result is the same as the valueType of the input.
ALIGN_RATE

Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignmentPeriod.

This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with valueType DOUBLE.

If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
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 GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignmentPeriod.

If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.

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 >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. 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 valueType DOUBLE.

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
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.

ComparisonType

Specifies an ordering relationship on two arguments, called left and right.

Enums
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 
{

  // Union field type can be only one of the following:
  "count": integer,
  "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

integer

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

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 timeSeries.list request (that call is useful to verify the time series that will be retrieved / processed). The filter must specify the metric type and the resource type. Optionally, it can specify resource labels and metric labels. This field must 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 timeSeries.list 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. 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 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.

MonitoringQueryLanguageCondition

A condition type that allows alert policies to be defined using Monitoring Query Language.

JSON representation 
{
  "query": string,
  "duration": string,
  "trigger": {
    object (Trigger)
  }
}
Fields
query

string

Monitoring Query Language query that outputs a boolean stream.
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.

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 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.

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.

JSON representation 
{
  "code": integer,
  "message": string,
  "details": [
    {
      "@type": string,
      field1: ...,
      ...
    }
  ]
}
Fields
code

integer

The status code, which should be an enum value of google.rpc.Code.
message

string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
details[]

object

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 "@type" contains a URI identifying the type. Example: { "id": 1234, "@type": "types.example.com/standard/id" }.

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 workspace.

patch

 Updates an alerting policy.