From 68d24a114ed9d8fdb71288d9e9699896a8c7e5a4 Mon Sep 17 00:00:00 2001 From: Yoshi Automation Date: Tue, 4 Oct 2022 07:14:52 +0000 Subject: [PATCH] feat(monitoring): update the api #### monitoring:v1 The following keys were added: - schemas.TimeSeriesQuery.properties.prometheusQuery.type (Total Keys: 1) #### monitoring:v3 The following keys were added: - schemas.BasicService (Total Keys: 5) - schemas.Service.properties.basicService.$ref (Total Keys: 1) --- .../monitoring_v1.projects.dashboards.html | 72 +++++++++++++++++++ .../monitoring_v3.projects.alertPolicies.html | 12 ++-- ...ring_v3.projects.notificationChannels.html | 12 ++-- docs/dyn/monitoring_v3.services.html | 48 +++++++++++-- .../documents/monitoring.v1.json | 6 +- .../documents/monitoring.v3.json | 38 +++++++--- 6 files changed, 161 insertions(+), 27 deletions(-) diff --git a/docs/dyn/monitoring_v1.projects.dashboards.html b/docs/dyn/monitoring_v1.projects.dashboards.html index a30703fdfa5..42e5be2d2f9 100644 --- a/docs/dyn/monitoring_v1.projects.dashboards.html +++ b/docs/dyn/monitoring_v1.projects.dashboards.html @@ -150,6 +150,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -235,6 +236,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -323,6 +325,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -472,6 +475,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -557,6 +561,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -645,6 +650,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -787,6 +793,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -872,6 +879,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -960,6 +968,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1104,6 +1113,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1189,6 +1199,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1277,6 +1288,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1430,6 +1442,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1515,6 +1528,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1603,6 +1617,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1752,6 +1767,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1837,6 +1853,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -1925,6 +1942,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2067,6 +2085,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2152,6 +2171,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2240,6 +2260,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2384,6 +2405,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2469,6 +2491,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2557,6 +2580,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2734,6 +2758,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2819,6 +2844,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -2907,6 +2933,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3056,6 +3083,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3141,6 +3169,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3229,6 +3258,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3371,6 +3401,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3456,6 +3487,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3544,6 +3576,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3688,6 +3721,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3773,6 +3807,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -3861,6 +3896,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4024,6 +4060,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4109,6 +4146,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4197,6 +4235,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4346,6 +4385,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4431,6 +4471,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4519,6 +4560,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4661,6 +4703,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4746,6 +4789,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4834,6 +4878,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -4978,6 +5023,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5063,6 +5109,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5151,6 +5198,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5322,6 +5370,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5407,6 +5456,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5495,6 +5545,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5644,6 +5695,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5729,6 +5781,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5817,6 +5870,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -5959,6 +6013,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6044,6 +6099,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6132,6 +6188,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6276,6 +6333,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6361,6 +6419,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6449,6 +6508,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6602,6 +6662,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6687,6 +6748,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6775,6 +6837,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -6924,6 +6987,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7009,6 +7073,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7097,6 +7162,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7239,6 +7305,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7324,6 +7391,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7412,6 +7480,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7556,6 +7625,7 @@

Method Details

}, ], "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7641,6 +7711,7 @@

Method Details

}, "tableTemplate": "A String", # Optional. A template string for naming TimeSeries in the resulting data set. This should be a string with interpolations of the form ${label_name}, which will resolve to the label's value i.e. "${resource.labels.project_id}." "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. @@ -7729,6 +7800,7 @@

Method Details

"plotType": "A String", # How this data should be plotted on the chart. "targetAxis": "A String", # Optional. The target axis to use for plotting the metric. "timeSeriesQuery": { # TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API. # Required. Fields for querying time series data from the Stackdriver metrics API. + "prometheusQuery": "A String", # A query used to fetch time series with PromQL. "timeSeriesFilter": { # A filter that defines a subset of time series data that is displayed in a widget. Time series data is fetched using the ListTimeSeries (https://cloud.google.com/monitoring/api/ref_v3/rest/v3/projects.timeSeries/list) method. # Filter parameters to fetch time series. "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 per_series_aligner operation to each time series after its data has been divided into regular alignment_period 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 cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, 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 (https://cloud.google.com/monitoring/api/v3/aggregation). # By default, the raw time series data is returned. Use this field to combine multiple time series for different views of the data. "alignmentPeriod": "A String", # The alignment_period 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 alignment_period is 2 years, or 104 weeks. diff --git a/docs/dyn/monitoring_v3.projects.alertPolicies.html b/docs/dyn/monitoring_v3.projects.alertPolicies.html index 2322f5a207e..60f5e375eec 100644 --- a/docs/dyn/monitoring_v3.projects.alertPolicies.html +++ b/docs/dyn/monitoring_v3.projects.alertPolicies.html @@ -79,10 +79,10 @@

Instance Methods

Close httplib2 connections.

create(name, body=None, x__xgafv=None)

-

Creates a new alerting policy.

+

Creates a new alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.

delete(name, x__xgafv=None)

-

Deletes an alerting policy.

+

Deletes an alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.

get(name, x__xgafv=None)

Gets a single alerting policy.

@@ -94,7 +94,7 @@

Instance Methods

Retrieves the next page of results.

patch(name, body=None, updateMask=None, x__xgafv=None)

-

Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.

+

Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.

Method Details

close() @@ -103,7 +103,7 @@

Method Details

create(name, body=None, x__xgafv=None) - 
Creates a new alerting policy.
+  
Creates a new alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.
 
 Args:
   name: string, Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) in which to create the alerting policy. The format is: projects/[PROJECT_ID_OR_NUMBER] Note that this field names the parent container in which the alerting policy will be written, not the name of the created policy. |name| must be a host project of a Metrics Scope, otherwise INVALID_ARGUMENT error will return. The alerting policy that is returned will have a name that contains a normalized representation of this name as a prefix but adds a suffix of the form /alertPolicies/[ALERT_POLICY_ID], identifying the policy in the container. (required)
@@ -343,7 +343,7 @@ 
Method Details

 
 

     delete(name, x__xgafv=None)
-  
Deletes an alerting policy.
+  
Deletes an alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.
 
 Args:
   name: string, Required. The alerting policy to delete. The format is: projects/[PROJECT_ID_OR_NUMBER]/alertPolicies/[ALERT_POLICY_ID] For more information, see AlertPolicy. (required)
@@ -637,7 +637,7 @@ 
Method Details

 
 

     patch(name, body=None, updateMask=None, x__xgafv=None)
-  
Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.
+  
Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.
 
 Args:
   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 Cloud 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. (required)
diff --git a/docs/dyn/monitoring_v3.projects.notificationChannels.html b/docs/dyn/monitoring_v3.projects.notificationChannels.html
index 315f5ece1be..d191c9cb37f 100644
--- a/docs/dyn/monitoring_v3.projects.notificationChannels.html
+++ b/docs/dyn/monitoring_v3.projects.notificationChannels.html
@@ -79,10 +79,10 @@ 
Instance Methods

 
Close httplib2 connections.

 

   create(name, body=None, x__xgafv=None)

-
Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.

+
Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.

 

   delete(name, force=None, x__xgafv=None)

-
Deletes a notification channel.

+
Deletes a notification channel.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.

 

   get(name, x__xgafv=None)

 
Gets a single notification channel. The channel includes the relevant configuration details with which the channel was created. However, the response may truncate or omit passwords, API keys, or other private key matter and thus the response may not be 100% identical to the information that was supplied in the call to the create method.

@@ -97,7 +97,7 @@ 
Instance Methods

 
Retrieves the next page of results.

 

   patch(name, body=None, updateMask=None, x__xgafv=None)

-
Updates a notification channel. Fields not specified in the field mask remain unchanged.

+
Updates a notification channel. Fields not specified in the field mask remain unchanged.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.

 

   sendVerificationCode(name, body=None, x__xgafv=None)

 
Causes a verification code to be delivered to the channel. The code can then be supplied in VerifyNotificationChannel to verify the channel.

@@ -112,7 +112,7 @@ 
Method Details

 
 

     create(name, body=None, x__xgafv=None)
-  
Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.
+  
Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.
 
 Args:
   name: string, Required. The project (https://cloud.google.com/monitoring/api/v3#project_name) on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] This names the container into which the channel will be written, this does not name the newly created channel. The resulting channel's name will have a normalized version of this field as a prefix, but will add /notificationChannels/[CHANNEL_ID] to identify the channel. (required)
@@ -180,7 +180,7 @@ 
Method Details

 
 

     delete(name, force=None, x__xgafv=None)
-  
Deletes a notification channel.
+  
Deletes a notification channel.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.
 
 Args:
   name: string, Required. The channel for which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID]  (required)
@@ -330,7 +330,7 @@ 
Method Details

 
 

     patch(name, body=None, updateMask=None, x__xgafv=None)
-  
Updates a notification channel. Fields not specified in the field mask remain unchanged.
+  
Updates a notification channel. Fields not specified in the field mask remain unchanged.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.
 
 Args:
   name: string, The full REST resource name for this channel. The format is: projects/[PROJECT_ID_OR_NUMBER]/notificationChannels/[CHANNEL_ID] The [CHANNEL_ID] is automatically assigned by the server on creation. (required)
diff --git a/docs/dyn/monitoring_v3.services.html b/docs/dyn/monitoring_v3.services.html
index 47b41b9d1da..29b7b6726b7 100644
--- a/docs/dyn/monitoring_v3.services.html
+++ b/docs/dyn/monitoring_v3.services.html
@@ -119,6 +119,12 @@ 
Method Details

   "appEngine": { # App Engine service. Learn more at https://cloud.google.com/appengine. # Type used for App Engine services.
     "moduleId": "A String", # The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app).
   },
+  "basicService": { # A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli). # Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+    "serviceLabels": { # Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+      "a_key": "A String",
+    },
+    "serviceType": "A String", # The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+  },
   "cloudEndpoints": { # Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. # Type used for Cloud Endpoints services.
     "service": "A String", # The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api).
   },
@@ -132,7 +138,7 @@ 
Method Details

     "serviceName": "A String", # The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics.
     "serviceNamespace": "A String", # The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics.
   },
-  "custom": { # Custom view of service telemetry. Currently a place-holder pending final design. # Custom service type.
+  "custom": { # Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. # Custom service type.
   },
   "displayName": "A String", # Name used for UI elements listing this Service.
   "gkeNamespace": { # GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). # Type used for GKE Namespaces.
@@ -188,6 +194,12 @@ 
Method Details

   "appEngine": { # App Engine service. Learn more at https://cloud.google.com/appengine. # Type used for App Engine services.
     "moduleId": "A String", # The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app).
   },
+  "basicService": { # A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli). # Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+    "serviceLabels": { # Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+      "a_key": "A String",
+    },
+    "serviceType": "A String", # The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+  },
   "cloudEndpoints": { # Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. # Type used for Cloud Endpoints services.
     "service": "A String", # The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api).
   },
@@ -201,7 +213,7 @@ 
Method Details

     "serviceName": "A String", # The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics.
     "serviceNamespace": "A String", # The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics.
   },
-  "custom": { # Custom view of service telemetry. Currently a place-holder pending final design. # Custom service type.
+  "custom": { # Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. # Custom service type.
   },
   "displayName": "A String", # Name used for UI elements listing this Service.
   "gkeNamespace": { # GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). # Type used for GKE Namespaces.
@@ -281,6 +293,12 @@ 
Method Details

   "appEngine": { # App Engine service. Learn more at https://cloud.google.com/appengine. # Type used for App Engine services.
     "moduleId": "A String", # The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app).
   },
+  "basicService": { # A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli). # Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+    "serviceLabels": { # Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+      "a_key": "A String",
+    },
+    "serviceType": "A String", # The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+  },
   "cloudEndpoints": { # Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. # Type used for Cloud Endpoints services.
     "service": "A String", # The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api).
   },
@@ -294,7 +312,7 @@ 
Method Details

     "serviceName": "A String", # The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics.
     "serviceNamespace": "A String", # The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics.
   },
-  "custom": { # Custom view of service telemetry. Currently a place-holder pending final design. # Custom service type.
+  "custom": { # Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. # Custom service type.
   },
   "displayName": "A String", # Name used for UI elements listing this Service.
   "gkeNamespace": { # GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). # Type used for GKE Namespaces.
@@ -362,6 +380,12 @@ 
Method Details

       "appEngine": { # App Engine service. Learn more at https://cloud.google.com/appengine. # Type used for App Engine services.
         "moduleId": "A String", # The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app).
       },
+      "basicService": { # A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli). # Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+        "serviceLabels": { # Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+          "a_key": "A String",
+        },
+        "serviceType": "A String", # The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+      },
       "cloudEndpoints": { # Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. # Type used for Cloud Endpoints services.
         "service": "A String", # The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api).
       },
@@ -375,7 +399,7 @@ 
Method Details

         "serviceName": "A String", # The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics.
         "serviceNamespace": "A String", # The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics.
       },
-      "custom": { # Custom view of service telemetry. Currently a place-holder pending final design. # Custom service type.
+      "custom": { # Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. # Custom service type.
       },
       "displayName": "A String", # Name used for UI elements listing this Service.
       "gkeNamespace": { # GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). # Type used for GKE Namespaces.
@@ -448,6 +472,12 @@ 
Method Details

   "appEngine": { # App Engine service. Learn more at https://cloud.google.com/appengine. # Type used for App Engine services.
     "moduleId": "A String", # The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app).
   },
+  "basicService": { # A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli). # Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+    "serviceLabels": { # Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+      "a_key": "A String",
+    },
+    "serviceType": "A String", # The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+  },
   "cloudEndpoints": { # Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. # Type used for Cloud Endpoints services.
     "service": "A String", # The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api).
   },
@@ -461,7 +491,7 @@ 
Method Details

     "serviceName": "A String", # The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics.
     "serviceNamespace": "A String", # The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics.
   },
-  "custom": { # Custom view of service telemetry. Currently a place-holder pending final design. # Custom service type.
+  "custom": { # Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. # Custom service type.
   },
   "displayName": "A String", # Name used for UI elements listing this Service.
   "gkeNamespace": { # GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). # Type used for GKE Namespaces.
@@ -517,6 +547,12 @@ 
Method Details

   "appEngine": { # App Engine service. Learn more at https://cloud.google.com/appengine. # Type used for App Engine services.
     "moduleId": "A String", # The ID of the App Engine module underlying this service. Corresponds to the module_id resource label in the gae_app monitored resource (https://cloud.google.com/monitoring/api/resources#tag_gae_app).
   },
+  "basicService": { # A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli). # Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+    "serviceLabels": { # Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+      "a_key": "A String",
+    },
+    "serviceType": "A String", # The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).
+  },
   "cloudEndpoints": { # Cloud Endpoints service. Learn more at https://cloud.google.com/endpoints. # Type used for Cloud Endpoints services.
     "service": "A String", # The name of the Cloud Endpoints service underlying this service. Corresponds to the service resource label in the api monitored resource (https://cloud.google.com/monitoring/api/resources#tag_api).
   },
@@ -530,7 +566,7 @@ 
Method Details

     "serviceName": "A String", # The name of the Istio service underlying this service. Corresponds to the destination_service_name metric label in Istio metrics.
     "serviceNamespace": "A String", # The namespace of the Istio service underlying this service. Corresponds to the destination_service_namespace metric label in Istio metrics.
   },
-  "custom": { # Custom view of service telemetry. Currently a place-holder pending final design. # Custom service type.
+  "custom": { # Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service. # Custom service type.
   },
   "displayName": "A String", # Name used for UI elements listing this Service.
   "gkeNamespace": { # GKE Namespace. The field names correspond to the resource metadata labels on monitored resources that fall under a namespace (for example, k8s_container or k8s_pod). # Type used for GKE Namespaces.
diff --git a/googleapiclient/discovery_cache/documents/monitoring.v1.json b/googleapiclient/discovery_cache/documents/monitoring.v1.json
index 63b9f3bdeb8..957532132fe 100644
--- a/googleapiclient/discovery_cache/documents/monitoring.v1.json
+++ b/googleapiclient/discovery_cache/documents/monitoring.v1.json
@@ -769,7 +769,7 @@
       }
     }
   },
-  "revision": "20220916",
+  "revision": "20220930",
   "rootUrl": "https://monitoring.googleapis.com/",
   "schemas": {
     "Aggregation": {
@@ -2012,6 +2012,10 @@
       "description": "TimeSeriesQuery collects the set of supported methods for querying time series data from the Stackdriver metrics API.",
       "id": "TimeSeriesQuery",
       "properties": {
+        "prometheusQuery": {
+          "description": "A query used to fetch time series with PromQL.",
+          "type": "string"
+        },
         "timeSeriesFilter": {
           "$ref": "TimeSeriesFilter",
           "description": "Filter parameters to fetch time series."
diff --git a/googleapiclient/discovery_cache/documents/monitoring.v3.json b/googleapiclient/discovery_cache/documents/monitoring.v3.json
index 3fe6c7ffa0a..ddd561afe13 100644
--- a/googleapiclient/discovery_cache/documents/monitoring.v3.json
+++ b/googleapiclient/discovery_cache/documents/monitoring.v3.json
@@ -665,7 +665,7 @@
         "alertPolicies": {
           "methods": {
             "create": {
-              "description": "Creates a new alerting policy.",
+              "description": "Creates a new alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.",
               "flatPath": "v3/projects/{projectsId}/alertPolicies",
               "httpMethod": "POST",
               "id": "monitoring.projects.alertPolicies.create",
@@ -694,7 +694,7 @@
               ]
             },
             "delete": {
-              "description": "Deletes an alerting policy.",
+              "description": "Deletes an alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.",
               "flatPath": "v3/projects/{projectsId}/alertPolicies/{alertPoliciesId}",
               "httpMethod": "DELETE",
               "id": "monitoring.projects.alertPolicies.delete",
@@ -795,7 +795,7 @@
               ]
             },
             "patch": {
-              "description": "Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.",
+              "description": "Updates an alerting policy. You can either replace the entire policy with a new one or replace only certain fields in the current alerting policy by specifying the fields to be updated via updateMask. Returns the updated alerting policy.Design your application to single-thread API calls that modify the state of alerting policies in a single project. This includes calls to CreateAlertPolicy, DeleteAlertPolicy and UpdateAlertPolicy.",
               "flatPath": "v3/projects/{projectsId}/alertPolicies/{alertPoliciesId}",
               "httpMethod": "PATCH",
               "id": "monitoring.projects.alertPolicies.patch",
@@ -1389,7 +1389,7 @@
         "notificationChannels": {
           "methods": {
             "create": {
-              "description": "Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.",
+              "description": "Creates a new notification channel, representing a single notification endpoint such as an email address, SMS number, or PagerDuty service.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.",
               "flatPath": "v3/projects/{projectsId}/notificationChannels",
               "httpMethod": "POST",
               "id": "monitoring.projects.notificationChannels.create",
@@ -1418,7 +1418,7 @@
               ]
             },
             "delete": {
-              "description": "Deletes a notification channel.",
+              "description": "Deletes a notification channel.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.",
               "flatPath": "v3/projects/{projectsId}/notificationChannels/{notificationChannelsId}",
               "httpMethod": "DELETE",
               "id": "monitoring.projects.notificationChannels.delete",
@@ -1553,7 +1553,7 @@
               ]
             },
             "patch": {
-              "description": "Updates a notification channel. Fields not specified in the field mask remain unchanged.",
+              "description": "Updates a notification channel. Fields not specified in the field mask remain unchanged.Design your application to single-thread API calls that modify the state of notification channels in a single project. This includes calls to CreateNotificationChannel, DeleteNotificationChannel and UpdateNotificationChannel.",
               "flatPath": "v3/projects/{projectsId}/notificationChannels/{notificationChannelsId}",
               "httpMethod": "PATCH",
               "id": "monitoring.projects.notificationChannels.patch",
@@ -2576,7 +2576,7 @@
       }
     }
   },
-  "revision": "20220916",
+  "revision": "20220930",
   "rootUrl": "https://monitoring.googleapis.com/",
   "schemas": {
     "Aggregation": {
@@ -2804,6 +2804,24 @@
       },
       "type": "object"
     },
+    "BasicService": {
+      "description": "A well-known service type, defined by its service type and service labels. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).",
+      "id": "BasicService",
+      "properties": {
+        "serviceLabels": {
+          "additionalProperties": {
+            "type": "string"
+          },
+          "description": "Labels that specify the resource that emits the monitoring data which is used for SLO reporting of this Service. Documentation and valid values for given service types here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).",
+          "type": "object"
+        },
+        "serviceType": {
+          "description": "The type of service that this basic service defines, e.g. APP_ENGINE service type. Documentation and valid values here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli).",
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
     "BasicSli": {
       "description": "An SLI measuring performance on a well-known service type. Performance will be computed on the basis of pre-defined metrics. The type of the service_resource determines the metrics to use and the service_resource.labels and metric_labels are used to construct a monitoring filter to filter that metric down to just the data relevant to this service.",
       "id": "BasicSli",
@@ -3174,7 +3192,7 @@
       "type": "object"
     },
     "Custom": {
-      "description": "Custom view of service telemetry. Currently a place-holder pending final design.",
+      "description": "Use a custom service to designate a service that you want to monitor when none of the other service types (like App Engine, Cloud Run, or a GKE type) matches your intended service.",
       "id": "Custom",
       "properties": {},
       "type": "object"
@@ -5007,6 +5025,10 @@
           "$ref": "AppEngine",
           "description": "Type used for App Engine services."
         },
+        "basicService": {
+          "$ref": "BasicService",
+          "description": "Message that contains the service type and service labels of this service if it is a basic service. Documentation and examples here (https://cloud.google.com/stackdriver/docs/solutions/slo-monitoring/api/api-structures#basic-svc-w-basic-sli)."
+        },
         "cloudEndpoints": {
           "$ref": "CloudEndpoints",
           "description": "Type used for Cloud Endpoints services."