list method

Future<ListTimeSeriesResponse> list(
  1. String name, {
  2. String? aggregation_alignmentPeriod,
  3. String? aggregation_crossSeriesReducer,
  4. List<String>? aggregation_groupByFields,
  5. String? aggregation_perSeriesAligner,
  6. String? filter,
  7. String? interval_endTime,
  8. String? interval_startTime,
  9. String? orderBy,
  10. int? pageSize,
  11. String? pageToken,
  12. String? secondaryAggregation_alignmentPeriod,
  13. String? secondaryAggregation_crossSeriesReducer,
  14. List<String>? secondaryAggregation_groupByFields,
  15. String? secondaryAggregation_perSeriesAligner,
  16. String? view,
  17. String? $fields,
})

Lists time series that match a filter.

Request parameters:

name - Required. The project (https://cloud.google.com/monitoring/api/v3#project_name), organization or folder on which to execute the request. The format is: projects/[PROJECT_ID_OR_NUMBER] organizations/[ORGANIZATION_ID] folders/[FOLDER_ID] Value must have pattern ^projects/\[^/\]+$.

aggregation_alignmentPeriod - 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 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.

aggregation_crossSeriesReducer - The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Possible string values are:

  • "REDUCE_NONE" : No cross-time series reduction. The output of the Aligner is returned.
  • "REDUCE_MEAN" : Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
  • "REDUCE_MIN" : Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
  • "REDUCE_MAX" : Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
  • "REDUCE_SUM" : Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
  • "REDUCE_STDDEV" : Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
  • "REDUCE_COUNT" : Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
  • "REDUCE_COUNT_TRUE" : Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
  • "REDUCE_COUNT_FALSE" : Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
  • "REDUCE_FRACTION_TRUE" : Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
  • "REDUCE_PERCENTILE_99" : Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  • "REDUCE_PERCENTILE_95" : Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  • "REDUCE_PERCENTILE_50" : Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  • "REDUCE_PERCENTILE_05" : Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

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

aggregation_perSeriesAligner - An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Possible string values are:

  • "ALIGN_NONE" : No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
  • "ALIGN_DELTA" : Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_RATE" : Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
  • "ALIGN_INTERPOLATE" : Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_NEXT_OLDER" : Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_MIN" : Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_MAX" : Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_MEAN" : Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
  • "ALIGN_COUNT" : Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
  • "ALIGN_SUM" : Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_STDDEV" : Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
  • "ALIGN_COUNT_TRUE" : Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
  • "ALIGN_COUNT_FALSE" : Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
  • "ALIGN_FRACTION_TRUE" : Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
  • "ALIGN_PERCENTILE_99" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENTILE_95" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENTILE_50" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENTILE_05" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENT_CHANGE" : Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

filter - Required. A monitoring filter (https://cloud.google.com/monitoring/api/v3/filters) that specifies which time series should be returned. The filter must specify a single metric type, and can additionally specify metric labels and other information. For example: metric.type = "compute.googleapis.com/instance/cpu/usage_time" AND metric.labels.instance_name = "my-instance-name"

interval_endTime - Required. The end of the time interval.

interval_startTime - Optional. The beginning of the time interval. The default value for the start time is the end time. The start time must not be later than the end time.

orderBy - Unsupported: must be left blank. The points in each time series are currently returned in reverse time order (most recent to oldest).

pageSize - A positive number that is the maximum number of results to return. If page_size is empty or more than 100,000 results, the effective page_size is 100,000 results. If view is set to FULL, this is the maximum number of Points returned. If view is set to HEADERS, this is the maximum number of TimeSeries returned.

pageToken - If this field is not empty then it must contain the nextPageToken value returned by a previous call to this method. Using this field causes the method to return additional results from the previous method call.

secondaryAggregation_alignmentPeriod - 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 104 weeks (2 years) for charts, and 90,000 seconds (25 hours) for alerting policies.

secondaryAggregation_crossSeriesReducer - The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned. Possible string values are:

  • "REDUCE_NONE" : No cross-time series reduction. The output of the Aligner is returned.
  • "REDUCE_MEAN" : Reduce by computing the mean value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
  • "REDUCE_MIN" : Reduce by computing the minimum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
  • "REDUCE_MAX" : Reduce by computing the maximum value across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric values. The value_type of the output is the same as the value_type of the input.
  • "REDUCE_SUM" : Reduce by computing the sum across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric and distribution values. The value_type of the output is the same as the value_type of the input.
  • "REDUCE_STDDEV" : Reduce by computing the standard deviation across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics with numeric or distribution values. The value_type of the output is DOUBLE.
  • "REDUCE_COUNT" : Reduce by computing the number of data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of numeric, Boolean, distribution, and string value_type. The value_type of the output is INT64.
  • "REDUCE_COUNT_TRUE" : Reduce by computing the number of True-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
  • "REDUCE_COUNT_FALSE" : Reduce by computing the number of False-valued data points across time series for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The value_type of the output is INT64.
  • "REDUCE_FRACTION_TRUE" : Reduce by computing the ratio of the number of True-valued data points to the total number of data points for each alignment period. This reducer is valid for DELTA and GAUGE metrics of Boolean value_type. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
  • "REDUCE_PERCENTILE_99" : Reduce by computing the 99th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  • "REDUCE_PERCENTILE_95" : Reduce by computing the 95th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  • "REDUCE_PERCENTILE_50" : Reduce by computing the 50th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
  • "REDUCE_PERCENTILE_05" : Reduce by computing the 5th percentile (https://en.wikipedia.org/wiki/Percentile) of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.

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

secondaryAggregation_perSeriesAligner - An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.Time series data must be aligned in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified and not equal to ALIGN_NONE and alignment_period must be specified; otherwise, an error is returned. Possible string values are:

  • "ALIGN_NONE" : No alignment. Raw data is returned. Not valid if cross-series reduction is requested. The value_type of the result is the same as the value_type of the input.
  • "ALIGN_DELTA" : Align and convert to DELTA. The output is delta = y1 - y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_RATE" : Align and convert to a rate. The result is computed as rate = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as providing the slope of the line that passes through the value at the start and at the end of the alignment_period.This aligner is valid for CUMULATIVE and DELTA metrics with numeric values. If the selected alignment period results in periods with no data, then the aligned value for such a period is created by interpolation. The output is a GAUGE metric with value_type DOUBLE.If, by "rate", you mean "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
  • "ALIGN_INTERPOLATE" : Align by interpolating between adjacent points around the alignment period boundary. This aligner is valid for GAUGE metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_NEXT_OLDER" : Align by moving the most recent data point before the end of the alignment period to the boundary at the end of the alignment period. This aligner is valid for GAUGE metrics. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_MIN" : Align the time series by returning the minimum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_MAX" : Align the time series by returning the maximum value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_MEAN" : Align the time series by returning the mean value in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the aligned result is DOUBLE.
  • "ALIGN_COUNT" : Align the time series by returning the number of values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric or Boolean values. The value_type of the aligned result is INT64.
  • "ALIGN_SUM" : Align the time series by returning the sum of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric and distribution values. The value_type of the aligned result is the same as the value_type of the input.
  • "ALIGN_STDDEV" : Align the time series by returning the standard deviation of the values in each alignment period. This aligner is valid for GAUGE and DELTA metrics with numeric values. The value_type of the output is DOUBLE.
  • "ALIGN_COUNT_TRUE" : Align the time series by returning the number of True values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
  • "ALIGN_COUNT_FALSE" : Align the time series by returning the number of False values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The value_type of the output is INT64.
  • "ALIGN_FRACTION_TRUE" : Align the time series by returning the ratio of the number of True values to the total number of values in each alignment period. This aligner is valid for GAUGE metrics with Boolean values. The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
  • "ALIGN_PERCENTILE_99" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 99th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENTILE_95" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 95th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENTILE_50" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 50th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENTILE_05" : Align the time series by using percentile aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting data point in each alignment period is the 5th percentile of all data points in the period. This aligner is valid for GAUGE and DELTA metrics with distribution values. The output is a GAUGE metric with value_type DOUBLE.
  • "ALIGN_PERCENT_CHANGE" : Align and convert to a percentage change. This aligner is valid for GAUGE and DELTA metrics with numeric values. This alignment returns ((current - previous)/previous) * 100, where the value of previous is determined based on the alignment_period.If the values of current and previous are both 0, then the returned value is 0. If only previous is 0, the returned value is infinity.A 10-minute moving mean is computed at each point of the alignment period prior to the above calculation to smooth the metric and prevent false positives from very short-lived spikes. The moving mean is only applicable for data whose values are >= 0. Any values < 0 are treated as a missing datapoint, and are ignored. While DELTA metrics are accepted by this alignment, special care should be taken that the values for the metric will always be positive. The output is a GAUGE metric with value_type DOUBLE.

view - Required. Specifies which information is returned about the time series. Possible string values are:

  • "FULL" : Returns the identity of the metric(s), the time series, and the time series data.
  • "HEADERS" : Returns the identity of the metric and the time series resource, but not the time series data.

$fields - Selector specifying which fields to include in a partial response.

Completes with a ListTimeSeriesResponse.

Completes with a commons.ApiRequestError if the API endpoint returned an error.

If the used http.Client completes with an error when making a REST call, this method will complete with the same error.

Implementation

async.Future<ListTimeSeriesResponse> list(
  core.String name, {
  core.String? aggregation_alignmentPeriod,
  core.String? aggregation_crossSeriesReducer,
  core.List<core.String>? aggregation_groupByFields,
  core.String? aggregation_perSeriesAligner,
  core.String? filter,
  core.String? interval_endTime,
  core.String? interval_startTime,
  core.String? orderBy,
  core.int? pageSize,
  core.String? pageToken,
  core.String? secondaryAggregation_alignmentPeriod,
  core.String? secondaryAggregation_crossSeriesReducer,
  core.List<core.String>? secondaryAggregation_groupByFields,
  core.String? secondaryAggregation_perSeriesAligner,
  core.String? view,
  core.String? $fields,
}) async {
  final queryParams_ = <core.String, core.List<core.String>>{
    if (aggregation_alignmentPeriod != null)
      'aggregation.alignmentPeriod': [aggregation_alignmentPeriod],
    if (aggregation_crossSeriesReducer != null)
      'aggregation.crossSeriesReducer': [aggregation_crossSeriesReducer],
    if (aggregation_groupByFields != null)
      'aggregation.groupByFields': aggregation_groupByFields,
    if (aggregation_perSeriesAligner != null)
      'aggregation.perSeriesAligner': [aggregation_perSeriesAligner],
    if (filter != null) 'filter': [filter],
    if (interval_endTime != null) 'interval.endTime': [interval_endTime],
    if (interval_startTime != null)
      'interval.startTime': [interval_startTime],
    if (orderBy != null) 'orderBy': [orderBy],
    if (pageSize != null) 'pageSize': ['${pageSize}'],
    if (pageToken != null) 'pageToken': [pageToken],
    if (secondaryAggregation_alignmentPeriod != null)
      'secondaryAggregation.alignmentPeriod': [
        secondaryAggregation_alignmentPeriod
      ],
    if (secondaryAggregation_crossSeriesReducer != null)
      'secondaryAggregation.crossSeriesReducer': [
        secondaryAggregation_crossSeriesReducer
      ],
    if (secondaryAggregation_groupByFields != null)
      'secondaryAggregation.groupByFields':
          secondaryAggregation_groupByFields,
    if (secondaryAggregation_perSeriesAligner != null)
      'secondaryAggregation.perSeriesAligner': [
        secondaryAggregation_perSeriesAligner
      ],
    if (view != null) 'view': [view],
    if ($fields != null) 'fields': [$fields],
  };

  final url_ = 'v3/' + core.Uri.encodeFull('$name') + '/timeSeries';

  final response_ = await _requester.request(
    url_,
    'GET',
    queryParams: queryParams_,
  );
  return ListTimeSeriesResponse.fromJson(
      response_ as core.Map<core.String, core.dynamic>);
}