Types for BigQuery API Client#

class google.cloud.bigquery_v2.types.BoolValue#
value#

Field google.protobuf.BoolValue.value

class google.cloud.bigquery_v2.types.BytesValue#
value#

Field google.protobuf.BytesValue.value

class google.cloud.bigquery_v2.types.DeleteModelRequest#

Protocol buffer.

project_id#

Project ID of the model to delete.

dataset_id#

Dataset ID of the model to delete.

model_id#

Model ID of the model to delete.

dataset_id

Field google.cloud.bigquery.v2.DeleteModelRequest.dataset_id

model_id

Field google.cloud.bigquery.v2.DeleteModelRequest.model_id

project_id

Field google.cloud.bigquery.v2.DeleteModelRequest.project_id

class google.cloud.bigquery_v2.types.DoubleValue#
value#

Field google.protobuf.DoubleValue.value

class google.cloud.bigquery_v2.types.Empty#
class google.cloud.bigquery_v2.types.FloatValue#
value#

Field google.protobuf.FloatValue.value

class google.cloud.bigquery_v2.types.GetModelRequest#

Protocol buffer.

project_id#

Project ID of the requested model.

dataset_id#

Dataset ID of the requested model.

model_id#

Model ID of the requested model.

dataset_id

Field google.cloud.bigquery.v2.GetModelRequest.dataset_id

model_id

Field google.cloud.bigquery.v2.GetModelRequest.model_id

project_id

Field google.cloud.bigquery.v2.GetModelRequest.project_id

class google.cloud.bigquery_v2.types.Int32Value#
value#

Field google.protobuf.Int32Value.value

class google.cloud.bigquery_v2.types.Int64Value#
value#

Field google.protobuf.Int64Value.value

class google.cloud.bigquery_v2.types.ListModelsRequest#

Protocol buffer.

project_id#

Project ID of the models to list.

dataset_id#

Dataset ID of the models to list.

max_results#

The maximum number of results to return in a single response page. Leverage the page tokens to iterate through the entire collection.

page_token#

Page token, returned by a previous call to request the next page of results

dataset_id

Field google.cloud.bigquery.v2.ListModelsRequest.dataset_id

max_results

Field google.cloud.bigquery.v2.ListModelsRequest.max_results

page_token

Field google.cloud.bigquery.v2.ListModelsRequest.page_token

project_id

Field google.cloud.bigquery.v2.ListModelsRequest.project_id

class google.cloud.bigquery_v2.types.ListModelsResponse#

Protocol buffer.

models#

Models in the requested dataset. Only the following fields are populated: model_reference, model_type, creation_time, last_modified_time and labels.

next_page_token#

A token to request the next page of results.

models

Field google.cloud.bigquery.v2.ListModelsResponse.models

next_page_token

Field google.cloud.bigquery.v2.ListModelsResponse.next_page_token

class google.cloud.bigquery_v2.types.Model#

Protocol buffer.

etag#

Output only. A hash of this resource.

model_reference#

Required. Unique identifier for this model.

creation_time#

Output only. The time when this model was created, in millisecs since the epoch.

last_modified_time#

Output only. The time when this model was last modified, in millisecs since the epoch.

description#

[Optional] A user-friendly description of this model.

friendly_name#

[Optional] A descriptive name for this model.

labels#

[Optional] The labels associated with this model. You can use these to organize and group your models. Label keys and values can be no longer than 63 characters, can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter and each label in the list must have a different key.

expiration_time#

[Optional] The time when this model expires, in milliseconds since the epoch. If not present, the model will persist indefinitely. Expired models will be deleted and their storage reclaimed. The defaultTableExpirationMs property of the encapsulating dataset can be used to set a default expirationTime on newly created models.

location#

Output only. The geographic location where the model resides. This value is inherited from the dataset.

model_type#

Output only. Type of the model resource.

training_runs#

Output only. Information for all training runs in increasing order of start_time.

feature_columns#

Output only. Input feature columns that were used to train this model.

label_columns#

Output only. Label columns that were used to train this model. The output of the model will have a “predicted_” prefix to these columns.

class AggregateClassificationMetrics#

Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.

precision#

Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro- averaged metric treating each class as a binary classifier.

recall#

Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro- averaged metric.

accuracy#

Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.

threshold#

Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.

f1_score#

The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.

log_loss#

Logarithmic Loss. For multiclass this is a macro-averaged metric.

roc_auc#

Area Under a ROC Curve. For multiclass this is a macro- averaged metric.

accuracy

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.accuracy

f1_score

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.f1_score

log_loss

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.log_loss

precision

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.precision

recall

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.recall

roc_auc

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.roc_auc

threshold

Field google.cloud.bigquery.v2.Model.AggregateClassificationMetrics.threshold

class BinaryClassificationMetrics#

Evaluation metrics for binary classification/classifier models.

aggregate_classification_metrics#

Aggregate classification metrics.

binary_confusion_matrix_list#

Binary confusion matrix at multiple thresholds.

positive_label#

Label representing the positive class.

negative_label#

Label representing the negative class.

class BinaryConfusionMatrix#

Confusion matrix for binary classification models.

positive_class_threshold#

Threshold value used when computing each of the following metric.

true_positives#

Number of true samples predicted as true.

false_positives#

Number of false samples predicted as true.

true_negatives#

Number of true samples predicted as false.

false_negatives#

Number of false samples predicted as false.

precision#

The fraction of actual positive predictions that had positive actual labels.

recall#

The fraction of actual positive labels that were given a positive prediction.

f1_score#

The equally weighted average of recall and precision.

accuracy#

The fraction of predictions given the correct label.

accuracy

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.accuracy

f1_score

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.f1_score

false_negatives

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.false_negatives

false_positives

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.false_positives

positive_class_threshold

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.positive_class_threshold

precision

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.precision

recall

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.recall

true_negatives

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.true_negatives

true_positives

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.BinaryConfusionMatrix.true_positives

aggregate_classification_metrics

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.aggregate_classification_metrics

binary_confusion_matrix_list

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.binary_confusion_matrix_list

negative_label

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.negative_label

positive_label

Field google.cloud.bigquery.v2.Model.BinaryClassificationMetrics.positive_label

class ClusteringMetrics#

Evaluation metrics for clustering models.

davies_bouldin_index#

Davies-Bouldin index.

mean_squared_distance#

Mean of squared distances between each sample to its cluster centroid.

clusters#

[Beta] Information for all clusters.

class Cluster#

Message containing the information about one cluster.

centroid_id#

Centroid id.

feature_values#

Values of highly variant features for this cluster.

count#

Count of training data rows that were assigned to this cluster.

class FeatureValue#

Representative value of a single feature within the cluster.

feature_column#

The feature column name.

numerical_value#

The numerical feature value. This is the centroid value for this feature.

categorical_value#

The categorical feature value.

class CategoricalValue#

Representative value of a categorical feature.

category_counts#

Counts of all categories for the categorical feature. If there are more than ten categories, we return top ten (by count) and return one more CategoryCount with category ‘OTHER’ and count as aggregate counts of remaining categories.

class CategoryCount#

Represents the count of a single category within the cluster.

category#

The name of category.

count#

The count of training samples matching the category within the cluster.

category

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount.category

count

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.CategoryCount.count

category_counts

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.CategoricalValue.category_counts

categorical_value

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.categorical_value

feature_column

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.feature_column

numerical_value

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.FeatureValue.numerical_value

centroid_id

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.centroid_id

count

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.count

feature_values

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.Cluster.feature_values

clusters

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.clusters

davies_bouldin_index

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.davies_bouldin_index

mean_squared_distance

Field google.cloud.bigquery.v2.Model.ClusteringMetrics.mean_squared_distance

class EvaluationMetrics#

Evaluation metrics of a model. These are either computed on all training data or just the eval data based on whether eval data was used during training. These are not present for imported models.

regression_metrics#

Populated for regression models and explicit feedback type matrix factorization models.

binary_classification_metrics#

Populated for binary classification/classifier models.

multi_class_classification_metrics#

Populated for multi-class classification/classifier models.

clustering_metrics#

Populated for clustering models.

binary_classification_metrics

Field google.cloud.bigquery.v2.Model.EvaluationMetrics.binary_classification_metrics

clustering_metrics

Field google.cloud.bigquery.v2.Model.EvaluationMetrics.clustering_metrics

multi_class_classification_metrics

Field google.cloud.bigquery.v2.Model.EvaluationMetrics.multi_class_classification_metrics

regression_metrics

Field google.cloud.bigquery.v2.Model.EvaluationMetrics.regression_metrics

class KmeansEnums#
class LabelsEntry#
key#

Field google.cloud.bigquery.v2.Model.LabelsEntry.key

value#

Field google.cloud.bigquery.v2.Model.LabelsEntry.value

class MultiClassClassificationMetrics#

Evaluation metrics for multi-class classification/classifier models.

aggregate_classification_metrics#

Aggregate classification metrics.

confusion_matrix_list#

Confusion matrix at different thresholds.

class ConfusionMatrix#

Confusion matrix for multi-class classification models.

confidence_threshold#

Confidence threshold used when computing the entries of the confusion matrix.

rows#

One row per actual label.

class Entry#

A single entry in the confusion matrix.

predicted_label#

The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.

item_count#

Number of items being predicted as this label.

item_count

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry.item_count

predicted_label

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Entry.predicted_label

class Row#

A single row in the confusion matrix.

actual_label#

The original label of this row.

entries#

Info describing predicted label distribution.

actual_label

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row.actual_label

entries

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.Row.entries

confidence_threshold

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.confidence_threshold

rows

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.ConfusionMatrix.rows

aggregate_classification_metrics

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.aggregate_classification_metrics

confusion_matrix_list

Field google.cloud.bigquery.v2.Model.MultiClassClassificationMetrics.confusion_matrix_list

class RegressionMetrics#

Evaluation metrics for regression and explicit feedback type matrix factorization models.

mean_absolute_error#

Mean absolute error.

mean_squared_error#

Mean squared error.

mean_squared_log_error#

Mean squared log error.

median_absolute_error#

Median absolute error.

r_squared#

R^2 score.

mean_absolute_error

Field google.cloud.bigquery.v2.Model.RegressionMetrics.mean_absolute_error

mean_squared_error

Field google.cloud.bigquery.v2.Model.RegressionMetrics.mean_squared_error

mean_squared_log_error

Field google.cloud.bigquery.v2.Model.RegressionMetrics.mean_squared_log_error

median_absolute_error

Field google.cloud.bigquery.v2.Model.RegressionMetrics.median_absolute_error

r_squared

Field google.cloud.bigquery.v2.Model.RegressionMetrics.r_squared

class TrainingRun#

Information about a single training query run for the model.

training_options#

Options that were used for this training run, includes user specified and default options that were used.

start_time#

The start time of this training run.

results#

Output of each iteration run, results.size() <= max_iterations.

evaluation_metrics#

The evaluation metrics over training/eval data that were computed at the end of training.

class IterationResult#

Information about a single iteration of the training run.

index#

Index of the iteration, 0 based.

duration_ms#

Time taken to run the iteration in milliseconds.

training_loss#

Loss computed on the training data at the end of iteration.

eval_loss#

Loss computed on the eval data at the end of iteration.

learn_rate#

Learn rate used for this iteration.

cluster_infos#

Information about top clusters for clustering models.

class ClusterInfo#

Information about a single cluster for clustering model.

centroid_id#

Centroid id.

cluster_radius#

Cluster radius, the average distance from centroid to each point assigned to the cluster.

cluster_size#

Cluster size, the total number of points assigned to the cluster.

centroid_id

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo.centroid_id

cluster_radius

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo.cluster_radius

cluster_size

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.ClusterInfo.cluster_size

cluster_infos

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.cluster_infos

duration_ms

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.duration_ms

eval_loss

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.eval_loss

index

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.index

learn_rate

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.learn_rate

training_loss

Field google.cloud.bigquery.v2.Model.TrainingRun.IterationResult.training_loss

class TrainingOptions#

Protocol buffer.

max_iterations#

The maximum number of iterations in training. Used only for iterative training algorithms.

loss_type#

Type of loss function used during training run.

learn_rate#

Learning rate in training. Used only for iterative training algorithms.

l1_regularization#

L1 regularization coefficient.

l2_regularization#

L2 regularization coefficient.

min_relative_progress#

When early_stop is true, stops training when accuracy improvement is less than ‘min_relative_progress’. Used only for iterative training algorithms.

warm_start#

Whether to train a model from the last checkpoint.

early_stop#

Whether to stop early when the loss doesn’t improve significantly any more (compared to min_relative_progress). Used only for iterative training algorithms.

input_label_columns#

Name of input label columns in training data.

data_split_method#

The data split type for training and evaluation, e.g. RANDOM.

data_split_eval_fraction#

The fraction of evaluation data over the whole input data. The rest of data will be used as training data. The format should be double. Accurate to two decimal places. Default value is 0.2.

data_split_column#

The column to split data with. This column won’t be used as a feature. 1. When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data. 2. When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION rows (from smallest to largest) in the corresponding column are used as training data, and the rest are eval data. It respects the order in Orderable data types: https://cloud.google.com/bigquery/docs/reference/standard- sql/data-types#data-type-properties

learn_rate_strategy#

The strategy to determine learn rate for the current iteration.

initial_learn_rate#

Specifies the initial learning rate for the line search learn rate strategy.

label_class_weights#

Weights associated with each label class, for rebalancing the training data. Only applicable for classification models.

distance_type#

Distance type for clustering models.

num_clusters#

Number of clusters for clustering models.

model_uri#

[Beta] Google Cloud Storage URI from which the model was imported. Only applicable for imported models.

optimization_strategy#

Optimization strategy for training linear regression models.

kmeans_initialization_method#

The method used to initialize the centroids for kmeans algorithm.

kmeans_initialization_column#

The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.

class LabelClassWeightsEntry#
key#

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.LabelClassWeightsEntry.key

value#

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.LabelClassWeightsEntry.value

data_split_column

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.data_split_column

data_split_eval_fraction

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.data_split_eval_fraction

data_split_method

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.data_split_method

distance_type

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.distance_type

early_stop

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.early_stop

initial_learn_rate

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.initial_learn_rate

input_label_columns

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.input_label_columns

kmeans_initialization_column

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.kmeans_initialization_column

kmeans_initialization_method

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.kmeans_initialization_method

l1_regularization

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.l1_regularization

l2_regularization

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.l2_regularization

label_class_weights

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.label_class_weights

learn_rate

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.learn_rate

learn_rate_strategy

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.learn_rate_strategy

loss_type

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.loss_type

max_iterations

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.max_iterations

min_relative_progress

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.min_relative_progress

model_uri

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.model_uri

num_clusters

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.num_clusters

optimization_strategy

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.optimization_strategy

warm_start

Field google.cloud.bigquery.v2.Model.TrainingRun.TrainingOptions.warm_start

evaluation_metrics

Field google.cloud.bigquery.v2.Model.TrainingRun.evaluation_metrics

results

Field google.cloud.bigquery.v2.Model.TrainingRun.results

start_time

Field google.cloud.bigquery.v2.Model.TrainingRun.start_time

training_options

Field google.cloud.bigquery.v2.Model.TrainingRun.training_options

creation_time

Field google.cloud.bigquery.v2.Model.creation_time

description

Field google.cloud.bigquery.v2.Model.description

etag

Field google.cloud.bigquery.v2.Model.etag

expiration_time

Field google.cloud.bigquery.v2.Model.expiration_time

feature_columns

Field google.cloud.bigquery.v2.Model.feature_columns

friendly_name

Field google.cloud.bigquery.v2.Model.friendly_name

label_columns

Field google.cloud.bigquery.v2.Model.label_columns

labels

Field google.cloud.bigquery.v2.Model.labels

last_modified_time

Field google.cloud.bigquery.v2.Model.last_modified_time

location

Field google.cloud.bigquery.v2.Model.location

model_reference

Field google.cloud.bigquery.v2.Model.model_reference

model_type

Field google.cloud.bigquery.v2.Model.model_type

training_runs

Field google.cloud.bigquery.v2.Model.training_runs

class google.cloud.bigquery_v2.types.ModelReference#

Id path of a model.

project_id#

[Required] The ID of the project containing this model.

dataset_id#

[Required] The ID of the dataset containing this model.

model_id#

[Required] The ID of the model. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.

dataset_id

Field google.cloud.bigquery.v2.ModelReference.dataset_id

model_id

Field google.cloud.bigquery.v2.ModelReference.model_id

project_id

Field google.cloud.bigquery.v2.ModelReference.project_id

class google.cloud.bigquery_v2.types.PatchModelRequest#

Protocol buffer.

project_id#

Project ID of the model to patch.

dataset_id#

Dataset ID of the model to patch.

model_id#

Model ID of the model to patch.

model#

Patched model. Follows RFC5789 patch semantics. Missing fields are not updated. To clear a field, explicitly set to default value.

dataset_id

Field google.cloud.bigquery.v2.PatchModelRequest.dataset_id

model

Field google.cloud.bigquery.v2.PatchModelRequest.model

model_id

Field google.cloud.bigquery.v2.PatchModelRequest.model_id

project_id

Field google.cloud.bigquery.v2.PatchModelRequest.project_id

class google.cloud.bigquery_v2.types.StandardSqlDataType#

The type of a variable, e.g., a function argument. Examples: INT64: {type_kind=”INT64”} ARRAY: {type_kind=”ARRAY”, array_element_type=”STRING”} STRUCT<x STRING, y ARRAY>: {type_kind=”STRUCT”, struct_type={fields=[ {name=”x”, type={type_kind=”STRING”}}, {name=”y”, type={type_kind=”ARRAY”, array_element_type=”DATE”}} ]}}

type_kind#

Required. The top level type of this field. Can be any standard SQL data type (e.g., “INT64”, “DATE”, “ARRAY”).

array_element_type#

The type of the array’s elements, if type_kind = “ARRAY”.

struct_type#

The fields of this struct, in order, if type_kind = “STRUCT”.

array_element_type

Field google.cloud.bigquery.v2.StandardSqlDataType.array_element_type

struct_type

Field google.cloud.bigquery.v2.StandardSqlDataType.struct_type

type_kind

Field google.cloud.bigquery.v2.StandardSqlDataType.type_kind

class google.cloud.bigquery_v2.types.StandardSqlField#

A field or a column.

name#

Optional. The name of this field. Can be absent for struct fields.

type#

Optional. The type of this parameter. Absent if not explicitly specified (e.g., CREATE FUNCTION statement can omit the return type; in this case the output parameter does not have this “type” field).

name

Field google.cloud.bigquery.v2.StandardSqlField.name

type

Field google.cloud.bigquery.v2.StandardSqlField.type

class google.cloud.bigquery_v2.types.StandardSqlStructType#
fields#

Field google.cloud.bigquery.v2.StandardSqlStructType.fields

class google.cloud.bigquery_v2.types.StringValue#
value#

Field google.protobuf.StringValue.value

class google.cloud.bigquery_v2.types.Timestamp#
nanos#

Field google.protobuf.Timestamp.nanos

seconds#

Field google.protobuf.Timestamp.seconds

class google.cloud.bigquery_v2.types.UInt32Value#
value#

Field google.protobuf.UInt32Value.value

class google.cloud.bigquery_v2.types.UInt64Value#
value#

Field google.protobuf.UInt64Value.value