@google-cloud/bigquery/build/src/types.d.ts

Google BigQuery Client Library for Node.js

// Copyright 2023 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/**
 * BigQuery API
 */
declare namespace bigquery {
  /**
   * 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.
   */
  type IAggregateClassificationMetrics = {
    /**
     * Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
     */
    accuracy?: number;
    /**
     * The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
     */
    f1Score?: number;
    /**
     * Logarithmic Loss. For multiclass this is a macro-averaged metric.
     */
    logLoss?: number;
    /**
     * 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.
     */
    precision?: number;
    /**
     * Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
     */
    recall?: number;
    /**
     * Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
     */
    rocAuc?: number;
    /**
     * 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.
     */
    threshold?: number;
  };

  /**
   * Input/output argument of a function or a stored procedure.
   */
  type IArgument = {
    /**
     * Optional. Defaults to FIXED_TYPE.
     */
    argumentKind?: 'ARGUMENT_KIND_UNSPECIFIED' | 'FIXED_TYPE' | 'ANY_TYPE';
    /**
     * Required unless argument_kind = ANY_TYPE.
     */
    dataType?: IStandardSqlDataType;
    /**
     * Optional. Specifies whether the argument is input or output. Can be set for procedures only.
     */
    mode?: 'MODE_UNSPECIFIED' | 'IN' | 'OUT' | 'INOUT';
    /**
     * Optional. The name of this argument. Can be absent for function return argument.
     */
    name?: string;
  };

  /**
   * Arima coefficients.
   */
  type IArimaCoefficients = {
    /**
     * Auto-regressive coefficients, an array of double.
     */
    autoRegressiveCoefficients?: Array<number>;
    /**
     * Intercept coefficient, just a double not an array.
     */
    interceptCoefficient?: number;
    /**
     * Moving-average coefficients, an array of double.
     */
    movingAverageCoefficients?: Array<number>;
  };

  /**
   * ARIMA model fitting metrics.
   */
  type IArimaFittingMetrics = {
    /**
     * AIC.
     */
    aic?: number;
    /**
     * Log-likelihood.
     */
    logLikelihood?: number;
    /**
     * Variance.
     */
    variance?: number;
  };

  /**
   * Model evaluation metrics for ARIMA forecasting models.
   */
  type IArimaForecastingMetrics = {
    /**
     * Arima model fitting metrics.
     */
    arimaFittingMetrics?: Array<IArimaFittingMetrics>;
    /**
     * Repeated as there can be many metric sets (one for each model) in auto-arima and the large-scale case.
     */
    arimaSingleModelForecastingMetrics?: Array<IArimaSingleModelForecastingMetrics>;
    /**
     * Whether Arima model fitted with drift or not. It is always false when d is not 1.
     */
    hasDrift?: Array<boolean>;
    /**
     * Non-seasonal order.
     */
    nonSeasonalOrder?: Array<IArimaOrder>;
    /**
     * Seasonal periods. Repeated because multiple periods are supported for one time series.
     */
    seasonalPeriods?: Array<
      | 'SEASONAL_PERIOD_TYPE_UNSPECIFIED'
      | 'NO_SEASONALITY'
      | 'DAILY'
      | 'WEEKLY'
      | 'MONTHLY'
      | 'QUARTERLY'
      | 'YEARLY'
    >;
    /**
     * Id to differentiate different time series for the large-scale case.
     */
    timeSeriesId?: Array<string>;
  };

  /**
   * Arima model information.
   */
  type IArimaModelInfo = {
    /**
     * Arima coefficients.
     */
    arimaCoefficients?: IArimaCoefficients;
    /**
     * Arima fitting metrics.
     */
    arimaFittingMetrics?: IArimaFittingMetrics;
    /**
     * Whether Arima model fitted with drift or not. It is always false when d is not 1.
     */
    hasDrift?: boolean;
    /**
     * If true, holiday_effect is a part of time series decomposition result.
     */
    hasHolidayEffect?: boolean;
    /**
     * If true, spikes_and_dips is a part of time series decomposition result.
     */
    hasSpikesAndDips?: boolean;
    /**
     * If true, step_changes is a part of time series decomposition result.
     */
    hasStepChanges?: boolean;
    /**
     * Non-seasonal order.
     */
    nonSeasonalOrder?: IArimaOrder;
    /**
     * Seasonal periods. Repeated because multiple periods are supported for one time series.
     */
    seasonalPeriods?: Array<
      | 'SEASONAL_PERIOD_TYPE_UNSPECIFIED'
      | 'NO_SEASONALITY'
      | 'DAILY'
      | 'WEEKLY'
      | 'MONTHLY'
      | 'QUARTERLY'
      | 'YEARLY'
    >;
    /**
     * The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
     */
    timeSeriesId?: string;
    /**
     * The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
     */
    timeSeriesIds?: Array<string>;
  };

  /**
   * Arima order, can be used for both non-seasonal and seasonal parts.
   */
  type IArimaOrder = {
    /**
     * Order of the differencing part.
     */
    d?: string;
    /**
     * Order of the autoregressive part.
     */
    p?: string;
    /**
     * Order of the moving-average part.
     */
    q?: string;
  };

  /**
   * (Auto-)arima fitting result. Wrap everything in ArimaResult for easier refactoring if we want to use model-specific iteration results.
   */
  type IArimaResult = {
    /**
     * This message is repeated because there are multiple arima models fitted in auto-arima. For non-auto-arima model, its size is one.
     */
    arimaModelInfo?: Array<IArimaModelInfo>;
    /**
     * Seasonal periods. Repeated because multiple periods are supported for one time series.
     */
    seasonalPeriods?: Array<
      | 'SEASONAL_PERIOD_TYPE_UNSPECIFIED'
      | 'NO_SEASONALITY'
      | 'DAILY'
      | 'WEEKLY'
      | 'MONTHLY'
      | 'QUARTERLY'
      | 'YEARLY'
    >;
  };

  /**
   * Model evaluation metrics for a single ARIMA forecasting model.
   */
  type IArimaSingleModelForecastingMetrics = {
    /**
     * Arima fitting metrics.
     */
    arimaFittingMetrics?: IArimaFittingMetrics;
    /**
     * Is arima model fitted with drift or not. It is always false when d is not 1.
     */
    hasDrift?: boolean;
    /**
     * If true, holiday_effect is a part of time series decomposition result.
     */
    hasHolidayEffect?: boolean;
    /**
     * If true, spikes_and_dips is a part of time series decomposition result.
     */
    hasSpikesAndDips?: boolean;
    /**
     * If true, step_changes is a part of time series decomposition result.
     */
    hasStepChanges?: boolean;
    /**
     * Non-seasonal order.
     */
    nonSeasonalOrder?: IArimaOrder;
    /**
     * Seasonal periods. Repeated because multiple periods are supported for one time series.
     */
    seasonalPeriods?: Array<
      | 'SEASONAL_PERIOD_TYPE_UNSPECIFIED'
      | 'NO_SEASONALITY'
      | 'DAILY'
      | 'WEEKLY'
      | 'MONTHLY'
      | 'QUARTERLY'
      | 'YEARLY'
    >;
    /**
     * The time_series_id value for this time series. It will be one of the unique values from the time_series_id_column specified during ARIMA model training. Only present when time_series_id_column training option was used.
     */
    timeSeriesId?: string;
    /**
     * The tuple of time_series_ids identifying this time series. It will be one of the unique tuples of values present in the time_series_id_columns specified during ARIMA model training. Only present when time_series_id_columns training option was used and the order of values here are same as the order of time_series_id_columns.
     */
    timeSeriesIds?: Array<string>;
  };

  /**
   * Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs. If there are AuditConfigs for both `allServices` and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted. Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts `jose@example.com` from DATA_READ logging, and `aliya@example.com` from DATA_WRITE logging.
   */
  type IAuditConfig = {
    /**
     * The configuration for logging of each type of permission.
     */
    auditLogConfigs?: Array<IAuditLogConfig>;
    /**
     * Specifies a service that will be enabled for audit logging. For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. `allServices` is a special value that covers all services.
     */
    service?: string;
  };

  /**
   * Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
   */
  type IAuditLogConfig = {
    /**
     * Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
     */
    exemptedMembers?: Array<string>;
    /**
     * The log type that this config enables.
     */
    logType?:
      | 'LOG_TYPE_UNSPECIFIED'
      | 'ADMIN_READ'
      | 'DATA_WRITE'
      | 'DATA_READ';
  };

  type IAvroOptions = {
    /**
     * [Optional] If sourceFormat is set to "AVRO", indicates whether to interpret logical types as the corresponding BigQuery data type (for example, TIMESTAMP), instead of using the raw type (for example, INTEGER).
     */
    useAvroLogicalTypes?: boolean;
  };

  type IBiEngineReason = {
    /**
     * [Output-only] High-level BI Engine reason for partial or disabled acceleration.
     */
    code?: string;
    /**
     * [Output-only] Free form human-readable reason for partial or disabled acceleration.
     */
    message?: string;
  };

  type IBiEngineStatistics = {
    /**
     * [Output-only] Specifies which mode of BI Engine acceleration was performed (if any).
     */
    accelerationMode?: string;
    /**
     * [Output-only] Specifies which mode of BI Engine acceleration was performed (if any).
     */
    biEngineMode?: string;
    /**
     * In case of DISABLED or PARTIAL bi_engine_mode, these contain the explanatory reasons as to why BI Engine could not accelerate. In case the full query was accelerated, this field is not populated.
     */
    biEngineReasons?: Array<IBiEngineReason>;
  };

  type IBigLakeConfiguration = {
    /**
     * [Required] Required and immutable. Credential reference for accessing external storage system. Normalized as project_id.location_id.connection_id.
     */
    connectionId?: string;
    /**
     * [Required] Required and immutable. Open source file format that the table data is stored in. Currently only PARQUET is supported.
     */
    fileFormat?: string;
    /**
     * [Required] Required and immutable. Fully qualified location prefix of the external folder where data is stored. Normalized to standard format: "gs:////". Starts with "gs://" rather than "/bigstore/". Ends with "/". Does not contain "*". See also BigLakeStorageMetadata on how it is used.
     */
    storageUri?: string;
    /**
     * [Required] Required and immutable. Open source file format that the table data is stored in. Currently only PARQUET is supported.
     */
    tableFormat?: string;
  };

  type IBigQueryModelTraining = {
    /**
     * [Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.
     */
    currentIteration?: number;
    /**
     * [Output-only, Beta] Expected number of iterations for the create model query job specified as num_iterations in the input query. The actual total number of iterations may be less than this number due to early stop.
     */
    expectedTotalIterations?: string;
  };

  type IBigtableColumn = {
    /**
     * [Optional] The encoding of the values when the type is not STRING. Acceptable encoding values are: TEXT - indicates values are alphanumeric text strings. BINARY - indicates values are encoded using HBase Bytes.toBytes family of functions. 'encoding' can also be set at the column family level. However, the setting at this level takes precedence if 'encoding' is set at both levels.
     */
    encoding?: string;
    /**
     * [Optional] If the qualifier is not a valid BigQuery field identifier i.e. does not match [a-zA-Z][a-zA-Z0-9_]*, a valid identifier must be provided as the column field name and is used as field name in queries.
     */
    fieldName?: string;
    /**
     * [Optional] If this is set, only the latest version of value in this column are exposed. 'onlyReadLatest' can also be set at the column family level. However, the setting at this level takes precedence if 'onlyReadLatest' is set at both levels.
     */
    onlyReadLatest?: boolean;
    /**
     * [Required] Qualifier of the column. Columns in the parent column family that has this exact qualifier are exposed as . field. If the qualifier is valid UTF-8 string, it can be specified in the qualifier_string field. Otherwise, a base-64 encoded value must be set to qualifier_encoded. The column field name is the same as the column qualifier. However, if the qualifier is not a valid BigQuery field identifier i.e. does not match [a-zA-Z][a-zA-Z0-9_]*, a valid identifier must be provided as field_name.
     */
    qualifierEncoded?: string;
    qualifierString?: string;
    /**
     * [Optional] The type to convert the value in cells of this column. The values are expected to be encoded using HBase Bytes.toBytes function when using the BINARY encoding value. Following BigQuery types are allowed (case-sensitive) - BYTES STRING INTEGER FLOAT BOOLEAN Default type is BYTES. 'type' can also be set at the column family level. However, the setting at this level takes precedence if 'type' is set at both levels.
     */
    type?: string;
  };

  type IBigtableColumnFamily = {
    /**
     * [Optional] Lists of columns that should be exposed as individual fields as opposed to a list of (column name, value) pairs. All columns whose qualifier matches a qualifier in this list can be accessed as .. Other columns can be accessed as a list through .Column field.
     */
    columns?: Array<IBigtableColumn>;
    /**
     * [Optional] The encoding of the values when the type is not STRING. Acceptable encoding values are: TEXT - indicates values are alphanumeric text strings. BINARY - indicates values are encoded using HBase Bytes.toBytes family of functions. This can be overridden for a specific column by listing that column in 'columns' and specifying an encoding for it.
     */
    encoding?: string;
    /**
     * Identifier of the column family.
     */
    familyId?: string;
    /**
     * [Optional] If this is set only the latest version of value are exposed for all columns in this column family. This can be overridden for a specific column by listing that column in 'columns' and specifying a different setting for that column.
     */
    onlyReadLatest?: boolean;
    /**
     * [Optional] The type to convert the value in cells of this column family. The values are expected to be encoded using HBase Bytes.toBytes function when using the BINARY encoding value. Following BigQuery types are allowed (case-sensitive) - BYTES STRING INTEGER FLOAT BOOLEAN Default type is BYTES. This can be overridden for a specific column by listing that column in 'columns' and specifying a type for it.
     */
    type?: string;
  };

  type IBigtableOptions = {
    /**
     * [Optional] List of column families to expose in the table schema along with their types. This list restricts the column families that can be referenced in queries and specifies their value types. You can use this list to do type conversions - see the 'type' field for more details. If you leave this list empty, all column families are present in the table schema and their values are read as BYTES. During a query only the column families referenced in that query are read from Bigtable.
     */
    columnFamilies?: Array<IBigtableColumnFamily>;
    /**
     * [Optional] If field is true, then the column families that are not specified in columnFamilies list are not exposed in the table schema. Otherwise, they are read with BYTES type values. The default value is false.
     */
    ignoreUnspecifiedColumnFamilies?: boolean;
    /**
     * [Optional] If field is true, then the rowkey column families will be read and converted to string. Otherwise they are read with BYTES type values and users need to manually cast them with CAST if necessary. The default value is false.
     */
    readRowkeyAsString?: boolean;
  };

  /**
   * Evaluation metrics for binary classification/classifier models.
   */
  type IBinaryClassificationMetrics = {
    /**
     * Aggregate classification metrics.
     */
    aggregateClassificationMetrics?: IAggregateClassificationMetrics;
    /**
     * Binary confusion matrix at multiple thresholds.
     */
    binaryConfusionMatrixList?: Array<IBinaryConfusionMatrix>;
    /**
     * Label representing the negative class.
     */
    negativeLabel?: string;
    /**
     * Label representing the positive class.
     */
    positiveLabel?: string;
  };

  /**
   * Confusion matrix for binary classification models.
   */
  type IBinaryConfusionMatrix = {
    /**
     * The fraction of predictions given the correct label.
     */
    accuracy?: number;
    /**
     * The equally weighted average of recall and precision.
     */
    f1Score?: number;
    /**
     * Number of false samples predicted as false.
     */
    falseNegatives?: string;
    /**
     * Number of false samples predicted as true.
     */
    falsePositives?: string;
    /**
     * Threshold value used when computing each of the following metric.
     */
    positiveClassThreshold?: number;
    /**
     * The fraction of actual positive predictions that had positive actual labels.
     */
    precision?: number;
    /**
     * The fraction of actual positive labels that were given a positive prediction.
     */
    recall?: number;
    /**
     * Number of true samples predicted as false.
     */
    trueNegatives?: string;
    /**
     * Number of true samples predicted as true.
     */
    truePositives?: string;
  };

  /**
   * Associates `members`, or principals, with a `role`.
   */
  type IBinding = {
    /**
     * The condition that is associated with this binding. If the condition evaluates to `true`, then this binding applies to the current request. If the condition evaluates to `false`, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies).
     */
    condition?: IExpr;
    /**
     * Specifies the principals requesting access for a Google Cloud resource. `members` can have the following values: * `allUsers`: A special identifier that represents anyone who is on the internet; with or without a Google account. * `allAuthenticatedUsers`: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * `user:{emailid}`: An email address that represents a specific Google account. For example, `alice@example.com` . * `serviceAccount:{emailid}`: An email address that represents a Google service account. For example, `my-other-app@appspot.gserviceaccount.com`. * `serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]`: An identifier for a [Kubernetes service account](https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, `my-project.svc.id.goog[my-namespace/my-kubernetes-sa]`. * `group:{emailid}`: An email address that represents a Google group. For example, `admins@example.com`. * `domain:{domain}`: The G Suite domain (primary) that represents all the users of that domain. For example, `google.com` or `example.com`. * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a user that has been recently deleted. For example, `alice@example.com?uid=123456789012345678901`. If the user is recovered, this value reverts to `user:{emailid}` and the recovered user retains the role in the binding. * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. If the service account is undeleted, this value reverts to `serviceAccount:{emailid}` and the undeleted service account retains the role in the binding. * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, `admins@example.com?uid=123456789012345678901`. If the group is recovered, this value reverts to `group:{emailid}` and the recovered group retains the role in the binding.
     */
    members?: Array<string>;
    /**
     * Role that is assigned to the list of `members`, or principals. For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
     */
    role?: string;
  };

  type IBqmlIterationResult = {
    /**
     * [Output-only, Beta] Time taken to run the training iteration in milliseconds.
     */
    durationMs?: string;
    /**
     * [Output-only, Beta] Eval loss computed on the eval data at the end of the iteration. The eval loss is used for early stopping to avoid overfitting. No eval loss if eval_split_method option is specified as no_split or auto_split with input data size less than 500 rows.
     */
    evalLoss?: number;
    /**
     * [Output-only, Beta] Index of the ML training iteration, starting from zero for each training run.
     */
    index?: number;
    /**
     * [Output-only, Beta] Learning rate used for this iteration, it varies for different training iterations if learn_rate_strategy option is not constant.
     */
    learnRate?: number;
    /**
     * [Output-only, Beta] Training loss computed on the training data at the end of the iteration. The training loss function is defined by model type.
     */
    trainingLoss?: number;
  };

  type IBqmlTrainingRun = {
    /**
     * [Output-only, Beta] List of each iteration results.
     */
    iterationResults?: Array<IBqmlIterationResult>;
    /**
     * [Output-only, Beta] Training run start time in milliseconds since the epoch.
     */
    startTime?: string;
    /**
     * [Output-only, Beta] Different state applicable for a training run. IN PROGRESS: Training run is in progress. FAILED: Training run ended due to a non-retryable failure. SUCCEEDED: Training run successfully completed. CANCELLED: Training run cancelled by the user.
     */
    state?: string;
    /**
     * [Output-only, Beta] Training options used by this training run. These options are mutable for subsequent training runs. Default values are explicitly stored for options not specified in the input query of the first training run. For subsequent training runs, any option not explicitly specified in the input query will be copied from the previous training run.
     */
    trainingOptions?: {
      earlyStop?: boolean;
      l1Reg?: number;
      l2Reg?: number;
      learnRate?: number;
      learnRateStrategy?: string;
      lineSearchInitLearnRate?: number;
      maxIteration?: string;
      minRelProgress?: number;
      warmStart?: boolean;
    };
  };

  /**
   * Representative value of a categorical feature.
   */
  type ICategoricalValue = {
    /**
     * 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.
     */
    categoryCounts?: Array<ICategoryCount>;
  };

  /**
   * Represents the count of a single category within the cluster.
   */
  type ICategoryCount = {
    /**
     * The name of category.
     */
    category?: string;
    /**
     * The count of training samples matching the category within the cluster.
     */
    count?: string;
  };

  type ICloneDefinition = {
    /**
     * [Required] Reference describing the ID of the table that was cloned.
     */
    baseTableReference?: ITableReference;
    /**
     * [Required] The time at which the base table was cloned. This value is reported in the JSON response using RFC3339 format.
     */
    cloneTime?: string;
  };

  /**
   * Message containing the information about one cluster.
   */
  type ICluster = {
    /**
     * Centroid id.
     */
    centroidId?: string;
    /**
     * Count of training data rows that were assigned to this cluster.
     */
    count?: string;
    /**
     * Values of highly variant features for this cluster.
     */
    featureValues?: Array<IFeatureValue>;
  };

  /**
   * Information about a single cluster for clustering model.
   */
  type IClusterInfo = {
    /**
     * Centroid id.
     */
    centroidId?: string;
    /**
     * Cluster radius, the average distance from centroid to each point assigned to the cluster.
     */
    clusterRadius?: number;
    /**
     * Cluster size, the total number of points assigned to the cluster.
     */
    clusterSize?: string;
  };

  type IClustering = {
    /**
     * [Repeated] One or more fields on which data should be clustered. Only top-level, non-repeated, simple-type fields are supported. When you cluster a table using multiple columns, the order of columns you specify is important. The order of the specified columns determines the sort order of the data.
     */
    fields?: Array<string>;
  };

  /**
   * Evaluation metrics for clustering models.
   */
  type IClusteringMetrics = {
    /**
     * Information for all clusters.
     */
    clusters?: Array<ICluster>;
    /**
     * Davies-Bouldin index.
     */
    daviesBouldinIndex?: number;
    /**
     * Mean of squared distances between each sample to its cluster centroid.
     */
    meanSquaredDistance?: number;
  };

  /**
   * Confusion matrix for multi-class classification models.
   */
  type IConfusionMatrix = {
    /**
     * Confidence threshold used when computing the entries of the confusion matrix.
     */
    confidenceThreshold?: number;
    /**
     * One row per actual label.
     */
    rows?: Array<IRow>;
  };

  type IConnectionProperty = {
    /**
     * [Required] Name of the connection property to set.
     */
    key?: string;
    /**
     * [Required] Value of the connection property.
     */
    value?: string;
  };

  type ICsvOptions = {
    /**
     * [Optional] Indicates if BigQuery should accept rows that are missing trailing optional columns. If true, BigQuery treats missing trailing columns as null values. If false, records with missing trailing columns are treated as bad records, and if there are too many bad records, an invalid error is returned in the job result. The default value is false.
     */
    allowJaggedRows?: boolean;
    /**
     * [Optional] Indicates if BigQuery should allow quoted data sections that contain newline characters in a CSV file. The default value is false.
     */
    allowQuotedNewlines?: boolean;
    /**
     * [Optional] The character encoding of the data. The supported values are UTF-8 or ISO-8859-1. The default value is UTF-8. BigQuery decodes the data after the raw, binary data has been split using the values of the quote and fieldDelimiter properties.
     */
    encoding?: string;
    /**
     * [Optional] The separator for fields in a CSV file. BigQuery converts the string to ISO-8859-1 encoding, and then uses the first byte of the encoded string to split the data in its raw, binary state. BigQuery also supports the escape sequence "\t" to specify a tab separator. The default value is a comma (',').
     */
    fieldDelimiter?: string;
    /**
     * [Optional] An custom string that will represent a NULL value in CSV import data.
     */
    null_marker?: string;
    /**
     * [Optional] Preserves the embedded ASCII control characters (the first 32 characters in the ASCII-table, from '\x00' to '\x1F') when loading from CSV. Only applicable to CSV, ignored for other formats.
     */
    preserveAsciiControlCharacters?: boolean;
    /**
     * [Optional] The value that is used to quote data sections in a CSV file. BigQuery converts the string to ISO-8859-1 encoding, and then uses the first byte of the encoded string to split the data in its raw, binary state. The default value is a double-quote ('"'). If your data does not contain quoted sections, set the property value to an empty string. If your data contains quoted newline characters, you must also set the allowQuotedNewlines property to true.
     */
    quote?: string;
    /**
     * [Optional] The number of rows at the top of a CSV file that BigQuery will skip when reading the data. The default value is 0. This property is useful if you have header rows in the file that should be skipped. When autodetect is on, the behavior is the following: * skipLeadingRows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. * skipLeadingRows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. * skipLeadingRows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise row N is used to extract column names for the detected schema.
     */
    skipLeadingRows?: string;
  };

  type IDataMaskingStatistics = {
    /**
     * [Output-only] [Preview] Whether any accessed data was protected by data masking. The actual evaluation is done by accessStats.masked_field_count > 0. Since this is only used for the discovery_doc generation purpose, as long as the type (boolean) matches, client library can leverage this. The actual evaluation of the variable is done else-where.
     */
    dataMaskingApplied?: boolean;
  };

  /**
   * Data split result. This contains references to the training and evaluation data tables that were used to train the model.
   */
  type IDataSplitResult = {
    /**
     * Table reference of the evaluation data after split.
     */
    evaluationTable?: ITableReference;
    /**
     * Table reference of the test data after split.
     */
    testTable?: ITableReference;
    /**
     * Table reference of the training data after split.
     */
    trainingTable?: ITableReference;
  };

  type IDataset = {
    /**
     * [Optional] An array of objects that define dataset access for one or more entities. You can set this property when inserting or updating a dataset in order to control who is allowed to access the data. If unspecified at dataset creation time, BigQuery adds default dataset access for the following entities: access.specialGroup: projectReaders; access.role: READER; access.specialGroup: projectWriters; access.role: WRITER; access.specialGroup: projectOwners; access.role: OWNER; access.userByEmail: [dataset creator email]; access.role: OWNER;
     */
    access?: Array<{
      /**
       * [Pick one] A grant authorizing all resources of a particular type in a particular dataset access to this dataset. Only views are supported for now. The role field is not required when this field is set. If that dataset is deleted and re-created, its access needs to be granted again via an update operation.
       */
      dataset?: IDatasetAccessEntry;
      /**
       * [Pick one] A domain to grant access to. Any users signed in with the domain specified will be granted the specified access. Example: "example.com". Maps to IAM policy member "domain:DOMAIN".
       */
      domain?: string;
      /**
       * [Pick one] An email address of a Google Group to grant access to. Maps to IAM policy member "group:GROUP".
       */
      groupByEmail?: string;
      /**
       * [Pick one] Some other type of member that appears in the IAM Policy but isn't a user, group, domain, or special group.
       */
      iamMember?: string;
      /**
       * [Required] An IAM role ID that should be granted to the user, group, or domain specified in this access entry. The following legacy mappings will be applied: OWNER  roles/bigquery.dataOwner WRITER  roles/bigquery.dataEditor READER  roles/bigquery.dataViewer This field will accept any of the above formats, but will return only the legacy format. For example, if you set this field to "roles/bigquery.dataOwner", it will be returned back as "OWNER".
       */
      role?: string;
      /**
       * [Pick one] A routine from a different dataset to grant access to. Queries executed against that routine will have read access to views/tables/routines in this dataset. Only UDF is supported for now. The role field is not required when this field is set. If that routine is updated by any user, access to the routine needs to be granted again via an update operation.
       */
      routine?: IRoutineReference;
      /**
       * [Pick one] A special group to grant access to. Possible values include: projectOwners: Owners of the enclosing project. projectReaders: Readers of the enclosing project. projectWriters: Writers of the enclosing project. allAuthenticatedUsers: All authenticated BigQuery users. Maps to similarly-named IAM members.
       */
      specialGroup?: string;
      /**
       * [Pick one] An email address of a user to grant access to. For example: fred@example.com. Maps to IAM policy member "user:EMAIL" or "serviceAccount:EMAIL".
       */
      userByEmail?: string;
      /**
       * [Pick one] A view from a different dataset to grant access to. Queries executed against that view will have read access to tables in this dataset. The role field is not required when this field is set. If that view is updated by any user, access to the view needs to be granted again via an update operation.
       */
      view?: ITableReference;
    }>;
    /**
     * [Output-only] The time when this dataset was created, in milliseconds since the epoch.
     */
    creationTime?: string;
    /**
     * [Required] A reference that identifies the dataset.
     */
    datasetReference?: IDatasetReference;
    /**
     * [Output-only] The default collation of the dataset.
     */
    defaultCollation?: string;
    defaultEncryptionConfiguration?: IEncryptionConfiguration;
    /**
     * [Optional] The default partition expiration for all partitioned tables in the dataset, in milliseconds. Once this property is set, all newly-created partitioned tables in the dataset will have an expirationMs property in the timePartitioning settings set to this value, and changing the value will only affect new tables, not existing ones. The storage in a partition will have an expiration time of its partition time plus this value. Setting this property overrides the use of defaultTableExpirationMs for partitioned tables: only one of defaultTableExpirationMs and defaultPartitionExpirationMs will be used for any new partitioned table. If you provide an explicit timePartitioning.expirationMs when creating or updating a partitioned table, that value takes precedence over the default partition expiration time indicated by this property.
     */
    defaultPartitionExpirationMs?: string;
    /**
     * [Output-only] The default rounding mode of the dataset.
     */
    defaultRoundingMode?: string;
    /**
     * [Optional] The default lifetime of all tables in the dataset, in milliseconds. The minimum value is 3600000 milliseconds (one hour). Once this property is set, all newly-created tables in the dataset will have an expirationTime property set to the creation time plus the value in this property, and changing the value will only affect new tables, not existing ones. When the expirationTime for a given table is reached, that table will be deleted automatically. If a table's expirationTime is modified or removed before the table expires, or if you provide an explicit expirationTime when creating a table, that value takes precedence over the default expiration time indicated by this property.
     */
    defaultTableExpirationMs?: string;
    /**
     * [Optional] A user-friendly description of the dataset.
     */
    description?: string;
    /**
     * [Output-only] A hash of the resource.
     */
    etag?: string;
    /**
     * [Optional] Information about the external metadata storage where the dataset is defined. Filled out when the dataset type is EXTERNAL.
     */
    externalDatasetReference?: IExternalDatasetReference;
    /**
     * [Optional] A descriptive name for the dataset.
     */
    friendlyName?: string;
    /**
     * [Output-only] The fully-qualified unique name of the dataset in the format projectId:datasetId. The dataset name without the project name is given in the datasetId field. When creating a new dataset, leave this field blank, and instead specify the datasetId field.
     */
    id?: string;
    /**
     * [Optional] Indicates if table names are case insensitive in the dataset.
     */
    isCaseInsensitive?: boolean;
    /**
     * [Output-only] The resource type.
     */
    kind?: string;
    /**
     * The labels associated with this dataset. You can use these to organize and group your datasets. You can set this property when inserting or updating a dataset. See Creating and Updating Dataset Labels for more information.
     */
    labels?: {[key: string]: string};
    /**
     * [Output-only] The date when this dataset or any of its tables was last modified, in milliseconds since the epoch.
     */
    lastModifiedTime?: string;
    /**
     * The geographic location where the dataset should reside. The default value is US. See details at https://cloud.google.com/bigquery/docs/locations.
     */
    location?: string;
    /**
     * [Optional] Number of hours for the max time travel for all tables in the dataset.
     */
    maxTimeTravelHours?: string;
    /**
     * [Output-only] Reserved for future use.
     */
    satisfiesPzs?: boolean;
    /**
     * [Output-only] A URL that can be used to access the resource again. You can use this URL in Get or Update requests to the resource.
     */
    selfLink?: string;
    /**
     * [Optional] Storage billing model to be used for all tables in the dataset. Can be set to PHYSICAL. Default is LOGICAL.
     */
    storageBillingModel?: string;
    /**
     * [Optional]The tags associated with this dataset. Tag keys are globally unique.
     */
    tags?: Array<{
      /**
       * [Required] The namespaced friendly name of the tag key, e.g. "12345/environment" where 12345 is org id.
       */
      tagKey?: string;
      /**
       * [Required] Friendly short name of the tag value, e.g. "production".
       */
      tagValue?: string;
    }>;
  };

  type IDatasetAccessEntry = {
    /**
     * [Required] The dataset this entry applies to.
     */
    dataset?: IDatasetReference;
    targetTypes?: Array<'TARGET_TYPE_UNSPECIFIED' | 'VIEWS' | 'ROUTINES'>;
  };

  type IDatasetList = {
    /**
     * An array of the dataset resources in the project. Each resource contains basic information. For full information about a particular dataset resource, use the Datasets: get method. This property is omitted when there are no datasets in the project.
     */
    datasets?: Array<{
      /**
       * The dataset reference. Use this property to access specific parts of the dataset's ID, such as project ID or dataset ID.
       */
      datasetReference?: IDatasetReference;
      /**
       * A descriptive name for the dataset, if one exists.
       */
      friendlyName?: string;
      /**
       * The fully-qualified, unique, opaque ID of the dataset.
       */
      id?: string;
      /**
       * The resource type. This property always returns the value "bigquery#dataset".
       */
      kind?: string;
      /**
       * The labels associated with this dataset. You can use these to organize and group your datasets.
       */
      labels?: {[key: string]: string};
      /**
       * The geographic location where the data resides.
       */
      location?: string;
    }>;
    /**
     * A hash value of the results page. You can use this property to determine if the page has changed since the last request.
     */
    etag?: string;
    /**
     * The list type. This property always returns the value "bigquery#datasetList".
     */
    kind?: string;
    /**
     * A token that can be used to request the next results page. This property is omitted on the final results page.
     */
    nextPageToken?: string;
  };

  type IDatasetReference = {
    /**
     * [Required] A unique ID for this dataset, without the project name. The ID must contain only letters (a-z, A-Z), numbers (0-9), or underscores (_). The maximum length is 1,024 characters.
     */
    datasetId?: string;
    /**
     * [Optional] The ID of the project containing this dataset.
     */
    projectId?: string;
  };

  type IDestinationTableProperties = {
    /**
     * [Optional] The description for the destination table. This will only be used if the destination table is newly created. If the table already exists and a value different than the current description is provided, the job will fail.
     */
    description?: string;
    /**
     * [Internal] This field is for Google internal use only.
     */
    expirationTime?: string;
    /**
     * [Optional] The friendly name for the destination table. This will only be used if the destination table is newly created. If the table already exists and a value different than the current friendly name is provided, the job will fail.
     */
    friendlyName?: string;
    /**
     * [Optional] The labels associated with this table. You can use these to organize and group your tables. This will only be used if the destination table is newly created. If the table already exists and labels are different than the current labels are provided, the job will fail.
     */
    labels?: {[key: string]: string};
  };

  /**
   * Model evaluation metrics for dimensionality reduction models.
   */
  type IDimensionalityReductionMetrics = {
    /**
     * Total percentage of variance explained by the selected principal components.
     */
    totalExplainedVarianceRatio?: number;
  };

  type IDmlStatistics = {
    /**
     * Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.
     */
    deletedRowCount?: string;
    /**
     * Number of inserted Rows. Populated by DML INSERT and MERGE statements.
     */
    insertedRowCount?: string;
    /**
     * Number of updated Rows. Populated by DML UPDATE and MERGE statements.
     */
    updatedRowCount?: string;
  };

  /**
   * Discrete candidates of a double hyperparameter.
   */
  type IDoubleCandidates = {
    /**
     * Candidates for the double parameter in increasing order.
     */
    candidates?: Array<number>;
  };

  /**
   * Search space for a double hyperparameter.
   */
  type IDoubleHparamSearchSpace = {
    /**
     * Candidates of the double hyperparameter.
     */
    candidates?: IDoubleCandidates;
    /**
     * Range of the double hyperparameter.
     */
    range?: IDoubleRange;
  };

  /**
   * Range of a double hyperparameter.
   */
  type IDoubleRange = {
    /**
     * Max value of the double parameter.
     */
    max?: number;
    /**
     * Min value of the double parameter.
     */
    min?: number;
  };

  type IEncryptionConfiguration = {
    /**
     * Optional. Describes the Cloud KMS encryption key that will be used to protect destination BigQuery table. The BigQuery Service Account associated with your project requires access to this encryption key.
     */
    kmsKeyName?: string;
  };

  /**
   * A single entry in the confusion matrix.
   */
  type IEntry = {
    /**
     * Number of items being predicted as this label.
     */
    itemCount?: string;
    /**
     * The predicted label. For confidence_threshold > 0, we will also add an entry indicating the number of items under the confidence threshold.
     */
    predictedLabel?: string;
  };

  type IErrorProto = {
    /**
     * Debugging information. This property is internal to Google and should not be used.
     */
    debugInfo?: string;
    /**
     * Specifies where the error occurred, if present.
     */
    location?: string;
    /**
     * A human-readable description of the error.
     */
    message?: string;
    /**
     * A short error code that summarizes the error.
     */
    reason?: string;
  };

  /**
   * 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.
   */
  type IEvaluationMetrics = {
    /**
     * Populated for ARIMA models.
     */
    arimaForecastingMetrics?: IArimaForecastingMetrics;
    /**
     * Populated for binary classification/classifier models.
     */
    binaryClassificationMetrics?: IBinaryClassificationMetrics;
    /**
     * Populated for clustering models.
     */
    clusteringMetrics?: IClusteringMetrics;
    /**
     * Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
     */
    dimensionalityReductionMetrics?: IDimensionalityReductionMetrics;
    /**
     * Populated for multi-class classification/classifier models.
     */
    multiClassClassificationMetrics?: IMultiClassClassificationMetrics;
    /**
     * Populated for implicit feedback type matrix factorization models.
     */
    rankingMetrics?: IRankingMetrics;
    /**
     * Populated for regression models and explicit feedback type matrix factorization models.
     */
    regressionMetrics?: IRegressionMetrics;
  };

  type IExplainQueryStage = {
    /**
     * Number of parallel input segments completed.
     */
    completedParallelInputs?: string;
    /**
     * Milliseconds the average shard spent on CPU-bound tasks.
     */
    computeMsAvg?: string;
    /**
     * Milliseconds the slowest shard spent on CPU-bound tasks.
     */
    computeMsMax?: string;
    /**
     * Relative amount of time the average shard spent on CPU-bound tasks.
     */
    computeRatioAvg?: number;
    /**
     * Relative amount of time the slowest shard spent on CPU-bound tasks.
     */
    computeRatioMax?: number;
    /**
     * Stage end time represented as milliseconds since epoch.
     */
    endMs?: string;
    /**
     * Unique ID for stage within plan.
     */
    id?: string;
    /**
     * IDs for stages that are inputs to this stage.
     */
    inputStages?: Array<string>;
    /**
     * Human-readable name for stage.
     */
    name?: string;
    /**
     * Number of parallel input segments to be processed.
     */
    parallelInputs?: string;
    /**
     * Milliseconds the average shard spent reading input.
     */
    readMsAvg?: string;
    /**
     * Milliseconds the slowest shard spent reading input.
     */
    readMsMax?: string;
    /**
     * Relative amount of time the average shard spent reading input.
     */
    readRatioAvg?: number;
    /**
     * Relative amount of time the slowest shard spent reading input.
     */
    readRatioMax?: number;
    /**
     * Number of records read into the stage.
     */
    recordsRead?: string;
    /**
     * Number of records written by the stage.
     */
    recordsWritten?: string;
    /**
     * Total number of bytes wri