google-native.bigquery/v2.Job

[Required] Describes the job configuration.

[Optional] Reference describing the unique-per-user name of the job.

[Required] Describes the job configuration.

[Optional] Reference describing the unique-per-user name of the job.

[Required] Describes the job configuration.

[Optional] Reference describing the unique-per-user name of the job.

[Required] Describes the job configuration.

[Optional] Reference describing the unique-per-user name of the job.

[Required] Describes the job configuration.

[Optional] Reference describing the unique-per-user name of the job.

[Required] Describes the job configuration.

[Optional] Reference describing the unique-per-user name of the job.

A hash of this resource.

The provider-assigned unique ID for this managed resource.

If set, it provides the reason why a Job was created. If not set, it should be treated as the default: REQUESTED. This feature is not yet available. Jobs will always be created.

The type of the resource.

A URL that can be used to access this resource again.

Information about the job, including starting time and ending time of the job.

The status of this job. Examine this value when polling an asynchronous job to see if the job is complete.

Email address of the user who ran the job.

A hash of this resource.

The provider-assigned unique ID for this managed resource.

If set, it provides the reason why a Job was created. If not set, it should be treated as the default: REQUESTED. This feature is not yet available. Jobs will always be created.

The type of the resource.

A URL that can be used to access this resource again.

Information about the job, including starting time and ending time of the job.

The status of this job. Examine this value when polling an asynchronous job to see if the job is complete.

Email address of the user who ran the job.

A hash of this resource.

The provider-assigned unique ID for this managed resource.

If set, it provides the reason why a Job was created. If not set, it should be treated as the default: REQUESTED. This feature is not yet available. Jobs will always be created.

The type of the resource.

A URL that can be used to access this resource again.

Information about the job, including starting time and ending time of the job.

The status of this job. Examine this value when polling an asynchronous job to see if the job is complete.

Email address of the user who ran the job.

A hash of this resource.

The provider-assigned unique ID for this managed resource.

If set, it provides the reason why a Job was created. If not set, it should be treated as the default: REQUESTED. This feature is not yet available. Jobs will always be created.

The type of the resource.

A URL that can be used to access this resource again.

Information about the job, including starting time and ending time of the job.

The status of this job. Examine this value when polling an asynchronous job to see if the job is complete.

Email address of the user who ran the job.

A hash of this resource.

The provider-assigned unique ID for this managed resource.

If set, it provides the reason why a Job was created. If not set, it should be treated as the default: REQUESTED. This feature is not yet available. Jobs will always be created.

The type of the resource.

A URL that can be used to access this resource again.

Information about the job, including starting time and ending time of the job.

The status of this job. Examine this value when polling an asynchronous job to see if the job is complete.

Email address of the user who ran the job.

A hash of this resource.

The provider-assigned unique ID for this managed resource.

If set, it provides the reason why a Job was created. If not set, it should be treated as the default: REQUESTED. This feature is not yet available. Jobs will always be created.

The type of the resource.

A URL that can be used to access this resource again.

Information about the job, including starting time and ending time of the job.

The status of this job. Examine this value when polling an asynchronous job to see if the job is complete.

Email address of the user who ran the job.

High-level BI Engine reason for partial or disabled acceleration.

Free form human-readable reason for partial or disabled acceleration.

High-level BI Engine reason for partial or disabled acceleration.

Free form human-readable reason for partial or disabled acceleration.

High-level BI Engine reason for partial or disabled acceleration.

Free form human-readable reason for partial or disabled acceleration.

High-level BI Engine reason for partial or disabled acceleration.

Free form human-readable reason for partial or disabled acceleration.

High-level BI Engine reason for partial or disabled acceleration.

Free form human-readable reason for partial or disabled acceleration.

High-level BI Engine reason for partial or disabled acceleration.

Free form human-readable reason for partial or disabled acceleration.

Specifies which mode of BI Engine acceleration was performed (if any).

Specifies which mode of BI Engine acceleration was performed (if any).

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.

Specifies which mode of BI Engine acceleration was performed (if any).

Specifies which mode of BI Engine acceleration was performed (if any).

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.

Specifies which mode of BI Engine acceleration was performed (if any).

Specifies which mode of BI Engine acceleration was performed (if any).

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.

Specifies which mode of BI Engine acceleration was performed (if any).

Specifies which mode of BI Engine acceleration was performed (if any).

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.

Specifies which mode of BI Engine acceleration was performed (if any).

Specifies which mode of BI Engine acceleration was performed (if any).

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.

Specifies which mode of BI Engine acceleration was performed (if any).

Specifies which mode of BI Engine acceleration was performed (if any).

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.

[Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.

[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.

[Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.

[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.

[Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.

[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.

[Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.

[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.

[Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.

[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.

[Output-only, Beta] Index of current ML training iteration. Updated during create model query job to show job progress.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[Required] Name of the connection property to set.

[Required] Value of the connection property.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Optional] The ID of the project containing this dataset.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

[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.

[Internal] This field is for Google internal use only.

[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.

[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.

Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.

Number of inserted Rows. Populated by DML INSERT and MERGE statements.

Number of updated Rows. Populated by DML UPDATE and MERGE statements.

Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.

Number of inserted Rows. Populated by DML INSERT and MERGE statements.

Number of updated Rows. Populated by DML UPDATE and MERGE statements.

Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.

Number of inserted Rows. Populated by DML INSERT and MERGE statements.

Number of updated Rows. Populated by DML UPDATE and MERGE statements.

Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.

Number of inserted Rows. Populated by DML INSERT and MERGE statements.

Number of updated Rows. Populated by DML UPDATE and MERGE statements.

Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.

Number of inserted Rows. Populated by DML INSERT and MERGE statements.

Number of updated Rows. Populated by DML UPDATE and MERGE statements.

Number of deleted Rows. populated by DML DELETE, MERGE and TRUNCATE statements.

Number of inserted Rows. Populated by DML INSERT and MERGE statements.

Number of updated Rows. Populated by DML UPDATE and MERGE statements.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Debugging information. This property is internal to Google and should not be used.

Specifies where the error occurred, if present.

A human-readable description of the error.

A short error code that summarizes the error.

Debugging information. This property is internal to Google and should not be used.

Specifies where the error occurred, if present.

A human-readable description of the error.

A short error code that summarizes the error.

Debugging information. This property is internal to Google and should not be used.

Specifies where the error occurred, if present.

A human-readable description of the error.

A short error code that summarizes the error.

Debugging information. This property is internal to Google and should not be used.

Specifies where the error occurred, if present.

A human-readable description of the error.

A short error code that summarizes the error.

Debugging information. This property is internal to Google and should not be used.

Specifies where the error occurred, if present.

A human-readable description of the error.

A short error code that summarizes the error.

Debugging information. This property is internal to Google and should not be used.

Specifies where the error occurred, if present.

A human-readable description of the error.

A short error code that summarizes the error.

Number of parallel input segments completed.

Milliseconds the average shard spent on CPU-bound tasks.

Milliseconds the slowest shard spent on CPU-bound tasks.

Relative amount of time the average shard spent on CPU-bound tasks.

Relative amount of time the slowest shard spent on CPU-bound tasks.

Stage end time represented as milliseconds since epoch.

IDs for stages that are inputs to this stage.

Human-readable name for stage.

Number of parallel input segments to be processed.

Milliseconds the average shard spent reading input.

Milliseconds the slowest shard spent reading input.

Relative amount of time the average shard spent reading input.

Relative amount of time the slowest shard spent reading input.

Number of records read into the stage.

Number of records written by the stage.

Total number of bytes written to shuffle.

Total number of bytes written to shuffle and spilled to disk.

Slot-milliseconds used by the stage.

Stage start time represented as milliseconds since epoch.

Current status for the stage.

List of operations within the stage in dependency order (approximately chronological).

Milliseconds the average shard spent waiting to be scheduled.

Milliseconds the slowest shard spent waiting to be scheduled.

Relative amount of time the average shard spent waiting to be scheduled.

Relative amount of time the slowest shard spent waiting to be scheduled.

Milliseconds the average shard spent on writing output.

Milliseconds the slowest shard spent on writing output.

Relative amount of time the average shard spent on writing output.

Relative amount of time the slowest shard spent on writing output.

Number of parallel input segments completed.

Milliseconds the average shard spent on CPU-bound tasks.

Milliseconds the slowest shard spent on CPU-bound tasks.

Relative amount of time the average shard spent on CPU-bound tasks.

Relative amount of time the slowest shard spent on CPU-bound tasks.

Stage end time represented as milliseconds since epoch.

IDs for stages that are inputs to this stage.

Human-readable name for stage.

Number of parallel input segments to be processed.

Milliseconds the average shard spent reading input.

Milliseconds the slowest shard spent reading input.

Relative amount of time the average shard spent reading input.

Relative amount of time the slowest shard spent reading input.

Number of records read into the stage.

Number of records written by the stage.

Total number of bytes written to shuffle.

Total number of bytes written to shuffle and spilled to disk.

Slot-milliseconds used by the stage.

Stage start time represented as milliseconds since epoch.

Current status for the stage.

List of operations within the stage in dependency order (approximately chronological).

Milliseconds the average shard spent waiting to be scheduled.

Milliseconds the slowest shard spent waiting to be scheduled.

Relative amount of time the average shard spent waiting to be scheduled.

Relative amount of time the slowest shard spent waiting to be scheduled.

Milliseconds the average shard spent on writing output.

Milliseconds the slowest shard spent on writing output.

Relative amount of time the average shard spent on writing output.

Relative amount of time the slowest shard spent on writing output.

Number of parallel input segments completed.

Milliseconds the average shard spent on CPU-bound tasks.

Milliseconds the slowest shard spent on CPU-bound tasks.

Relative amount of time the average shard spent on CPU-bound tasks.

Relative amount of time the slowest shard spent on CPU-bound tasks.

Stage end time represented as milliseconds since epoch.

IDs for stages that are inputs to this stage.

Human-readable name for stage.

Number of parallel input segments to be processed.

Milliseconds the average shard spent reading input.

Milliseconds the slowest shard spent reading input.

Relative amount of time the average shard spent reading input.

Relative amount of time the slowest shard spent reading input.

Number of records read into the stage.

Number of records written by the stage.

Total number of bytes written to shuffle.

Total number of bytes written to shuffle and spilled to disk.

Slot-milliseconds used by the stage.

Stage start time represented as milliseconds since epoch.

Current status for the stage.

List of operations within the stage in dependency order (approximately chronological).

Milliseconds the average shard spent waiting to be scheduled.

Milliseconds the slowest shard spent waiting to be scheduled.

Relative amount of time the average shard spent waiting to be scheduled.

Relative amount of time the slowest shard spent waiting to be scheduled.

Milliseconds the average shard spent on writing output.

Milliseconds the slowest shard spent on writing output.

Relative amount of time the average shard spent on writing output.

Relative amount of time the slowest shard spent on writing output.

Number of parallel input segments completed.

Milliseconds the average shard spent on CPU-bound tasks.

Milliseconds the slowest shard spent on CPU-bound tasks.

Relative amount of time the average shard spent on CPU-bound tasks.

Relative amount of time the slowest shard spent on CPU-bound tasks.

Stage end time represented as milliseconds since epoch.

IDs for stages that are inputs to this stage.

Human-readable name for stage.

Number of parallel input segments to be processed.

Milliseconds the average shard spent reading input.

Milliseconds the slowest shard spent reading input.

Relative amount of time the average shard spent reading input.

Relative amount of time the slowest shard spent reading input.

Number of records read into the stage.

Number of records written by the stage.

Total number of bytes written to shuffle.

Total number of bytes written to shuffle and spilled to disk.

Slot-milliseconds used by the stage.

Stage start time represented as milliseconds since epoch.

Current status for the stage.

List of operations within the stage in dependency order (approximately chronological).

Milliseconds the average shard spent waiting to be scheduled.

Milliseconds the slowest shard spent waiting to be scheduled.

Relative amount of time the average shard spent waiting to be scheduled.

Relative amount of time the slowest shard spent waiting to be scheduled.

Milliseconds the average shard spent on writing output.

Milliseconds the slowest shard spent on writing output.

Relative amount of time the average shard spent on writing output.

Relative amount of time the slowest shard spent on writing output.

Number of parallel input segments completed.

Milliseconds the average shard spent on CPU-bound tasks.

Milliseconds the slowest shard spent on CPU-bound tasks.

Relative amount of time the average shard spent on CPU-bound tasks.

Relative amount of time the slowest shard spent on CPU-bound tasks.

Stage end time represented as milliseconds since epoch.

IDs for stages that are inputs to this stage.

Human-readable name for stage.

Number of parallel input segments to be processed.

Milliseconds the average shard spent reading input.

Milliseconds the slowest shard spent reading input.

Relative amount of time the average shard spent reading input.

Relative amount of time the slowest shard spent reading input.

Number of records read into the stage.

Number of records written by the stage.

Total number of bytes written to shuffle.

Total number of bytes written to shuffle and spilled to disk.

Slot-milliseconds used by the stage.

Stage start time represented as milliseconds since epoch.

Current status for the stage.

List of operations within the stage in dependency order (approximately chronological).

Milliseconds the average shard spent waiting to be scheduled.

Milliseconds the slowest shard spent waiting to be scheduled.

Relative amount of time the average shard spent waiting to be scheduled.

Relative amount of time the slowest shard spent waiting to be scheduled.

Milliseconds the average shard spent on writing output.

Milliseconds the slowest shard spent on writing output.

Relative amount of time the average shard spent on writing output.

Relative amount of time the slowest shard spent on writing output.

Number of parallel input segments completed.

Milliseconds the average shard spent on CPU-bound tasks.

Milliseconds the slowest shard spent on CPU-bound tasks.

Relative amount of time the average shard spent on CPU-bound tasks.

Relative amount of time the slowest shard spent on CPU-bound tasks.

Stage end time represented as milliseconds since epoch.

IDs for stages that are inputs to this stage.

Human-readable name for stage.

Number of parallel input segments to be processed.

Milliseconds the average shard spent reading input.

Milliseconds the slowest shard spent reading input.

Relative amount of time the average shard spent reading input.

Relative amount of time the slowest shard spent reading input.

Number of records read into the stage.

Number of records written by the stage.

Total number of bytes written to shuffle.

Total number of bytes written to shuffle and spilled to disk.

Slot-milliseconds used by the stage.

Stage start time represented as milliseconds since epoch.

Current status for the stage.

List of operations within the stage in dependency order (approximately chronological).

Milliseconds the average shard spent waiting to be scheduled.

Milliseconds the slowest shard spent waiting to be scheduled.

Relative amount of time the average shard spent waiting to be scheduled.

Relative amount of time the slowest shard spent waiting to be scheduled.

Milliseconds the average shard spent on writing output.

Milliseconds the slowest shard spent on writing output.

Relative amount of time the average shard spent on writing output.

Relative amount of time the slowest shard spent on writing output.

Machine-readable operation type.

Human-readable stage descriptions.

Machine-readable operation type.

Human-readable stage descriptions.

Machine-readable operation type.

Human-readable stage descriptions.

Machine-readable operation type.

Human-readable stage descriptions.

Machine-readable operation type.

Human-readable stage descriptions.

Machine-readable operation type.

Human-readable stage descriptions.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

For permanent external tables, this field is populated with the hive partition keys in the order they were inferred. The types of the partition keys can be deduced by checking the table schema (which will include the partition keys). Not every API will populate this field in the output. For example, Tables.Get will populate it, but Tables.List will not contain this field.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

For permanent external tables, this field is populated with the hive partition keys in the order they were inferred. The types of the partition keys can be deduced by checking the table schema (which will include the partition keys). Not every API will populate this field in the output. For example, Tables.Get will populate it, but Tables.List will not contain this field.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

For permanent external tables, this field is populated with the hive partition keys in the order they were inferred. The types of the partition keys can be deduced by checking the table schema (which will include the partition keys). Not every API will populate this field in the output. For example, Tables.Get will populate it, but Tables.List will not contain this field.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

For permanent external tables, this field is populated with the hive partition keys in the order they were inferred. The types of the partition keys can be deduced by checking the table schema (which will include the partition keys). Not every API will populate this field in the output. For example, Tables.Get will populate it, but Tables.List will not contain this field.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

For permanent external tables, this field is populated with the hive partition keys in the order they were inferred. The types of the partition keys can be deduced by checking the table schema (which will include the partition keys). Not every API will populate this field in the output. For example, Tables.Get will populate it, but Tables.List will not contain this field.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

For permanent external tables, this field is populated with the hive partition keys in the order they were inferred. The types of the partition keys can be deduced by checking the table schema (which will include the partition keys). Not every API will populate this field in the output. For example, Tables.Get will populate it, but Tables.List will not contain this field.

[Optional] When set, what mode of hive partitioning to use when reading data. The following modes are supported. (1) AUTO: automatically infer partition key name(s) and type(s). (2) STRINGS: automatically infer partition key name(s). All types are interpreted as strings. (3) CUSTOM: partition key schema is encoded in the source URI prefix. Not all storage formats support hive partitioning. Requesting hive partitioning on an unsupported format will lead to an error. Currently supported types include: AVRO, CSV, JSON, ORC and Parquet.

[Optional] If set to true, queries over this table require a partition filter that can be used for partition elimination to be specified. Note that this field should only be true when creating a permanent external table or querying a temporary external table. Hive-partitioned loads with requirePartitionFilter explicitly set to true will fail.

[Optional] When hive partition detection is requested, a common prefix for all source uris should be supplied. The prefix must end immediately before the partition key encoding begins. For example, consider files following this data layout. gs://bucket/path_to_table/dt=2019-01-01/country=BR/id=7/file.avro gs://bucket/path_to_table/dt=2018-12-31/country=CA/id=3/file.avro When hive partitioning is requested with either AUTO or STRINGS detection, the common prefix can be either of gs://bucket/path_to_table or gs://bucket/path_to_table/ (trailing slash does not matter).

Specifies the base table involved in the reason that no search index was used.

Specifies the high-level reason for the scenario when no search index was used.

Specifies the name of the unused search index, if available.

Free form human-readable reason for the scenario when no search index was used.

Specifies the base table involved in the reason that no search index was used.

Specifies the high-level reason for the scenario when no search index was used.

Specifies the name of the unused search index, if available.

Free form human-readable reason for the scenario when no search index was used.

Specifies the base table involved in the reason that no search index was used.

Specifies the high-level reason for the scenario when no search index was used.

Specifies the name of the unused search index, if available.

Free form human-readable reason for the scenario when no search index was used.

Specifies the base table involved in the reason that no search index was used.

Specifies the high-level reason for the scenario when no search index was used.

Specifies the name of the unused search index, if available.

Free form human-readable reason for the scenario when no search index was used.

Specifies the base table involved in the reason that no search index was used.

Specifies the high-level reason for the scenario when no search index was used.

Specifies the name of the unused search index, if available.

Free form human-readable reason for the scenario when no search index was used.

Specifies the base table involved in the reason that no search index was used.

Specifies the high-level reason for the scenario when no search index was used.

Specifies the name of the unused search index, if available.

Free form human-readable reason for the scenario when no search index was used.

Time taken to run the iteration in milliseconds.

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

Index of the iteration, 0 based.

Learn rate used for this iteration.

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

Time taken to run the iteration in milliseconds.

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

Index of the iteration, 0 based.

Learn rate used for this iteration.

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

Time taken to run the iteration in milliseconds.

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

Index of the iteration, 0 based.

Learn rate used for this iteration.

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

Time taken to run the iteration in milliseconds.

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

Index of the iteration, 0 based.

Learn rate used for this iteration.

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

Time taken to run the iteration in milliseconds.

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

Index of the iteration, 0 based.

Learn rate used for this iteration.

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

Time taken to run the iteration in milliseconds.

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

Index of the iteration, 0 based.

Learn rate used for this iteration.

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

[Pick one] Copies a table.

[Optional] If set, don't actually run this job. A valid query will return a mostly empty response with some processing statistics, while an invalid query will return the same error it would if it wasn't a dry run. Behavior of non-query jobs is undefined.

[Pick one] Configures an extract job.

[Optional] Job timeout in milliseconds. If this time limit is exceeded, BigQuery may attempt to terminate the job.

The labels associated with this job. You can use these to organize and group your jobs. 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.

[Pick one] Configures a load job.

[Pick one] Configures a query job.

[Pick one] Copies a table.

[Optional] If set, don't actually run this job. A valid query will return a mostly empty response with some processing statistics, while an invalid query will return the same error it would if it wasn't a dry run. Behavior of non-query jobs is undefined.

[Pick one] Configures an extract job.

[Optional] Job timeout in milliseconds. If this time limit is exceeded, BigQuery may attempt to terminate the job.

The labels associated with this job. You can use these to organize and group your jobs. 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.

[Pick one] Configures a load job.

[Pick one] Configures a query job.

[Pick one] Copies a table.

[Optional] If set, don't actually run this job. A valid query will return a mostly empty response with some processing statistics, while an invalid query will return the same error it would if it wasn't a dry run. Behavior of non-query jobs is undefined.

[Pick one] Configures an extract job.

[Optional] Job timeout in milliseconds. If this time limit is exceeded, BigQuery may attempt to terminate the job.

The labels associated with this job. You can use these to organize and group your jobs. 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.

[Pick one] Configures a load job.

[Pick one] Configures a query job.

[Pick one] Copies a table.

[Optional] If set, don't actually run this job. A valid query will return a mostly empty response with some processing statistics, while an invalid query will return the same error it would if it wasn't a dry run. Behavior of non-query jobs is undefined.

[Pick one] Configures an extract job.

[Optional] Job timeout in milliseconds. If this time limit is exceeded, BigQuery may attempt to terminate the job.

The labels associated with this job. You can use these to organize and group your jobs. 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.

[Pick one] Configures a load job.

[Pick one] Configures a query job.

[Pick one] Copies a table.

[Optional] If set, don't actually run this job. A valid query will return a mostly empty response with some processing statistics, while an invalid query will return the same error it would if it wasn't a dry run. Behavior of non-query jobs is undefined.

[Pick one] Configures an extract job.

[Optional] Job timeout in milliseconds. If this time limit is exceeded, BigQuery may attempt to terminate the job.

The labels associated with this job. You can use these to organize and group your jobs. 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.

[Pick one] Configures a load job.

[Pick one] Configures a query job.

[Pick one] Copies a table.

[Optional] If set, don't actually run this job. A valid query will return a mostly empty response with some processing statistics, while an invalid query will return the same error it would if it wasn't a dry run. Behavior of non-query jobs is undefined.

[Pick one] Configures an extract job.

[Optional] Job timeout in milliseconds. If this time limit is exceeded, BigQuery may attempt to terminate the job.

The labels associated with this job. You can use these to organize and group your jobs. 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.

[Pick one] Configures a load job.

[Pick one] Configures a query job.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] The compression type to use for exported files. Possible values include GZIP, DEFLATE, SNAPPY, and NONE. The default value is NONE. DEFLATE and SNAPPY are only supported for Avro. Not applicable when extracting models.

[Optional] The exported file format. Possible values include CSV, NEWLINE_DELIMITED_JSON, PARQUET or AVRO for tables and ML_TF_SAVED_MODEL or ML_XGBOOST_BOOSTER for models. The default value for tables is CSV. Tables with nested or repeated fields cannot be exported as CSV. The default value for models is ML_TF_SAVED_MODEL.

[Pick one] DEPRECATED: Use destinationUris instead, passing only one URI as necessary. The fully-qualified Google Cloud Storage URI where the extracted table should be written.

[Pick one] A list of fully-qualified Google Cloud Storage URIs where the extracted table should be written.

[Optional] Delimiter to use between fields in the exported data. Default is ','. Not applicable when extracting models.

[Optional] Whether to print out a header row in the results. Default is true. Not applicable when extracting models.

A reference to the model being exported.

A reference to the table being exported.

[Optional] If destinationFormat is set to "AVRO", this flag indicates whether to enable extracting applicable column types (such as TIMESTAMP) to their corresponding AVRO logical types (timestamp-micros), instead of only using their raw types (avro-long). Not applicable when extracting models.

[Optional] Accept rows that are missing trailing optional columns. The missing values are treated as nulls. 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. Only applicable to CSV, ignored for other formats.

Indicates if BigQuery should allow quoted data sections that contain newline characters in a CSV file. The default value is false.

[Optional] Indicates if we should automatically infer the options and schema for CSV and JSON sources.

[Beta] Clustering specification for the destination table. Must be specified with time-based partitioning, data in the table will be first partitioned and subsequently clustered.

Connection properties.

[Optional] Specifies whether the job is allowed to create new tables. The following values are supported: CREATE_IF_NEEDED: If the table does not exist, BigQuery creates the table. CREATE_NEVER: The table must already exist. If it does not, a 'notFound' error is returned in the job result. The default value is CREATE_IF_NEEDED. Creation, truncation and append actions occur as one atomic update upon job completion.

If true, creates a new session, where session id will be a server generated random id. If false, runs query with an existing session_id passed in ConnectionProperty, otherwise runs the load job in non-session mode.

[Optional] Defines the list of possible SQL data types to which the source decimal values are converted. This list and the precision and the scale parameters of the decimal field determine the target type. In the order of NUMERIC, BIGNUMERIC, and STRING, a type is picked if it is in the specified list and if it supports the precision and the scale. STRING supports all precision and scale values. If none of the listed types supports the precision and the scale, the type supporting the widest range in the specified list is picked, and if a value exceeds the supported range when reading the data, an error will be thrown. Example: Suppose the value of this field is ["NUMERIC", "BIGNUMERIC"]. If (precision,scale) is: (38,9) -> NUMERIC; (39,9) -> BIGNUMERIC (NUMERIC cannot hold 30 integer digits); (38,10) -> BIGNUMERIC (NUMERIC cannot hold 10 fractional digits); (76,38) -> BIGNUMERIC; (77,38) -> BIGNUMERIC (error if value exeeds supported range). This field cannot contain duplicate types. The order of the types in this field is ignored. For example, ["BIGNUMERIC", "NUMERIC"] is the same as ["NUMERIC", "BIGNUMERIC"] and NUMERIC always takes precedence over BIGNUMERIC. Defaults to ["NUMERIC", "STRING"] for ORC and ["NUMERIC"] for the other file formats.

Custom encryption configuration (e.g., Cloud KMS keys).

[Required] The destination table to load the data into.

[Beta] [Optional] Properties with which to create the destination table if it is new.

[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.

[Optional] The separator for fields in a CSV file. The separator can be any ISO-8859-1 single-byte character. To use a character in the range 128-255, you must encode the character as UTF8. 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 (',').

[Optional] Specifies how source URIs are interpreted for constructing the file set to load. By default source URIs are expanded against the underlying storage. Other options include specifying manifest files. Only applicable to object storage systems.

[Optional] Options to configure hive partitioning support.

[Optional] Indicates if BigQuery should allow extra values that are not represented in the table schema. If true, the extra values are ignored. If false, records with extra 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. The sourceFormat property determines what BigQuery treats as an extra value: CSV: Trailing columns JSON: Named values that don't match any column names

[Optional] If sourceFormat is set to newline-delimited JSON, indicates whether it should be processed as a JSON variant such as GeoJSON. For a sourceFormat other than JSON, omit this field. If the sourceFormat is newline-delimited JSON: - for newline-delimited GeoJSON: set to GEOJSON.

[Optional] The maximum number of bad records that BigQuery can ignore when running the job. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV and JSON. The default value is 0, which requires that all records are valid.

[Optional] Specifies a string that represents a null value in a CSV file. For example, if you specify "\N", BigQuery interprets "\N" as a null value when loading a CSV file. The default value is the empty string. If you set this property to a custom value, BigQuery throws an error if an empty string is present for all data types except for STRING and BYTE. For STRING and BYTE columns, BigQuery interprets the empty string as an empty value.

[Optional] Options to configure parquet support.

[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.

If sourceFormat is set to "DATASTORE_BACKUP", indicates which entity properties to load into BigQuery from a Cloud Datastore backup. Property names are case sensitive and must be top-level properties. If no properties are specified, BigQuery loads all properties. If any named property isn't found in the Cloud Datastore backup, an invalid error is returned in the job result.

[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.

[TrustedTester] Range partitioning specification for this table. Only one of timePartitioning and rangePartitioning should be specified.

User provided referencing file with the expected reader schema, Available for the format: AVRO, PARQUET, ORC.

[Optional] The schema for the destination table. The schema can be omitted if the destination table already exists, or if you're loading data from Google Cloud Datastore.

[Deprecated] The inline schema. For CSV schemas, specify as "Field1:Type1[,Field2:Type2]*". For example, "foo:STRING, bar:INTEGER, baz:FLOAT".

Deprecated:

[Deprecated] The inline schema. For CSV schemas, specify as "Field1:Type1[,Field2:Type2]*". For example, "foo:STRING, bar:INTEGER, baz:FLOAT".

[Deprecated] The format of the schemaInline property.

Deprecated:

[Deprecated] The format of the schemaInline property.

Allows the schema of the destination table to be updated as a side effect of the load job if a schema is autodetected or supplied in the job configuration. Schema update options are supported in two cases: when writeDisposition is WRITE_APPEND; when writeDisposition is WRITE_TRUNCATE and the destination table is a partition of a table, specified by partition decorators. For normal tables, WRITE_TRUNCATE will always overwrite the schema. One or more of the following values are specified: ALLOW_FIELD_ADDITION: allow adding a nullable field to the schema. ALLOW_FIELD_RELAXATION: allow relaxing a required field in the original schema to nullable.

[Optional] The number of rows at the top of a CSV file that BigQuery will skip when loading the data. The default value is 0. This property is useful if you have header rows in the file that should be skipped.

[Optional] The format of the data files. For CSV files, specify "CSV". For datastore backups, specify "DATASTORE_BACKUP". For newline-delimited JSON, specify "NEWLINE_DELIMITED_JSON". For Avro, specify "AVRO". For parquet, specify "PARQUET". For orc, specify "ORC". The default value is CSV.

[Required] The fully-qualified URIs that point to your data in Google Cloud. For Google Cloud Storage URIs: Each URI can contain one '' wildcard character and it must come after the 'bucket' name. Size limits related to load jobs apply to external data sources. For Google Cloud Bigtable URIs: Exactly one URI can be specified and it has be a fully specified and valid HTTPS URL for a Google Cloud Bigtable table. For Google Cloud Datastore backups: Exactly one URI can be specified. Also, the '' wildcard character is not allowed.

Time-based partitioning specification for the destination table. Only one of timePartitioning and rangePartitioning should be specified.

[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).

[Optional] Specifies the action that occurs if the destination table already exists. The following values are supported: WRITE_TRUNCATE: If the table already exists, BigQuery overwrites the table data. WRITE_APPEND: If the table already exists, BigQuery appends the data to the table. WRITE_EMPTY: If the table already exists and contains data, a 'duplicate' error is returned in the job result. The default value is WRITE_APPEND. Each action is atomic and only occurs if BigQuery is able to complete the job successfully. Creation, truncation and append actions occur as one atomic update upon job completion.

[Optional] Accept rows that are missing trailing optional columns. The missing values are treated as nulls. 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. Only applicable to CSV, ignored for other formats.

Indicates if BigQuery should allow quoted data sections that contain newline characters in a CSV file. The default value is false.

[Optional] Indicates if we should automatically infer the options and schema for CSV and JSON sources.

[Beta] Clustering specification for the destination table. Must be specified with time-based partitioning, data in the table will be first partitioned and subsequently clustered.

Connection properties.

[Optional] Specifies whether the job is allowed to create new tables. The following values are supported: CREATE_IF_NEEDED: If the table does not exist, BigQuery creates the table. CREATE_NEVER: The table must already exist. If it does not, a 'notFound' error is returned in the job result. The default value is CREATE_IF_NEEDED. Creation, truncation and append actions occur as one atomic update upon job completion.

If true, creates a new session, where session id will be a server generated random id. If false, runs query with an existing session_id passed in ConnectionProperty, otherwise runs the load job in non-session mode.

[Optional] Defines the list of possible SQL data types to which the source decimal values are converted. This list and the precision and the scale parameters of the decimal field determine the target type. In the order of NUMERIC, BIGNUMERIC, and STRING, a type is picked if it is in the specified list and if it supports the precision and the scale. STRING supports all precision and scale values. If none of the listed types supports the precision and the scale, the type supporting the widest range in the specified list is picked, and if a value exceeds the supported range when reading the data, an error will be thrown. Example: Suppose the value of this field is ["NUMERIC", "BIGNUMERIC"]. If (precision,scale) is: (38,9) -> NUMERIC; (39,9) -> BIGNUMERIC (NUMERIC cannot hold 30 integer digits); (38,10) -> BIGNUMERIC (NUMERIC cannot hold 10 fractional digits); (76,38) -> BIGNUMERIC; (77,38) -> BIGNUMERIC (error if value exeeds supported range). This field cannot contain duplicate types. The order of the types in this field is ignored. For example, ["BIGNUMERIC", "NUMERIC"] is the same as ["NUMERIC", "BIGNUMERIC"] and NUMERIC always takes precedence over BIGNUMERIC. Defaults to ["NUMERIC", "STRING"] for ORC and ["NUMERIC"] for the other file formats.

Custom encryption configuration (e.g., Cloud KMS keys).

[Required] The destination table to load the data into.

[Beta] [Optional] Properties with which to create the destination table if it is new.

[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.

[Optional] The separator for fields in a CSV file. The separator can be any ISO-8859-1 single-byte character. To use a character in the range 128-255, you must encode the character as UTF8. 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 (',').

[Optional] Specifies how source URIs are interpreted for constructing the file set to load. By default source URIs are expanded against the underlying storage. Other options include specifying manifest files. Only applicable to object storage systems.

[Optional] Options to configure hive partitioning support.

[Optional] Indicates if BigQuery should allow extra values that are not represented in the table schema. If true, the extra values are ignored. If false, records with extra 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. The sourceFormat property determines what BigQuery treats as an extra value: CSV: Trailing columns JSON: Named values that don't match any column names

[Optional] If sourceFormat is set to newline-delimited JSON, indicates whether it should be processed as a JSON variant such as GeoJSON. For a sourceFormat other than JSON, omit this field. If the sourceFormat is newline-delimited JSON: - for newline-delimited GeoJSON: set to GEOJSON.

[Optional] The maximum number of bad records that BigQuery can ignore when running the job. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV and JSON. The default value is 0, which requires that all records are valid.

[Optional] Specifies a string that represents a null value in a CSV file. For example, if you specify "\N", BigQuery interprets "\N" as a null value when loading a CSV file. The default value is the empty string. If you set this property to a custom value, BigQuery throws an error if an empty string is present for all data types except for STRING and BYTE. For STRING and BYTE columns, BigQuery interprets the empty string as an empty value.

[Optional] Options to configure parquet support.

[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.

If sourceFormat is set to "DATASTORE_BACKUP", indicates which entity properties to load into BigQuery from a Cloud Datastore backup. Property names are case sensitive and must be top-level properties. If no properties are specified, BigQuery loads all properties. If any named property isn't found in the Cloud Datastore backup, an invalid error is returned in the job result.

[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.

[TrustedTester] Range partitioning specification for this table. Only one of timePartitioning and rangePartitioning should be specified.

User provided referencing file with the expected reader schema, Available for the format: AVRO, PARQUET, ORC.

[Optional] The schema for the destination table. The schema can be omitted if the destination table already exists, or if you're loading data from Google Cloud Datastore.

[Deprecated] The inline schema. For CSV schemas, specify as "Field1:Type1[,Field2:Type2]*". For example, "foo:STRING, bar:INTEGER, baz:FLOAT".

Deprecated:

[Deprecated] The inline schema. For CSV schemas, specify as "Field1:Type1[,Field2:Type2]*". For example, "foo:STRING, bar:INTEGER, baz:FLOAT".

[Deprecated] The format of the schemaInline property.

Deprecated:

[Deprecated] The format of the schemaInline property.

Allows the schema of the destination table to be updated as a side effect of the load job if a schema is autodetected or supplied in the job configuration. Schema update options are supported in two cases: when writeDisposition is WRITE_APPEND; when writeDisposition is WRITE_TRUNCATE and the destination table is a partition of a table, specified by partition decorators. For normal tables, WRITE_TRUNCATE will always overwrite the schema. One or more of the following values are specified: ALLOW_FIELD_ADDITION: allow adding a nullable field to the schema. ALLOW_FIELD_RELAXATION: allow relaxing a required field in the original schema to nullable.

[Optional] The number of rows at the top of a CSV file that BigQuery will skip when loading the data. The default value is 0. This property is useful if you have header rows in the file that should be skipped.

[Optional] The format of the data files. For CSV files, specify "CSV". For datastore backups, specify "DATASTORE_BACKUP". For newline-delimited JSON, specify "NEWLINE_DELIMITED_JSON". For Avro, specify "AVRO". For parquet, specify "PARQUET". For orc, specify "ORC". The default value is CSV.

[Required] The fully-qualified URIs that point to your data in Google Cloud. For Google Cloud Storage URIs: Each URI can contain one '' wildcard character and it must come after the 'bucket' name. Size limits related to load jobs apply to external data sources. For Google Cloud Bigtable URIs: Exactly one URI can be specified and it has be a fully specified and valid HTTPS URL for a Google Cloud Bigtable table. For Google Cloud Datastore backups: Exactly one URI can be specified. Also, the '' wildcard character is not allowed.

Time-based partitioning specification for the destination table. Only one of timePartitioning and rangePartitioning should be specified.

[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).

[Optional] Specifies the action that occurs if the destination table already exists. The following values are supported: WRITE_TRUNCATE: If the table already exists, BigQuery overwrites the table data. WRITE_APPEND: If the table already exists, BigQuery appends the data to the table. WRITE_EMPTY: If the table already exists and contains data, a 'duplicate' error is returned in the job result. The default value is WRITE_APPEND. Each action is atomic and only occurs if BigQuery is able to complete the job successfully. Creation, truncation and append actions occur as one atomic update upon job completion.

[Optional] Accept rows that are missing trailing optional columns. The missing values are treated as nulls. 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. Only applicable to CSV, ignored for other formats.

Indicates if BigQuery should allow quoted data sections that contain newline characters in a CSV file. The default value is false.

[Optional] Indicates if we should automatically infer the options and schema for CSV and JSON sources.

[Beta] Clustering specification for the destination table. Must be specified with time-based partitioning, data in the table will be first partitioned and subsequently clustered.

Connection properties.

[Optional] Specifies whether the job is allowed to create new tables. The following values are supported: CREATE_IF_NEEDED: If the table does not exist, BigQuery creates the table. CREATE_NEVER: The table must already exist. If it does not, a 'notFound' error is returned in the job result. The default value is CREATE_IF_NEEDED. Creation, truncation and append actions occur as one atomic update upon job completion.

If true, creates a new session, where session id will be a server generated random id. If false, runs query with an existing session_id passed in ConnectionProperty, otherwise runs the load job in non-session mode.

[Optional] Defines the list of possible SQL data types to which the source decimal values are converted. This list and the precision and the scale parameters of the decimal field determine the target type. In the order of NUMERIC, BIGNUMERIC, and STRING, a type is picked if it is in the specified list and if it supports the precision and the scale. STRING supports all precision and scale values. If none of the listed types supports the precision and the scale, the type supporting the widest range in the specified list is picked, and if a value exceeds the supported range when reading the data, an error will be thrown. Example: Suppose the value of this field is ["NUMERIC", "BIGNUMERIC"]. If (precision,scale) is: (38,9) -> NUMERIC; (39,9) -> BIGNUMERIC (NUMERIC cannot hold 30 integer digits); (38,10) -> BIGNUMERIC (NUMERIC cannot hold 10 fractional digits); (76,38) -> BIGNUMERIC; (77,38) -> BIGNUMERIC (error if value exeeds supported range). This field cannot contain duplicate types. The order of the types in this field is ignored. For example, ["BIGNUMERIC", "NUMERIC"] is the same as ["NUMERIC", "BIGNUMERIC"] and NUMERIC always takes precedence over BIGNUMERIC. Defaults to ["NUMERIC", "STRING"] for ORC and ["NUMERIC"] for the other file formats.

Custom encryption configuration (e.g., Cloud KMS keys).

[Required] The destination table to load the data into.

[Beta] [Optional] Properties with which to create the destination table if it is new.

[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.

[Optional] The separator for fields in a CSV file. The separator can be any ISO-8859-1 single-byte character. To use a character in the range 128-255, you must encode the character as UTF8. 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 (',').

[Optional] Specifies how source URIs are interpreted for constructing the file set to load. By default source URIs are expanded against the underlying storage. Other options include specifying manifest files. Only applicable to object storage systems.

[Optional] Options to configure hive partitioning support.

[Optional] Indicates if BigQuery should allow extra values that are not represented in the table schema. If true, the extra values are ignored. If false, records with extra 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. The sourceFormat property determines what BigQuery treats as an extra value: CSV: Trailing columns JSON: Named values that don't match any column names

[Optional] If sourceFormat is set to newline-delimited JSON, indicates whether it should be processed as a JSON variant such as GeoJSON. For a sourceFormat other than JSON, omit this field. If the sourceFormat is newline-delimited JSON: - for newline-delimited GeoJSON: set to GEOJSON.

[Optional] The maximum number of bad records that BigQuery can ignore when running the job. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV and JSON. The default value is 0, which requires that all records are valid.