Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::TableSpec.
A specification of a relational table.
The table's schema is represented via its child column specs. It is
pre-populated as part of ImportData by schema inference algorithm, the
version of which is a required parameter of ImportData InputConfig.
Note: While working with a table, at times the schema may be
inconsistent with the data in the table (e.g. string in a FLOAT64 column).
The consistency validation is done upon creation of a model.
Used by:
Tables
Inherits
Object
Extended By
Google::Protobuf::MessageExts::ClassMethods
Includes
Google::Protobuf::MessageExts
Methods
#column_count
defcolumn_count()->::Integer
Returns
(::Integer) — Output only. The number of columns of the table. That is, the number of
child ColumnSpec-s.
#column_count=
defcolumn_count=(value)->::Integer
Parameter
value (::Integer) — Output only. The number of columns of the table. That is, the number of
child ColumnSpec-s.
Returns
(::Integer) — Output only. The number of columns of the table. That is, the number of
child ColumnSpec-s.
#etag
defetag()->::String
Returns
(::String) — Used to perform consistent read-modify-write updates. If not set, a blind
"overwrite" update happens.
#etag=
defetag=(value)->::String
Parameter
value (::String) — Used to perform consistent read-modify-write updates. If not set, a blind
"overwrite" update happens.
Returns
(::String) — Used to perform consistent read-modify-write updates. If not set, a blind
"overwrite" update happens.
(::Integer) — Output only. The number of rows (i.e. examples) in the table.
#row_count=
defrow_count=(value)->::Integer
Parameter
value (::Integer) — Output only. The number of rows (i.e. examples) in the table.
Returns
(::Integer) — Output only. The number of rows (i.e. examples) in the table.
#time_column_spec_id
deftime_column_spec_id()->::String
Returns
(::String) — column_spec_id of the time column. Only used if the parent dataset's
ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE
and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and
those in between to VALIDATE.
Required type: TIMESTAMP.
If both this column and ml_use_column are not set, then ML use of all rows
will be assigned by AutoML. NOTE: Updates of this field will instantly
affect any other users concurrently working with the dataset.
#time_column_spec_id=
deftime_column_spec_id=(value)->::String
Parameter
value (::String) — column_spec_id of the time column. Only used if the parent dataset's
ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE
and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and
those in between to VALIDATE.
Required type: TIMESTAMP.
If both this column and ml_use_column are not set, then ML use of all rows
will be assigned by AutoML. NOTE: Updates of this field will instantly
affect any other users concurrently working with the dataset.
Returns
(::String) — column_spec_id of the time column. Only used if the parent dataset's
ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE
and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and
those in between to VALIDATE.
Required type: TIMESTAMP.
If both this column and ml_use_column are not set, then ML use of all rows
will be assigned by AutoML. NOTE: Updates of this field will instantly
affect any other users concurrently working with the dataset.
#valid_row_count
defvalid_row_count()->::Integer
Returns
(::Integer) — Output only. The number of valid rows (i.e. without values that don't match
DataType-s of their columns).
#valid_row_count=
defvalid_row_count=(value)->::Integer
Parameter
value (::Integer) — Output only. The number of valid rows (i.e. without values that don't match
DataType-s of their columns).
Returns
(::Integer) — Output only. The number of valid rows (i.e. without values that don't match
DataType-s of their columns).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[],[],null,["# Cloud AutoML V1beta1 API - Class Google::Cloud::AutoML::V1beta1::TableSpec (v0.14.1)\n\nVersion latestkeyboard_arrow_down\n\n- [0.14.1 (latest)](/ruby/docs/reference/google-cloud-automl-v1beta1/latest/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.14.0](/ruby/docs/reference/google-cloud-automl-v1beta1/0.14.0/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.13.1](/ruby/docs/reference/google-cloud-automl-v1beta1/0.13.1/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.12.0](/ruby/docs/reference/google-cloud-automl-v1beta1/0.12.0/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.11.1](/ruby/docs/reference/google-cloud-automl-v1beta1/0.11.1/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.10.2](/ruby/docs/reference/google-cloud-automl-v1beta1/0.10.2/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.9.0](/ruby/docs/reference/google-cloud-automl-v1beta1/0.9.0/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.8.0](/ruby/docs/reference/google-cloud-automl-v1beta1/0.8.0/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.7.0](/ruby/docs/reference/google-cloud-automl-v1beta1/0.7.0/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.6.1](/ruby/docs/reference/google-cloud-automl-v1beta1/0.6.1/Google-Cloud-AutoML-V1beta1-TableSpec)\n- [0.5.5](/ruby/docs/reference/google-cloud-automl-v1beta1/0.5.5/Google-Cloud-AutoML-V1beta1-TableSpec) \nReference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::TableSpec.\n\nA specification of a relational table.\nThe table's schema is represented via its child column specs. It is\npre-populated as part of ImportData by schema inference algorithm, the\nversion of which is a required parameter of ImportData InputConfig.\nNote: While working with a table, at times the schema may be\ninconsistent with the data in the table (e.g. string in a FLOAT64 column).\nThe consistency validation is done upon creation of a model.\nUsed by:\n\n- Tables \n\nInherits\n--------\n\n- Object \n\nExtended By\n-----------\n\n- Google::Protobuf::MessageExts::ClassMethods \n\nIncludes\n--------\n\n- Google::Protobuf::MessageExts\n\nMethods\n-------\n\n### #column_count\n\n def column_count() -\u003e ::Integer\n\n**Returns**\n\n- (::Integer) --- Output only. The number of columns of the table. That is, the number of child ColumnSpec-s.\n\n### #column_count=\n\n def column_count=(value) -\u003e ::Integer\n\n**Parameter**\n\n- **value** (::Integer) --- Output only. The number of columns of the table. That is, the number of child ColumnSpec-s. \n**Returns**\n\n- (::Integer) --- Output only. The number of columns of the table. That is, the number of child ColumnSpec-s.\n\n### #etag\n\n def etag() -\u003e ::String\n\n**Returns**\n\n- (::String) --- Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.\n\n### #etag=\n\n def etag=(value) -\u003e ::String\n\n**Parameter**\n\n- **value** (::String) --- Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens. \n**Returns**\n\n- (::String) --- Used to perform consistent read-modify-write updates. If not set, a blind \"overwrite\" update happens.\n\n### #input_configs\n\n def input_configs() -\u003e ::Array\u003c::Google::Cloud::AutoML::V1beta1::InputConfig\u003e\n\n**Returns**\n\n- (::Array\\\u003c[::Google::Cloud::AutoML::V1beta1::InputConfig](./Google-Cloud-AutoML-V1beta1-InputConfig)\\\u003e) --- Output only. Input configs via which data currently residing in the table had been imported.\n\n### #input_configs=\n\n def input_configs=(value) -\u003e ::Array\u003c::Google::Cloud::AutoML::V1beta1::InputConfig\u003e\n\n**Parameter**\n\n- **value** (::Array\\\u003c[::Google::Cloud::AutoML::V1beta1::InputConfig](./Google-Cloud-AutoML-V1beta1-InputConfig)\\\u003e) --- Output only. Input configs via which data currently residing in the table had been imported. \n**Returns**\n\n- (::Array\\\u003c[::Google::Cloud::AutoML::V1beta1::InputConfig](./Google-Cloud-AutoML-V1beta1-InputConfig)\\\u003e) --- Output only. Input configs via which data currently residing in the table had been imported.\n\n### #name\n\n def name() -\u003e ::String\n\n**Returns**\n\n- (::String) --- Output only. The resource name of the table spec. Form:\n\n `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}`\n\n### #name=\n\n def name=(value) -\u003e ::String\n\n**Parameter**\n\n- **value** (::String) --- Output only. The resource name of the table spec. Form:\n\n\n`projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}` \n**Returns**\n\n- (::String) --- Output only. The resource name of the table spec. Form:\n\n `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}`\n\n### #row_count\n\n def row_count() -\u003e ::Integer\n\n**Returns**\n\n- (::Integer) --- Output only. The number of rows (i.e. examples) in the table.\n\n### #row_count=\n\n def row_count=(value) -\u003e ::Integer\n\n**Parameter**\n\n- **value** (::Integer) --- Output only. The number of rows (i.e. examples) in the table. \n**Returns**\n\n- (::Integer) --- Output only. The number of rows (i.e. examples) in the table.\n\n### #time_column_spec_id\n\n def time_column_spec_id() -\u003e ::String\n\n**Returns**\n\n- (::String) --- column_spec_id of the time column. Only used if the parent dataset's ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and those in between to VALIDATE. Required type: TIMESTAMP. If both this column and ml_use_column are not set, then ML use of all rows will be assigned by AutoML. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.\n\n### #time_column_spec_id=\n\n def time_column_spec_id=(value) -\u003e ::String\n\n**Parameter**\n\n- **value** (::String) --- column_spec_id of the time column. Only used if the parent dataset's ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and those in between to VALIDATE. Required type: TIMESTAMP. If both this column and ml_use_column are not set, then ML use of all rows will be assigned by AutoML. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset. \n**Returns**\n\n- (::String) --- column_spec_id of the time column. Only used if the parent dataset's ml_use_column_spec_id is not set. Used to split rows into TRAIN, VALIDATE and TEST sets such that oldest rows go to TRAIN set, newest to TEST, and those in between to VALIDATE. Required type: TIMESTAMP. If both this column and ml_use_column are not set, then ML use of all rows will be assigned by AutoML. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.\n\n### #valid_row_count\n\n def valid_row_count() -\u003e ::Integer\n\n**Returns**\n\n- (::Integer) --- Output only. The number of valid rows (i.e. without values that don't match DataType-s of their columns).\n\n### #valid_row_count=\n\n def valid_row_count=(value) -\u003e ::Integer\n\n**Parameter**\n\n- **value** (::Integer) --- Output only. The number of valid rows (i.e. without values that don't match DataType-s of their columns). \n**Returns**\n\n- (::Integer) --- Output only. The number of valid rows (i.e. without values that don't match DataType-s of their columns)."]]
