chore: Update discovery artifacts (#1195)
* chore(accesscontextmanager): update the api
* chore(adexchangebuyer2): update the api
* chore(admin): update the api
* chore(alertcenter): update the api
* chore(analyticsadmin): update the api
* chore(analyticsdata): update the api
* chore(androidmanagement): update the api
* chore(apigateway): update the api
* chore(apigee): update the api
* chore(appengine): update the api
* chore(area120tables): update the api
* chore(artifactregistry): update the api
* chore(bigquery): update the api
* chore(bigqueryconnection): update the api
* chore(bigqueryreservation): update the api
* chore(billingbudgets): update the api
* chore(binaryauthorization): update the api
* chore(blogger): update the api
* chore(calendar): update the api
* chore(chat): update the api
* chore(cloudasset): update the api
* chore(cloudbuild): update the api
* chore(cloudfunctions): update the api
* chore(cloudidentity): update the api
* chore(cloudkms): update the api
* chore(cloudresourcemanager): update the api
* chore(cloudscheduler): update the api
* chore(cloudtasks): update the api
* chore(composer): update the api
* chore(compute): update the api
* chore(container): update the api
* chore(containeranalysis): update the api
* chore(content): update the api
* chore(datacatalog): update the api
* chore(dataflow): update the api
* chore(datafusion): update the api
* chore(datamigration): update the api
* chore(dataproc): update the api
* chore(deploymentmanager): update the api
* chore(dialogflow): update the api
* chore(displayvideo): update the api
* chore(dlp): update the api
* chore(dns): update the api
* chore(documentai): update the api
* chore(eventarc): update the api
* chore(file): update the api
* chore(firebaseml): update the api
* chore(games): update the api
* chore(gameservices): update the api
* chore(genomics): update the api
* chore(healthcare): update the api
* chore(homegraph): update the api
* chore(iam): update the api
* chore(iap): update the api
* chore(jobs): update the api
* chore(lifesciences): update the api
* chore(localservices): update the api
* chore(managedidentities): update the api
* chore(manufacturers): update the api
* chore(memcache): update the api
* chore(ml): update the api
* chore(monitoring): update the api
* chore(notebooks): update the api
* chore(osconfig): update the api
* chore(pagespeedonline): update the api
* chore(people): update the api
* chore(privateca): update the api
* chore(prod_tt_sasportal): update the api
* chore(pubsub): update the api
* chore(pubsublite): update the api
* chore(recommender): update the api
* chore(remotebuildexecution): update the api
* chore(reseller): update the api
* chore(run): update the api
* chore(safebrowsing): update the api
* chore(sasportal): update the api
* chore(searchconsole): update the api
* chore(secretmanager): update the api
* chore(securitycenter): update the api
* chore(serviceconsumermanagement): update the api
* chore(servicecontrol): update the api
* chore(servicenetworking): update the api
* chore(serviceusage): update the api
* chore(sheets): update the api
* chore(slides): update the api
* chore(spanner): update the api
* chore(speech): update the api
* chore(sqladmin): update the api
* chore(storage): update the api
* chore(storagetransfer): update the api
* chore(sts): update the api
* chore(tagmanager): update the api
* chore(testing): update the api
* chore(toolresults): update the api
* chore(transcoder): update the api
* chore(vectortile): update the api
* chore(videointelligence): update the api
* chore(vision): update the api
* chore(webmasters): update the api
* chore(workflowexecutions): update the api
* chore(youtube): update the api
diff --git a/docs/dyn/bigquery_v2.jobs.html b/docs/dyn/bigquery_v2.jobs.html
index b8e4d82..ea7f12b 100644
--- a/docs/dyn/bigquery_v2.jobs.html
+++ b/docs/dyn/bigquery_v2.jobs.html
@@ -180,7 +180,7 @@
],
},
"createDisposition": "A String", # [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.
- "decimalTargetTypes": [ # [Trusted Tester] 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. For example: suppose decimal_target_type = ["NUMERIC", "BIGNUMERIC"]. Then 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). For duplicated types in this field, only one will be considered and the rest will be ignored. 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.
+ "decimalTargetTypes": [ # 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 ([Preview](/products/#product-launch-stages)), 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.
"A String",
],
"destinationEncryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys).
@@ -200,14 +200,19 @@
},
"encoding": "A String", # [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.
"fieldDelimiter": "A String", # [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 (',').
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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
+ "jsonExtension": "A String", # [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.
"maxBadRecords": 42, # [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.
"nullMarker": "A String", # [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.
+ "parquetOptions": { # [Optional] Options to configure parquet support.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"projectionFields": [ # 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.
"A String",
],
@@ -239,7 +244,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -370,13 +375,17 @@
"range": "A String", # [Optional] Range of a sheet to query from. Only used when non-empty. Typical format: sheet_name!top_left_cell_id:bottom_right_cell_id For example: sheet1!A1:B20
"skipLeadingRows": "A String", # [Optional] The number of rows at the top of a sheet that BigQuery will skip when reading the data. The default value is 0. This property is useful if you have header rows that should be skipped. When autodetect is on, behavior is the following: * skipLeadingRows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. * skipLeadingRows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. * skipLeadingRows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise row N is used to extract column names for the detected schema.
},
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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 Google Cloud Bigtable: This setting is ignored. Google Cloud Datastore backups: This setting is ignored. Avro: This setting is ignored.
"maxBadRecords": 42, # [Optional] The maximum number of bad records that BigQuery can ignore when reading data. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV, JSON, and Google Sheets. The default value is 0, which requires that all records are valid. This setting is ignored for Google Cloud Bigtable, Google Cloud Datastore backups and Avro formats.
+ "parquetOptions": { # Additional properties to set if sourceFormat is set to Parquet.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"schema": { # [Optional] The schema for the data. Schema is required for CSV and JSON formats. Schema is disallowed for Google Cloud Bigtable, Cloud Datastore backups, and Avro formats.
"fields": [ # Describes the fields in a table.
{
@@ -396,7 +405,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -456,6 +465,10 @@
"cacheHit": True or False, # [Output-only] Whether the query result was fetched from the query cache.
"ddlAffectedRowAccessPolicyCount": "A String", # [Output-only] [Preview] The number of row access policies affected by a DDL statement. Present only for DROP ALL ROW ACCESS POLICIES queries.
"ddlOperationPerformed": "A String", # The DDL operation performed, possibly dependent on the pre-existence of the DDL target. Possible values (new values might be added in the future): "CREATE": The query created the DDL target. "SKIP": No-op. Example cases: the query is CREATE TABLE IF NOT EXISTS while the table already exists, or the query is DROP TABLE IF EXISTS while the table does not exist. "REPLACE": The query replaced the DDL target. Example case: the query is CREATE OR REPLACE TABLE, and the table already exists. "DROP": The query deleted the DDL target.
+ "ddlTargetDataset": { # [Output-only] The DDL target dataset. Present only for CREATE/ALTER/DROP SCHEMA queries.
+ "datasetId": "A String", # [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.
+ "projectId": "A String", # [Optional] The ID of the project containing this dataset.
+ },
"ddlTargetRoutine": { # The DDL target routine. Present only for CREATE/DROP FUNCTION/PROCEDURE queries.
"datasetId": "A String", # [Required] The ID of the dataset containing this routine.
"projectId": "A String", # [Required] The ID of the project containing this routine.
@@ -562,7 +575,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -745,7 +758,7 @@
],
},
"createDisposition": "A String", # [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.
- "decimalTargetTypes": [ # [Trusted Tester] 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. For example: suppose decimal_target_type = ["NUMERIC", "BIGNUMERIC"]. Then 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). For duplicated types in this field, only one will be considered and the rest will be ignored. 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.
+ "decimalTargetTypes": [ # 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 ([Preview](/products/#product-launch-stages)), 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.
"A String",
],
"destinationEncryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys).
@@ -765,14 +778,19 @@
},
"encoding": "A String", # [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.
"fieldDelimiter": "A String", # [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 (',').
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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
+ "jsonExtension": "A String", # [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.
"maxBadRecords": 42, # [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.
"nullMarker": "A String", # [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.
+ "parquetOptions": { # [Optional] Options to configure parquet support.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"projectionFields": [ # 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.
"A String",
],
@@ -804,7 +822,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -935,13 +953,17 @@
"range": "A String", # [Optional] Range of a sheet to query from. Only used when non-empty. Typical format: sheet_name!top_left_cell_id:bottom_right_cell_id For example: sheet1!A1:B20
"skipLeadingRows": "A String", # [Optional] The number of rows at the top of a sheet that BigQuery will skip when reading the data. The default value is 0. This property is useful if you have header rows that should be skipped. When autodetect is on, behavior is the following: * skipLeadingRows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. * skipLeadingRows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. * skipLeadingRows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise row N is used to extract column names for the detected schema.
},
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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 Google Cloud Bigtable: This setting is ignored. Google Cloud Datastore backups: This setting is ignored. Avro: This setting is ignored.
"maxBadRecords": 42, # [Optional] The maximum number of bad records that BigQuery can ignore when reading data. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV, JSON, and Google Sheets. The default value is 0, which requires that all records are valid. This setting is ignored for Google Cloud Bigtable, Google Cloud Datastore backups and Avro formats.
+ "parquetOptions": { # Additional properties to set if sourceFormat is set to Parquet.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"schema": { # [Optional] The schema for the data. Schema is required for CSV and JSON formats. Schema is disallowed for Google Cloud Bigtable, Cloud Datastore backups, and Avro formats.
"fields": [ # Describes the fields in a table.
{
@@ -961,7 +983,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -1021,6 +1043,10 @@
"cacheHit": True or False, # [Output-only] Whether the query result was fetched from the query cache.
"ddlAffectedRowAccessPolicyCount": "A String", # [Output-only] [Preview] The number of row access policies affected by a DDL statement. Present only for DROP ALL ROW ACCESS POLICIES queries.
"ddlOperationPerformed": "A String", # The DDL operation performed, possibly dependent on the pre-existence of the DDL target. Possible values (new values might be added in the future): "CREATE": The query created the DDL target. "SKIP": No-op. Example cases: the query is CREATE TABLE IF NOT EXISTS while the table already exists, or the query is DROP TABLE IF EXISTS while the table does not exist. "REPLACE": The query replaced the DDL target. Example case: the query is CREATE OR REPLACE TABLE, and the table already exists. "DROP": The query deleted the DDL target.
+ "ddlTargetDataset": { # [Output-only] The DDL target dataset. Present only for CREATE/ALTER/DROP SCHEMA queries.
+ "datasetId": "A String", # [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.
+ "projectId": "A String", # [Optional] The ID of the project containing this dataset.
+ },
"ddlTargetRoutine": { # The DDL target routine. Present only for CREATE/DROP FUNCTION/PROCEDURE queries.
"datasetId": "A String", # [Required] The ID of the dataset containing this routine.
"projectId": "A String", # [Required] The ID of the project containing this routine.
@@ -1127,7 +1153,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -1290,7 +1316,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -1387,7 +1413,7 @@
],
},
"createDisposition": "A String", # [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.
- "decimalTargetTypes": [ # [Trusted Tester] 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. For example: suppose decimal_target_type = ["NUMERIC", "BIGNUMERIC"]. Then 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). For duplicated types in this field, only one will be considered and the rest will be ignored. 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.
+ "decimalTargetTypes": [ # 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 ([Preview](/products/#product-launch-stages)), 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.
"A String",
],
"destinationEncryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys).
@@ -1407,14 +1433,19 @@
},
"encoding": "A String", # [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.
"fieldDelimiter": "A String", # [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 (',').
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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
+ "jsonExtension": "A String", # [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.
"maxBadRecords": 42, # [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.
"nullMarker": "A String", # [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.
+ "parquetOptions": { # [Optional] Options to configure parquet support.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"projectionFields": [ # 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.
"A String",
],
@@ -1446,7 +1477,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -1577,13 +1608,17 @@
"range": "A String", # [Optional] Range of a sheet to query from. Only used when non-empty. Typical format: sheet_name!top_left_cell_id:bottom_right_cell_id For example: sheet1!A1:B20
"skipLeadingRows": "A String", # [Optional] The number of rows at the top of a sheet that BigQuery will skip when reading the data. The default value is 0. This property is useful if you have header rows that should be skipped. When autodetect is on, behavior is the following: * skipLeadingRows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. * skipLeadingRows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. * skipLeadingRows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise row N is used to extract column names for the detected schema.
},
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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 Google Cloud Bigtable: This setting is ignored. Google Cloud Datastore backups: This setting is ignored. Avro: This setting is ignored.
"maxBadRecords": 42, # [Optional] The maximum number of bad records that BigQuery can ignore when reading data. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV, JSON, and Google Sheets. The default value is 0, which requires that all records are valid. This setting is ignored for Google Cloud Bigtable, Google Cloud Datastore backups and Avro formats.
+ "parquetOptions": { # Additional properties to set if sourceFormat is set to Parquet.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"schema": { # [Optional] The schema for the data. Schema is required for CSV and JSON formats. Schema is disallowed for Google Cloud Bigtable, Cloud Datastore backups, and Avro formats.
"fields": [ # Describes the fields in a table.
{
@@ -1603,7 +1638,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -1663,6 +1698,10 @@
"cacheHit": True or False, # [Output-only] Whether the query result was fetched from the query cache.
"ddlAffectedRowAccessPolicyCount": "A String", # [Output-only] [Preview] The number of row access policies affected by a DDL statement. Present only for DROP ALL ROW ACCESS POLICIES queries.
"ddlOperationPerformed": "A String", # The DDL operation performed, possibly dependent on the pre-existence of the DDL target. Possible values (new values might be added in the future): "CREATE": The query created the DDL target. "SKIP": No-op. Example cases: the query is CREATE TABLE IF NOT EXISTS while the table already exists, or the query is DROP TABLE IF EXISTS while the table does not exist. "REPLACE": The query replaced the DDL target. Example case: the query is CREATE OR REPLACE TABLE, and the table already exists. "DROP": The query deleted the DDL target.
+ "ddlTargetDataset": { # [Output-only] The DDL target dataset. Present only for CREATE/ALTER/DROP SCHEMA queries.
+ "datasetId": "A String", # [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.
+ "projectId": "A String", # [Optional] The ID of the project containing this dataset.
+ },
"ddlTargetRoutine": { # The DDL target routine. Present only for CREATE/DROP FUNCTION/PROCEDURE queries.
"datasetId": "A String", # [Required] The ID of the dataset containing this routine.
"projectId": "A String", # [Required] The ID of the project containing this routine.
@@ -1769,7 +1808,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -1938,7 +1977,7 @@
],
},
"createDisposition": "A String", # [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.
- "decimalTargetTypes": [ # [Trusted Tester] 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. For example: suppose decimal_target_type = ["NUMERIC", "BIGNUMERIC"]. Then 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). For duplicated types in this field, only one will be considered and the rest will be ignored. 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.
+ "decimalTargetTypes": [ # 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 ([Preview](/products/#product-launch-stages)), 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.
"A String",
],
"destinationEncryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys).
@@ -1958,14 +1997,19 @@
},
"encoding": "A String", # [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.
"fieldDelimiter": "A String", # [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 (',').
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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
+ "jsonExtension": "A String", # [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.
"maxBadRecords": 42, # [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.
"nullMarker": "A String", # [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.
+ "parquetOptions": { # [Optional] Options to configure parquet support.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"projectionFields": [ # 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.
"A String",
],
@@ -1997,7 +2041,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -2128,13 +2172,17 @@
"range": "A String", # [Optional] Range of a sheet to query from. Only used when non-empty. Typical format: sheet_name!top_left_cell_id:bottom_right_cell_id For example: sheet1!A1:B20
"skipLeadingRows": "A String", # [Optional] The number of rows at the top of a sheet that BigQuery will skip when reading the data. The default value is 0. This property is useful if you have header rows that should be skipped. When autodetect is on, behavior is the following: * skipLeadingRows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. * skipLeadingRows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. * skipLeadingRows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise row N is used to extract column names for the detected schema.
},
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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 Google Cloud Bigtable: This setting is ignored. Google Cloud Datastore backups: This setting is ignored. Avro: This setting is ignored.
"maxBadRecords": 42, # [Optional] The maximum number of bad records that BigQuery can ignore when reading data. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV, JSON, and Google Sheets. The default value is 0, which requires that all records are valid. This setting is ignored for Google Cloud Bigtable, Google Cloud Datastore backups and Avro formats.
+ "parquetOptions": { # Additional properties to set if sourceFormat is set to Parquet.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"schema": { # [Optional] The schema for the data. Schema is required for CSV and JSON formats. Schema is disallowed for Google Cloud Bigtable, Cloud Datastore backups, and Avro formats.
"fields": [ # Describes the fields in a table.
{
@@ -2154,7 +2202,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -2214,6 +2262,10 @@
"cacheHit": True or False, # [Output-only] Whether the query result was fetched from the query cache.
"ddlAffectedRowAccessPolicyCount": "A String", # [Output-only] [Preview] The number of row access policies affected by a DDL statement. Present only for DROP ALL ROW ACCESS POLICIES queries.
"ddlOperationPerformed": "A String", # The DDL operation performed, possibly dependent on the pre-existence of the DDL target. Possible values (new values might be added in the future): "CREATE": The query created the DDL target. "SKIP": No-op. Example cases: the query is CREATE TABLE IF NOT EXISTS while the table already exists, or the query is DROP TABLE IF EXISTS while the table does not exist. "REPLACE": The query replaced the DDL target. Example case: the query is CREATE OR REPLACE TABLE, and the table already exists. "DROP": The query deleted the DDL target.
+ "ddlTargetDataset": { # [Output-only] The DDL target dataset. Present only for CREATE/ALTER/DROP SCHEMA queries.
+ "datasetId": "A String", # [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.
+ "projectId": "A String", # [Optional] The ID of the project containing this dataset.
+ },
"ddlTargetRoutine": { # The DDL target routine. Present only for CREATE/DROP FUNCTION/PROCEDURE queries.
"datasetId": "A String", # [Required] The ID of the dataset containing this routine.
"projectId": "A String", # [Required] The ID of the project containing this routine.
@@ -2320,7 +2372,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -2512,7 +2564,7 @@
],
},
"createDisposition": "A String", # [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.
- "decimalTargetTypes": [ # [Trusted Tester] 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. For example: suppose decimal_target_type = ["NUMERIC", "BIGNUMERIC"]. Then 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). For duplicated types in this field, only one will be considered and the rest will be ignored. 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.
+ "decimalTargetTypes": [ # 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 ([Preview](/products/#product-launch-stages)), 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.
"A String",
],
"destinationEncryptionConfiguration": { # Custom encryption configuration (e.g., Cloud KMS keys).
@@ -2532,14 +2584,19 @@
},
"encoding": "A String", # [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.
"fieldDelimiter": "A String", # [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 (',').
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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
+ "jsonExtension": "A String", # [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.
"maxBadRecords": 42, # [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.
"nullMarker": "A String", # [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.
+ "parquetOptions": { # [Optional] Options to configure parquet support.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"projectionFields": [ # 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.
"A String",
],
@@ -2571,7 +2628,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -2702,13 +2759,17 @@
"range": "A String", # [Optional] Range of a sheet to query from. Only used when non-empty. Typical format: sheet_name!top_left_cell_id:bottom_right_cell_id For example: sheet1!A1:B20
"skipLeadingRows": "A String", # [Optional] The number of rows at the top of a sheet that BigQuery will skip when reading the data. The default value is 0. This property is useful if you have header rows that should be skipped. When autodetect is on, behavior is the following: * skipLeadingRows unspecified - Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. * skipLeadingRows is 0 - Instructs autodetect that there are no headers and data should be read starting from the first row. * skipLeadingRows = N > 0 - Autodetect skips N-1 rows and tries to detect headers in row N. If headers are not detected, row N is just skipped. Otherwise row N is used to extract column names for the detected schema.
},
- "hivePartitioningOptions": { # [Optional, Trusted Tester] Options to configure hive partitioning support.
+ "hivePartitioningOptions": { # [Optional] Options to configure hive partitioning support.
"mode": "A String", # [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.
"requirePartitionFilter": True or False, # [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.
"sourceUriPrefix": "A String", # [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).
},
"ignoreUnknownValues": True or False, # [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 Google Cloud Bigtable: This setting is ignored. Google Cloud Datastore backups: This setting is ignored. Avro: This setting is ignored.
"maxBadRecords": 42, # [Optional] The maximum number of bad records that BigQuery can ignore when reading data. If the number of bad records exceeds this value, an invalid error is returned in the job result. This is only valid for CSV, JSON, and Google Sheets. The default value is 0, which requires that all records are valid. This setting is ignored for Google Cloud Bigtable, Google Cloud Datastore backups and Avro formats.
+ "parquetOptions": { # Additional properties to set if sourceFormat is set to Parquet.
+ "enableListInference": True or False, # [Optional] Indicates whether to use schema inference specifically for Parquet LIST logical type.
+ "enumAsString": True or False, # [Optional] Indicates whether to infer Parquet ENUM logical type as STRING instead of BYTES by default.
+ },
"schema": { # [Optional] The schema for the data. Schema is required for CSV and JSON formats. Schema is disallowed for Google Cloud Bigtable, Cloud Datastore backups, and Avro formats.
"fields": [ # Describes the fields in a table.
{
@@ -2728,7 +2789,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -2793,6 +2854,10 @@
"cacheHit": True or False, # [Output-only] Whether the query result was fetched from the query cache.
"ddlAffectedRowAccessPolicyCount": "A String", # [Output-only] [Preview] The number of row access policies affected by a DDL statement. Present only for DROP ALL ROW ACCESS POLICIES queries.
"ddlOperationPerformed": "A String", # The DDL operation performed, possibly dependent on the pre-existence of the DDL target. Possible values (new values might be added in the future): "CREATE": The query created the DDL target. "SKIP": No-op. Example cases: the query is CREATE TABLE IF NOT EXISTS while the table already exists, or the query is DROP TABLE IF EXISTS while the table does not exist. "REPLACE": The query replaced the DDL target. Example case: the query is CREATE OR REPLACE TABLE, and the table already exists. "DROP": The query deleted the DDL target.
+ "ddlTargetDataset": { # [Output-only] The DDL target dataset. Present only for CREATE/ALTER/DROP SCHEMA queries.
+ "datasetId": "A String", # [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.
+ "projectId": "A String", # [Optional] The ID of the project containing this dataset.
+ },
"ddlTargetRoutine": { # The DDL target routine. Present only for CREATE/DROP FUNCTION/PROCEDURE queries.
"datasetId": "A String", # [Required] The ID of the dataset containing this routine.
"projectId": "A String", # [Required] The ID of the project containing this routine.
@@ -2899,7 +2964,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},
@@ -3129,7 +3194,7 @@
"A String",
],
},
- "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
+ "type": "A String", # [Required] The field data type. Possible values include STRING, BYTES, INTEGER, INT64 (same as INTEGER), FLOAT, FLOAT64 (same as FLOAT), NUMERIC, BIGNUMERIC, BOOLEAN, BOOL (same as BOOLEAN), TIMESTAMP, DATE, TIME, DATETIME, RECORD (where RECORD indicates that the field contains a nested schema) or STRUCT (same as RECORD).
},
],
},