close()

Close httplib2 connections.

create(parent, body=None, dataScanId=None, validateOnly=None, x__xgafv=None)

Creates a DataScan resource.

Args:
parent: string, Required. The resource name of the parent location: projects/{project}/locations/{location_id} where project refers to a project_id or project_number and location_id refers to a GCP region. (required)
body: object, The request body.
The object takes the form of:

{ # Represents a user-visible job which provides the insights for the related data source.For example: Data Quality: generates queries based on the rules and runs against the data to get data quality check results. Data Profile: analyzes the data in table(s) and generates insights about the structure, content and relationships (such as null percent, cardinality, min/max/mean, etc).
"createTime": "A String", # Output only. The time when the scan was created.
"data": { # The data source for DataScan. # Required. The data source for DataScan.
"entity": "A String", # Immutable. The Dataplex entity that represents the data source (e.g. BigQuery table) for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
"resource": "A String", # Immutable. The service-qualified full resource name of the cloud resource for a DataScan job to scan against. The field could be: BigQuery table of type "TABLE" for DataProfileScan/DataQualityScan Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"dataDiscoveryResult": { # The output of a data discovery scan. # Output only. The result of a data discovery scan.
"bigqueryPublishing": { # Describes BigQuery publishing configurations. # Output only. Configuration for metadata publishing.
"dataset": "A String", # Output only. The BigQuery dataset to publish to. It takes the form projects/{project_id}/datasets/{dataset_id}. If not set, the service creates a default publishing dataset.
},
},
"dataDiscoverySpec": { # Spec for a data discovery scan. # Settings for a data discovery scan.
"bigqueryPublishingConfig": { # Describes BigQuery publishing configurations. # Optional. Configuration for metadata publishing.
"connection": "A String", # Optional. The BigQuery connection used to create BigLake tables. Must be in the form projects/{project_id}/locations/{location_id}/connections/{connection_id}
"tableType": "A String", # Optional. Determines whether to publish discovered tables as BigLake external tables or non-BigLake external tables.
},
"storageConfig": { # Configurations related to Cloud Storage as the data source. # Cloud Storage related configurations.
"csvOptions": { # Describes CSV and similar semi-structured data formats. # Optional. Configuration for CSV data.
"delimiter": "A String", # Optional. The delimiter that is used to separate values. The default is , (comma).
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"headerRows": 42, # Optional. The number of rows to interpret as header rows that should be skipped when reading data rows.
"quote": "A String", # Optional. The character used to quote column values. Accepts " (double quotation mark) or ' (single quotation mark). If unspecified, defaults to " (double quotation mark).
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for CSV data. If true, all columns are registered as strings.
},
"excludePatterns": [ # Optional. Defines the data to exclude during discovery. Provide a list of patterns that identify the data to exclude. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"includePatterns": [ # Optional. Defines the data to include during discovery when only a subset of the data should be considered. Provide a list of patterns that identify the data to include. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"jsonOptions": { # Describes JSON data format. # Optional. Configuration for JSON data.
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for JSON data. If true, all columns are registered as their primitive types (strings, number, or boolean).
},
},
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of a data profile scan.
"postScanActionsResult": { # The result of post scan actions of DataProfileScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"profile": { # Contains name, type, mode and field type specific profile information. # The profile information per field.
"fields": [ # List of fields with structural and profile information for each field.
{ # A field within a table.
"mode": "A String", # The mode of the field. Possible values include: REQUIRED, if it is a required field. NULLABLE, if it is an optional field. REPEATED, if it is a repeated field.
"name": "A String", # The name of the field.
"profile": { # The profile information for each field type. # Profile information for the corresponding field.
"distinctRatio": 3.14, # Ratio of rows with distinct values against total scanned rows. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
"doubleProfile": { # The profile information for a double type field. # Double type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": 3.14, # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": 3.14, # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"integerProfile": { # The profile information for an integer type field. # Integer type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": "A String", # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": "A String", # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of approximate quartile values for the scanned data, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"nullRatio": 3.14, # Ratio of rows with null value against total scanned rows.
"stringProfile": { # The profile information for a string type field. # String type field information.
"averageLength": 3.14, # Average length of non-null values in the scanned data.
"maxLength": "A String", # Maximum length of non-null values in the scanned data.
"minLength": "A String", # Minimum length of non-null values in the scanned data.
},
"topNValues": [ # The list of top N non-null values, frequency and ratio with which they occur in the scanned data. N is 10 or equal to the number of distinct values in the field, whichever is smaller. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
{ # Top N non-null values in the scanned data.
"count": "A String", # Count of the corresponding value in the scanned data.
"ratio": 3.14, # Ratio of the corresponding value in the field against the total number of rows in the scanned data.
"value": "A String", # String value of a top N non-null value.
},
],
},
"type": "A String", # The data type retrieved from the schema of the data source. For instance, for a BigQuery native table, it is the BigQuery Table Schema (https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#tablefieldschema). For a Dataplex Entity, it is the Entity Schema (https://cloud.google.com/dataplex/docs/reference/rpc/google.cloud.dataplex.v1#type_3).
},
],
},
"rowCount": "A String", # The count of rows scanned.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # Settings for a data profile scan.
"excludeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to exclude from data profile.If specified, the fields will be excluded from data profile, regardless of include_fields value.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"includeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to include in data profile.If not specified, all fields at the time of profile scan job execution are included, except for ones listed in exclude_fields.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"postScanActions": { # The configuration of post scan actions of DataProfileScan job. # Optional. Actions to take upon job completion..
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataProfileScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of a data quality scan.
"columns": [ # Output only. A list of results at the column level.A column will have a corresponding DataQualityColumnResult if and only if there is at least one rule with the 'column' field set to it.
{ # DataQualityColumnResult provides a more detailed, per-column view of the results.
"column": "A String", # Output only. The column specified in the DataQualityRule.
"score": 3.14, # Output only. The column-level data quality score for this data scan job if and only if the 'column' field is set.The score ranges between between 0, 100 (up to two decimal points).
},
],
"dimensions": [ # A list of results at the dimension level.A dimension will have a corresponding DataQualityDimensionResult if and only if there is at least one rule with the 'dimension' field set to it.
{ # DataQualityDimensionResult provides a more detailed, per-dimension view of the results.
"dimension": { # A dimension captures data quality intent about a defined subset of the rules specified. # Output only. The dimension config specified in the DataQualitySpec, as is.
"name": "A String", # The dimension name a rule belongs to. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
},
"passed": True or False, # Whether the dimension passed or failed.
"score": 3.14, # Output only. The dimension-level data quality score for this data scan job if and only if the 'dimension' field is set.The score ranges between 0, 100 (up to two decimal points).
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"postScanActionsResult": { # The result of post scan actions of DataQualityScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"rowCount": "A String", # The count of rows processed.
"rules": [ # A list of all the rules in a job, and their results.
{ # DataQualityRuleResult provides a more detailed, per-rule view of the results.
"assertionRowCount": "A String", # Output only. The number of rows returned by the SQL statement in a SQL assertion rule.This field is only valid for SQL assertion rules.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against.This field is only valid for row-level type rules.Evaluated count can be configured to either include all rows (default) - with null rows automatically failing rule evaluation, or exclude null rows from the evaluated_count, by setting ignore_nulls = true.
"failingRowsQuery": "A String", # The query to find rows that did not pass this rule.This field is only valid for row-level type rules.
"nullCount": "A String", # The number of rows with null values in the specified column.
"passRatio": 3.14, # The ratio of passed_count / evaluated_count.This field is only valid for row-level type rules.
"passed": True or False, # Whether the rule passed or failed.
"passedCount": "A String", # The number of rows which passed a rule evaluation.This field is only valid for row-level type rules.
"rule": { # A rule captures data quality intent about a data source. # The rule specified in the DataQualitySpec, as is.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
},
],
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
"score": 3.14, # Output only. The overall data quality score.The score ranges between 0, 100 (up to two decimal points).
},
"dataQualitySpec": { # DataQualityScan related setting. # Settings for a data quality scan.
"postScanActions": { # The configuration of post scan actions of DataQualityScan. # Optional. Actions to take upon job completion.
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataQualityScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"notificationReport": { # The configuration of notification report post scan action. # Optional. If set, results will be sent to the provided notification receipts upon triggers.
"jobEndTrigger": { # This trigger is triggered whenever a scan job run ends, regardless of the result. # Optional. If set, report will be sent when a scan job ends.
},
"jobFailureTrigger": { # This trigger is triggered when the scan job itself fails, regardless of the result. # Optional. If set, report will be sent when a scan job fails.
},
"recipients": { # The individuals or groups who are designated to receive notifications upon triggers. # Required. The recipients who will receive the notification report.
"emails": [ # Optional. The email recipients who will receive the DataQualityScan results report.
"A String",
],
},
"scoreThresholdTrigger": { # This trigger is triggered when the DQ score in the job result is less than a specified input score. # Optional. If set, report will be sent when score threshold is met.
"scoreThreshold": 3.14, # Optional. The score range is in 0,100.
},
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"rules": [ # Required. The list of rules to evaluate against a data source. At least one rule is required.
{ # A rule captures data quality intent about a data source.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
],
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"description": "A String", # Optional. Description of the scan. Must be between 1-1024 characters.
"displayName": "A String", # Optional. User friendly display name. Must be between 1-256 characters.
"executionSpec": { # DataScan execution settings. # Optional. DataScan execution settings.If not specified, the fields in it will use their default values.
"field": "A String", # Immutable. The unnested field (of type Date or Timestamp) that contains values which monotonically increase over time.If not specified, a data scan will run for all data in the table.
"trigger": { # DataScan scheduling and trigger settings. # Optional. Spec related to how often and when a scan should be triggered.If not specified, the default is OnDemand, which means the scan will not run until the user calls RunDataScan API.
"onDemand": { # The scan runs once via RunDataScan API. # The scan runs once via RunDataScan API.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron (https://en.wikipedia.org/wiki/Cron) schedule for running scans periodically.To explicitly set a timezone in the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database (wikipedia (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List)). For example, CRON_TZ=America/New_York 1 * * * *, or TZ=America/New_York 1 * * * *.This field is required for Schedule scans.
},
},
},
"executionStatus": { # Status of the data scan execution. # Output only. Status of the data scan execution.
"latestJobCreateTime": "A String", # Optional. The time when the DataScanJob execution was created.
"latestJobEndTime": "A String", # Optional. The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # Optional. The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. Identifier. The relative resource name of the scan, of the form: projects/{project}/locations/{location_id}/dataScans/{datascan_id}, where project refers to a project_id or project_number and location_id refers to a GCP region.
"state": "A String", # Output only. Current state of the DataScan.
"type": "A String", # Output only. The type of DataScan.
"uid": "A String", # Output only. System generated globally unique ID for the scan. This ID will be different if the scan is deleted and re-created with the same name.
"updateTime": "A String", # Output only. The time when the scan was last updated.
}

dataScanId: string, Required. DataScan identifier. Must contain only lowercase letters, numbers and hyphens. Must start with a letter. Must end with a number or a letter. Must be between 1-63 characters. Must be unique within the customer project / location.
validateOnly: boolean, Optional. Only validate the request, but do not perform mutations. The default is false.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

delete(name, force=None, x__xgafv=None)

Deletes a DataScan resource.

Args:
name: string, Required. The resource name of the dataScan: projects/{project}/locations/{location_id}/dataScans/{data_scan_id} where project refers to a project_id or project_number and location_id refers to a GCP region. (required)
force: boolean, Optional. If set to true, any child resources of this data scan will also be deleted. (Otherwise, the request will only work if the data scan has no child resources.)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

generateDataQualityRules(name, body=None, x__xgafv=None)

Generates recommended data quality rules based on the results of a data profiling scan.Use the recommendations to build rules for a data quality scan.

Args:
name: string, Required. The name must be one of the following: The name of a data scan with at least one successful, completed data profiling job The name of a successful, completed data profiling job (a data scan job where the job type is data profiling) (required)
body: object, The request body.
The object takes the form of:

{ # Request details for generating data quality rule recommendations.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Response details for data quality rule recommendations.
"rule": [ # The data quality rules that Dataplex generates based on the results of a data profiling scan.
{ # A rule captures data quality intent about a data source.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
],
}

get(name, view=None, x__xgafv=None)

Gets a DataScan resource.

Args:
name: string, Required. The resource name of the dataScan: projects/{project}/locations/{location_id}/dataScans/{data_scan_id} where project refers to a project_id or project_number and location_id refers to a GCP region. (required)
view: string, Optional. Select the DataScan view to return. Defaults to BASIC.
Allowed values
DATA_SCAN_VIEW_UNSPECIFIED - The API will default to the BASIC view.
BASIC - Basic view that does not include spec and result.
FULL - Include everything.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Represents a user-visible job which provides the insights for the related data source.For example: Data Quality: generates queries based on the rules and runs against the data to get data quality check results. Data Profile: analyzes the data in table(s) and generates insights about the structure, content and relationships (such as null percent, cardinality, min/max/mean, etc).
"createTime": "A String", # Output only. The time when the scan was created.
"data": { # The data source for DataScan. # Required. The data source for DataScan.
"entity": "A String", # Immutable. The Dataplex entity that represents the data source (e.g. BigQuery table) for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
"resource": "A String", # Immutable. The service-qualified full resource name of the cloud resource for a DataScan job to scan against. The field could be: BigQuery table of type "TABLE" for DataProfileScan/DataQualityScan Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"dataDiscoveryResult": { # The output of a data discovery scan. # Output only. The result of a data discovery scan.
"bigqueryPublishing": { # Describes BigQuery publishing configurations. # Output only. Configuration for metadata publishing.
"dataset": "A String", # Output only. The BigQuery dataset to publish to. It takes the form projects/{project_id}/datasets/{dataset_id}. If not set, the service creates a default publishing dataset.
},
},
"dataDiscoverySpec": { # Spec for a data discovery scan. # Settings for a data discovery scan.
"bigqueryPublishingConfig": { # Describes BigQuery publishing configurations. # Optional. Configuration for metadata publishing.
"connection": "A String", # Optional. The BigQuery connection used to create BigLake tables. Must be in the form projects/{project_id}/locations/{location_id}/connections/{connection_id}
"tableType": "A String", # Optional. Determines whether to publish discovered tables as BigLake external tables or non-BigLake external tables.
},
"storageConfig": { # Configurations related to Cloud Storage as the data source. # Cloud Storage related configurations.
"csvOptions": { # Describes CSV and similar semi-structured data formats. # Optional. Configuration for CSV data.
"delimiter": "A String", # Optional. The delimiter that is used to separate values. The default is , (comma).
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"headerRows": 42, # Optional. The number of rows to interpret as header rows that should be skipped when reading data rows.
"quote": "A String", # Optional. The character used to quote column values. Accepts " (double quotation mark) or ' (single quotation mark). If unspecified, defaults to " (double quotation mark).
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for CSV data. If true, all columns are registered as strings.
},
"excludePatterns": [ # Optional. Defines the data to exclude during discovery. Provide a list of patterns that identify the data to exclude. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"includePatterns": [ # Optional. Defines the data to include during discovery when only a subset of the data should be considered. Provide a list of patterns that identify the data to include. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"jsonOptions": { # Describes JSON data format. # Optional. Configuration for JSON data.
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for JSON data. If true, all columns are registered as their primitive types (strings, number, or boolean).
},
},
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of a data profile scan.
"postScanActionsResult": { # The result of post scan actions of DataProfileScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"profile": { # Contains name, type, mode and field type specific profile information. # The profile information per field.
"fields": [ # List of fields with structural and profile information for each field.
{ # A field within a table.
"mode": "A String", # The mode of the field. Possible values include: REQUIRED, if it is a required field. NULLABLE, if it is an optional field. REPEATED, if it is a repeated field.
"name": "A String", # The name of the field.
"profile": { # The profile information for each field type. # Profile information for the corresponding field.
"distinctRatio": 3.14, # Ratio of rows with distinct values against total scanned rows. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
"doubleProfile": { # The profile information for a double type field. # Double type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": 3.14, # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": 3.14, # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"integerProfile": { # The profile information for an integer type field. # Integer type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": "A String", # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": "A String", # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of approximate quartile values for the scanned data, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"nullRatio": 3.14, # Ratio of rows with null value against total scanned rows.
"stringProfile": { # The profile information for a string type field. # String type field information.
"averageLength": 3.14, # Average length of non-null values in the scanned data.
"maxLength": "A String", # Maximum length of non-null values in the scanned data.
"minLength": "A String", # Minimum length of non-null values in the scanned data.
},
"topNValues": [ # The list of top N non-null values, frequency and ratio with which they occur in the scanned data. N is 10 or equal to the number of distinct values in the field, whichever is smaller. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
{ # Top N non-null values in the scanned data.
"count": "A String", # Count of the corresponding value in the scanned data.
"ratio": 3.14, # Ratio of the corresponding value in the field against the total number of rows in the scanned data.
"value": "A String", # String value of a top N non-null value.
},
],
},
"type": "A String", # The data type retrieved from the schema of the data source. For instance, for a BigQuery native table, it is the BigQuery Table Schema (https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#tablefieldschema). For a Dataplex Entity, it is the Entity Schema (https://cloud.google.com/dataplex/docs/reference/rpc/google.cloud.dataplex.v1#type_3).
},
],
},
"rowCount": "A String", # The count of rows scanned.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # Settings for a data profile scan.
"excludeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to exclude from data profile.If specified, the fields will be excluded from data profile, regardless of include_fields value.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"includeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to include in data profile.If not specified, all fields at the time of profile scan job execution are included, except for ones listed in exclude_fields.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"postScanActions": { # The configuration of post scan actions of DataProfileScan job. # Optional. Actions to take upon job completion..
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataProfileScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of a data quality scan.
"columns": [ # Output only. A list of results at the column level.A column will have a corresponding DataQualityColumnResult if and only if there is at least one rule with the 'column' field set to it.
{ # DataQualityColumnResult provides a more detailed, per-column view of the results.
"column": "A String", # Output only. The column specified in the DataQualityRule.
"score": 3.14, # Output only. The column-level data quality score for this data scan job if and only if the 'column' field is set.The score ranges between between 0, 100 (up to two decimal points).
},
],
"dimensions": [ # A list of results at the dimension level.A dimension will have a corresponding DataQualityDimensionResult if and only if there is at least one rule with the 'dimension' field set to it.
{ # DataQualityDimensionResult provides a more detailed, per-dimension view of the results.
"dimension": { # A dimension captures data quality intent about a defined subset of the rules specified. # Output only. The dimension config specified in the DataQualitySpec, as is.
"name": "A String", # The dimension name a rule belongs to. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
},
"passed": True or False, # Whether the dimension passed or failed.
"score": 3.14, # Output only. The dimension-level data quality score for this data scan job if and only if the 'dimension' field is set.The score ranges between 0, 100 (up to two decimal points).
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"postScanActionsResult": { # The result of post scan actions of DataQualityScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"rowCount": "A String", # The count of rows processed.
"rules": [ # A list of all the rules in a job, and their results.
{ # DataQualityRuleResult provides a more detailed, per-rule view of the results.
"assertionRowCount": "A String", # Output only. The number of rows returned by the SQL statement in a SQL assertion rule.This field is only valid for SQL assertion rules.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against.This field is only valid for row-level type rules.Evaluated count can be configured to either include all rows (default) - with null rows automatically failing rule evaluation, or exclude null rows from the evaluated_count, by setting ignore_nulls = true.
"failingRowsQuery": "A String", # The query to find rows that did not pass this rule.This field is only valid for row-level type rules.
"nullCount": "A String", # The number of rows with null values in the specified column.
"passRatio": 3.14, # The ratio of passed_count / evaluated_count.This field is only valid for row-level type rules.
"passed": True or False, # Whether the rule passed or failed.
"passedCount": "A String", # The number of rows which passed a rule evaluation.This field is only valid for row-level type rules.
"rule": { # A rule captures data quality intent about a data source. # The rule specified in the DataQualitySpec, as is.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
},
],
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
"score": 3.14, # Output only. The overall data quality score.The score ranges between 0, 100 (up to two decimal points).
},
"dataQualitySpec": { # DataQualityScan related setting. # Settings for a data quality scan.
"postScanActions": { # The configuration of post scan actions of DataQualityScan. # Optional. Actions to take upon job completion.
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataQualityScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"notificationReport": { # The configuration of notification report post scan action. # Optional. If set, results will be sent to the provided notification receipts upon triggers.
"jobEndTrigger": { # This trigger is triggered whenever a scan job run ends, regardless of the result. # Optional. If set, report will be sent when a scan job ends.
},
"jobFailureTrigger": { # This trigger is triggered when the scan job itself fails, regardless of the result. # Optional. If set, report will be sent when a scan job fails.
},
"recipients": { # The individuals or groups who are designated to receive notifications upon triggers. # Required. The recipients who will receive the notification report.
"emails": [ # Optional. The email recipients who will receive the DataQualityScan results report.
"A String",
],
},
"scoreThresholdTrigger": { # This trigger is triggered when the DQ score in the job result is less than a specified input score. # Optional. If set, report will be sent when score threshold is met.
"scoreThreshold": 3.14, # Optional. The score range is in 0,100.
},
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"rules": [ # Required. The list of rules to evaluate against a data source. At least one rule is required.
{ # A rule captures data quality intent about a data source.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
],
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"description": "A String", # Optional. Description of the scan. Must be between 1-1024 characters.
"displayName": "A String", # Optional. User friendly display name. Must be between 1-256 characters.
"executionSpec": { # DataScan execution settings. # Optional. DataScan execution settings.If not specified, the fields in it will use their default values.
"field": "A String", # Immutable. The unnested field (of type Date or Timestamp) that contains values which monotonically increase over time.If not specified, a data scan will run for all data in the table.
"trigger": { # DataScan scheduling and trigger settings. # Optional. Spec related to how often and when a scan should be triggered.If not specified, the default is OnDemand, which means the scan will not run until the user calls RunDataScan API.
"onDemand": { # The scan runs once via RunDataScan API. # The scan runs once via RunDataScan API.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron (https://en.wikipedia.org/wiki/Cron) schedule for running scans periodically.To explicitly set a timezone in the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database (wikipedia (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List)). For example, CRON_TZ=America/New_York 1 * * * *, or TZ=America/New_York 1 * * * *.This field is required for Schedule scans.
},
},
},
"executionStatus": { # Status of the data scan execution. # Output only. Status of the data scan execution.
"latestJobCreateTime": "A String", # Optional. The time when the DataScanJob execution was created.
"latestJobEndTime": "A String", # Optional. The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # Optional. The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. Identifier. The relative resource name of the scan, of the form: projects/{project}/locations/{location_id}/dataScans/{datascan_id}, where project refers to a project_id or project_number and location_id refers to a GCP region.
"state": "A String", # Output only. Current state of the DataScan.
"type": "A String", # Output only. The type of DataScan.
"uid": "A String", # Output only. System generated globally unique ID for the scan. This ID will be different if the scan is deleted and re-created with the same name.
"updateTime": "A String", # Output only. The time when the scan was last updated.
}

getIamPolicy(resource, options_requestedPolicyVersion=None, x__xgafv=None)

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

Args:
  resource: string, REQUIRED: The resource for which the policy is being requested. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  options_requestedPolicyVersion: integer, Optional. The maximum policy version that will be used to format the policy.Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected.Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset.The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/).
  "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
    { # Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs.If there are AuditConfigs for both allServices and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted.Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts jose@example.com from DATA_READ logging, and aliya@example.com from DATA_WRITE logging.
      "auditLogConfigs": [ # The configuration for logging of each type of permission.
        { # Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
          "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
            "A String",
          ],
          "logType": "A String", # The log type that this config enables.
        },
      ],
      "service": "A String", # Specifies a service that will be enabled for audit logging. For example, storage.googleapis.com, cloudsql.googleapis.com. allServices is a special value that covers all services.
    },
  ],
  "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
    { # Associates members, or principals, with a role.
      "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
        "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
        "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
        "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
        "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
      },
      "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
        "A String",
      ],
      "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
    },
  ],
  "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
  "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
}

list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)

Lists DataScans.

Args:
parent: string, Required. The resource name of the parent location: projects/{project}/locations/{location_id} where project refers to a project_id or project_number and location_id refers to a GCP region. (required)
filter: string, Optional. Filter request.
orderBy: string, Optional. Order by fields (name or create_time) for the result. If not specified, the ordering is undefined.
pageSize: integer, Optional. Maximum number of dataScans to return. The service may return fewer than this value. If unspecified, at most 500 scans will be returned. The maximum value is 1000; values above 1000 will be coerced to 1000.
pageToken: string, Optional. Page token received from a previous ListDataScans call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to ListDataScans must match the call that provided the page token.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # List dataScans response.
"dataScans": [ # DataScans (BASIC view only) under the given parent location.
{ # Represents a user-visible job which provides the insights for the related data source.For example: Data Quality: generates queries based on the rules and runs against the data to get data quality check results. Data Profile: analyzes the data in table(s) and generates insights about the structure, content and relationships (such as null percent, cardinality, min/max/mean, etc).
"createTime": "A String", # Output only. The time when the scan was created.
"data": { # The data source for DataScan. # Required. The data source for DataScan.
"entity": "A String", # Immutable. The Dataplex entity that represents the data source (e.g. BigQuery table) for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
"resource": "A String", # Immutable. The service-qualified full resource name of the cloud resource for a DataScan job to scan against. The field could be: BigQuery table of type "TABLE" for DataProfileScan/DataQualityScan Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"dataDiscoveryResult": { # The output of a data discovery scan. # Output only. The result of a data discovery scan.
"bigqueryPublishing": { # Describes BigQuery publishing configurations. # Output only. Configuration for metadata publishing.
"dataset": "A String", # Output only. The BigQuery dataset to publish to. It takes the form projects/{project_id}/datasets/{dataset_id}. If not set, the service creates a default publishing dataset.
},
},
"dataDiscoverySpec": { # Spec for a data discovery scan. # Settings for a data discovery scan.
"bigqueryPublishingConfig": { # Describes BigQuery publishing configurations. # Optional. Configuration for metadata publishing.
"connection": "A String", # Optional. The BigQuery connection used to create BigLake tables. Must be in the form projects/{project_id}/locations/{location_id}/connections/{connection_id}
"tableType": "A String", # Optional. Determines whether to publish discovered tables as BigLake external tables or non-BigLake external tables.
},
"storageConfig": { # Configurations related to Cloud Storage as the data source. # Cloud Storage related configurations.
"csvOptions": { # Describes CSV and similar semi-structured data formats. # Optional. Configuration for CSV data.
"delimiter": "A String", # Optional. The delimiter that is used to separate values. The default is , (comma).
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"headerRows": 42, # Optional. The number of rows to interpret as header rows that should be skipped when reading data rows.
"quote": "A String", # Optional. The character used to quote column values. Accepts " (double quotation mark) or ' (single quotation mark). If unspecified, defaults to " (double quotation mark).
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for CSV data. If true, all columns are registered as strings.
},
"excludePatterns": [ # Optional. Defines the data to exclude during discovery. Provide a list of patterns that identify the data to exclude. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"includePatterns": [ # Optional. Defines the data to include during discovery when only a subset of the data should be considered. Provide a list of patterns that identify the data to include. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"jsonOptions": { # Describes JSON data format. # Optional. Configuration for JSON data.
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for JSON data. If true, all columns are registered as their primitive types (strings, number, or boolean).
},
},
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of a data profile scan.
"postScanActionsResult": { # The result of post scan actions of DataProfileScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"profile": { # Contains name, type, mode and field type specific profile information. # The profile information per field.
"fields": [ # List of fields with structural and profile information for each field.
{ # A field within a table.
"mode": "A String", # The mode of the field. Possible values include: REQUIRED, if it is a required field. NULLABLE, if it is an optional field. REPEATED, if it is a repeated field.
"name": "A String", # The name of the field.
"profile": { # The profile information for each field type. # Profile information for the corresponding field.
"distinctRatio": 3.14, # Ratio of rows with distinct values against total scanned rows. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
"doubleProfile": { # The profile information for a double type field. # Double type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": 3.14, # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": 3.14, # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"integerProfile": { # The profile information for an integer type field. # Integer type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": "A String", # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": "A String", # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of approximate quartile values for the scanned data, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"nullRatio": 3.14, # Ratio of rows with null value against total scanned rows.
"stringProfile": { # The profile information for a string type field. # String type field information.
"averageLength": 3.14, # Average length of non-null values in the scanned data.
"maxLength": "A String", # Maximum length of non-null values in the scanned data.
"minLength": "A String", # Minimum length of non-null values in the scanned data.
},
"topNValues": [ # The list of top N non-null values, frequency and ratio with which they occur in the scanned data. N is 10 or equal to the number of distinct values in the field, whichever is smaller. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
{ # Top N non-null values in the scanned data.
"count": "A String", # Count of the corresponding value in the scanned data.
"ratio": 3.14, # Ratio of the corresponding value in the field against the total number of rows in the scanned data.
"value": "A String", # String value of a top N non-null value.
},
],
},
"type": "A String", # The data type retrieved from the schema of the data source. For instance, for a BigQuery native table, it is the BigQuery Table Schema (https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#tablefieldschema). For a Dataplex Entity, it is the Entity Schema (https://cloud.google.com/dataplex/docs/reference/rpc/google.cloud.dataplex.v1#type_3).
},
],
},
"rowCount": "A String", # The count of rows scanned.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # Settings for a data profile scan.
"excludeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to exclude from data profile.If specified, the fields will be excluded from data profile, regardless of include_fields value.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"includeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to include in data profile.If not specified, all fields at the time of profile scan job execution are included, except for ones listed in exclude_fields.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"postScanActions": { # The configuration of post scan actions of DataProfileScan job. # Optional. Actions to take upon job completion..
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataProfileScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of a data quality scan.
"columns": [ # Output only. A list of results at the column level.A column will have a corresponding DataQualityColumnResult if and only if there is at least one rule with the 'column' field set to it.
{ # DataQualityColumnResult provides a more detailed, per-column view of the results.
"column": "A String", # Output only. The column specified in the DataQualityRule.
"score": 3.14, # Output only. The column-level data quality score for this data scan job if and only if the 'column' field is set.The score ranges between between 0, 100 (up to two decimal points).
},
],
"dimensions": [ # A list of results at the dimension level.A dimension will have a corresponding DataQualityDimensionResult if and only if there is at least one rule with the 'dimension' field set to it.
{ # DataQualityDimensionResult provides a more detailed, per-dimension view of the results.
"dimension": { # A dimension captures data quality intent about a defined subset of the rules specified. # Output only. The dimension config specified in the DataQualitySpec, as is.
"name": "A String", # The dimension name a rule belongs to. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
},
"passed": True or False, # Whether the dimension passed or failed.
"score": 3.14, # Output only. The dimension-level data quality score for this data scan job if and only if the 'dimension' field is set.The score ranges between 0, 100 (up to two decimal points).
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"postScanActionsResult": { # The result of post scan actions of DataQualityScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"rowCount": "A String", # The count of rows processed.
"rules": [ # A list of all the rules in a job, and their results.
{ # DataQualityRuleResult provides a more detailed, per-rule view of the results.
"assertionRowCount": "A String", # Output only. The number of rows returned by the SQL statement in a SQL assertion rule.This field is only valid for SQL assertion rules.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against.This field is only valid for row-level type rules.Evaluated count can be configured to either include all rows (default) - with null rows automatically failing rule evaluation, or exclude null rows from the evaluated_count, by setting ignore_nulls = true.
"failingRowsQuery": "A String", # The query to find rows that did not pass this rule.This field is only valid for row-level type rules.
"nullCount": "A String", # The number of rows with null values in the specified column.
"passRatio": 3.14, # The ratio of passed_count / evaluated_count.This field is only valid for row-level type rules.
"passed": True or False, # Whether the rule passed or failed.
"passedCount": "A String", # The number of rows which passed a rule evaluation.This field is only valid for row-level type rules.
"rule": { # A rule captures data quality intent about a data source. # The rule specified in the DataQualitySpec, as is.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
},
],
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
"score": 3.14, # Output only. The overall data quality score.The score ranges between 0, 100 (up to two decimal points).
},
"dataQualitySpec": { # DataQualityScan related setting. # Settings for a data quality scan.
"postScanActions": { # The configuration of post scan actions of DataQualityScan. # Optional. Actions to take upon job completion.
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataQualityScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"notificationReport": { # The configuration of notification report post scan action. # Optional. If set, results will be sent to the provided notification receipts upon triggers.
"jobEndTrigger": { # This trigger is triggered whenever a scan job run ends, regardless of the result. # Optional. If set, report will be sent when a scan job ends.
},
"jobFailureTrigger": { # This trigger is triggered when the scan job itself fails, regardless of the result. # Optional. If set, report will be sent when a scan job fails.
},
"recipients": { # The individuals or groups who are designated to receive notifications upon triggers. # Required. The recipients who will receive the notification report.
"emails": [ # Optional. The email recipients who will receive the DataQualityScan results report.
"A String",
],
},
"scoreThresholdTrigger": { # This trigger is triggered when the DQ score in the job result is less than a specified input score. # Optional. If set, report will be sent when score threshold is met.
"scoreThreshold": 3.14, # Optional. The score range is in 0,100.
},
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"rules": [ # Required. The list of rules to evaluate against a data source. At least one rule is required.
{ # A rule captures data quality intent about a data source.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
],
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"description": "A String", # Optional. Description of the scan. Must be between 1-1024 characters.
"displayName": "A String", # Optional. User friendly display name. Must be between 1-256 characters.
"executionSpec": { # DataScan execution settings. # Optional. DataScan execution settings.If not specified, the fields in it will use their default values.
"field": "A String", # Immutable. The unnested field (of type Date or Timestamp) that contains values which monotonically increase over time.If not specified, a data scan will run for all data in the table.
"trigger": { # DataScan scheduling and trigger settings. # Optional. Spec related to how often and when a scan should be triggered.If not specified, the default is OnDemand, which means the scan will not run until the user calls RunDataScan API.
"onDemand": { # The scan runs once via RunDataScan API. # The scan runs once via RunDataScan API.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron (https://en.wikipedia.org/wiki/Cron) schedule for running scans periodically.To explicitly set a timezone in the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database (wikipedia (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List)). For example, CRON_TZ=America/New_York 1 * * * *, or TZ=America/New_York 1 * * * *.This field is required for Schedule scans.
},
},
},
"executionStatus": { # Status of the data scan execution. # Output only. Status of the data scan execution.
"latestJobCreateTime": "A String", # Optional. The time when the DataScanJob execution was created.
"latestJobEndTime": "A String", # Optional. The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # Optional. The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. Identifier. The relative resource name of the scan, of the form: projects/{project}/locations/{location_id}/dataScans/{datascan_id}, where project refers to a project_id or project_number and location_id refers to a GCP region.
"state": "A String", # Output only. Current state of the DataScan.
"type": "A String", # Output only. The type of DataScan.
"uid": "A String", # Output only. System generated globally unique ID for the scan. This ID will be different if the scan is deleted and re-created with the same name.
"updateTime": "A String", # Output only. The time when the scan was last updated.
},
],
"nextPageToken": "A String", # Token to retrieve the next page of results, or empty if there are no more results in the list.
"unreachable": [ # Locations that could not be reached.
"A String",
],
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.

patch(name, body=None, updateMask=None, validateOnly=None, x__xgafv=None)

Updates a DataScan resource.

Args:
name: string, Output only. Identifier. The relative resource name of the scan, of the form: projects/{project}/locations/{location_id}/dataScans/{datascan_id}, where project refers to a project_id or project_number and location_id refers to a GCP region. (required)
body: object, The request body.
The object takes the form of:

{ # Represents a user-visible job which provides the insights for the related data source.For example: Data Quality: generates queries based on the rules and runs against the data to get data quality check results. Data Profile: analyzes the data in table(s) and generates insights about the structure, content and relationships (such as null percent, cardinality, min/max/mean, etc).
"createTime": "A String", # Output only. The time when the scan was created.
"data": { # The data source for DataScan. # Required. The data source for DataScan.
"entity": "A String", # Immutable. The Dataplex entity that represents the data source (e.g. BigQuery table) for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
"resource": "A String", # Immutable. The service-qualified full resource name of the cloud resource for a DataScan job to scan against. The field could be: BigQuery table of type "TABLE" for DataProfileScan/DataQualityScan Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"dataDiscoveryResult": { # The output of a data discovery scan. # Output only. The result of a data discovery scan.
"bigqueryPublishing": { # Describes BigQuery publishing configurations. # Output only. Configuration for metadata publishing.
"dataset": "A String", # Output only. The BigQuery dataset to publish to. It takes the form projects/{project_id}/datasets/{dataset_id}. If not set, the service creates a default publishing dataset.
},
},
"dataDiscoverySpec": { # Spec for a data discovery scan. # Settings for a data discovery scan.
"bigqueryPublishingConfig": { # Describes BigQuery publishing configurations. # Optional. Configuration for metadata publishing.
"connection": "A String", # Optional. The BigQuery connection used to create BigLake tables. Must be in the form projects/{project_id}/locations/{location_id}/connections/{connection_id}
"tableType": "A String", # Optional. Determines whether to publish discovered tables as BigLake external tables or non-BigLake external tables.
},
"storageConfig": { # Configurations related to Cloud Storage as the data source. # Cloud Storage related configurations.
"csvOptions": { # Describes CSV and similar semi-structured data formats. # Optional. Configuration for CSV data.
"delimiter": "A String", # Optional. The delimiter that is used to separate values. The default is , (comma).
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"headerRows": 42, # Optional. The number of rows to interpret as header rows that should be skipped when reading data rows.
"quote": "A String", # Optional. The character used to quote column values. Accepts " (double quotation mark) or ' (single quotation mark). If unspecified, defaults to " (double quotation mark).
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for CSV data. If true, all columns are registered as strings.
},
"excludePatterns": [ # Optional. Defines the data to exclude during discovery. Provide a list of patterns that identify the data to exclude. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"includePatterns": [ # Optional. Defines the data to include during discovery when only a subset of the data should be considered. Provide a list of patterns that identify the data to include. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"jsonOptions": { # Describes JSON data format. # Optional. Configuration for JSON data.
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for JSON data. If true, all columns are registered as their primitive types (strings, number, or boolean).
},
},
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of a data profile scan.
"postScanActionsResult": { # The result of post scan actions of DataProfileScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"profile": { # Contains name, type, mode and field type specific profile information. # The profile information per field.
"fields": [ # List of fields with structural and profile information for each field.
{ # A field within a table.
"mode": "A String", # The mode of the field. Possible values include: REQUIRED, if it is a required field. NULLABLE, if it is an optional field. REPEATED, if it is a repeated field.
"name": "A String", # The name of the field.
"profile": { # The profile information for each field type. # Profile information for the corresponding field.
"distinctRatio": 3.14, # Ratio of rows with distinct values against total scanned rows. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
"doubleProfile": { # The profile information for a double type field. # Double type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": 3.14, # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": 3.14, # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"integerProfile": { # The profile information for an integer type field. # Integer type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": "A String", # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": "A String", # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of approximate quartile values for the scanned data, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"nullRatio": 3.14, # Ratio of rows with null value against total scanned rows.
"stringProfile": { # The profile information for a string type field. # String type field information.
"averageLength": 3.14, # Average length of non-null values in the scanned data.
"maxLength": "A String", # Maximum length of non-null values in the scanned data.
"minLength": "A String", # Minimum length of non-null values in the scanned data.
},
"topNValues": [ # The list of top N non-null values, frequency and ratio with which they occur in the scanned data. N is 10 or equal to the number of distinct values in the field, whichever is smaller. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
{ # Top N non-null values in the scanned data.
"count": "A String", # Count of the corresponding value in the scanned data.
"ratio": 3.14, # Ratio of the corresponding value in the field against the total number of rows in the scanned data.
"value": "A String", # String value of a top N non-null value.
},
],
},
"type": "A String", # The data type retrieved from the schema of the data source. For instance, for a BigQuery native table, it is the BigQuery Table Schema (https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#tablefieldschema). For a Dataplex Entity, it is the Entity Schema (https://cloud.google.com/dataplex/docs/reference/rpc/google.cloud.dataplex.v1#type_3).
},
],
},
"rowCount": "A String", # The count of rows scanned.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # Settings for a data profile scan.
"excludeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to exclude from data profile.If specified, the fields will be excluded from data profile, regardless of include_fields value.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"includeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to include in data profile.If not specified, all fields at the time of profile scan job execution are included, except for ones listed in exclude_fields.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"postScanActions": { # The configuration of post scan actions of DataProfileScan job. # Optional. Actions to take upon job completion..
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataProfileScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of a data quality scan.
"columns": [ # Output only. A list of results at the column level.A column will have a corresponding DataQualityColumnResult if and only if there is at least one rule with the 'column' field set to it.
{ # DataQualityColumnResult provides a more detailed, per-column view of the results.
"column": "A String", # Output only. The column specified in the DataQualityRule.
"score": 3.14, # Output only. The column-level data quality score for this data scan job if and only if the 'column' field is set.The score ranges between between 0, 100 (up to two decimal points).
},
],
"dimensions": [ # A list of results at the dimension level.A dimension will have a corresponding DataQualityDimensionResult if and only if there is at least one rule with the 'dimension' field set to it.
{ # DataQualityDimensionResult provides a more detailed, per-dimension view of the results.
"dimension": { # A dimension captures data quality intent about a defined subset of the rules specified. # Output only. The dimension config specified in the DataQualitySpec, as is.
"name": "A String", # The dimension name a rule belongs to. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
},
"passed": True or False, # Whether the dimension passed or failed.
"score": 3.14, # Output only. The dimension-level data quality score for this data scan job if and only if the 'dimension' field is set.The score ranges between 0, 100 (up to two decimal points).
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"postScanActionsResult": { # The result of post scan actions of DataQualityScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"rowCount": "A String", # The count of rows processed.
"rules": [ # A list of all the rules in a job, and their results.
{ # DataQualityRuleResult provides a more detailed, per-rule view of the results.
"assertionRowCount": "A String", # Output only. The number of rows returned by the SQL statement in a SQL assertion rule.This field is only valid for SQL assertion rules.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against.This field is only valid for row-level type rules.Evaluated count can be configured to either include all rows (default) - with null rows automatically failing rule evaluation, or exclude null rows from the evaluated_count, by setting ignore_nulls = true.
"failingRowsQuery": "A String", # The query to find rows that did not pass this rule.This field is only valid for row-level type rules.
"nullCount": "A String", # The number of rows with null values in the specified column.
"passRatio": 3.14, # The ratio of passed_count / evaluated_count.This field is only valid for row-level type rules.
"passed": True or False, # Whether the rule passed or failed.
"passedCount": "A String", # The number of rows which passed a rule evaluation.This field is only valid for row-level type rules.
"rule": { # A rule captures data quality intent about a data source. # The rule specified in the DataQualitySpec, as is.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
},
],
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
"score": 3.14, # Output only. The overall data quality score.The score ranges between 0, 100 (up to two decimal points).
},
"dataQualitySpec": { # DataQualityScan related setting. # Settings for a data quality scan.
"postScanActions": { # The configuration of post scan actions of DataQualityScan. # Optional. Actions to take upon job completion.
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataQualityScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"notificationReport": { # The configuration of notification report post scan action. # Optional. If set, results will be sent to the provided notification receipts upon triggers.
"jobEndTrigger": { # This trigger is triggered whenever a scan job run ends, regardless of the result. # Optional. If set, report will be sent when a scan job ends.
},
"jobFailureTrigger": { # This trigger is triggered when the scan job itself fails, regardless of the result. # Optional. If set, report will be sent when a scan job fails.
},
"recipients": { # The individuals or groups who are designated to receive notifications upon triggers. # Required. The recipients who will receive the notification report.
"emails": [ # Optional. The email recipients who will receive the DataQualityScan results report.
"A String",
],
},
"scoreThresholdTrigger": { # This trigger is triggered when the DQ score in the job result is less than a specified input score. # Optional. If set, report will be sent when score threshold is met.
"scoreThreshold": 3.14, # Optional. The score range is in 0,100.
},
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"rules": [ # Required. The list of rules to evaluate against a data source. At least one rule is required.
{ # A rule captures data quality intent about a data source.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
],
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"description": "A String", # Optional. Description of the scan. Must be between 1-1024 characters.
"displayName": "A String", # Optional. User friendly display name. Must be between 1-256 characters.
"executionSpec": { # DataScan execution settings. # Optional. DataScan execution settings.If not specified, the fields in it will use their default values.
"field": "A String", # Immutable. The unnested field (of type Date or Timestamp) that contains values which monotonically increase over time.If not specified, a data scan will run for all data in the table.
"trigger": { # DataScan scheduling and trigger settings. # Optional. Spec related to how often and when a scan should be triggered.If not specified, the default is OnDemand, which means the scan will not run until the user calls RunDataScan API.
"onDemand": { # The scan runs once via RunDataScan API. # The scan runs once via RunDataScan API.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron (https://en.wikipedia.org/wiki/Cron) schedule for running scans periodically.To explicitly set a timezone in the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database (wikipedia (https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List)). For example, CRON_TZ=America/New_York 1 * * * *, or TZ=America/New_York 1 * * * *.This field is required for Schedule scans.
},
},
},
"executionStatus": { # Status of the data scan execution. # Output only. Status of the data scan execution.
"latestJobCreateTime": "A String", # Optional. The time when the DataScanJob execution was created.
"latestJobEndTime": "A String", # Optional. The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # Optional. The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. Identifier. The relative resource name of the scan, of the form: projects/{project}/locations/{location_id}/dataScans/{datascan_id}, where project refers to a project_id or project_number and location_id refers to a GCP region.
"state": "A String", # Output only. Current state of the DataScan.
"type": "A String", # Output only. The type of DataScan.
"uid": "A String", # Output only. System generated globally unique ID for the scan. This ID will be different if the scan is deleted and re-created with the same name.
"updateTime": "A String", # Output only. The time when the scan was last updated.
}

updateMask: string, Optional. Mask of fields to update.
validateOnly: boolean, Optional. Only validate the request, but do not perform mutations. The default is false.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

run(name, body=None, x__xgafv=None)

Runs an on-demand execution of a DataScan

Args:
name: string, Required. The resource name of the DataScan: projects/{project}/locations/{location_id}/dataScans/{data_scan_id}. where project refers to a project_id or project_number and location_id refers to a GCP region.Only OnDemand data scans are allowed. (required)
body: object, The request body.
The object takes the form of:

{ # Run DataScan Request
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Run DataScan Response.
"job": { # A DataScanJob represents an instance of DataScan execution. # DataScanJob created by RunDataScan request.
"createTime": "A String", # Output only. The time when the DataScanJob was created.
"dataDiscoveryResult": { # The output of a data discovery scan. # Output only. The result of a data discovery scan.
"bigqueryPublishing": { # Describes BigQuery publishing configurations. # Output only. Configuration for metadata publishing.
"dataset": "A String", # Output only. The BigQuery dataset to publish to. It takes the form projects/{project_id}/datasets/{dataset_id}. If not set, the service creates a default publishing dataset.
},
},
"dataDiscoverySpec": { # Spec for a data discovery scan. # Output only. Settings for a data discovery scan.
"bigqueryPublishingConfig": { # Describes BigQuery publishing configurations. # Optional. Configuration for metadata publishing.
"connection": "A String", # Optional. The BigQuery connection used to create BigLake tables. Must be in the form projects/{project_id}/locations/{location_id}/connections/{connection_id}
"tableType": "A String", # Optional. Determines whether to publish discovered tables as BigLake external tables or non-BigLake external tables.
},
"storageConfig": { # Configurations related to Cloud Storage as the data source. # Cloud Storage related configurations.
"csvOptions": { # Describes CSV and similar semi-structured data formats. # Optional. Configuration for CSV data.
"delimiter": "A String", # Optional. The delimiter that is used to separate values. The default is , (comma).
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"headerRows": 42, # Optional. The number of rows to interpret as header rows that should be skipped when reading data rows.
"quote": "A String", # Optional. The character used to quote column values. Accepts " (double quotation mark) or ' (single quotation mark). If unspecified, defaults to " (double quotation mark).
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for CSV data. If true, all columns are registered as strings.
},
"excludePatterns": [ # Optional. Defines the data to exclude during discovery. Provide a list of patterns that identify the data to exclude. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"includePatterns": [ # Optional. Defines the data to include during discovery when only a subset of the data should be considered. Provide a list of patterns that identify the data to include. For Cloud Storage bucket assets, these patterns are interpreted as glob patterns used to match object names. For BigQuery dataset assets, these patterns are interpreted as patterns to match table names.
"A String",
],
"jsonOptions": { # Describes JSON data format. # Optional. Configuration for JSON data.
"encoding": "A String", # Optional. The character encoding of the data. The default is UTF-8.
"typeInferenceDisabled": True or False, # Optional. Whether to disable the inference of data types for JSON data. If true, all columns are registered as their primitive types (strings, number, or boolean).
},
},
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of a data profile scan.
"postScanActionsResult": { # The result of post scan actions of DataProfileScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"profile": { # Contains name, type, mode and field type specific profile information. # The profile information per field.
"fields": [ # List of fields with structural and profile information for each field.
{ # A field within a table.
"mode": "A String", # The mode of the field. Possible values include: REQUIRED, if it is a required field. NULLABLE, if it is an optional field. REPEATED, if it is a repeated field.
"name": "A String", # The name of the field.
"profile": { # The profile information for each field type. # Profile information for the corresponding field.
"distinctRatio": 3.14, # Ratio of rows with distinct values against total scanned rows. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
"doubleProfile": { # The profile information for a double type field. # Double type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": 3.14, # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": 3.14, # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"integerProfile": { # The profile information for an integer type field. # Integer type field information.
"average": 3.14, # Average of non-null values in the scanned data. NaN, if the field has a NaN.
"max": "A String", # Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
"min": "A String", # Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
"quartiles": [ # A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of approximate quartile values for the scanned data, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
},
"nullRatio": 3.14, # Ratio of rows with null value against total scanned rows.
"stringProfile": { # The profile information for a string type field. # String type field information.
"averageLength": 3.14, # Average length of non-null values in the scanned data.
"maxLength": "A String", # Maximum length of non-null values in the scanned data.
"minLength": "A String", # Minimum length of non-null values in the scanned data.
},
"topNValues": [ # The list of top N non-null values, frequency and ratio with which they occur in the scanned data. N is 10 or equal to the number of distinct values in the field, whichever is smaller. Not available for complex non-groupable field type, including RECORD, ARRAY, GEOGRAPHY, and JSON, as well as fields with REPEATABLE mode.
{ # Top N non-null values in the scanned data.
"count": "A String", # Count of the corresponding value in the scanned data.
"ratio": 3.14, # Ratio of the corresponding value in the field against the total number of rows in the scanned data.
"value": "A String", # String value of a top N non-null value.
},
],
},
"type": "A String", # The data type retrieved from the schema of the data source. For instance, for a BigQuery native table, it is the BigQuery Table Schema (https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#tablefieldschema). For a Dataplex Entity, it is the Entity Schema (https://cloud.google.com/dataplex/docs/reference/rpc/google.cloud.dataplex.v1#type_3).
},
],
},
"rowCount": "A String", # The count of rows scanned.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # Output only. Settings for a data profile scan.
"excludeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to exclude from data profile.If specified, the fields will be excluded from data profile, regardless of include_fields value.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"includeFields": { # The specification for fields to include or exclude in data profile scan. # Optional. The fields to include in data profile.If not specified, all fields at the time of profile scan job execution are included, except for ones listed in exclude_fields.
"fieldNames": [ # Optional. Expected input is a list of fully qualified names of fields as in the schema.Only top-level field names for nested fields are supported. For instance, if 'x' is of nested field type, listing 'x' is supported but 'x.y.z' is not supported. Here 'y' and 'y.z' are nested fields of 'x'.
"A String",
],
},
"postScanActions": { # The configuration of post scan actions of DataProfileScan job. # Optional. Actions to take upon job completion..
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataProfileScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of a data quality scan.
"columns": [ # Output only. A list of results at the column level.A column will have a corresponding DataQualityColumnResult if and only if there is at least one rule with the 'column' field set to it.
{ # DataQualityColumnResult provides a more detailed, per-column view of the results.
"column": "A String", # Output only. The column specified in the DataQualityRule.
"score": 3.14, # Output only. The column-level data quality score for this data scan job if and only if the 'column' field is set.The score ranges between between 0, 100 (up to two decimal points).
},
],
"dimensions": [ # A list of results at the dimension level.A dimension will have a corresponding DataQualityDimensionResult if and only if there is at least one rule with the 'dimension' field set to it.
{ # DataQualityDimensionResult provides a more detailed, per-dimension view of the results.
"dimension": { # A dimension captures data quality intent about a defined subset of the rules specified. # Output only. The dimension config specified in the DataQualitySpec, as is.
"name": "A String", # The dimension name a rule belongs to. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
},
"passed": True or False, # Whether the dimension passed or failed.
"score": 3.14, # Output only. The dimension-level data quality score for this data scan job if and only if the 'dimension' field is set.The score ranges between 0, 100 (up to two decimal points).
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"postScanActionsResult": { # The result of post scan actions of DataQualityScan job. # Output only. The result of post scan actions.
"bigqueryExportResult": { # The result of BigQuery export post scan action. # Output only. The result of BigQuery export post scan action.
"message": "A String", # Output only. Additional information about the BigQuery exporting.
"state": "A String", # Output only. Execution state for the BigQuery exporting.
},
},
"rowCount": "A String", # The count of rows processed.
"rules": [ # A list of all the rules in a job, and their results.
{ # DataQualityRuleResult provides a more detailed, per-rule view of the results.
"assertionRowCount": "A String", # Output only. The number of rows returned by the SQL statement in a SQL assertion rule.This field is only valid for SQL assertion rules.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against.This field is only valid for row-level type rules.Evaluated count can be configured to either include all rows (default) - with null rows automatically failing rule evaluation, or exclude null rows from the evaluated_count, by setting ignore_nulls = true.
"failingRowsQuery": "A String", # The query to find rows that did not pass this rule.This field is only valid for row-level type rules.
"nullCount": "A String", # The number of rows with null values in the specified column.
"passRatio": 3.14, # The ratio of passed_count / evaluated_count.This field is only valid for row-level type rules.
"passed": True or False, # Whether the rule passed or failed.
"passedCount": "A String", # The number of rows which passed a rule evaluation.This field is only valid for row-level type rules.
"rule": { # A rule captures data quality intent about a data source. # The rule specified in the DataQualitySpec, as is.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
},
],
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this result.
"incrementalField": { # A data range denoted by a pair of start/end values of a field. # The range denoted by values of an incremental field
"end": "A String", # Value that marks the end of the range.
"field": "A String", # The field that contains values which monotonically increases over time (e.g. a timestamp column).
"start": "A String", # Value that marks the start of the range.
},
},
"score": 3.14, # Output only. The overall data quality score.The score ranges between 0, 100 (up to two decimal points).
},
"dataQualitySpec": { # DataQualityScan related setting. # Output only. Settings for a data quality scan.
"postScanActions": { # The configuration of post scan actions of DataQualityScan. # Optional. Actions to take upon job completion.
"bigqueryExport": { # The configuration of BigQuery export post scan action. # Optional. If set, results will be exported to the provided BigQuery table.
"resultsTable": "A String", # Optional. The BigQuery table to export DataQualityScan results to. Format: //bigquery.googleapis.com/projects/PROJECT_ID/datasets/DATASET_ID/tables/TABLE_ID
},
"notificationReport": { # The configuration of notification report post scan action. # Optional. If set, results will be sent to the provided notification receipts upon triggers.
"jobEndTrigger": { # This trigger is triggered whenever a scan job run ends, regardless of the result. # Optional. If set, report will be sent when a scan job ends.
},
"jobFailureTrigger": { # This trigger is triggered when the scan job itself fails, regardless of the result. # Optional. If set, report will be sent when a scan job fails.
},
"recipients": { # The individuals or groups who are designated to receive notifications upon triggers. # Required. The recipients who will receive the notification report.
"emails": [ # Optional. The email recipients who will receive the DataQualityScan results report.
"A String",
],
},
"scoreThresholdTrigger": { # This trigger is triggered when the DQ score in the job result is less than a specified input score. # Optional. If set, report will be sent when score threshold is met.
"scoreThreshold": 3.14, # Optional. The score range is in 0,100.
},
},
},
"rowFilter": "A String", # Optional. A filter applied to all rows in a single DataScan job. The filter needs to be a valid SQL expression for a WHERE clause in BigQuery standard SQL syntax. Example: col1 >= 0 AND col2 < 10
"rules": [ # Required. The list of rules to evaluate against a data source. At least one rule is required.
{ # A rule captures data quality intent about a data source.
"column": "A String", # Optional. The unnested column which this rule is evaluated against.
"description": "A String", # Optional. Description of the rule. The maximum length is 1,024 characters.
"dimension": "A String", # Required. The dimension a rule belongs to. Results are also aggregated at the dimension level. Supported dimensions are "COMPLETENESS", "ACCURACY", "CONSISTENCY", "VALIDITY", "UNIQUENESS", "FRESHNESS", "VOLUME"
"ignoreNull": True or False, # Optional. Rows with null values will automatically fail a rule, unless ignore_null is true. In that case, such null rows are trivially considered passing.This field is only valid for the following type of rules: RangeExpectation RegexExpectation SetExpectation UniquenessExpectation
"name": "A String", # Optional. A mutable name for the rule. The name must contain only letters (a-z, A-Z), numbers (0-9), or hyphens (-). The maximum length is 63 characters. Must start with a letter. Must end with a number or a letter.
"nonNullExpectation": { # Evaluates whether each column value is null. # Row-level rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # Row-level rule which evaluates whether each column value lies between a specified range.
"maxValue": "A String", # Optional. The maximum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column value allowed for a row to pass this validation. At least one of min_value and max_value need to be provided.
"strictMaxEnabled": True or False, # Optional. Whether each value needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether each value needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"regexExpectation": { # Evaluates whether each column value matches a specified regex. # Row-level rule which evaluates whether each column value matches a specified regex.
"regex": "A String", # Optional. A regular expression the column value is expected to match.
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition.The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean value per row as the result.Example: col1 >= 0 AND col2 < 10 # Row-level rule which evaluates whether each row in a table passes the specified condition.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # Row-level rule which evaluates whether each column value is contained by a specified set.
"values": [ # Optional. Expected values for the column value.
"A String",
],
},
"sqlAssertion": { # A SQL statement that is evaluated to return rows that match an invalid state. If any rows are are returned, this rule fails.The SQL statement must use BigQuery standard SQL syntax, and must not contain any semicolons.You can use the data reference parameter ${data()} to reference the source table with all of its precondition filters applied. Examples of precondition filters include row filters, incremental data filters, and sampling. For more information, see Data reference parameter (https://cloud.google.com/dataplex/docs/auto-data-quality-overview#data-reference-parameter).Example: SELECT * FROM ${data()} WHERE price < 0 # Aggregate rule which evaluates the number of rows returned for the provided statement. If any rows are returned, this rule fails.
"sqlStatement": "A String", # Optional. The SQL statement.
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # Aggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # Optional. The maximum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"minValue": "A String", # Optional. The minimum column statistic value allowed for a row to pass this validation.At least one of min_value and max_value need to be provided.
"statistic": "A String", # Optional. The aggregate metric to evaluate.
"strictMaxEnabled": True or False, # Optional. Whether column statistic needs to be strictly lesser than ('<') the maximum, or if equality is allowed.Only relevant if a max_value has been defined. Default = false.
"strictMinEnabled": True or False, # Optional. Whether column statistic needs to be strictly greater than ('>') the minimum, or if equality is allowed.Only relevant if a min_value has been defined. Default = false.
},
"suspended": True or False, # Optional. Whether the Rule is active or suspended. Default is false.
"tableConditionExpectation": { # Evaluates whether the provided expression is true.The SQL expression needs to use BigQuery standard SQL syntax and should produce a scalar boolean result.Example: MIN(col1) >= 0 # Aggregate rule which evaluates whether the provided expression is true for a table.
"sqlExpression": "A String", # Optional. The SQL expression.
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.0.0 indicates default value (i.e. 1.0).This field is only valid for row-level type rules.
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # Row-level rule which evaluates whether each column value is unique.
},
},
],
"samplingPercent": 3.14, # Optional. The percentage of the records to be selected from the dataset for DataScan. Value can range between 0.0 and 100.0 with up to 3 significant decimal digits. Sampling is not applied if sampling_percent is not specified, 0 or 100.
},
"endTime": "A String", # Output only. The time when the DataScanJob ended.
"message": "A String", # Output only. Additional information about the current state.
"name": "A String", # Output only. Identifier. The relative resource name of the DataScanJob, of the form: projects/{project}/locations/{location_id}/dataScans/{datascan_id}/jobs/{job_id}, where project refers to a project_id or project_number and location_id refers to a GCP region.
"startTime": "A String", # Output only. The time when the DataScanJob was started.
"state": "A String", # Output only. Execution state for the DataScanJob.
"type": "A String", # Output only. The type of the parent DataScan.
"uid": "A String", # Output only. System generated globally unique ID for the DataScanJob.
},
}

setIamPolicy(resource, body=None, x__xgafv=None)

Sets the access control policy on the specified resource. Replaces any existing policy.Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

Args:
  resource: string, REQUIRED: The resource for which the policy is being specified. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for SetIamPolicy method.
  "policy": { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/). # REQUIRED: The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.
    "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
      { # Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs.If there are AuditConfigs for both allServices and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted.Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts jose@example.com from DATA_READ logging, and aliya@example.com from DATA_WRITE logging.
        "auditLogConfigs": [ # The configuration for logging of each type of permission.
          { # Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
            "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
              "A String",
            ],
            "logType": "A String", # The log type that this config enables.
          },
        ],
        "service": "A String", # Specifies a service that will be enabled for audit logging. For example, storage.googleapis.com, cloudsql.googleapis.com. allServices is a special value that covers all services.
      },
    ],
    "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
      { # Associates members, or principals, with a role.
        "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
          "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
          "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
          "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
          "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
        },
        "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
          "A String",
        ],
        "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
      },
    ],
    "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
    "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
  },
  "updateMask": "A String", # OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only the fields in the mask will be modified. If no mask is provided, the following default mask is used:paths: "bindings, etag"
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/).
  "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
    { # Specifies the audit configuration for a service. The configuration determines which permission types are logged, and what identities, if any, are exempted from logging. An AuditConfig must have one or more AuditLogConfigs.If there are AuditConfigs for both allServices and a specific service, the union of the two AuditConfigs is used for that service: the log_types specified in each AuditConfig are enabled, and the exempted_members in each AuditLogConfig are exempted.Example Policy with multiple AuditConfigs: { "audit_configs": [ { "service": "allServices", "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" }, { "log_type": "ADMIN_READ" } ] }, { "service": "sampleservice.googleapis.com", "audit_log_configs": [ { "log_type": "DATA_READ" }, { "log_type": "DATA_WRITE", "exempted_members": [ "user:aliya@example.com" ] } ] } ] } For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also exempts jose@example.com from DATA_READ logging, and aliya@example.com from DATA_WRITE logging.
      "auditLogConfigs": [ # The configuration for logging of each type of permission.
        { # Provides the configuration for logging a type of permissions. Example: { "audit_log_configs": [ { "log_type": "DATA_READ", "exempted_members": [ "user:jose@example.com" ] }, { "log_type": "DATA_WRITE" } ] } This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from DATA_READ logging.
          "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of permission. Follows the same format of Binding.members.
            "A String",
          ],
          "logType": "A String", # The log type that this config enables.
        },
      ],
      "service": "A String", # Specifies a service that will be enabled for audit logging. For example, storage.googleapis.com, cloudsql.googleapis.com. allServices is a special value that covers all services.
    },
  ],
  "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
    { # Associates members, or principals, with a role.
      "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
        "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
        "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
        "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
        "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
      },
      "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
        "A String",
      ],
      "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
    },
  ],
  "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
  "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
}

testIamPermissions(resource, body=None, x__xgafv=None)

Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a NOT_FOUND error.Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

Args:
  resource: string, REQUIRED: The resource for which the policy detail is being requested. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for TestIamPermissions method.
  "permissions": [ # The set of permissions to check for the resource. Permissions with wildcards (such as * or storage.*) are not allowed. For more information see IAM Overview (https://cloud.google.com/iam/docs/overview#permissions).
    "A String",
  ],
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for TestIamPermissions method.
  "permissions": [ # A subset of TestPermissionsRequest.permissions that the caller is allowed.
    "A String",
  ],
}

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Instance Methods

Method Details