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dataplex_v1.projects.locations.dataScans.html
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dataplex_v1.projects.locations.dataScans.html
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<h1><a href="dataplex_v1.html">Cloud Dataplex API</a> . <a href="dataplex_v1.projects.html">projects</a> . <a href="dataplex_v1.projects.locations.html">locations</a> . <a href="dataplex_v1.projects.locations.dataScans.html">dataScans</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
<code><a href="dataplex_v1.projects.locations.dataScans.jobs.html">jobs()</a></code>
</p>
<p class="firstline">Returns the jobs Resource.</p>
<p class="toc_element">
<code><a href="#close">close()</a></code></p>
<p class="firstline">Close httplib2 connections.</p>
<p class="toc_element">
<code><a href="#create">create(parent, body=None, dataScanId=None, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a dataScan resource.</p>
<p class="toc_element">
<code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
<p class="firstline">Delete the dataScan resource.</p>
<p class="toc_element">
<code><a href="#get">get(name, view=None, x__xgafv=None)</a></code></p>
<p class="firstline">Get dataScan resource.</p>
<p class="toc_element">
<code><a href="#getIamPolicy">getIamPolicy(resource, options_requestedPolicyVersion=None, x__xgafv=None)</a></code></p>
<p class="firstline">Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.</p>
<p class="toc_element">
<code><a href="#list">list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
<p class="firstline">Lists dataScans.</p>
<p class="toc_element">
<code><a href="#list_next">list_next()</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
<code><a href="#patch">patch(name, body=None, updateMask=None, x__xgafv=None)</a></code></p>
<p class="firstline">Update the dataScan resource.</p>
<p class="toc_element">
<code><a href="#run">run(name, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Run an on demand execution of a DataScan.</p>
<p class="toc_element">
<code><a href="#setIamPolicy">setIamPolicy(resource, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Sets the access control policy on the specified resource. Replaces any existing policy.Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.</p>
<p class="toc_element">
<code><a href="#testIamPermissions">testIamPermissions(resource, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">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.</p>
<h3>Method Details</h3>
<div class="method">
<code class="details" id="close">close()</code>
<pre>Close httplib2 connections.</pre>
</div>
<div class="method">
<code class="details" id="create">create(parent, body=None, dataScanId=None, x__xgafv=None)</code>
<pre>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 examples: - Data Quality: generates queries based on the rules and run 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 contains the data for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of the data profile scan.
"profile": { # Profile information describing the structure and layout of the data and contains the profile info. # This represents the profile information per field.
"fields": [ # The sequence of fields describing data in table entities.
{ # Represents a column field within a table schema.
"mode": "A String", # The mode of the field. Its value will be: 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": { # ProfileInfo defines the profile information for each schema field type. # The profile information for the corresponding field.
"distinctRatio": 3.14, # The ratio of rows that are distinct against the rows in the sampled data.
"doubleProfile": { # DoubleFieldInfo defines output for any double type field. # The corresponding double field profile.
"average": 3.14, # The average of non-null values of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": 3.14, # The maximum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": 3.14, # The minimum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide the numebr 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # The standard deviation of non-null of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"integerProfile": { # IntegerFieldInfo defines output for any integer type field. # The corresponding integer field profile.
"average": 3.14, # The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": "A String", # The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": "A String", # The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"nullRatio": 3.14, # The ratio of null rows against the rows in the sampled data.
"stringProfile": { # StringFieldInfo defines output info for any string type field. # The corresponding string field profile.
"averageLength": 3.14, # The average length of a string field in the sampled data. Optional if zero non-null rows.
"maxLength": "A String", # The maximum length of a string field in the sampled data. Optional if zero non-null rows.
"minLength": "A String", # The minimum length of the string field in the sampled data. Optional if zero non-null rows.
},
"topNValues": [ # The array of top N values of the field in the sampled data. Currently N is set as 10 or equal to distinct values in the field, whichever is smaller. This will be optional for complex non-groupable data-types such as JSON, ARRAY, JSON, STRUCT.
{ # The TopNValue defines the structure of output of top N values of a field.
"count": "A String", # The frequency count of the corresponding value in the field.
"value": "A String", # The value is the string value of the actual value from the field.
},
],
},
"type": "A String", # The field data type. Possible values include: STRING BYTE INT64 INT32 INT16 DOUBLE FLOAT DECIMAL BOOLEAN BINARY TIMESTAMP DATE TIME NULL RECORD
},
],
},
"rowCount": "A String", # The count of all rows in the sampled data. Return 0, if zero rows.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this profile.
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # DataProfileScan related setting.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of the data quality scan.
"dimensions": [ # A list of results at the dimension-level.
{ # DataQualityDimensionResult provides a more detailed, per-dimension level view of the results.
"passed": True or False, # Whether the dimension passed or failed.
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"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 level view of the results.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against. This field is only valid for ColumnMap type rules. Evaluated count can be configured to either (1) include all rows (default) - with null rows automatically failing rule evaluation OR (2) 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. Only applies to ColumnMap and RowCondition 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 ColumnMap 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 ColumnMap 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
},
],
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataQualitySpec": { # DataQualityScan related setting. # DataQualityScan related setting.
"rules": [ # 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
],
},
"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 under it will use their default values.
"field": "A String", # Immutable. The unnested field (Date or Timestamp) that contains values that monotonically increase over time.
"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 one-time via RunDataScan API. # The scan runs one-time shortly after DataScan Creation.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron schedule (https://en.wikipedia.org/wiki/Cron) for running scans periodically. To explicitly set a timezone to 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. 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.
"latestJobEndTime": "A String", # The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. 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.
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 response of the operation in case of success. 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.
},
}</pre>
</div>
<div class="method">
<code class="details" id="delete">delete(name, x__xgafv=None)</code>
<pre>Delete the 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)
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 response of the operation in case of success. 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.
},
}</pre>
</div>
<div class="method">
<code class="details" id="get">get(name, view=None, x__xgafv=None)</code>
<pre>Get 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. Used to select the subset of DataScan information 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 examples: - Data Quality: generates queries based on the rules and run 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 contains the data for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of the data profile scan.
"profile": { # Profile information describing the structure and layout of the data and contains the profile info. # This represents the profile information per field.
"fields": [ # The sequence of fields describing data in table entities.
{ # Represents a column field within a table schema.
"mode": "A String", # The mode of the field. Its value will be: 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": { # ProfileInfo defines the profile information for each schema field type. # The profile information for the corresponding field.
"distinctRatio": 3.14, # The ratio of rows that are distinct against the rows in the sampled data.
"doubleProfile": { # DoubleFieldInfo defines output for any double type field. # The corresponding double field profile.
"average": 3.14, # The average of non-null values of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": 3.14, # The maximum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": 3.14, # The minimum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide the numebr 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # The standard deviation of non-null of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"integerProfile": { # IntegerFieldInfo defines output for any integer type field. # The corresponding integer field profile.
"average": 3.14, # The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": "A String", # The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": "A String", # The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"nullRatio": 3.14, # The ratio of null rows against the rows in the sampled data.
"stringProfile": { # StringFieldInfo defines output info for any string type field. # The corresponding string field profile.
"averageLength": 3.14, # The average length of a string field in the sampled data. Optional if zero non-null rows.
"maxLength": "A String", # The maximum length of a string field in the sampled data. Optional if zero non-null rows.
"minLength": "A String", # The minimum length of the string field in the sampled data. Optional if zero non-null rows.
},
"topNValues": [ # The array of top N values of the field in the sampled data. Currently N is set as 10 or equal to distinct values in the field, whichever is smaller. This will be optional for complex non-groupable data-types such as JSON, ARRAY, JSON, STRUCT.
{ # The TopNValue defines the structure of output of top N values of a field.
"count": "A String", # The frequency count of the corresponding value in the field.
"value": "A String", # The value is the string value of the actual value from the field.
},
],
},
"type": "A String", # The field data type. Possible values include: STRING BYTE INT64 INT32 INT16 DOUBLE FLOAT DECIMAL BOOLEAN BINARY TIMESTAMP DATE TIME NULL RECORD
},
],
},
"rowCount": "A String", # The count of all rows in the sampled data. Return 0, if zero rows.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this profile.
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # DataProfileScan related setting.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of the data quality scan.
"dimensions": [ # A list of results at the dimension-level.
{ # DataQualityDimensionResult provides a more detailed, per-dimension level view of the results.
"passed": True or False, # Whether the dimension passed or failed.
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"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 level view of the results.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against. This field is only valid for ColumnMap type rules. Evaluated count can be configured to either (1) include all rows (default) - with null rows automatically failing rule evaluation OR (2) 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. Only applies to ColumnMap and RowCondition 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 ColumnMap 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 ColumnMap 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
},
],
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataQualitySpec": { # DataQualityScan related setting. # DataQualityScan related setting.
"rules": [ # 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
],
},
"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 under it will use their default values.
"field": "A String", # Immutable. The unnested field (Date or Timestamp) that contains values that monotonically increase over time.
"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 one-time via RunDataScan API. # The scan runs one-time shortly after DataScan Creation.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron schedule (https://en.wikipedia.org/wiki/Cron) for running scans periodically. To explicitly set a timezone to 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. 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.
"latestJobEndTime": "A String", # The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. 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.
}</pre>
</div>
<div class="method">
<code class="details" id="getIamPolicy">getIamPolicy(resource, options_requestedPolicyVersion=None, x__xgafv=None)</code>
<pre>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. 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. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com.
"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.
},
],
"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).
}</pre>
</div>
<div class="method">
<code class="details" id="list">list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</code>
<pre>Lists dataScans.
Args:
parent: string, Required. 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 10 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 (metadata only) under the given parent location.
{ # Represents a user-visible job which provides the insights for the related data source. For examples: - Data Quality: generates queries based on the rules and run 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 contains the data for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of the data profile scan.
"profile": { # Profile information describing the structure and layout of the data and contains the profile info. # This represents the profile information per field.
"fields": [ # The sequence of fields describing data in table entities.
{ # Represents a column field within a table schema.
"mode": "A String", # The mode of the field. Its value will be: 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": { # ProfileInfo defines the profile information for each schema field type. # The profile information for the corresponding field.
"distinctRatio": 3.14, # The ratio of rows that are distinct against the rows in the sampled data.
"doubleProfile": { # DoubleFieldInfo defines output for any double type field. # The corresponding double field profile.
"average": 3.14, # The average of non-null values of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": 3.14, # The maximum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": 3.14, # The minimum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide the numebr 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # The standard deviation of non-null of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"integerProfile": { # IntegerFieldInfo defines output for any integer type field. # The corresponding integer field profile.
"average": 3.14, # The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": "A String", # The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": "A String", # The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"nullRatio": 3.14, # The ratio of null rows against the rows in the sampled data.
"stringProfile": { # StringFieldInfo defines output info for any string type field. # The corresponding string field profile.
"averageLength": 3.14, # The average length of a string field in the sampled data. Optional if zero non-null rows.
"maxLength": "A String", # The maximum length of a string field in the sampled data. Optional if zero non-null rows.
"minLength": "A String", # The minimum length of the string field in the sampled data. Optional if zero non-null rows.
},
"topNValues": [ # The array of top N values of the field in the sampled data. Currently N is set as 10 or equal to distinct values in the field, whichever is smaller. This will be optional for complex non-groupable data-types such as JSON, ARRAY, JSON, STRUCT.
{ # The TopNValue defines the structure of output of top N values of a field.
"count": "A String", # The frequency count of the corresponding value in the field.
"value": "A String", # The value is the string value of the actual value from the field.
},
],
},
"type": "A String", # The field data type. Possible values include: STRING BYTE INT64 INT32 INT16 DOUBLE FLOAT DECIMAL BOOLEAN BINARY TIMESTAMP DATE TIME NULL RECORD
},
],
},
"rowCount": "A String", # The count of all rows in the sampled data. Return 0, if zero rows.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this profile.
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # DataProfileScan related setting.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of the data quality scan.
"dimensions": [ # A list of results at the dimension-level.
{ # DataQualityDimensionResult provides a more detailed, per-dimension level view of the results.
"passed": True or False, # Whether the dimension passed or failed.
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"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 level view of the results.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against. This field is only valid for ColumnMap type rules. Evaluated count can be configured to either (1) include all rows (default) - with null rows automatically failing rule evaluation OR (2) 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. Only applies to ColumnMap and RowCondition 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 ColumnMap 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 ColumnMap 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
},
],
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataQualitySpec": { # DataQualityScan related setting. # DataQualityScan related setting.
"rules": [ # 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
],
},
"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 under it will use their default values.
"field": "A String", # Immutable. The unnested field (Date or Timestamp) that contains values that monotonically increase over time.
"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 one-time via RunDataScan API. # The scan runs one-time shortly after DataScan Creation.
},
"schedule": { # The scan is scheduled to run periodically. # The scan is scheduled to run periodically.
"cron": "A String", # Required. Cron schedule (https://en.wikipedia.org/wiki/Cron) for running scans periodically. To explicitly set a timezone to 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. 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.
"latestJobEndTime": "A String", # The time when the latest DataScanJob ended.
"latestJobStartTime": "A String", # The time when the latest DataScanJob started.
},
"labels": { # Optional. User-defined labels for the scan.
"a_key": "A String",
},
"name": "A String", # Output only. 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",
],
}</pre>
</div>
<div class="method">
<code class="details" id="list_next">list_next()</code>
<pre>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.
</pre>
</div>
<div class="method">
<code class="details" id="patch">patch(name, body=None, updateMask=None, x__xgafv=None)</code>
<pre>Update the dataScan resource.
Args:
name: string, Output only. 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 examples: - Data Quality: generates queries based on the rules and run 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 contains the data for DataScan, of the form: projects/{project_number}/locations/{location_id}/lakes/{lake_id}/zones/{zone_id}/entities/{entity_id}.
},
"dataProfileResult": { # DataProfileResult defines the output of DataProfileScan. Each field of the table will have field type specific profile result. # Output only. The result of the data profile scan.
"profile": { # Profile information describing the structure and layout of the data and contains the profile info. # This represents the profile information per field.
"fields": [ # The sequence of fields describing data in table entities.
{ # Represents a column field within a table schema.
"mode": "A String", # The mode of the field. Its value will be: 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": { # ProfileInfo defines the profile information for each schema field type. # The profile information for the corresponding field.
"distinctRatio": 3.14, # The ratio of rows that are distinct against the rows in the sampled data.
"doubleProfile": { # DoubleFieldInfo defines output for any double type field. # The corresponding double field profile.
"average": 3.14, # The average of non-null values of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": 3.14, # The maximum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": 3.14, # The minimum value of a double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide the numebr 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
3.14,
],
"standardDeviation": 3.14, # The standard deviation of non-null of double field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"integerProfile": { # IntegerFieldInfo defines output for any integer type field. # The corresponding integer field profile.
"average": 3.14, # The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"max": "A String", # The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"min": "A String", # The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
"quartiles": [ # A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
"A String",
],
"standardDeviation": 3.14, # The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
},
"nullRatio": 3.14, # The ratio of null rows against the rows in the sampled data.
"stringProfile": { # StringFieldInfo defines output info for any string type field. # The corresponding string field profile.
"averageLength": 3.14, # The average length of a string field in the sampled data. Optional if zero non-null rows.
"maxLength": "A String", # The maximum length of a string field in the sampled data. Optional if zero non-null rows.
"minLength": "A String", # The minimum length of the string field in the sampled data. Optional if zero non-null rows.
},
"topNValues": [ # The array of top N values of the field in the sampled data. Currently N is set as 10 or equal to distinct values in the field, whichever is smaller. This will be optional for complex non-groupable data-types such as JSON, ARRAY, JSON, STRUCT.
{ # The TopNValue defines the structure of output of top N values of a field.
"count": "A String", # The frequency count of the corresponding value in the field.
"value": "A String", # The value is the string value of the actual value from the field.
},
],
},
"type": "A String", # The field data type. Possible values include: STRING BYTE INT64 INT32 INT16 DOUBLE FLOAT DECIMAL BOOLEAN BINARY TIMESTAMP DATE TIME NULL RECORD
},
],
},
"rowCount": "A String", # The count of all rows in the sampled data. Return 0, if zero rows.
"scannedData": { # The data scanned during processing (e.g. in incremental DataScan) # The data scanned for this profile.
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},
},
"dataProfileSpec": { # DataProfileScan related setting. # DataProfileScan related setting.
},
"dataQualityResult": { # The output of a DataQualityScan. # Output only. The result of the data quality scan.
"dimensions": [ # A list of results at the dimension-level.
{ # DataQualityDimensionResult provides a more detailed, per-dimension level view of the results.
"passed": True or False, # Whether the dimension passed or failed.
},
],
"passed": True or False, # Overall data quality result -- true if all rules passed.
"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 level view of the results.
"evaluatedCount": "A String", # The number of rows a rule was evaluated against. This field is only valid for ColumnMap type rules. Evaluated count can be configured to either (1) include all rows (default) - with null rows automatically failing rule evaluation OR (2) 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. Only applies to ColumnMap and RowCondition 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 ColumnMap 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 ColumnMap 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.
"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", "INTEGRITY"
"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. Only applicable to ColumnMap rules.
"nonNullExpectation": { # Evaluates whether each column value is null. # ColumnMap rule which evaluates whether each column value is null.
},
"rangeExpectation": { # Evaluates whether each column value lies between a specified range. # ColumnMap 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. # ColumnMap rule which evaluates whether each column value matches a specified regex.
"regex": "A String",
},
"rowConditionExpectation": { # Evaluates whether each row passes the specified condition. The SQL expression needs to use BigQuery standard SQL syntax and should produce a boolean per row as the result. Example: col1 >= 0 AND col2 < 10 # Table rule which evaluates whether each row passes the specified condition.
"sqlExpression": "A String",
},
"setExpectation": { # Evaluates whether each column value is contained by a specified set. # ColumnMap rule which evaluates whether each column value is contained by a specified set.
"values": [
"A String",
],
},
"statisticRangeExpectation": { # Evaluates whether the column aggregate statistic lies between a specified range. # ColumnAggregate rule which evaluates whether the column aggregate statistic lies between a specified range.
"maxValue": "A String", # 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", # 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",
"strictMaxEnabled": True or False, # 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, # 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.
},
"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 # Table rule which evaluates whether the provided expression is true.
"sqlExpression": "A String",
},
"threshold": 3.14, # Optional. The minimum ratio of passing_rows / total_rows required to pass this rule, with a range of 0.0, 1.00 indicates default value (i.e. 1.0)
"uniquenessExpectation": { # Evaluates whether the column has duplicates. # ColumnAggregate rule which evaluates whether the column has duplicates.
},
},
},
],
"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. timestamp).
"start": "A String", # Value that marks the start of the range
},
},