| :py:mod:`airflow.providers.google.cloud.sensors.bigquery` |
| ========================================================= |
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| .. py:module:: airflow.providers.google.cloud.sensors.bigquery |
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| .. autoapi-nested-parse:: |
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| This module contains Google BigQuery sensors. |
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| Module Contents |
| --------------- |
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| Classes |
| ~~~~~~~ |
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| .. autoapisummary:: |
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| airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistenceSensor |
| airflow.providers.google.cloud.sensors.bigquery.BigQueryTablePartitionExistenceSensor |
| airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistenceAsyncSensor |
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| .. py:class:: BigQueryTableExistenceSensor(*, project_id, dataset_id, table_id, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs) |
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| Bases: :py:obj:`airflow.sensors.base.BaseSensorOperator` |
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| Checks for the existence of a table in Google Bigquery. |
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| :param project_id: The Google cloud project in which to look for the table. |
| The connection supplied to the hook must provide |
| access to the specified project. |
| :param dataset_id: The name of the dataset in which to look for the table. |
| storage bucket. |
| :param table_id: The name of the table to check the existence of. |
| :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud. |
| :param delegate_to: The account to impersonate using domain-wide delegation of authority, |
| if any. For this to work, the service account making the request must have |
| domain-wide delegation enabled. |
| :param impersonation_chain: Optional service account to impersonate using short-term |
| credentials, or chained list of accounts required to get the access_token |
| of the last account in the list, which will be impersonated in the request. |
| If set as a string, the account must grant the originating account |
| the Service Account Token Creator IAM role. |
| If set as a sequence, the identities from the list must grant |
| Service Account Token Creator IAM role to the directly preceding identity, with first |
| account from the list granting this role to the originating account (templated). |
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| .. py:attribute:: template_fields |
| :annotation: :Sequence[str] = ['project_id', 'dataset_id', 'table_id', 'impersonation_chain'] |
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| .. py:attribute:: ui_color |
| :annotation: = #f0eee4 |
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| .. py:method:: poke(context) |
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| Function that the sensors defined while deriving this class should |
| override. |
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| .. py:class:: BigQueryTablePartitionExistenceSensor(*, project_id, dataset_id, table_id, partition_id, gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs) |
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| Bases: :py:obj:`airflow.sensors.base.BaseSensorOperator` |
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| Checks for the existence of a partition within a table in Google Bigquery. |
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| :param project_id: The Google cloud project in which to look for the table. |
| The connection supplied to the hook must provide |
| access to the specified project. |
| :param dataset_id: The name of the dataset in which to look for the table. |
| storage bucket. |
| :param table_id: The name of the table to check the existence of. |
| :param partition_id: The name of the partition to check the existence of. |
| :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud. |
| :param delegate_to: The account to impersonate, if any. |
| For this to work, the service account making the request must |
| have domain-wide delegation enabled. |
| :param impersonation_chain: Optional service account to impersonate using short-term |
| credentials, or chained list of accounts required to get the access_token |
| of the last account in the list, which will be impersonated in the request. |
| If set as a string, the account must grant the originating account |
| the Service Account Token Creator IAM role. |
| If set as a sequence, the identities from the list must grant |
| Service Account Token Creator IAM role to the directly preceding identity, with first |
| account from the list granting this role to the originating account (templated). |
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| .. py:attribute:: template_fields |
| :annotation: :Sequence[str] = ['project_id', 'dataset_id', 'table_id', 'partition_id', 'impersonation_chain'] |
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| .. py:attribute:: ui_color |
| :annotation: = #f0eee4 |
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| .. py:method:: poke(context) |
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| Function that the sensors defined while deriving this class should |
| override. |
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| .. py:class:: BigQueryTableExistenceAsyncSensor(gcp_conn_id = 'google_cloud_default', polling_interval = 5.0, **kwargs) |
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| Bases: :py:obj:`BigQueryTableExistenceSensor` |
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| Checks for the existence of a table in Google Big Query. |
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| :param project_id: The Google cloud project in which to look for the table. |
| The connection supplied to the hook must provide |
| access to the specified project. |
| :param dataset_id: The name of the dataset in which to look for the table. |
| storage bucket. |
| :param table_id: The name of the table to check the existence of. |
| :param gcp_conn_id: The connection ID used to connect to Google Cloud. |
| :param bigquery_conn_id: (Deprecated) The connection ID used to connect to Google Cloud. |
| This parameter has been deprecated. You should pass the gcp_conn_id parameter instead. |
| :param delegate_to: The account to impersonate using domain-wide delegation of authority, |
| if any. For this to work, the service account making the request must have |
| domain-wide delegation enabled. |
| :param impersonation_chain: Optional service account to impersonate using short-term |
| credentials, or chained list of accounts required to get the access_token |
| of the last account in the list, which will be impersonated in the request. |
| If set as a string, the account must grant the originating account |
| the Service Account Token Creator IAM role. |
| If set as a sequence, the identities from the list must grant |
| Service Account Token Creator IAM role to the directly preceding identity, with first |
| account from the list granting this role to the originating account (templated). |
| :param polling_interval: The interval in seconds to wait between checks table existence. |
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| .. py:method:: execute(context) |
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| Airflow runs this method on the worker and defers using the trigger. |
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| .. py:method:: execute_complete(context, event = None) |
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| Callback for when the trigger fires - returns immediately. |
| Relies on trigger to throw an exception, otherwise it assumes execution was |
| successful. |
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