Source code for airflow.contrib.operators.bigquery_to_mysql_operator

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This module contains a Google BigQuery to MySQL operator.

from airflow.contrib.hooks.bigquery_hook import BigQueryHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.hooks.mysql_hook import MySqlHook

[docs]class BigQueryToMySqlOperator(BaseOperator): """ Fetches the data from a BigQuery table (alternatively fetch data for selected columns) and insert that data into a MySQL table. .. note:: If you pass fields to ``selected_fields`` which are in different order than the order of columns already in BQ table, the data will still be in the order of BQ table. For example if the BQ table has 3 columns as ``[A,B,C]`` and you pass 'B,A' in the ``selected_fields`` the data would still be of the form ``'A,B'`` and passed through this form to MySQL **Example**: :: transfer_data = BigQueryToMySqlOperator( task_id='task_id', dataset_table='origin_bq_table', mysql_table='dest_table_name', replace=True, ) :param dataset_table: A dotted ``<dataset>.<table>``: the big query table of origin :type dataset_table: str :param max_results: The maximum number of records (rows) to be fetched from the table. (templated) :type max_results: str :param selected_fields: List of fields to return (comma-separated). If unspecified, all fields are returned. :type selected_fields: str :param gcp_conn_id: reference to a specific GCP hook. :type gcp_conn_id: str :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. :type delegate_to: str :param mysql_conn_id: reference to a specific mysql hook :type mysql_conn_id: str :param database: name of database which overwrite defined one in connection :type database: str :param replace: Whether to replace instead of insert :type replace: bool :param batch_size: The number of rows to take in each batch :type batch_size: int """
[docs] template_fields = ('dataset_id', 'table_id', 'mysql_table')
@apply_defaults def __init__(self, dataset_table, mysql_table, selected_fields=None, gcp_conn_id='google_cloud_default', mysql_conn_id='mysql_default', database=None, delegate_to=None, replace=False, batch_size=1000, *args, **kwargs): super(BigQueryToMySqlOperator, self).__init__(*args, **kwargs) self.selected_fields = selected_fields self.gcp_conn_id = gcp_conn_id self.mysql_conn_id = mysql_conn_id self.database = database self.mysql_table = mysql_table self.replace = replace self.delegate_to = delegate_to self.batch_size = batch_size try: self.dataset_id, self.table_id = dataset_table.split('.') except ValueError: raise ValueError('Could not parse {} as <dataset>.<table>' .format(dataset_table))
[docs] def _bq_get_data(self):'Fetching Data from:')'Dataset: %s ; Table: %s', self.dataset_id, self.table_id) hook = BigQueryHook(bigquery_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to) conn = hook.get_conn() cursor = conn.cursor() i = 0 while True: response = cursor.get_tabledata(dataset_id=self.dataset_id, table_id=self.table_id, max_results=self.batch_size, selected_fields=self.selected_fields, start_index=i * self.batch_size) if 'rows' in response: rows = response['rows'] else:'Job Finished') return'Total Extracted rows: %s', len(rows) + i * self.batch_size) table_data = [] for dict_row in rows: single_row = [] for fields in dict_row['f']: single_row.append(fields['v']) table_data.append(single_row) yield table_data i += 1
[docs] def execute(self, context): mysql_hook = MySqlHook(schema=self.database, mysql_conn_id=self.mysql_conn_id) for rows in self._bq_get_data(): mysql_hook.insert_rows(self.mysql_table, rows, replace=self.replace)

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