Source code for airflow.operators.mssql_to_hive

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from builtins import chr
from collections import OrderedDict
import unicodecsv as csv
from tempfile import NamedTemporaryFile
import pymssql

from airflow.hooks.hive_hooks import HiveCliHook
from airflow.hooks.mssql_hook import MsSqlHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults

[docs]class MsSqlToHiveTransfer(BaseOperator): """ Moves data from Microsoft SQL Server to Hive. The operator runs your query against Microsoft SQL Server, stores the file locally before loading it into a Hive table. If the ``create`` or ``recreate`` arguments are set to ``True``, a ``CREATE TABLE`` and ``DROP TABLE`` statements are generated. Hive data types are inferred from the cursor's metadata. Note that the table generated in Hive uses ``STORED AS textfile`` which isn't the most efficient serialization format. If a large amount of data is loaded and/or if the table gets queried considerably, you may want to use this operator only to stage the data into a temporary table before loading it into its final destination using a ``HiveOperator``. :param sql: SQL query to execute against the Microsoft SQL Server database. (templated) :type sql: str :param hive_table: target Hive table, use dot notation to target a specific database. (templated) :type hive_table: str :param create: whether to create the table if it doesn't exist :type create: bool :param recreate: whether to drop and recreate the table at every execution :type recreate: bool :param partition: target partition as a dict of partition columns and values. (templated) :type partition: dict :param delimiter: field delimiter in the file :type delimiter: str :param mssql_conn_id: source Microsoft SQL Server connection :type mssql_conn_id: str :param hive_conn_id: destination hive connection :type hive_conn_id: str :param tblproperties: TBLPROPERTIES of the hive table being created :type tblproperties: dict """
[docs] template_fields = ('sql', 'partition', 'hive_table')
[docs] template_ext = ('.sql',)
[docs] ui_color = '#a0e08c'
@apply_defaults def __init__( self, sql, hive_table, create=True, recreate=False, partition=None, delimiter=chr(1), mssql_conn_id='mssql_default', hive_cli_conn_id='hive_cli_default', tblproperties=None, *args, **kwargs): super(MsSqlToHiveTransfer, self).__init__(*args, **kwargs) self.sql = sql self.hive_table = hive_table self.partition = partition self.create = create self.recreate = recreate self.delimiter = delimiter self.mssql_conn_id = mssql_conn_id self.hive_cli_conn_id = hive_cli_conn_id self.partition = partition or {} self.tblproperties = tblproperties @classmethod
[docs] def type_map(cls, mssql_type): t = pymssql d = { t.BINARY.value: 'INT', t.DECIMAL.value: 'FLOAT', t.NUMBER.value: 'INT', } return d[mssql_type] if mssql_type in d else 'STRING'
[docs] def execute(self, context): hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id) mssql = MsSqlHook(mssql_conn_id=self.mssql_conn_id)"Dumping Microsoft SQL Server query results to local file") conn = mssql.get_conn() cursor = conn.cursor() cursor.execute(self.sql) with NamedTemporaryFile("w") as f: csv_writer = csv.writer(f, delimiter=self.delimiter, encoding='utf-8') field_dict = OrderedDict() col_count = 0 for field in cursor.description: col_count += 1 col_position = "Column{position}".format(position=col_count) field_dict[col_position if field[0] == '' else field[0]] \ = self.type_map(field[1]) csv_writer.writerows(cursor) f.flush() cursor.close() conn.close()"Loading file into Hive") hive.load_file(, self.hive_table, field_dict=field_dict, create=self.create, partition=self.partition, delimiter=self.delimiter, recreate=self.recreate, tblproperties=self.tblproperties)