Coverage for airflow.operators.mssql_to_hive : 34%

from builtins import chr 

from collections import OrderedDict 

import unicodecsv as csv 

import logging 

from tempfile import NamedTemporaryFile 

import pymssql 

from airflow.hooks import HiveCliHook, MsSqlHook 

from airflow.models import BaseOperator 

from airflow.utils import apply_defaults 

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 

    :type sql: str 

    :param hive_table: target Hive table, use dot notation to target a 

    specific database 

    :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 

    :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 

    """ 

    template_fields = ('sql', 'partition', 'hive_table') 

    template_ext = ('.sql',) 

    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', 

            *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 {} 

    @classmethod 

    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' 

    def execute(self, context): 

        hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id) 

        mssql = MsSqlHook(mssql_conn_id=self.mssql_conn_id) 

        logging.info("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() 

            logging.info("Loading file into Hive") 

            hive.load_file( 

                f.name, 

                self.hive_table, 

                field_dict=field_dict, 

                create=self.create, 

                partition=self.partition, 

                delimiter=self.delimiter, 

                recreate=self.recreate)