Coverage for airflow.contrib.operators.vertica_to_hive : 35%

from builtins import chr 

from collections import OrderedDict 

import unicodecsv as csv 

import logging 

from tempfile import NamedTemporaryFile 

from airflow.hooks import HiveCliHook 

from airflow.contrib.hooks import VerticaHook 

from airflow.models import BaseOperator 

from airflow.utils import apply_defaults 

class VerticaToHiveTransfer(BaseOperator): 

    """ 

    Moves data from Vertia to Hive. The operator runs 

    your query against Vertia, 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 Vertia 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 vertica_conn_id: source Vertica connection 

    :type vertica_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 = '#b4e0ff' 

    @apply_defaults 

    def __init__( 

            self, 

            sql, 

            hive_table, 

            create=True, 

            recreate=False, 

            partition=None, 

            delimiter=chr(1), 

            vertica_conn_id='vertica_default', 

            hive_cli_conn_id='hive_cli_default', 

            *args, **kwargs): 

        super(VerticaToHiveTransfer, self).__init__(*args, **kwargs) 

        self.sql = sql 

        self.hive_table = hive_table 

        self.partition = partition 

        self.create = create 

        self.recreate = recreate 

        self.delimiter = str(delimiter) 

        self.vertica_conn_id = vertica_conn_id 

        self.hive_cli_conn_id = hive_cli_conn_id 

        self.partition = partition or {} 

    @classmethod 

    def type_map(cls, vertica_type): 

        # vertica-python datatype.py donot provied the full type mapping access. 

        # Manual hack. Reference: https://github.com/uber/vertica-python/blob/master/vertica_python/vertica/column.py 

        d = { 

            5: 'BOOLEAN', 

            6: 'INT', 

            7: 'FLOAT', 

            8: 'STRING', 

            9: 'STRING', 

            16: 'FLOAT', 

        } 

        return d[vertica_type] if vertica_type in d else 'STRING' 

    def execute(self, context): 

        hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id) 

        vertica = VerticaHook(vertica_conn_id=self.vertica_conn_id) 

        logging.info("Dumping Vertica query results to local file") 

        conn = vertica.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.iterate()) 

            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)