Source code for airflow.contrib.operators.vertica_to_hive

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

from airflow.hooks.hive_hooks import HiveCliHook
from airflow.contrib.hooks.vertica_hook import VerticaHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults

[docs]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. (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 vertica_conn_id: source Vertica connection :type vertica_conn_id: str :param hive_conn_id: destination hive connection :type hive_conn_id: str """
[docs] template_fields = ('sql', 'partition', 'hive_table')
[docs] template_ext = ('.sql',)
[docs] 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
[docs] def type_map(cls, vertica_type): # vertica-python donot provied the full type mapping access. # Manual hack. # Reference: # d = { 5: 'BOOLEAN', 6: 'INT', 7: 'FLOAT', 8: 'STRING', 9: 'STRING', 16: 'FLOAT', } return d[vertica_type] if vertica_type in d else 'STRING'
[docs] def execute(self, context): hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id) vertica = VerticaHook(vertica_conn_id=self.vertica_conn_id)"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()"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)