Source code for airflow.contrib.operators.vertica_to_hive
# -*- coding: utf-8 -*-
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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',)
@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 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'
[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)
self.log.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()
self.log.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)