Source code for airflow.providers.apache.hive.transfers.vertica_to_hive
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"""This module contains operator to move data from Vertica to Hive."""
from collections import OrderedDict
from tempfile import NamedTemporaryFile
import unicodecsv as csv
from airflow.models import BaseOperator
from airflow.providers.apache.hive.hooks.hive import HiveCliHook
from airflow.providers.vertica.hooks.vertica import VerticaHook
from airflow.utils.decorators import apply_defaults
[docs]class VerticaToHiveOperator(BaseOperator):
"""
Moves data from Vertica to Hive. The operator runs
your query against Vertica, 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 Vertica 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',
**kwargs,
):
super().__init__(**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 does not provide the full type mapping access.
Manual hack. Reference:
https://github.com/uber/vertica-python/blob/master/vertica_python/vertica/column.py
"""
type_map = {
5: 'BOOLEAN',
6: 'INT',
7: 'FLOAT',
8: 'STRING',
9: 'STRING',
16: 'FLOAT',
}
return type_map.get(vertica_type, '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 = f"Column{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,
)
