Source code for airflow.providers.apache.hive.transfers.vertica_to_hive
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"""This module contains an operator to move data from Vertica to Hive."""
from collections import OrderedDict
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence
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
if TYPE_CHECKING:
from airflow.utils.context import Context
[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)
:param hive_table: target Hive table, use dot notation to target a
specific database. (templated)
:param create: whether to create the table if it doesn't exist
:param recreate: whether to drop and recreate the table at every execution
:param partition: target partition as a dict of partition columns
and values. (templated)
:param delimiter: field delimiter in the file
:param vertica_conn_id: source Vertica connection
:param hive_cli_conn_id: Reference to the
:ref:`Hive CLI connection id <howto/connection:hive_cli>`.
"""
[docs] template_fields: Sequence[str] = ('sql', 'partition', 'hive_table')
[docs] template_ext: Sequence[str] = ('.sql',)
[docs] template_fields_renderers = {'sql': 'sql'}
def __init__(
self,
*,
sql: str,
hive_table: str,
create: bool = True,
recreate: bool = False,
partition: Optional[Dict] = None,
delimiter: str = chr(1),
vertica_conn_id: str = 'vertica_default',
hive_cli_conn_id: str = 'hive_cli_default',
**kwargs: Any,
) -> None:
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: '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,
)
