#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains an operator to move data from an S3 bucket to Hive."""
import bz2
import gzip
import os
import tempfile
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import TYPE_CHECKING, Dict, Optional, Sequence, Union
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.apache.hive.hooks.hive import HiveCliHook
from airflow.utils.compression import uncompress_file
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class S3ToHiveOperator(BaseOperator):
"""
Moves data from S3 to Hive. The operator downloads a file from S3,
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 from.
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 tables 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 s3_key: The key to be retrieved from S3. (templated)
:type s3_key: str
:param field_dict: A dictionary of the fields name in the file
as keys and their Hive types as values
:type field_dict: dict
:param hive_table: target Hive table, use dot notation to target a
specific database. (templated)
:type hive_table: str
:param delimiter: field delimiter in the file
:type delimiter: 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 headers: whether the file contains column names on the first
line
:type headers: bool
:param check_headers: whether the column names on the first line should be
checked against the keys of field_dict
:type check_headers: bool
:param wildcard_match: whether the s3_key should be interpreted as a Unix
wildcard pattern
:type wildcard_match: bool
:param aws_conn_id: source s3 connection
:type aws_conn_id: str
:param verify: Whether or not to verify SSL certificates for S3 connection.
By default SSL certificates are verified.
You can provide the following values:
- ``False``: do not validate SSL certificates. SSL will still be used
(unless use_ssl is False), but SSL certificates will not be
verified.
- ``path/to/cert/bundle.pem``: A filename of the CA cert bundle to uses.
You can specify this argument if you want to use a different
CA cert bundle than the one used by botocore.
:type verify: bool or str
:param hive_cli_conn_id: Reference to the
:ref:`Hive CLI connection id <howto/connection:hive_cli>`.
:type hive_cli_conn_id: str
:param input_compressed: Boolean to determine if file decompression is
required to process headers
:type input_compressed: bool
:param tblproperties: TBLPROPERTIES of the hive table being created
:type tblproperties: dict
:param select_expression: S3 Select expression
:type select_expression: str
"""
[docs] template_fields: Sequence[str] = ('s3_key', 'partition', 'hive_table')
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
s3_key: str,
field_dict: Dict,
hive_table: str,
delimiter: str = ',',
create: bool = True,
recreate: bool = False,
partition: Optional[Dict] = None,
headers: bool = False,
check_headers: bool = False,
wildcard_match: bool = False,
aws_conn_id: str = 'aws_default',
verify: Optional[Union[bool, str]] = None,
hive_cli_conn_id: str = 'hive_cli_default',
input_compressed: bool = False,
tblproperties: Optional[Dict] = None,
select_expression: Optional[str] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.s3_key = s3_key
self.field_dict = field_dict
self.hive_table = hive_table
self.delimiter = delimiter
self.create = create
self.recreate = recreate
self.partition = partition
self.headers = headers
self.check_headers = check_headers
self.wildcard_match = wildcard_match
self.hive_cli_conn_id = hive_cli_conn_id
self.aws_conn_id = aws_conn_id
self.verify = verify
self.input_compressed = input_compressed
self.tblproperties = tblproperties
self.select_expression = select_expression
if self.check_headers and not (self.field_dict is not None and self.headers):
raise AirflowException("To check_headers provide field_dict and headers")
[docs] def execute(self, context: 'Context'):
# Downloading file from S3
s3_hook = S3Hook(aws_conn_id=self.aws_conn_id, verify=self.verify)
hive_hook = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)
self.log.info("Downloading S3 file")
if self.wildcard_match:
if not s3_hook.check_for_wildcard_key(self.s3_key):
raise AirflowException(f"No key matches {self.s3_key}")
s3_key_object = s3_hook.get_wildcard_key(self.s3_key)
else:
if not s3_hook.check_for_key(self.s3_key):
raise AirflowException(f"The key {self.s3_key} does not exists")
s3_key_object = s3_hook.get_key(self.s3_key)
_, file_ext = os.path.splitext(s3_key_object.key)
if self.select_expression and self.input_compressed and file_ext.lower() != '.gz':
raise AirflowException("GZIP is the only compression format Amazon S3 Select supports")
with TemporaryDirectory(prefix='tmps32hive_') as tmp_dir, NamedTemporaryFile(
mode="wb", dir=tmp_dir, suffix=file_ext
) as f:
self.log.info("Dumping S3 key %s contents to local file %s", s3_key_object.key, f.name)
if self.select_expression:
option = {}
if self.headers:
option['FileHeaderInfo'] = 'USE'
if self.delimiter:
option['FieldDelimiter'] = self.delimiter
input_serialization = {'CSV': option}
if self.input_compressed:
input_serialization['CompressionType'] = 'GZIP'
content = s3_hook.select_key(
bucket_name=s3_key_object.bucket_name,
key=s3_key_object.key,
expression=self.select_expression,
input_serialization=input_serialization,
)
f.write(content.encode("utf-8"))
else:
s3_key_object.download_fileobj(f)
f.flush()
if self.select_expression or not self.headers:
self.log.info("Loading file %s into Hive", f.name)
hive_hook.load_file(
f.name,
self.hive_table,
field_dict=self.field_dict,
create=self.create,
partition=self.partition,
delimiter=self.delimiter,
recreate=self.recreate,
tblproperties=self.tblproperties,
)
else:
# Decompressing file
if self.input_compressed:
self.log.info("Uncompressing file %s", f.name)
fn_uncompressed = uncompress_file(f.name, file_ext, tmp_dir)
self.log.info("Uncompressed to %s", fn_uncompressed)
# uncompressed file available now so deleting
# compressed file to save disk space
f.close()
else:
fn_uncompressed = f.name
# Testing if header matches field_dict
if self.check_headers:
self.log.info("Matching file header against field_dict")
header_list = self._get_top_row_as_list(fn_uncompressed)
if not self._match_headers(header_list):
raise AirflowException("Header check failed")
# Deleting top header row
self.log.info("Removing header from file %s", fn_uncompressed)
headless_file = self._delete_top_row_and_compress(fn_uncompressed, file_ext, tmp_dir)
self.log.info("Headless file %s", headless_file)
self.log.info("Loading file %s into Hive", headless_file)
hive_hook.load_file(
headless_file,
self.hive_table,
field_dict=self.field_dict,
create=self.create,
partition=self.partition,
delimiter=self.delimiter,
recreate=self.recreate,
tblproperties=self.tblproperties,
)
def _get_top_row_as_list(self, file_name):
with open(file_name) as file:
header_line = file.readline().strip()
header_list = header_line.split(self.delimiter)
return header_list
def _match_headers(self, header_list):
if not header_list:
raise AirflowException("Unable to retrieve header row from file")
field_names = self.field_dict.keys()
if len(field_names) != len(header_list):
self.log.warning(
"Headers count mismatch File headers:\n %s\nField names: \n %s\n", header_list, field_names
)
return False
test_field_match = [h1.lower() == h2.lower() for h1, h2 in zip(header_list, field_names)]
if not all(test_field_match):
self.log.warning(
"Headers do not match field names File headers:\n %s\nField names: \n %s\n",
header_list,
field_names,
)
return False
else:
return True
@staticmethod
def _delete_top_row_and_compress(input_file_name, output_file_ext, dest_dir):
# When output_file_ext is not defined, file is not compressed
open_fn = open
if output_file_ext.lower() == '.gz':
open_fn = gzip.GzipFile
elif output_file_ext.lower() == '.bz2':
open_fn = bz2.BZ2File
_, fn_output = tempfile.mkstemp(suffix=output_file_ext, dir=dest_dir)
with open(input_file_name, 'rb') as f_in, open_fn(fn_output, 'wb') as f_out:
f_in.seek(0)
next(f_in)
for line in f_in:
f_out.write(line)
return fn_output