Source code for airflow.operators.s3_to_hive_operator

# -*- coding: utf-8 -*-
# Licensed 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from builtins import next
from builtins import zip
from tempfile import NamedTemporaryFile
from airflow.utils.file import TemporaryDirectory
import gzip
import bz2
import tempfile
import os

from airflow.exceptions import AirflowException
from airflow.hooks.S3_hook import S3Hook
from airflow.hooks.hive_hooks import HiveCliHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.utils.compression import uncompress_file

[docs]class S3ToHiveTransfer(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 :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 :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 :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 delimiter: field delimiter in the file :type delimiter: str :param aws_conn_id: source s3 connection :type aws_conn_id: str :param hive_cli_conn_id: destination hive connection :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 """ template_fields = ('s3_key', 'partition', 'hive_table') template_ext = () ui_color = '#a0e08c' @apply_defaults def __init__( self, s3_key, field_dict, hive_table, delimiter=',', create=True, recreate=False, partition=None, headers=False, check_headers=False, wildcard_match=False, aws_conn_id='aws_default', hive_cli_conn_id='hive_cli_default', input_compressed=False, tblproperties=None, *args, **kwargs): super(S3ToHiveTransfer, self).__init__(*args, **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.input_compressed = input_compressed self.tblproperties = tblproperties 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") def execute(self, context): # Downloading file from S3 self.s3 = S3Hook(aws_conn_id=self.aws_conn_id) self.hive = HiveCliHook(hive_cli_conn_id=self.hive_cli_conn_id)"Downloading S3 file") if self.wildcard_match: if not self.s3.check_for_wildcard_key(self.s3_key): raise AirflowException("No key matches {0}" .format(self.s3_key)) s3_key_object = self.s3.get_wildcard_key(self.s3_key) else: if not self.s3.check_for_key(self.s3_key): raise AirflowException( "The key {0} does not exists".format(self.s3_key)) s3_key_object = self.s3.get_key(self.s3_key) root, file_ext = os.path.splitext(s3_key_object.key) with TemporaryDirectory(prefix='tmps32hive_') as tmp_dir,\ NamedTemporaryFile(mode="wb", dir=tmp_dir, suffix=file_ext) as f:"Dumping S3 key {0} contents to local file {1}" .format(s3_key_object.key, s3_key_object.download_fileobj(f) f.flush() if not self.headers:"Loading file %s into Hive", self.hive.load_file(, 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:"Uncompressing file %s", fn_uncompressed = uncompress_file(, file_ext, tmp_dir)"Uncompressed to %s", fn_uncompressed) # uncompressed file available now so deleting # compressed file to save disk space f.close() else: fn_uncompressed = # Testing if header matches field_dict if self.check_headers:"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"Removing header from file %s", fn_uncompressed) headless_file = ( self._delete_top_row_and_compress(fn_uncompressed, file_ext, tmp_dir))"Headless file %s", headless_file)"Loading file %s into Hive", headless_file) self.hive.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, 'rt') as f: header_line = f.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 {header_list}\n" "Field names: \n {field_names}\n" "".format(**locals())) 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 {header_list}\n" "Field names: \n {field_names}\n" "".format(**locals())) return False else: return True def _delete_top_row_and_compress( self, 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 os_fh_output, 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: next(f_in) for line in f_in: f_out.write(line) return fn_output