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# -*- coding: utf-8 -*-
#
# 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 operator for copying
data from Cassandra to Google cloud storage in JSON format.
"""
import json
import warnings
from base64 import b64encode
from datetime import datetime
from decimal import Decimal
from tempfile import NamedTemporaryFile
from uuid import UUID
from cassandra.util import Date, Time, SortedSet, OrderedMapSerializedKey
from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook
from airflow.contrib.hooks.cassandra_hook import CassandraHook
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
class CassandraToGoogleCloudStorageOperator(BaseOperator):
"""
Copy data from Cassandra to Google cloud storage in JSON format
Note: Arrays of arrays are not supported.
:param cql: The CQL to execute on the Cassandra table.
:type cql: str
:param bucket: The bucket to upload to.
:type bucket: str
:param filename: The filename to use as the object name when uploading
to Google cloud storage. A {} should be specified in the filename
to allow the operator to inject file numbers in cases where the
file is split due to size.
:type filename: str
:param schema_filename: If set, the filename to use as the object name
when uploading a .json file containing the BigQuery schema fields
for the table that was dumped from MySQL.
:type schema_filename: str
:param approx_max_file_size_bytes: This operator supports the ability
to split large table dumps into multiple files (see notes in the
filename param docs above). This param allows developers to specify the
file size of the splits. Check https://cloud.google.com/storage/quotas
to see the maximum allowed file size for a single object.
:type approx_max_file_size_bytes: long
:param cassandra_conn_id: Reference to a specific Cassandra hook.
:type cassandra_conn_id: str
:param gzip: Option to compress file for upload
:type gzip: bool
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud Platform.
:type gcp_conn_id: str
:param google_cloud_storage_conn_id: (Deprecated) The connection ID used to connect to Google Cloud
Platform. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.
:type google_cloud_storage_conn_id: str
:param delegate_to: The account to impersonate, if any. For this to
work, the service account making the request must have domain-wide
delegation enabled.
:type delegate_to: str
"""
template_fields = ('cql', 'bucket', 'filename', 'schema_filename',)
template_ext = ('.cql',)
ui_color = '#a0e08c'
@apply_defaults
def __init__(self,
cql,
bucket,
filename,
schema_filename=None,
approx_max_file_size_bytes=1900000000,
gzip=False,
cassandra_conn_id='cassandra_default',
gcp_conn_id='google_cloud_default',
google_cloud_storage_conn_id=None,
delegate_to=None,
*args,
**kwargs):
super().__init__(*args, **kwargs)
if google_cloud_storage_conn_id:
warnings.warn(
"The google_cloud_storage_conn_id parameter has been deprecated. You should pass "
"the gcp_conn_id parameter.", DeprecationWarning, stacklevel=3)
gcp_conn_id = google_cloud_storage_conn_id
self.cql = cql
self.bucket = bucket
self.filename = filename
self.schema_filename = schema_filename
self.approx_max_file_size_bytes = approx_max_file_size_bytes
self.cassandra_conn_id = cassandra_conn_id
self.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
self.gzip = gzip
self.hook = None
# Default Cassandra to BigQuery type mapping
CQL_TYPE_MAP = {
'BytesType': 'BYTES',
'DecimalType': 'FLOAT',
'UUIDType': 'BYTES',
'BooleanType': 'BOOL',
'ByteType': 'INTEGER',
'AsciiType': 'STRING',
'FloatType': 'FLOAT',
'DoubleType': 'FLOAT',
'LongType': 'INTEGER',
'Int32Type': 'INTEGER',
'IntegerType': 'INTEGER',
'InetAddressType': 'STRING',
'CounterColumnType': 'INTEGER',
'DateType': 'TIMESTAMP',
'SimpleDateType': 'DATE',
'TimestampType': 'TIMESTAMP',
'TimeUUIDType': 'BYTES',
'ShortType': 'INTEGER',
'TimeType': 'TIME',
'DurationType': 'INTEGER',
'UTF8Type': 'STRING',
'VarcharType': 'STRING',
}
def execute(self, context):
cursor = self._query_cassandra()
files_to_upload = self._write_local_data_files(cursor)
# If a schema is set, create a BQ schema JSON file.
if self.schema_filename:
files_to_upload.update(self._write_local_schema_file(cursor))
# Flush all files before uploading
for file_handle in files_to_upload.values():
file_handle.flush()
self._upload_to_gcs(files_to_upload)
# Close all temp file handles.
for file_handle in files_to_upload.values():
file_handle.close()
# Close all sessions and connection associated with this Cassandra cluster
self.hook.shutdown_cluster()
def _query_cassandra(self):
"""
Queries cassandra and returns a cursor to the results.
"""
self.hook = CassandraHook(cassandra_conn_id=self.cassandra_conn_id)
session = self.hook.get_conn()
cursor = session.execute(self.cql)
return cursor
def _write_local_data_files(self, cursor):
"""
Takes a cursor, and writes results to a local file.
:return: A dictionary where keys are filenames to be used as object
names in GCS, and values are file handles to local files that
contain the data for the GCS objects.
"""
file_no = 0
tmp_file_handle = NamedTemporaryFile(delete=True)
tmp_file_handles = {self.filename.format(file_no): tmp_file_handle}
for row in cursor:
row_dict = self.generate_data_dict(row._fields, row)
s = json.dumps(row_dict).encode('utf-8')
tmp_file_handle.write(s)
# Append newline to make dumps BigQuery compatible.
tmp_file_handle.write(b'\n')
if tmp_file_handle.tell() >= self.approx_max_file_size_bytes:
file_no += 1
tmp_file_handle = NamedTemporaryFile(delete=True)
tmp_file_handles[self.filename.format(file_no)] = tmp_file_handle
return tmp_file_handles
def _write_local_schema_file(self, cursor):
"""
Takes a cursor, and writes the BigQuery schema for the results to a
local file system.
:return: A dictionary where key is a filename to be used as an object
name in GCS, and values are file handles to local files that
contains the BigQuery schema fields in .json format.
"""
schema = []
tmp_schema_file_handle = NamedTemporaryFile(delete=True)
for name, type in zip(cursor.column_names, cursor.column_types):
schema.append(self.generate_schema_dict(name, type))
json_serialized_schema = json.dumps(schema).encode('utf-8')
tmp_schema_file_handle.write(json_serialized_schema)
return {self.schema_filename: tmp_schema_file_handle}
def _upload_to_gcs(self, files_to_upload):
hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to)
for object, tmp_file_handle in files_to_upload.items():
hook.upload(self.bucket, object, tmp_file_handle.name, 'application/json', self.gzip)
@classmethod
def generate_data_dict(cls, names, values):
row_dict = {}
for name, value in zip(names, values):
row_dict.update({name: cls.convert_value(name, value)})
return row_dict
@classmethod
def convert_value(cls, name, value):
if not value:
return value
elif isinstance(value, (str, int, float, bool, dict)):
return value
elif isinstance(value, bytes):
return b64encode(value).decode('ascii')
elif isinstance(value, UUID):
return b64encode(value.bytes).decode('ascii')
elif isinstance(value, (datetime, Date)):
return str(value)
elif isinstance(value, Decimal):
return float(value)
elif isinstance(value, Time):
return str(value).split('.')[0]
elif isinstance(value, (list, SortedSet)):
return cls.convert_array_types(name, value)
elif hasattr(value, '_fields'):
return cls.convert_user_type(name, value)
elif isinstance(value, tuple):
return cls.convert_tuple_type(name, value)
elif isinstance(value, OrderedMapSerializedKey):
return cls.convert_map_type(name, value)
else:
raise AirflowException('unexpected value: ' + str(value))
@classmethod
def convert_array_types(cls, name, value):
return [cls.convert_value(name, nested_value) for nested_value in value]
@classmethod
def convert_user_type(cls, name, value):
"""
Converts a user type to RECORD that contains n fields, where n is the
number of attributes. Each element in the user type class will be converted to its
corresponding data type in BQ.
"""
names = value._fields
values = [cls.convert_value(name, getattr(value, name)) for name in names]
return cls.generate_data_dict(names, values)
@classmethod
def convert_tuple_type(cls, name, value):
"""
Converts a tuple to RECORD that contains n fields, each will be converted
to its corresponding data type in bq and will be named 'field_<index>', where
index is determined by the order of the tuple elements defined in cassandra.
"""
names = ['field_' + str(i) for i in range(len(value))]
values = [cls.convert_value(name, value) for name, value in zip(names, value)]
return cls.generate_data_dict(names, values)
@classmethod
def convert_map_type(cls, name, value):
"""
Converts a map to a repeated RECORD that contains two fields: 'key' and 'value',
each will be converted to its corresponding data type in BQ.
"""
converted_map = []
for k, v in zip(value.keys(), value.values()):
converted_map.append({
'key': cls.convert_value('key', k),
'value': cls.convert_value('value', v)
})
return converted_map
@classmethod
def generate_schema_dict(cls, name, type):
field_schema = dict()
field_schema.update({'name': name})
field_schema.update({'type': cls.get_bq_type(type)})
field_schema.update({'mode': cls.get_bq_mode(type)})
fields = cls.get_bq_fields(name, type)
if fields:
field_schema.update({'fields': fields})
return field_schema
@classmethod
def get_bq_fields(cls, name, type):
fields = []
if not cls.is_simple_type(type):
names, types = [], []
if cls.is_array_type(type) and cls.is_record_type(type.subtypes[0]):
names = type.subtypes[0].fieldnames
types = type.subtypes[0].subtypes
elif cls.is_record_type(type):
names = type.fieldnames
types = type.subtypes
if types and not names and type.cassname == 'TupleType':
names = ['field_' + str(i) for i in range(len(types))]
elif types and not names and type.cassname == 'MapType':
names = ['key', 'value']
for name, type in zip(names, types):
field = cls.generate_schema_dict(name, type)
fields.append(field)
return fields
@classmethod
def is_simple_type(cls, type):
return type.cassname in CassandraToGoogleCloudStorageOperator.CQL_TYPE_MAP
@classmethod
def is_array_type(cls, type):
return type.cassname in ['ListType', 'SetType']
@classmethod
def is_record_type(cls, type):
return type.cassname in ['UserType', 'TupleType', 'MapType']
@classmethod
def get_bq_type(cls, type):
if cls.is_simple_type(type):
return CassandraToGoogleCloudStorageOperator.CQL_TYPE_MAP[type.cassname]
elif cls.is_record_type(type):
return 'RECORD'
elif cls.is_array_type(type):
return cls.get_bq_type(type.subtypes[0])
else:
raise AirflowException('Not a supported type: ' + type.cassname)
@classmethod
def get_bq_mode(cls, type):
if cls.is_array_type(type) or type.cassname == 'MapType':
return 'REPEATED'
elif cls.is_record_type(type) or cls.is_simple_type(type):
return 'NULLABLE'
else:
raise AirflowException('Not a supported type: ' + type.cassname)
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