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"""
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 typing import TYPE_CHECKING, Any, Dict, Iterable, List, NewType, Optional, Sequence, Tuple, Union
from uuid import UUID
from cassandra.util import Date, OrderedMapSerializedKey, SortedSet, Time
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.apache.cassandra.hooks.cassandra import CassandraHook
from airflow.providers.google.cloud.hooks.gcs import GCSHook
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]NotSetType = NewType('NotSetType', object)
[docs]NOT_SET = NotSetType(object())
[docs]class CassandraToGCSOperator(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.
:param bucket: The bucket to upload to.
: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.
: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.
: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.
:param cassandra_conn_id: Reference to a specific Cassandra hook.
:param gzip: Option to compress file for upload
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param google_cloud_storage_conn_id: (Deprecated) The connection ID used to connect to Google Cloud.
This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.
:param delegate_to: The account to impersonate using domain-wide delegation of authority,
if any. For this to work, the service account making the request must have
domain-wide delegation enabled.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
:param query_timeout: (Optional) The amount of time, in seconds, used to execute the Cassandra query.
If not set, the timeout value will be set in Session.execute() by Cassandra driver.
If set to None, there is no timeout.
"""
[docs] template_fields: Sequence[str] = (
'cql',
'bucket',
'filename',
'schema_filename',
'impersonation_chain',
)
[docs] template_ext: Sequence[str] = ('.cql',)
def __init__(
self,
*,
cql: str,
bucket: str,
filename: str,
schema_filename: Optional[str] = None,
approx_max_file_size_bytes: int = 1900000000,
gzip: bool = False,
cassandra_conn_id: str = 'cassandra_default',
gcp_conn_id: str = 'google_cloud_default',
google_cloud_storage_conn_id: Optional[str] = None,
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
query_timeout: Union[float, None, NotSetType] = NOT_SET,
**kwargs,
) -> None:
super().__init__(**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.impersonation_chain = impersonation_chain
self.query_timeout = query_timeout
# Default Cassandra to BigQuery type mapping
[docs] CQL_TYPE_MAP = {
'BytesType': 'STRING',
'DecimalType': 'FLOAT',
'UUIDType': 'STRING',
'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': 'STRING',
'ShortType': 'INTEGER',
'TimeType': 'TIME',
'DurationType': 'INTEGER',
'UTF8Type': 'STRING',
'VarcharType': 'STRING',
}
[docs] def execute(self, context: 'Context'):
hook = CassandraHook(cassandra_conn_id=self.cassandra_conn_id)
query_extra = {}
if self.query_timeout is not NOT_SET:
query_extra['timeout'] = self.query_timeout
cursor = hook.get_conn().execute(self.cql, **query_extra)
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
hook.shutdown_cluster()
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)
content = json.dumps(row_dict).encode('utf-8')
tmp_file_handle.write(content)
# 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: Dict[str, Any]):
hook = GCSHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
for obj, tmp_file_handle in files_to_upload.items():
hook.upload(
bucket_name=self.bucket,
object_name=obj,
filename=tmp_file_handle.name,
mime_type='application/json',
gzip=self.gzip,
)
@classmethod
[docs] def generate_data_dict(cls, names: Iterable[str], values: Any) -> Dict[str, Any]:
"""Generates data structure that will be stored as file in GCS."""
return {n: cls.convert_value(v) for n, v in zip(names, values)}
@classmethod
[docs] def convert_value(cls, value: Optional[Any]) -> Optional[Any]:
"""Convert value to BQ type."""
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(value)
elif hasattr(value, '_fields'):
return cls.convert_user_type(value)
elif isinstance(value, tuple):
return cls.convert_tuple_type(value)
elif isinstance(value, OrderedMapSerializedKey):
return cls.convert_map_type(value)
else:
raise AirflowException('Unexpected value: ' + str(value))
@classmethod
[docs] def convert_array_types(cls, value: Union[List[Any], SortedSet]) -> List[Any]:
"""Maps convert_value over array."""
return [cls.convert_value(nested_value) for nested_value in value]
@classmethod
[docs] def convert_user_type(cls, value: Any) -> Dict[str, Any]:
"""
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(getattr(value, name)) for name in names]
return cls.generate_data_dict(names, values)
@classmethod
[docs] def convert_tuple_type(cls, values: Tuple[Any]) -> Dict[str, Any]:
"""
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(values))]
return cls.generate_data_dict(names, values)
@classmethod
[docs] def convert_map_type(cls, value: OrderedMapSerializedKey) -> List[Dict[str, Any]]:
"""
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(k), 'value': cls.convert_value(v)})
return converted_map
@classmethod
[docs] def generate_schema_dict(cls, name: str, type_: Any) -> Dict[str, Any]:
"""Generates BQ schema."""
field_schema: Dict[str, Any] = {}
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(type_)
if fields:
field_schema.update({'fields': fields})
return field_schema
@classmethod
[docs] def get_bq_fields(cls, type_: Any) -> List[Dict[str, Any]]:
"""Converts non simple type value to BQ representation."""
if cls.is_simple_type(type_):
return []
# In case of not simple type
names: List[str] = []
types: List[Any] = []
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']
return [cls.generate_schema_dict(n, t) for n, t in zip(names, types)]
@staticmethod
[docs] def is_simple_type(type_: Any) -> bool:
"""Check if type is a simple type."""
return type_.cassname in CassandraToGCSOperator.CQL_TYPE_MAP
@staticmethod
[docs] def is_array_type(type_: Any) -> bool:
"""Check if type is an array type."""
return type_.cassname in ['ListType', 'SetType']
@staticmethod
[docs] def is_record_type(type_: Any) -> bool:
"""Checks the record type."""
return type_.cassname in ['UserType', 'TupleType', 'MapType']
@classmethod
[docs] def get_bq_type(cls, type_: Any) -> str:
"""Converts type to equivalent BQ type."""
if cls.is_simple_type(type_):
return CassandraToGCSOperator.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
[docs] def get_bq_mode(cls, type_: Any) -> str:
"""Converts type to equivalent BQ mode."""
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)