repo_name stringclasses 4
values | method_name stringlengths 3 72 | method_code stringlengths 87 3.59k | method_summary stringlengths 12 196 | original_method_code stringlengths 129 8.98k | method_path stringlengths 15 136 |
|---|---|---|---|---|---|
apache/airflow | S3Hook.read_key | def read_key(self, key, bucket_name=None):
obj = self.get_key(key, bucket_name)
return obj.get()['Body'].read().decode('utf-8') | Reads a key from S3 | def read_key(self, key, bucket_name=None):
"""
Reads a key from S3
:param key: S3 key that will point to the file
:type key: str
:param bucket_name: Name of the bucket in which the file is stored
:type bucket_name: str
"""
obj = self.get_key(key, bucket_... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.select_key | def select_key(self, key, bucket_name=None,
expression='SELECT * FROM S3Object',
expression_type='SQL',
input_serialization=None,
output_serialization=None):
if input_serialization is None:
input_serialization = {'CSV': {}}
... | Reads a key with S3 Select. | def select_key(self, key, bucket_name=None,
expression='SELECT * FROM S3Object',
expression_type='SQL',
input_serialization=None,
output_serialization=None):
"""
Reads a key with S3 Select.
:param key: S3 key that will ... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.check_for_wildcard_key | def check_for_wildcard_key(self,
wildcard_key, bucket_name=None, delimiter=''):
return self.get_wildcard_key(wildcard_key=wildcard_key,
bucket_name=bucket_name,
delimiter=delimiter) is not None | Checks that a key matching a wildcard expression exists in a bucket | def check_for_wildcard_key(self,
wildcard_key, bucket_name=None, delimiter=''):
"""
Checks that a key matching a wildcard expression exists in a bucket
:param wildcard_key: the path to the key
:type wildcard_key: str
:param bucket_name: the name of... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.load_file | def load_file(self,
filename,
key,
bucket_name=None,
replace=False,
encrypt=False):
if not bucket_name:
(bucket_name, key) = self.parse_s3_url(key)
if not replace and self.check_for_key(key, bucket_nam... | Loads a local file to S3 | def load_file(self,
filename,
key,
bucket_name=None,
replace=False,
encrypt=False):
"""
Loads a local file to S3
:param filename: name of the file to load.
:type filename: str
:param key: S... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.load_string | def load_string(self,
string_data,
key,
bucket_name=None,
replace=False,
encrypt=False,
encoding='utf-8'):
self.load_bytes(string_data.encode(encoding),
key=key,
... | Loads a string to S3 This is provided as a convenience to drop a string in S3. It uses the boto infrastructure to ship a file to s3. | def load_string(self,
string_data,
key,
bucket_name=None,
replace=False,
encrypt=False,
encoding='utf-8'):
"""
Loads a string to S3
This is provided as a convenience to drop a... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.load_bytes | def load_bytes(self,
bytes_data,
key,
bucket_name=None,
replace=False,
encrypt=False):
if not bucket_name:
(bucket_name, key) = self.parse_s3_url(key)
if not replace and self.check_for_key(key, bu... | Loads bytes to S3 This is provided as a convenience to drop a string in S3. It uses the boto infrastructure to ship a file to s3. | def load_bytes(self,
bytes_data,
key,
bucket_name=None,
replace=False,
encrypt=False):
"""
Loads bytes to S3
This is provided as a convenience to drop a string in S3. It uses the
boto infrastr... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.load_file_obj | def load_file_obj(self,
file_obj,
key,
bucket_name=None,
replace=False,
encrypt=False):
if not bucket_name:
(bucket_name, key) = self.parse_s3_url(key)
if not replace and self.check... | Loads a file object to S3 | def load_file_obj(self,
file_obj,
key,
bucket_name=None,
replace=False,
encrypt=False):
"""
Loads a file object to S3
:param file_obj: The file-like object to set as the content for the... | airflow/hooks/S3_hook.py |
apache/airflow | S3Hook.copy_object | def copy_object(self,
source_bucket_key,
dest_bucket_key,
source_bucket_name=None,
dest_bucket_name=None,
source_version_id=None):
if dest_bucket_name is None:
dest_bucket_name, dest_bucket_key = self... | Creates a copy of an object that is already stored in S3. | def copy_object(self,
source_bucket_key,
dest_bucket_key,
source_bucket_name=None,
dest_bucket_name=None,
source_version_id=None):
"""
Creates a copy of an object that is already stored in S3.
No... | airflow/hooks/S3_hook.py |
apache/airflow | CassandraToGoogleCloudStorageOperator._query_cassandra | def _query_cassandra(self):
self.hook = CassandraHook(cassandra_conn_id=self.cassandra_conn_id)
session = self.hook.get_conn()
cursor = session.execute(self.cql)
return cursor | Queries cassandra and returns a cursor to the results. | 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 | airflow/contrib/operators/cassandra_to_gcs.py |
apache/airflow | CassandraToGoogleCloudStorageOperator.convert_user_type | def convert_user_type(cls, name, value):
names = value._fields
values = [cls.convert_value(name, getattr(value, name)) for name in names]
return cls.generate_data_dict(names, values) | 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. | 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 = ... | airflow/contrib/operators/cassandra_to_gcs.py |
apache/airflow | send_email | def send_email(to, subject, html_content, files=None, dryrun=False, cc=None,
bcc=None, mime_subtype='mixed', sandbox_mode=False, **kwargs):
if files is None:
files = []
mail = Mail()
from_email = kwargs.get('from_email') or os.environ.get('SENDGRID_MAIL_FROM')
from_name = kwargs.... | Send an email with html content using sendgrid. To use this | def send_email(to, subject, html_content, files=None, dryrun=False, cc=None,
bcc=None, mime_subtype='mixed', sandbox_mode=False, **kwargs):
"""
Send an email with html content using sendgrid.
To use this plugin:
0. include sendgrid subpackage as part of your Airflow installation, e.g.,
... | airflow/contrib/utils/sendgrid.py |
apache/airflow | GCPSpeechToTextHook.get_conn | def get_conn(self):
if not self._client:
self._client = SpeechClient(credentials=self._get_credentials())
return self._client | Retrieves connection to Cloud Speech. | def get_conn(self):
"""
Retrieves connection to Cloud Speech.
:return: Google Cloud Speech client object.
:rtype: google.cloud.speech_v1.SpeechClient
"""
if not self._client:
self._client = SpeechClient(credentials=self._get_credentials())
return self... | airflow/contrib/hooks/gcp_speech_to_text_hook.py |
apache/airflow | GCPSpeechToTextHook.recognize_speech | def recognize_speech(self, config, audio, retry=None, timeout=None):
client = self.get_conn()
response = client.recognize(config=config, audio=audio, retry=retry, timeout=timeout)
self.log.info("Recognised speech: %s" % response)
return response | Recognizes audio input | def recognize_speech(self, config, audio, retry=None, timeout=None):
"""
Recognizes audio input
:param config: information to the recognizer that specifies how to process the request.
https://googleapis.github.io/google-cloud-python/latest/speech/gapic/v1/types.html#google.cloud.spe... | airflow/contrib/hooks/gcp_speech_to_text_hook.py |
apache/airflow | SparkSqlOperator.execute | def execute(self, context):
self._hook = SparkSqlHook(sql=self._sql,
conf=self._conf,
conn_id=self._conn_id,
total_executor_cores=self._total_executor_cores,
executor_cores=sel... | Call the SparkSqlHook to run the provided sql query | def execute(self, context):
"""
Call the SparkSqlHook to run the provided sql query
"""
self._hook = SparkSqlHook(sql=self._sql,
conf=self._conf,
conn_id=self._conn_id,
total_executor_co... | airflow/contrib/operators/spark_sql_operator.py |
apache/airflow | load_entrypoint_plugins | def load_entrypoint_plugins(entry_points, airflow_plugins):
for entry_point in entry_points:
log.debug('Importing entry_point plugin %s', entry_point.name)
plugin_obj = entry_point.load()
if is_valid_plugin(plugin_obj, airflow_plugins):
if callable(getattr(plugin_obj, 'on_load', ... | Load AirflowPlugin subclasses from the entrypoints provided. The entry_point group should be 'airflow.plugins'. | def load_entrypoint_plugins(entry_points, airflow_plugins):
"""
Load AirflowPlugin subclasses from the entrypoints
provided. The entry_point group should be 'airflow.plugins'.
:param entry_points: A collection of entrypoints to search for plugins
:type entry_points: Generator[setuptools.EntryPoint,... | airflow/plugins_manager.py |
apache/airflow | is_valid_plugin | def is_valid_plugin(plugin_obj, existing_plugins):
if (
inspect.isclass(plugin_obj) and
issubclass(plugin_obj, AirflowPlugin) and
(plugin_obj is not AirflowPlugin)
):
plugin_obj.validate()
return plugin_obj not in existing_plugins
return False | Check whether a potential object is a subclass of the AirflowPlugin class. | def is_valid_plugin(plugin_obj, existing_plugins):
"""
Check whether a potential object is a subclass of
the AirflowPlugin class.
:param plugin_obj: potential subclass of AirflowPlugin
:param existing_plugins: Existing list of AirflowPlugin subclasses
:return: Whether or not the obj is a valid ... | airflow/plugins_manager.py |
apache/airflow | SkipMixin.skip | def skip(self, dag_run, execution_date, tasks, session=None):
if not tasks:
return
task_ids = [d.task_id for d in tasks]
now = timezone.utcnow()
if dag_run:
session.query(TaskInstance).filter(
TaskInstance.dag_id == dag_run.dag_id,
... | Sets tasks instances to skipped from the same dag run. | def skip(self, dag_run, execution_date, tasks, session=None):
"""
Sets tasks instances to skipped from the same dag run.
:param dag_run: the DagRun for which to set the tasks to skipped
:param execution_date: execution_date
:param tasks: tasks to skip (not task_ids)
:par... | airflow/models/skipmixin.py |
apache/airflow | AzureDataLakeHook.get_conn | def get_conn(self):
conn = self.get_connection(self.conn_id)
service_options = conn.extra_dejson
self.account_name = service_options.get('account_name')
adlCreds = lib.auth(tenant_id=service_options.get('tenant'),
client_secret=conn.password,
... | Return a AzureDLFileSystem object. | def get_conn(self):
"""Return a AzureDLFileSystem object."""
conn = self.get_connection(self.conn_id)
service_options = conn.extra_dejson
self.account_name = service_options.get('account_name')
adlCreds = lib.auth(tenant_id=service_options.get('tenant'),
... | airflow/contrib/hooks/azure_data_lake_hook.py |
apache/airflow | AzureDataLakeHook.check_for_file | def check_for_file(self, file_path):
try:
files = self.connection.glob(file_path, details=False, invalidate_cache=True)
return len(files) == 1
except FileNotFoundError:
return False | Check if a file exists on Azure Data Lake. | def check_for_file(self, file_path):
"""
Check if a file exists on Azure Data Lake.
:param file_path: Path and name of the file.
:type file_path: str
:return: True if the file exists, False otherwise.
:rtype: bool
"""
try:
files = self.connect... | airflow/contrib/hooks/azure_data_lake_hook.py |
apache/airflow | AzureDataLakeHook.upload_file | def upload_file(self, local_path, remote_path, nthreads=64, overwrite=True,
buffersize=4194304, blocksize=4194304):
multithread.ADLUploader(self.connection,
lpath=local_path,
rpath=remote_path,
nt... | Upload a file to Azure Data Lake. | def upload_file(self, local_path, remote_path, nthreads=64, overwrite=True,
buffersize=4194304, blocksize=4194304):
"""
Upload a file to Azure Data Lake.
:param local_path: local path. Can be single file, directory (in which case,
upload recursively) or glob patt... | airflow/contrib/hooks/azure_data_lake_hook.py |
apache/airflow | AzureDataLakeHook.list | def list(self, path):
if "*" in path:
return self.connection.glob(path)
else:
return self.connection.walk(path) | List files in Azure Data Lake Storage | def list(self, path):
"""
List files in Azure Data Lake Storage
:param path: full path/globstring to use to list files in ADLS
:type path: str
"""
if "*" in path:
return self.connection.glob(path)
else:
return self.connection.walk(path) | airflow/contrib/hooks/azure_data_lake_hook.py |
apache/airflow | AWSAthenaOperator.execute | def execute(self, context):
self.hook = self.get_hook()
self.hook.get_conn()
self.query_execution_context['Database'] = self.database
self.result_configuration['OutputLocation'] = self.output_location
self.query_execution_id = self.hook.run_query(self.query, self.query_execution... | Run Presto Query on Athena | def execute(self, context):
"""
Run Presto Query on Athena
"""
self.hook = self.get_hook()
self.hook.get_conn()
self.query_execution_context['Database'] = self.database
self.result_configuration['OutputLocation'] = self.output_location
self.query_executio... | airflow/contrib/operators/aws_athena_operator.py |
apache/airflow | uncompress_file | def uncompress_file(input_file_name, file_extension, dest_dir):
if file_extension.lower() not in ('.gz', '.bz2'):
raise NotImplementedError("Received {} format. Only gz and bz2 "
"files can currently be uncompressed."
.format(file_extension... | Uncompress gz and bz2 files | def uncompress_file(input_file_name, file_extension, dest_dir):
"""
Uncompress gz and bz2 files
"""
if file_extension.lower() not in ('.gz', '.bz2'):
raise NotImplementedError("Received {} format. Only gz and bz2 "
"files can currently be uncompressed."
... | airflow/utils/compression.py |
apache/airflow | MsSqlToGoogleCloudStorageOperator._query_mssql | def _query_mssql(self):
mssql = MsSqlHook(mssql_conn_id=self.mssql_conn_id)
conn = mssql.get_conn()
cursor = conn.cursor()
cursor.execute(self.sql)
return cursor | Queries MSSQL and returns a cursor of results. | def _query_mssql(self):
"""
Queries MSSQL and returns a cursor of results.
:return: mssql cursor
"""
mssql = MsSqlHook(mssql_conn_id=self.mssql_conn_id)
conn = mssql.get_conn()
cursor = conn.cursor()
cursor.execute(self.sql)
return cursor | airflow/contrib/operators/mssql_to_gcs.py |
apache/airflow | CgroupTaskRunner._create_cgroup | def _create_cgroup(self, path):
node = trees.Tree().root
path_split = path.split(os.sep)
for path_element in path_split:
name_to_node = {x.name: x for x in node.children}
if path_element not in name_to_node:
self.log.debug("Creating cgroup %s in %s", path_... | Create the specified cgroup. | def _create_cgroup(self, path):
"""
Create the specified cgroup.
:param path: The path of the cgroup to create.
E.g. cpu/mygroup/mysubgroup
:return: the Node associated with the created cgroup.
:rtype: cgroupspy.nodes.Node
"""
node = trees.Tree().root
... | airflow/contrib/task_runner/cgroup_task_runner.py |
apache/airflow | CgroupTaskRunner._delete_cgroup | def _delete_cgroup(self, path):
node = trees.Tree().root
path_split = path.split("/")
for path_element in path_split:
name_to_node = {x.name: x for x in node.children}
if path_element not in name_to_node:
self.log.warning("Cgroup does not exist: %s", path)... | Delete the specified cgroup. | def _delete_cgroup(self, path):
"""
Delete the specified cgroup.
:param path: The path of the cgroup to delete.
E.g. cpu/mygroup/mysubgroup
"""
node = trees.Tree().root
path_split = path.split("/")
for path_element in path_split:
name_to_node ... | airflow/contrib/task_runner/cgroup_task_runner.py |
apache/airflow | DatabricksHook._parse_host | def _parse_host(host):
urlparse_host = urlparse.urlparse(host).hostname
if urlparse_host:
return urlparse_host
else:
return host | The purpose of this function is to be robust to improper connections settings provided by users, specifically in the host field. For example -- when users supply `` | def _parse_host(host):
"""
The purpose of this function is to be robust to improper connections
settings provided by users, specifically in the host field.
For example -- when users supply ``https://xx.cloud.databricks.com`` as the
host, we must strip out the protocol to get the... | airflow/contrib/hooks/databricks_hook.py |
apache/airflow | DatabricksHook._do_api_call | def _do_api_call(self, endpoint_info, json):
method, endpoint = endpoint_info
url = 'https://{host}/{endpoint}'.format(
host=self._parse_host(self.databricks_conn.host),
endpoint=endpoint)
if 'token' in self.databricks_conn.extra_dejson:
self.log.info('Using t... | Utility function to perform an API call with retries | def _do_api_call(self, endpoint_info, json):
"""
Utility function to perform an API call with retries
:param endpoint_info: Tuple of method and endpoint
:type endpoint_info: tuple[string, string]
:param json: Parameters for this API call.
:type json: dict
:return... | airflow/contrib/hooks/databricks_hook.py |
apache/airflow | SalesforceHook.get_conn | def get_conn(self):
if not self.conn:
connection = self.get_connection(self.conn_id)
extras = connection.extra_dejson
self.conn = Salesforce(
username=connection.login,
password=connection.password,
security_token=extras['securi... | Sign into Salesforce, only if we are not already signed in. | def get_conn(self):
"""
Sign into Salesforce, only if we are not already signed in.
"""
if not self.conn:
connection = self.get_connection(self.conn_id)
extras = connection.extra_dejson
self.conn = Salesforce(
username=connection.login,... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | SalesforceHook.make_query | def make_query(self, query):
conn = self.get_conn()
self.log.info("Querying for all objects")
query_results = conn.query_all(query)
self.log.info("Received results: Total size: %s; Done: %s",
query_results['totalSize'], query_results['done'])
return query... | Make a query to Salesforce. | def make_query(self, query):
"""
Make a query to Salesforce.
:param query: The query to make to Salesforce.
:type query: str
:return: The query result.
:rtype: dict
"""
conn = self.get_conn()
self.log.info("Querying for all objects")
quer... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | SalesforceHook.describe_object | def describe_object(self, obj):
conn = self.get_conn()
return conn.__getattr__(obj).describe() | Get the description of an object from Salesforce. This description is the object's schema and some extra metadata that Salesforce stores for each object. | def describe_object(self, obj):
"""
Get the description of an object from Salesforce.
This description is the object's schema and
some extra metadata that Salesforce stores for each object.
:param obj: The name of the Salesforce object that we are getting a description of.
... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | SalesforceHook.get_available_fields | def get_available_fields(self, obj):
self.get_conn()
obj_description = self.describe_object(obj)
return [field['name'] for field in obj_description['fields']] | Get a list of all available fields for an object. | def get_available_fields(self, obj):
"""
Get a list of all available fields for an object.
:param obj: The name of the Salesforce object that we are getting a description of.
:type obj: str
:return: the names of the fields.
:rtype: list of str
"""
self.ge... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | SalesforceHook.get_object_from_salesforce | def get_object_from_salesforce(self, obj, fields):
query = "SELECT {} FROM {}".format(",".join(fields), obj)
self.log.info("Making query to Salesforce: %s",
query if len(query) < 30 else " ... ".join([query[:15], query[-15:]]))
return self.make_query(query) | Get all instances of the `object` from Salesforce. For each model, only get the fields specified in fields. All we really do underneath the hood is | def get_object_from_salesforce(self, obj, fields):
"""
Get all instances of the `object` from Salesforce.
For each model, only get the fields specified in fields.
All we really do underneath the hood is run:
SELECT <fields> FROM <obj>;
:param obj: The object name to... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | SalesforceHook._to_timestamp | def _to_timestamp(cls, column):
try:
column = pd.to_datetime(column)
except ValueError:
log = LoggingMixin().log
log.warning("Could not convert field to timestamps: %s", column.name)
... | Convert a column of a dataframe to UNIX timestamps if applicable | def _to_timestamp(cls, column):
"""
Convert a column of a dataframe to UNIX timestamps if applicable
:param column: A Series object representing a column of a dataframe.
:type column: pd.Series
:return: a new series that maintains the same index as the original
:rtype: p... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | SalesforceHook.write_object_to_file | def write_object_to_file(self,
query_results,
filename,
fmt="csv",
coerce_to_timestamp=False,
record_time_added=False):
fmt = fmt.lower()
if fmt not in ['csv',... | Write query results to file. Acceptable formats | def write_object_to_file(self,
query_results,
filename,
fmt="csv",
coerce_to_timestamp=False,
record_time_added=False):
"""
Write query results to file.
... | airflow/contrib/hooks/salesforce_hook.py |
apache/airflow | MongoHook.get_conn | def get_conn(self):
if self.client is not None:
return self.client
options = self.extras
if options.get('ssl', False):
options.update({'ssl_cert_reqs': CERT_NONE})
self.client = MongoClient(self.uri, **options)
return self.client | Fetches PyMongo Client | def get_conn(self):
"""
Fetches PyMongo Client
"""
if self.client is not None:
return self.client
# Mongo Connection Options dict that is unpacked when passed to MongoClient
options = self.extras
# If we are using SSL disable requiring certs from spe... | airflow/contrib/hooks/mongo_hook.py |
apache/airflow | MongoHook.get_collection | def get_collection(self, mongo_collection, mongo_db=None):
mongo_db = mongo_db if mongo_db is not None else self.connection.schema
mongo_conn = self.get_conn()
return mongo_conn.get_database(mongo_db).get_collection(mongo_collection) | Fetches a mongo collection object for querying. Uses connection schema as DB unless specified. | def get_collection(self, mongo_collection, mongo_db=None):
"""
Fetches a mongo collection object for querying.
Uses connection schema as DB unless specified.
"""
mongo_db = mongo_db if mongo_db is not None else self.connection.schema
mongo_conn = self.get_conn()
... | airflow/contrib/hooks/mongo_hook.py |
apache/airflow | MongoHook.replace_many | def replace_many(self, mongo_collection, docs,
filter_docs=None, mongo_db=None, upsert=False, collation=None,
**kwargs):
collection = self.get_collection(mongo_collection, mongo_db=mongo_db)
if not filter_docs:
filter_docs = [{'_id': doc['_id']} for... | Replaces many documents in a mongo collection. Uses bulk_write with multiple ReplaceOne operations | def replace_many(self, mongo_collection, docs,
filter_docs=None, mongo_db=None, upsert=False, collation=None,
**kwargs):
"""
Replaces many documents in a mongo collection.
Uses bulk_write with multiple ReplaceOne operations
https://api.mongodb.c... | airflow/contrib/hooks/mongo_hook.py |
apache/airflow | ImapHook.has_mail_attachment | def has_mail_attachment(self, name, mail_folder='INBOX', check_regex=False):
mail_attachments = self._retrieve_mails_attachments_by_name(name,
mail_folder,
check_regex,
... | Checks the mail folder for mails containing attachments with the given name. | def has_mail_attachment(self, name, mail_folder='INBOX', check_regex=False):
"""
Checks the mail folder for mails containing attachments with the given name.
:param name: The name of the attachment that will be searched for.
:type name: str
:param mail_folder: The mail folder wh... | airflow/contrib/hooks/imap_hook.py |
apache/airflow | ImapHook.retrieve_mail_attachments | def retrieve_mail_attachments(self,
name,
mail_folder='INBOX',
check_regex=False,
latest_only=False,
not_found_mode='raise'):
mail_attachments... | Retrieves mail's attachments in the mail folder by its name. | def retrieve_mail_attachments(self,
name,
mail_folder='INBOX',
check_regex=False,
latest_only=False,
not_found_mode='raise'):
"""
Retr... | airflow/contrib/hooks/imap_hook.py |
apache/airflow | ImapHook.download_mail_attachments | def download_mail_attachments(self,
name,
local_output_directory,
mail_folder='INBOX',
check_regex=False,
latest_only=False,
... | Downloads mail's attachments in the mail folder by its name to the local directory. | def download_mail_attachments(self,
name,
local_output_directory,
mail_folder='INBOX',
check_regex=False,
latest_only=False,
... | airflow/contrib/hooks/imap_hook.py |
apache/airflow | Mail.get_attachments_by_name | def get_attachments_by_name(self, name, check_regex, find_first=False):
attachments = []
for part in self.mail.walk():
mail_part = MailPart(part)
if mail_part.is_attachment():
found_attachment = mail_part.has_matching_name(name) if check_regex \
... | Gets all attachments by name for the mail. | def get_attachments_by_name(self, name, check_regex, find_first=False):
"""
Gets all attachments by name for the mail.
:param name: The name of the attachment to look for.
:type name: str
:param check_regex: Checks the name for a regular expression.
:type check_regex: bo... | airflow/contrib/hooks/imap_hook.py |
apache/airflow | MailPart.get_file | def get_file(self):
return self.part.get_filename(), self.part.get_payload(decode=True) | Gets the file including name and payload. | def get_file(self):
"""
Gets the file including name and payload.
:returns: the part's name and payload.
:rtype: tuple
"""
return self.part.get_filename(), self.part.get_payload(decode=True) | airflow/contrib/hooks/imap_hook.py |
apache/airflow | AwsFirehoseHook.put_records | def put_records(self, records):
firehose_conn = self.get_conn()
response = firehose_conn.put_record_batch(
DeliveryStreamName=self.delivery_stream,
Records=records
)
return response | Write batch records to Kinesis Firehose | def put_records(self, records):
"""
Write batch records to Kinesis Firehose
"""
firehose_conn = self.get_conn()
response = firehose_conn.put_record_batch(
DeliveryStreamName=self.delivery_stream,
Records=records
)
return response | airflow/contrib/hooks/aws_firehose_hook.py |
apache/airflow | send_email | def send_email(to, subject, html_content,
files=None, dryrun=False, cc=None, bcc=None,
mime_subtype='mixed', mime_charset='utf-8', **kwargs):
path, attr = configuration.conf.get('email', 'EMAIL_BACKEND').rsplit('.', 1)
module = importlib.import_module(path)
backend = getattr(mo... | Send email using backend specified in EMAIL_BACKEND. | def send_email(to, subject, html_content,
files=None, dryrun=False, cc=None, bcc=None,
mime_subtype='mixed', mime_charset='utf-8', **kwargs):
"""
Send email using backend specified in EMAIL_BACKEND.
"""
path, attr = configuration.conf.get('email', 'EMAIL_BACKEND').rsplit('.... | airflow/utils/email.py |
apache/airflow | send_email_smtp | def send_email_smtp(to, subject, html_content, files=None,
dryrun=False, cc=None, bcc=None,
mime_subtype='mixed', mime_charset='utf-8',
**kwargs):
smtp_mail_from = configuration.conf.get('smtp', 'SMTP_MAIL_FROM')
to = get_email_address_list(to)
m... | Send an email with html content | def send_email_smtp(to, subject, html_content, files=None,
dryrun=False, cc=None, bcc=None,
mime_subtype='mixed', mime_charset='utf-8',
**kwargs):
"""
Send an email with html content
>>> send_email('test@example.com', 'foo', '<b>Foo</b> bar', ['/d... | airflow/utils/email.py |
apache/airflow | WasbHook.check_for_blob | def check_for_blob(self, container_name, blob_name, **kwargs):
return self.connection.exists(container_name, blob_name, **kwargs) | Check if a blob exists on Azure Blob Storage. | def check_for_blob(self, container_name, blob_name, **kwargs):
"""
Check if a blob exists on Azure Blob Storage.
:param container_name: Name of the container.
:type container_name: str
:param blob_name: Name of the blob.
:type blob_name: str
:param kwargs: Option... | airflow/contrib/hooks/wasb_hook.py |
apache/airflow | WasbHook.check_for_prefix | def check_for_prefix(self, container_name, prefix, **kwargs):
matches = self.connection.list_blobs(container_name, prefix,
num_results=1, **kwargs)
return len(list(matches)) > 0 | Check if a prefix exists on Azure Blob storage. | def check_for_prefix(self, container_name, prefix, **kwargs):
"""
Check if a prefix exists on Azure Blob storage.
:param container_name: Name of the container.
:type container_name: str
:param prefix: Prefix of the blob.
:type prefix: str
:param kwargs: Optional ... | airflow/contrib/hooks/wasb_hook.py |
apache/airflow | WasbHook.load_string | def load_string(self, string_data, container_name, blob_name, **kwargs):
self.connection.create_blob_from_text(container_name, blob_name,
string_data, **kwargs) | Upload a string to Azure Blob Storage. | def load_string(self, string_data, container_name, blob_name, **kwargs):
"""
Upload a string to Azure Blob Storage.
:param string_data: String to load.
:type string_data: str
:param container_name: Name of the container.
:type container_name: str
:param blob_name... | airflow/contrib/hooks/wasb_hook.py |
apache/airflow | WasbHook.read_file | def read_file(self, container_name, blob_name, **kwargs):
return self.connection.get_blob_to_text(container_name,
blob_name,
**kwargs).content | Read a file from Azure Blob Storage and return as a string. | def read_file(self, container_name, blob_name, **kwargs):
"""
Read a file from Azure Blob Storage and return as a string.
:param container_name: Name of the container.
:type container_name: str
:param blob_name: Name of the blob.
:type blob_name: str
:param kwarg... | airflow/contrib/hooks/wasb_hook.py |
apache/airflow | WasbHook.delete_file | def delete_file(self, container_name, blob_name, is_prefix=False,
ignore_if_missing=False, **kwargs):
if is_prefix:
blobs_to_delete = [
blob.name for blob in self.connection.list_blobs(
container_name, prefix=blob_name, **kwargs
... | Delete a file from Azure Blob Storage. | def delete_file(self, container_name, blob_name, is_prefix=False,
ignore_if_missing=False, **kwargs):
"""
Delete a file from Azure Blob Storage.
:param container_name: Name of the container.
:type container_name: str
:param blob_name: Name of the blob.
... | airflow/contrib/hooks/wasb_hook.py |
apache/airflow | DiscordWebhookOperator.execute | def execute(self, context):
self.hook = DiscordWebhookHook(
self.http_conn_id,
self.webhook_endpoint,
self.message,
self.username,
self.avatar_url,
self.tts,
self.proxy
)
self.hook.execute() | Call the DiscordWebhookHook to post message | def execute(self, context):
"""
Call the DiscordWebhookHook to post message
"""
self.hook = DiscordWebhookHook(
self.http_conn_id,
self.webhook_endpoint,
self.message,
self.username,
self.avatar_url,
self.tts,
... | airflow/contrib/operators/discord_webhook_operator.py |
apache/airflow | AzureFileShareHook.get_conn | def get_conn(self):
conn = self.get_connection(self.conn_id)
service_options = conn.extra_dejson
return FileService(account_name=conn.login,
account_key=conn.password, **service_options) | Return the FileService object. | def get_conn(self):
"""Return the FileService object."""
conn = self.get_connection(self.conn_id)
service_options = conn.extra_dejson
return FileService(account_name=conn.login,
account_key=conn.password, **service_options) | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.check_for_directory | def check_for_directory(self, share_name, directory_name, **kwargs):
return self.connection.exists(share_name, directory_name,
**kwargs) | Check if a directory exists on Azure File Share. | def check_for_directory(self, share_name, directory_name, **kwargs):
"""
Check if a directory exists on Azure File Share.
:param share_name: Name of the share.
:type share_name: str
:param directory_name: Name of the directory.
:type directory_name: str
:param kw... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.check_for_file | def check_for_file(self, share_name, directory_name, file_name, **kwargs):
return self.connection.exists(share_name, directory_name,
file_name, **kwargs) | Check if a file exists on Azure File Share. | def check_for_file(self, share_name, directory_name, file_name, **kwargs):
"""
Check if a file exists on Azure File Share.
:param share_name: Name of the share.
:type share_name: str
:param directory_name: Name of the directory.
:type directory_name: str
:param f... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.list_directories_and_files | def list_directories_and_files(self, share_name, directory_name=None, **kwargs):
return self.connection.list_directories_and_files(share_name,
directory_name,
**kwargs) | Return the list of directories and files stored on a Azure File Share. | def list_directories_and_files(self, share_name, directory_name=None, **kwargs):
"""
Return the list of directories and files stored on a Azure File Share.
:param share_name: Name of the share.
:type share_name: str
:param directory_name: Name of the directory.
:type dir... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.create_directory | def create_directory(self, share_name, directory_name, **kwargs):
return self.connection.create_directory(share_name, directory_name, **kwargs) | Create a new directory on a Azure File Share. | def create_directory(self, share_name, directory_name, **kwargs):
"""
Create a new directory on a Azure File Share.
:param share_name: Name of the share.
:type share_name: str
:param directory_name: Name of the directory.
:type directory_name: str
:param kwargs: ... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.load_file | def load_file(self, file_path, share_name, directory_name, file_name, **kwargs):
self.connection.create_file_from_path(share_name, directory_name,
file_name, file_path, **kwargs) | Upload a file to Azure File Share. | def load_file(self, file_path, share_name, directory_name, file_name, **kwargs):
"""
Upload a file to Azure File Share.
:param file_path: Path to the file to load.
:type file_path: str
:param share_name: Name of the share.
:type share_name: str
:param directory_n... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.load_string | def load_string(self, string_data, share_name, directory_name, file_name, **kwargs):
self.connection.create_file_from_text(share_name, directory_name,
file_name, string_data, **kwargs) | Upload a string to Azure File Share. | def load_string(self, string_data, share_name, directory_name, file_name, **kwargs):
"""
Upload a string to Azure File Share.
:param string_data: String to load.
:type string_data: str
:param share_name: Name of the share.
:type share_name: str
:param directory_n... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | AzureFileShareHook.load_stream | def load_stream(self, stream, share_name, directory_name, file_name, count, **kwargs):
self.connection.create_file_from_stream(share_name, directory_name,
file_name, stream, count, **kwargs) | Upload a stream to Azure File Share. | def load_stream(self, stream, share_name, directory_name, file_name, count, **kwargs):
"""
Upload a stream to Azure File Share.
:param stream: Opened file/stream to upload as the file content.
:type stream: file-like
:param share_name: Name of the share.
:type share_name... | airflow/contrib/hooks/azure_fileshare_hook.py |
apache/airflow | GoogleCloudStorageHook.copy | def copy(self, source_bucket, source_object, destination_bucket=None,
destination_object=None):
destination_bucket = destination_bucket or source_bucket
destination_object = destination_object or source_object
if source_bucket == destination_bucket and \
source_objec... | Copies an object from a bucket to another, with renaming if requested. destination_bucket or destination_object can be omitted, in which case source bucket/object is used, but not both. | def copy(self, source_bucket, source_object, destination_bucket=None,
destination_object=None):
"""
Copies an object from a bucket to another, with renaming if requested.
destination_bucket or destination_object can be omitted, in which case
source bucket/object is used, bu... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.download | def download(self, bucket_name, object_name, filename=None):
client = self.get_conn()
bucket = client.get_bucket(bucket_name)
blob = bucket.blob(blob_name=object_name)
if filename:
blob.download_to_filename(filename)
self.log.info('File downloaded to %s', filenam... | Get a file from Google Cloud Storage. | def download(self, bucket_name, object_name, filename=None):
"""
Get a file from Google Cloud Storage.
:param bucket_name: The bucket to fetch from.
:type bucket_name: str
:param object_name: The object to fetch.
:type object_name: str
:param filename: If set, a ... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.upload | def upload(self, bucket_name, object_name, filename,
mime_type='application/octet-stream', gzip=False):
if gzip:
filename_gz = filename + '.gz'
with open(filename, 'rb') as f_in:
with gz.open(filename_gz, 'wb') as f_out:
shutil.copyfile... | Uploads a local file to Google Cloud Storage. | def upload(self, bucket_name, object_name, filename,
mime_type='application/octet-stream', gzip=False):
"""
Uploads a local file to Google Cloud Storage.
:param bucket_name: The bucket to upload to.
:type bucket_name: str
:param object_name: The object name to set... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.exists | def exists(self, bucket_name, object_name):
client = self.get_conn()
bucket = client.get_bucket(bucket_name=bucket_name)
blob = bucket.blob(blob_name=object_name)
return blob.exists() | Checks for the existence of a file in Google Cloud Storage. | def exists(self, bucket_name, object_name):
"""
Checks for the existence of a file in Google Cloud Storage.
:param bucket_name: The Google cloud storage bucket where the object is.
:type bucket_name: str
:param object_name: The name of the blob_name to check in the Google cloud
... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.is_updated_after | def is_updated_after(self, bucket_name, object_name, ts):
client = self.get_conn()
bucket = storage.Bucket(client=client, name=bucket_name)
blob = bucket.get_blob(blob_name=object_name)
blob.reload()
blob_update_time = blob.updated
if blob_update_time is not None:
... | Checks if an blob_name is updated in Google Cloud Storage. | def is_updated_after(self, bucket_name, object_name, ts):
"""
Checks if an blob_name is updated in Google Cloud Storage.
:param bucket_name: The Google cloud storage bucket where the object is.
:type bucket_name: str
:param object_name: The name of the object to check in the Goo... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.delete | def delete(self, bucket_name, object_name):
client = self.get_conn()
bucket = client.get_bucket(bucket_name=bucket_name)
blob = bucket.blob(blob_name=object_name)
blob.delete()
self.log.info('Blob %s deleted.', object_name) | Deletes an object from the bucket. | def delete(self, bucket_name, object_name):
"""
Deletes an object from the bucket.
:param bucket_name: name of the bucket, where the object resides
:type bucket_name: str
:param object_name: name of the object to delete
:type object_name: str
"""
client =... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.list | def list(self, bucket_name, versions=None, max_results=None, prefix=None, delimiter=None):
client = self.get_conn()
bucket = client.get_bucket(bucket_name=bucket_name)
ids = []
pageToken = None
while True:
blobs = bucket.list_blobs(
max_results=max_re... | List all objects from the bucket with the give string prefix in name | def list(self, bucket_name, versions=None, max_results=None, prefix=None, delimiter=None):
"""
List all objects from the bucket with the give string prefix in name
:param bucket_name: bucket name
:type bucket_name: str
:param versions: if true, list all versions of the objects
... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.get_size | def get_size(self, bucket_name, object_name):
self.log.info('Checking the file size of object: %s in bucket_name: %s',
object_name,
bucket_name)
client = self.get_conn()
bucket = client.get_bucket(bucket_name=bucket_name)
blob = bucket.get_blob... | Gets the size of a file in Google Cloud Storage. | def get_size(self, bucket_name, object_name):
"""
Gets the size of a file in Google Cloud Storage.
:param bucket_name: The Google cloud storage bucket where the blob_name is.
:type bucket_name: str
:param object_name: The name of the object to check in the Google
clo... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.get_crc32c | def get_crc32c(self, bucket_name, object_name):
self.log.info('Retrieving the crc32c checksum of '
'object_name: %s in bucket_name: %s', object_name, bucket_name)
client = self.get_conn()
bucket = client.get_bucket(bucket_name=bucket_name)
blob = bucket.get_blob(blo... | Gets the CRC32c checksum of an object in Google Cloud Storage. | def get_crc32c(self, bucket_name, object_name):
"""
Gets the CRC32c checksum of an object in Google Cloud Storage.
:param bucket_name: The Google cloud storage bucket where the blob_name is.
:type bucket_name: str
:param object_name: The name of the object to check in the Google... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.get_md5hash | def get_md5hash(self, bucket_name, object_name):
self.log.info('Retrieving the MD5 hash of '
'object: %s in bucket: %s', object_name, bucket_name)
client = self.get_conn()
bucket = client.get_bucket(bucket_name=bucket_name)
blob = bucket.get_blob(blob_name=object_na... | Gets the MD5 hash of an object in Google Cloud Storage. | def get_md5hash(self, bucket_name, object_name):
"""
Gets the MD5 hash of an object in Google Cloud Storage.
:param bucket_name: The Google cloud storage bucket where the blob_name is.
:type bucket_name: str
:param object_name: The name of the object to check in the Google cloud... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.create_bucket | def create_bucket(self,
bucket_name,
resource=None,
storage_class='MULTI_REGIONAL',
location='US',
project_id=None,
labels=None
):
self.log.info('Creating Buc... | Creates a new bucket. Google Cloud Storage uses a flat namespace, so you can't create a bucket with a name that is already in use. | def create_bucket(self,
bucket_name,
resource=None,
storage_class='MULTI_REGIONAL',
location='US',
project_id=None,
labels=None
):
"""
Creates a new b... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | GoogleCloudStorageHook.compose | def compose(self, bucket_name, source_objects, destination_object):
if not source_objects or not len(source_objects):
raise ValueError('source_objects cannot be empty.')
if not bucket_name or not destination_object:
raise ValueError('bucket_name and destination_object cannot be ... | Composes a list of existing object into a new object in the same storage bucket_name Currently it only supports up to 32 objects that can be concatenated in a single operation | def compose(self, bucket_name, source_objects, destination_object):
"""
Composes a list of existing object into a new object in the same storage bucket_name
Currently it only supports up to 32 objects that can be concatenated
in a single operation
https://cloud.google.com/stora... | airflow/contrib/hooks/gcs_hook.py |
apache/airflow | SageMakerHook.tar_and_s3_upload | def tar_and_s3_upload(self, path, key, bucket):
with tempfile.TemporaryFile() as temp_file:
if os.path.isdir(path):
files = [os.path.join(path, name) for name in os.listdir(path)]
else:
files = [path]
with tarfile.open(mode='w:gz', fileobj=temp... | Tar the local file or directory and upload to s3 | def tar_and_s3_upload(self, path, key, bucket):
"""
Tar the local file or directory and upload to s3
:param path: local file or directory
:type path: str
:param key: s3 key
:type key: str
:param bucket: s3 bucket
:type bucket: str
:return: None
... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.configure_s3_resources | def configure_s3_resources(self, config):
s3_operations = config.pop('S3Operations', None)
if s3_operations is not None:
create_bucket_ops = s3_operations.get('S3CreateBucket', [])
upload_ops = s3_operations.get('S3Upload', [])
for op in create_bucket_ops:
... | Extract the S3 operations from the configuration and execute them. | def configure_s3_resources(self, config):
"""
Extract the S3 operations from the configuration and execute them.
:param config: config of SageMaker operation
:type config: dict
:rtype: dict
"""
s3_operations = config.pop('S3Operations', None)
if s3_opera... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.check_s3_url | def check_s3_url(self, s3url):
bucket, key = S3Hook.parse_s3_url(s3url)
if not self.s3_hook.check_for_bucket(bucket_name=bucket):
raise AirflowException(
"The input S3 Bucket {} does not exist ".format(bucket))
if key and not self.s3_hook.check_for_key(key=key, bucket... | Check if an S3 URL exists | def check_s3_url(self, s3url):
"""
Check if an S3 URL exists
:param s3url: S3 url
:type s3url: str
:rtype: bool
"""
bucket, key = S3Hook.parse_s3_url(s3url)
if not self.s3_hook.check_for_bucket(bucket_name=bucket):
raise AirflowException(
... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.get_log_conn | def get_log_conn(self):
config = botocore.config.Config(retries={'max_attempts': 15})
return self.get_client_type('logs', config=config) | Establish an AWS connection for retrieving logs during training | def get_log_conn(self):
"""
Establish an AWS connection for retrieving logs during training
:rtype: CloudWatchLogs.Client
"""
config = botocore.config.Config(retries={'max_attempts': 15})
return self.get_client_type('logs', config=config) | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.create_training_job | def create_training_job(self, config, wait_for_completion=True, print_log=True,
check_interval=30, max_ingestion_time=None):
self.check_training_config(config)
response = self.get_conn().create_training_job(**config)
if print_log:
self.check_training_stat... | Create a training job | def create_training_job(self, config, wait_for_completion=True, print_log=True,
check_interval=30, max_ingestion_time=None):
"""
Create a training job
:param config: the config for training
:type config: dict
:param wait_for_completion: if the program... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.create_tuning_job | def create_tuning_job(self, config, wait_for_completion=True,
check_interval=30, max_ingestion_time=None):
self.check_tuning_config(config)
response = self.get_conn().create_hyper_parameter_tuning_job(**config)
if wait_for_completion:
self.check_status(conf... | Create a tuning job | def create_tuning_job(self, config, wait_for_completion=True,
check_interval=30, max_ingestion_time=None):
"""
Create a tuning job
:param config: the config for tuning
:type config: dict
:param wait_for_completion: if the program should keep running unt... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.create_transform_job | def create_transform_job(self, config, wait_for_completion=True,
check_interval=30, max_ingestion_time=None):
self.check_s3_url(config['TransformInput']['DataSource']['S3DataSource']['S3Uri'])
response = self.get_conn().create_transform_job(**config)
if wait_for_com... | Create a transform job | def create_transform_job(self, config, wait_for_completion=True,
check_interval=30, max_ingestion_time=None):
"""
Create a transform job
:param config: the config for transform job
:type config: dict
:param wait_for_completion: if the program should ... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.create_endpoint | def create_endpoint(self, config, wait_for_completion=True,
check_interval=30, max_ingestion_time=None):
response = self.get_conn().create_endpoint(**config)
if wait_for_completion:
self.check_status(config['EndpointName'],
'EndpointStatu... | Create an endpoint | def create_endpoint(self, config, wait_for_completion=True,
check_interval=30, max_ingestion_time=None):
"""
Create an endpoint
:param config: the config for endpoint
:type config: dict
:param wait_for_completion: if the program should keep running until ... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.describe_training_job_with_log | def describe_training_job_with_log(self, job_name, positions, stream_names,
instance_count, state, last_description,
last_describe_job_call):
log_group = '/aws/sagemaker/TrainingJobs'
if len(stream_names) < instance_count:
... | Return the training job info associated with job_name and print CloudWatch logs | def describe_training_job_with_log(self, job_name, positions, stream_names,
instance_count, state, last_description,
last_describe_job_call):
"""
Return the training job info associated with job_name and print CloudWatch logs
... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.check_status | def check_status(self, job_name, key,
describe_function, check_interval,
max_ingestion_time,
non_terminal_states=None):
if not non_terminal_states:
non_terminal_states = self.non_terminal_states
sec = 0
running = True
... | Check status of a SageMaker job | def check_status(self, job_name, key,
describe_function, check_interval,
max_ingestion_time,
non_terminal_states=None):
"""
Check status of a SageMaker job
:param job_name: name of the job to check status
:type job_name: str... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | SageMakerHook.check_training_status_with_log | def check_training_status_with_log(self, job_name, non_terminal_states, failed_states,
wait_for_completion, check_interval, max_ingestion_time):
sec = 0
description = self.describe_training_job(job_name)
self.log.info(secondary_training_status_message(descr... | Display the logs for a given training job, optionally tailing them until the job is complete. | def check_training_status_with_log(self, job_name, non_terminal_states, failed_states,
wait_for_completion, check_interval, max_ingestion_time):
"""
Display the logs for a given training job, optionally tailing them until the
job is complete.
:para... | airflow/contrib/hooks/sagemaker_hook.py |
apache/airflow | DataFlowPythonOperator.execute | def execute(self, context):
bucket_helper = GoogleCloudBucketHelper(
self.gcp_conn_id, self.delegate_to)
self.py_file = bucket_helper.google_cloud_to_local(self.py_file)
hook = DataFlowHook(gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
... | Execute the python dataflow job. | def execute(self, context):
"""Execute the python dataflow job."""
bucket_helper = GoogleCloudBucketHelper(
self.gcp_conn_id, self.delegate_to)
self.py_file = bucket_helper.google_cloud_to_local(self.py_file)
hook = DataFlowHook(gcp_conn_id=self.gcp_conn_id,
... | airflow/contrib/operators/dataflow_operator.py |
apache/airflow | run_migrations_online | def run_migrations_online():
connectable = settings.engine
with connectable.connect() as connection:
context.configure(
connection=connection,
transaction_per_migration=True,
target_metadata=target_metadata,
compare_type=COMPARE_TYPE,
)
w... | Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. | def run_migrations_online():
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
connectable = settings.engine
with connectable.connect() as connection:
context.configure(
connection=connection,
... | airflow/migrations/env.py |
apache/airflow | BigtableHook.delete_instance | def delete_instance(self, instance_id, project_id=None):
instance = self.get_instance(instance_id=instance_id, project_id=project_id)
if instance:
instance.delete()
else:
self.log.info("The instance '%s' does not exist in project '%s'. Exiting", instance_id,
... | Deletes the specified Cloud Bigtable instance. | def delete_instance(self, instance_id, project_id=None):
"""
Deletes the specified Cloud Bigtable instance.
Raises google.api_core.exceptions.NotFound if the Cloud Bigtable instance does
not exist.
:param project_id: Optional, Google Cloud Platform project ID where the
... | airflow/contrib/hooks/gcp_bigtable_hook.py |
apache/airflow | BigtableHook.create_instance | def create_instance(self,
instance_id,
main_cluster_id,
main_cluster_zone,
project_id=None,
replica_cluster_id=None,
replica_cluster_zone=None,
instance... | Creates new instance. | def create_instance(self,
instance_id,
main_cluster_id,
main_cluster_zone,
project_id=None,
replica_cluster_id=None,
replica_cluster_zone=None,
instance... | airflow/contrib/hooks/gcp_bigtable_hook.py |
apache/airflow | BigtableHook.create_table | def create_table(instance,
table_id,
initial_split_keys=None,
column_families=None):
if column_families is None:
column_families = {}
if initial_split_keys is None:
initial_split_keys = []
table = Table(table_... | Creates the specified Cloud Bigtable table. | def create_table(instance,
table_id,
initial_split_keys=None,
column_families=None):
"""
Creates the specified Cloud Bigtable table.
Raises ``google.api_core.exceptions.AlreadyExists`` if the table exists.
:type instance: In... | airflow/contrib/hooks/gcp_bigtable_hook.py |
apache/airflow | BigtableHook.delete_table | def delete_table(self, instance_id, table_id, project_id=None):
table = self.get_instance(instance_id=instance_id, project_id=project_id).table(table_id=table_id)
table.delete() | Deletes the specified table in Cloud Bigtable. | def delete_table(self, instance_id, table_id, project_id=None):
"""
Deletes the specified table in Cloud Bigtable.
Raises google.api_core.exceptions.NotFound if the table does not exist.
:type instance_id: str
:param instance_id: The ID of the Cloud Bigtable instance.
:t... | airflow/contrib/hooks/gcp_bigtable_hook.py |
apache/airflow | BigtableHook.update_cluster | def update_cluster(instance, cluster_id, nodes):
cluster = Cluster(cluster_id, instance)
cluster.serve_nodes = nodes
cluster.update() | Updates number of nodes in the specified Cloud Bigtable cluster. | def update_cluster(instance, cluster_id, nodes):
"""
Updates number of nodes in the specified Cloud Bigtable cluster.
Raises google.api_core.exceptions.NotFound if the cluster does not exist.
:type instance: Instance
:param instance: The Cloud Bigtable instance that owns the clu... | airflow/contrib/hooks/gcp_bigtable_hook.py |
apache/airflow | HiveCliHook._prepare_cli_cmd | def _prepare_cli_cmd(self):
conn = self.conn
hive_bin = 'hive'
cmd_extra = []
if self.use_beeline:
hive_bin = 'beeline'
jdbc_url = "jdbc:hive2://{host}:{port}/{schema}".format(
host=conn.host, port=conn.port, schema=conn.schema)
if con... | This function creates the command list from available information | def _prepare_cli_cmd(self):
"""
This function creates the command list from available information
"""
conn = self.conn
hive_bin = 'hive'
cmd_extra = []
if self.use_beeline:
hive_bin = 'beeline'
jdbc_url = "jdbc:hive2://{host}:{port}/{schem... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveCliHook._prepare_hiveconf | def _prepare_hiveconf(d):
if not d:
return []
return as_flattened_list(
zip(["-hiveconf"] * len(d),
["{}={}".format(k, v) for k, v in d.items()])
) | This function prepares a list of hiveconf params from a dictionary of key value pairs. | def _prepare_hiveconf(d):
"""
This function prepares a list of hiveconf params
from a dictionary of key value pairs.
:param d:
:type d: dict
>>> hh = HiveCliHook()
>>> hive_conf = {"hive.exec.dynamic.partition": "true",
... "hive.exec.dynamic.partition.m... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveCliHook.load_df | def load_df(
self,
df,
table,
field_dict=None,
delimiter=',',
encoding='utf8',
pandas_kwargs=None, **kwargs):
def _infer_field_types_from_df(df):
DTYPE_KIND_HIVE_TYPE = {
'b': 'BOOLEAN',
... | Loads a pandas DataFrame into hive. Hive data types will be inferred if not passed but column names will not be sanitized. | def load_df(
self,
df,
table,
field_dict=None,
delimiter=',',
encoding='utf8',
pandas_kwargs=None, **kwargs):
"""
Loads a pandas DataFrame into hive.
Hive data types will be inferred if not passed but column nam... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveMetastoreHook.check_for_named_partition | def check_for_named_partition(self, schema, table, partition_name):
with self.metastore as client:
return client.check_for_named_partition(schema, table, partition_name) | Checks whether a partition with a given name exists | def check_for_named_partition(self, schema, table, partition_name):
"""
Checks whether a partition with a given name exists
:param schema: Name of hive schema (database) @table belongs to
:type schema: str
:param table: Name of hive table @partition belongs to
:type sche... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveMetastoreHook.table_exists | def table_exists(self, table_name, db='default'):
try:
self.get_table(table_name, db)
return True
except Exception:
return False | Check if table exists | def table_exists(self, table_name, db='default'):
"""
Check if table exists
>>> hh = HiveMetastoreHook()
>>> hh.table_exists(db='airflow', table_name='static_babynames')
True
>>> hh.table_exists(db='airflow', table_name='does_not_exist')
False
"""
... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveServer2Hook.get_results | def get_results(self, hql, schema='default', fetch_size=None, hive_conf=None):
results_iter = self._get_results(hql, schema,
fetch_size=fetch_size, hive_conf=hive_conf)
header = next(results_iter)
results = {
'data': list(results_iter),
... | Get results of the provided hql in target schema. | def get_results(self, hql, schema='default', fetch_size=None, hive_conf=None):
"""
Get results of the provided hql in target schema.
:param hql: hql to be executed.
:type hql: str or list
:param schema: target schema, default to 'default'.
:type schema: str
:para... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveServer2Hook.to_csv | def to_csv(
self,
hql,
csv_filepath,
schema='default',
delimiter=',',
lineterminator='\r\n',
output_header=True,
fetch_size=1000,
hive_conf=None):
results_iter = self._get_results(hql, schema,
... | Execute hql in target schema and write results to a csv file. | def to_csv(
self,
hql,
csv_filepath,
schema='default',
delimiter=',',
lineterminator='\r\n',
output_header=True,
fetch_size=1000,
hive_conf=None):
"""
Execute hql in target schema and write result... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveServer2Hook.get_records | def get_records(self, hql, schema='default', hive_conf=None):
return self.get_results(hql, schema=schema, hive_conf=hive_conf)['data'] | Get a set of records from a Hive query. | def get_records(self, hql, schema='default', hive_conf=None):
"""
Get a set of records from a Hive query.
:param hql: hql to be executed.
:type hql: str or list
:param schema: target schema, default to 'default'.
:type schema: str
:param hive_conf: hive_conf to e... | airflow/hooks/hive_hooks.py |
apache/airflow | HiveServer2Hook.get_pandas_df | def get_pandas_df(self, hql, schema='default'):
import pandas as pd
res = self.get_results(hql, schema=schema)
df = pd.DataFrame(res['data'])
df.columns = [c[0] for c in res['header']]
return df | Get a pandas dataframe from a Hive query | def get_pandas_df(self, hql, schema='default'):
"""
Get a pandas dataframe from a Hive query
:param hql: hql to be executed.
:type hql: str or list
:param schema: target schema, default to 'default'.
:type schema: str
:return: result of hql execution
:rty... | airflow/hooks/hive_hooks.py |
apache/airflow | CloudVisionHook.get_conn | def get_conn(self):
if not self._client:
self._client = ProductSearchClient(credentials=self._get_credentials())
return self._client | Retrieves connection to Cloud Vision. | def get_conn(self):
"""
Retrieves connection to Cloud Vision.
:return: Google Cloud Vision client object.
:rtype: google.cloud.vision_v1.ProductSearchClient
"""
if not self._client:
self._client = ProductSearchClient(credentials=self._get_credentials())
... | airflow/contrib/hooks/gcp_vision_hook.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.