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apache/airflow | BaseExecutor.has_task | def has_task(self, task_instance):
if task_instance.key in self.queued_tasks or task_instance.key in self.running:
return True | Checks if a task is either queued or running in this executor | def has_task(self, task_instance):
"""
Checks if a task is either queued or running in this executor
:param task_instance: TaskInstance
:return: True if the task is known to this executor
"""
if task_instance.key in self.queued_tasks or task_instance.key in self.running:
return True | airflow/executors/base_executor.py |
apache/airflow | SnowflakeHook._get_aws_credentials | def _get_aws_credentials(self):
if self.snowflake_conn_id:
connection_object = self.get_connection(self.snowflake_conn_id)
if 'aws_secret_access_key' in connection_object.extra_dejson:
aws_access_key_id = connection_object.extra_dejson.get(
'aws_access_key_id')
aws_secret_access_key = connection_object.extra_dejson.get(
'aws_secret_access_key')
return aws_access_key_id, aws_secret_access_key | returns aws_access_key_id, aws_secret_access_key from extra intended to be used by external import and export statements | def _get_aws_credentials(self):
"""
returns aws_access_key_id, aws_secret_access_key
from extra
intended to be used by external import and export statements
"""
if self.snowflake_conn_id:
connection_object = self.get_connection(self.snowflake_conn_id)
if 'aws_secret_access_key' in connection_object.extra_dejson:
aws_access_key_id = connection_object.extra_dejson.get(
'aws_access_key_id')
aws_secret_access_key = connection_object.extra_dejson.get(
'aws_secret_access_key')
return aws_access_key_id, aws_secret_access_key | airflow/contrib/hooks/snowflake_hook.py |
apache/airflow | PostgresHook.copy_expert | def copy_expert(self, sql, filename, open=open):
if not os.path.isfile(filename):
with open(filename, 'w'):
pass
with open(filename, 'r+') as f:
with closing(self.get_conn()) as conn:
with closing(conn.cursor()) as cur:
cur.copy_expert(sql, f)
f.truncate(f.tell())
conn.commit() | Executes SQL using psycopg2 copy_expert method. Necessary to execute COPY command without access to a superuser. | def copy_expert(self, sql, filename, open=open):
"""
Executes SQL using psycopg2 copy_expert method.
Necessary to execute COPY command without access to a superuser.
Note: if this method is called with a "COPY FROM" statement and
the specified input file does not exist, it creates an empty
file and no data is loaded, but the operation succeeds.
So if users want to be aware when the input file does not exist,
they have to check its existence by themselves.
"""
if not os.path.isfile(filename):
with open(filename, 'w'):
pass
with open(filename, 'r+') as f:
with closing(self.get_conn()) as conn:
with closing(conn.cursor()) as cur:
cur.copy_expert(sql, f)
f.truncate(f.tell())
conn.commit() | airflow/hooks/postgres_hook.py |
apache/airflow | PostgresHook.bulk_dump | def bulk_dump(self, table, tmp_file):
self.copy_expert("COPY {table} TO STDOUT".format(table=table), tmp_file) | Dumps a database table into a tab-delimited file | def bulk_dump(self, table, tmp_file):
"""
Dumps a database table into a tab-delimited file
"""
self.copy_expert("COPY {table} TO STDOUT".format(table=table), tmp_file) | airflow/hooks/postgres_hook.py |
apache/airflow | FileToGoogleCloudStorageOperator.execute | def execute(self, context):
hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
hook.upload(
bucket_name=self.bucket,
object_name=self.dst,
mime_type=self.mime_type,
filename=self.src,
gzip=self.gzip,
) | Uploads the file to Google cloud storage | def execute(self, context):
"""
Uploads the file to Google cloud storage
"""
hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
hook.upload(
bucket_name=self.bucket,
object_name=self.dst,
mime_type=self.mime_type,
filename=self.src,
gzip=self.gzip,
) | airflow/contrib/operators/file_to_gcs.py |
apache/airflow | max_partition | def max_partition(
table, schema="default", field=None, filter_map=None,
metastore_conn_id='metastore_default'):
from airflow.hooks.hive_hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
return hh.max_partition(
schema=schema, table_name=table, field=field, filter_map=filter_map) | Gets the max partition for a table. | def max_partition(
table, schema="default", field=None, filter_map=None,
metastore_conn_id='metastore_default'):
"""
Gets the max partition for a table.
:param schema: The hive schema the table lives in
:type schema: str
:param table: The hive table you are interested in, supports the dot
notation as in "my_database.my_table", if a dot is found,
the schema param is disregarded
:type table: str
:param metastore_conn_id: The hive connection you are interested in.
If your default is set you don't need to use this parameter.
:type metastore_conn_id: str
:param filter_map: partition_key:partition_value map used for partition filtering,
e.g. {'key1': 'value1', 'key2': 'value2'}.
Only partitions matching all partition_key:partition_value
pairs will be considered as candidates of max partition.
:type filter_map: map
:param field: the field to get the max value from. If there's only
one partition field, this will be inferred
:type field: str
>>> max_partition('airflow.static_babynames_partitioned')
'2015-01-01'
"""
from airflow.hooks.hive_hooks import HiveMetastoreHook
if '.' in table:
schema, table = table.split('.')
hh = HiveMetastoreHook(metastore_conn_id=metastore_conn_id)
return hh.max_partition(
schema=schema, table_name=table, field=field, filter_map=filter_map) | airflow/macros/hive.py |
apache/airflow | CloudTranslateHook.get_conn | def get_conn(self):
if not self._client:
self._client = Client(credentials=self._get_credentials())
return self._client | Retrieves connection to Cloud Translate | def get_conn(self):
"""
Retrieves connection to Cloud Translate
:return: Google Cloud Translate client object.
:rtype: Client
"""
if not self._client:
self._client = Client(credentials=self._get_credentials())
return self._client | airflow/contrib/hooks/gcp_translate_hook.py |
apache/airflow | CloudTranslateHook.translate | def translate(
self, values, target_language, format_=None, source_language=None, model=None
):
client = self.get_conn()
return client.translate(
values=values,
target_language=target_language,
format_=format_,
source_language=source_language,
model=model,
) | Translate a string or list of strings. See | def translate(
self, values, target_language, format_=None, source_language=None, model=None
):
"""Translate a string or list of strings.
See https://cloud.google.com/translate/docs/translating-text
:type values: str or list
:param values: String or list of strings to translate.
:type target_language: str
:param target_language: The language to translate results into. This
is required by the API and defaults to
the target language of the current instance.
:type format_: str
:param format_: (Optional) One of ``text`` or ``html``, to specify
if the input text is plain text or HTML.
:type source_language: str or None
:param source_language: (Optional) The language of the text to
be translated.
:type model: str or None
:param model: (Optional) The model used to translate the text, such
as ``'base'`` or ``'nmt'``.
:rtype: str or list
:returns: A list of dictionaries for each queried value. Each
dictionary typically contains three keys (though not
all will be present in all cases)
* ``detectedSourceLanguage``: The detected language (as an
ISO 639-1 language code) of the text.
* ``translatedText``: The translation of the text into the
target language.
* ``input``: The corresponding input value.
* ``model``: The model used to translate the text.
If only a single value is passed, then only a single
dictionary will be returned.
:raises: :class:`~exceptions.ValueError` if the number of
values and translations differ.
"""
client = self.get_conn()
return client.translate(
values=values,
target_language=target_language,
format_=format_,
source_language=source_language,
model=model,
) | airflow/contrib/hooks/gcp_translate_hook.py |
apache/airflow | CloudSqlHook.get_instance | def get_instance(self, instance, project_id=None):
return self.get_conn().instances().get(
project=project_id,
instance=instance
).execute(num_retries=self.num_retries) | Retrieves a resource containing information about a Cloud SQL instance. | def get_instance(self, instance, project_id=None):
"""
Retrieves a resource containing information about a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: A Cloud SQL instance resource.
:rtype: dict
"""
return self.get_conn().instances().get(
project=project_id,
instance=instance
).execute(num_retries=self.num_retries) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.create_instance | def create_instance(self, body, project_id=None):
response = self.get_conn().instances().insert(
project=project_id,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | Creates a new Cloud SQL instance. | def create_instance(self, body, project_id=None):
"""
Creates a new Cloud SQL instance.
:param body: Body required by the Cloud SQL insert API, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/instances/insert#request-body.
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().instances().insert(
project=project_id,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.patch_instance | def patch_instance(self, body, instance, project_id=None):
response = self.get_conn().instances().patch(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | Updates settings of a Cloud SQL instance. | def patch_instance(self, body, instance, project_id=None):
"""
Updates settings of a Cloud SQL instance.
Caution: This is not a partial update, so you must include values for
all the settings that you want to retain.
:param body: Body required by the Cloud SQL patch API, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/instances/patch#request-body.
:type body: dict
:param instance: Cloud SQL instance ID. This does not include the project ID.
:type instance: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().instances().patch(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.delete_instance | def delete_instance(self, instance, project_id=None):
response = self.get_conn().instances().delete(
project=project_id,
instance=instance,
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | Deletes a Cloud SQL instance. | def delete_instance(self, instance, project_id=None):
"""
Deletes a Cloud SQL instance.
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:param instance: Cloud SQL instance ID. This does not include the project ID.
:type instance: str
:return: None
"""
response = self.get_conn().instances().delete(
project=project_id,
instance=instance,
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.get_database | def get_database(self, instance, database, project_id=None):
return self.get_conn().databases().get(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries) | Retrieves a database resource from a Cloud SQL instance. | def get_database(self, instance, database, project_id=None):
"""
Retrieves a database resource from a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param database: Name of the database in the instance.
:type database: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: A Cloud SQL database resource, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases#resource.
:rtype: dict
"""
return self.get_conn().databases().get(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.create_database | def create_database(self, instance, body, project_id=None):
response = self.get_conn().databases().insert(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | Creates a new database inside a Cloud SQL instance. | def create_database(self, instance, body, project_id=None):
"""
Creates a new database inside a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param body: The request body, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases/insert#request-body.
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().databases().insert(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.patch_database | def patch_database(self, instance, database, body, project_id=None):
response = self.get_conn().databases().patch(
project=project_id,
instance=instance,
database=database,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | Updates a database resource inside a Cloud SQL instance. This method supports patch semantics. See | def patch_database(self, instance, database, body, project_id=None):
"""
Updates a database resource inside a Cloud SQL instance.
This method supports patch semantics.
See https://cloud.google.com/sql/docs/mysql/admin-api/how-tos/performance#patch.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param database: Name of the database to be updated in the instance.
:type database: str
:param body: The request body, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/databases/insert#request-body.
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().databases().patch(
project=project_id,
instance=instance,
database=database,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.delete_database | def delete_database(self, instance, database, project_id=None):
response = self.get_conn().databases().delete(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | Deletes a database from a Cloud SQL instance. | def delete_database(self, instance, database, project_id=None):
"""
Deletes a database from a Cloud SQL instance.
:param instance: Database instance ID. This does not include the project ID.
:type instance: str
:param database: Name of the database to be deleted in the instance.
:type database: str
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().databases().delete(
project=project_id,
instance=instance,
database=database
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlHook.export_instance | def export_instance(self, instance, body, project_id=None):
try:
response = self.get_conn().instances().export(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
except HttpError as ex:
raise AirflowException(
'Exporting instance {} failed: {}'.format(instance, ex.content)
) | Exports data from a Cloud SQL instance to a Cloud Storage bucket as a SQL dump or CSV file. | def export_instance(self, instance, body, project_id=None):
"""
Exports data from a Cloud SQL instance to a Cloud Storage bucket as a SQL dump
or CSV file.
:param instance: Database instance ID of the Cloud SQL instance. This does not include the
project ID.
:type instance: str
:param body: The request body, as described in
https://cloud.google.com/sql/docs/mysql/admin-api/v1beta4/instances/export#request-body
:type body: dict
:param project_id: Project ID of the project that contains the instance. If set
to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
try:
response = self.get_conn().instances().export(
project=project_id,
instance=instance,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(project_id=project_id,
operation_name=operation_name)
except HttpError as ex:
raise AirflowException(
'Exporting instance {} failed: {}'.format(instance, ex.content)
) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlProxyRunner.start_proxy | def start_proxy(self):
self._download_sql_proxy_if_needed()
if self.sql_proxy_process:
raise AirflowException("The sql proxy is already running: {}".format(
self.sql_proxy_process))
else:
command_to_run = [self.sql_proxy_path]
command_to_run.extend(self.command_line_parameters)
try:
self.log.info("Creating directory %s",
self.cloud_sql_proxy_socket_directory)
os.makedirs(self.cloud_sql_proxy_socket_directory)
except OSError:
pass
command_to_run.extend(self._get_credential_parameters())
self.log.info("Running the command: `%s`", " ".join(command_to_run))
self.sql_proxy_process = Popen(command_to_run,
stdin=PIPE, stdout=PIPE, stderr=PIPE)
self.log.info("The pid of cloud_sql_proxy: %s", self.sql_proxy_process.pid)
while True:
line = self.sql_proxy_process.stderr.readline().decode('utf-8')
return_code = self.sql_proxy_process.poll()
if line == '' and return_code is not None:
self.sql_proxy_process = None
raise AirflowException(
"The cloud_sql_proxy finished early with return code {}!".format(
return_code))
if line != '':
self.log.info(line)
if "googleapi: Error" in line or "invalid instance name:" in line:
self.stop_proxy()
raise AirflowException(
"Error when starting the cloud_sql_proxy {}!".format(
line))
if "Ready for new connections" in line:
return | Starts Cloud SQL Proxy. You have to remember to stop the proxy if you started it! | def start_proxy(self):
"""
Starts Cloud SQL Proxy.
You have to remember to stop the proxy if you started it!
"""
self._download_sql_proxy_if_needed()
if self.sql_proxy_process:
raise AirflowException("The sql proxy is already running: {}".format(
self.sql_proxy_process))
else:
command_to_run = [self.sql_proxy_path]
command_to_run.extend(self.command_line_parameters)
try:
self.log.info("Creating directory %s",
self.cloud_sql_proxy_socket_directory)
os.makedirs(self.cloud_sql_proxy_socket_directory)
except OSError:
# Needed for python 2 compatibility (exists_ok missing)
pass
command_to_run.extend(self._get_credential_parameters())
self.log.info("Running the command: `%s`", " ".join(command_to_run))
self.sql_proxy_process = Popen(command_to_run,
stdin=PIPE, stdout=PIPE, stderr=PIPE)
self.log.info("The pid of cloud_sql_proxy: %s", self.sql_proxy_process.pid)
while True:
line = self.sql_proxy_process.stderr.readline().decode('utf-8')
return_code = self.sql_proxy_process.poll()
if line == '' and return_code is not None:
self.sql_proxy_process = None
raise AirflowException(
"The cloud_sql_proxy finished early with return code {}!".format(
return_code))
if line != '':
self.log.info(line)
if "googleapi: Error" in line or "invalid instance name:" in line:
self.stop_proxy()
raise AirflowException(
"Error when starting the cloud_sql_proxy {}!".format(
line))
if "Ready for new connections" in line:
return | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlProxyRunner.stop_proxy | def stop_proxy(self):
if not self.sql_proxy_process:
raise AirflowException("The sql proxy is not started yet")
else:
self.log.info("Stopping the cloud_sql_proxy pid: %s",
self.sql_proxy_process.pid)
self.sql_proxy_process.kill()
self.sql_proxy_process = None
self.log.info("Removing the socket directory: %s",
self.cloud_sql_proxy_socket_directory)
shutil.rmtree(self.cloud_sql_proxy_socket_directory, ignore_errors=True)
if self.sql_proxy_was_downloaded:
self.log.info("Removing downloaded proxy: %s", self.sql_proxy_path)
try:
os.remove(self.sql_proxy_path)
except OSError as e:
if not e.errno == errno.ENOENT:
raise
else:
self.log.info("Skipped removing proxy - it was not downloaded: %s",
self.sql_proxy_path)
if os.path.isfile(self.credentials_path):
self.log.info("Removing generated credentials file %s",
self.credentials_path)
os.remove(self.credentials_path) | Stops running proxy. You should stop the proxy after you stop using it. | def stop_proxy(self):
"""
Stops running proxy.
You should stop the proxy after you stop using it.
"""
if not self.sql_proxy_process:
raise AirflowException("The sql proxy is not started yet")
else:
self.log.info("Stopping the cloud_sql_proxy pid: %s",
self.sql_proxy_process.pid)
self.sql_proxy_process.kill()
self.sql_proxy_process = None
# Cleanup!
self.log.info("Removing the socket directory: %s",
self.cloud_sql_proxy_socket_directory)
shutil.rmtree(self.cloud_sql_proxy_socket_directory, ignore_errors=True)
if self.sql_proxy_was_downloaded:
self.log.info("Removing downloaded proxy: %s", self.sql_proxy_path)
# Silently ignore if the file has already been removed (concurrency)
try:
os.remove(self.sql_proxy_path)
except OSError as e:
if not e.errno == errno.ENOENT:
raise
else:
self.log.info("Skipped removing proxy - it was not downloaded: %s",
self.sql_proxy_path)
if os.path.isfile(self.credentials_path):
self.log.info("Removing generated credentials file %s",
self.credentials_path)
# Here file cannot be delete by concurrent task (each task has its own copy)
os.remove(self.credentials_path) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.create_connection | def create_connection(self, session=None):
connection = Connection(conn_id=self.db_conn_id)
uri = self._generate_connection_uri()
self.log.info("Creating connection %s", self.db_conn_id)
connection.parse_from_uri(uri)
session.add(connection)
session.commit() | Create connection in the Connection table, according to whether it uses proxy, TCP, UNIX sockets, SSL. Connection ID will be randomly generated. | def create_connection(self, session=None):
"""
Create connection in the Connection table, according to whether it uses
proxy, TCP, UNIX sockets, SSL. Connection ID will be randomly generated.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
"""
connection = Connection(conn_id=self.db_conn_id)
uri = self._generate_connection_uri()
self.log.info("Creating connection %s", self.db_conn_id)
connection.parse_from_uri(uri)
session.add(connection)
session.commit() | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.retrieve_connection | def retrieve_connection(self, session=None):
self.log.info("Retrieving connection %s", self.db_conn_id)
connections = session.query(Connection).filter(
Connection.conn_id == self.db_conn_id)
if connections.count():
return connections[0]
return None | Retrieves the dynamically created connection from the Connection table. | def retrieve_connection(self, session=None):
"""
Retrieves the dynamically created connection from the Connection table.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
"""
self.log.info("Retrieving connection %s", self.db_conn_id)
connections = session.query(Connection).filter(
Connection.conn_id == self.db_conn_id)
if connections.count():
return connections[0]
return None | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.delete_connection | def delete_connection(self, session=None):
self.log.info("Deleting connection %s", self.db_conn_id)
connections = session.query(Connection).filter(
Connection.conn_id == self.db_conn_id)
if connections.count():
connection = connections[0]
session.delete(connection)
session.commit()
else:
self.log.info("Connection was already deleted!") | Delete the dynamically created connection from the Connection table. | def delete_connection(self, session=None):
"""
Delete the dynamically created connection from the Connection table.
:param session: Session of the SQL Alchemy ORM (automatically generated with
decorator).
"""
self.log.info("Deleting connection %s", self.db_conn_id)
connections = session.query(Connection).filter(
Connection.conn_id == self.db_conn_id)
if connections.count():
connection = connections[0]
session.delete(connection)
session.commit()
else:
self.log.info("Connection was already deleted!") | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.get_sqlproxy_runner | def get_sqlproxy_runner(self):
if not self.use_proxy:
raise AirflowException("Proxy runner can only be retrieved in case of use_proxy = True")
return CloudSqlProxyRunner(
path_prefix=self.sql_proxy_unique_path,
instance_specification=self._get_sqlproxy_instance_specification(),
project_id=self.project_id,
sql_proxy_version=self.sql_proxy_version,
sql_proxy_binary_path=self.sql_proxy_binary_path
) | Retrieve Cloud SQL Proxy runner. It is used to manage the proxy lifecycle per task. | def get_sqlproxy_runner(self):
"""
Retrieve Cloud SQL Proxy runner. It is used to manage the proxy
lifecycle per task.
:return: The Cloud SQL Proxy runner.
:rtype: CloudSqlProxyRunner
"""
if not self.use_proxy:
raise AirflowException("Proxy runner can only be retrieved in case of use_proxy = True")
return CloudSqlProxyRunner(
path_prefix=self.sql_proxy_unique_path,
instance_specification=self._get_sqlproxy_instance_specification(),
project_id=self.project_id,
sql_proxy_version=self.sql_proxy_version,
sql_proxy_binary_path=self.sql_proxy_binary_path
) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.get_database_hook | def get_database_hook(self):
if self.database_type == 'postgres':
self.db_hook = PostgresHook(postgres_conn_id=self.db_conn_id,
schema=self.database)
else:
self.db_hook = MySqlHook(mysql_conn_id=self.db_conn_id,
schema=self.database)
return self.db_hook | Retrieve database hook. This is the actual Postgres or MySQL database hook that uses proxy or connects directly to the Google Cloud SQL database. | def get_database_hook(self):
"""
Retrieve database hook. This is the actual Postgres or MySQL database hook
that uses proxy or connects directly to the Google Cloud SQL database.
"""
if self.database_type == 'postgres':
self.db_hook = PostgresHook(postgres_conn_id=self.db_conn_id,
schema=self.database)
else:
self.db_hook = MySqlHook(mysql_conn_id=self.db_conn_id,
schema=self.database)
return self.db_hook | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.cleanup_database_hook | def cleanup_database_hook(self):
if self.database_type == 'postgres':
if hasattr(self.db_hook,
'conn') and self.db_hook.conn and self.db_hook.conn.notices:
for output in self.db_hook.conn.notices:
self.log.info(output) | Clean up database hook after it was used. | def cleanup_database_hook(self):
"""
Clean up database hook after it was used.
"""
if self.database_type == 'postgres':
if hasattr(self.db_hook,
'conn') and self.db_hook.conn and self.db_hook.conn.notices:
for output in self.db_hook.conn.notices:
self.log.info(output) | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | CloudSqlDatabaseHook.reserve_free_tcp_port | def reserve_free_tcp_port(self):
self.reserved_tcp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.reserved_tcp_socket.bind(('127.0.0.1', 0))
self.sql_proxy_tcp_port = self.reserved_tcp_socket.getsockname()[1] | Reserve free TCP port to be used by Cloud SQL Proxy | def reserve_free_tcp_port(self):
"""
Reserve free TCP port to be used by Cloud SQL Proxy
"""
self.reserved_tcp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.reserved_tcp_socket.bind(('127.0.0.1', 0))
self.sql_proxy_tcp_port = self.reserved_tcp_socket.getsockname()[1] | airflow/contrib/hooks/gcp_sql_hook.py |
apache/airflow | _normalize_mlengine_job_id | def _normalize_mlengine_job_id(job_id):
match = re.search(r'\d|\{{2}', job_id)
if match and match.start() == 0:
job = 'z_{}'.format(job_id)
else:
job = job_id
tracker = 0
cleansed_job_id = ''
for m in re.finditer(r'\{{2}.+?\}{2}', job):
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_',
job[tracker:m.start()])
cleansed_job_id += job[m.start():m.end()]
tracker = m.end()
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_', job[tracker:])
return cleansed_job_id | Replaces invalid MLEngine job_id characters with '_'. This also adds a leading 'z' in case job_id starts with an invalid character. | def _normalize_mlengine_job_id(job_id):
"""
Replaces invalid MLEngine job_id characters with '_'.
This also adds a leading 'z' in case job_id starts with an invalid
character.
Args:
job_id: A job_id str that may have invalid characters.
Returns:
A valid job_id representation.
"""
# Add a prefix when a job_id starts with a digit or a template
match = re.search(r'\d|\{{2}', job_id)
if match and match.start() == 0:
job = 'z_{}'.format(job_id)
else:
job = job_id
# Clean up 'bad' characters except templates
tracker = 0
cleansed_job_id = ''
for m in re.finditer(r'\{{2}.+?\}{2}', job):
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_',
job[tracker:m.start()])
cleansed_job_id += job[m.start():m.end()]
tracker = m.end()
# Clean up last substring or the full string if no templates
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_', job[tracker:])
return cleansed_job_id | airflow/contrib/operators/mlengine_operator.py |
apache/airflow | FTPSensor._get_error_code | def _get_error_code(self, e):
try:
matches = self.error_code_pattern.match(str(e))
code = int(matches.group(0))
return code
except ValueError:
return e | Extract error code from ftp exception | def _get_error_code(self, e):
"""Extract error code from ftp exception"""
try:
matches = self.error_code_pattern.match(str(e))
code = int(matches.group(0))
return code
except ValueError:
return e | airflow/contrib/sensors/ftp_sensor.py |
apache/airflow | clear_dag_runs | def clear_dag_runs():
session = settings.Session()
drs = session.query(DagRun).filter(
DagRun.dag_id.in_(DAG_IDS),
).all()
for dr in drs:
logging.info('Deleting DagRun :: {}'.format(dr))
session.delete(dr) | Remove any existing DAG runs for the perf test DAGs. | def clear_dag_runs():
"""
Remove any existing DAG runs for the perf test DAGs.
"""
session = settings.Session()
drs = session.query(DagRun).filter(
DagRun.dag_id.in_(DAG_IDS),
).all()
for dr in drs:
logging.info('Deleting DagRun :: {}'.format(dr))
session.delete(dr) | scripts/perf/scheduler_ops_metrics.py |
apache/airflow | clear_dag_task_instances | def clear_dag_task_instances():
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
for ti in tis:
logging.info('Deleting TaskInstance :: {}'.format(ti))
session.delete(ti)
session.commit() | Remove any existing task instances for the perf test DAGs. | def clear_dag_task_instances():
"""
Remove any existing task instances for the perf test DAGs.
"""
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
for ti in tis:
logging.info('Deleting TaskInstance :: {}'.format(ti))
session.delete(ti)
session.commit() | scripts/perf/scheduler_ops_metrics.py |
apache/airflow | set_dags_paused_state | def set_dags_paused_state(is_paused):
session = settings.Session()
dms = session.query(DagModel).filter(
DagModel.dag_id.in_(DAG_IDS))
for dm in dms:
logging.info('Setting DAG :: {} is_paused={}'.format(dm, is_paused))
dm.is_paused = is_paused
session.commit() | Toggle the pause state of the DAGs in the test. | def set_dags_paused_state(is_paused):
"""
Toggle the pause state of the DAGs in the test.
"""
session = settings.Session()
dms = session.query(DagModel).filter(
DagModel.dag_id.in_(DAG_IDS))
for dm in dms:
logging.info('Setting DAG :: {} is_paused={}'.format(dm, is_paused))
dm.is_paused = is_paused
session.commit() | scripts/perf/scheduler_ops_metrics.py |
apache/airflow | SchedulerMetricsJob.print_stats | def print_stats(self):
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
successful_tis = [x for x in tis if x.state == State.SUCCESS]
ti_perf = [(ti.dag_id, ti.task_id, ti.execution_date,
(ti.queued_dttm - self.start_date).total_seconds(),
(ti.start_date - self.start_date).total_seconds(),
(ti.end_date - self.start_date).total_seconds(),
ti.duration) for ti in successful_tis]
ti_perf_df = pd.DataFrame(ti_perf, columns=['dag_id', 'task_id',
'execution_date',
'queue_delay',
'start_delay', 'land_time',
'duration'])
print('Performance Results')
print('###################')
for dag_id in DAG_IDS:
print('DAG {}'.format(dag_id))
print(ti_perf_df[ti_perf_df['dag_id'] == dag_id])
print('###################')
if len(tis) > len(successful_tis):
print("WARNING!! The following task instances haven't completed")
print(pd.DataFrame([(ti.dag_id, ti.task_id, ti.execution_date, ti.state)
for ti in filter(lambda x: x.state != State.SUCCESS, tis)],
columns=['dag_id', 'task_id', 'execution_date', 'state']))
session.commit() | Print operational metrics for the scheduler test. | def print_stats(self):
"""
Print operational metrics for the scheduler test.
"""
session = settings.Session()
TI = TaskInstance
tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.all()
)
successful_tis = [x for x in tis if x.state == State.SUCCESS]
ti_perf = [(ti.dag_id, ti.task_id, ti.execution_date,
(ti.queued_dttm - self.start_date).total_seconds(),
(ti.start_date - self.start_date).total_seconds(),
(ti.end_date - self.start_date).total_seconds(),
ti.duration) for ti in successful_tis]
ti_perf_df = pd.DataFrame(ti_perf, columns=['dag_id', 'task_id',
'execution_date',
'queue_delay',
'start_delay', 'land_time',
'duration'])
print('Performance Results')
print('###################')
for dag_id in DAG_IDS:
print('DAG {}'.format(dag_id))
print(ti_perf_df[ti_perf_df['dag_id'] == dag_id])
print('###################')
if len(tis) > len(successful_tis):
print("WARNING!! The following task instances haven't completed")
print(pd.DataFrame([(ti.dag_id, ti.task_id, ti.execution_date, ti.state)
for ti in filter(lambda x: x.state != State.SUCCESS, tis)],
columns=['dag_id', 'task_id', 'execution_date', 'state']))
session.commit() | scripts/perf/scheduler_ops_metrics.py |
apache/airflow | SchedulerMetricsJob.heartbeat | def heartbeat(self):
super(SchedulerMetricsJob, self).heartbeat()
session = settings.Session()
TI = TaskInstance
successful_tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.filter(TI.state.in_([State.SUCCESS]))
.all()
)
session.commit()
dagbag = DagBag(SUBDIR)
dags = [dagbag.dags[dag_id] for dag_id in DAG_IDS]
num_task_instances = sum([(timezone.utcnow() - task.start_date).days
for dag in dags for task in dag.tasks])
if (len(successful_tis) == num_task_instances or
(timezone.utcnow() - self.start_date).total_seconds() >
MAX_RUNTIME_SECS):
if len(successful_tis) == num_task_instances:
self.log.info("All tasks processed! Printing stats.")
else:
self.log.info("Test timeout reached. Printing available stats.")
self.print_stats()
set_dags_paused_state(True)
sys.exit() | Override the scheduler heartbeat to determine when the test is complete | def heartbeat(self):
"""
Override the scheduler heartbeat to determine when the test is complete
"""
super(SchedulerMetricsJob, self).heartbeat()
session = settings.Session()
# Get all the relevant task instances
TI = TaskInstance
successful_tis = (
session
.query(TI)
.filter(TI.dag_id.in_(DAG_IDS))
.filter(TI.state.in_([State.SUCCESS]))
.all()
)
session.commit()
dagbag = DagBag(SUBDIR)
dags = [dagbag.dags[dag_id] for dag_id in DAG_IDS]
# the tasks in perf_dag_1 and per_dag_2 have a daily schedule interval.
num_task_instances = sum([(timezone.utcnow() - task.start_date).days
for dag in dags for task in dag.tasks])
if (len(successful_tis) == num_task_instances or
(timezone.utcnow() - self.start_date).total_seconds() >
MAX_RUNTIME_SECS):
if len(successful_tis) == num_task_instances:
self.log.info("All tasks processed! Printing stats.")
else:
self.log.info("Test timeout reached. Printing available stats.")
self.print_stats()
set_dags_paused_state(True)
sys.exit() | scripts/perf/scheduler_ops_metrics.py |
apache/airflow | AwsLambdaHook.invoke_lambda | def invoke_lambda(self, payload):
awslambda_conn = self.get_conn()
response = awslambda_conn.invoke(
FunctionName=self.function_name,
InvocationType=self.invocation_type,
LogType=self.log_type,
Payload=payload,
Qualifier=self.qualifier
)
return response | Invoke Lambda Function | def invoke_lambda(self, payload):
"""
Invoke Lambda Function
"""
awslambda_conn = self.get_conn()
response = awslambda_conn.invoke(
FunctionName=self.function_name,
InvocationType=self.invocation_type,
LogType=self.log_type,
Payload=payload,
Qualifier=self.qualifier
)
return response | airflow/contrib/hooks/aws_lambda_hook.py |
apache/airflow | mkdirs | def mkdirs(path, mode):
try:
o_umask = os.umask(0)
os.makedirs(path, mode)
except OSError:
if not os.path.isdir(path):
raise
finally:
os.umask(o_umask) | Creates the directory specified by path, creating intermediate directories as necessary. If directory already exists, this is a no-op. | def mkdirs(path, mode):
"""
Creates the directory specified by path, creating intermediate directories
as necessary. If directory already exists, this is a no-op.
:param path: The directory to create
:type path: str
:param mode: The mode to give to the directory e.g. 0o755, ignores umask
:type mode: int
"""
try:
o_umask = os.umask(0)
os.makedirs(path, mode)
except OSError:
if not os.path.isdir(path):
raise
finally:
os.umask(o_umask) | airflow/utils/file.py |
apache/airflow | _convert_to_float_if_possible | def _convert_to_float_if_possible(s):
try:
ret = float(s)
except (ValueError, TypeError):
ret = s
return ret | A small helper function to convert a string to a numeric value if appropriate | def _convert_to_float_if_possible(s):
"""
A small helper function to convert a string to a numeric value
if appropriate
:param s: the string to be converted
:type s: str
"""
try:
ret = float(s)
except (ValueError, TypeError):
ret = s
return ret | airflow/operators/check_operator.py |
apache/airflow | make_aware | def make_aware(value, timezone=None):
if timezone is None:
timezone = TIMEZONE
if is_localized(value):
raise ValueError(
"make_aware expects a naive datetime, got %s" % value)
if hasattr(value, 'fold'):
value = value.replace(fold=1)
if hasattr(timezone, 'localize'):
return timezone.localize(value)
elif hasattr(timezone, 'convert'):
return timezone.convert(value)
else:
return value.replace(tzinfo=timezone) | Make a naive datetime.datetime in a given time zone aware. | def make_aware(value, timezone=None):
"""
Make a naive datetime.datetime in a given time zone aware.
:param value: datetime
:param timezone: timezone
:return: localized datetime in settings.TIMEZONE or timezone
"""
if timezone is None:
timezone = TIMEZONE
# Check that we won't overwrite the timezone of an aware datetime.
if is_localized(value):
raise ValueError(
"make_aware expects a naive datetime, got %s" % value)
if hasattr(value, 'fold'):
# In case of python 3.6 we want to do the same that pendulum does for python3.5
# i.e in case we move clock back we want to schedule the run at the time of the second
# instance of the same clock time rather than the first one.
# Fold parameter has no impact in other cases so we can safely set it to 1 here
value = value.replace(fold=1)
if hasattr(timezone, 'localize'):
# This method is available for pytz time zones.
return timezone.localize(value)
elif hasattr(timezone, 'convert'):
# For pendulum
return timezone.convert(value)
else:
# This may be wrong around DST changes!
return value.replace(tzinfo=timezone) | airflow/utils/timezone.py |
apache/airflow | make_naive | def make_naive(value, timezone=None):
if timezone is None:
timezone = TIMEZONE
if is_naive(value):
raise ValueError("make_naive() cannot be applied to a naive datetime")
o = value.astimezone(timezone)
naive = dt.datetime(o.year,
o.month,
o.day,
o.hour,
o.minute,
o.second,
o.microsecond)
return naive | Make an aware datetime.datetime naive in a given time zone. | def make_naive(value, timezone=None):
"""
Make an aware datetime.datetime naive in a given time zone.
:param value: datetime
:param timezone: timezone
:return: naive datetime
"""
if timezone is None:
timezone = TIMEZONE
# Emulate the behavior of astimezone() on Python < 3.6.
if is_naive(value):
raise ValueError("make_naive() cannot be applied to a naive datetime")
o = value.astimezone(timezone)
# cross library compatibility
naive = dt.datetime(o.year,
o.month,
o.day,
o.hour,
o.minute,
o.second,
o.microsecond)
return naive | airflow/utils/timezone.py |
apache/airflow | datetime | def datetime(*args, **kwargs):
if 'tzinfo' not in kwargs:
kwargs['tzinfo'] = TIMEZONE
return dt.datetime(*args, **kwargs) | Wrapper around datetime.datetime that adds settings.TIMEZONE if tzinfo not specified | def datetime(*args, **kwargs):
"""
Wrapper around datetime.datetime that adds settings.TIMEZONE if tzinfo not specified
:return: datetime.datetime
"""
if 'tzinfo' not in kwargs:
kwargs['tzinfo'] = TIMEZONE
return dt.datetime(*args, **kwargs) | airflow/utils/timezone.py |
apache/airflow | DruidDbApiHook.get_conn | def get_conn(self):
conn = self.get_connection(self.druid_broker_conn_id)
druid_broker_conn = connect(
host=conn.host,
port=conn.port,
path=conn.extra_dejson.get('endpoint', '/druid/v2/sql'),
scheme=conn.extra_dejson.get('schema', 'http')
)
self.log.info('Get the connection to druid broker on %s', conn.host)
return druid_broker_conn | Establish a connection to druid broker. | def get_conn(self):
"""
Establish a connection to druid broker.
"""
conn = self.get_connection(self.druid_broker_conn_id)
druid_broker_conn = connect(
host=conn.host,
port=conn.port,
path=conn.extra_dejson.get('endpoint', '/druid/v2/sql'),
scheme=conn.extra_dejson.get('schema', 'http')
)
self.log.info('Get the connection to druid broker on %s', conn.host)
return druid_broker_conn | airflow/hooks/druid_hook.py |
apache/airflow | HttpHook.run | def run(self, endpoint, data=None, headers=None, extra_options=None):
extra_options = extra_options or {}
session = self.get_conn(headers)
if self.base_url and not self.base_url.endswith('/') and \
endpoint and not endpoint.startswith('/'):
url = self.base_url + '/' + endpoint
else:
url = (self.base_url or '') + (endpoint or '')
req = None
if self.method == 'GET':
req = requests.Request(self.method,
url,
params=data,
headers=headers)
elif self.method == 'HEAD':
req = requests.Request(self.method,
url,
headers=headers)
else:
req = requests.Request(self.method,
url,
data=data,
headers=headers)
prepped_request = session.prepare_request(req)
self.log.info("Sending '%s' to url: %s", self.method, url)
return self.run_and_check(session, prepped_request, extra_options) | Performs the request | def run(self, endpoint, data=None, headers=None, extra_options=None):
"""
Performs the request
:param endpoint: the endpoint to be called i.e. resource/v1/query?
:type endpoint: str
:param data: payload to be uploaded or request parameters
:type data: dict
:param headers: additional headers to be passed through as a dictionary
:type headers: dict
:param extra_options: additional options to be used when executing the request
i.e. {'check_response': False} to avoid checking raising exceptions on non
2XX or 3XX status codes
:type extra_options: dict
"""
extra_options = extra_options or {}
session = self.get_conn(headers)
if self.base_url and not self.base_url.endswith('/') and \
endpoint and not endpoint.startswith('/'):
url = self.base_url + '/' + endpoint
else:
url = (self.base_url or '') + (endpoint or '')
req = None
if self.method == 'GET':
# GET uses params
req = requests.Request(self.method,
url,
params=data,
headers=headers)
elif self.method == 'HEAD':
# HEAD doesn't use params
req = requests.Request(self.method,
url,
headers=headers)
else:
# Others use data
req = requests.Request(self.method,
url,
data=data,
headers=headers)
prepped_request = session.prepare_request(req)
self.log.info("Sending '%s' to url: %s", self.method, url)
return self.run_and_check(session, prepped_request, extra_options) | airflow/hooks/http_hook.py |
apache/airflow | HttpHook.check_response | def check_response(self, response):
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
self.log.error("HTTP error: %s", response.reason)
if self.method not in ['GET', 'HEAD']:
self.log.error(response.text)
raise AirflowException(str(response.status_code) + ":" + response.reason) | Checks the status code and raise an AirflowException exception on non 2XX or 3XX status codes | def check_response(self, response):
"""
Checks the status code and raise an AirflowException exception on non 2XX or 3XX
status codes
:param response: A requests response object
:type response: requests.response
"""
try:
response.raise_for_status()
except requests.exceptions.HTTPError:
self.log.error("HTTP error: %s", response.reason)
if self.method not in ['GET', 'HEAD']:
self.log.error(response.text)
raise AirflowException(str(response.status_code) + ":" + response.reason) | airflow/hooks/http_hook.py |
apache/airflow | HttpHook.run_and_check | def run_and_check(self, session, prepped_request, extra_options):
extra_options = extra_options or {}
try:
response = session.send(
prepped_request,
stream=extra_options.get("stream", False),
verify=extra_options.get("verify", True),
proxies=extra_options.get("proxies", {}),
cert=extra_options.get("cert"),
timeout=extra_options.get("timeout"),
allow_redirects=extra_options.get("allow_redirects", True))
if extra_options.get('check_response', True):
self.check_response(response)
return response
except requests.exceptions.ConnectionError as ex:
self.log.warn(str(ex) + ' Tenacity will retry to execute the operation')
raise ex | Grabs extra options like timeout and actually runs the request, checking for the result | def run_and_check(self, session, prepped_request, extra_options):
"""
Grabs extra options like timeout and actually runs the request,
checking for the result
:param session: the session to be used to execute the request
:type session: requests.Session
:param prepped_request: the prepared request generated in run()
:type prepped_request: session.prepare_request
:param extra_options: additional options to be used when executing the request
i.e. {'check_response': False} to avoid checking raising exceptions on non 2XX
or 3XX status codes
:type extra_options: dict
"""
extra_options = extra_options or {}
try:
response = session.send(
prepped_request,
stream=extra_options.get("stream", False),
verify=extra_options.get("verify", True),
proxies=extra_options.get("proxies", {}),
cert=extra_options.get("cert"),
timeout=extra_options.get("timeout"),
allow_redirects=extra_options.get("allow_redirects", True))
if extra_options.get('check_response', True):
self.check_response(response)
return response
except requests.exceptions.ConnectionError as ex:
self.log.warn(str(ex) + ' Tenacity will retry to execute the operation')
raise ex | airflow/hooks/http_hook.py |
apache/airflow | create_session | def create_session():
session = settings.Session()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close() | Contextmanager that will create and teardown a session. | def create_session():
"""
Contextmanager that will create and teardown a session.
"""
session = settings.Session()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close() | airflow/utils/db.py |
apache/airflow | resetdb | def resetdb():
from airflow import models
from alembic.migration import MigrationContext
log.info("Dropping tables that exist")
models.base.Base.metadata.drop_all(settings.engine)
mc = MigrationContext.configure(settings.engine)
if mc._version.exists(settings.engine):
mc._version.drop(settings.engine)
from flask_appbuilder.models.sqla import Base
Base.metadata.drop_all(settings.engine)
initdb() | Clear out the database | def resetdb():
"""
Clear out the database
"""
from airflow import models
# alembic adds significant import time, so we import it lazily
from alembic.migration import MigrationContext
log.info("Dropping tables that exist")
models.base.Base.metadata.drop_all(settings.engine)
mc = MigrationContext.configure(settings.engine)
if mc._version.exists(settings.engine):
mc._version.drop(settings.engine)
from flask_appbuilder.models.sqla import Base
Base.metadata.drop_all(settings.engine)
initdb() | airflow/utils/db.py |
apache/airflow | PrestoHook._get_pretty_exception_message | def _get_pretty_exception_message(e):
if (hasattr(e, 'message') and
'errorName' in e.message and
'message' in e.message):
return ('{name}: {message}'.format(
name=e.message['errorName'],
message=e.message['message']))
else:
return str(e) | Parses some DatabaseError to provide a better error message | def _get_pretty_exception_message(e):
"""
Parses some DatabaseError to provide a better error message
"""
if (hasattr(e, 'message') and
'errorName' in e.message and
'message' in e.message):
return ('{name}: {message}'.format(
name=e.message['errorName'],
message=e.message['message']))
else:
return str(e) | airflow/hooks/presto_hook.py |
apache/airflow | PrestoHook.get_records | def get_records(self, hql, parameters=None):
try:
return super().get_records(
self._strip_sql(hql), parameters)
except DatabaseError as e:
raise PrestoException(self._get_pretty_exception_message(e)) | Get a set of records from Presto | def get_records(self, hql, parameters=None):
"""
Get a set of records from Presto
"""
try:
return super().get_records(
self._strip_sql(hql), parameters)
except DatabaseError as e:
raise PrestoException(self._get_pretty_exception_message(e)) | airflow/hooks/presto_hook.py |
apache/airflow | PrestoHook.get_pandas_df | def get_pandas_df(self, hql, parameters=None):
import pandas
cursor = self.get_cursor()
try:
cursor.execute(self._strip_sql(hql), parameters)
data = cursor.fetchall()
except DatabaseError as e:
raise PrestoException(self._get_pretty_exception_message(e))
column_descriptions = cursor.description
if data:
df = pandas.DataFrame(data)
df.columns = [c[0] for c in column_descriptions]
else:
df = pandas.DataFrame()
return df | Get a pandas dataframe from a sql query. | def get_pandas_df(self, hql, parameters=None):
"""
Get a pandas dataframe from a sql query.
"""
import pandas
cursor = self.get_cursor()
try:
cursor.execute(self._strip_sql(hql), parameters)
data = cursor.fetchall()
except DatabaseError as e:
raise PrestoException(self._get_pretty_exception_message(e))
column_descriptions = cursor.description
if data:
df = pandas.DataFrame(data)
df.columns = [c[0] for c in column_descriptions]
else:
df = pandas.DataFrame()
return df | airflow/hooks/presto_hook.py |
apache/airflow | PrestoHook.run | def run(self, hql, parameters=None):
return super().run(self._strip_sql(hql), parameters) | Execute the statement against Presto. Can be used to create views. | def run(self, hql, parameters=None):
"""
Execute the statement against Presto. Can be used to create views.
"""
return super().run(self._strip_sql(hql), parameters) | airflow/hooks/presto_hook.py |
apache/airflow | PrestoHook.insert_rows | def insert_rows(self, table, rows, target_fields=None):
super().insert_rows(table, rows, target_fields, 0) | A generic way to insert a set of tuples into a table. | def insert_rows(self, table, rows, target_fields=None):
"""
A generic way to insert a set of tuples into a table.
:param table: Name of the target table
:type table: str
:param rows: The rows to insert into the table
:type rows: iterable of tuples
:param target_fields: The names of the columns to fill in the table
:type target_fields: iterable of strings
"""
super().insert_rows(table, rows, target_fields, 0) | airflow/hooks/presto_hook.py |
apache/airflow | AzureCosmosDBHook.get_conn | def get_conn(self):
if self.cosmos_client is not None:
return self.cosmos_client
self.cosmos_client = cosmos_client.CosmosClient(self.endpoint_uri, {'masterKey': self.master_key})
return self.cosmos_client | Return a cosmos db client. | def get_conn(self):
"""
Return a cosmos db client.
"""
if self.cosmos_client is not None:
return self.cosmos_client
# Initialize the Python Azure Cosmos DB client
self.cosmos_client = cosmos_client.CosmosClient(self.endpoint_uri, {'masterKey': self.master_key})
return self.cosmos_client | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.does_collection_exist | def does_collection_exist(self, collection_name, database_name=None):
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
existing_container = list(self.get_conn().QueryContainers(
get_database_link(self.__get_database_name(database_name)), {
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": collection_name}
]
}))
if len(existing_container) == 0:
return False
return True | Checks if a collection exists in CosmosDB. | def does_collection_exist(self, collection_name, database_name=None):
"""
Checks if a collection exists in CosmosDB.
"""
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
existing_container = list(self.get_conn().QueryContainers(
get_database_link(self.__get_database_name(database_name)), {
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": collection_name}
]
}))
if len(existing_container) == 0:
return False
return True | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.create_collection | def create_collection(self, collection_name, database_name=None):
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
existing_container = list(self.get_conn().QueryContainers(
get_database_link(self.__get_database_name(database_name)), {
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": collection_name}
]
}))
if len(existing_container) == 0:
self.get_conn().CreateContainer(
get_database_link(self.__get_database_name(database_name)),
{"id": collection_name}) | Creates a new collection in the CosmosDB database. | def create_collection(self, collection_name, database_name=None):
"""
Creates a new collection in the CosmosDB database.
"""
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
# We need to check to see if this container already exists so we don't try
# to create it twice
existing_container = list(self.get_conn().QueryContainers(
get_database_link(self.__get_database_name(database_name)), {
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": collection_name}
]
}))
# Only create if we did not find it already existing
if len(existing_container) == 0:
self.get_conn().CreateContainer(
get_database_link(self.__get_database_name(database_name)),
{"id": collection_name}) | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.does_database_exist | def does_database_exist(self, database_name):
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
existing_database = list(self.get_conn().QueryDatabases({
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": database_name}
]
}))
if len(existing_database) == 0:
return False
return True | Checks if a database exists in CosmosDB. | def does_database_exist(self, database_name):
"""
Checks if a database exists in CosmosDB.
"""
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
existing_database = list(self.get_conn().QueryDatabases({
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": database_name}
]
}))
if len(existing_database) == 0:
return False
return True | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.create_database | def create_database(self, database_name):
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
existing_database = list(self.get_conn().QueryDatabases({
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": database_name}
]
}))
if len(existing_database) == 0:
self.get_conn().CreateDatabase({"id": database_name}) | Creates a new database in CosmosDB. | def create_database(self, database_name):
"""
Creates a new database in CosmosDB.
"""
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
# We need to check to see if this database already exists so we don't try
# to create it twice
existing_database = list(self.get_conn().QueryDatabases({
"query": "SELECT * FROM r WHERE r.id=@id",
"parameters": [
{"name": "@id", "value": database_name}
]
}))
# Only create if we did not find it already existing
if len(existing_database) == 0:
self.get_conn().CreateDatabase({"id": database_name}) | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.delete_database | def delete_database(self, database_name):
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
self.get_conn().DeleteDatabase(get_database_link(database_name)) | Deletes an existing database in CosmosDB. | def delete_database(self, database_name):
"""
Deletes an existing database in CosmosDB.
"""
if database_name is None:
raise AirflowBadRequest("Database name cannot be None.")
self.get_conn().DeleteDatabase(get_database_link(database_name)) | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.delete_collection | def delete_collection(self, collection_name, database_name=None):
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
self.get_conn().DeleteContainer(
get_collection_link(self.__get_database_name(database_name), collection_name)) | Deletes an existing collection in the CosmosDB database. | def delete_collection(self, collection_name, database_name=None):
"""
Deletes an existing collection in the CosmosDB database.
"""
if collection_name is None:
raise AirflowBadRequest("Collection name cannot be None.")
self.get_conn().DeleteContainer(
get_collection_link(self.__get_database_name(database_name), collection_name)) | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.insert_documents | def insert_documents(self, documents, database_name=None, collection_name=None):
if documents is None:
raise AirflowBadRequest("You cannot insert empty documents")
created_documents = []
for single_document in documents:
created_documents.append(
self.get_conn().CreateItem(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
single_document))
return created_documents | Insert a list of new documents into an existing collection in the CosmosDB database. | def insert_documents(self, documents, database_name=None, collection_name=None):
"""
Insert a list of new documents into an existing collection in the CosmosDB database.
"""
if documents is None:
raise AirflowBadRequest("You cannot insert empty documents")
created_documents = []
for single_document in documents:
created_documents.append(
self.get_conn().CreateItem(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
single_document))
return created_documents | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.delete_document | def delete_document(self, document_id, database_name=None, collection_name=None):
if document_id is None:
raise AirflowBadRequest("Cannot delete a document without an id")
self.get_conn().DeleteItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id)) | Delete an existing document out of a collection in the CosmosDB database. | def delete_document(self, document_id, database_name=None, collection_name=None):
"""
Delete an existing document out of a collection in the CosmosDB database.
"""
if document_id is None:
raise AirflowBadRequest("Cannot delete a document without an id")
self.get_conn().DeleteItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id)) | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.get_document | def get_document(self, document_id, database_name=None, collection_name=None):
if document_id is None:
raise AirflowBadRequest("Cannot get a document without an id")
try:
return self.get_conn().ReadItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id))
except HTTPFailure:
return None | Get a document from an existing collection in the CosmosDB database. | def get_document(self, document_id, database_name=None, collection_name=None):
"""
Get a document from an existing collection in the CosmosDB database.
"""
if document_id is None:
raise AirflowBadRequest("Cannot get a document without an id")
try:
return self.get_conn().ReadItem(
get_document_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name),
document_id))
except HTTPFailure:
return None | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | AzureCosmosDBHook.get_documents | def get_documents(self, sql_string, database_name=None, collection_name=None, partition_key=None):
if sql_string is None:
raise AirflowBadRequest("SQL query string cannot be None")
query = {'query': sql_string}
try:
result_iterable = self.get_conn().QueryItems(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
query,
partition_key)
return list(result_iterable)
except HTTPFailure:
return None | Get a list of documents from an existing collection in the CosmosDB database via SQL query. | def get_documents(self, sql_string, database_name=None, collection_name=None, partition_key=None):
"""
Get a list of documents from an existing collection in the CosmosDB database via SQL query.
"""
if sql_string is None:
raise AirflowBadRequest("SQL query string cannot be None")
# Query them in SQL
query = {'query': sql_string}
try:
result_iterable = self.get_conn().QueryItems(
get_collection_link(
self.__get_database_name(database_name),
self.__get_collection_name(collection_name)),
query,
partition_key)
return list(result_iterable)
except HTTPFailure:
return None | airflow/contrib/hooks/azure_cosmos_hook.py |
apache/airflow | GcfHook.create_new_function | def create_new_function(self, location, body, project_id=None):
response = self.get_conn().projects().locations().functions().create(
location=self._full_location(project_id, location),
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | Creates a new function in Cloud Function in the location specified in the body. | def create_new_function(self, location, body, project_id=None):
"""
Creates a new function in Cloud Function in the location specified in the body.
:param location: The location of the function.
:type location: str
:param body: The body required by the Cloud Functions insert API.
:type body: dict
:param project_id: Optional, Google Cloud Project project_id where the function belongs.
If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: None
"""
response = self.get_conn().projects().locations().functions().create(
location=self._full_location(project_id, location),
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | airflow/contrib/hooks/gcp_function_hook.py |
apache/airflow | GcfHook.update_function | def update_function(self, name, body, update_mask):
response = self.get_conn().projects().locations().functions().patch(
updateMask=",".join(update_mask),
name=name,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | Updates Cloud Functions according to the specified update mask. | def update_function(self, name, body, update_mask):
"""
Updates Cloud Functions according to the specified update mask.
:param name: The name of the function.
:type name: str
:param body: The body required by the cloud function patch API.
:type body: dict
:param update_mask: The update mask - array of fields that should be patched.
:type update_mask: [str]
:return: None
"""
response = self.get_conn().projects().locations().functions().patch(
updateMask=",".join(update_mask),
name=name,
body=body
).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | airflow/contrib/hooks/gcp_function_hook.py |
apache/airflow | GcfHook.upload_function_zip | def upload_function_zip(self, location, zip_path, project_id=None):
response = self.get_conn().projects().locations().functions().generateUploadUrl(
parent=self._full_location(project_id, location)
).execute(num_retries=self.num_retries)
upload_url = response.get('uploadUrl')
with open(zip_path, 'rb') as fp:
requests.put(
url=upload_url,
data=fp,
headers={
'Content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600',
}
)
return upload_url | Uploads zip file with sources. | def upload_function_zip(self, location, zip_path, project_id=None):
"""
Uploads zip file with sources.
:param location: The location where the function is created.
:type location: str
:param zip_path: The path of the valid .zip file to upload.
:type zip_path: str
:param project_id: Optional, Google Cloud Project project_id where the function belongs.
If set to None or missing, the default project_id from the GCP connection is used.
:type project_id: str
:return: The upload URL that was returned by generateUploadUrl method.
"""
response = self.get_conn().projects().locations().functions().generateUploadUrl(
parent=self._full_location(project_id, location)
).execute(num_retries=self.num_retries)
upload_url = response.get('uploadUrl')
with open(zip_path, 'rb') as fp:
requests.put(
url=upload_url,
data=fp,
# Those two headers needs to be specified according to:
# https://cloud.google.com/functions/docs/reference/rest/v1/projects.locations.functions/generateUploadUrl
# nopep8
headers={
'Content-type': 'application/zip',
'x-goog-content-length-range': '0,104857600',
}
)
return upload_url | airflow/contrib/hooks/gcp_function_hook.py |
apache/airflow | GcfHook.delete_function | def delete_function(self, name):
response = self.get_conn().projects().locations().functions().delete(
name=name).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | Deletes the specified Cloud Function. | def delete_function(self, name):
"""
Deletes the specified Cloud Function.
:param name: The name of the function.
:type name: str
:return: None
"""
response = self.get_conn().projects().locations().functions().delete(
name=name).execute(num_retries=self.num_retries)
operation_name = response["name"]
self._wait_for_operation_to_complete(operation_name=operation_name) | airflow/contrib/hooks/gcp_function_hook.py |
apache/airflow | BaseTIDep.get_dep_statuses | def get_dep_statuses(self, ti, session, dep_context=None):
from airflow.ti_deps.dep_context import DepContext
if dep_context is None:
dep_context = DepContext()
if self.IGNOREABLE and dep_context.ignore_all_deps:
yield self._passing_status(
reason="Context specified all dependencies should be ignored.")
return
if self.IS_TASK_DEP and dep_context.ignore_task_deps:
yield self._passing_status(
reason="Context specified all task dependencies should be ignored.")
return
for dep_status in self._get_dep_statuses(ti, session, dep_context):
yield dep_status | Wrapper around the private _get_dep_statuses method that contains some global checks for all dependencies. | def get_dep_statuses(self, ti, session, dep_context=None):
"""
Wrapper around the private _get_dep_statuses method that contains some global
checks for all dependencies.
:param ti: the task instance to get the dependency status for
:type ti: airflow.models.TaskInstance
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param dep_context: the context for which this dependency should be evaluated for
:type dep_context: DepContext
"""
# this avoids a circular dependency
from airflow.ti_deps.dep_context import DepContext
if dep_context is None:
dep_context = DepContext()
if self.IGNOREABLE and dep_context.ignore_all_deps:
yield self._passing_status(
reason="Context specified all dependencies should be ignored.")
return
if self.IS_TASK_DEP and dep_context.ignore_task_deps:
yield self._passing_status(
reason="Context specified all task dependencies should be ignored.")
return
for dep_status in self._get_dep_statuses(ti, session, dep_context):
yield dep_status | airflow/ti_deps/deps/base_ti_dep.py |
apache/airflow | _parse_s3_config | def _parse_s3_config(config_file_name, config_format='boto', profile=None):
config = configparser.ConfigParser()
if config.read(config_file_name):
sections = config.sections()
else:
raise AirflowException("Couldn't read {0}".format(config_file_name))
if config_format is None:
config_format = 'boto'
conf_format = config_format.lower()
if conf_format == 'boto':
if profile is not None and 'profile ' + profile in sections:
cred_section = 'profile ' + profile
else:
cred_section = 'Credentials'
elif conf_format == 'aws' and profile is not None:
cred_section = profile
else:
cred_section = 'default'
if conf_format in ('boto', 'aws'):
key_id_option = 'aws_access_key_id'
secret_key_option = 'aws_secret_access_key'
else:
key_id_option = 'access_key'
secret_key_option = 'secret_key'
if cred_section not in sections:
raise AirflowException("This config file format is not recognized")
else:
try:
access_key = config.get(cred_section, key_id_option)
secret_key = config.get(cred_section, secret_key_option)
except Exception:
logging.warning("Option Error in parsing s3 config file")
raise
return access_key, secret_key | Parses a config file for s3 credentials. Can currently parse boto, s3cmd.conf and AWS SDK config formats | def _parse_s3_config(config_file_name, config_format='boto', profile=None):
"""
Parses a config file for s3 credentials. Can currently
parse boto, s3cmd.conf and AWS SDK config formats
:param config_file_name: path to the config file
:type config_file_name: str
:param config_format: config type. One of "boto", "s3cmd" or "aws".
Defaults to "boto"
:type config_format: str
:param profile: profile name in AWS type config file
:type profile: str
"""
config = configparser.ConfigParser()
if config.read(config_file_name): # pragma: no cover
sections = config.sections()
else:
raise AirflowException("Couldn't read {0}".format(config_file_name))
# Setting option names depending on file format
if config_format is None:
config_format = 'boto'
conf_format = config_format.lower()
if conf_format == 'boto': # pragma: no cover
if profile is not None and 'profile ' + profile in sections:
cred_section = 'profile ' + profile
else:
cred_section = 'Credentials'
elif conf_format == 'aws' and profile is not None:
cred_section = profile
else:
cred_section = 'default'
# Option names
if conf_format in ('boto', 'aws'): # pragma: no cover
key_id_option = 'aws_access_key_id'
secret_key_option = 'aws_secret_access_key'
# security_token_option = 'aws_security_token'
else:
key_id_option = 'access_key'
secret_key_option = 'secret_key'
# Actual Parsing
if cred_section not in sections:
raise AirflowException("This config file format is not recognized")
else:
try:
access_key = config.get(cred_section, key_id_option)
secret_key = config.get(cred_section, secret_key_option)
except Exception:
logging.warning("Option Error in parsing s3 config file")
raise
return access_key, secret_key | airflow/contrib/hooks/aws_hook.py |
apache/airflow | AwsHook.get_credentials | def get_credentials(self, region_name=None):
session, _ = self._get_credentials(region_name)
return session.get_credentials().get_frozen_credentials() | Get the underlying `botocore.Credentials` object. This contains the following authentication | def get_credentials(self, region_name=None):
"""Get the underlying `botocore.Credentials` object.
This contains the following authentication attributes: access_key, secret_key and token.
"""
session, _ = self._get_credentials(region_name)
# Credentials are refreshable, so accessing your access key and
# secret key separately can lead to a race condition.
# See https://stackoverflow.com/a/36291428/8283373
return session.get_credentials().get_frozen_credentials() | airflow/contrib/hooks/aws_hook.py |
apache/airflow | StreamLogWriter.flush | def flush(self):
if len(self._buffer) > 0:
self.logger.log(self.level, self._buffer)
self._buffer = str() | Ensure all logging output has been flushed | def flush(self):
"""
Ensure all logging output has been flushed
"""
if len(self._buffer) > 0:
self.logger.log(self.level, self._buffer)
self._buffer = str() | airflow/utils/log/logging_mixin.py |
apache/airflow | correct_maybe_zipped | def correct_maybe_zipped(fileloc):
_, archive, filename = re.search(
r'((.*\.zip){})?(.*)'.format(re.escape(os.sep)), fileloc).groups()
if archive and zipfile.is_zipfile(archive):
return archive
else:
return fileloc | If the path contains a folder with a .zip suffix, then the folder is treated as a zip archive and path to zip is returned. | def correct_maybe_zipped(fileloc):
"""
If the path contains a folder with a .zip suffix, then
the folder is treated as a zip archive and path to zip is returned.
"""
_, archive, filename = re.search(
r'((.*\.zip){})?(.*)'.format(re.escape(os.sep)), fileloc).groups()
if archive and zipfile.is_zipfile(archive):
return archive
else:
return fileloc | airflow/utils/dag_processing.py |
apache/airflow | list_py_file_paths | def list_py_file_paths(directory, safe_mode=True,
include_examples=None):
if include_examples is None:
include_examples = conf.getboolean('core', 'LOAD_EXAMPLES')
file_paths = []
if directory is None:
return []
elif os.path.isfile(directory):
return [directory]
elif os.path.isdir(directory):
patterns_by_dir = {}
for root, dirs, files in os.walk(directory, followlinks=True):
patterns = patterns_by_dir.get(root, [])
ignore_file = os.path.join(root, '.airflowignore')
if os.path.isfile(ignore_file):
with open(ignore_file, 'r') as f:
patterns += [re.compile(p) for p in f.read().split('\n') if p]
dirs[:] = [
d
for d in dirs
if not any(p.search(os.path.join(root, d)) for p in patterns)
]
for d in dirs:
patterns_by_dir[os.path.join(root, d)] = patterns
for f in files:
try:
file_path = os.path.join(root, f)
if not os.path.isfile(file_path):
continue
mod_name, file_ext = os.path.splitext(
os.path.split(file_path)[-1])
if file_ext != '.py' and not zipfile.is_zipfile(file_path):
continue
if any([re.findall(p, file_path) for p in patterns]):
continue
might_contain_dag = True
if safe_mode and not zipfile.is_zipfile(file_path):
with open(file_path, 'rb') as fp:
content = fp.read()
might_contain_dag = all(
[s in content for s in (b'DAG', b'airflow')])
if not might_contain_dag:
continue
file_paths.append(file_path)
except Exception:
log = LoggingMixin().log
log.exception("Error while examining %s", f)
if include_examples:
import airflow.example_dags
example_dag_folder = airflow.example_dags.__path__[0]
file_paths.extend(list_py_file_paths(example_dag_folder, safe_mode, False))
return file_paths | Traverse a directory and look for Python files. | def list_py_file_paths(directory, safe_mode=True,
include_examples=None):
"""
Traverse a directory and look for Python files.
:param directory: the directory to traverse
:type directory: unicode
:param safe_mode: whether to use a heuristic to determine whether a file
contains Airflow DAG definitions
:return: a list of paths to Python files in the specified directory
:rtype: list[unicode]
"""
if include_examples is None:
include_examples = conf.getboolean('core', 'LOAD_EXAMPLES')
file_paths = []
if directory is None:
return []
elif os.path.isfile(directory):
return [directory]
elif os.path.isdir(directory):
patterns_by_dir = {}
for root, dirs, files in os.walk(directory, followlinks=True):
patterns = patterns_by_dir.get(root, [])
ignore_file = os.path.join(root, '.airflowignore')
if os.path.isfile(ignore_file):
with open(ignore_file, 'r') as f:
# If we have new patterns create a copy so we don't change
# the previous list (which would affect other subdirs)
patterns += [re.compile(p) for p in f.read().split('\n') if p]
# If we can ignore any subdirs entirely we should - fewer paths
# to walk is better. We have to modify the ``dirs`` array in
# place for this to affect os.walk
dirs[:] = [
d
for d in dirs
if not any(p.search(os.path.join(root, d)) for p in patterns)
]
# We want patterns defined in a parent folder's .airflowignore to
# apply to subdirs too
for d in dirs:
patterns_by_dir[os.path.join(root, d)] = patterns
for f in files:
try:
file_path = os.path.join(root, f)
if not os.path.isfile(file_path):
continue
mod_name, file_ext = os.path.splitext(
os.path.split(file_path)[-1])
if file_ext != '.py' and not zipfile.is_zipfile(file_path):
continue
if any([re.findall(p, file_path) for p in patterns]):
continue
# Heuristic that guesses whether a Python file contains an
# Airflow DAG definition.
might_contain_dag = True
if safe_mode and not zipfile.is_zipfile(file_path):
with open(file_path, 'rb') as fp:
content = fp.read()
might_contain_dag = all(
[s in content for s in (b'DAG', b'airflow')])
if not might_contain_dag:
continue
file_paths.append(file_path)
except Exception:
log = LoggingMixin().log
log.exception("Error while examining %s", f)
if include_examples:
import airflow.example_dags
example_dag_folder = airflow.example_dags.__path__[0]
file_paths.extend(list_py_file_paths(example_dag_folder, safe_mode, False))
return file_paths | airflow/utils/dag_processing.py |
apache/airflow | SimpleTaskInstance.construct_task_instance | def construct_task_instance(self, session=None, lock_for_update=False):
TI = airflow.models.TaskInstance
qry = session.query(TI).filter(
TI.dag_id == self._dag_id,
TI.task_id == self._task_id,
TI.execution_date == self._execution_date)
if lock_for_update:
ti = qry.with_for_update().first()
else:
ti = qry.first()
return ti | Construct a TaskInstance from the database based on the primary key | def construct_task_instance(self, session=None, lock_for_update=False):
"""
Construct a TaskInstance from the database based on the primary key
:param session: DB session.
:param lock_for_update: if True, indicates that the database should
lock the TaskInstance (issuing a FOR UPDATE clause) until the
session is committed.
"""
TI = airflow.models.TaskInstance
qry = session.query(TI).filter(
TI.dag_id == self._dag_id,
TI.task_id == self._task_id,
TI.execution_date == self._execution_date)
if lock_for_update:
ti = qry.with_for_update().first()
else:
ti = qry.first()
return ti | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorAgent.start | def start(self):
self._process = self._launch_process(self._dag_directory,
self._file_paths,
self._max_runs,
self._processor_factory,
self._child_signal_conn,
self._stat_queue,
self._result_queue,
self._async_mode)
self.log.info("Launched DagFileProcessorManager with pid: %s", self._process.pid) | Launch DagFileProcessorManager processor and start DAG parsing loop in manager. | def start(self):
"""
Launch DagFileProcessorManager processor and start DAG parsing loop in manager.
"""
self._process = self._launch_process(self._dag_directory,
self._file_paths,
self._max_runs,
self._processor_factory,
self._child_signal_conn,
self._stat_queue,
self._result_queue,
self._async_mode)
self.log.info("Launched DagFileProcessorManager with pid: %s", self._process.pid) | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorAgent.terminate | def terminate(self):
self.log.info("Sending termination message to manager.")
self._child_signal_conn.send(DagParsingSignal.TERMINATE_MANAGER) | Send termination signal to DAG parsing processor manager and expect it to terminate all DAG file processors. | def terminate(self):
"""
Send termination signal to DAG parsing processor manager
and expect it to terminate all DAG file processors.
"""
self.log.info("Sending termination message to manager.")
self._child_signal_conn.send(DagParsingSignal.TERMINATE_MANAGER) | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager._exit_gracefully | def _exit_gracefully(self, signum, frame):
self.log.info("Exiting gracefully upon receiving signal %s", signum)
self.terminate()
self.end()
self.log.debug("Finished terminating DAG processors.")
sys.exit(os.EX_OK) | Helper method to clean up DAG file processors to avoid leaving orphan processes. | def _exit_gracefully(self, signum, frame):
"""
Helper method to clean up DAG file processors to avoid leaving orphan processes.
"""
self.log.info("Exiting gracefully upon receiving signal %s", signum)
self.terminate()
self.end()
self.log.debug("Finished terminating DAG processors.")
sys.exit(os.EX_OK) | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.start | def start(self):
self.log.info("Processing files using up to %s processes at a time ", self._parallelism)
self.log.info("Process each file at most once every %s seconds", self._file_process_interval)
self.log.info(
"Checking for new files in %s every %s seconds", self._dag_directory, self.dag_dir_list_interval
)
if self._async_mode:
self.log.debug("Starting DagFileProcessorManager in async mode")
self.start_in_async()
else:
self.log.debug("Starting DagFileProcessorManager in sync mode")
self.start_in_sync() | Use multiple processes to parse and generate tasks for the DAGs in parallel. By processing them in separate processes, we can get parallelism and isolation from potentially harmful user code. | def start(self):
"""
Use multiple processes to parse and generate tasks for the
DAGs in parallel. By processing them in separate processes,
we can get parallelism and isolation from potentially harmful
user code.
"""
self.log.info("Processing files using up to %s processes at a time ", self._parallelism)
self.log.info("Process each file at most once every %s seconds", self._file_process_interval)
self.log.info(
"Checking for new files in %s every %s seconds", self._dag_directory, self.dag_dir_list_interval
)
if self._async_mode:
self.log.debug("Starting DagFileProcessorManager in async mode")
self.start_in_async()
else:
self.log.debug("Starting DagFileProcessorManager in sync mode")
self.start_in_sync() | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.start_in_async | def start_in_async(self):
while True:
loop_start_time = time.time()
if self._signal_conn.poll():
agent_signal = self._signal_conn.recv()
if agent_signal == DagParsingSignal.TERMINATE_MANAGER:
self.terminate()
break
elif agent_signal == DagParsingSignal.END_MANAGER:
self.end()
sys.exit(os.EX_OK)
self._refresh_dag_dir()
simple_dags = self.heartbeat()
for simple_dag in simple_dags:
self._result_queue.put(simple_dag)
self._print_stat()
all_files_processed = all(self.get_last_finish_time(x) is not None
for x in self.file_paths)
max_runs_reached = self.max_runs_reached()
dag_parsing_stat = DagParsingStat(self._file_paths,
self.get_all_pids(),
max_runs_reached,
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
break
loop_duration = time.time() - loop_start_time
if loop_duration < 1:
sleep_length = 1 - loop_duration
self.log.debug("Sleeping for %.2f seconds to prevent excessive logging", sleep_length)
time.sleep(sleep_length) | Parse DAG files repeatedly in a standalone loop. | def start_in_async(self):
"""
Parse DAG files repeatedly in a standalone loop.
"""
while True:
loop_start_time = time.time()
if self._signal_conn.poll():
agent_signal = self._signal_conn.recv()
if agent_signal == DagParsingSignal.TERMINATE_MANAGER:
self.terminate()
break
elif agent_signal == DagParsingSignal.END_MANAGER:
self.end()
sys.exit(os.EX_OK)
self._refresh_dag_dir()
simple_dags = self.heartbeat()
for simple_dag in simple_dags:
self._result_queue.put(simple_dag)
self._print_stat()
all_files_processed = all(self.get_last_finish_time(x) is not None
for x in self.file_paths)
max_runs_reached = self.max_runs_reached()
dag_parsing_stat = DagParsingStat(self._file_paths,
self.get_all_pids(),
max_runs_reached,
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
break
loop_duration = time.time() - loop_start_time
if loop_duration < 1:
sleep_length = 1 - loop_duration
self.log.debug("Sleeping for %.2f seconds to prevent excessive logging", sleep_length)
time.sleep(sleep_length) | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.start_in_sync | def start_in_sync(self):
while True:
agent_signal = self._signal_conn.recv()
if agent_signal == DagParsingSignal.TERMINATE_MANAGER:
self.terminate()
break
elif agent_signal == DagParsingSignal.END_MANAGER:
self.end()
sys.exit(os.EX_OK)
elif agent_signal == DagParsingSignal.AGENT_HEARTBEAT:
self._refresh_dag_dir()
simple_dags = self.heartbeat()
for simple_dag in simple_dags:
self._result_queue.put(simple_dag)
self._print_stat()
all_files_processed = all(self.get_last_finish_time(x) is not None
for x in self.file_paths)
max_runs_reached = self.max_runs_reached()
dag_parsing_stat = DagParsingStat(self._file_paths,
self.get_all_pids(),
self.max_runs_reached(),
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
self.wait_until_finished()
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
break | Parse DAG files in a loop controlled by DagParsingSignal. Actual DAG parsing loop will run once upon receiving one agent heartbeat message and will report done when finished the loop. | def start_in_sync(self):
"""
Parse DAG files in a loop controlled by DagParsingSignal.
Actual DAG parsing loop will run once upon receiving one
agent heartbeat message and will report done when finished the loop.
"""
while True:
agent_signal = self._signal_conn.recv()
if agent_signal == DagParsingSignal.TERMINATE_MANAGER:
self.terminate()
break
elif agent_signal == DagParsingSignal.END_MANAGER:
self.end()
sys.exit(os.EX_OK)
elif agent_signal == DagParsingSignal.AGENT_HEARTBEAT:
self._refresh_dag_dir()
simple_dags = self.heartbeat()
for simple_dag in simple_dags:
self._result_queue.put(simple_dag)
self._print_stat()
all_files_processed = all(self.get_last_finish_time(x) is not None
for x in self.file_paths)
max_runs_reached = self.max_runs_reached()
dag_parsing_stat = DagParsingStat(self._file_paths,
self.get_all_pids(),
self.max_runs_reached(),
all_files_processed,
len(simple_dags))
self._stat_queue.put(dag_parsing_stat)
self.wait_until_finished()
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
if max_runs_reached:
self.log.info("Exiting dag parsing loop as all files "
"have been processed %s times", self._max_runs)
self._signal_conn.send(DagParsingSignal.MANAGER_DONE)
break | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager._refresh_dag_dir | def _refresh_dag_dir(self):
elapsed_time_since_refresh = (timezone.utcnow() -
self.last_dag_dir_refresh_time).total_seconds()
if elapsed_time_since_refresh > self.dag_dir_list_interval:
self.log.info("Searching for files in %s", self._dag_directory)
self._file_paths = list_py_file_paths(self._dag_directory)
self.last_dag_dir_refresh_time = timezone.utcnow()
self.log.info("There are %s files in %s", len(self._file_paths), self._dag_directory)
self.set_file_paths(self._file_paths)
try:
self.log.debug("Removing old import errors")
self.clear_nonexistent_import_errors()
except Exception:
self.log.exception("Error removing old import errors") | Refresh file paths from dag dir if we haven't done it for too long. | def _refresh_dag_dir(self):
"""
Refresh file paths from dag dir if we haven't done it for too long.
"""
elapsed_time_since_refresh = (timezone.utcnow() -
self.last_dag_dir_refresh_time).total_seconds()
if elapsed_time_since_refresh > self.dag_dir_list_interval:
# Build up a list of Python files that could contain DAGs
self.log.info("Searching for files in %s", self._dag_directory)
self._file_paths = list_py_file_paths(self._dag_directory)
self.last_dag_dir_refresh_time = timezone.utcnow()
self.log.info("There are %s files in %s", len(self._file_paths), self._dag_directory)
self.set_file_paths(self._file_paths)
try:
self.log.debug("Removing old import errors")
self.clear_nonexistent_import_errors()
except Exception:
self.log.exception("Error removing old import errors") | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager._print_stat | def _print_stat(self):
if ((timezone.utcnow() - self.last_stat_print_time).total_seconds() >
self.print_stats_interval):
if len(self._file_paths) > 0:
self._log_file_processing_stats(self._file_paths)
self.last_stat_print_time = timezone.utcnow() | Occasionally print out stats about how fast the files are getting processed | def _print_stat(self):
"""
Occasionally print out stats about how fast the files are getting processed
"""
if ((timezone.utcnow() - self.last_stat_print_time).total_seconds() >
self.print_stats_interval):
if len(self._file_paths) > 0:
self._log_file_processing_stats(self._file_paths)
self.last_stat_print_time = timezone.utcnow() | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.clear_nonexistent_import_errors | def clear_nonexistent_import_errors(self, session):
query = session.query(errors.ImportError)
if self._file_paths:
query = query.filter(
~errors.ImportError.filename.in_(self._file_paths)
)
query.delete(synchronize_session='fetch')
session.commit() | Clears import errors for files that no longer exist. | def clear_nonexistent_import_errors(self, session):
"""
Clears import errors for files that no longer exist.
:param session: session for ORM operations
:type session: sqlalchemy.orm.session.Session
"""
query = session.query(errors.ImportError)
if self._file_paths:
query = query.filter(
~errors.ImportError.filename.in_(self._file_paths)
)
query.delete(synchronize_session='fetch')
session.commit() | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager._log_file_processing_stats | def _log_file_processing_stats(self, known_file_paths):
headers = ["File Path",
"PID",
"Runtime",
"Last Runtime",
"Last Run"]
rows = []
for file_path in known_file_paths:
last_runtime = self.get_last_runtime(file_path)
file_name = os.path.basename(file_path)
file_name = os.path.splitext(file_name)[0].replace(os.sep, '.')
if last_runtime:
Stats.gauge(
'dag_processing.last_runtime.{}'.format(file_name),
last_runtime
)
processor_pid = self.get_pid(file_path)
processor_start_time = self.get_start_time(file_path)
runtime = ((timezone.utcnow() - processor_start_time).total_seconds()
if processor_start_time else None)
last_run = self.get_last_finish_time(file_path)
if last_run:
seconds_ago = (timezone.utcnow() - last_run).total_seconds()
Stats.gauge(
'dag_processing.last_run.seconds_ago.{}'.format(file_name),
seconds_ago
)
rows.append((file_path,
processor_pid,
runtime,
last_runtime,
last_run))
rows = sorted(rows, key=lambda x: x[3] or 0.0)
formatted_rows = []
for file_path, pid, runtime, last_runtime, last_run in rows:
formatted_rows.append((file_path,
pid,
"{:.2f}s".format(runtime)
if runtime else None,
"{:.2f}s".format(last_runtime)
if last_runtime else None,
last_run.strftime("%Y-%m-%dT%H:%M:%S")
if last_run else None))
log_str = ("\n" +
"=" * 80 +
"\n" +
"DAG File Processing Stats\n\n" +
tabulate(formatted_rows, headers=headers) +
"\n" +
"=" * 80)
self.log.info(log_str) | Print out stats about how files are getting processed. | def _log_file_processing_stats(self, known_file_paths):
"""
Print out stats about how files are getting processed.
:param known_file_paths: a list of file paths that may contain Airflow
DAG definitions
:type known_file_paths: list[unicode]
:return: None
"""
# File Path: Path to the file containing the DAG definition
# PID: PID associated with the process that's processing the file. May
# be empty.
# Runtime: If the process is currently running, how long it's been
# running for in seconds.
# Last Runtime: If the process ran before, how long did it take to
# finish in seconds
# Last Run: When the file finished processing in the previous run.
headers = ["File Path",
"PID",
"Runtime",
"Last Runtime",
"Last Run"]
rows = []
for file_path in known_file_paths:
last_runtime = self.get_last_runtime(file_path)
file_name = os.path.basename(file_path)
file_name = os.path.splitext(file_name)[0].replace(os.sep, '.')
if last_runtime:
Stats.gauge(
'dag_processing.last_runtime.{}'.format(file_name),
last_runtime
)
processor_pid = self.get_pid(file_path)
processor_start_time = self.get_start_time(file_path)
runtime = ((timezone.utcnow() - processor_start_time).total_seconds()
if processor_start_time else None)
last_run = self.get_last_finish_time(file_path)
if last_run:
seconds_ago = (timezone.utcnow() - last_run).total_seconds()
Stats.gauge(
'dag_processing.last_run.seconds_ago.{}'.format(file_name),
seconds_ago
)
rows.append((file_path,
processor_pid,
runtime,
last_runtime,
last_run))
# Sort by longest last runtime. (Can't sort None values in python3)
rows = sorted(rows, key=lambda x: x[3] or 0.0)
formatted_rows = []
for file_path, pid, runtime, last_runtime, last_run in rows:
formatted_rows.append((file_path,
pid,
"{:.2f}s".format(runtime)
if runtime else None,
"{:.2f}s".format(last_runtime)
if last_runtime else None,
last_run.strftime("%Y-%m-%dT%H:%M:%S")
if last_run else None))
log_str = ("\n" +
"=" * 80 +
"\n" +
"DAG File Processing Stats\n\n" +
tabulate(formatted_rows, headers=headers) +
"\n" +
"=" * 80)
self.log.info(log_str) | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.set_file_paths | def set_file_paths(self, new_file_paths):
self._file_paths = new_file_paths
self._file_path_queue = [x for x in self._file_path_queue
if x in new_file_paths]
filtered_processors = {}
for file_path, processor in self._processors.items():
if file_path in new_file_paths:
filtered_processors[file_path] = processor
else:
self.log.warning("Stopping processor for %s", file_path)
processor.terminate()
self._processors = filtered_processors | Update this with a new set of paths to DAG definition files. | def set_file_paths(self, new_file_paths):
"""
Update this with a new set of paths to DAG definition files.
:param new_file_paths: list of paths to DAG definition files
:type new_file_paths: list[unicode]
:return: None
"""
self._file_paths = new_file_paths
self._file_path_queue = [x for x in self._file_path_queue
if x in new_file_paths]
# Stop processors that are working on deleted files
filtered_processors = {}
for file_path, processor in self._processors.items():
if file_path in new_file_paths:
filtered_processors[file_path] = processor
else:
self.log.warning("Stopping processor for %s", file_path)
processor.terminate()
self._processors = filtered_processors | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.wait_until_finished | def wait_until_finished(self):
for file_path, processor in self._processors.items():
while not processor.done:
time.sleep(0.1) | Sleeps until all the processors are done. | def wait_until_finished(self):
"""
Sleeps until all the processors are done.
"""
for file_path, processor in self._processors.items():
while not processor.done:
time.sleep(0.1) | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.heartbeat | def heartbeat(self):
finished_processors = {}
running_processors = {}
for file_path, processor in self._processors.items():
if processor.done:
self.log.debug("Processor for %s finished", file_path)
now = timezone.utcnow()
finished_processors[file_path] = processor
self._last_runtime[file_path] = (now -
processor.start_time).total_seconds()
self._last_finish_time[file_path] = now
self._run_count[file_path] += 1
else:
running_processors[file_path] = processor
self._processors = running_processors
self.log.debug("%s/%s DAG parsing processes running",
len(self._processors), self._parallelism)
self.log.debug("%s file paths queued for processing",
len(self._file_path_queue))
simple_dags = []
for file_path, processor in finished_processors.items():
if processor.result is None:
self.log.warning(
"Processor for %s exited with return code %s.",
processor.file_path, processor.exit_code
)
else:
for simple_dag in processor.result:
simple_dags.append(simple_dag)
if len(self._file_path_queue) == 0:
file_paths_in_progress = self._processors.keys()
now = timezone.utcnow()
file_paths_recently_processed = []
for file_path in self._file_paths:
last_finish_time = self.get_last_finish_time(file_path)
if (last_finish_time is not None and
(now - last_finish_time).total_seconds() <
self._file_process_interval):
file_paths_recently_processed.append(file_path)
files_paths_at_run_limit = [file_path
for file_path, num_runs in self._run_count.items()
if num_runs == self._max_runs]
files_paths_to_queue = list(set(self._file_paths) -
set(file_paths_in_progress) -
set(file_paths_recently_processed) -
set(files_paths_at_run_limit))
for file_path, processor in self._processors.items():
self.log.debug(
"File path %s is still being processed (started: %s)",
processor.file_path, processor.start_time.isoformat()
)
self.log.debug(
"Queuing the following files for processing:\n\t%s",
"\n\t".join(files_paths_to_queue)
)
self._file_path_queue.extend(files_paths_to_queue)
zombies = self._find_zombies()
while (self._parallelism - len(self._processors) > 0 and
len(self._file_path_queue) > 0):
file_path = self._file_path_queue.pop(0)
processor = self._processor_factory(file_path, zombies)
processor.start()
self.log.debug(
"Started a process (PID: %s) to generate tasks for %s",
processor.pid, file_path
)
self._processors[file_path] = processor
self._run_count[self._heart_beat_key] += 1
return simple_dags | This should be periodically called by the manager loop. This method will kick off new processes to process DAG definition files and read the results from the finished processors. | def heartbeat(self):
"""
This should be periodically called by the manager loop. This method will
kick off new processes to process DAG definition files and read the
results from the finished processors.
:return: a list of SimpleDags that were produced by processors that
have finished since the last time this was called
:rtype: list[airflow.utils.dag_processing.SimpleDag]
"""
finished_processors = {}
""":type : dict[unicode, AbstractDagFileProcessor]"""
running_processors = {}
""":type : dict[unicode, AbstractDagFileProcessor]"""
for file_path, processor in self._processors.items():
if processor.done:
self.log.debug("Processor for %s finished", file_path)
now = timezone.utcnow()
finished_processors[file_path] = processor
self._last_runtime[file_path] = (now -
processor.start_time).total_seconds()
self._last_finish_time[file_path] = now
self._run_count[file_path] += 1
else:
running_processors[file_path] = processor
self._processors = running_processors
self.log.debug("%s/%s DAG parsing processes running",
len(self._processors), self._parallelism)
self.log.debug("%s file paths queued for processing",
len(self._file_path_queue))
# Collect all the DAGs that were found in the processed files
simple_dags = []
for file_path, processor in finished_processors.items():
if processor.result is None:
self.log.warning(
"Processor for %s exited with return code %s.",
processor.file_path, processor.exit_code
)
else:
for simple_dag in processor.result:
simple_dags.append(simple_dag)
# Generate more file paths to process if we processed all the files
# already.
if len(self._file_path_queue) == 0:
# If the file path is already being processed, or if a file was
# processed recently, wait until the next batch
file_paths_in_progress = self._processors.keys()
now = timezone.utcnow()
file_paths_recently_processed = []
for file_path in self._file_paths:
last_finish_time = self.get_last_finish_time(file_path)
if (last_finish_time is not None and
(now - last_finish_time).total_seconds() <
self._file_process_interval):
file_paths_recently_processed.append(file_path)
files_paths_at_run_limit = [file_path
for file_path, num_runs in self._run_count.items()
if num_runs == self._max_runs]
files_paths_to_queue = list(set(self._file_paths) -
set(file_paths_in_progress) -
set(file_paths_recently_processed) -
set(files_paths_at_run_limit))
for file_path, processor in self._processors.items():
self.log.debug(
"File path %s is still being processed (started: %s)",
processor.file_path, processor.start_time.isoformat()
)
self.log.debug(
"Queuing the following files for processing:\n\t%s",
"\n\t".join(files_paths_to_queue)
)
self._file_path_queue.extend(files_paths_to_queue)
zombies = self._find_zombies()
# Start more processors if we have enough slots and files to process
while (self._parallelism - len(self._processors) > 0 and
len(self._file_path_queue) > 0):
file_path = self._file_path_queue.pop(0)
processor = self._processor_factory(file_path, zombies)
processor.start()
self.log.debug(
"Started a process (PID: %s) to generate tasks for %s",
processor.pid, file_path
)
self._processors[file_path] = processor
# Update heartbeat count.
self._run_count[self._heart_beat_key] += 1
return simple_dags | airflow/utils/dag_processing.py |
apache/airflow | DagFileProcessorManager.end | def end(self):
pids_to_kill = self.get_all_pids()
if len(pids_to_kill) > 0:
this_process = psutil.Process(os.getpid())
child_processes = [x for x in this_process.children(recursive=True)
if x.is_running() and x.pid in pids_to_kill]
for child in child_processes:
self.log.info("Terminating child PID: %s", child.pid)
child.terminate()
timeout = 5
self.log.info("Waiting up to %s seconds for processes to exit...", timeout)
try:
psutil.wait_procs(
child_processes, timeout=timeout,
callback=lambda x: self.log.info('Terminated PID %s', x.pid))
except psutil.TimeoutExpired:
self.log.debug("Ran out of time while waiting for processes to exit")
child_processes = [x for x in this_process.children(recursive=True)
if x.is_running() and x.pid in pids_to_kill]
if len(child_processes) > 0:
self.log.info("SIGKILL processes that did not terminate gracefully")
for child in child_processes:
self.log.info("Killing child PID: %s", child.pid)
child.kill()
child.wait() | Kill all child processes on exit since we don't want to leave them as orphaned. | def end(self):
"""
Kill all child processes on exit since we don't want to leave
them as orphaned.
"""
pids_to_kill = self.get_all_pids()
if len(pids_to_kill) > 0:
# First try SIGTERM
this_process = psutil.Process(os.getpid())
# Only check child processes to ensure that we don't have a case
# where we kill the wrong process because a child process died
# but the PID got reused.
child_processes = [x for x in this_process.children(recursive=True)
if x.is_running() and x.pid in pids_to_kill]
for child in child_processes:
self.log.info("Terminating child PID: %s", child.pid)
child.terminate()
# TODO: Remove magic number
timeout = 5
self.log.info("Waiting up to %s seconds for processes to exit...", timeout)
try:
psutil.wait_procs(
child_processes, timeout=timeout,
callback=lambda x: self.log.info('Terminated PID %s', x.pid))
except psutil.TimeoutExpired:
self.log.debug("Ran out of time while waiting for processes to exit")
# Then SIGKILL
child_processes = [x for x in this_process.children(recursive=True)
if x.is_running() and x.pid in pids_to_kill]
if len(child_processes) > 0:
self.log.info("SIGKILL processes that did not terminate gracefully")
for child in child_processes:
self.log.info("Killing child PID: %s", child.pid)
child.kill()
child.wait() | airflow/utils/dag_processing.py |
apache/airflow | SSHHook.get_conn | def get_conn(self):
self.log.debug('Creating SSH client for conn_id: %s', self.ssh_conn_id)
client = paramiko.SSHClient()
if not self.allow_host_key_change:
self.log.warning('Remote Identification Change is not verified. '
'This wont protect against Man-In-The-Middle attacks')
client.load_system_host_keys()
if self.no_host_key_check:
self.log.warning('No Host Key Verification. This wont protect '
'against Man-In-The-Middle attacks')
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
if self.password and self.password.strip():
client.connect(hostname=self.remote_host,
username=self.username,
password=self.password,
key_filename=self.key_file,
timeout=self.timeout,
compress=self.compress,
port=self.port,
sock=self.host_proxy)
else:
client.connect(hostname=self.remote_host,
username=self.username,
key_filename=self.key_file,
timeout=self.timeout,
compress=self.compress,
port=self.port,
sock=self.host_proxy)
if self.keepalive_interval:
client.get_transport().set_keepalive(self.keepalive_interval)
self.client = client
return client | Opens a ssh connection to the remote host. | def get_conn(self):
"""
Opens a ssh connection to the remote host.
:rtype: paramiko.client.SSHClient
"""
self.log.debug('Creating SSH client for conn_id: %s', self.ssh_conn_id)
client = paramiko.SSHClient()
if not self.allow_host_key_change:
self.log.warning('Remote Identification Change is not verified. '
'This wont protect against Man-In-The-Middle attacks')
client.load_system_host_keys()
if self.no_host_key_check:
self.log.warning('No Host Key Verification. This wont protect '
'against Man-In-The-Middle attacks')
# Default is RejectPolicy
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
if self.password and self.password.strip():
client.connect(hostname=self.remote_host,
username=self.username,
password=self.password,
key_filename=self.key_file,
timeout=self.timeout,
compress=self.compress,
port=self.port,
sock=self.host_proxy)
else:
client.connect(hostname=self.remote_host,
username=self.username,
key_filename=self.key_file,
timeout=self.timeout,
compress=self.compress,
port=self.port,
sock=self.host_proxy)
if self.keepalive_interval:
client.get_transport().set_keepalive(self.keepalive_interval)
self.client = client
return client | airflow/contrib/hooks/ssh_hook.py |
apache/airflow | GCPTransferServiceHook.create_transfer_job | def create_transfer_job(self, body):
body = self._inject_project_id(body, BODY, PROJECT_ID)
return self.get_conn().transferJobs().create(body=body).execute(num_retries=self.num_retries) | Creates a transfer job that runs periodically. | def create_transfer_job(self, body):
"""
Creates a transfer job that runs periodically.
:param body: (Required) A request body, as described in
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferJobs/patch#request-body
:type body: dict
:return: transfer job.
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferJobs#TransferJob
:rtype: dict
"""
body = self._inject_project_id(body, BODY, PROJECT_ID)
return self.get_conn().transferJobs().create(body=body).execute(num_retries=self.num_retries) | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.get_transfer_job | def get_transfer_job(self, job_name, project_id=None):
return (
self.get_conn()
.transferJobs()
.get(jobName=job_name, projectId=project_id)
.execute(num_retries=self.num_retries)
) | Gets the latest state of a long-running operation in Google Storage Transfer Service. | def get_transfer_job(self, job_name, project_id=None):
"""
Gets the latest state of a long-running operation in Google Storage
Transfer Service.
:param job_name: (Required) Name of the job to be fetched
:type job_name: str
:param project_id: (Optional) the ID of the project that owns the Transfer
Job. If set to None or missing, the default project_id from the GCP
connection is used.
:type project_id: str
:return: Transfer Job
:rtype: dict
"""
return (
self.get_conn()
.transferJobs()
.get(jobName=job_name, projectId=project_id)
.execute(num_retries=self.num_retries)
) | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.list_transfer_job | def list_transfer_job(self, filter):
conn = self.get_conn()
filter = self._inject_project_id(filter, FILTER, FILTER_PROJECT_ID)
request = conn.transferJobs().list(filter=json.dumps(filter))
jobs = []
while request is not None:
response = request.execute(num_retries=self.num_retries)
jobs.extend(response[TRANSFER_JOBS])
request = conn.transferJobs().list_next(previous_request=request, previous_response=response)
return jobs | Lists long-running operations in Google Storage Transfer Service that match the specified filter. | def list_transfer_job(self, filter):
"""
Lists long-running operations in Google Storage Transfer
Service that match the specified filter.
:param filter: (Required) A request filter, as described in
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferJobs/list#body.QUERY_PARAMETERS.filter
:type filter: dict
:return: List of Transfer Jobs
:rtype: list[dict]
"""
conn = self.get_conn()
filter = self._inject_project_id(filter, FILTER, FILTER_PROJECT_ID)
request = conn.transferJobs().list(filter=json.dumps(filter))
jobs = []
while request is not None:
response = request.execute(num_retries=self.num_retries)
jobs.extend(response[TRANSFER_JOBS])
request = conn.transferJobs().list_next(previous_request=request, previous_response=response)
return jobs | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.update_transfer_job | def update_transfer_job(self, job_name, body):
body = self._inject_project_id(body, BODY, PROJECT_ID)
return (
self.get_conn()
.transferJobs()
.patch(jobName=job_name, body=body)
.execute(num_retries=self.num_retries)
) | Updates a transfer job that runs periodically. | def update_transfer_job(self, job_name, body):
"""
Updates a transfer job that runs periodically.
:param job_name: (Required) Name of the job to be updated
:type job_name: str
:param body: A request body, as described in
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferJobs/patch#request-body
:type body: dict
:return: If successful, TransferJob.
:rtype: dict
"""
body = self._inject_project_id(body, BODY, PROJECT_ID)
return (
self.get_conn()
.transferJobs()
.patch(jobName=job_name, body=body)
.execute(num_retries=self.num_retries)
) | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.cancel_transfer_operation | def cancel_transfer_operation(self, operation_name):
self.get_conn().transferOperations().cancel(name=operation_name).execute(num_retries=self.num_retries) | Cancels an transfer operation in Google Storage Transfer Service. | def cancel_transfer_operation(self, operation_name):
"""
Cancels an transfer operation in Google Storage Transfer Service.
:param operation_name: Name of the transfer operation.
:type operation_name: str
:rtype: None
"""
self.get_conn().transferOperations().cancel(name=operation_name).execute(num_retries=self.num_retries) | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.pause_transfer_operation | def pause_transfer_operation(self, operation_name):
self.get_conn().transferOperations().pause(name=operation_name).execute(num_retries=self.num_retries) | Pauses an transfer operation in Google Storage Transfer Service. | def pause_transfer_operation(self, operation_name):
"""
Pauses an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:rtype: None
"""
self.get_conn().transferOperations().pause(name=operation_name).execute(num_retries=self.num_retries) | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.resume_transfer_operation | def resume_transfer_operation(self, operation_name):
self.get_conn().transferOperations().resume(name=operation_name).execute(num_retries=self.num_retries) | Resumes an transfer operation in Google Storage Transfer Service. | def resume_transfer_operation(self, operation_name):
"""
Resumes an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:rtype: None
"""
self.get_conn().transferOperations().resume(name=operation_name).execute(num_retries=self.num_retries) | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | GCPTransferServiceHook.wait_for_transfer_job | def wait_for_transfer_job(self, job, expected_statuses=(GcpTransferOperationStatus.SUCCESS,), timeout=60):
while timeout > 0:
operations = self.list_transfer_operations(
filter={FILTER_PROJECT_ID: job[PROJECT_ID], FILTER_JOB_NAMES: [job[NAME]]}
)
if GCPTransferServiceHook.operations_contain_expected_statuses(operations, expected_statuses):
return
time.sleep(TIME_TO_SLEEP_IN_SECONDS)
timeout -= TIME_TO_SLEEP_IN_SECONDS
raise AirflowException("Timeout. The operation could not be completed within the allotted time.") | Waits until the job reaches the expected state. | def wait_for_transfer_job(self, job, expected_statuses=(GcpTransferOperationStatus.SUCCESS,), timeout=60):
"""
Waits until the job reaches the expected state.
:param job: Transfer job
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferJobs#TransferJob
:type job: dict
:param expected_statuses: State that is expected
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferOperations#Status
:type expected_statuses: set[str]
:param timeout:
:type timeout: time in which the operation must end in seconds
:rtype: None
"""
while timeout > 0:
operations = self.list_transfer_operations(
filter={FILTER_PROJECT_ID: job[PROJECT_ID], FILTER_JOB_NAMES: [job[NAME]]}
)
if GCPTransferServiceHook.operations_contain_expected_statuses(operations, expected_statuses):
return
time.sleep(TIME_TO_SLEEP_IN_SECONDS)
timeout -= TIME_TO_SLEEP_IN_SECONDS
raise AirflowException("Timeout. The operation could not be completed within the allotted time.") | airflow/contrib/hooks/gcp_transfer_hook.py |
apache/airflow | run_command | def run_command(command):
process = subprocess.Popen(
shlex.split(command),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
output, stderr = [stream.decode(sys.getdefaultencoding(), 'ignore')
for stream in process.communicate()]
if process.returncode != 0:
raise AirflowConfigException(
"Cannot execute {}. Error code is: {}. Output: {}, Stderr: {}"
.format(command, process.returncode, output, stderr)
)
return output | Runs command and returns stdout | def run_command(command):
"""
Runs command and returns stdout
"""
process = subprocess.Popen(
shlex.split(command),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
output, stderr = [stream.decode(sys.getdefaultencoding(), 'ignore')
for stream in process.communicate()]
if process.returncode != 0:
raise AirflowConfigException(
"Cannot execute {}. Error code is: {}. Output: {}, Stderr: {}"
.format(command, process.returncode, output, stderr)
)
return output | airflow/configuration.py |
apache/airflow | AirflowConfigParser.remove_option | def remove_option(self, section, option, remove_default=True):
if super().has_option(section, option):
super().remove_option(section, option)
if self.airflow_defaults.has_option(section, option) and remove_default:
self.airflow_defaults.remove_option(section, option) | Remove an option if it exists in config from a file or default config. If both of config have the same option, this removes the option in both configs unless remove_default=False. | def remove_option(self, section, option, remove_default=True):
"""
Remove an option if it exists in config from a file or
default config. If both of config have the same option, this removes
the option in both configs unless remove_default=False.
"""
if super().has_option(section, option):
super().remove_option(section, option)
if self.airflow_defaults.has_option(section, option) and remove_default:
self.airflow_defaults.remove_option(section, option) | airflow/configuration.py |
apache/airflow | DatastoreHook.allocate_ids | def allocate_ids(self, partial_keys):
conn = self.get_conn()
resp = (conn
.projects()
.allocateIds(projectId=self.project_id, body={'keys': partial_keys})
.execute(num_retries=self.num_retries))
return resp['keys'] | Allocate IDs for incomplete keys. | def allocate_ids(self, partial_keys):
"""
Allocate IDs for incomplete keys.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/allocateIds
:param partial_keys: a list of partial keys.
:type partial_keys: list
:return: a list of full keys.
:rtype: list
"""
conn = self.get_conn()
resp = (conn
.projects()
.allocateIds(projectId=self.project_id, body={'keys': partial_keys})
.execute(num_retries=self.num_retries))
return resp['keys'] | airflow/contrib/hooks/datastore_hook.py |
apache/airflow | DatastoreHook.begin_transaction | def begin_transaction(self):
conn = self.get_conn()
resp = (conn
.projects()
.beginTransaction(projectId=self.project_id, body={})
.execute(num_retries=self.num_retries))
return resp['transaction'] | Begins a new transaction. | def begin_transaction(self):
"""
Begins a new transaction.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/beginTransaction
:return: a transaction handle.
:rtype: str
"""
conn = self.get_conn()
resp = (conn
.projects()
.beginTransaction(projectId=self.project_id, body={})
.execute(num_retries=self.num_retries))
return resp['transaction'] | airflow/contrib/hooks/datastore_hook.py |
apache/airflow | DatastoreHook.commit | def commit(self, body):
conn = self.get_conn()
resp = (conn
.projects()
.commit(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | Commit a transaction, optionally creating, deleting or modifying some entities. | def commit(self, body):
"""
Commit a transaction, optionally creating, deleting or modifying some entities.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/commit
:param body: the body of the commit request.
:type body: dict
:return: the response body of the commit request.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.commit(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | airflow/contrib/hooks/datastore_hook.py |
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