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test | SSHHook.get_conn | Opens a ssh connection to the remote host.
:rtype: paramiko.client.SSHClient | airflow/contrib/hooks/ssh_hook.py | 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 | 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 | [
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"connection",
"to",
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"remote",
"host",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ssh_hook.py#L144-L185 | [
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test | SSHHook.get_tunnel | Creates a tunnel between two hosts. Like ssh -L <LOCAL_PORT>:host:<REMOTE_PORT>.
:param remote_port: The remote port to create a tunnel to
:type remote_port: int
:param remote_host: The remote host to create a tunnel to (default localhost)
:type remote_host: str
:param local_port: The local port to attach the tunnel to
:type local_port: int
:return: sshtunnel.SSHTunnelForwarder object | airflow/contrib/hooks/ssh_hook.py | def get_tunnel(self, remote_port, remote_host="localhost", local_port=None):
"""
Creates a tunnel between two hosts. Like ssh -L <LOCAL_PORT>:host:<REMOTE_PORT>.
:param remote_port: The remote port to create a tunnel to
:type remote_port: int
:param remote_host: The remote host to create a tunnel to (default localhost)
:type remote_host: str
:param local_port: The local port to attach the tunnel to
:type local_port: int
:return: sshtunnel.SSHTunnelForwarder object
"""
if local_port:
local_bind_address = ('localhost', local_port)
else:
local_bind_address = ('localhost',)
if self.password and self.password.strip():
client = SSHTunnelForwarder(self.remote_host,
ssh_port=self.port,
ssh_username=self.username,
ssh_password=self.password,
ssh_pkey=self.key_file,
ssh_proxy=self.host_proxy,
local_bind_address=local_bind_address,
remote_bind_address=(remote_host, remote_port),
logger=self.log)
else:
client = SSHTunnelForwarder(self.remote_host,
ssh_port=self.port,
ssh_username=self.username,
ssh_pkey=self.key_file,
ssh_proxy=self.host_proxy,
local_bind_address=local_bind_address,
remote_bind_address=(remote_host, remote_port),
host_pkey_directories=[],
logger=self.log)
return client | def get_tunnel(self, remote_port, remote_host="localhost", local_port=None):
"""
Creates a tunnel between two hosts. Like ssh -L <LOCAL_PORT>:host:<REMOTE_PORT>.
:param remote_port: The remote port to create a tunnel to
:type remote_port: int
:param remote_host: The remote host to create a tunnel to (default localhost)
:type remote_host: str
:param local_port: The local port to attach the tunnel to
:type local_port: int
:return: sshtunnel.SSHTunnelForwarder object
"""
if local_port:
local_bind_address = ('localhost', local_port)
else:
local_bind_address = ('localhost',)
if self.password and self.password.strip():
client = SSHTunnelForwarder(self.remote_host,
ssh_port=self.port,
ssh_username=self.username,
ssh_password=self.password,
ssh_pkey=self.key_file,
ssh_proxy=self.host_proxy,
local_bind_address=local_bind_address,
remote_bind_address=(remote_host, remote_port),
logger=self.log)
else:
client = SSHTunnelForwarder(self.remote_host,
ssh_port=self.port,
ssh_username=self.username,
ssh_pkey=self.key_file,
ssh_proxy=self.host_proxy,
local_bind_address=local_bind_address,
remote_bind_address=(remote_host, remote_port),
host_pkey_directories=[],
logger=self.log)
return client | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/ssh_hook.py#L199-L239 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | GCPTransferServiceHook.create_transfer_job | 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 | airflow/contrib/hooks/gcp_transfer_hook.py | 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) | 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) | [
"Creates",
"a",
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"job",
"that",
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test | GCPTransferServiceHook.get_transfer_job | 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 | airflow/contrib/hooks/gcp_transfer_hook.py | 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)
) | 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)
) | [
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"long",
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"running",
"operation",
"in",
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"Transfer",
"Service",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L136-L155 | [
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test | GCPTransferServiceHook.list_transfer_job | 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] | airflow/contrib/hooks/gcp_transfer_hook.py | 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 | 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 | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L157-L179 | [
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test | GCPTransferServiceHook.update_transfer_job | 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 | airflow/contrib/hooks/gcp_transfer_hook.py | 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)
) | 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)
) | [
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"a",
"transfer",
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"that",
"runs",
"periodically",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L182-L200 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | GCPTransferServiceHook.delete_transfer_job | Deletes a transfer job. This is a soft delete. After a transfer job is
deleted, the job and all the transfer executions are subject to garbage
collection. Transfer jobs become eligible for garbage collection
30 days after soft delete.
:param job_name: (Required) Name of the job to be deleted
: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
:rtype: None | airflow/contrib/hooks/gcp_transfer_hook.py | def delete_transfer_job(self, job_name, project_id):
"""
Deletes a transfer job. This is a soft delete. After a transfer job is
deleted, the job and all the transfer executions are subject to garbage
collection. Transfer jobs become eligible for garbage collection
30 days after soft delete.
:param job_name: (Required) Name of the job to be deleted
: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
:rtype: None
"""
return (
self.get_conn()
.transferJobs()
.patch(
jobName=job_name,
body={
PROJECT_ID: project_id,
TRANSFER_JOB: {STATUS1: GcpTransferJobsStatus.DELETED},
TRANSFER_JOB_FIELD_MASK: STATUS1,
},
)
.execute(num_retries=self.num_retries)
) | def delete_transfer_job(self, job_name, project_id):
"""
Deletes a transfer job. This is a soft delete. After a transfer job is
deleted, the job and all the transfer executions are subject to garbage
collection. Transfer jobs become eligible for garbage collection
30 days after soft delete.
:param job_name: (Required) Name of the job to be deleted
: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
:rtype: None
"""
return (
self.get_conn()
.transferJobs()
.patch(
jobName=job_name,
body={
PROJECT_ID: project_id,
TRANSFER_JOB: {STATUS1: GcpTransferJobsStatus.DELETED},
TRANSFER_JOB_FIELD_MASK: STATUS1,
},
)
.execute(num_retries=self.num_retries)
) | [
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test | GCPTransferServiceHook.cancel_transfer_operation | Cancels an transfer operation in Google Storage Transfer Service.
:param operation_name: Name of the transfer operation.
:type operation_name: str
:rtype: None | airflow/contrib/hooks/gcp_transfer_hook.py | 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) | 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) | [
"Cancels",
"an",
"transfer",
"operation",
"in",
"Google",
"Storage",
"Transfer",
"Service",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L235-L243 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | GCPTransferServiceHook.get_transfer_operation | Gets an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:return: transfer operation
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/Operation
:rtype: dict | airflow/contrib/hooks/gcp_transfer_hook.py | def get_transfer_operation(self, operation_name):
"""
Gets an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:return: transfer operation
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/Operation
:rtype: dict
"""
return (
self.get_conn()
.transferOperations()
.get(name=operation_name)
.execute(num_retries=self.num_retries)
) | def get_transfer_operation(self, operation_name):
"""
Gets an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:return: transfer operation
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/Operation
:rtype: dict
"""
return (
self.get_conn()
.transferOperations()
.get(name=operation_name)
.execute(num_retries=self.num_retries)
) | [
"Gets",
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"in",
"Google",
"Storage",
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"Service",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L246-L262 | [
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test | GCPTransferServiceHook.list_transfer_operations | Gets an transfer operation in Google Storage Transfer Service.
: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
With one additional improvement:
* project_id is optional if you have a project id defined
in the connection
See: :ref:`howto/connection:gcp`
:type filter: dict
:return: transfer operation
:rtype: list[dict] | airflow/contrib/hooks/gcp_transfer_hook.py | def list_transfer_operations(self, filter):
"""
Gets an transfer operation in Google Storage Transfer Service.
: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
With one additional improvement:
* project_id is optional if you have a project id defined
in the connection
See: :ref:`howto/connection:gcp`
:type filter: dict
:return: transfer operation
:rtype: list[dict]
"""
conn = self.get_conn()
filter = self._inject_project_id(filter, FILTER, FILTER_PROJECT_ID)
operations = []
request = conn.transferOperations().list(name=TRANSFER_OPERATIONS, filter=json.dumps(filter))
while request is not None:
response = request.execute(num_retries=self.num_retries)
if OPERATIONS in response:
operations.extend(response[OPERATIONS])
request = conn.transferOperations().list_next(
previous_request=request, previous_response=response
)
return operations | def list_transfer_operations(self, filter):
"""
Gets an transfer operation in Google Storage Transfer Service.
: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
With one additional improvement:
* project_id is optional if you have a project id defined
in the connection
See: :ref:`howto/connection:gcp`
:type filter: dict
:return: transfer operation
:rtype: list[dict]
"""
conn = self.get_conn()
filter = self._inject_project_id(filter, FILTER, FILTER_PROJECT_ID)
operations = []
request = conn.transferOperations().list(name=TRANSFER_OPERATIONS, filter=json.dumps(filter))
while request is not None:
response = request.execute(num_retries=self.num_retries)
if OPERATIONS in response:
operations.extend(response[OPERATIONS])
request = conn.transferOperations().list_next(
previous_request=request, previous_response=response
)
return operations | [
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"transfer",
"operation",
"in",
"Google",
"Storage",
"Transfer",
"Service",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L265-L298 | [
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test | GCPTransferServiceHook.pause_transfer_operation | Pauses an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:rtype: None | airflow/contrib/hooks/gcp_transfer_hook.py | 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) | 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) | [
"Pauses",
"an",
"transfer",
"operation",
"in",
"Google",
"Storage",
"Transfer",
"Service",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L301-L309 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | GCPTransferServiceHook.resume_transfer_operation | Resumes an transfer operation in Google Storage Transfer Service.
:param operation_name: (Required) Name of the transfer operation.
:type operation_name: str
:rtype: None | airflow/contrib/hooks/gcp_transfer_hook.py | 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) | 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) | [
"Resumes",
"an",
"transfer",
"operation",
"in",
"Google",
"Storage",
"Transfer",
"Service",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L312-L320 | [
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test | GCPTransferServiceHook.wait_for_transfer_job | 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 | airflow/contrib/hooks/gcp_transfer_hook.py | 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.") | 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.") | [
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test | GCPTransferServiceHook.operations_contain_expected_statuses | Checks whether the operation list has an operation with the
expected status, then returns true
If it encounters operations in FAILED or ABORTED state
throw :class:`airflow.exceptions.AirflowException`.
:param operations: (Required) List of transfer operations to check.
:type operations: list[dict]
:param expected_statuses: (Required) status that is expected
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferOperations#Status
:type expected_statuses: set[str]
:return: If there is an operation with the expected state
in the operation list, returns true,
:raises: airflow.exceptions.AirflowException If it encounters operations
with a state in the list,
:rtype: bool | airflow/contrib/hooks/gcp_transfer_hook.py | def operations_contain_expected_statuses(operations, expected_statuses):
"""
Checks whether the operation list has an operation with the
expected status, then returns true
If it encounters operations in FAILED or ABORTED state
throw :class:`airflow.exceptions.AirflowException`.
:param operations: (Required) List of transfer operations to check.
:type operations: list[dict]
:param expected_statuses: (Required) status that is expected
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferOperations#Status
:type expected_statuses: set[str]
:return: If there is an operation with the expected state
in the operation list, returns true,
:raises: airflow.exceptions.AirflowException If it encounters operations
with a state in the list,
:rtype: bool
"""
expected_statuses = (
{expected_statuses} if isinstance(expected_statuses, six.string_types) else set(expected_statuses)
)
if len(operations) == 0:
return False
current_statuses = {operation[METADATA][STATUS] for operation in operations}
if len(current_statuses - set(expected_statuses)) != len(current_statuses):
return True
if len(NEGATIVE_STATUSES - current_statuses) != len(NEGATIVE_STATUSES):
raise AirflowException(
'An unexpected operation status was encountered. Expected: {}'.format(
", ".join(expected_statuses)
)
)
return False | def operations_contain_expected_statuses(operations, expected_statuses):
"""
Checks whether the operation list has an operation with the
expected status, then returns true
If it encounters operations in FAILED or ABORTED state
throw :class:`airflow.exceptions.AirflowException`.
:param operations: (Required) List of transfer operations to check.
:type operations: list[dict]
:param expected_statuses: (Required) status that is expected
See:
https://cloud.google.com/storage-transfer/docs/reference/rest/v1/transferOperations#Status
:type expected_statuses: set[str]
:return: If there is an operation with the expected state
in the operation list, returns true,
:raises: airflow.exceptions.AirflowException If it encounters operations
with a state in the list,
:rtype: bool
"""
expected_statuses = (
{expected_statuses} if isinstance(expected_statuses, six.string_types) else set(expected_statuses)
)
if len(operations) == 0:
return False
current_statuses = {operation[METADATA][STATUS] for operation in operations}
if len(current_statuses - set(expected_statuses)) != len(current_statuses):
return True
if len(NEGATIVE_STATUSES - current_statuses) != len(NEGATIVE_STATUSES):
raise AirflowException(
'An unexpected operation status was encountered. Expected: {}'.format(
", ".join(expected_statuses)
)
)
return False | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_transfer_hook.py#L361-L397 | [
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test | TaskReschedule.find_for_task_instance | Returns all task reschedules for the task instance and try number,
in ascending order.
:param task_instance: the task instance to find task reschedules for
:type task_instance: airflow.models.TaskInstance | airflow/models/taskreschedule.py | def find_for_task_instance(task_instance, session):
"""
Returns all task reschedules for the task instance and try number,
in ascending order.
:param task_instance: the task instance to find task reschedules for
:type task_instance: airflow.models.TaskInstance
"""
TR = TaskReschedule
return (
session
.query(TR)
.filter(TR.dag_id == task_instance.dag_id,
TR.task_id == task_instance.task_id,
TR.execution_date == task_instance.execution_date,
TR.try_number == task_instance.try_number)
.order_by(asc(TR.id))
.all()
) | def find_for_task_instance(task_instance, session):
"""
Returns all task reschedules for the task instance and try number,
in ascending order.
:param task_instance: the task instance to find task reschedules for
:type task_instance: airflow.models.TaskInstance
"""
TR = TaskReschedule
return (
session
.query(TR)
.filter(TR.dag_id == task_instance.dag_id,
TR.task_id == task_instance.task_id,
TR.execution_date == task_instance.execution_date,
TR.try_number == task_instance.try_number)
.order_by(asc(TR.id))
.all()
) | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/taskreschedule.py#L67-L85 | [
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test | AirflowKubernetesScheduler._strip_unsafe_kubernetes_special_chars | Kubernetes only supports lowercase alphanumeric characters and "-" and "." in
the pod name
However, there are special rules about how "-" and "." can be used so let's
only keep
alphanumeric chars see here for detail:
https://kubernetes.io/docs/concepts/overview/working-with-objects/names/
:param string: The requested Pod name
:return: ``str`` Pod name stripped of any unsafe characters | airflow/contrib/executors/kubernetes_executor.py | def _strip_unsafe_kubernetes_special_chars(string):
"""
Kubernetes only supports lowercase alphanumeric characters and "-" and "." in
the pod name
However, there are special rules about how "-" and "." can be used so let's
only keep
alphanumeric chars see here for detail:
https://kubernetes.io/docs/concepts/overview/working-with-objects/names/
:param string: The requested Pod name
:return: ``str`` Pod name stripped of any unsafe characters
"""
return ''.join(ch.lower() for ind, ch in enumerate(string) if ch.isalnum()) | def _strip_unsafe_kubernetes_special_chars(string):
"""
Kubernetes only supports lowercase alphanumeric characters and "-" and "." in
the pod name
However, there are special rules about how "-" and "." can be used so let's
only keep
alphanumeric chars see here for detail:
https://kubernetes.io/docs/concepts/overview/working-with-objects/names/
:param string: The requested Pod name
:return: ``str`` Pod name stripped of any unsafe characters
"""
return ''.join(ch.lower() for ind, ch in enumerate(string) if ch.isalnum()) | [
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test | AirflowKubernetesScheduler._make_safe_pod_id | Kubernetes pod names must be <= 253 chars and must pass the following regex for
validation
"^[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*$"
:param safe_dag_id: a dag_id with only alphanumeric characters
:param safe_task_id: a task_id with only alphanumeric characters
:param random_uuid: a uuid
:return: ``str`` valid Pod name of appropriate length | airflow/contrib/executors/kubernetes_executor.py | def _make_safe_pod_id(safe_dag_id, safe_task_id, safe_uuid):
"""
Kubernetes pod names must be <= 253 chars and must pass the following regex for
validation
"^[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*$"
:param safe_dag_id: a dag_id with only alphanumeric characters
:param safe_task_id: a task_id with only alphanumeric characters
:param random_uuid: a uuid
:return: ``str`` valid Pod name of appropriate length
"""
MAX_POD_ID_LEN = 253
safe_key = safe_dag_id + safe_task_id
safe_pod_id = safe_key[:MAX_POD_ID_LEN - len(safe_uuid) - 1] + "-" + safe_uuid
return safe_pod_id | def _make_safe_pod_id(safe_dag_id, safe_task_id, safe_uuid):
"""
Kubernetes pod names must be <= 253 chars and must pass the following regex for
validation
"^[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*$"
:param safe_dag_id: a dag_id with only alphanumeric characters
:param safe_task_id: a task_id with only alphanumeric characters
:param random_uuid: a uuid
:return: ``str`` valid Pod name of appropriate length
"""
MAX_POD_ID_LEN = 253
safe_key = safe_dag_id + safe_task_id
safe_pod_id = safe_key[:MAX_POD_ID_LEN - len(safe_uuid) - 1] + "-" + safe_uuid
return safe_pod_id | [
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test | AirflowKubernetesScheduler._make_safe_label_value | Valid label values must be 63 characters or less and must be empty or begin and
end with an alphanumeric character ([a-z0-9A-Z]) with dashes (-), underscores (_),
dots (.), and alphanumerics between.
If the label value is then greater than 63 chars once made safe, or differs in any
way from the original value sent to this function, then we need to truncate to
53chars, and append it with a unique hash. | airflow/contrib/executors/kubernetes_executor.py | def _make_safe_label_value(string):
"""
Valid label values must be 63 characters or less and must be empty or begin and
end with an alphanumeric character ([a-z0-9A-Z]) with dashes (-), underscores (_),
dots (.), and alphanumerics between.
If the label value is then greater than 63 chars once made safe, or differs in any
way from the original value sent to this function, then we need to truncate to
53chars, and append it with a unique hash.
"""
MAX_LABEL_LEN = 63
safe_label = re.sub(r'^[^a-z0-9A-Z]*|[^a-zA-Z0-9_\-\.]|[^a-z0-9A-Z]*$', '', string)
if len(safe_label) > MAX_LABEL_LEN or string != safe_label:
safe_hash = hashlib.md5(string.encode()).hexdigest()[:9]
safe_label = safe_label[:MAX_LABEL_LEN - len(safe_hash) - 1] + "-" + safe_hash
return safe_label | def _make_safe_label_value(string):
"""
Valid label values must be 63 characters or less and must be empty or begin and
end with an alphanumeric character ([a-z0-9A-Z]) with dashes (-), underscores (_),
dots (.), and alphanumerics between.
If the label value is then greater than 63 chars once made safe, or differs in any
way from the original value sent to this function, then we need to truncate to
53chars, and append it with a unique hash.
"""
MAX_LABEL_LEN = 63
safe_label = re.sub(r'^[^a-z0-9A-Z]*|[^a-zA-Z0-9_\-\.]|[^a-z0-9A-Z]*$', '', string)
if len(safe_label) > MAX_LABEL_LEN or string != safe_label:
safe_hash = hashlib.md5(string.encode()).hexdigest()[:9]
safe_label = safe_label[:MAX_LABEL_LEN - len(safe_hash) - 1] + "-" + safe_hash
return safe_label | [
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test | KubernetesExecutor.clear_not_launched_queued_tasks | If the airflow scheduler restarts with pending "Queued" tasks, the tasks may or
may not
have been launched Thus, on starting up the scheduler let's check every
"Queued" task to
see if it has been launched (ie: if there is a corresponding pod on kubernetes)
If it has been launched then do nothing, otherwise reset the state to "None" so
the task
will be rescheduled
This will not be necessary in a future version of airflow in which there is
proper support
for State.LAUNCHED | airflow/contrib/executors/kubernetes_executor.py | def clear_not_launched_queued_tasks(self, session=None):
"""
If the airflow scheduler restarts with pending "Queued" tasks, the tasks may or
may not
have been launched Thus, on starting up the scheduler let's check every
"Queued" task to
see if it has been launched (ie: if there is a corresponding pod on kubernetes)
If it has been launched then do nothing, otherwise reset the state to "None" so
the task
will be rescheduled
This will not be necessary in a future version of airflow in which there is
proper support
for State.LAUNCHED
"""
queued_tasks = session\
.query(TaskInstance)\
.filter(TaskInstance.state == State.QUEUED).all()
self.log.info(
'When executor started up, found %s queued task instances',
len(queued_tasks)
)
for task in queued_tasks:
dict_string = (
"dag_id={},task_id={},execution_date={},airflow-worker={}".format(
AirflowKubernetesScheduler._make_safe_label_value(task.dag_id),
AirflowKubernetesScheduler._make_safe_label_value(task.task_id),
AirflowKubernetesScheduler._datetime_to_label_safe_datestring(
task.execution_date
),
self.worker_uuid
)
)
kwargs = dict(label_selector=dict_string)
pod_list = self.kube_client.list_namespaced_pod(
self.kube_config.kube_namespace, **kwargs)
if len(pod_list.items) == 0:
self.log.info(
'TaskInstance: %s found in queued state but was not launched, '
'rescheduling', task
)
session.query(TaskInstance).filter(
TaskInstance.dag_id == task.dag_id,
TaskInstance.task_id == task.task_id,
TaskInstance.execution_date == task.execution_date
).update({TaskInstance.state: State.NONE}) | def clear_not_launched_queued_tasks(self, session=None):
"""
If the airflow scheduler restarts with pending "Queued" tasks, the tasks may or
may not
have been launched Thus, on starting up the scheduler let's check every
"Queued" task to
see if it has been launched (ie: if there is a corresponding pod on kubernetes)
If it has been launched then do nothing, otherwise reset the state to "None" so
the task
will be rescheduled
This will not be necessary in a future version of airflow in which there is
proper support
for State.LAUNCHED
"""
queued_tasks = session\
.query(TaskInstance)\
.filter(TaskInstance.state == State.QUEUED).all()
self.log.info(
'When executor started up, found %s queued task instances',
len(queued_tasks)
)
for task in queued_tasks:
dict_string = (
"dag_id={},task_id={},execution_date={},airflow-worker={}".format(
AirflowKubernetesScheduler._make_safe_label_value(task.dag_id),
AirflowKubernetesScheduler._make_safe_label_value(task.task_id),
AirflowKubernetesScheduler._datetime_to_label_safe_datestring(
task.execution_date
),
self.worker_uuid
)
)
kwargs = dict(label_selector=dict_string)
pod_list = self.kube_client.list_namespaced_pod(
self.kube_config.kube_namespace, **kwargs)
if len(pod_list.items) == 0:
self.log.info(
'TaskInstance: %s found in queued state but was not launched, '
'rescheduling', task
)
session.query(TaskInstance).filter(
TaskInstance.dag_id == task.dag_id,
TaskInstance.task_id == task.task_id,
TaskInstance.execution_date == task.execution_date
).update({TaskInstance.state: State.NONE}) | [
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test | Pool.open_slots | Returns the number of slots open at the moment | airflow/models/pool.py | def open_slots(self, session):
"""
Returns the number of slots open at the moment
"""
from airflow.models.taskinstance import \
TaskInstance as TI # Avoid circular import
used_slots = session.query(func.count()).filter(TI.pool == self.pool).filter(
TI.state.in_([State.RUNNING, State.QUEUED])).scalar()
return self.slots - used_slots | def open_slots(self, session):
"""
Returns the number of slots open at the moment
"""
from airflow.models.taskinstance import \
TaskInstance as TI # Avoid circular import
used_slots = session.query(func.count()).filter(TI.pool == self.pool).filter(
TI.state.in_([State.RUNNING, State.QUEUED])).scalar()
return self.slots - used_slots | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/pool.py#L60-L69 | [
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test | expand_env_var | Expands (potentially nested) env vars by repeatedly applying
`expandvars` and `expanduser` until interpolation stops having
any effect. | airflow/configuration.py | def expand_env_var(env_var):
"""
Expands (potentially nested) env vars by repeatedly applying
`expandvars` and `expanduser` until interpolation stops having
any effect.
"""
if not env_var:
return env_var
while True:
interpolated = os.path.expanduser(os.path.expandvars(str(env_var)))
if interpolated == env_var:
return interpolated
else:
env_var = interpolated | def expand_env_var(env_var):
"""
Expands (potentially nested) env vars by repeatedly applying
`expandvars` and `expanduser` until interpolation stops having
any effect.
"""
if not env_var:
return env_var
while True:
interpolated = os.path.expanduser(os.path.expandvars(str(env_var)))
if interpolated == env_var:
return interpolated
else:
env_var = interpolated | [
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test | run_command | Runs command and returns stdout | airflow/configuration.py | 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 | 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 | [
"Runs",
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"stdout"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/configuration.py#L75-L93 | [
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test | parameterized_config | Generates a configuration from the provided template + variables defined in
current scope
:param template: a config content templated with {{variables}} | airflow/configuration.py | def parameterized_config(template):
"""
Generates a configuration from the provided template + variables defined in
current scope
:param template: a config content templated with {{variables}}
"""
all_vars = {k: v for d in [globals(), locals()] for k, v in d.items()}
return template.format(**all_vars) | def parameterized_config(template):
"""
Generates a configuration from the provided template + variables defined in
current scope
:param template: a config content templated with {{variables}}
"""
all_vars = {k: v for d in [globals(), locals()] for k, v in d.items()}
return template.format(**all_vars) | [
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test | AirflowConfigParser.remove_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. | airflow/configuration.py | 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) | 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) | [
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test | AirflowConfigParser.getsection | Returns the section as a dict. Values are converted to int, float, bool
as required.
:param section: section from the config
:rtype: dict | airflow/configuration.py | def getsection(self, section):
"""
Returns the section as a dict. Values are converted to int, float, bool
as required.
:param section: section from the config
:rtype: dict
"""
if (section not in self._sections and
section not in self.airflow_defaults._sections):
return None
_section = copy.deepcopy(self.airflow_defaults._sections[section])
if section in self._sections:
_section.update(copy.deepcopy(self._sections[section]))
section_prefix = 'AIRFLOW__{S}__'.format(S=section.upper())
for env_var in sorted(os.environ.keys()):
if env_var.startswith(section_prefix):
key = env_var.replace(section_prefix, '').lower()
_section[key] = self._get_env_var_option(section, key)
for key, val in iteritems(_section):
try:
val = int(val)
except ValueError:
try:
val = float(val)
except ValueError:
if val.lower() in ('t', 'true'):
val = True
elif val.lower() in ('f', 'false'):
val = False
_section[key] = val
return _section | def getsection(self, section):
"""
Returns the section as a dict. Values are converted to int, float, bool
as required.
:param section: section from the config
:rtype: dict
"""
if (section not in self._sections and
section not in self.airflow_defaults._sections):
return None
_section = copy.deepcopy(self.airflow_defaults._sections[section])
if section in self._sections:
_section.update(copy.deepcopy(self._sections[section]))
section_prefix = 'AIRFLOW__{S}__'.format(S=section.upper())
for env_var in sorted(os.environ.keys()):
if env_var.startswith(section_prefix):
key = env_var.replace(section_prefix, '').lower()
_section[key] = self._get_env_var_option(section, key)
for key, val in iteritems(_section):
try:
val = int(val)
except ValueError:
try:
val = float(val)
except ValueError:
if val.lower() in ('t', 'true'):
val = True
elif val.lower() in ('f', 'false'):
val = False
_section[key] = val
return _section | [
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test | AirflowConfigParser.as_dict | Returns the current configuration as an OrderedDict of OrderedDicts.
:param display_source: If False, the option value is returned. If True,
a tuple of (option_value, source) is returned. Source is either
'airflow.cfg', 'default', 'env var', or 'cmd'.
:type display_source: bool
:param display_sensitive: If True, the values of options set by env
vars and bash commands will be displayed. If False, those options
are shown as '< hidden >'
:type display_sensitive: bool
:param raw: Should the values be output as interpolated values, or the
"raw" form that can be fed back in to ConfigParser
:type raw: bool | airflow/configuration.py | def as_dict(
self, display_source=False, display_sensitive=False, raw=False):
"""
Returns the current configuration as an OrderedDict of OrderedDicts.
:param display_source: If False, the option value is returned. If True,
a tuple of (option_value, source) is returned. Source is either
'airflow.cfg', 'default', 'env var', or 'cmd'.
:type display_source: bool
:param display_sensitive: If True, the values of options set by env
vars and bash commands will be displayed. If False, those options
are shown as '< hidden >'
:type display_sensitive: bool
:param raw: Should the values be output as interpolated values, or the
"raw" form that can be fed back in to ConfigParser
:type raw: bool
"""
cfg = {}
configs = [
('default', self.airflow_defaults),
('airflow.cfg', self),
]
for (source_name, config) in configs:
for section in config.sections():
sect = cfg.setdefault(section, OrderedDict())
for (k, val) in config.items(section=section, raw=raw):
if display_source:
val = (val, source_name)
sect[k] = val
# add env vars and overwrite because they have priority
for ev in [ev for ev in os.environ if ev.startswith('AIRFLOW__')]:
try:
_, section, key = ev.split('__')
opt = self._get_env_var_option(section, key)
except ValueError:
continue
if not display_sensitive and ev != 'AIRFLOW__CORE__UNIT_TEST_MODE':
opt = '< hidden >'
elif raw:
opt = opt.replace('%', '%%')
if display_source:
opt = (opt, 'env var')
cfg.setdefault(section.lower(), OrderedDict()).update(
{key.lower(): opt})
# add bash commands
for (section, key) in self.as_command_stdout:
opt = self._get_cmd_option(section, key)
if opt:
if not display_sensitive:
opt = '< hidden >'
if display_source:
opt = (opt, 'cmd')
elif raw:
opt = opt.replace('%', '%%')
cfg.setdefault(section, OrderedDict()).update({key: opt})
del cfg[section][key + '_cmd']
return cfg | def as_dict(
self, display_source=False, display_sensitive=False, raw=False):
"""
Returns the current configuration as an OrderedDict of OrderedDicts.
:param display_source: If False, the option value is returned. If True,
a tuple of (option_value, source) is returned. Source is either
'airflow.cfg', 'default', 'env var', or 'cmd'.
:type display_source: bool
:param display_sensitive: If True, the values of options set by env
vars and bash commands will be displayed. If False, those options
are shown as '< hidden >'
:type display_sensitive: bool
:param raw: Should the values be output as interpolated values, or the
"raw" form that can be fed back in to ConfigParser
:type raw: bool
"""
cfg = {}
configs = [
('default', self.airflow_defaults),
('airflow.cfg', self),
]
for (source_name, config) in configs:
for section in config.sections():
sect = cfg.setdefault(section, OrderedDict())
for (k, val) in config.items(section=section, raw=raw):
if display_source:
val = (val, source_name)
sect[k] = val
# add env vars and overwrite because they have priority
for ev in [ev for ev in os.environ if ev.startswith('AIRFLOW__')]:
try:
_, section, key = ev.split('__')
opt = self._get_env_var_option(section, key)
except ValueError:
continue
if not display_sensitive and ev != 'AIRFLOW__CORE__UNIT_TEST_MODE':
opt = '< hidden >'
elif raw:
opt = opt.replace('%', '%%')
if display_source:
opt = (opt, 'env var')
cfg.setdefault(section.lower(), OrderedDict()).update(
{key.lower(): opt})
# add bash commands
for (section, key) in self.as_command_stdout:
opt = self._get_cmd_option(section, key)
if opt:
if not display_sensitive:
opt = '< hidden >'
if display_source:
opt = (opt, 'cmd')
elif raw:
opt = opt.replace('%', '%%')
cfg.setdefault(section, OrderedDict()).update({key: opt})
del cfg[section][key + '_cmd']
return cfg | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | DatastoreHook.allocate_ids | 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 | airflow/contrib/hooks/datastore_hook.py | 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'] | 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'] | [
"Allocate",
"IDs",
"for",
"incomplete",
"keys",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L62-L81 | [
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test | DatastoreHook.begin_transaction | Begins a new transaction.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/beginTransaction
:return: a transaction handle.
:rtype: str | airflow/contrib/hooks/datastore_hook.py | 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'] | 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'] | [
"Begins",
"a",
"new",
"transaction",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L83-L100 | [
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test | DatastoreHook.commit | 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 | airflow/contrib/hooks/datastore_hook.py | 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 | 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 | [
"Commit",
"a",
"transaction",
"optionally",
"creating",
"deleting",
"or",
"modifying",
"some",
"entities",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L102-L121 | [
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test | DatastoreHook.lookup | Lookup some entities by key.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/lookup
:param keys: the keys to lookup.
:type keys: list
:param read_consistency: the read consistency to use. default, strong or eventual.
Cannot be used with a transaction.
:type read_consistency: str
:param transaction: the transaction to use, if any.
:type transaction: str
:return: the response body of the lookup request.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def lookup(self, keys, read_consistency=None, transaction=None):
"""
Lookup some entities by key.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/lookup
:param keys: the keys to lookup.
:type keys: list
:param read_consistency: the read consistency to use. default, strong or eventual.
Cannot be used with a transaction.
:type read_consistency: str
:param transaction: the transaction to use, if any.
:type transaction: str
:return: the response body of the lookup request.
:rtype: dict
"""
conn = self.get_conn()
body = {'keys': keys}
if read_consistency:
body['readConsistency'] = read_consistency
if transaction:
body['transaction'] = transaction
resp = (conn
.projects()
.lookup(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | def lookup(self, keys, read_consistency=None, transaction=None):
"""
Lookup some entities by key.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/lookup
:param keys: the keys to lookup.
:type keys: list
:param read_consistency: the read consistency to use. default, strong or eventual.
Cannot be used with a transaction.
:type read_consistency: str
:param transaction: the transaction to use, if any.
:type transaction: str
:return: the response body of the lookup request.
:rtype: dict
"""
conn = self.get_conn()
body = {'keys': keys}
if read_consistency:
body['readConsistency'] = read_consistency
if transaction:
body['transaction'] = transaction
resp = (conn
.projects()
.lookup(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | [
"Lookup",
"some",
"entities",
"by",
"key",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L123-L152 | [
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test | DatastoreHook.rollback | Roll back a transaction.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/rollback
:param transaction: the transaction to roll back.
:type transaction: str | airflow/contrib/hooks/datastore_hook.py | def rollback(self, transaction):
"""
Roll back a transaction.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/rollback
:param transaction: the transaction to roll back.
:type transaction: str
"""
conn = self.get_conn()
conn.projects().rollback(
projectId=self.project_id, body={'transaction': transaction}
).execute(num_retries=self.num_retries) | def rollback(self, transaction):
"""
Roll back a transaction.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/rollback
:param transaction: the transaction to roll back.
:type transaction: str
"""
conn = self.get_conn()
conn.projects().rollback(
projectId=self.project_id, body={'transaction': transaction}
).execute(num_retries=self.num_retries) | [
"Roll",
"back",
"a",
"transaction",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L154-L168 | [
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test | DatastoreHook.run_query | Run a query for entities.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/runQuery
:param body: the body of the query request.
:type body: dict
:return: the batch of query results.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def run_query(self, body):
"""
Run a query for entities.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/runQuery
:param body: the body of the query request.
:type body: dict
:return: the batch of query results.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.runQuery(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp['batch'] | def run_query(self, body):
"""
Run a query for entities.
.. seealso::
https://cloud.google.com/datastore/docs/reference/rest/v1/projects/runQuery
:param body: the body of the query request.
:type body: dict
:return: the batch of query results.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.runQuery(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp['batch'] | [
"Run",
"a",
"query",
"for",
"entities",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L170-L189 | [
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test | DatastoreHook.get_operation | Gets the latest state of a long-running operation.
.. seealso::
https://cloud.google.com/datastore/docs/reference/data/rest/v1/projects.operations/get
:param name: the name of the operation resource.
:type name: str
:return: a resource operation instance.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def get_operation(self, name):
"""
Gets the latest state of a long-running operation.
.. seealso::
https://cloud.google.com/datastore/docs/reference/data/rest/v1/projects.operations/get
:param name: the name of the operation resource.
:type name: str
:return: a resource operation instance.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.operations()
.get(name=name)
.execute(num_retries=self.num_retries))
return resp | def get_operation(self, name):
"""
Gets the latest state of a long-running operation.
.. seealso::
https://cloud.google.com/datastore/docs/reference/data/rest/v1/projects.operations/get
:param name: the name of the operation resource.
:type name: str
:return: a resource operation instance.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.operations()
.get(name=name)
.execute(num_retries=self.num_retries))
return resp | [
"Gets",
"the",
"latest",
"state",
"of",
"a",
"long",
"-",
"running",
"operation",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L191-L211 | [
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test | DatastoreHook.delete_operation | Deletes the long-running operation.
.. seealso::
https://cloud.google.com/datastore/docs/reference/data/rest/v1/projects.operations/delete
:param name: the name of the operation resource.
:type name: str
:return: none if successful.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def delete_operation(self, name):
"""
Deletes the long-running operation.
.. seealso::
https://cloud.google.com/datastore/docs/reference/data/rest/v1/projects.operations/delete
:param name: the name of the operation resource.
:type name: str
:return: none if successful.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.operations()
.delete(name=name)
.execute(num_retries=self.num_retries))
return resp | def delete_operation(self, name):
"""
Deletes the long-running operation.
.. seealso::
https://cloud.google.com/datastore/docs/reference/data/rest/v1/projects.operations/delete
:param name: the name of the operation resource.
:type name: str
:return: none if successful.
:rtype: dict
"""
conn = self.get_conn()
resp = (conn
.projects()
.operations()
.delete(name=name)
.execute(num_retries=self.num_retries))
return resp | [
"Deletes",
"the",
"long",
"-",
"running",
"operation",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L213-L233 | [
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test | DatastoreHook.poll_operation_until_done | Poll backup operation state until it's completed.
:param name: the name of the operation resource
:type name: str
:param polling_interval_in_seconds: The number of seconds to wait before calling another request.
:type polling_interval_in_seconds: int
:return: a resource operation instance.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def poll_operation_until_done(self, name, polling_interval_in_seconds):
"""
Poll backup operation state until it's completed.
:param name: the name of the operation resource
:type name: str
:param polling_interval_in_seconds: The number of seconds to wait before calling another request.
:type polling_interval_in_seconds: int
:return: a resource operation instance.
:rtype: dict
"""
while True:
result = self.get_operation(name)
state = result['metadata']['common']['state']
if state == 'PROCESSING':
self.log.info('Operation is processing. Re-polling state in {} seconds'
.format(polling_interval_in_seconds))
time.sleep(polling_interval_in_seconds)
else:
return result | def poll_operation_until_done(self, name, polling_interval_in_seconds):
"""
Poll backup operation state until it's completed.
:param name: the name of the operation resource
:type name: str
:param polling_interval_in_seconds: The number of seconds to wait before calling another request.
:type polling_interval_in_seconds: int
:return: a resource operation instance.
:rtype: dict
"""
while True:
result = self.get_operation(name)
state = result['metadata']['common']['state']
if state == 'PROCESSING':
self.log.info('Operation is processing. Re-polling state in {} seconds'
.format(polling_interval_in_seconds))
time.sleep(polling_interval_in_seconds)
else:
return result | [
"Poll",
"backup",
"operation",
"state",
"until",
"it",
"s",
"completed",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L235-L255 | [
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test | DatastoreHook.export_to_storage_bucket | Export entities from Cloud Datastore to Cloud Storage for backup.
.. note::
Keep in mind that this requests the Admin API not the Data API.
.. seealso::
https://cloud.google.com/datastore/docs/reference/admin/rest/v1/projects/export
:param bucket: The name of the Cloud Storage bucket.
:type bucket: str
:param namespace: The Cloud Storage namespace path.
:type namespace: str
:param entity_filter: Description of what data from the project is included in the export.
:type entity_filter: dict
:param labels: Client-assigned labels.
:type labels: dict of str
:return: a resource operation instance.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def export_to_storage_bucket(self, bucket, namespace=None, entity_filter=None, labels=None):
"""
Export entities from Cloud Datastore to Cloud Storage for backup.
.. note::
Keep in mind that this requests the Admin API not the Data API.
.. seealso::
https://cloud.google.com/datastore/docs/reference/admin/rest/v1/projects/export
:param bucket: The name of the Cloud Storage bucket.
:type bucket: str
:param namespace: The Cloud Storage namespace path.
:type namespace: str
:param entity_filter: Description of what data from the project is included in the export.
:type entity_filter: dict
:param labels: Client-assigned labels.
:type labels: dict of str
:return: a resource operation instance.
:rtype: dict
"""
admin_conn = self.get_conn()
output_uri_prefix = 'gs://' + '/'.join(filter(None, [bucket, namespace]))
if not entity_filter:
entity_filter = {}
if not labels:
labels = {}
body = {
'outputUrlPrefix': output_uri_prefix,
'entityFilter': entity_filter,
'labels': labels,
}
resp = (admin_conn
.projects()
.export(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | def export_to_storage_bucket(self, bucket, namespace=None, entity_filter=None, labels=None):
"""
Export entities from Cloud Datastore to Cloud Storage for backup.
.. note::
Keep in mind that this requests the Admin API not the Data API.
.. seealso::
https://cloud.google.com/datastore/docs/reference/admin/rest/v1/projects/export
:param bucket: The name of the Cloud Storage bucket.
:type bucket: str
:param namespace: The Cloud Storage namespace path.
:type namespace: str
:param entity_filter: Description of what data from the project is included in the export.
:type entity_filter: dict
:param labels: Client-assigned labels.
:type labels: dict of str
:return: a resource operation instance.
:rtype: dict
"""
admin_conn = self.get_conn()
output_uri_prefix = 'gs://' + '/'.join(filter(None, [bucket, namespace]))
if not entity_filter:
entity_filter = {}
if not labels:
labels = {}
body = {
'outputUrlPrefix': output_uri_prefix,
'entityFilter': entity_filter,
'labels': labels,
}
resp = (admin_conn
.projects()
.export(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | [
"Export",
"entities",
"from",
"Cloud",
"Datastore",
"to",
"Cloud",
"Storage",
"for",
"backup",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L257-L295 | [
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test | DatastoreHook.import_from_storage_bucket | Import a backup from Cloud Storage to Cloud Datastore.
.. note::
Keep in mind that this requests the Admin API not the Data API.
.. seealso::
https://cloud.google.com/datastore/docs/reference/admin/rest/v1/projects/import
:param bucket: The name of the Cloud Storage bucket.
:type bucket: str
:param file: the metadata file written by the projects.export operation.
:type file: str
:param namespace: The Cloud Storage namespace path.
:type namespace: str
:param entity_filter: specify which kinds/namespaces are to be imported.
:type entity_filter: dict
:param labels: Client-assigned labels.
:type labels: dict of str
:return: a resource operation instance.
:rtype: dict | airflow/contrib/hooks/datastore_hook.py | def import_from_storage_bucket(self, bucket, file, namespace=None, entity_filter=None, labels=None):
"""
Import a backup from Cloud Storage to Cloud Datastore.
.. note::
Keep in mind that this requests the Admin API not the Data API.
.. seealso::
https://cloud.google.com/datastore/docs/reference/admin/rest/v1/projects/import
:param bucket: The name of the Cloud Storage bucket.
:type bucket: str
:param file: the metadata file written by the projects.export operation.
:type file: str
:param namespace: The Cloud Storage namespace path.
:type namespace: str
:param entity_filter: specify which kinds/namespaces are to be imported.
:type entity_filter: dict
:param labels: Client-assigned labels.
:type labels: dict of str
:return: a resource operation instance.
:rtype: dict
"""
admin_conn = self.get_conn()
input_url = 'gs://' + '/'.join(filter(None, [bucket, namespace, file]))
if not entity_filter:
entity_filter = {}
if not labels:
labels = {}
body = {
'inputUrl': input_url,
'entityFilter': entity_filter,
'labels': labels,
}
resp = (admin_conn
.projects()
.import_(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | def import_from_storage_bucket(self, bucket, file, namespace=None, entity_filter=None, labels=None):
"""
Import a backup from Cloud Storage to Cloud Datastore.
.. note::
Keep in mind that this requests the Admin API not the Data API.
.. seealso::
https://cloud.google.com/datastore/docs/reference/admin/rest/v1/projects/import
:param bucket: The name of the Cloud Storage bucket.
:type bucket: str
:param file: the metadata file written by the projects.export operation.
:type file: str
:param namespace: The Cloud Storage namespace path.
:type namespace: str
:param entity_filter: specify which kinds/namespaces are to be imported.
:type entity_filter: dict
:param labels: Client-assigned labels.
:type labels: dict of str
:return: a resource operation instance.
:rtype: dict
"""
admin_conn = self.get_conn()
input_url = 'gs://' + '/'.join(filter(None, [bucket, namespace, file]))
if not entity_filter:
entity_filter = {}
if not labels:
labels = {}
body = {
'inputUrl': input_url,
'entityFilter': entity_filter,
'labels': labels,
}
resp = (admin_conn
.projects()
.import_(projectId=self.project_id, body=body)
.execute(num_retries=self.num_retries))
return resp | [
"Import",
"a",
"backup",
"from",
"Cloud",
"Storage",
"to",
"Cloud",
"Datastore",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/datastore_hook.py#L297-L337 | [
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test | AwsSnsHook.publish_to_target | Publish a message to a topic or an endpoint.
:param target_arn: either a TopicArn or an EndpointArn
:type target_arn: str
:param message: the default message you want to send
:param message: str | airflow/contrib/hooks/aws_sns_hook.py | def publish_to_target(self, target_arn, message):
"""
Publish a message to a topic or an endpoint.
:param target_arn: either a TopicArn or an EndpointArn
:type target_arn: str
:param message: the default message you want to send
:param message: str
"""
conn = self.get_conn()
messages = {
'default': message
}
return conn.publish(
TargetArn=target_arn,
Message=json.dumps(messages),
MessageStructure='json'
) | def publish_to_target(self, target_arn, message):
"""
Publish a message to a topic or an endpoint.
:param target_arn: either a TopicArn or an EndpointArn
:type target_arn: str
:param message: the default message you want to send
:param message: str
"""
conn = self.get_conn()
messages = {
'default': message
}
return conn.publish(
TargetArn=target_arn,
Message=json.dumps(messages),
MessageStructure='json'
) | [
"Publish",
"a",
"message",
"to",
"a",
"topic",
"or",
"an",
"endpoint",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_sns_hook.py#L40-L60 | [
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test | get_hostname | Fetch the hostname using the callable from the config or using
`socket.getfqdn` as a fallback. | airflow/utils/net.py | def get_hostname():
"""
Fetch the hostname using the callable from the config or using
`socket.getfqdn` as a fallback.
"""
# First we attempt to fetch the callable path from the config.
try:
callable_path = conf.get('core', 'hostname_callable')
except AirflowConfigException:
callable_path = None
# Then we handle the case when the config is missing or empty. This is the
# default behavior.
if not callable_path:
return socket.getfqdn()
# Since we have a callable path, we try to import and run it next.
module_path, attr_name = callable_path.split(':')
module = importlib.import_module(module_path)
callable = getattr(module, attr_name)
return callable() | def get_hostname():
"""
Fetch the hostname using the callable from the config or using
`socket.getfqdn` as a fallback.
"""
# First we attempt to fetch the callable path from the config.
try:
callable_path = conf.get('core', 'hostname_callable')
except AirflowConfigException:
callable_path = None
# Then we handle the case when the config is missing or empty. This is the
# default behavior.
if not callable_path:
return socket.getfqdn()
# Since we have a callable path, we try to import and run it next.
module_path, attr_name = callable_path.split(':')
module = importlib.import_module(module_path)
callable = getattr(module, attr_name)
return callable() | [
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"config",
"or",
"using",
"socket",
".",
"getfqdn",
"as",
"a",
"fallback",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/net.py#L25-L45 | [
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test | CloudNaturalLanguageHook.get_conn | Retrieves connection to Cloud Natural Language service.
:return: Cloud Natural Language service object
:rtype: google.cloud.language_v1.LanguageServiceClient | airflow/contrib/hooks/gcp_natural_language_hook.py | def get_conn(self):
"""
Retrieves connection to Cloud Natural Language service.
:return: Cloud Natural Language service object
:rtype: google.cloud.language_v1.LanguageServiceClient
"""
if not self._conn:
self._conn = LanguageServiceClient(credentials=self._get_credentials())
return self._conn | def get_conn(self):
"""
Retrieves connection to Cloud Natural Language service.
:return: Cloud Natural Language service object
:rtype: google.cloud.language_v1.LanguageServiceClient
"""
if not self._conn:
self._conn = LanguageServiceClient(credentials=self._get_credentials())
return self._conn | [
"Retrieves",
"connection",
"to",
"Cloud",
"Natural",
"Language",
"service",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L44-L53 | [
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test | CloudNaturalLanguageHook.analyze_entities | Finds named entities in the text along with entity types,
salience, mentions for each entity, and other properties.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or class google.cloud.language_v1.types.Document
:param encoding_type: The encoding type used by the API to calculate offsets.
:type encoding_type: google.cloud.language_v1.types.EncodingType
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse | airflow/contrib/hooks/gcp_natural_language_hook.py | def analyze_entities(self, document, encoding_type=None, retry=None, timeout=None, metadata=None):
"""
Finds named entities in the text along with entity types,
salience, mentions for each entity, and other properties.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or class google.cloud.language_v1.types.Document
:param encoding_type: The encoding type used by the API to calculate offsets.
:type encoding_type: google.cloud.language_v1.types.EncodingType
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse
"""
client = self.get_conn()
return client.analyze_entities(
document=document, encoding_type=encoding_type, retry=retry, timeout=timeout, metadata=metadata
) | def analyze_entities(self, document, encoding_type=None, retry=None, timeout=None, metadata=None):
"""
Finds named entities in the text along with entity types,
salience, mentions for each entity, and other properties.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or class google.cloud.language_v1.types.Document
:param encoding_type: The encoding type used by the API to calculate offsets.
:type encoding_type: google.cloud.language_v1.types.EncodingType
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse
"""
client = self.get_conn()
return client.analyze_entities(
document=document, encoding_type=encoding_type, retry=retry, timeout=timeout, metadata=metadata
) | [
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"with",
"entity",
"types",
"salience",
"mentions",
"for",
"each",
"entity",
"and",
"other",
"properties",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L56-L80 | [
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test | CloudNaturalLanguageHook.annotate_text | A convenience method that provides all the features that analyzeSentiment,
analyzeEntities, and analyzeSyntax provide in one call.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or google.cloud.language_v1.types.Document
:param features: The enabled features.
If a dict is provided, it must be of the same form as the protobuf message Features
:type features: dict or google.cloud.language_v1.enums.Features
:param encoding_type: The encoding type used by the API to calculate offsets.
:type encoding_type: google.cloud.language_v1.types.EncodingType
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnnotateTextResponse | airflow/contrib/hooks/gcp_natural_language_hook.py | def annotate_text(self, document, features, encoding_type=None, retry=None, timeout=None, metadata=None):
"""
A convenience method that provides all the features that analyzeSentiment,
analyzeEntities, and analyzeSyntax provide in one call.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or google.cloud.language_v1.types.Document
:param features: The enabled features.
If a dict is provided, it must be of the same form as the protobuf message Features
:type features: dict or google.cloud.language_v1.enums.Features
:param encoding_type: The encoding type used by the API to calculate offsets.
:type encoding_type: google.cloud.language_v1.types.EncodingType
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnnotateTextResponse
"""
client = self.get_conn()
return client.annotate_text(
document=document,
features=features,
encoding_type=encoding_type,
retry=retry,
timeout=timeout,
metadata=metadata,
) | def annotate_text(self, document, features, encoding_type=None, retry=None, timeout=None, metadata=None):
"""
A convenience method that provides all the features that analyzeSentiment,
analyzeEntities, and analyzeSyntax provide in one call.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or google.cloud.language_v1.types.Document
:param features: The enabled features.
If a dict is provided, it must be of the same form as the protobuf message Features
:type features: dict or google.cloud.language_v1.enums.Features
:param encoding_type: The encoding type used by the API to calculate offsets.
:type encoding_type: google.cloud.language_v1.types.EncodingType
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnnotateTextResponse
"""
client = self.get_conn()
return client.annotate_text(
document=document,
features=features,
encoding_type=encoding_type,
retry=retry,
timeout=timeout,
metadata=metadata,
) | [
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test | CloudNaturalLanguageHook.classify_text | Classifies a document into categories.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or class google.cloud.language_v1.types.Document
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse | airflow/contrib/hooks/gcp_natural_language_hook.py | def classify_text(self, document, retry=None, timeout=None, metadata=None):
"""
Classifies a document into categories.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or class google.cloud.language_v1.types.Document
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse
"""
client = self.get_conn()
return client.classify_text(document=document, retry=retry, timeout=timeout, metadata=metadata) | def classify_text(self, document, retry=None, timeout=None, metadata=None):
"""
Classifies a document into categories.
:param document: Input document.
If a dict is provided, it must be of the same form as the protobuf message Document
:type document: dict or class google.cloud.language_v1.types.Document
:param retry: A retry object used to retry requests. If None is specified, requests will not be
retried.
:type retry: google.api_core.retry.Retry
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
retry is specified, the timeout applies to each individual attempt.
:type timeout: float
:param metadata: Additional metadata that is provided to the method.
:type metadata: sequence[tuple[str, str]]]
:rtype: google.cloud.language_v1.types.AnalyzeEntitiesResponse
"""
client = self.get_conn()
return client.classify_text(document=document, retry=retry, timeout=timeout, metadata=metadata) | [
"Classifies",
"a",
"document",
"into",
"categories",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_natural_language_hook.py#L198-L217 | [
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test | get_task | Return the task object identified by the given dag_id and task_id. | airflow/api/common/experimental/get_task.py | def get_task(dag_id, task_id):
"""Return the task object identified by the given dag_id and task_id."""
dagbag = DagBag()
# Check DAG exists.
if dag_id not in dagbag.dags:
error_message = "Dag id {} not found".format(dag_id)
raise DagNotFound(error_message)
# Get DAG object and check Task Exists
dag = dagbag.get_dag(dag_id)
if not dag.has_task(task_id):
error_message = 'Task {} not found in dag {}'.format(task_id, dag_id)
raise TaskNotFound(error_message)
# Return the task.
return dag.get_task(task_id) | def get_task(dag_id, task_id):
"""Return the task object identified by the given dag_id and task_id."""
dagbag = DagBag()
# Check DAG exists.
if dag_id not in dagbag.dags:
error_message = "Dag id {} not found".format(dag_id)
raise DagNotFound(error_message)
# Get DAG object and check Task Exists
dag = dagbag.get_dag(dag_id)
if not dag.has_task(task_id):
error_message = 'Task {} not found in dag {}'.format(task_id, dag_id)
raise TaskNotFound(error_message)
# Return the task.
return dag.get_task(task_id) | [
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"task_id",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/get_task.py#L24-L40 | [
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test | get_template_field | Gets template fields for specific operator class.
:param fullname: Full path to operator class.
For example: ``airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetCreateOperator``
:return: List of template field
:rtype: list[str] | docs/exts/docroles.py | def get_template_field(env, fullname):
"""
Gets template fields for specific operator class.
:param fullname: Full path to operator class.
For example: ``airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetCreateOperator``
:return: List of template field
:rtype: list[str]
"""
modname, classname = fullname.rsplit(".", 1)
try:
with mock(env.config.autodoc_mock_imports):
mod = import_module(modname)
except ImportError:
raise RoleException("Error loading %s module." % (modname, ))
clazz = getattr(mod, classname)
if not clazz:
raise RoleException("Error finding %s class in %s module." % (classname, modname))
template_fields = getattr(clazz, "template_fields")
if not template_fields:
raise RoleException(
"Could not find the template fields for %s class in %s module." % (classname, modname)
)
return list(template_fields) | def get_template_field(env, fullname):
"""
Gets template fields for specific operator class.
:param fullname: Full path to operator class.
For example: ``airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetCreateOperator``
:return: List of template field
:rtype: list[str]
"""
modname, classname = fullname.rsplit(".", 1)
try:
with mock(env.config.autodoc_mock_imports):
mod = import_module(modname)
except ImportError:
raise RoleException("Error loading %s module." % (modname, ))
clazz = getattr(mod, classname)
if not clazz:
raise RoleException("Error finding %s class in %s module." % (classname, modname))
template_fields = getattr(clazz, "template_fields")
if not template_fields:
raise RoleException(
"Could not find the template fields for %s class in %s module." % (classname, modname)
)
return list(template_fields) | [
"Gets",
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"class",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/docs/exts/docroles.py#L27-L55 | [
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test | template_field_role | A role that allows you to include a list of template fields in the middle of the text. This is especially
useful when writing guides describing how to use the operator.
The result is a list of fields where each field is shorted in the literal block.
Sample usage::
:template-fields:`airflow.contrib.operators.gcp_natural_language_operator.CloudLanguageAnalyzeSentimentOperator`
For further information look at:
* [http://docutils.sourceforge.net/docs/howto/rst-roles.html](Creating reStructuredText Interpreted
Text Roles) | docs/exts/docroles.py | def template_field_role(app, typ, rawtext, text, lineno, inliner, options={}, content=[]):
"""
A role that allows you to include a list of template fields in the middle of the text. This is especially
useful when writing guides describing how to use the operator.
The result is a list of fields where each field is shorted in the literal block.
Sample usage::
:template-fields:`airflow.contrib.operators.gcp_natural_language_operator.CloudLanguageAnalyzeSentimentOperator`
For further information look at:
* [http://docutils.sourceforge.net/docs/howto/rst-roles.html](Creating reStructuredText Interpreted
Text Roles)
"""
text = utils.unescape(text)
try:
template_fields = get_template_field(app.env, text)
except RoleException as e:
msg = inliner.reporter.error("invalid class name %s \n%s" % (text, e, ), line=lineno)
prb = inliner.problematic(rawtext, rawtext, msg)
return [prb], [msg]
node = nodes.inline(rawtext=rawtext)
for i, field in enumerate(template_fields):
if i != 0:
node += nodes.Text(", ")
node += nodes.literal(field, "", nodes.Text(field))
return [node], [] | def template_field_role(app, typ, rawtext, text, lineno, inliner, options={}, content=[]):
"""
A role that allows you to include a list of template fields in the middle of the text. This is especially
useful when writing guides describing how to use the operator.
The result is a list of fields where each field is shorted in the literal block.
Sample usage::
:template-fields:`airflow.contrib.operators.gcp_natural_language_operator.CloudLanguageAnalyzeSentimentOperator`
For further information look at:
* [http://docutils.sourceforge.net/docs/howto/rst-roles.html](Creating reStructuredText Interpreted
Text Roles)
"""
text = utils.unescape(text)
try:
template_fields = get_template_field(app.env, text)
except RoleException as e:
msg = inliner.reporter.error("invalid class name %s \n%s" % (text, e, ), line=lineno)
prb = inliner.problematic(rawtext, rawtext, msg)
return [prb], [msg]
node = nodes.inline(rawtext=rawtext)
for i, field in enumerate(template_fields):
if i != 0:
node += nodes.Text(", ")
node += nodes.literal(field, "", nodes.Text(field))
return [node], [] | [
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test | dispose_orm | Properly close pooled database connections | airflow/settings.py | def dispose_orm():
""" Properly close pooled database connections """
log.debug("Disposing DB connection pool (PID %s)", os.getpid())
global engine
global Session
if Session:
Session.remove()
Session = None
if engine:
engine.dispose()
engine = None | def dispose_orm():
""" Properly close pooled database connections """
log.debug("Disposing DB connection pool (PID %s)", os.getpid())
global engine
global Session
if Session:
Session.remove()
Session = None
if engine:
engine.dispose()
engine = None | [
"Properly",
"close",
"pooled",
"database",
"connections"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/settings.py#L166-L177 | [
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test | prepare_classpath | Ensures that certain subfolders of AIRFLOW_HOME are on the classpath | airflow/settings.py | def prepare_classpath():
"""
Ensures that certain subfolders of AIRFLOW_HOME are on the classpath
"""
if DAGS_FOLDER not in sys.path:
sys.path.append(DAGS_FOLDER)
# Add ./config/ for loading custom log parsers etc, or
# airflow_local_settings etc.
config_path = os.path.join(AIRFLOW_HOME, 'config')
if config_path not in sys.path:
sys.path.append(config_path)
if PLUGINS_FOLDER not in sys.path:
sys.path.append(PLUGINS_FOLDER) | def prepare_classpath():
"""
Ensures that certain subfolders of AIRFLOW_HOME are on the classpath
"""
if DAGS_FOLDER not in sys.path:
sys.path.append(DAGS_FOLDER)
# Add ./config/ for loading custom log parsers etc, or
# airflow_local_settings etc.
config_path = os.path.join(AIRFLOW_HOME, 'config')
if config_path not in sys.path:
sys.path.append(config_path)
if PLUGINS_FOLDER not in sys.path:
sys.path.append(PLUGINS_FOLDER) | [
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"are",
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"classpath"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/settings.py#L220-L235 | [
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".",
"path",
".",
"append",
"(",
"PLUGINS_FOLDER",
")"
] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | CeleryQueueSensor._check_task_id | Gets the returned Celery result from the Airflow task
ID provided to the sensor, and returns True if the
celery result has been finished execution.
:param context: Airflow's execution context
:type context: dict
:return: True if task has been executed, otherwise False
:rtype: bool | airflow/contrib/sensors/celery_queue_sensor.py | def _check_task_id(self, context):
"""
Gets the returned Celery result from the Airflow task
ID provided to the sensor, and returns True if the
celery result has been finished execution.
:param context: Airflow's execution context
:type context: dict
:return: True if task has been executed, otherwise False
:rtype: bool
"""
ti = context['ti']
celery_result = ti.xcom_pull(task_ids=self.target_task_id)
return celery_result.ready() | def _check_task_id(self, context):
"""
Gets the returned Celery result from the Airflow task
ID provided to the sensor, and returns True if the
celery result has been finished execution.
:param context: Airflow's execution context
:type context: dict
:return: True if task has been executed, otherwise False
:rtype: bool
"""
ti = context['ti']
celery_result = ti.xcom_pull(task_ids=self.target_task_id)
return celery_result.ready() | [
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"ready",
"(",
")"
] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | detect_conf_var | Return true if the ticket cache contains "conf" information as is found
in ticket caches of Kerberos 1.8.1 or later. This is incompatible with the
Sun Java Krb5LoginModule in Java6, so we need to take an action to work
around it. | airflow/security/kerberos.py | def detect_conf_var():
"""Return true if the ticket cache contains "conf" information as is found
in ticket caches of Kerberos 1.8.1 or later. This is incompatible with the
Sun Java Krb5LoginModule in Java6, so we need to take an action to work
around it.
"""
ticket_cache = configuration.conf.get('kerberos', 'ccache')
with open(ticket_cache, 'rb') as f:
# Note: this file is binary, so we check against a bytearray.
return b'X-CACHECONF:' in f.read() | def detect_conf_var():
"""Return true if the ticket cache contains "conf" information as is found
in ticket caches of Kerberos 1.8.1 or later. This is incompatible with the
Sun Java Krb5LoginModule in Java6, so we need to take an action to work
around it.
"""
ticket_cache = configuration.conf.get('kerberos', 'ccache')
with open(ticket_cache, 'rb') as f:
# Note: this file is binary, so we check against a bytearray.
return b'X-CACHECONF:' in f.read() | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/security/kerberos.py#L100-L110 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | alchemy_to_dict | Transforms a SQLAlchemy model instance into a dictionary | airflow/utils/helpers.py | def alchemy_to_dict(obj):
"""
Transforms a SQLAlchemy model instance into a dictionary
"""
if not obj:
return None
d = {}
for c in obj.__table__.columns:
value = getattr(obj, c.name)
if type(value) == datetime:
value = value.isoformat()
d[c.name] = value
return d | def alchemy_to_dict(obj):
"""
Transforms a SQLAlchemy model instance into a dictionary
"""
if not obj:
return None
d = {}
for c in obj.__table__.columns:
value = getattr(obj, c.name)
if type(value) == datetime:
value = value.isoformat()
d[c.name] = value
return d | [
"Transforms",
"a",
"SQLAlchemy",
"model",
"instance",
"into",
"a",
"dictionary"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L60-L72 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | chunks | Yield successive chunks of a given size from a list of items | airflow/utils/helpers.py | def chunks(items, chunk_size):
"""
Yield successive chunks of a given size from a list of items
"""
if chunk_size <= 0:
raise ValueError('Chunk size must be a positive integer')
for i in range(0, len(items), chunk_size):
yield items[i:i + chunk_size] | def chunks(items, chunk_size):
"""
Yield successive chunks of a given size from a list of items
"""
if chunk_size <= 0:
raise ValueError('Chunk size must be a positive integer')
for i in range(0, len(items), chunk_size):
yield items[i:i + chunk_size] | [
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"of",
"a",
"given",
"size",
"from",
"a",
"list",
"of",
"items"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L121-L128 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | reduce_in_chunks | Reduce the given list of items by splitting it into chunks
of the given size and passing each chunk through the reducer | airflow/utils/helpers.py | def reduce_in_chunks(fn, iterable, initializer, chunk_size=0):
"""
Reduce the given list of items by splitting it into chunks
of the given size and passing each chunk through the reducer
"""
if len(iterable) == 0:
return initializer
if chunk_size == 0:
chunk_size = len(iterable)
return reduce(fn, chunks(iterable, chunk_size), initializer) | def reduce_in_chunks(fn, iterable, initializer, chunk_size=0):
"""
Reduce the given list of items by splitting it into chunks
of the given size and passing each chunk through the reducer
"""
if len(iterable) == 0:
return initializer
if chunk_size == 0:
chunk_size = len(iterable)
return reduce(fn, chunks(iterable, chunk_size), initializer) | [
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"and",
"passing",
"each",
"chunk",
"through",
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"reducer"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L131-L140 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | chain | Given a number of tasks, builds a dependency chain.
chain(task_1, task_2, task_3, task_4)
is equivalent to
task_1.set_downstream(task_2)
task_2.set_downstream(task_3)
task_3.set_downstream(task_4) | airflow/utils/helpers.py | def chain(*tasks):
"""
Given a number of tasks, builds a dependency chain.
chain(task_1, task_2, task_3, task_4)
is equivalent to
task_1.set_downstream(task_2)
task_2.set_downstream(task_3)
task_3.set_downstream(task_4)
"""
for up_task, down_task in zip(tasks[:-1], tasks[1:]):
up_task.set_downstream(down_task) | def chain(*tasks):
"""
Given a number of tasks, builds a dependency chain.
chain(task_1, task_2, task_3, task_4)
is equivalent to
task_1.set_downstream(task_2)
task_2.set_downstream(task_3)
task_3.set_downstream(task_4)
"""
for up_task, down_task in zip(tasks[:-1], tasks[1:]):
up_task.set_downstream(down_task) | [
"Given",
"a",
"number",
"of",
"tasks",
"builds",
"a",
"dependency",
"chain",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L153-L166 | [
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")"
] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | pprinttable | Returns a pretty ascii table from tuples
If namedtuple are used, the table will have headers | airflow/utils/helpers.py | def pprinttable(rows):
"""Returns a pretty ascii table from tuples
If namedtuple are used, the table will have headers
"""
if not rows:
return
if hasattr(rows[0], '_fields'): # if namedtuple
headers = rows[0]._fields
else:
headers = ["col{}".format(i) for i in range(len(rows[0]))]
lens = [len(s) for s in headers]
for row in rows:
for i in range(len(rows[0])):
slenght = len("{}".format(row[i]))
if slenght > lens[i]:
lens[i] = slenght
formats = []
hformats = []
for i in range(len(rows[0])):
if isinstance(rows[0][i], int):
formats.append("%%%dd" % lens[i])
else:
formats.append("%%-%ds" % lens[i])
hformats.append("%%-%ds" % lens[i])
pattern = " | ".join(formats)
hpattern = " | ".join(hformats)
separator = "-+-".join(['-' * n for n in lens])
s = ""
s += separator + '\n'
s += (hpattern % tuple(headers)) + '\n'
s += separator + '\n'
def f(t):
return "{}".format(t) if isinstance(t, basestring) else t
for line in rows:
s += pattern % tuple(f(t) for t in line) + '\n'
s += separator + '\n'
return s | def pprinttable(rows):
"""Returns a pretty ascii table from tuples
If namedtuple are used, the table will have headers
"""
if not rows:
return
if hasattr(rows[0], '_fields'): # if namedtuple
headers = rows[0]._fields
else:
headers = ["col{}".format(i) for i in range(len(rows[0]))]
lens = [len(s) for s in headers]
for row in rows:
for i in range(len(rows[0])):
slenght = len("{}".format(row[i]))
if slenght > lens[i]:
lens[i] = slenght
formats = []
hformats = []
for i in range(len(rows[0])):
if isinstance(rows[0][i], int):
formats.append("%%%dd" % lens[i])
else:
formats.append("%%-%ds" % lens[i])
hformats.append("%%-%ds" % lens[i])
pattern = " | ".join(formats)
hpattern = " | ".join(hformats)
separator = "-+-".join(['-' * n for n in lens])
s = ""
s += separator + '\n'
s += (hpattern % tuple(headers)) + '\n'
s += separator + '\n'
def f(t):
return "{}".format(t) if isinstance(t, basestring) else t
for line in rows:
s += pattern % tuple(f(t) for t in line) + '\n'
s += separator + '\n'
return s | [
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"pretty",
"ascii",
"table",
"from",
"tuples"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L200-L240 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | reap_process_group | Tries really hard to terminate all children (including grandchildren). Will send
sig (SIGTERM) to the process group of pid. If any process is alive after timeout
a SIGKILL will be send.
:param log: log handler
:param pid: pid to kill
:param sig: signal type
:param timeout: how much time a process has to terminate | airflow/utils/helpers.py | def reap_process_group(pid, log, sig=signal.SIGTERM,
timeout=DEFAULT_TIME_TO_WAIT_AFTER_SIGTERM):
"""
Tries really hard to terminate all children (including grandchildren). Will send
sig (SIGTERM) to the process group of pid. If any process is alive after timeout
a SIGKILL will be send.
:param log: log handler
:param pid: pid to kill
:param sig: signal type
:param timeout: how much time a process has to terminate
"""
def on_terminate(p):
log.info("Process %s (%s) terminated with exit code %s", p, p.pid, p.returncode)
if pid == os.getpid():
raise RuntimeError("I refuse to kill myself")
parent = psutil.Process(pid)
children = parent.children(recursive=True)
children.append(parent)
try:
pg = os.getpgid(pid)
except OSError as err:
# Skip if not such process - we experience a race and it just terminated
if err.errno == errno.ESRCH:
return
raise
log.info("Sending %s to GPID %s", sig, pg)
os.killpg(os.getpgid(pid), sig)
gone, alive = psutil.wait_procs(children, timeout=timeout, callback=on_terminate)
if alive:
for p in alive:
log.warn("process %s (%s) did not respond to SIGTERM. Trying SIGKILL", p, pid)
os.killpg(os.getpgid(pid), signal.SIGKILL)
gone, alive = psutil.wait_procs(alive, timeout=timeout, callback=on_terminate)
if alive:
for p in alive:
log.error("Process %s (%s) could not be killed. Giving up.", p, p.pid) | def reap_process_group(pid, log, sig=signal.SIGTERM,
timeout=DEFAULT_TIME_TO_WAIT_AFTER_SIGTERM):
"""
Tries really hard to terminate all children (including grandchildren). Will send
sig (SIGTERM) to the process group of pid. If any process is alive after timeout
a SIGKILL will be send.
:param log: log handler
:param pid: pid to kill
:param sig: signal type
:param timeout: how much time a process has to terminate
"""
def on_terminate(p):
log.info("Process %s (%s) terminated with exit code %s", p, p.pid, p.returncode)
if pid == os.getpid():
raise RuntimeError("I refuse to kill myself")
parent = psutil.Process(pid)
children = parent.children(recursive=True)
children.append(parent)
try:
pg = os.getpgid(pid)
except OSError as err:
# Skip if not such process - we experience a race and it just terminated
if err.errno == errno.ESRCH:
return
raise
log.info("Sending %s to GPID %s", sig, pg)
os.killpg(os.getpgid(pid), sig)
gone, alive = psutil.wait_procs(children, timeout=timeout, callback=on_terminate)
if alive:
for p in alive:
log.warn("process %s (%s) did not respond to SIGTERM. Trying SIGKILL", p, pid)
os.killpg(os.getpgid(pid), signal.SIGKILL)
gone, alive = psutil.wait_procs(alive, timeout=timeout, callback=on_terminate)
if alive:
for p in alive:
log.error("Process %s (%s) could not be killed. Giving up.", p, p.pid) | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L243-L289 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | render_log_filename | Given task instance, try_number, filename_template, return the rendered log
filename
:param ti: task instance
:param try_number: try_number of the task
:param filename_template: filename template, which can be jinja template or
python string template | airflow/utils/helpers.py | def render_log_filename(ti, try_number, filename_template):
"""
Given task instance, try_number, filename_template, return the rendered log
filename
:param ti: task instance
:param try_number: try_number of the task
:param filename_template: filename template, which can be jinja template or
python string template
"""
filename_template, filename_jinja_template = parse_template_string(filename_template)
if filename_jinja_template:
jinja_context = ti.get_template_context()
jinja_context['try_number'] = try_number
return filename_jinja_template.render(**jinja_context)
return filename_template.format(dag_id=ti.dag_id,
task_id=ti.task_id,
execution_date=ti.execution_date.isoformat(),
try_number=try_number) | def render_log_filename(ti, try_number, filename_template):
"""
Given task instance, try_number, filename_template, return the rendered log
filename
:param ti: task instance
:param try_number: try_number of the task
:param filename_template: filename template, which can be jinja template or
python string template
"""
filename_template, filename_jinja_template = parse_template_string(filename_template)
if filename_jinja_template:
jinja_context = ti.get_template_context()
jinja_context['try_number'] = try_number
return filename_jinja_template.render(**jinja_context)
return filename_template.format(dag_id=ti.dag_id,
task_id=ti.task_id,
execution_date=ti.execution_date.isoformat(),
try_number=try_number) | [
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"task",
"instance",
"try_number",
"filename_template",
"return",
"the",
"rendered",
"log",
"filename"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/utils/helpers.py#L299-L318 | [
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test | get_task_instance | Return the task object identified by the given dag_id and task_id. | airflow/api/common/experimental/get_task_instance.py | def get_task_instance(dag_id, task_id, execution_date):
"""Return the task object identified by the given dag_id and task_id."""
dagbag = DagBag()
# Check DAG exists.
if dag_id not in dagbag.dags:
error_message = "Dag id {} not found".format(dag_id)
raise DagNotFound(error_message)
# Get DAG object and check Task Exists
dag = dagbag.get_dag(dag_id)
if not dag.has_task(task_id):
error_message = 'Task {} not found in dag {}'.format(task_id, dag_id)
raise TaskNotFound(error_message)
# Get DagRun object and check that it exists
dagrun = dag.get_dagrun(execution_date=execution_date)
if not dagrun:
error_message = ('Dag Run for date {} not found in dag {}'
.format(execution_date, dag_id))
raise DagRunNotFound(error_message)
# Get task instance object and check that it exists
task_instance = dagrun.get_task_instance(task_id)
if not task_instance:
error_message = ('Task {} instance for date {} not found'
.format(task_id, execution_date))
raise TaskInstanceNotFound(error_message)
return task_instance | def get_task_instance(dag_id, task_id, execution_date):
"""Return the task object identified by the given dag_id and task_id."""
dagbag = DagBag()
# Check DAG exists.
if dag_id not in dagbag.dags:
error_message = "Dag id {} not found".format(dag_id)
raise DagNotFound(error_message)
# Get DAG object and check Task Exists
dag = dagbag.get_dag(dag_id)
if not dag.has_task(task_id):
error_message = 'Task {} not found in dag {}'.format(task_id, dag_id)
raise TaskNotFound(error_message)
# Get DagRun object and check that it exists
dagrun = dag.get_dagrun(execution_date=execution_date)
if not dagrun:
error_message = ('Dag Run for date {} not found in dag {}'
.format(execution_date, dag_id))
raise DagRunNotFound(error_message)
# Get task instance object and check that it exists
task_instance = dagrun.get_task_instance(task_id)
if not task_instance:
error_message = ('Task {} instance for date {} not found'
.format(task_id, execution_date))
raise TaskInstanceNotFound(error_message)
return task_instance | [
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"task",
"object",
"identified",
"by",
"the",
"given",
"dag_id",
"and",
"task_id",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/api/common/experimental/get_task_instance.py#L25-L55 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | _integrate_plugins | Integrate plugins to the context | airflow/operators/__init__.py | def _integrate_plugins():
"""Integrate plugins to the context"""
import sys
from airflow.plugins_manager import operators_modules
for operators_module in operators_modules:
sys.modules[operators_module.__name__] = operators_module
globals()[operators_module._name] = operators_module | def _integrate_plugins():
"""Integrate plugins to the context"""
import sys
from airflow.plugins_manager import operators_modules
for operators_module in operators_modules:
sys.modules[operators_module.__name__] = operators_module
globals()[operators_module._name] = operators_module | [
"Integrate",
"plugins",
"to",
"the",
"context"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/operators/__init__.py#L21-L27 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | DataProcHook.get_conn | Returns a Google Cloud Dataproc service object. | airflow/contrib/hooks/gcp_dataproc_hook.py | def get_conn(self):
"""Returns a Google Cloud Dataproc service object."""
http_authorized = self._authorize()
return build(
'dataproc', self.api_version, http=http_authorized,
cache_discovery=False) | def get_conn(self):
"""Returns a Google Cloud Dataproc service object."""
http_authorized = self._authorize()
return build(
'dataproc', self.api_version, http=http_authorized,
cache_discovery=False) | [
"Returns",
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"Google",
"Cloud",
"Dataproc",
"service",
"object",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_dataproc_hook.py#L218-L223 | [
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test | DataProcHook.wait | Awaits for Google Cloud Dataproc Operation to complete. | airflow/contrib/hooks/gcp_dataproc_hook.py | def wait(self, operation):
"""Awaits for Google Cloud Dataproc Operation to complete."""
submitted = _DataProcOperation(self.get_conn(), operation,
self.num_retries)
submitted.wait_for_done() | def wait(self, operation):
"""Awaits for Google Cloud Dataproc Operation to complete."""
submitted = _DataProcOperation(self.get_conn(), operation,
self.num_retries)
submitted.wait_for_done() | [
"Awaits",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_dataproc_hook.py#L243-L247 | [
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test | _deep_string_coerce | Coerces content or all values of content if it is a dict to a string. The
function will throw if content contains non-string or non-numeric types.
The reason why we have this function is because the ``self.json`` field must be a
dict with only string values. This is because ``render_template`` will fail
for numerical values. | airflow/contrib/operators/databricks_operator.py | def _deep_string_coerce(content, json_path='json'):
"""
Coerces content or all values of content if it is a dict to a string. The
function will throw if content contains non-string or non-numeric types.
The reason why we have this function is because the ``self.json`` field must be a
dict with only string values. This is because ``render_template`` will fail
for numerical values.
"""
c = _deep_string_coerce
if isinstance(content, six.string_types):
return content
elif isinstance(content, six.integer_types + (float,)):
# Databricks can tolerate either numeric or string types in the API backend.
return str(content)
elif isinstance(content, (list, tuple)):
return [c(e, '{0}[{1}]'.format(json_path, i)) for i, e in enumerate(content)]
elif isinstance(content, dict):
return {k: c(v, '{0}[{1}]'.format(json_path, k))
for k, v in list(content.items())}
else:
param_type = type(content)
msg = 'Type {0} used for parameter {1} is not a number or a string' \
.format(param_type, json_path)
raise AirflowException(msg) | def _deep_string_coerce(content, json_path='json'):
"""
Coerces content or all values of content if it is a dict to a string. The
function will throw if content contains non-string or non-numeric types.
The reason why we have this function is because the ``self.json`` field must be a
dict with only string values. This is because ``render_template`` will fail
for numerical values.
"""
c = _deep_string_coerce
if isinstance(content, six.string_types):
return content
elif isinstance(content, six.integer_types + (float,)):
# Databricks can tolerate either numeric or string types in the API backend.
return str(content)
elif isinstance(content, (list, tuple)):
return [c(e, '{0}[{1}]'.format(json_path, i)) for i, e in enumerate(content)]
elif isinstance(content, dict):
return {k: c(v, '{0}[{1}]'.format(json_path, k))
for k, v in list(content.items())}
else:
param_type = type(content)
msg = 'Type {0} used for parameter {1} is not a number or a string' \
.format(param_type, json_path)
raise AirflowException(msg) | [
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test | _handle_databricks_operator_execution | Handles the Airflow + Databricks lifecycle logic for a Databricks operator
:param operator: Databricks operator being handled
:param context: Airflow context | airflow/contrib/operators/databricks_operator.py | def _handle_databricks_operator_execution(operator, hook, log, context):
"""
Handles the Airflow + Databricks lifecycle logic for a Databricks operator
:param operator: Databricks operator being handled
:param context: Airflow context
"""
if operator.do_xcom_push:
context['ti'].xcom_push(key=XCOM_RUN_ID_KEY, value=operator.run_id)
log.info('Run submitted with run_id: %s', operator.run_id)
run_page_url = hook.get_run_page_url(operator.run_id)
if operator.do_xcom_push:
context['ti'].xcom_push(key=XCOM_RUN_PAGE_URL_KEY, value=run_page_url)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
while True:
run_state = hook.get_run_state(operator.run_id)
if run_state.is_terminal:
if run_state.is_successful:
log.info('%s completed successfully.', operator.task_id)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
return
else:
error_message = '{t} failed with terminal state: {s}'.format(
t=operator.task_id,
s=run_state)
raise AirflowException(error_message)
else:
log.info('%s in run state: %s', operator.task_id, run_state)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
log.info('Sleeping for %s seconds.', operator.polling_period_seconds)
time.sleep(operator.polling_period_seconds) | def _handle_databricks_operator_execution(operator, hook, log, context):
"""
Handles the Airflow + Databricks lifecycle logic for a Databricks operator
:param operator: Databricks operator being handled
:param context: Airflow context
"""
if operator.do_xcom_push:
context['ti'].xcom_push(key=XCOM_RUN_ID_KEY, value=operator.run_id)
log.info('Run submitted with run_id: %s', operator.run_id)
run_page_url = hook.get_run_page_url(operator.run_id)
if operator.do_xcom_push:
context['ti'].xcom_push(key=XCOM_RUN_PAGE_URL_KEY, value=run_page_url)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
while True:
run_state = hook.get_run_state(operator.run_id)
if run_state.is_terminal:
if run_state.is_successful:
log.info('%s completed successfully.', operator.task_id)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
return
else:
error_message = '{t} failed with terminal state: {s}'.format(
t=operator.task_id,
s=run_state)
raise AirflowException(error_message)
else:
log.info('%s in run state: %s', operator.task_id, run_state)
log.info('View run status, Spark UI, and logs at %s', run_page_url)
log.info('Sleeping for %s seconds.', operator.polling_period_seconds)
time.sleep(operator.polling_period_seconds) | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | PigCliHook.run_cli | Run an pig script using the pig cli
>>> ph = PigCliHook()
>>> result = ph.run_cli("ls /;")
>>> ("hdfs://" in result)
True | airflow/hooks/pig_hook.py | def run_cli(self, pig, verbose=True):
"""
Run an pig script using the pig cli
>>> ph = PigCliHook()
>>> result = ph.run_cli("ls /;")
>>> ("hdfs://" in result)
True
"""
with TemporaryDirectory(prefix='airflow_pigop_') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir) as f:
f.write(pig.encode('utf-8'))
f.flush()
fname = f.name
pig_bin = 'pig'
cmd_extra = []
pig_cmd = [pig_bin, '-f', fname] + cmd_extra
if self.pig_properties:
pig_properties_list = self.pig_properties.split()
pig_cmd.extend(pig_properties_list)
if verbose:
self.log.info("%s", " ".join(pig_cmd))
sp = subprocess.Popen(
pig_cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=tmp_dir,
close_fds=True)
self.sp = sp
stdout = ''
for line in iter(sp.stdout.readline, b''):
stdout += line.decode('utf-8')
if verbose:
self.log.info(line.strip())
sp.wait()
if sp.returncode:
raise AirflowException(stdout)
return stdout | def run_cli(self, pig, verbose=True):
"""
Run an pig script using the pig cli
>>> ph = PigCliHook()
>>> result = ph.run_cli("ls /;")
>>> ("hdfs://" in result)
True
"""
with TemporaryDirectory(prefix='airflow_pigop_') as tmp_dir:
with NamedTemporaryFile(dir=tmp_dir) as f:
f.write(pig.encode('utf-8'))
f.flush()
fname = f.name
pig_bin = 'pig'
cmd_extra = []
pig_cmd = [pig_bin, '-f', fname] + cmd_extra
if self.pig_properties:
pig_properties_list = self.pig_properties.split()
pig_cmd.extend(pig_properties_list)
if verbose:
self.log.info("%s", " ".join(pig_cmd))
sp = subprocess.Popen(
pig_cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=tmp_dir,
close_fds=True)
self.sp = sp
stdout = ''
for line in iter(sp.stdout.readline, b''):
stdout += line.decode('utf-8')
if verbose:
self.log.info(line.strip())
sp.wait()
if sp.returncode:
raise AirflowException(stdout)
return stdout | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/pig_hook.py#L45-L87 | [
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"(",
"stdout",
")",
"return",
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | fetch_celery_task_state | Fetch and return the state of the given celery task. The scope of this function is
global so that it can be called by subprocesses in the pool.
:param celery_task: a tuple of the Celery task key and the async Celery object used
to fetch the task's state
:type celery_task: tuple(str, celery.result.AsyncResult)
:return: a tuple of the Celery task key and the Celery state of the task
:rtype: tuple[str, str] | airflow/executors/celery_executor.py | def fetch_celery_task_state(celery_task):
"""
Fetch and return the state of the given celery task. The scope of this function is
global so that it can be called by subprocesses in the pool.
:param celery_task: a tuple of the Celery task key and the async Celery object used
to fetch the task's state
:type celery_task: tuple(str, celery.result.AsyncResult)
:return: a tuple of the Celery task key and the Celery state of the task
:rtype: tuple[str, str]
"""
try:
with timeout(seconds=2):
# Accessing state property of celery task will make actual network request
# to get the current state of the task.
res = (celery_task[0], celery_task[1].state)
except Exception as e:
exception_traceback = "Celery Task ID: {}\n{}".format(celery_task[0],
traceback.format_exc())
res = ExceptionWithTraceback(e, exception_traceback)
return res | def fetch_celery_task_state(celery_task):
"""
Fetch and return the state of the given celery task. The scope of this function is
global so that it can be called by subprocesses in the pool.
:param celery_task: a tuple of the Celery task key and the async Celery object used
to fetch the task's state
:type celery_task: tuple(str, celery.result.AsyncResult)
:return: a tuple of the Celery task key and the Celery state of the task
:rtype: tuple[str, str]
"""
try:
with timeout(seconds=2):
# Accessing state property of celery task will make actual network request
# to get the current state of the task.
res = (celery_task[0], celery_task[1].state)
except Exception as e:
exception_traceback = "Celery Task ID: {}\n{}".format(celery_task[0],
traceback.format_exc())
res = ExceptionWithTraceback(e, exception_traceback)
return res | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | CeleryExecutor._num_tasks_per_send_process | How many Celery tasks should each worker process send.
:return: Number of tasks that should be sent per process
:rtype: int | airflow/executors/celery_executor.py | def _num_tasks_per_send_process(self, to_send_count):
"""
How many Celery tasks should each worker process send.
:return: Number of tasks that should be sent per process
:rtype: int
"""
return max(1,
int(math.ceil(1.0 * to_send_count / self._sync_parallelism))) | def _num_tasks_per_send_process(self, to_send_count):
"""
How many Celery tasks should each worker process send.
:return: Number of tasks that should be sent per process
:rtype: int
"""
return max(1,
int(math.ceil(1.0 * to_send_count / self._sync_parallelism))) | [
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"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L158-L166 | [
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")",
")",
")"
] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | CeleryExecutor._num_tasks_per_fetch_process | How many Celery tasks should be sent to each worker process.
:return: Number of tasks that should be used per process
:rtype: int | airflow/executors/celery_executor.py | def _num_tasks_per_fetch_process(self):
"""
How many Celery tasks should be sent to each worker process.
:return: Number of tasks that should be used per process
:rtype: int
"""
return max(1,
int(math.ceil(1.0 * len(self.tasks) / self._sync_parallelism))) | def _num_tasks_per_fetch_process(self):
"""
How many Celery tasks should be sent to each worker process.
:return: Number of tasks that should be used per process
:rtype: int
"""
return max(1,
int(math.ceil(1.0 * len(self.tasks) / self._sync_parallelism))) | [
"How",
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"sent",
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"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/celery_executor.py#L168-L176 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | Variable.setdefault | Like a Python builtin dict object, setdefault returns the current value
for a key, and if it isn't there, stores the default value and returns it.
:param key: Dict key for this Variable
:type key: str
:param default: Default value to set and return if the variable
isn't already in the DB
:type default: Mixed
:param deserialize_json: Store this as a JSON encoded value in the DB
and un-encode it when retrieving a value
:return: Mixed | airflow/models/variable.py | def setdefault(cls, key, default, deserialize_json=False):
"""
Like a Python builtin dict object, setdefault returns the current value
for a key, and if it isn't there, stores the default value and returns it.
:param key: Dict key for this Variable
:type key: str
:param default: Default value to set and return if the variable
isn't already in the DB
:type default: Mixed
:param deserialize_json: Store this as a JSON encoded value in the DB
and un-encode it when retrieving a value
:return: Mixed
"""
obj = Variable.get(key, default_var=None,
deserialize_json=deserialize_json)
if obj is None:
if default is not None:
Variable.set(key, default, serialize_json=deserialize_json)
return default
else:
raise ValueError('Default Value must be set')
else:
return obj | def setdefault(cls, key, default, deserialize_json=False):
"""
Like a Python builtin dict object, setdefault returns the current value
for a key, and if it isn't there, stores the default value and returns it.
:param key: Dict key for this Variable
:type key: str
:param default: Default value to set and return if the variable
isn't already in the DB
:type default: Mixed
:param deserialize_json: Store this as a JSON encoded value in the DB
and un-encode it when retrieving a value
:return: Mixed
"""
obj = Variable.get(key, default_var=None,
deserialize_json=deserialize_json)
if obj is None:
if default is not None:
Variable.set(key, default, serialize_json=deserialize_json)
return default
else:
raise ValueError('Default Value must be set')
else:
return obj | [
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"isn",
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"there",
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"it",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/models/variable.py#L76-L99 | [
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test | MLEngineHook.get_conn | Returns a Google MLEngine service object. | airflow/contrib/hooks/gcp_mlengine_hook.py | def get_conn(self):
"""
Returns a Google MLEngine service object.
"""
authed_http = self._authorize()
return build('ml', 'v1', http=authed_http, cache_discovery=False) | def get_conn(self):
"""
Returns a Google MLEngine service object.
"""
authed_http = self._authorize()
return build('ml', 'v1', http=authed_http, cache_discovery=False) | [
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test | MLEngineHook.create_job | Launches a MLEngine job and wait for it to reach a terminal state.
:param project_id: The Google Cloud project id within which MLEngine
job will be launched.
:type project_id: str
:param job: MLEngine Job object that should be provided to the MLEngine
API, such as: ::
{
'jobId': 'my_job_id',
'trainingInput': {
'scaleTier': 'STANDARD_1',
...
}
}
:type job: dict
:param use_existing_job_fn: In case that a MLEngine job with the same
job_id already exist, this method (if provided) will decide whether
we should use this existing job, continue waiting for it to finish
and returning the job object. It should accepts a MLEngine job
object, and returns a boolean value indicating whether it is OK to
reuse the existing job. If 'use_existing_job_fn' is not provided,
we by default reuse the existing MLEngine job.
:type use_existing_job_fn: function
:return: The MLEngine job object if the job successfully reach a
terminal state (which might be FAILED or CANCELLED state).
:rtype: dict | airflow/contrib/hooks/gcp_mlengine_hook.py | def create_job(self, project_id, job, use_existing_job_fn=None):
"""
Launches a MLEngine job and wait for it to reach a terminal state.
:param project_id: The Google Cloud project id within which MLEngine
job will be launched.
:type project_id: str
:param job: MLEngine Job object that should be provided to the MLEngine
API, such as: ::
{
'jobId': 'my_job_id',
'trainingInput': {
'scaleTier': 'STANDARD_1',
...
}
}
:type job: dict
:param use_existing_job_fn: In case that a MLEngine job with the same
job_id already exist, this method (if provided) will decide whether
we should use this existing job, continue waiting for it to finish
and returning the job object. It should accepts a MLEngine job
object, and returns a boolean value indicating whether it is OK to
reuse the existing job. If 'use_existing_job_fn' is not provided,
we by default reuse the existing MLEngine job.
:type use_existing_job_fn: function
:return: The MLEngine job object if the job successfully reach a
terminal state (which might be FAILED or CANCELLED state).
:rtype: dict
"""
request = self._mlengine.projects().jobs().create(
parent='projects/{}'.format(project_id),
body=job)
job_id = job['jobId']
try:
request.execute()
except HttpError as e:
# 409 means there is an existing job with the same job ID.
if e.resp.status == 409:
if use_existing_job_fn is not None:
existing_job = self._get_job(project_id, job_id)
if not use_existing_job_fn(existing_job):
self.log.error(
'Job with job_id %s already exist, but it does '
'not match our expectation: %s',
job_id, existing_job
)
raise
self.log.info(
'Job with job_id %s already exist. Will waiting for it to finish',
job_id
)
else:
self.log.error('Failed to create MLEngine job: {}'.format(e))
raise
return self._wait_for_job_done(project_id, job_id) | def create_job(self, project_id, job, use_existing_job_fn=None):
"""
Launches a MLEngine job and wait for it to reach a terminal state.
:param project_id: The Google Cloud project id within which MLEngine
job will be launched.
:type project_id: str
:param job: MLEngine Job object that should be provided to the MLEngine
API, such as: ::
{
'jobId': 'my_job_id',
'trainingInput': {
'scaleTier': 'STANDARD_1',
...
}
}
:type job: dict
:param use_existing_job_fn: In case that a MLEngine job with the same
job_id already exist, this method (if provided) will decide whether
we should use this existing job, continue waiting for it to finish
and returning the job object. It should accepts a MLEngine job
object, and returns a boolean value indicating whether it is OK to
reuse the existing job. If 'use_existing_job_fn' is not provided,
we by default reuse the existing MLEngine job.
:type use_existing_job_fn: function
:return: The MLEngine job object if the job successfully reach a
terminal state (which might be FAILED or CANCELLED state).
:rtype: dict
"""
request = self._mlengine.projects().jobs().create(
parent='projects/{}'.format(project_id),
body=job)
job_id = job['jobId']
try:
request.execute()
except HttpError as e:
# 409 means there is an existing job with the same job ID.
if e.resp.status == 409:
if use_existing_job_fn is not None:
existing_job = self._get_job(project_id, job_id)
if not use_existing_job_fn(existing_job):
self.log.error(
'Job with job_id %s already exist, but it does '
'not match our expectation: %s',
job_id, existing_job
)
raise
self.log.info(
'Job with job_id %s already exist. Will waiting for it to finish',
job_id
)
else:
self.log.error('Failed to create MLEngine job: {}'.format(e))
raise
return self._wait_for_job_done(project_id, job_id) | [
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test | MLEngineHook._get_job | Gets a MLEngine job based on the job name.
:return: MLEngine job object if succeed.
:rtype: dict
Raises:
googleapiclient.errors.HttpError: if HTTP error is returned from server | airflow/contrib/hooks/gcp_mlengine_hook.py | def _get_job(self, project_id, job_id):
"""
Gets a MLEngine job based on the job name.
:return: MLEngine job object if succeed.
:rtype: dict
Raises:
googleapiclient.errors.HttpError: if HTTP error is returned from server
"""
job_name = 'projects/{}/jobs/{}'.format(project_id, job_id)
request = self._mlengine.projects().jobs().get(name=job_name)
while True:
try:
return request.execute()
except HttpError as e:
if e.resp.status == 429:
# polling after 30 seconds when quota failure occurs
time.sleep(30)
else:
self.log.error('Failed to get MLEngine job: {}'.format(e))
raise | def _get_job(self, project_id, job_id):
"""
Gets a MLEngine job based on the job name.
:return: MLEngine job object if succeed.
:rtype: dict
Raises:
googleapiclient.errors.HttpError: if HTTP error is returned from server
"""
job_name = 'projects/{}/jobs/{}'.format(project_id, job_id)
request = self._mlengine.projects().jobs().get(name=job_name)
while True:
try:
return request.execute()
except HttpError as e:
if e.resp.status == 429:
# polling after 30 seconds when quota failure occurs
time.sleep(30)
else:
self.log.error('Failed to get MLEngine job: {}'.format(e))
raise | [
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"based",
"on",
"the",
"job",
"name",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L123-L144 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | MLEngineHook._wait_for_job_done | Waits for the Job to reach a terminal state.
This method will periodically check the job state until the job reach
a terminal state.
Raises:
googleapiclient.errors.HttpError: if HTTP error is returned when getting
the job | airflow/contrib/hooks/gcp_mlengine_hook.py | def _wait_for_job_done(self, project_id, job_id, interval=30):
"""
Waits for the Job to reach a terminal state.
This method will periodically check the job state until the job reach
a terminal state.
Raises:
googleapiclient.errors.HttpError: if HTTP error is returned when getting
the job
"""
if interval <= 0:
raise ValueError("Interval must be > 0")
while True:
job = self._get_job(project_id, job_id)
if job['state'] in ['SUCCEEDED', 'FAILED', 'CANCELLED']:
return job
time.sleep(interval) | def _wait_for_job_done(self, project_id, job_id, interval=30):
"""
Waits for the Job to reach a terminal state.
This method will periodically check the job state until the job reach
a terminal state.
Raises:
googleapiclient.errors.HttpError: if HTTP error is returned when getting
the job
"""
if interval <= 0:
raise ValueError("Interval must be > 0")
while True:
job = self._get_job(project_id, job_id)
if job['state'] in ['SUCCEEDED', 'FAILED', 'CANCELLED']:
return job
time.sleep(interval) | [
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"for",
"the",
"Job",
"to",
"reach",
"a",
"terminal",
"state",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L146-L163 | [
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test | MLEngineHook.create_version | Creates the Version on Google Cloud ML Engine.
Returns the operation if the version was created successfully and
raises an error otherwise. | airflow/contrib/hooks/gcp_mlengine_hook.py | def create_version(self, project_id, model_name, version_spec):
"""
Creates the Version on Google Cloud ML Engine.
Returns the operation if the version was created successfully and
raises an error otherwise.
"""
parent_name = 'projects/{}/models/{}'.format(project_id, model_name)
create_request = self._mlengine.projects().models().versions().create(
parent=parent_name, body=version_spec)
response = create_request.execute()
get_request = self._mlengine.projects().operations().get(
name=response['name'])
return _poll_with_exponential_delay(
request=get_request,
max_n=9,
is_done_func=lambda resp: resp.get('done', False),
is_error_func=lambda resp: resp.get('error', None) is not None) | def create_version(self, project_id, model_name, version_spec):
"""
Creates the Version on Google Cloud ML Engine.
Returns the operation if the version was created successfully and
raises an error otherwise.
"""
parent_name = 'projects/{}/models/{}'.format(project_id, model_name)
create_request = self._mlengine.projects().models().versions().create(
parent=parent_name, body=version_spec)
response = create_request.execute()
get_request = self._mlengine.projects().operations().get(
name=response['name'])
return _poll_with_exponential_delay(
request=get_request,
max_n=9,
is_done_func=lambda resp: resp.get('done', False),
is_error_func=lambda resp: resp.get('error', None) is not None) | [
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"Version",
"on",
"Google",
"Cloud",
"ML",
"Engine",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L165-L183 | [
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test | MLEngineHook.set_default_version | Sets a version to be the default. Blocks until finished. | airflow/contrib/hooks/gcp_mlengine_hook.py | def set_default_version(self, project_id, model_name, version_name):
"""
Sets a version to be the default. Blocks until finished.
"""
full_version_name = 'projects/{}/models/{}/versions/{}'.format(
project_id, model_name, version_name)
request = self._mlengine.projects().models().versions().setDefault(
name=full_version_name, body={})
try:
response = request.execute()
self.log.info('Successfully set version: %s to default', response)
return response
except HttpError as e:
self.log.error('Something went wrong: %s', e)
raise | def set_default_version(self, project_id, model_name, version_name):
"""
Sets a version to be the default. Blocks until finished.
"""
full_version_name = 'projects/{}/models/{}/versions/{}'.format(
project_id, model_name, version_name)
request = self._mlengine.projects().models().versions().setDefault(
name=full_version_name, body={})
try:
response = request.execute()
self.log.info('Successfully set version: %s to default', response)
return response
except HttpError as e:
self.log.error('Something went wrong: %s', e)
raise | [
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"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L185-L200 | [
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test | MLEngineHook.list_versions | Lists all available versions of a model. Blocks until finished. | airflow/contrib/hooks/gcp_mlengine_hook.py | def list_versions(self, project_id, model_name):
"""
Lists all available versions of a model. Blocks until finished.
"""
result = []
full_parent_name = 'projects/{}/models/{}'.format(
project_id, model_name)
request = self._mlengine.projects().models().versions().list(
parent=full_parent_name, pageSize=100)
response = request.execute()
next_page_token = response.get('nextPageToken', None)
result.extend(response.get('versions', []))
while next_page_token is not None:
next_request = self._mlengine.projects().models().versions().list(
parent=full_parent_name,
pageToken=next_page_token,
pageSize=100)
response = next_request.execute()
next_page_token = response.get('nextPageToken', None)
result.extend(response.get('versions', []))
time.sleep(5)
return result | def list_versions(self, project_id, model_name):
"""
Lists all available versions of a model. Blocks until finished.
"""
result = []
full_parent_name = 'projects/{}/models/{}'.format(
project_id, model_name)
request = self._mlengine.projects().models().versions().list(
parent=full_parent_name, pageSize=100)
response = request.execute()
next_page_token = response.get('nextPageToken', None)
result.extend(response.get('versions', []))
while next_page_token is not None:
next_request = self._mlengine.projects().models().versions().list(
parent=full_parent_name,
pageToken=next_page_token,
pageSize=100)
response = next_request.execute()
next_page_token = response.get('nextPageToken', None)
result.extend(response.get('versions', []))
time.sleep(5)
return result | [
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"of",
"a",
"model",
".",
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"until",
"finished",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L202-L224 | [
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test | MLEngineHook.delete_version | Deletes the given version of a model. Blocks until finished. | airflow/contrib/hooks/gcp_mlengine_hook.py | def delete_version(self, project_id, model_name, version_name):
"""
Deletes the given version of a model. Blocks until finished.
"""
full_name = 'projects/{}/models/{}/versions/{}'.format(
project_id, model_name, version_name)
delete_request = self._mlengine.projects().models().versions().delete(
name=full_name)
response = delete_request.execute()
get_request = self._mlengine.projects().operations().get(
name=response['name'])
return _poll_with_exponential_delay(
request=get_request,
max_n=9,
is_done_func=lambda resp: resp.get('done', False),
is_error_func=lambda resp: resp.get('error', None) is not None) | def delete_version(self, project_id, model_name, version_name):
"""
Deletes the given version of a model. Blocks until finished.
"""
full_name = 'projects/{}/models/{}/versions/{}'.format(
project_id, model_name, version_name)
delete_request = self._mlengine.projects().models().versions().delete(
name=full_name)
response = delete_request.execute()
get_request = self._mlengine.projects().operations().get(
name=response['name'])
return _poll_with_exponential_delay(
request=get_request,
max_n=9,
is_done_func=lambda resp: resp.get('done', False),
is_error_func=lambda resp: resp.get('error', None) is not None) | [
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"given",
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"of",
"a",
"model",
".",
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"until",
"finished",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L226-L242 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | MLEngineHook.create_model | Create a Model. Blocks until finished. | airflow/contrib/hooks/gcp_mlengine_hook.py | def create_model(self, project_id, model):
"""
Create a Model. Blocks until finished.
"""
if not model['name']:
raise ValueError("Model name must be provided and "
"could not be an empty string")
project = 'projects/{}'.format(project_id)
request = self._mlengine.projects().models().create(
parent=project, body=model)
return request.execute() | def create_model(self, project_id, model):
"""
Create a Model. Blocks until finished.
"""
if not model['name']:
raise ValueError("Model name must be provided and "
"could not be an empty string")
project = 'projects/{}'.format(project_id)
request = self._mlengine.projects().models().create(
parent=project, body=model)
return request.execute() | [
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"a",
"Model",
".",
"Blocks",
"until",
"finished",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L244-L255 | [
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test | MLEngineHook.get_model | Gets a Model. Blocks until finished. | airflow/contrib/hooks/gcp_mlengine_hook.py | def get_model(self, project_id, model_name):
"""
Gets a Model. Blocks until finished.
"""
if not model_name:
raise ValueError("Model name must be provided and "
"it could not be an empty string")
full_model_name = 'projects/{}/models/{}'.format(
project_id, model_name)
request = self._mlengine.projects().models().get(name=full_model_name)
try:
return request.execute()
except HttpError as e:
if e.resp.status == 404:
self.log.error('Model was not found: %s', e)
return None
raise | def get_model(self, project_id, model_name):
"""
Gets a Model. Blocks until finished.
"""
if not model_name:
raise ValueError("Model name must be provided and "
"it could not be an empty string")
full_model_name = 'projects/{}/models/{}'.format(
project_id, model_name)
request = self._mlengine.projects().models().get(name=full_model_name)
try:
return request.execute()
except HttpError as e:
if e.resp.status == 404:
self.log.error('Model was not found: %s', e)
return None
raise | [
"Gets",
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"Model",
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"Blocks",
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"finished",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/gcp_mlengine_hook.py#L257-L273 | [
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test | LocalWorker.execute_work | Executes command received and stores result state in queue.
:param key: the key to identify the TI
:type key: tuple(dag_id, task_id, execution_date)
:param command: the command to execute
:type command: str | airflow/executors/local_executor.py | def execute_work(self, key, command):
"""
Executes command received and stores result state in queue.
:param key: the key to identify the TI
:type key: tuple(dag_id, task_id, execution_date)
:param command: the command to execute
:type command: str
"""
if key is None:
return
self.log.info("%s running %s", self.__class__.__name__, command)
try:
subprocess.check_call(command, close_fds=True)
state = State.SUCCESS
except subprocess.CalledProcessError as e:
state = State.FAILED
self.log.error("Failed to execute task %s.", str(e))
# TODO: Why is this commented out?
# raise e
self.result_queue.put((key, state)) | def execute_work(self, key, command):
"""
Executes command received and stores result state in queue.
:param key: the key to identify the TI
:type key: tuple(dag_id, task_id, execution_date)
:param command: the command to execute
:type command: str
"""
if key is None:
return
self.log.info("%s running %s", self.__class__.__name__, command)
try:
subprocess.check_call(command, close_fds=True)
state = State.SUCCESS
except subprocess.CalledProcessError as e:
state = State.FAILED
self.log.error("Failed to execute task %s.", str(e))
# TODO: Why is this commented out?
# raise e
self.result_queue.put((key, state)) | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | AwsDynamoDBHook.write_batch_data | Write batch items to dynamodb table with provisioned throughout capacity. | airflow/contrib/hooks/aws_dynamodb_hook.py | def write_batch_data(self, items):
"""
Write batch items to dynamodb table with provisioned throughout capacity.
"""
dynamodb_conn = self.get_conn()
try:
table = dynamodb_conn.Table(self.table_name)
with table.batch_writer(overwrite_by_pkeys=self.table_keys) as batch:
for item in items:
batch.put_item(Item=item)
return True
except Exception as general_error:
raise AirflowException(
'Failed to insert items in dynamodb, error: {error}'.format(
error=str(general_error)
)
) | def write_batch_data(self, items):
"""
Write batch items to dynamodb table with provisioned throughout capacity.
"""
dynamodb_conn = self.get_conn()
try:
table = dynamodb_conn.Table(self.table_name)
with table.batch_writer(overwrite_by_pkeys=self.table_keys) as batch:
for item in items:
batch.put_item(Item=item)
return True
except Exception as general_error:
raise AirflowException(
'Failed to insert items in dynamodb, error: {error}'.format(
error=str(general_error)
)
) | [
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"table",
"with",
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"throughout",
"capacity",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/aws_dynamodb_hook.py#L50-L69 | [
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test | _integrate_plugins | Integrate plugins to the context. | airflow/executors/__init__.py | def _integrate_plugins():
"""Integrate plugins to the context."""
from airflow.plugins_manager import executors_modules
for executors_module in executors_modules:
sys.modules[executors_module.__name__] = executors_module
globals()[executors_module._name] = executors_module | def _integrate_plugins():
"""Integrate plugins to the context."""
from airflow.plugins_manager import executors_modules
for executors_module in executors_modules:
sys.modules[executors_module.__name__] = executors_module
globals()[executors_module._name] = executors_module | [
"Integrate",
"plugins",
"to",
"the",
"context",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/__init__.py#L31-L36 | [
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test | get_default_executor | Creates a new instance of the configured executor if none exists and returns it | airflow/executors/__init__.py | def get_default_executor():
"""Creates a new instance of the configured executor if none exists and returns it"""
global DEFAULT_EXECUTOR
if DEFAULT_EXECUTOR is not None:
return DEFAULT_EXECUTOR
executor_name = configuration.conf.get('core', 'EXECUTOR')
DEFAULT_EXECUTOR = _get_executor(executor_name)
log = LoggingMixin().log
log.info("Using executor %s", executor_name)
return DEFAULT_EXECUTOR | def get_default_executor():
"""Creates a new instance of the configured executor if none exists and returns it"""
global DEFAULT_EXECUTOR
if DEFAULT_EXECUTOR is not None:
return DEFAULT_EXECUTOR
executor_name = configuration.conf.get('core', 'EXECUTOR')
DEFAULT_EXECUTOR = _get_executor(executor_name)
log = LoggingMixin().log
log.info("Using executor %s", executor_name)
return DEFAULT_EXECUTOR | [
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"if",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/executors/__init__.py#L39-L53 | [
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test | _get_executor | Creates a new instance of the named executor.
In case the executor name is not know in airflow,
look for it in the plugins | airflow/executors/__init__.py | def _get_executor(executor_name):
"""
Creates a new instance of the named executor.
In case the executor name is not know in airflow,
look for it in the plugins
"""
if executor_name == Executors.LocalExecutor:
return LocalExecutor()
elif executor_name == Executors.SequentialExecutor:
return SequentialExecutor()
elif executor_name == Executors.CeleryExecutor:
from airflow.executors.celery_executor import CeleryExecutor
return CeleryExecutor()
elif executor_name == Executors.DaskExecutor:
from airflow.executors.dask_executor import DaskExecutor
return DaskExecutor()
elif executor_name == Executors.KubernetesExecutor:
from airflow.contrib.executors.kubernetes_executor import KubernetesExecutor
return KubernetesExecutor()
else:
# Loading plugins
_integrate_plugins()
executor_path = executor_name.split('.')
if len(executor_path) != 2:
raise AirflowException(
"Executor {0} not supported: "
"please specify in format plugin_module.executor".format(executor_name))
if executor_path[0] in globals():
return globals()[executor_path[0]].__dict__[executor_path[1]]()
else:
raise AirflowException("Executor {0} not supported.".format(executor_name)) | def _get_executor(executor_name):
"""
Creates a new instance of the named executor.
In case the executor name is not know in airflow,
look for it in the plugins
"""
if executor_name == Executors.LocalExecutor:
return LocalExecutor()
elif executor_name == Executors.SequentialExecutor:
return SequentialExecutor()
elif executor_name == Executors.CeleryExecutor:
from airflow.executors.celery_executor import CeleryExecutor
return CeleryExecutor()
elif executor_name == Executors.DaskExecutor:
from airflow.executors.dask_executor import DaskExecutor
return DaskExecutor()
elif executor_name == Executors.KubernetesExecutor:
from airflow.contrib.executors.kubernetes_executor import KubernetesExecutor
return KubernetesExecutor()
else:
# Loading plugins
_integrate_plugins()
executor_path = executor_name.split('.')
if len(executor_path) != 2:
raise AirflowException(
"Executor {0} not supported: "
"please specify in format plugin_module.executor".format(executor_name))
if executor_path[0] in globals():
return globals()[executor_path[0]].__dict__[executor_path[1]]()
else:
raise AirflowException("Executor {0} not supported.".format(executor_name)) | [
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test | SegmentHook.on_error | Handles error callbacks when using Segment with segment_debug_mode set to True | airflow/contrib/hooks/segment_hook.py | def on_error(self, error, items):
"""
Handles error callbacks when using Segment with segment_debug_mode set to True
"""
self.log.error('Encountered Segment error: {segment_error} with '
'items: {with_items}'.format(segment_error=error,
with_items=items))
raise AirflowException('Segment error: {}'.format(error)) | def on_error(self, error, items):
"""
Handles error callbacks when using Segment with segment_debug_mode set to True
"""
self.log.error('Encountered Segment error: {segment_error} with '
'items: {with_items}'.format(segment_error=error,
with_items=items))
raise AirflowException('Segment error: {}'.format(error)) | [
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"set",
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/segment_hook.py#L83-L90 | [
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"format",
"(",
"error",
")",
")"
] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | PodLauncher.run_pod | Launches the pod synchronously and waits for completion.
Args:
pod (Pod):
startup_timeout (int): Timeout for startup of the pod (if pod is pending for
too long, considers task a failure | airflow/contrib/kubernetes/pod_launcher.py | def run_pod(self, pod, startup_timeout=120, get_logs=True):
# type: (Pod, int, bool) -> Tuple[State, Optional[str]]
"""
Launches the pod synchronously and waits for completion.
Args:
pod (Pod):
startup_timeout (int): Timeout for startup of the pod (if pod is pending for
too long, considers task a failure
"""
resp = self.run_pod_async(pod)
curr_time = dt.now()
if resp.status.start_time is None:
while self.pod_not_started(pod):
delta = dt.now() - curr_time
if delta.seconds >= startup_timeout:
raise AirflowException("Pod took too long to start")
time.sleep(1)
self.log.debug('Pod not yet started')
return self._monitor_pod(pod, get_logs) | def run_pod(self, pod, startup_timeout=120, get_logs=True):
# type: (Pod, int, bool) -> Tuple[State, Optional[str]]
"""
Launches the pod synchronously and waits for completion.
Args:
pod (Pod):
startup_timeout (int): Timeout for startup of the pod (if pod is pending for
too long, considers task a failure
"""
resp = self.run_pod_async(pod)
curr_time = dt.now()
if resp.status.start_time is None:
while self.pod_not_started(pod):
delta = dt.now() - curr_time
if delta.seconds >= startup_timeout:
raise AirflowException("Pod took too long to start")
time.sleep(1)
self.log.debug('Pod not yet started')
return self._monitor_pod(pod, get_logs) | [
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test | MsSqlHook.get_conn | Returns a mssql connection object | airflow/hooks/mssql_hook.py | def get_conn(self):
"""
Returns a mssql connection object
"""
conn = self.get_connection(self.mssql_conn_id)
conn = pymssql.connect(
server=conn.host,
user=conn.login,
password=conn.password,
database=self.schema or conn.schema,
port=conn.port)
return conn | def get_conn(self):
"""
Returns a mssql connection object
"""
conn = self.get_connection(self.mssql_conn_id)
conn = pymssql.connect(
server=conn.host,
user=conn.login,
password=conn.password,
database=self.schema or conn.schema,
port=conn.port)
return conn | [
"Returns",
"a",
"mssql",
"connection",
"object"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/hooks/mssql_hook.py#L38-L49 | [
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test | SparkSubmitOperator.execute | Call the SparkSubmitHook to run the provided spark job | airflow/contrib/operators/spark_submit_operator.py | def execute(self, context):
"""
Call the SparkSubmitHook to run the provided spark job
"""
self._hook = SparkSubmitHook(
conf=self._conf,
conn_id=self._conn_id,
files=self._files,
py_files=self._py_files,
archives=self._archives,
driver_class_path=self._driver_class_path,
jars=self._jars,
java_class=self._java_class,
packages=self._packages,
exclude_packages=self._exclude_packages,
repositories=self._repositories,
total_executor_cores=self._total_executor_cores,
executor_cores=self._executor_cores,
executor_memory=self._executor_memory,
driver_memory=self._driver_memory,
keytab=self._keytab,
principal=self._principal,
name=self._name,
num_executors=self._num_executors,
application_args=self._application_args,
env_vars=self._env_vars,
verbose=self._verbose,
spark_binary=self._spark_binary
)
self._hook.submit(self._application) | def execute(self, context):
"""
Call the SparkSubmitHook to run the provided spark job
"""
self._hook = SparkSubmitHook(
conf=self._conf,
conn_id=self._conn_id,
files=self._files,
py_files=self._py_files,
archives=self._archives,
driver_class_path=self._driver_class_path,
jars=self._jars,
java_class=self._java_class,
packages=self._packages,
exclude_packages=self._exclude_packages,
repositories=self._repositories,
total_executor_cores=self._total_executor_cores,
executor_cores=self._executor_cores,
executor_memory=self._executor_memory,
driver_memory=self._driver_memory,
keytab=self._keytab,
principal=self._principal,
name=self._name,
num_executors=self._num_executors,
application_args=self._application_args,
env_vars=self._env_vars,
verbose=self._verbose,
spark_binary=self._spark_binary
)
self._hook.submit(self._application) | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/operators/spark_submit_operator.py#L147-L176 | [
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test | trigger_dag | Trigger a new dag run for a Dag with an execution date of now unless
specified in the data. | airflow/www/api/experimental/endpoints.py | def trigger_dag(dag_id):
"""
Trigger a new dag run for a Dag with an execution date of now unless
specified in the data.
"""
data = request.get_json(force=True)
run_id = None
if 'run_id' in data:
run_id = data['run_id']
conf = None
if 'conf' in data:
conf = data['conf']
execution_date = None
if 'execution_date' in data and data['execution_date'] is not None:
execution_date = data['execution_date']
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'
.format(execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
dr = trigger.trigger_dag(dag_id, run_id, conf, execution_date)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
if getattr(g, 'user', None):
_log.info("User %s created %s", g.user, dr)
response = jsonify(message="Created {}".format(dr))
return response | def trigger_dag(dag_id):
"""
Trigger a new dag run for a Dag with an execution date of now unless
specified in the data.
"""
data = request.get_json(force=True)
run_id = None
if 'run_id' in data:
run_id = data['run_id']
conf = None
if 'conf' in data:
conf = data['conf']
execution_date = None
if 'execution_date' in data and data['execution_date'] is not None:
execution_date = data['execution_date']
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'
.format(execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
dr = trigger.trigger_dag(dag_id, run_id, conf, execution_date)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
if getattr(g, 'user', None):
_log.info("User %s created %s", g.user, dr)
response = jsonify(message="Created {}".format(dr))
return response | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | delete_dag | Delete all DB records related to the specified Dag. | airflow/www/api/experimental/endpoints.py | def delete_dag(dag_id):
"""
Delete all DB records related to the specified Dag.
"""
try:
count = delete.delete_dag(dag_id)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
return jsonify(message="Removed {} record(s)".format(count), count=count) | def delete_dag(dag_id):
"""
Delete all DB records related to the specified Dag.
"""
try:
count = delete.delete_dag(dag_id)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
return jsonify(message="Removed {} record(s)".format(count), count=count) | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L98-L109 | [
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test | dag_runs | Returns a list of Dag Runs for a specific DAG ID.
:query param state: a query string parameter '?state=queued|running|success...'
:param dag_id: String identifier of a DAG
:return: List of DAG runs of a DAG with requested state,
or all runs if the state is not specified | airflow/www/api/experimental/endpoints.py | def dag_runs(dag_id):
"""
Returns a list of Dag Runs for a specific DAG ID.
:query param state: a query string parameter '?state=queued|running|success...'
:param dag_id: String identifier of a DAG
:return: List of DAG runs of a DAG with requested state,
or all runs if the state is not specified
"""
try:
state = request.args.get('state')
dagruns = get_dag_runs(dag_id, state)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = 400
return response
return jsonify(dagruns) | def dag_runs(dag_id):
"""
Returns a list of Dag Runs for a specific DAG ID.
:query param state: a query string parameter '?state=queued|running|success...'
:param dag_id: String identifier of a DAG
:return: List of DAG runs of a DAG with requested state,
or all runs if the state is not specified
"""
try:
state = request.args.get('state')
dagruns = get_dag_runs(dag_id, state)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = 400
return response
return jsonify(dagruns) | [
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test | get_dag_code | Return python code of a given dag_id. | airflow/www/api/experimental/endpoints.py | def get_dag_code(dag_id):
"""Return python code of a given dag_id."""
try:
return get_code(dag_id)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response | def get_dag_code(dag_id):
"""Return python code of a given dag_id."""
try:
return get_code(dag_id)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L142-L150 | [
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"status_code",
"return",
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | task_info | Returns a JSON with a task's public instance variables. | airflow/www/api/experimental/endpoints.py | def task_info(dag_id, task_id):
"""Returns a JSON with a task's public instance variables. """
try:
info = get_task(dag_id, task_id)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
# JSONify and return.
fields = {k: str(v)
for k, v in vars(info).items()
if not k.startswith('_')}
return jsonify(fields) | def task_info(dag_id, task_id):
"""Returns a JSON with a task's public instance variables. """
try:
info = get_task(dag_id, task_id)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
# JSONify and return.
fields = {k: str(v)
for k, v in vars(info).items()
if not k.startswith('_')}
return jsonify(fields) | [
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] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L155-L169 | [
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test | dag_paused | (Un)pauses a dag | airflow/www/api/experimental/endpoints.py | def dag_paused(dag_id, paused):
"""(Un)pauses a dag"""
DagModel = models.DagModel
with create_session() as session:
orm_dag = (
session.query(DagModel)
.filter(DagModel.dag_id == dag_id).first()
)
if paused == 'true':
orm_dag.is_paused = True
else:
orm_dag.is_paused = False
session.merge(orm_dag)
session.commit()
return jsonify({'response': 'ok'}) | def dag_paused(dag_id, paused):
"""(Un)pauses a dag"""
DagModel = models.DagModel
with create_session() as session:
orm_dag = (
session.query(DagModel)
.filter(DagModel.dag_id == dag_id).first()
)
if paused == 'true':
orm_dag.is_paused = True
else:
orm_dag.is_paused = False
session.merge(orm_dag)
session.commit()
return jsonify({'response': 'ok'}) | [
"(",
"Un",
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"pauses",
"a",
"dag"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L175-L191 | [
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test | task_instance_info | Returns a JSON with a task instance's public instance variables.
The format for the exec_date is expected to be
"YYYY-mm-DDTHH:MM:SS", for example: "2016-11-16T11:34:15". This will
of course need to have been encoded for URL in the request. | airflow/www/api/experimental/endpoints.py | def task_instance_info(dag_id, execution_date, task_id):
"""
Returns a JSON with a task instance's public instance variables.
The format for the exec_date is expected to be
"YYYY-mm-DDTHH:MM:SS", for example: "2016-11-16T11:34:15". This will
of course need to have been encoded for URL in the request.
"""
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'
.format(execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
info = get_task_instance(dag_id, task_id, execution_date)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
# JSONify and return.
fields = {k: str(v)
for k, v in vars(info).items()
if not k.startswith('_')}
return jsonify(fields) | def task_instance_info(dag_id, execution_date, task_id):
"""
Returns a JSON with a task instance's public instance variables.
The format for the exec_date is expected to be
"YYYY-mm-DDTHH:MM:SS", for example: "2016-11-16T11:34:15". This will
of course need to have been encoded for URL in the request.
"""
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'
.format(execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
info = get_task_instance(dag_id, task_id, execution_date)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
# JSONify and return.
fields = {k: str(v)
for k, v in vars(info).items()
if not k.startswith('_')}
return jsonify(fields) | [
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test | dag_run_status | Returns a JSON with a dag_run's public instance variables.
The format for the exec_date is expected to be
"YYYY-mm-DDTHH:MM:SS", for example: "2016-11-16T11:34:15". This will
of course need to have been encoded for URL in the request. | airflow/www/api/experimental/endpoints.py | def dag_run_status(dag_id, execution_date):
"""
Returns a JSON with a dag_run's public instance variables.
The format for the exec_date is expected to be
"YYYY-mm-DDTHH:MM:SS", for example: "2016-11-16T11:34:15". This will
of course need to have been encoded for URL in the request.
"""
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'.format(
execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
info = get_dag_run_state(dag_id, execution_date)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
return jsonify(info) | def dag_run_status(dag_id, execution_date):
"""
Returns a JSON with a dag_run's public instance variables.
The format for the exec_date is expected to be
"YYYY-mm-DDTHH:MM:SS", for example: "2016-11-16T11:34:15". This will
of course need to have been encoded for URL in the request.
"""
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'.format(
execution_date))
_log.info(error_message)
response = jsonify({'error': error_message})
response.status_code = 400
return response
try:
info = get_dag_run_state(dag_id, execution_date)
except AirflowException as err:
_log.info(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
return jsonify(info) | [
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test | get_pools | Get all pools. | airflow/www/api/experimental/endpoints.py | def get_pools():
"""Get all pools."""
try:
pools = pool_api.get_pools()
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify([p.to_json() for p in pools]) | def get_pools():
"""Get all pools."""
try:
pools = pool_api.get_pools()
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify([p.to_json() for p in pools]) | [
"Get",
"all",
"pools",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L309-L319 | [
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test | create_pool | Create a pool. | airflow/www/api/experimental/endpoints.py | def create_pool():
"""Create a pool."""
params = request.get_json(force=True)
try:
pool = pool_api.create_pool(**params)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify(pool.to_json()) | def create_pool():
"""Create a pool."""
params = request.get_json(force=True)
try:
pool = pool_api.create_pool(**params)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify(pool.to_json()) | [
"Create",
"a",
"pool",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L325-L336 | [
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test | delete_pool | Delete pool. | airflow/www/api/experimental/endpoints.py | def delete_pool(name):
"""Delete pool."""
try:
pool = pool_api.delete_pool(name=name)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify(pool.to_json()) | def delete_pool(name):
"""Delete pool."""
try:
pool = pool_api.delete_pool(name=name)
except AirflowException as err:
_log.error(err)
response = jsonify(error="{}".format(err))
response.status_code = err.status_code
return response
else:
return jsonify(pool.to_json()) | [
"Delete",
"pool",
"."
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/www/api/experimental/endpoints.py#L342-L352 | [
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test | AzureContainerInstanceHook.create_or_update | Create a new container group
:param resource_group: the name of the resource group
:type resource_group: str
:param name: the name of the container group
:type name: str
:param container_group: the properties of the container group
:type container_group: azure.mgmt.containerinstance.models.ContainerGroup | airflow/contrib/hooks/azure_container_instance_hook.py | def create_or_update(self, resource_group, name, container_group):
"""
Create a new container group
:param resource_group: the name of the resource group
:type resource_group: str
:param name: the name of the container group
:type name: str
:param container_group: the properties of the container group
:type container_group: azure.mgmt.containerinstance.models.ContainerGroup
"""
self.connection.container_groups.create_or_update(resource_group,
name,
container_group) | def create_or_update(self, resource_group, name, container_group):
"""
Create a new container group
:param resource_group: the name of the resource group
:type resource_group: str
:param name: the name of the container group
:type name: str
:param container_group: the properties of the container group
:type container_group: azure.mgmt.containerinstance.models.ContainerGroup
"""
self.connection.container_groups.create_or_update(resource_group,
name,
container_group) | [
"Create",
"a",
"new",
"container",
"group"
] | apache/airflow | python | https://github.com/apache/airflow/blob/b69c686ad8a0c89b9136bb4b31767257eb7b2597/airflow/contrib/hooks/azure_container_instance_hook.py#L80-L93 | [
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] | b69c686ad8a0c89b9136bb4b31767257eb7b2597 |
test | AzureContainerInstanceHook.get_state_exitcode_details | Get the state and exitcode of a container group
:param resource_group: the name of the resource group
:type resource_group: str
:param name: the name of the container group
:type name: str
:return: A tuple with the state, exitcode, and details.
If the exitcode is unknown 0 is returned.
:rtype: tuple(state,exitcode,details) | airflow/contrib/hooks/azure_container_instance_hook.py | def get_state_exitcode_details(self, resource_group, name):
"""
Get the state and exitcode of a container group
:param resource_group: the name of the resource group
:type resource_group: str
:param name: the name of the container group
:type name: str
:return: A tuple with the state, exitcode, and details.
If the exitcode is unknown 0 is returned.
:rtype: tuple(state,exitcode,details)
"""
current_state = self._get_instance_view(resource_group, name).current_state
return (current_state.state,
current_state.exit_code,
current_state.detail_status) | def get_state_exitcode_details(self, resource_group, name):
"""
Get the state and exitcode of a container group
:param resource_group: the name of the resource group
:type resource_group: str
:param name: the name of the container group
:type name: str
:return: A tuple with the state, exitcode, and details.
If the exitcode is unknown 0 is returned.
:rtype: tuple(state,exitcode,details)
"""
current_state = self._get_instance_view(resource_group, name).current_state
return (current_state.state,
current_state.exit_code,
current_state.detail_status) | [
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