# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpRequest, HttpResponse
from .. import models as _models
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
[docs]class SparkBatchOperations(object):
"""SparkBatchOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.synapse.spark.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
[docs] def get_spark_batch_jobs(
self,
from_parameter=None, # type: Optional[int]
size=None, # type: Optional[int]
detailed=None, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> "_models.SparkBatchJobCollection"
"""List all spark batch jobs which are running under a particular spark pool.
:param from_parameter: Optional param specifying which index the list should begin from.
:type from_parameter: int
:param size: Optional param specifying the size of the returned list.
By default it is 20 and that is the maximum.
:type size: int
:param detailed: Optional query param specifying whether detailed response is returned beyond
plain livy.
:type detailed: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SparkBatchJobCollection, or the result of cls(response)
:rtype: ~azure.synapse.spark.models.SparkBatchJobCollection
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.SparkBatchJobCollection"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/json"
# Construct URL
url = self.get_spark_batch_jobs.metadata['url'] # type: ignore
path_format_arguments = {
'endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'livyApiVersion': self._serialize.url("self._config.livy_api_version", self._config.livy_api_version, 'str', skip_quote=True),
'sparkPoolName': self._serialize.url("self._config.spark_pool_name", self._config.spark_pool_name, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
if from_parameter is not None:
query_parameters['from'] = self._serialize.query("from_parameter", from_parameter, 'int')
if size is not None:
query_parameters['size'] = self._serialize.query("size", size, 'int')
if detailed is not None:
query_parameters['detailed'] = self._serialize.query("detailed", detailed, 'bool')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = self._deserialize('SparkBatchJobCollection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_spark_batch_jobs.metadata = {'url': '/batches'} # type: ignore
[docs] def create_spark_batch_job(
self,
spark_batch_job_options, # type: "_models.SparkBatchJobOptions"
detailed=None, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> "_models.SparkBatchJob"
"""Create new spark batch job.
:param spark_batch_job_options: Livy compatible batch job request payload.
:type spark_batch_job_options: ~azure.synapse.spark.models.SparkBatchJobOptions
:param detailed: Optional query param specifying whether detailed response is returned beyond
plain livy.
:type detailed: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SparkBatchJob, or the result of cls(response)
:rtype: ~azure.synapse.spark.models.SparkBatchJob
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.SparkBatchJob"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.create_spark_batch_job.metadata['url'] # type: ignore
path_format_arguments = {
'endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'livyApiVersion': self._serialize.url("self._config.livy_api_version", self._config.livy_api_version, 'str', skip_quote=True),
'sparkPoolName': self._serialize.url("self._config.spark_pool_name", self._config.spark_pool_name, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
if detailed is not None:
query_parameters['detailed'] = self._serialize.query("detailed", detailed, 'bool')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(spark_batch_job_options, 'SparkBatchJobOptions')
body_content_kwargs['content'] = body_content
request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = self._deserialize('SparkBatchJob', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_spark_batch_job.metadata = {'url': '/batches'} # type: ignore
[docs] def get_spark_batch_job(
self,
batch_id, # type: int
detailed=None, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> "_models.SparkBatchJob"
"""Gets a single spark batch job.
:param batch_id: Identifier for the batch job.
:type batch_id: int
:param detailed: Optional query param specifying whether detailed response is returned beyond
plain livy.
:type detailed: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SparkBatchJob, or the result of cls(response)
:rtype: ~azure.synapse.spark.models.SparkBatchJob
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.SparkBatchJob"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
accept = "application/json"
# Construct URL
url = self.get_spark_batch_job.metadata['url'] # type: ignore
path_format_arguments = {
'endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'livyApiVersion': self._serialize.url("self._config.livy_api_version", self._config.livy_api_version, 'str', skip_quote=True),
'sparkPoolName': self._serialize.url("self._config.spark_pool_name", self._config.spark_pool_name, 'str', skip_quote=True),
'batchId': self._serialize.url("batch_id", batch_id, 'int'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
if detailed is not None:
query_parameters['detailed'] = self._serialize.query("detailed", detailed, 'bool')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = self._deserialize('SparkBatchJob', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_spark_batch_job.metadata = {'url': '/batches/{batchId}'} # type: ignore
[docs] def cancel_spark_batch_job(
self,
batch_id, # type: int
**kwargs # type: Any
):
# type: (...) -> None
"""Cancels a running spark batch job.
:param batch_id: Identifier for the batch job.
:type batch_id: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
# Construct URL
url = self.cancel_spark_batch_job.metadata['url'] # type: ignore
path_format_arguments = {
'endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
'livyApiVersion': self._serialize.url("self._config.livy_api_version", self._config.livy_api_version, 'str', skip_quote=True),
'sparkPoolName': self._serialize.url("self._config.spark_pool_name", self._config.spark_pool_name, 'str', skip_quote=True),
'batchId': self._serialize.url("batch_id", batch_id, 'int'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
# Construct headers
header_parameters = {} # type: Dict[str, Any]
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
if cls:
return cls(pipeline_response, None, {})
cancel_spark_batch_job.metadata = {'url': '/batches/{batchId}'} # type: ignore