Source code for azure.synapse.spark.operations._spark_batch_operations

# 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 HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpRequest, HttpResponse

from .. import 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 = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) # 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'] = 'application/json' 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 = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop("content_type", "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'] = 'application/json' 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 = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) # 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'] = 'application/json' 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 = {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