%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Using AWS Batch, you can run batch computing workloads on the AWS %% Cloud. %% %% Batch computing is a common means for developers, scientists, and %% engineers to access large amounts of compute resources. AWS Batch utilizes %% the advantages of this computing workload to remove the undifferentiated %% heavy lifting of configuring and managing required infrastructure, while %% also adopting a familiar batch computing software approach. Given these %% advantages, AWS Batch can help you to efficiently provision resources in %% response to jobs submitted, thus effectively helping to eliminate capacity %% constraints, reduce compute costs, and deliver your results more quickly. %% %% As a fully managed service, AWS Batch can run batch computing workloads of %% any scale. AWS Batch automatically provisions compute resources and %% optimizes workload distribution based on the quantity and scale of your %% specific workloads. With AWS Batch, there's no need to install or manage %% batch computing software. This means that you can focus your time and %% energy on analyzing results and solving your specific problems. -module(aws_batch). -export([cancel_job/2, cancel_job/3, create_compute_environment/2, create_compute_environment/3, create_job_queue/2, create_job_queue/3, delete_compute_environment/2, delete_compute_environment/3, delete_job_queue/2, delete_job_queue/3, deregister_job_definition/2, deregister_job_definition/3, describe_compute_environments/2, describe_compute_environments/3, describe_job_definitions/2, describe_job_definitions/3, describe_job_queues/2, describe_job_queues/3, describe_jobs/2, describe_jobs/3, list_jobs/2, list_jobs/3, list_tags_for_resource/2, list_tags_for_resource/4, list_tags_for_resource/5, register_job_definition/2, register_job_definition/3, submit_job/2, submit_job/3, tag_resource/3, tag_resource/4, terminate_job/2, terminate_job/3, untag_resource/3, untag_resource/4, update_compute_environment/2, update_compute_environment/3, update_job_queue/2, update_job_queue/3]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc Cancels a job in an AWS Batch job queue. %% %% Jobs that are in the `SUBMITTED', `PENDING', or `RUNNABLE' state are %% canceled. Jobs that have progressed to `STARTING' or `RUNNING' are not %% canceled (but the API operation still succeeds, even if no job is %% canceled); these jobs must be terminated with the `TerminateJob' %% operation. cancel_job(Client, Input) -> cancel_job(Client, Input, []). cancel_job(Client, Input0, Options0) -> Method = post, Path = ["/v1/canceljob"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Creates an AWS Batch compute environment. %% %% You can create `MANAGED' or `UNMANAGED' compute environments. `MANAGED' %% compute environments can use Amazon EC2 or AWS Fargate resources. %% `UNMANAGED' compute environments can only use EC2 resources. %% %% In a managed compute environment, AWS Batch manages the capacity and %% instance types of the compute resources within the environment. This is %% based on the compute resource specification that you define or the launch %% template that you specify when you create the compute environment. You can %% choose either to use EC2 On-Demand Instances and EC2 Spot Instances, or to %% use Fargate and Fargate Spot capacity in your managed compute environment. %% You can optionally set a maximum price so that Spot Instances only launch %% when the Spot Instance price is less than a specified percentage of the %% On-Demand price. %% %% Multi-node parallel jobs are not supported on Spot Instances. %% %% In an unmanaged compute environment, you can manage your own EC2 compute %% resources and have a lot of flexibility with how you configure your %% compute resources. For example, you can use custom AMI. However, you need %% to verify that your AMI meets the Amazon ECS container instance AMI %% specification. For more information, see container instance AMIs in the %% Amazon Elastic Container Service Developer Guide. After you have created %% your unmanaged compute environment, you can use the %% `DescribeComputeEnvironments' operation to find the Amazon ECS cluster %% that's associated with it. Then, manually launch your container instances %% into that Amazon ECS cluster. For more information, see Launching an %% Amazon ECS container instance in the Amazon Elastic Container Service %% Developer Guide. %% %% AWS Batch doesn't upgrade the AMIs in a compute environment after it's %% created. For example, it doesn't update the AMIs when a newer version of %% the Amazon ECS-optimized AMI is available. Therefore, you're responsible %% for the management of the guest operating system (including updates and %% security patches) and any additional application software or utilities %% that you install on the compute resources. To use a new AMI for your AWS %% Batch jobs, complete these steps: %% %% Create a new compute environment with the new AMI. %% %% Add the compute environment to an existing job queue. %% %% Remove the earlier compute environment from your job queue. %% %% Delete the earlier compute environment. create_compute_environment(Client, Input) -> create_compute_environment(Client, Input, []). create_compute_environment(Client, Input0, Options0) -> Method = post, Path = ["/v1/createcomputeenvironment"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Creates an AWS Batch job queue. %% %% When you create a job queue, you associate one or more compute %% environments to the queue and assign an order of preference for the %% compute environments. %% %% You also set a priority to the job queue that determines the order in %% which the AWS Batch scheduler places jobs onto its associated compute %% environments. For example, if a compute environment is associated with %% more than one job queue, the job queue with a higher priority is given %% preference for scheduling jobs to that compute environment. create_job_queue(Client, Input) -> create_job_queue(Client, Input, []). create_job_queue(Client, Input0, Options0) -> Method = post, Path = ["/v1/createjobqueue"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Deletes an AWS Batch compute environment. %% %% Before you can delete a compute environment, you must set its state to %% `DISABLED' with the `UpdateComputeEnvironment' API operation and %% disassociate it from any job queues with the `UpdateJobQueue' API %% operation. Compute environments that use AWS Fargate resources must %% terminate all active jobs on that compute environment before deleting the %% compute environment. If this isn't done, the compute environment will end %% up in an invalid state. delete_compute_environment(Client, Input) -> delete_compute_environment(Client, Input, []). delete_compute_environment(Client, Input0, Options0) -> Method = post, Path = ["/v1/deletecomputeenvironment"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified job queue. %% %% You must first disable submissions for a queue with the `UpdateJobQueue' %% operation. All jobs in the queue are eventually terminated when you delete %% a job queue. The jobs are terminated at a rate of about 16 jobs each %% second. %% %% It's not necessary to disassociate compute environments from a queue %% before submitting a `DeleteJobQueue' request. delete_job_queue(Client, Input) -> delete_job_queue(Client, Input, []). delete_job_queue(Client, Input0, Options0) -> Method = post, Path = ["/v1/deletejobqueue"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Deregisters an AWS Batch job definition. %% %% Job definitions are permanently deleted after 180 days. deregister_job_definition(Client, Input) -> deregister_job_definition(Client, Input, []). deregister_job_definition(Client, Input0, Options0) -> Method = post, Path = ["/v1/deregisterjobdefinition"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Describes one or more of your compute environments. %% %% If you're using an unmanaged compute environment, you can use the %% `DescribeComputeEnvironment' operation to determine the `ecsClusterArn' %% that you should launch your Amazon ECS container instances into. describe_compute_environments(Client, Input) -> describe_compute_environments(Client, Input, []). describe_compute_environments(Client, Input0, Options0) -> Method = post, Path = ["/v1/describecomputeenvironments"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Describes a list of job definitions. %% %% You can specify a `status' (such as `ACTIVE') to only return job %% definitions that match that status. describe_job_definitions(Client, Input) -> describe_job_definitions(Client, Input, []). describe_job_definitions(Client, Input0, Options0) -> Method = post, Path = ["/v1/describejobdefinitions"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Describes one or more of your job queues. describe_job_queues(Client, Input) -> describe_job_queues(Client, Input, []). describe_job_queues(Client, Input0, Options0) -> Method = post, Path = ["/v1/describejobqueues"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Describes a list of AWS Batch jobs. describe_jobs(Client, Input) -> describe_jobs(Client, Input, []). describe_jobs(Client, Input0, Options0) -> Method = post, Path = ["/v1/describejobs"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Returns a list of AWS Batch jobs. %% %% You must specify only one of the following items: %% %% You can filter the results by job status with the `jobStatus' %% parameter. If you don't specify a status, only `RUNNING' jobs are %% returned. list_jobs(Client, Input) -> list_jobs(Client, Input, []). list_jobs(Client, Input0, Options0) -> Method = post, Path = ["/v1/listjobs"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Lists the tags for an AWS Batch resource. %% %% AWS Batch resources that support tags are compute environments, jobs, job %% definitions, and job queues. ARNs for child jobs of array and multi-node %% parallel (MNP) jobs are not supported. list_tags_for_resource(Client, ResourceArn) when is_map(Client) -> list_tags_for_resource(Client, ResourceArn, #{}, #{}). list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, []). list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/v1/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Registers an AWS Batch job definition. register_job_definition(Client, Input) -> register_job_definition(Client, Input, []). register_job_definition(Client, Input0, Options0) -> Method = post, Path = ["/v1/registerjobdefinition"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Submits an AWS Batch job from a job definition. %% %% Parameters specified during `SubmitJob' override parameters defined in the %% job definition. %% %% Jobs run on Fargate resources don't run for more than 14 days. After 14 %% days, the Fargate resources might no longer be available and the job is %% terminated. submit_job(Client, Input) -> submit_job(Client, Input, []). submit_job(Client, Input0, Options0) -> Method = post, Path = ["/v1/submitjob"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Associates the specified tags to a resource with the specified %% `resourceArn'. %% %% If existing tags on a resource aren't specified in the request parameters, %% they aren't changed. When a resource is deleted, the tags associated with %% that resource are deleted as well. AWS Batch resources that support tags %% are compute environments, jobs, job definitions, and job queues. ARNs for %% child jobs of array and multi-node parallel (MNP) jobs are not supported. tag_resource(Client, ResourceArn, Input) -> tag_resource(Client, ResourceArn, Input, []). tag_resource(Client, ResourceArn, Input0, Options0) -> Method = post, Path = ["/v1/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Terminates a job in a job queue. %% %% Jobs that are in the `STARTING' or `RUNNING' state are terminated, which %% causes them to transition to `FAILED'. Jobs that have not progressed to %% the `STARTING' state are cancelled. terminate_job(Client, Input) -> terminate_job(Client, Input, []). terminate_job(Client, Input0, Options0) -> Method = post, Path = ["/v1/terminatejob"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Deletes specified tags from an AWS Batch resource. untag_resource(Client, ResourceArn, Input) -> untag_resource(Client, ResourceArn, Input, []). untag_resource(Client, ResourceArn, Input0, Options0) -> Method = delete, Path = ["/v1/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, QueryMapping = [ {<<"tagKeys">>, <<"tagKeys">>} ], {Query_, Input} = aws_request:build_headers(QueryMapping, Input1), request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Updates an AWS Batch compute environment. update_compute_environment(Client, Input) -> update_compute_environment(Client, Input, []). update_compute_environment(Client, Input0, Options0) -> Method = post, Path = ["/v1/updatecomputeenvironment"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %% @doc Updates a job queue. update_job_queue(Client, Input) -> update_job_queue(Client, Input, []). update_job_queue(Client, Input0, Options0) -> Method = post, Path = ["/v1/updatejobqueue"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, Query_ = [], Input = Input1, request(Client, Method, Path, Query_, Headers, Input, Options, SuccessStatusCode). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), atom(), iolist(), list(), list(), map() | undefined, list(), pos_integer() | undefined) -> {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map(), Error :: map(). request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) -> Client1 = Client#{service => <<"batch">>}, Host = build_host(<<"batch">>, Client1), URL0 = build_url(Host, Path, Client1), URL = aws_request:add_query(URL0, Query), AdditionalHeaders = [ {<<"Host">>, Host} , {<<"Content-Type">>, <<"application/x-amz-json-1.1">>} ], Headers1 = aws_request:add_headers(AdditionalHeaders, Headers0), Payload = case proplists:get_value(send_body_as_binary, Options) of true -> maps:get(<<"Body">>, Input, <<"">>); false -> encode_payload(Input) end, MethodBin = aws_request:method_to_binary(Method), SignedHeaders = aws_request:sign_request(Client1, MethodBin, URL, Headers1, Payload), Response = hackney:request(Method, URL, SignedHeaders, Payload, Options), DecodeBody = not proplists:get_value(receive_body_as_binary, Options), handle_response(Response, SuccessStatusCode, DecodeBody). handle_response({ok, StatusCode, ResponseHeaders, Client}, SuccessStatusCode, DecodeBody) when StatusCode =:= 200; StatusCode =:= 202; StatusCode =:= 204; StatusCode =:= SuccessStatusCode -> case hackney:body(Client) of {ok, <<>>} when StatusCode =:= 200; StatusCode =:= SuccessStatusCode -> {ok, #{}, {StatusCode, ResponseHeaders, Client}}; {ok, Body} -> Result = case DecodeBody of true -> jsx:decode(Body); false -> #{<<"Body">> => Body} end, {ok, Result, {StatusCode, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, ResponseHeaders, Client}, _, _DecodeBody) -> {ok, Body} = hackney:body(Client), Error = jsx:decode(Body), {error, Error, {StatusCode, ResponseHeaders, Client}}; handle_response({error, Reason}, _, _DecodeBody) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Path0, Client) -> Proto = maps:get(proto, Client), Path = erlang:iolist_to_binary(Path0), Port = maps:get(port, Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, Path], <<"">>). -spec encode_payload(undefined | map()) -> binary(). encode_payload(undefined) -> <<>>; encode_payload(Input) -> jsx:encode(Input).