%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Batch %% %% Using Batch, you can run batch computing workloads on the Amazon Web %% Services Cloud. %% %% Batch computing is a common means for developers, scientists, and %% engineers to access large amounts of compute resources. Batch uses the %% advantages of this computing workload to remove the undifferentiated heavy %% lifting of configuring and managing required infrastructure. At the same %% time, it also adopts a familiar batch computing software approach. Given %% these advantages, Batch can help you to efficiently provision resources in %% response to jobs submitted, thus effectively helping you to eliminate %% capacity constraints, reduce compute costs, and deliver your results more %% quickly. %% %% As a fully managed service, Batch can run batch computing workloads of any %% scale. Batch automatically provisions compute resources and optimizes %% workload distribution based on the quantity and scale of your specific %% workloads. With 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, create_scheduling_policy/2, create_scheduling_policy/3, delete_compute_environment/2, delete_compute_environment/3, delete_job_queue/2, delete_job_queue/3, delete_scheduling_policy/2, delete_scheduling_policy/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, describe_scheduling_policies/2, describe_scheduling_policies/3, list_jobs/2, list_jobs/3, list_scheduling_policies/2, list_scheduling_policies/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, update_scheduling_policy/2, update_scheduling_policy/3]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc Cancels a job in an Batch job queue. %% %% Jobs that are in the `SUBMITTED', `PENDING', or `RUNNABLE' state are %% canceled. Jobs that have progressed to `STARTING' or `RUNNING' aren't %% 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Creates an Batch compute environment. %% %% You can create `MANAGED' or `UNMANAGED' compute environments. `MANAGED' %% compute environments can use Amazon EC2 or Fargate resources. `UNMANAGED' %% compute environments can only use EC2 resources. %% %% In a managed compute environment, 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. Either, you can %% choose to use EC2 On-Demand Instances and EC2 Spot Instances. Or, you can %% 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 aren't 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 AMIs. However, you must %% verify that each of your AMIs meet the Amazon ECS container instance AMI %% specification. For more information, see container instance AMIs in the %% Amazon Elastic Container Service Developer Guide. After you created your %% unmanaged compute environment, you can use the %% `DescribeComputeEnvironments' operation to find the Amazon ECS cluster %% that's associated with it. Then, 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. %% %% Batch doesn't upgrade the AMIs in a compute environment after the %% environment is 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 managing the guest operating system (including its %% 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 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Creates an 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 that %% the 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Creates an Batch scheduling policy. create_scheduling_policy(Client, Input) -> create_scheduling_policy(Client, Input, []). create_scheduling_policy(Client, Input0, Options0) -> Method = post, Path = ["/v1/createschedulingpolicy"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes an 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 Fargate resources must terminate %% all active jobs on that compute environment before deleting the compute %% environment. If this isn't done, the compute environment enters 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified scheduling policy. %% %% You can't delete a scheduling policy that is used in any job queues. delete_scheduling_policy(Client, Input) -> delete_scheduling_policy(Client, Input, []). delete_scheduling_policy(Client, Input0, Options0) -> Method = post, Path = ["/v1/deleteschedulingpolicy"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deregisters an 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Describes a list of 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Describes one or more of your scheduling policies. describe_scheduling_policies(Client, Input) -> describe_scheduling_policies(Client, Input, []). describe_scheduling_policies(Client, Input0, Options0) -> Method = post, Path = ["/v1/describeschedulingpolicies"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Returns a list of 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Returns a list of Batch scheduling policies. list_scheduling_policies(Client, Input) -> list_scheduling_policies(Client, Input, []). list_scheduling_policies(Client, Input0, Options0) -> Method = post, Path = ["/v1/listschedulingpolicies"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Lists the tags for an Batch resource. %% %% Batch resources that support tags are compute environments, jobs, job %% definitions, job queues, and scheduling policies. 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 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Submits an Batch job from a job definition. %% %% Parameters that are specified during `SubmitJob' override parameters %% defined in the job definition. vCPU and memory requirements that are %% specified in the `resourceRequirements' objects in the job definition are %% the exception. They can't be overridden this way using the `memory' and %% `vcpus' parameters. Rather, you must specify updates to job definition %% parameters in a `ResourceRequirements' object that's included in the %% `containerOverrides' parameter. %% %% Job queues with a scheduling policy are limited to 500 active fair share %% identifiers at a time. %% %% Jobs that run on Fargate resources can't be guaranteed to run for more %% than 14 days. This is because, after 14 days, Fargate resources might %% become unavailable and job might be 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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 that are %% associated with that resource are deleted as well. Batch resources that %% support tags are compute environments, jobs, job definitions, job queues, %% and scheduling policies. 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes specified tags from an 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, CustomHeaders = [], Input2 = Input1, QueryMapping = [ {<<"tagKeys">>, <<"tagKeys">>} ], {Query_, Input} = aws_request:build_headers(QueryMapping, Input2), request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Updates an 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ 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, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Updates a scheduling policy. update_scheduling_policy(Client, Input) -> update_scheduling_policy(Client, Input, []). update_scheduling_policy(Client, Input0, Options0) -> Method = post, Path = ["/v1/updateschedulingpolicy"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), atom(), iolist(), list(), list(), map() | undefined, list(), pos_integer() | undefined) -> {ok, {integer(), list()}} | {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) -> RequestFun = fun() -> do_request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) end, aws_request:request(RequestFun, Options). do_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}, SuccessStatusCode, _DecodeBody) when StatusCode =:= 200; StatusCode =:= 202; StatusCode =:= 204; StatusCode =:= SuccessStatusCode -> {ok, {StatusCode, ResponseHeaders}}; handle_response({ok, StatusCode, ResponseHeaders}, _, _DecodeBody) -> {error, {StatusCode, ResponseHeaders}}; 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).