%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Amazon EMR on EKS provides a deployment option for Amazon EMR that %% allows you to run open-source big data frameworks on Amazon Elastic %% Kubernetes Service (Amazon EKS). %% %% With this deployment option, you can focus on running analytics workloads %% while Amazon EMR on EKS builds, configures, and manages containers for %% open-source applications. For more information about Amazon EMR on EKS %% concepts and tasks, see What is Amazon EMR on EKS. %% %% Amazon EMR containers is the API name for Amazon EMR on EKS. The %% `emr-containers' prefix is used in the following scenarios: %% %% -module(aws_emr_containers). -export([cancel_job_run/4, cancel_job_run/5, create_job_template/2, create_job_template/3, create_managed_endpoint/3, create_managed_endpoint/4, create_virtual_cluster/2, create_virtual_cluster/3, delete_job_template/3, delete_job_template/4, delete_managed_endpoint/4, delete_managed_endpoint/5, delete_virtual_cluster/3, delete_virtual_cluster/4, describe_job_run/3, describe_job_run/5, describe_job_run/6, describe_job_template/2, describe_job_template/4, describe_job_template/5, describe_managed_endpoint/3, describe_managed_endpoint/5, describe_managed_endpoint/6, describe_virtual_cluster/2, describe_virtual_cluster/4, describe_virtual_cluster/5, list_job_runs/2, list_job_runs/4, list_job_runs/5, list_job_templates/1, list_job_templates/3, list_job_templates/4, list_managed_endpoints/2, list_managed_endpoints/4, list_managed_endpoints/5, list_tags_for_resource/2, list_tags_for_resource/4, list_tags_for_resource/5, list_virtual_clusters/1, list_virtual_clusters/3, list_virtual_clusters/4, start_job_run/3, start_job_run/4, tag_resource/3, tag_resource/4, untag_resource/3, untag_resource/4]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc Cancels a job run. %% %% A job run is a unit of work, such as a Spark jar, PySpark script, or %% SparkSQL query, that you submit to Amazon EMR on EKS. cancel_job_run(Client, Id, VirtualClusterId, Input) -> cancel_job_run(Client, Id, VirtualClusterId, Input, []). cancel_job_run(Client, Id, VirtualClusterId, Input0, Options0) -> Method = delete, Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/jobruns/", aws_util:encode_uri(Id), ""], 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 a job template. %% %% Job template stores values of StartJobRun API request in a template and %% can be used to start a job run. Job template allows two use cases: avoid %% repeating recurring StartJobRun API request values, enforcing certain %% values in StartJobRun API request. create_job_template(Client, Input) -> create_job_template(Client, Input, []). create_job_template(Client, Input0, Options0) -> Method = post, Path = ["/jobtemplates"], 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 a managed endpoint. %% %% A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on %% EKS so that EMR Studio can communicate with your virtual cluster. create_managed_endpoint(Client, VirtualClusterId, Input) -> create_managed_endpoint(Client, VirtualClusterId, Input, []). create_managed_endpoint(Client, VirtualClusterId, Input0, Options0) -> Method = post, Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/endpoints"], 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 a virtual cluster. %% %% Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, %% describe, list and delete virtual clusters. They do not consume any %% additional resource in your system. A single virtual cluster maps to a %% single Kubernetes namespace. Given this relationship, you can model %% virtual clusters the same way you model Kubernetes namespaces to meet your %% requirements. create_virtual_cluster(Client, Input) -> create_virtual_cluster(Client, Input, []). create_virtual_cluster(Client, Input0, Options0) -> Method = post, Path = ["/virtualclusters"], 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 a job template. %% %% Job template stores values of StartJobRun API request in a template and %% can be used to start a job run. Job template allows two use cases: avoid %% repeating recurring StartJobRun API request values, enforcing certain %% values in StartJobRun API request. delete_job_template(Client, Id, Input) -> delete_job_template(Client, Id, Input, []). delete_job_template(Client, Id, Input0, Options0) -> Method = delete, Path = ["/jobtemplates/", aws_util:encode_uri(Id), ""], 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 a managed endpoint. %% %% A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on %% EKS so that EMR Studio can communicate with your virtual cluster. delete_managed_endpoint(Client, Id, VirtualClusterId, Input) -> delete_managed_endpoint(Client, Id, VirtualClusterId, Input, []). delete_managed_endpoint(Client, Id, VirtualClusterId, Input0, Options0) -> Method = delete, Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/endpoints/", aws_util:encode_uri(Id), ""], 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 a virtual cluster. %% %% Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, %% describe, list and delete virtual clusters. They do not consume any %% additional resource in your system. A single virtual cluster maps to a %% single Kubernetes namespace. Given this relationship, you can model %% virtual clusters the same way you model Kubernetes namespaces to meet your %% requirements. delete_virtual_cluster(Client, Id, Input) -> delete_virtual_cluster(Client, Id, Input, []). delete_virtual_cluster(Client, Id, Input0, Options0) -> Method = delete, Path = ["/virtualclusters/", aws_util:encode_uri(Id), ""], 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 Displays detailed information about a job run. %% %% A job run is a unit of work, such as a Spark jar, PySpark script, or %% SparkSQL query, that you submit to Amazon EMR on EKS. describe_job_run(Client, Id, VirtualClusterId) when is_map(Client) -> describe_job_run(Client, Id, VirtualClusterId, #{}, #{}). describe_job_run(Client, Id, VirtualClusterId, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> describe_job_run(Client, Id, VirtualClusterId, QueryMap, HeadersMap, []). describe_job_run(Client, Id, VirtualClusterId, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/jobruns/", aws_util:encode_uri(Id), ""], 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 Displays detailed information about a specified job template. %% %% Job template stores values of StartJobRun API request in a template and %% can be used to start a job run. Job template allows two use cases: avoid %% repeating recurring StartJobRun API request values, enforcing certain %% values in StartJobRun API request. describe_job_template(Client, Id) when is_map(Client) -> describe_job_template(Client, Id, #{}, #{}). describe_job_template(Client, Id, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> describe_job_template(Client, Id, QueryMap, HeadersMap, []). describe_job_template(Client, Id, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/jobtemplates/", aws_util:encode_uri(Id), ""], 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 Displays detailed information about a managed endpoint. %% %% A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on %% EKS so that EMR Studio can communicate with your virtual cluster. describe_managed_endpoint(Client, Id, VirtualClusterId) when is_map(Client) -> describe_managed_endpoint(Client, Id, VirtualClusterId, #{}, #{}). describe_managed_endpoint(Client, Id, VirtualClusterId, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> describe_managed_endpoint(Client, Id, VirtualClusterId, QueryMap, HeadersMap, []). describe_managed_endpoint(Client, Id, VirtualClusterId, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/endpoints/", aws_util:encode_uri(Id), ""], 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 Displays detailed information about a specified virtual cluster. %% %% Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, %% describe, list and delete virtual clusters. They do not consume any %% additional resource in your system. A single virtual cluster maps to a %% single Kubernetes namespace. Given this relationship, you can model %% virtual clusters the same way you model Kubernetes namespaces to meet your %% requirements. describe_virtual_cluster(Client, Id) when is_map(Client) -> describe_virtual_cluster(Client, Id, #{}, #{}). describe_virtual_cluster(Client, Id, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> describe_virtual_cluster(Client, Id, QueryMap, HeadersMap, []). describe_virtual_cluster(Client, Id, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/virtualclusters/", aws_util:encode_uri(Id), ""], 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 Lists job runs based on a set of parameters. %% %% A job run is a unit of work, such as a Spark jar, PySpark script, or %% SparkSQL query, that you submit to Amazon EMR on EKS. list_job_runs(Client, VirtualClusterId) when is_map(Client) -> list_job_runs(Client, VirtualClusterId, #{}, #{}). list_job_runs(Client, VirtualClusterId, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_job_runs(Client, VirtualClusterId, QueryMap, HeadersMap, []). list_job_runs(Client, VirtualClusterId, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/jobruns"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"createdAfter">>, maps:get(<<"createdAfter">>, QueryMap, undefined)}, {<<"createdBefore">>, maps:get(<<"createdBefore">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"name">>, maps:get(<<"name">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"states">>, maps:get(<<"states">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Lists job templates based on a set of parameters. %% %% Job template stores values of StartJobRun API request in a template and %% can be used to start a job run. Job template allows two use cases: avoid %% repeating recurring StartJobRun API request values, enforcing certain %% values in StartJobRun API request. list_job_templates(Client) when is_map(Client) -> list_job_templates(Client, #{}, #{}). list_job_templates(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_job_templates(Client, QueryMap, HeadersMap, []). list_job_templates(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/jobtemplates"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"createdAfter">>, maps:get(<<"createdAfter">>, QueryMap, undefined)}, {<<"createdBefore">>, maps:get(<<"createdBefore">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Lists managed endpoints based on a set of parameters. %% %% A managed endpoint is a gateway that connects EMR Studio to Amazon EMR on %% EKS so that EMR Studio can communicate with your virtual cluster. list_managed_endpoints(Client, VirtualClusterId) when is_map(Client) -> list_managed_endpoints(Client, VirtualClusterId, #{}, #{}). list_managed_endpoints(Client, VirtualClusterId, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_managed_endpoints(Client, VirtualClusterId, QueryMap, HeadersMap, []). list_managed_endpoints(Client, VirtualClusterId, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/endpoints"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"createdAfter">>, maps:get(<<"createdAfter">>, QueryMap, undefined)}, {<<"createdBefore">>, maps:get(<<"createdBefore">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"states">>, maps:get(<<"states">>, QueryMap, undefined)}, {<<"types">>, maps:get(<<"types">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Lists the tags assigned to the resources. 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 = ["/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 Lists information about the specified virtual cluster. %% %% Virtual cluster is a managed entity on Amazon EMR on EKS. You can create, %% describe, list and delete virtual clusters. They do not consume any %% additional resource in your system. A single virtual cluster maps to a %% single Kubernetes namespace. Given this relationship, you can model %% virtual clusters the same way you model Kubernetes namespaces to meet your %% requirements. list_virtual_clusters(Client) when is_map(Client) -> list_virtual_clusters(Client, #{}, #{}). list_virtual_clusters(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_virtual_clusters(Client, QueryMap, HeadersMap, []). list_virtual_clusters(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/virtualclusters"], SuccessStatusCode = undefined, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"containerProviderId">>, maps:get(<<"containerProviderId">>, QueryMap, undefined)}, {<<"containerProviderType">>, maps:get(<<"containerProviderType">>, QueryMap, undefined)}, {<<"createdAfter">>, maps:get(<<"createdAfter">>, QueryMap, undefined)}, {<<"createdBefore">>, maps:get(<<"createdBefore">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)}, {<<"states">>, maps:get(<<"states">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Starts a job run. %% %% A job run is a unit of work, such as a Spark jar, PySpark script, or %% SparkSQL query, that you submit to Amazon EMR on EKS. start_job_run(Client, VirtualClusterId, Input) -> start_job_run(Client, VirtualClusterId, Input, []). start_job_run(Client, VirtualClusterId, Input0, Options0) -> Method = post, Path = ["/virtualclusters/", aws_util:encode_uri(VirtualClusterId), "/jobruns"], 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 Assigns tags to resources. %% %% A tag is a label that you assign to an AWS resource. Each tag consists of %% a key and an optional value, both of which you define. Tags enable you to %% categorize your AWS resources by attributes such as purpose, owner, or %% environment. When you have many resources of the same type, you can %% quickly identify a specific resource based on the tags you've assigned to %% it. For example, you can define a set of tags for your Amazon EMR on EKS %% clusters to help you track each cluster's owner and stack level. We %% recommend that you devise a consistent set of tag keys for each resource %% type. You can then search and filter the resources based on the tags that %% you add. tag_resource(Client, ResourceArn, Input) -> tag_resource(Client, ResourceArn, Input, []). tag_resource(Client, ResourceArn, Input0, Options0) -> Method = post, Path = ["/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 Removes tags from resources. untag_resource(Client, ResourceArn, Input) -> untag_resource(Client, ResourceArn, Input, []). untag_resource(Client, ResourceArn, Input0, Options0) -> Method = delete, Path = ["/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). %%==================================================================== %% 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 => <<"emr-containers">>}, Host = build_host(<<"emr-containers">>, 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 =:= 206; 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 =:= 206; 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).