如何使用fabric8 kubernetes Java&;Scala客户端API

如何使用fabric8 kubernetes Java&;Scala客户端API,java,scala,client,kubernetes,fabric8,Java,Scala,Client,Kubernetes,Fabric8,fabric8 kubernetes Java&Scala客户端API非常适合与kubernetes(或OpenShift)交谈,但其文档非常稀少。向kubernetes吊舱中运行的容器添加资源需求的代码示例是什么?如果您使用的是fabric8 kubernetes客户端API for Java和Scala,下面是一段代码,演示如何向吊舱中运行的容器添加资源需求。此代码是从Scala复制的,但Java代码非常类似: // other fabric8 imports not included; j

fabric8 kubernetes Java&Scala客户端API非常适合与kubernetes(或OpenShift)交谈,但其文档非常稀少。向kubernetes吊舱中运行的容器添加资源需求的代码示例是什么?

如果您使用的是fabric8 kubernetes客户端API for Java和Scala,下面是一段代码,演示如何向吊舱中运行的容器添加资源需求。此代码是从Scala复制的,但Java代码非常类似:

// other fabric8 imports not included; just focusing on resource
// requirements logic in this example
import io.fabric8.kubernetes.api.model.Quantity
import io.fabric8.kubernetes.api.model.ResourceRequirementsBuilder

// Use Java style Map (as opposed to Scala's Map class)
val reqMap: java.util.Map[String, Quantity] =
  new java.util.HashMap[String, Quantity]()

// add CPU and memory requirements to the map
reqMap.put("cpu", new Quantity("1"))
reqMap.put("memory", new Quantity("1500Mi"))

// Build a ResourceRequirements object from the map
val reqs = new ResourceRequirementsBuilder()
  .withRequests(reqMap)
  .build()

// pass the ResourceRequirements object to the container spec
val pod = new PodBuilder()
  .withNewMetadata()
  .withName(podName)
  .endMetadata()
  .withNewSpec()
  .withRestartPolicy("OnFailure")
  .addNewContainer()
  .withName(containerName)
  .withImage(containerImage)
  .withImagePullPolicy("Always")
  .withResources(reqs)            // <-- resource reqs here
  .withCommand(commandName)
  .withArgs(commandArguments)
  .endContainer()
  .endSpec()
  .build()

// create the new pod with resource requirements via the 
// fabric8 kube client:
client.pods().inNamespace(nameSpace).withName(podName).create(pod)
//不包括其他fabric8导入;只关注资源
//本例中的需求逻辑
导入io.fabric8.kubernetes.api.model.Quantity
导入io.fabric8.kubernetes.api.model.ResourceRequirementsBuilder
//使用Java风格的映射(与Scala的映射类相反)
val reqMap:java.util.Map[字符串,数量]=
新的java.util.HashMap[String,Quantity]()
//将CPU和内存需求添加到映射中
需求映射放置(“cpu”,新数量(“1”))
请求映射放置(“内存”,新数量(“1500Mi”))
//从映射构建ResourceRequirements对象
val reqs=新资源需求构建器()
.withRequests(reqMap)
.build()
//将ResourceRequirements对象传递给容器规范
val pod=new PodBuilder()
.withNewMetadata()
.withName(podName)
.endMetadata()
.withNewSpec()
.withRestartPolicy(“OnFailure”)
.addNewContainer()
.withName(容器名称)
.withImage(集装箱图像)
.withImagePullPolicy(“始终”)
.withResources(reqs)/
if(EmptyUtil.isNotNull(template.getComputerRequest())||
EmptyUtil.isNotNull(template.getComputeLimit())){
withResources(buildResources(template.getComputerRequest(),template.getComputeLimit());
}
私有资源需求构建资源(InstanceType请求、InstanceType限制){
_logger.info(“建筑计算机资源”);
ResourceRequirementsBuilder requirementsBuilder=新的ResourceRequirementsBuilder(isValidationEnabled);
if(EmptyUtil.isNotNull(请求)){
requirementsBuilder.withRequests(k8computersourceutil.buildCompute(request));
}
if(EmptyUtil.isNotNull(限制)){
要求带限制的构建器(K8ComputerSourceUtil.buildCompute(限制));
}
返回要求builder.build();
}
公共静态映射buildCompute(InstanceType compute){
Map ComputerSourceMap=新HashMap();
if(EmptyUtil.isNotNull(compute.getCpu())){
computersourcemap.putAll(buildCpu(compute.getCpu());
}
if(EmptyUtil.isNotNull(compute.getMemory())){
computersourcemap.putAll(buildMemory(compute.getMemory());
}
返回计算机资源映射;
}
专用静态映射构建cpu(浮点cpu){
Map cpuMap=newhashmap();
试一试{
数量cpuQuantity=新数量();
if(EmptyUtil.isNotNull(cpu)){
cpuQuantity.setAmount(字符串.valueOf(cpu));
cpuMap.put(K8Constants.CPU,cpuQuantity);
}
}捕获(NumberFormatException nfe){
_logger.error(“未能转换cpu'{}',cpu,nfe);
}
返回cpuMap;
}
专用静态映射构建内存(整数内存){
Map cpuMap=newhashmap();
试一试{
数量cpu=新数量();
if(EmptyUtil.isNotNull(内存)){
setAmount(String.valueOf(memory));
cpuMap.put(K8Constants.MEMORY,cpu);
}
}捕获(NumberFormatException nfe){
_logger.error(“无法转换内存'{}',内存,nfe”);
}
返回cpuMap;
}
这里我有一些构建器来构建cpu和内存。 这只是为了了解流程。 您可以以整数或字符串形式给出cpu/内存值。

这很有用:)我通常发现,在使用fabric8的fluent builder时,以下内容以及YAML数据类型(list/map/…)都很有用。
    if(EmptyUtil.isNotNull(template.getComputeRequest()) ||
            EmptyUtil.isNotNull(template.getComputeLimit())) {
        containerBuilder.withResources(buildResources(template.getComputeRequest(), template.getComputeLimit()));
    }


private ResourceRequirements buildResources(InstanceType request, InstanceType limit){
    _logger.info("Building computeResources");
    ResourceRequirementsBuilder requirementsBuilder = new ResourceRequirementsBuilder(isValidationEnabled);

    if(EmptyUtil.isNotNull(request)){
        requirementsBuilder.withRequests(K8ComputeResourceUtil.buildCompute(request));
    }

    if(EmptyUtil.isNotNull(limit)){
        requirementsBuilder.withLimits(K8ComputeResourceUtil.buildCompute(limit));
    }

    return requirementsBuilder.build();
}



public static Map<String, Quantity> buildCompute(InstanceType compute){

    Map<String, Quantity> computeResourceMap = new HashMap<>();

    if(EmptyUtil.isNotNull(compute.getCpu())) {
        computeResourceMap.putAll(buildCpu(compute.getCpu()));
    }

    if(EmptyUtil.isNotNull(compute.getMemory())) {
        computeResourceMap.putAll(buildMemory(compute.getMemory()));
    }

    return computeResourceMap;
}

private static Map<String, Quantity> buildCpu(Float cpu){
    Map<String, Quantity> cpuMap = new HashMap<>();
    try {
        Quantity cpuQuantity = new Quantity();
        if (EmptyUtil.isNotNull(cpu)) {
            cpuQuantity.setAmount(String.valueOf(cpu));
            cpuMap.put(K8Constants.CPU, cpuQuantity);
        }
    } catch (NumberFormatException nfe){
        _logger.error("Failed to convert cpu '{}'", cpu, nfe);
    }

    return cpuMap;
}

private static Map<String, Quantity> buildMemory(Integer memory){
    Map<String, Quantity> cpuMap = new HashMap<>();
    try {
        Quantity cpu = new Quantity();
        if (EmptyUtil.isNotNull(memory)) {
            cpu.setAmount(String.valueOf(memory));
            cpuMap.put(K8Constants.MEMORY, cpu);
        }
    } catch (NumberFormatException nfe){
        _logger.error("Failed to convert memory '{}'", memory, nfe);
    }

    return cpuMap;
}