一、Yarn
以获取Yarn界面队列信息为例：
1. 接口（HTTP Request）
http://ip:port/ws/v1/cluster/...
ip和port：Yarn ResourceManager active节点的ip地址和端口号
2. 申请形式
GET
3. Response Header
HTTP/1.1 200 OK
Content-Type: application/json
Transfer-Encoding: chunked
Server: Jetty(6.1.26)4. Response Body
Yarn web ui显示的队列信息：
申请http://bigdatalearnshare01:80...：
{
"scheduler":{ "schedulerInfo":{ "type":"capacityScheduler", -- 调度器类型 "capacity":100, "usedCapacity":14.583333, "maxCapacity":100, "queueName":"root", -- root为根队列 "queues":{ "queue":[ { "type":"capacitySchedulerLeafQueueInfo", "capacity":20, -- 调配的容量（占整个队列的百分比） "usedCapacity":20.83418, -- 应用队列容量（占以后队列的百分比） "maxCapacity":20, "absoluteCapacity":20, "absoluteMaxCapacity":20, "absoluteUsedCapacity":4.166667, "numApplications":1, "queueName":"default", -- 队列名字 "state":"RUNNING", -- 运行状态 "resourcesUsed":{"memory":2048,"vCores":2}, "hideReservationQueues":false, "nodeLabels":["*"], "allocatedContainers":2, "reservedContainers":0, "pendingContainers":0, "capacities":{ "queueCapacitiesByPartition":[ {"partitionName":"","capacity":20,"usedCapacity":20.83418,"maxCapacity":20, "absoluteCapacity":20,"absoluteUsedCapacity":4.166667, "absoluteMaxCapacity":20,"maxAMLimitPercentage":50} ] }, "resources":{ "resourceUsagesByPartition":[ { "partitionName":"", "used":{"memory":2048,"vCores":2}, "reserved":{"memory":0,"vCores":0}, "pending":{"memory":0,"vCores":0}, "amUsed":{"memory":1024,"vCores":1}, "amLimit":{"memory":5120,"vCores":1} } ] }, "numActiveApplications":1, "numPendingApplications":0, "numContainers":2, "maxApplications":2000, "maxApplicationsPerUser":2000, "userLimit":100, "users":{ "user":[ { "username":"bigdatalearnshare", "resourcesUsed":{ "memory":2048, "vCores":2 }, "numPendingApplications":0, "numActiveApplications":1, "AMResourceUsed":{ "memory":1024, "vCores":1 }, "userResourceLimit":{ "memory":10240, "vCores":1 },....
....
二、Spark UI
以获取Spark UI界面executors指标信息为例：
以bigdatalearnshare01:8088的Yarn上的Spark利用大数据培训实例为例，对应的Spark UI界面Executors次要信息如下：
Spark提供了很多接口去获取这些信息，比方：