命名空间
Namespace = QCE/CKAFKA
监控指标
指标英文名 | 指标中文名 | 指标说明 | 单位 | 维度 | 统计规则
[period, statType] |
BConsumeLocalTime95thTime | 95th 消费本地耗时 | 消费本地耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeLocalTime999thTime | 999th 消费本地耗时 | 消费本地耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeRemoteTime95thTime | 95th 消费 ack=all 等待同步耗时 | 消费远程耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeRemoteTime999thTime | 999th 消费 ack=all 等待同步耗时 | 消费远程耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeRequestQueueTime95thTime | 95th 消费请求队列等待耗时 | 消费请求队列耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeRequestQueueTime999thTime | 999th 消费请求队列等待耗时 | 消费请求队列耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeResponseQueueTime95thTime | 95th 消费回包队列等待耗时 | 消费响应队列耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeResponseQueueTime999thTime | 999th 消费回包队列等待耗时 | 消费响应队列耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeThrottleTime95thTime | 95th 消费延时回包的耗时 | 消费延迟回包耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeThrottleTime999thTime | 999th 消费延时回包的耗时 | 消费延迟回包耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeTotalTime95thTime | 95th 消费总耗时 | 表示消费的总耗时，由请求队列耗时，本地耗时等指标汇总而成。注意，在每一个时间点，总耗时不会等于以上五个95th耗时的累加。原因是每个指标都是各自取平均得到的。故不相等 | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BConsumeTotalTime999thTime | 999th 消费总耗时 | 表示消费的总耗时，由请求队列耗时，本地耗时等指标汇总而成。注意，在每一个时间点，总耗时不会等于以上五个999th耗时的累加。原因是每个指标都是各自取平均得到的。故不相等 | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BIsrExpand | ISR 扩充次数 | Kafka ISR 扩充次数，即存在未同步副本的情况下，当未同步副本追上 leader 数据，会重新加入 ISR，此时该次数就会加1 | Count | broker_ip | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
BIsrShrink | ISR 缩小抖动次数 | Kafka ISR 收缩次数，即当出现 broker 宕机，Zookeeper 重连的情况，会出现 ISR 缩小的次数统计 | Count | broker_ip | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
BNetworkProcessorAvgIdlePercent | 网络繁忙程度 | 用于衡量实例当前网络线程处理能力的指标，越接近1越空闲 | % | broker_ip | [60s, first]
[300s, avg]
[3600s, avg]
[86400s, avg] |
BProduceLocalTime95thTime | 95th 生产本地耗时 | 生产本地耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceLocalTime999thTime | 999th 生产本地耗时 | 生产本地耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceRemoteTime95thTime | 95th 生产 ack=all 等待同步耗时 | 生产远程耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceRemoteTime999thTime | 999th 生产 ack=all 等待同步耗时 | 生产远程耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceRequestQueueTime95thTime | 95th 生产请求队列等待耗时 | 生产请求队列耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceRequestQueueTime999thTime | 999th 生产请求队列等待耗时 | 生产请求队列耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceResponseQueueTime95thTime | 95th 生产回包队列等待耗时 | 生产响应回包队列耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceResponseQueueTime999thTime | 999th 生产回包队列等待耗时 | 生产响应回包队列耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceThrottleTime95thTime | 95th 生产延时回包的耗时 | 生产延迟回包耗时95th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceThrottleTime999thTime | 999th 生产延时回包的耗时 | 生产延迟回包耗时999th | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceTotalTime95thTime | 95th 生产总耗时 | 表示生产请求的总耗时，由请求队列耗时，本地耗时，延时回包耗时等指标汇总而成。注意，在每一个时间点，总耗时不会等于以上五个耗时的累加。原因是每个指标都是各自取平均得到的。故不累加相等 | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BProduceTotalTime999thTime | 999th 生产总耗时 | 表示生产请求的总耗时，由请求队列耗时，本地耗时，延时回包耗时等指标汇总而成。注意，在每一个时间点，总耗时不会等于以上五个耗时的累加。原因是每个指标都是各自取平均得到的。故不累加相等 | ms | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BUnderAr | 低于 AR 分片数 | 集群中存在的未同步的副本个数，当实例存在未同步副本，就表示集群的健康度可能有问题 | None | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
BZookeeperDisConnectsCount | zk 断连次数 | Broker 和 Zookeeper 之间的长连接断开重连的次数。网络波动，集群负载较高有可能会引起连接断开&重连。发生时会发生 leader 切换。该值是一个累加值，Broker 启动后，断连一次加1，只有 Broker 重启才会置0 | None | broker_ip | [60s, first]
[300s, max]
[3600s, max]
[86400s, max] |
CgroupMaxOffset | 当前 partition 最大offset | 消费分组最大 offset | count | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
CpartitionConsumerSpeed | 消费速度/分钟 | 分区消费速度 | count/min | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, avg]
[3600s, avg]
[86400s, avg] |
CpartitionMaxOffset | 当前 partition 最大 offset | 分区最大 offset | count | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, 0] |
CpartitionOffset | 当前消费 offset | 分区当前消费 offset | count | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, 0] |
CpartitionUnconsume | 未消费的消息条数 | 分区当前未消费消息 | count | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, 0] |
CpuUsage | CPU 使用率 | CPU 使用率 | % | instanceid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
CtopicConCount | Topic 消费消息条数 | 消费者组消费速度是 Broker 通过 consume offset 统计的，而 Topic 或者实例的消费消息数是通过 Fetch 请求返回包统计的 | count | instanceId, topicId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
CtopicConFlow | Topic 消费流量 | Topic 消费流量（不包含副本产生的流量），按照所选择的时间粒度统计求和 | MB | instanceId, topicId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
CtopicConReqCount | Topic 级别消费请求次数 | Topic 级别消费请求次数，按照所选择的时间粒度统计求和 | count | instanceId, topicId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
CtopicConsumerSpeed | 消费速度/分钟 | 主题消费速度 | count/min | consumerGroup, instanceId, topicId, topicName | [60s, sum]
[300s, avg]
[3600s, avg]
[86400s, avg] |
CtopicMsgCount | Topic 落盘的消息总条数 | Topic 落盘的消息总条数（不包含副本），按照所选择的时间粒度取最新值 | count | instanceId, topicId | [60s, sum]
[300s, last]
[3600s, last]
[86400s, last] |
CtopicMsgHeap | Topic 占用磁盘的消息总量 | Topic 占用磁盘的消息总量（不包含副本），按照所选择的时间粒度取最新值 | MB | instanceId, topicId | [60s, sum]
[300s, last]
[3600s, last]
[86400s, last] |
CtopicMsgOffset | 当前消费 offset | 主题级别消费分组 offset | count | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
CtopicProCount | Topic 生产消息条数 | Topic 生产消息条数，按照所选择的时间粒度统计求和 | count | instanceId, topicId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
CtopicProFlow | Topic 生产流量 | Topic 生产流量（不包含副本产生的流量），按照所选择的时间粒度统计求和 | MB | instanceId, topicId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
CtopicProReqCount | Topic 级别生产请求次数 | Topic 级别生产请求次数，按照所选择的时间粒度统计求和 | count | instanceId, topicId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
CtopicUnconsumeMsgCount | 未消费的消息条数 | 主题级别未消费消息个数 | count | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
CtopicUnconsumeMsgOffset | 未消费消息堆积量 | 主题级别未消费消息 offset | MB | consumerGroup, instanceId, partition, topicId, topicName | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
DetectStatus | Broker 探测状态 | Broker 实例状态 | None | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
DiskUsage | 磁盘使用率 | 每台 CVM 上的硬盘实际使用情况。由于挂载 HDD/SSD 硬盘物理限制，且需要预留额外空间应对突发情况等原因，所有主机硬盘使用率之和与 CKafka 实例磁盘使用率不一定一致 | % | instanceid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceConCount | Topic 消费消息条数 | 实例消费消息条数，按照所选择的时间粒度统计求和 | count | instanceId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceConFlow | Topic 消费流量 | 实例消费流量（不包含副本产生的流量），按照所选择的时间粒度统计求和 | MB | instanceId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceConnectCount | 实例连接数 | 客户端和服务器的连接数 | count | instanceId | [60s, first]
[300s, last] |
InstanceConnectPercentage | 连接数百分比 | 实例连接数百分比(客户端和服务端连接数占配额百分比) | % | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceConReqCount | Topic 级别消费请求次数 | 实例级别消费请求次数，按照所选择的时间粒度统计求和 | count | instanceId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceConsumeBandwidthPercentage | 消费带宽百分比 | 实例消费带宽百分比（实例消费带宽占配额百分比） | % | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceConsumeGroupNum | 消费分组数 | 实例消费分组数量 | None | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceConsumeGroupPercentage | 消费分组百分比 | 实例消费分组百分比（实例消费组数占配额百分比） | % | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceConsumeThrottle | 消费限流次数 | 实例消费限流次数 | Count | instanceId | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceDiskUsage | 磁盘使用百分比 | 当前磁盘占用与实例规格磁盘总容量的百分比 | % | instanceId | [60s, expr]
[300s, max] |
InstanceMaxConFlow | 最大消费流量 | 实例消费消息峰值带宽（消费时无副本的概念） | MBytes/s | instanceId | [60s, first]
[300s, max] |
InstanceMaxProFlow | 实例最大生产流量（不含副本） | 实例生产消息峰值带宽（不包含副本生产的带宽） | MBytes/s | instanceId | [60s, first]
[300s, max] |
InstanceMsgCount | Topic 落盘的消息总条数 | 实例落盘的消息总条数（不包含副本），按照所选择的时间粒度取最新值 | count | instanceId | [60s, sum]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceMsgHeap | 实例消息堆积量 | 实例磁盘占用量（包含副本），按照所选择的时间粒度取最新值 | MB | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstancePartitionNum | partition 数量 | 实例 partition 数量 | None | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstancePartitionPercentage | partition 百分比 | 实例 partition 百分比（占用配额百分比） | % | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceProCount | Topic 生产消息条数 | 实例生产消息条数，按照所选择的时间粒度统计求和 | count | instanceId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceProduceBandwidthPercentage | 生产带宽百分比 | 实例生产带宽百分比（占用配额百分比） | % | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceProduceThrottle | 生产限流次数 | 实例生产限流次数 | Count | instanceId | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceProFlow | Topic 生产流量 | 实例生产流量（不包含副本产生的流量），按照所选择的时间粒度统计求和 | MB | instanceId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceProReqCount | Topic 级别生产请求次数 | 实例级别生产请求次数，按照所选择的时间粒度统计求和 | count | instanceId | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceReplicaProduceFlow | 生产全流量（包含副本流量） | 实例生产消息峰值带宽（包含副本生产的带宽） | MBytes/s | instanceId | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
InstanceTopicNum | Topic 数量 | 实例 Topic 数量 | None | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
InstanceTopicPercentage | Topic 百分比 | 实例 Topic 百分比(占用配额) | % | instanceId | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
Intraffic | 公网入带宽 | 公网入带宽 | Bit/s | instanceid | [60s, avg]
[300s, avg]
[3600s, avg]
[86400s, avg] |
LanIntraffic | 内网入带宽 | 内网入带宽。按照所选择的时间粒度统计求 | MBytes | instanceid | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
LanOuttraffic | 内网出带宽 | 内网出带宽。按照所选择的时间粒度统计求和 | MBytes | instanceid | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
LastOldGcCount | Broker Full GC 的次数 | Broker Full GC 的次数 | Count | brokerip | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
LastYoungGcCount | Broker Yong GC 的次数 | Broker Yong GC 的次数 | Count | brokerip | [60s, first]
[300s, sum]
[3600s, sum]
[86400s, sum] |
MaxOffsetTopic | 当前 partition 最大 offset | 消费分组对应当前 Topic 最大 offset | count | consumerGroup, instanceId, topicId, topicName | [60s, max]
[300s, last]
[3600s, last]
[86400s, last] |
MemUsage | 内存利用率 | 内存利用率 | % | instanceid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
OffsetTopic | 当前消费 offset | 消费分组当前消费 offset | count | consumerGroup, instanceId, topicId, topicName | [60s, max]
[300s, max]
[3600s, max]
[86400s, max] |
Outtraffic | 公网出带宽 | 公网出带宽 | Bit/s | instanceid | [60s, avg]
[300s, avg]
[3600s, avg]
[86400s, avg] |
PartitionConCount | Topic 消费消息条数 | Partition 消费消息条数，按照所选择的时间粒度统计求和 | Count | instanceid, partition, topicid | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
PartitionConFlow | Topic 消费流量 | Partition 消费流量（不包含副本产生的流量），按照所选择的时间粒度统计求和 | MBytes | instanceid, partition, topicid | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
PartitionMsgCount | Topic 落盘的消息总条数 | Partition 落盘的消息总条数（不包含副本），按照所选择的时间粒度取最新值 | Count | instanceid, partition, topicid | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
PartitionMsgHeap | Topic 占用磁盘的消息总量 | Partition 占用磁盘的消息总量（不包含副本），按照所选择的时间粒度取最新值 | MBytes | instanceid, partition, topicid | [60s, sum]
[300s, max]
[3600s, max]
[86400s, max] |
PartitionProCount | Topic 生产消息条数 | Partition 生产消息条数，按照所选择的时间粒度统计求和 | Count | instanceid, partition, topicid | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
PartitionProFlow | Topic 生产流量 | Partition 生产流量（不包含副本产生的流量），按照所选择的时间粒度统计求和 | MBytes | instanceid, partition, topicid | [60s, sum]
[300s, sum]
[3600s, sum]
[86400s, sum] |
QueueSize | 队列深度 | 请求队列深度反映当前未处理的生产请求个数，如果该值过大可能是同一时间请求量过大，CPU 负载过高或者磁盘 IO 出现瓶颈 | None | broker_ip | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SetBatchSizeAvg | 批次平均数据大小 | 每批平均处理数据记录数 | Count | setid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SetBatchSizeMax | 批次最大数据大小 | 批次最大处理数据记录数 | Count | setid | [60s, first]
[300s, max]
[3600s, max]
[86400s, max] |
SetMaxLag | 还未从源拉取到的条数（set 聚合） | 未从源拉取到的记录数 | Count | setid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SetPollBatchAvgTimeMs | 批次拉取的平均耗时 | 每批平均处理数据耗时 | ms | setid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SetPollBatchMaxTimeMs | 批次最大拉取耗时 | 批次拉取最大耗时 | ms | setid | [60s, max]
[300s, max]
[3600s, max]
[86400s, max] |
SetRecordSendTotal | 当有消息正同步时，累计写入的记录数（set 聚合） | 已写入目标的记录数 | Count | setid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SetRecordsLead | 已从源拉取的条数（set 聚合） | 已从源拉取到的记录数 | Count | setid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SetSourceRecordActiveCount | 此任务已生成但尚未完全写入 Kafka 的记录数（set 聚合） | 未写入的目标的记录数 | Count | setid | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SourceRecordPollTotal | 指定源连接器的任务生成/轮询（转换前）的记录总数 | 已拉取源数据的条数 | Count | connectorname, task | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
SourceRecordWriteTotal | 自该任务上次重新启动以来从转换输出并为此任务写入 Kafka 的记录数 | 源数据已写入的条数 | Count | connectorname, task | [60s, first]
[300s, last]
[3600s, last]
[86400s, last] |
TMaxConsumeFlow | Topic 最大消费流量 | Topic 最大消费流量 | MBytes/s | instanceId, topicId | [60s, first]
[300s, max]
[3600s, max]
[86400s, max] |
TMaxProduceFlow | 最大生产流量 | Topic 最大生产流量(不含副本流量) | MBytes/s | instanceId, topicId | [60s, first]
[300s, max]
[3600s, max]
[86400s, max] |
TTopicConsumeThrottle | Topic 消费限流次数 | Topic 消费限流次数 | Count/s | instanceId, topicId | [60s, avg]
[300s, avg]
[3600s, avg]
[86400s, avg] |
TTopicProduceThrottle | Topic 生产限流次数 | Topic 生产限流次数 | Count/s | instanceId, topicId | [60s, avg]
[300s, avg]
[3600s, avg]
[86400s, avg] |
UnconsumeSizeTopic | 未消费消息堆积量 | 消费分组未消费消息大小 | MB | consumerGroup, instanceId, topicId, topicName | [60s, sum]
[300s, last]
[3600s, last]
[86400s, last] |
UnconsumeTopic | 未消费的消息条数 | 消费分组未消费消息数 | count | consumerGroup, instanceId, topicId, topicName | [60s, sum]
[300s, last]
[3600s, last]
[86400s, last] |
各维度对应参数总览
参数名称 | 维度名称 | 维度解释 | 格式 |
Instances.N.Dimensions.0.Name | instanceId | ckafka 实例 ID 的维度名称 | 输入 String 类型维度名称：instanceId |
Instances.N.Dimensions.0.Value | instanceId | ckafka 具体实例的 ID | 输入实例具体 ID，例如：ckafka-test |
Instances.N.Dimensions.1.Name | instanceid | 专业版 ckafka 实例下的维度名称 | 输入 String 类型维度名称：instanceid |
Instances.N.Dimensions.1.Value | instanceid | 专业版 ckafka 实例下的broker ip | 输入实例具体 ID，例如：brokerip |
Instances.N.Dimensions.2.Name | topicId | 实例所在主题 ID 的维度名称 | 输入 String 类型维度名称：topicId |
Instances.N.Dimensions.2.Value | topicId | 实例所在主题的具体主题 ID | 输入主题具体 ID，例如：topic-test |
Instances.N.Dimensions.3.Name | consumerGroup | 消费分组的维度名称 | 输入 String 类型维度名称：consumerGroup |
Instances.N.Dimensions.3.Value | consumerGroup | 具体消费分组信息 | 输入用户需要查看的消费分组信息，例如：perf-consumer-8330 |
Instances.N.Dimensions.4.Name | partition | partition 的维度名称 | 输入 String 类型维度名称：partition |
Instances.N.Dimensions.4.Value | partition | 具体 partition 信息 | 输入 topic 分区信息，例如：0 |
Instances.N.Dimensions.5.Name | topicName | 主题的维度名称 | 输入 String 类型维度名称：topicName |
Instances.N.Dimensions.5.Value | topicName | 具体主题名称 | 输入用户消费主题的名称，例如：test |
Instances.N.Dimensions.6.Name | broker_ip | 主题 ID 的维度名称 | 输入 String 类型维度名称：broker_ip |
Instances.N.Dimensions.6.Value | broker_ip | 具体主题 ID | 输入用户要查看的主题 ID，例如：inter-topic-hwz9 |
入参说明
查询 QCE/CKAFKA 监控数据，入参取值如下：
&Namespace=QCE/CKAFKA
&Instances.N.Dimensions.0.Name=instanceId
&Instances.N.Dimensions.0.Value=实例 ID
&Instances.N.Dimensions.1.Name=instanceid
&Instances.N.Dimensions.1.Value=实例 ID
&Instances.N.Dimensions.2.Name=topicId
&Instances.N.Dimensions.2.Value=主题 ID
&Instances.N.Dimensions.3.Name=consumerGroup
&Instances.N.Dimensions.3.Value=消费分组
&Instances.N.Dimensions.4.Name=partition
&Instances.N.Dimensions.4.Value=分区
&Instances.N.Dimensions.5.Name=topicName
&Instances.N.Dimensions.5.Value=主题名称
&Instances.N.Dimensions.6.Name=broker_ip
&Instances.N.Dimensions.6.Value=主题 ID
说明：
不同监控指标维度不同，请根据对应指标维度填写入参。
