Kafka monitoring is a Gateway configuration file that enables monitoring of Kafka Brokers through a set of samplers with customised JMX plug-in settings.
Kafka is a distributed streaming platform that allows you to:
- Publish and subscribe to stream of records.
- Store streams of records in a fault-tolerant way.
- Process streams of records as they occur.
It is important to monitor Kafka because it carries crucial data that many applications rely on. Geneos provides a JMX server sampler configuration to monitor Kafka.
This technical reference provides information on the metrics and dataviews for the samplers available through the Kafka integration. If you are setting up the Kafka integration for the first time, see Kafka Monitoring User Guide.
The JMX Server sampler configurations are used to monitor Kafka.
This provides the state of the Kafka broker:
|Version||Kafka binary version.|
|State||State of the Kafka broker.|
|Kafka Status||Manipulated base of the Kafka state value. The following are available:
|PartitionCount||Total number of partitions for all topics in the broker which is is usually even across all brokers.|
|LeaderCount||Leader Replica Count. The Leader is the node responsible for all reads and writes for the given partition. Each node will be the leader for a randomly selected portion of the partitions.|
|UnderReplicatedPartitions||Number of partitions under replicated per broker. Replicas are the list of nodes that replicate the log for this partition regardless of whether they are the leader or even if they are currently active.|
|ActiveControllerCount||The number of active controllers in the cluster. One of the brokers is elected as the controller for the whole cluster. It will be responsible for:
|OfflinePartitionsCount||The number of partitions that do not have an active leader and are hence not writable or readable.|
|PreferredReplicaImbalanceCount||The imbalance count in the preferred replica.|
|IsrExpand||If a broker goes down, the ISR for some partitions will shrink. When that broker is up again, the ISR will be expanded once the replicas are fully caught up. Other than that, the expected value for both the ISR shrink and expansion rates is 0.|
|IsrShrink||When a broker is brought up after a failure, it starts syncing by reading from the leader. Once synced, it gets added back to the ISR.|
ISR is the set of in-sync replicas. This is the subset of the replicas list that is currently alive and synced with the leader.
This provides all the metrics available for a topic in the broker:
|ID||Topic and matrix names.|
|Count / EventType / MeanRate / RateUnit||Attribute values.|
MBeans for Kafka-Topics
This shows the number of partitions that do not have an active leader and are hence not writable or readable per topic for the entire cluster.
|Name||Topic name and partition number.|
|UnderReplicatedPartition||Number of under replicated partitions.|
MBeans for Kafka-Cluster Metrics
|Committed||Amount of memory in bytes that is committed for the Java virtual machine to use.|
|UsageInit||Amount of memory in bytes that the Java virtual machine initially requests from the operating system for memory management.|
|UsageMax||Maximum amount of memory in bytes that can be used for memory management.|
|UsageUsed||Amount of used memory in bytes.|
|PercentageUsed||Percentage of maximum usable memory currently used.|
MBeans for Kafka-HeapMemoryUsage