Monitoring

Monitoring

Kafka uses Yammer Metrics for metrics reporting in the server and Scala clients. The Java clients use Kafka Metrics, a built-in metrics registry that minimizes transitive dependencies pulled into client applications. Both expose metrics via JMX and can be configured to report stats using pluggable stats reporters to hook up to your monitoring system.

The easiest way to see the available metrics is to fire up jconsole and point it at a running kafka client or server; this will allow browsing all metrics with JMX.

of under replicated partitions (|ISR| < |all replicas|) | kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions | 0

Is controller active on broker | kafka.controller:type=KafkaController,name=ActiveControllerCount | only one broker in the cluster should have 1
Leader election rate | kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs | non-zero when there are broker failures
Unclean leader election rate | kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec | 0
Partition counts | kafka.server:type=ReplicaManager,name=PartitionCount | mostly even across brokers
Leader replica counts | kafka.server:type=ReplicaManager,name=LeaderCount | mostly even across brokers
ISR shrink rate | kafka.server:type=ReplicaManager,name=IsrShrinksPerSec | If a broker goes down, ISR for some of the partitions will shrink. When that broker is up again, ISR will be expanded once the replicas are fully caught up. Other than that, the expected value for both ISR shrink rate and expansion rate is 0.
ISR expansion rate | kafka.server:type=ReplicaManager,name=IsrExpandsPerSec | See above
Max lag in messages btw follower and leader replicas | kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica | lag should be proportional to the maximum batch size of a produce request.
Lag in messages per follower replica | kafka.server:type=FetcherLagMetrics,name=ConsumerLag,clientId=([-.\w]+),topic=([-.\w]+),partition=([0-9]+) | lag should be proportional to the maximum batch size of a produce request.
Requests waiting in the producer purgatory | kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Produce | non-zero if ack=-1 is used
Requests waiting in the fetch purgatory | kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Fetch | size depends on fetch.wait.max.ms in the consumer
Request total time | kafka.network:type=RequestMetrics,name=TotalTimeMs,request={Produce|FetchConsumer|FetchFollower} | broken into queue, local, remote and response send time
Time the request waits in the request queue | kafka.network:type=RequestMetrics,name=RequestQueueTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Time the request is processed at the leader | kafka.network:type=RequestMetrics,name=LocalTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Time the request waits for the follower | kafka.network:type=RequestMetrics,name=RemoteTimeMs,request={Produce|FetchConsumer|FetchFollower} | non-zero for produce requests when ack=-1
Time the request waits in the response queue | kafka.network:type=RequestMetrics,name=ResponseQueueTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Time to send the response | kafka.network:type=RequestMetrics,name=ResponseSendTimeMs,request={Produce|FetchConsumer|FetchFollower} |
Number of messages the consumer lags behind the producer by. Published by the consumer, not broker. | Old consumer: kafka.consumer:type=ConsumerFetcherManager,name=MaxLag,clientId=([-.\w]+) New consumer: kafka.consumer:type=consumer-fetch-manager-metrics,client-id={client-id} Attribute: records-lag-max |
The average fraction of time the network processors are idle | kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent | between 0 and 1, ideally > 0.3
The average fraction of time the request handler threads are idle | kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent | between 0 and 1, ideally > 0.3
Bandwidth quota metrics per (user, client-id), user or client-id | kafka.server:type={Produce|Fetch},user=([-.\w]+),client-id=([-.\w]+) | Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0. byte-rate indicates the data produce/consume rate of the client in bytes/sec. For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.
Request quota metrics per (user, client-id), user or client-id | kafka.server:type=Request,user=([-.\w]+),client-id=([-.\w]+) | Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0. request-time indicates the percentage of time spent in broker network and I/O threads to process requests from client group. For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.
Requests exempt from throttling | kafka.server:type=Request | exempt-throttle-time indicates the percentage of time spent in broker network and I/O threads to process requests that are exempt from throttling.

Common monitoring metrics for producer/consumer/connect/streams

Common Per-broker metrics for producer/consumer/connect/streams

Producer monitoring

New consumer monitoring

The following metrics are available on new consumer instances.

Consumer Group Metrics

Consumer Fetch Metrics

Streams Monitoring

A Kafka Streams instance contains all the producer and consumer metrics as well as additional metrics specific to streams. By default Kafka Streams has metrics with two recording levels: debug and info. The debug level records all metrics, while the info level records only the thread-level metrics.

Note that the metrics have a 3-layer hierarchy. At the top level there are per-thread metrics. Each thread has tasks, with their own metrics. Each task has a number of processor nodes, with their own metrics. Each task also has a number of state stores and record caches, all with their own metrics.

Use the following configuration option to specify which metrics you want collected:

metrics.recording.level="info"

Thread Metrics

Task Metrics

Processor Node Metrics

State Store Metrics

Record Cache Metrics

Others

We recommend monitoring GC time and other stats and various server stats such as CPU utilization, I/O service time, etc. On the client side, we recommend monitoring the message/byte rate (global and per topic), request rate/size/time, and on the consumer side, max lag in messages among all partitions and min fetch request rate. For a consumer to keep up, max lag needs to be less than a threshold and min fetch rate needs to be larger than 0.

Audit

The final alerting we do is on the correctness of the data delivery. We audit that every message that is sent is consumed by all consumers and measure the lag for this to occur. For important topics we alert if a certain completeness is not achieved in a certain time period. The details of this are discussed in KAFKA-260.