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Monitoring Streams Applications


Accessing Metrics

Accessing Metrics via JMX and Reporters

The Kafka Streams library reports a variety of metrics through JMX. It can also be configured to report stats using additional pluggable stats reporters using the metrics.reporters configuration option. The easiest way to view the available metrics is through tools such as JConsole, which allow you to browse JMX MBeans.

Accessing Metrics Programmatically

The entire metrics registry of a KafkaStreams instance can be accessed read-only through the method KafkaStreams#metrics(). The metrics registry will contain all the available metrics listed below. See the documentation of KafkaStreams in the Kafka Streams Javadocs for details.

Configuring Metrics Granularity

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 some of them. Use the metrics.recording.level configuration option to specify which metrics you want collected, see Optional configuration parameters.

Built-in Metrics

Thread Metrics

All the following metrics have a recording level of info.

MBean: kafka.streams:type=stream-metrics,thread.client-id=[threadId]

[commit | poll | process | punctuate]-latency-[avg | max]
The [average | maximum] execution time in ms, for the respective operation, across all running tasks of this thread.
[commit | poll | process | punctuate]-rate
The average number of respective operations per second across all tasks.
The average number of newly created tasks per second.
The average number of tasks closed per second.
The total number of skipped records. Malformed records are skipped for a number of reasons, depending on your configuration. In addition to incrementing this metric, Streams logs a warning for each skip, so you should check the logs to track down the reason for unexpected skips.
The average number of skipped records per second.

Task Metrics

MBean: kafka.streams:type=stream-task-metrics,client-id=[threadId],task-id=[taskId]

commit-latency-[avg | max]
(debug) The [average | maximum] commit time in ns for this task.
(debug) The average number of commit calls per second.
record-lateness-[avg | max]
(info) The [average | maximum] observed lateness (stream time - record timestamp).

Processor Node Metrics

All the following metrics have a recording level of debug.

MBean: kafka.streams:type=stream-processor-node-metrics,client-id=[threadId],task-id=[taskId],processor-node-id=[processorNodeId]

[process | punctuate | create | destroy]-latency-[avg | max]
The [average | maximum] execution time in ns, for the respective operation.
[process | punctuate | create | destroy]-rate
The average number of respective operations per second.
The average rate of records being forwarded downstream, from source nodes only, per second. This metric can be used to understand how fast the library is consuming from source topics.

State Store Metrics

All the following metrics have a recording level of debug.

MBean: kafka.streams:type=stream-[storeType]-state-metrics,client-id=[threadId],task-id=[taskId],[storeType]-state-id=[storeName]

[put | put-if-absent | get | delete | put-all | all | range | flush | restore]-latency-[avg | max]
The average execution time in ns, for the respective operation.
[put | put-if-absent | get | delete | put-all | all | range | flush | restore]-rate
The average rate of respective operations per second for this store.

Record Cache Metrics

All the following metrics have a recording level of debug.

MBean: kafka.streams:type=stream-record-cache-metrics,client-id=[threadId],task-id=[taskId],record-cache-id=[storeName]

hitRatio-[avg | min | max]
The [average | minimum | maximum] cache hit ratio defined as the ratio of cache read hits over the total cache read requests.

Adding Your Own Metrics

Application developers using the low-level Processor API can add additional metrics to their application. The ProcessorContext#metrics() method provides a handle to the StreamMetrics object, which you can use to:

  • Add latency and throughput metrics via StreamMetrics#addLatencyAndThroughputSensor and StreamMetrics#addThroughputSensor().
  • Add any other type of metric via StreamMetrics#addSensor().

Run-time Status Information

Status of KafkaStreams instances


Don’t confuse the run-time state of a KafkaStreams instance (e.g. created, rebalancing) with state stores!

A Kafka Streams instance may be in one of several run-time states, as defined in the enum KafkaStreams.State. For example, it might be created but not running; or it might be rebalancing and thus its state stores are not available for querying. Users can access the current run-time state programmatically using the method KafkaStreams#state(). The documentation of KafkaStreams.State in the Kafka Streams Javadocs lists all the available states.

Also, you can use KafkaStreams#setStateListener() to register a KafkaStreams#StateListener method that will be triggered whenever the state changes.

Use the KafkaStreams#localThreadsMetadata() method to check the runtime state of the current KafkaStreams instance. The localThreadsMetadata() method returns a ThreadMetadata object for each local stream thread. The ThreadMetadata object describes the runtime state of a thread and the metadata for the thread’s currently assigned tasks.

Monitoring the Restoration Progress of Fault-tolerant State Stores

When starting up your application any fault-tolerant state stores don’t need a restoration process as the persisted state is read from local disk. But there could be situations when a full restore from the backing changelog topic is required (e.g., a failure wiped out the local state or your application runs in a stateless environment and persisted data is lost on re-starts).

If you have a significant amount of data in the changelog topic, the restoration process could take a non-negligible amount of time. Given that processing of new data won’t start until the restoration process is completed, having a window into the progress of restoration is useful.

In order to observe the restoration of all state stores you provide your application an instance of the org.apache.kafka.streams.processor.StateRestoreListener interface. You set the org.apache.kafka.streams.processor.StateRestoreListener by calling the KafkaStreams#setGlobalStateRestoreListener method.

A basic implementation example that prints restoration status to the console:

import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.streams.processor.StateRestoreListener;

 public class ConsoleGlobalRestoreListerner implements StateRestoreListener {

    public void onRestoreStart(final TopicPartition topicPartition,
                               final String storeName,
                               final long startingOffset,
                               final long endingOffset) {

        System.out.print("Started restoration of " + storeName + " partition " + topicPartition.partition());
        System.out.println(" total records to be restored " + (endingOffset - startingOffset));

    public void onBatchRestored(final TopicPartition topicPartition,
                                final String storeName,
                                final long batchEndOffset,
                                final long numRestored) {

        System.out.println("Restored batch " + numRestored + " for " + storeName + " partition " + topicPartition.partition());


    public void onRestoreEnd(final TopicPartition topicPartition,
                             final String storeName,
                             final long totalRestored) {

        System.out.println("Restoration complete for " + storeName + " partition " + topicPartition.partition());


The StateRestoreListener instance is shared across all org.apache.kafka.streams.processor.internals.StreamThread instances and also used for global stores. Furthermore, it is assumed all methods are stateless. If any stateful operations are desired, then the user will need to provide synchronization internally

Integration with Confluent Control Center

Since the 3.2 release, Confluent Control Center will display the underlying producer metrics and consumer metrics of a Kafka Streams application, which the Streams API uses internally whenever data needs to be read from or written to Apache Kafka® topics. These metrics can be used, for example, to monitor the so-called “consumer lag” of an application, which indicates whether an application – at its current capacity and available computing resources – is able to keep up with the incoming data volume.

A Kafka Streams application, i.e. all its running instances, appear as a single consumer group in Control Center.


Restore consumers of an application are displayed separately: Behind the scenes, the Streams API uses a dedicated “restore” consumer for the purposes of fault tolerance and state management. This restore consumer manually assigns and manages the topic partitions it consumes from and is not a member of the application’s consumer group. As a result, the restore consumers will be displayed separately from their application.