Monitoring your application

Table of Contents

Metrics

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.
task-created-rate
The average number of newly created tasks per second.
task-closed-rate
The average number of tasks closed per second.
skipped-records-rate
The average number of skipped records per second. This metric helps monitor if the rate of record consumption and rate of record processing are equal or not. The difference should be negligible during normal operations.

Task Metrics

All the following metrics have a recording level of debug.

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

commit-latency-[avg | max]
The [average | maximum] commit time in ns for this task.
commit-rate
The average number of commit calls per second.

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.
forward-rate
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.
skippedDueToDeserializationError-rate
The average number of skipped records due to deserialization error per second. This metric is only registered for source nodes in the topology that are piping deserialized messages from Kafka topics and forwarding to downstream processors.

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

Important

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 {

    @Override
    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));
    }

    @Override
    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());

    }

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

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

Attention

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 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.

Note

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.