Monitor Kafka Streams Applications in Confluent Platform

Apache Kafka® reports a variety of metrics through JMX. You can configure your Kafka Streams applications to report stats using pluggable reporter configuration settings.

Metrics

Access metrics

Access metrics using 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.

For all Apache Kafka® metrics, see Monitoring Kafka with JMX. For Cluster Linking metrics, see Monitor Cluster Metrics and Optimize Links for Cluster Linking on Confluent Platform.

Access metrics programmatically

The entire metrics registry of a Kafka Streams instance can be accessed read-only through the method KafkaStreams#metrics(). The metrics registry contains all of the available metrics listed below. For more information, see Kafka Streams Javadocs.

The metrics for Kafka Streams have a four-level hierarchy:

  • At the top level, there are client-level metrics for each running Kafka Streams client.
  • Each client has stream threads with their own metrics.
  • Each stream thread has tasks with their own metrics.
  • Each task has a number of processor nodes with their own metrics. Also, each task has a number of state stores and record caches, all of which have their own metrics.

Configure metrics granularity

By default, Kafka Streams has metrics with three recording levels: info, debug, and trace. The debug level records most metrics, while the info level records only some of them. The trace level records all possible metrics.

Use the metrics.recording.level configuration option to specify which metrics you want collected, for example:

metrics.recording.level="info"

For more information, see Optional configuration parameters.

Built-in metrics

Client metrics

All of the following metrics have a recording level of info.

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

version
The version of the Kafka Streams client.
commit-id
The version control commit ID of the Kafka Streams client.
application-id
The application ID of the Kafka Streams client.
topology-description
The description of the topology executed in the Kafka Streams client.
state
The state of the Kafka Streams client.
alive-stream-threads
The current number of alive stream threads that are running or participating in rebalance.
failed-stream-threads
The number of failed stream threads since the start of the Kafka Streams client.

Thread metrics

All of the following metrics have a recording level of info.

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

blocked-time-ns-total

The total time the Kafka Streams thread spent blocked on Kafka since it was started, in nanoseconds (ns).

You can sample this metric periodically and use the difference between samples to measure time blocked during an interval, which is useful for debugging Kafka Streams application performance, because it gives the proportion of time the application was blocked on Kafka, versus processing messages.

[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.
[commit | poll | process | punctuate]-total
The total number of respective operations across all tasks.
[commit | poll | process | punctuate]-ratio
The fraction of time the thread spent on the respective operations for active tasks.
poll-records-[avg | max]
The [average | maximum] number of records polled from consumer within an iteration.
process-records-[avg | max]
The [average | maximum] number of records processed within an iteration.
task-created-rate
The average number of newly-created tasks per second.
task-created-total
The total number of newly-created tasks.
task-closed-rate
The average number of tasks closed per second.
task-closed-total
The total number of tasks closed.
thread-start-time
The epoch time when the Kafka Streams thread was started, which is useful for computing the processing ratio during the first interval after the thread starts.

Task metrics

All of the following metrics have a recording level of debug, except for the dropped-records-*, active-process-ratio, and record-e2e-latency-* metrics, which have a recording level info.

All latency metrics are reported in nanoseconds (ns).

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

commit-latency-[avg | max]
The [average | maximum] execution time in nanoseconds (ns), for committing.
commit-rate
The average number of commit calls per second.
commit-total
The total number of commit calls.
process-latency-[avg | max]
The [average | maximum] execution time, in nanoseconds (ns), for the respective operation for this task.
process-rate
The average number of respective operations per second for this task.
process-total
The total number of respective operations for this task.
record-lateness-[avg | max]
The [average | maximum] observed lateness (stream time - record timestamp) for this task. For more information on out-of-order records, see Out-of-Order Handling.
enforced-processing-rate
The average number of enforced processings per second for this task.
enforced-processing-total
The total number of enforced processings for this task.
dropped-records-rate
The average number of records dropped within this task.
dropped-records-total
The total number of records dropped within this task.
active-process-ratio
The fraction of time the thread spent on processing this active task among all assigned active tasks.

Processor node metrics

The following metrics are available only on certain types of nodes.

  • The process-rate and process-total metrics are available only for source processor nodes.
  • The suppression-emit-rate and suppression-emit-total metrics are available only for suppression operation nodes.
  • The record-e2e-latency-* metrics are available only for source processor nodes and terminal nodes (nodes without successor nodes).

All of the metrics have a recording level of debug, except for the record-e2e-latency-* metrics, which have a recording level of info.

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

bytes-consumed-total
The total number of bytes consumed by a source processor node.
bytes-produced-total
The total number of bytes produced by a sink processor node.
process-rate
The average number of records processed per second by a source node.
process-total
The total number of records processed by a source node.
suppression-emit-rate
The rate at which records that have been emitted downstream from suppression operation nodes. Compare with the process-rate metric to determine how many updates are being suppressed.
suppression-emit-total
The total number of records that have been emitted downstream from suppression operation nodes. Compare with the process-total metric to determine how many updates are being suppressed.
record-e2e-latency-[avg | min | max]
The [average | minimum | maximum] end-to-end latency of a record, measured by comparing the record timestamp with the system time when it has been fully processed by the node.
records-consumed-total
The total number of records consumed by a source processor node.
records-produced-total
The total number of records produced by a sink processor node.

State store metrics

All the following metrics have a recording level of debug, except for the record-e2e-latency-* metrics which have a recording level trace. The store-scope value is specified in StoreSupplier#metricsScope() for the user’s customized state stores; for built-in state stores, currently we have:

  • in-memory-state
  • in-memory-lru-state
  • in-memory-window-state
  • in-memory-suppression (for suppression buffers)
  • rocksdb-state (for RocksDB backed key-value store)
  • rocksdb-window-state (for RocksDB backed window store)
  • rocksdb-session-state (for RocksDB backed session store)

Metrics suppression-buffer-size-avg, suppression-buffer-size-max, suppression-buffer-count-avg, and suppression-buffer-count-max are only available for suppression buffers. All other metrics are not available for suppression buffers.

All latency metrics are reported in nanoseconds (ns).

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

[put | put-if-absent | get | delete | put-all | all | range | flush | restore]-latency-[avg | max]
The average execution time in nanoseconds (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.
[put | put-if-absent | get | delete | put-all | all | range | flush | restore]-total
The total number of respective operations for this store.
get-latency-[avg | max]
The [average | maximum] get execution time, in ns.
delete-latency-[avg | max]
The [average | maximum] delete execution time, in ns.
put-all-latency-[avg | max]
The [average | maximum] put-all execution time, in ns.
all-latency-[avg | max]
The [average | maximum] all operation execution time, in ns.
range-latency-[avg | max]
The [average | maximum] range execution time, in ns.
flush-latency-[avg | max]
The [average | maximum] flush execution time, in ns.
restore-latency-[avg | max]
The [average | maximum] restore execution time, in ns.
put-rate
The average put rate for this store.
put-if-absent-rate
The average put-if-absent rate for this store.
get-rate
The average get rate for this store.
delete-rate
The average delete rate for this store.
put-all-rate
The average put-all rate for this store.
all-rate
The average all operation rate for this store.
range-rate
The average range rate for this store.
flush-rate
The average flush rate for this store.
restore-rate
The average restore rate for this store.
suppression-buffer-size-[avg | max]
The average or maximum size of buffered data, in bytes. This helps you choose a value for BufferConfig.maxBytes(...), if desired.
suppression-buffer-count-[avg | max]
The average or maximum number of records in the buffer. This helps you choose a value for BufferConfig.maxRecords(...), if desired.
record-e2e-latency-[avg | min | max]
The [average | minimum | maximum] end-to-end latency of a record, measured by comparing the record timestamp with the system time when it has been fully processed by the node.

RocksDB metrics

RocksDB metrics are grouped into statistics-based metrics and properties-based metrics.

  • Statistics-based metrics are recorded from statistics that a RocksDB state store collects.
  • Properties-based metrics are recorded from properties that RocksDB exposes.

Statistics collected by RocksDB provide cumulative measurements over time, for example, bytes written to the state store.

Properties exposed by RocksDB provide current measurements, for example, the amount of memory currently used.

The built-in RocksDB state stores have these values for store-scope:

  • rocksdb-state (for RocksDB-backed key-value stores)
  • rocksdb-window-state (for RocksDB-backed window stores)
  • rocksdb-session-state (for RocksDB-backed session stores)

RocksDB Statistics-based Metrics

RocksDB Statistics-based Metrics: All of the following metrics have a recording level of debug, because collecting statistics in RocksDB may have an impact on performance.

Statistics-based metrics are collected every minute from the RocksDB state stores.

If a state store consists of multiple RocksDB instances, which is the case for WindowStores and SessionStores, each metric reports an aggregation over the RocksDB instances of the state store.

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

bytes-written-[rate | total]
The [average | total] number of bytes written per second to the RocksDB state store.
bytes-read-[rate | total]
The [average | total] number of bytes read per second from the RocksDB state store.
memtable-bytes-flushed-[rate | total]
The [average | total] number of bytes flushed per second from the memtable to disk.
memtable-hit-ratio
The ratio of memtable hits, relative to all lookups to the memtable.
memtable-flush-time-[avg | min | max]
The [average | minimum | maximum] duration of memtable flushes to disk, in ms.
block-cache-data-hit-ratio
The ratio of block cache hits for data blocks, relative to all lookups for data blocks to the block cache.
block-cache-index-hit-ratio
The ratio of block cache hits for index blocks, relative to all lookups for index blocks to the block cache.
block-cache-filter-hit-ratio
The ratio of block cache hits for filter blocks, relative to all lookups for filter blocks to the block cache.
write-stall-duration-[avg | total]
The [average | total] duration of write stalls, in ms.
bytes-read-compaction-rate
The average number of bytes read per second during compaction.
bytes-written-compaction-rate
The average number of bytes written per second during compaction.
compaction-time-[avg | min | max]
The [average | minimum | maximum] duration of disk compactions, in ms.
number-open-files
The number of current open files.
number-file-errors-total
The total number of file errors that occurred.

RocksDB Properties-based Metrics: All of the following properties-based metrics have a recording level of info and are recorded when the metrics are accessed.

If a state store consists of multiple RocksDB instances, which is the case for WindowStores and SessionStores, each metric reports the sum over all the RocksDB instances of the state store, except for the block cache metrics, named block-cache-*. The block cache metrics report the sum over all RocksDB instances if each instance uses its own block cache, and they report the recorded value from only one instance if a single block cache is shared among all instances.

num-immutable-mem-table
The number of immutable memtables that have not yet been flushed.
cur-size-active-mem-table
The approximate size of the active memtable in bytes.
cur-size-all-mem-tables
The approximate size of active and unflushed immutable memtables in bytes.
size-all-mem-tables
The approximate size of active, unflushed immutable, and pinned immutable memtables in bytes.
num-entries-active-mem-table
The number of entries in the active memtable.
num-entries-imm-mem-tables
The number of entries in the unflushed immutable memtables.
num-deletes-active-mem-table
The number of delete entries in the active memtable.
num-deletes-imm-mem-tables
The number of delete entries in the unflushed immutable memtables.
mem-table-flush-pending
This metric reports 1 if a memtable flush is pending, otherwise it reports 0.
num-running-flushes
The number of currently running flushes.
compaction-pending
This metric reports 1 if at least one compaction is pending, otherwise it reports 0.
num-running-compactions
The number of currently running compactions.
estimate-pending-compaction-bytes
The estimated total number of bytes a compaction needs to rewrite on disk to get all levels down to under target size (only valid for level compaction).
total-sst-files-size
The total size in bytes of all Sorted Sequence Table (SST) files.
live-sst-files-size
The total size in bytes of all Sorted Sequence Table (SST) files that belong to the latest log-structured merge (LSM) tree.
num-live-versions
Number of live versions of the log-structured merge (LSM) tree.
block-cache-capacity
The capacity of the block cache in bytes.
block-cache-usage
The memory size of the entries residing in block cache in bytes.
block-cache-pinned-usage
The memory size for the entries being pinned in the block cache in bytes.
estimate-num-keys
The estimated number of keys in the active and unflushed immutable memtables and storage.
estimate-table-readers-mem
The estimated memory in bytes used for reading Sorted Sequence Tables (SSTs), excluding memory used in block cache.
background-errors
The total number of background errors.

Record cache metrics

All of the following metrics have a recording level of debug.

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

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

Add 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#addLatencyRateTotalSensor() and StreamMetrics#addRateTotalSensor().
  • Add any other type of metric via StreamMetrics#addSensor().

Runtime status information

Status of Kafka Streams instances

Important

Don’t confuse the runtime state of a KafkaStreams instance, for example, created or 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 runtime 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.

Get runtime information on Kafka Streams clients

You can get runtime information on these local KafkaStreams clients:

There is one admin client per KafkaStreams instance, and all other clients are per StreamThread.

Get the names of local KafkaStreams clients by calling the client ID methods on the ThreadMetadata class, like producerClientIds().

Client names are based on a client ID value, which is assigned according to the StreamsConfig.CLIENT_ID_CONFIG and StreamsConfig.APPLICATION_ID_CONFIG configuration settings.

  • If CLIENT_ID_CONFIG is set, Kafka Streams uses CLIENT_ID_CONFIG for the client ID value.

  • If CLIENT_ID_CONFIG isn’t set, Kafka Streams uses APPLICATION_ID_CONFIG and appends a random unique identifier (UUID):

    clientId = StreamsConfig.APPLICATION_ID_CONFIG + "-" + <random-UUID>

Kafka Streams creates names for specific clients by appending a thread ID and a descriptive string to the main client ID.

specificClientId = clientId + "-StreamThread-" + <thread-number> + <description>

For example, if CLIENT_ID_CONFIG is set to “MyClientId”, the consumerClientId() method returns a value that resembles MyClientId-StreamThread-2-consumer. If CLIENT_ID_CONFIG isn’t set, and APPLICATION_ID_CONFIG is set to “MyApplicationId”, the consumerClientId() method returns a value that resembles MyApplicationId-8d8ce4a7-85bb-41f7-ac9c-fe6f3cc0959e-StreamThread-2-consumer.

Call the threadName() method to get the thread ID:

threadId = clientId + "-StreamThread-" + <thread-number>

Depending on the configuration settings, an example thread ID resembles MyClientId-StreamThread-2 or MyApplicationId-8d8ce4a7-85bb-41f7-ac9c-fe6f3cc0959e-StreamThread-2.

adminClientId()

Gets the ID of the client application, which is the main client ID value, appended with -admin. Depending on configuration settings, the return value resembles MyClientId-admin or MyApplicationId-8d8ce4a7-85bb-41f7-ac9c-fe6f3cc0959e-admin.

The admin client ID doesn’t contain a thread ID.

producerClientIds()

Gets the names of producer clients. If exactly-once semantics (EOS version 1) is active, returns the list of task producer names, otherwise (EOS disabled or EOS version 2) returns the thread producer name. All producer client names are the main thread ID appended with -producer. If EOS version 1 is active, a -<taskId> is included.

A task ID is a sub-topology ID and a partition number, <subTopologyId>_<partition>. The subTopologyId is an integer greater than or equal to zero.

If EOS version 1 is active, the producerClientIds() method returns a Set of client names that have different task IDs. Depending on configuration settings, the return value resembles MyClientId-StreamThread-2-1_4-producer.

If EOS isn’t active or EOS version 2 is active, the return value is a single client name that doesn’t have a task ID, for example, MyClientId-StreamThread-2-producer.

For more information, see Stream partitions and tasks.

consumerClientId()
Gets the name of the consumer client. The consumer client name is the main thread ID appended with -consumer, for example, MyClientId-StreamThread-2-consumer.
restoreConsumerClientId()
Gets the name of the restore consumer client. The restore consumer client name is the main thread ID appended with -restore-consumer, for example, MyClientId-StreamThread-2-restore-consumer

Monitor the restoration progress of fault-tolerant state stores

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

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.

To observe the restoration of all state stores, provide your application with an instance of the org.apache.kafka.streams.processor.StateRestoreListener interface. Set the org.apache.kafka.streams.processor.StateRestoreListener by calling the KafkaStreams#setGlobalStateRestoreListener method.

The following code shows 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());
    }
}

Important

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

Integration with Confluent Control Center

Since the 3.2 release, Confluent Control Center displays the underlying producer metrics and consumer metrics of a Kafka Streams application, which the Kafka 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.

In Control Center, all of the running instances of a Kafka Streams application appear as a single consumer group.

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 are displayed separately from their application.

Note

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