Processor API

The Processor API allows developers to define and connect custom processors and to interact with state stores. With the Processor API, you can define arbitrary stream processors that process one received record at a time, and connect these processors with their associated state stores to compose the processor topology that represents a customized processing logic.


The Processor API can be used to implement both stateless as well as stateful operations, where the latter is achieved through the use of state stores.


Combining the DSL and the Processor API: You can combine the convenience of the DSL with the power and flexibility of the Processor API as described in the section Applying processors and transformers (Processor API integration).

For a complete list of available API functionality, see the Kafka Streams API docs.

Defining a Stream Processor

A stream processor is a node in the processor topology that represents a single processing step. With the Processor API, you can define arbitrary stream processors that processes one received record at a time, and connect these processors with their associated state stores to compose the processor topology.

You can define a customized stream processor by implementing the Processor interface, which provides the process() API method. The process() method is called on each of the received records.

The Processor interface also has an init() method, which is called by the Kafka Streams library during task construction phase. Processor instances should perform any required initialization in this method. The init() method passes in a ProcessorContext instance, which provides access to the metadata of the currently processed record, including its source Kafka topic and partition, its corresponding message offset, and further such information. You can also use this context instance to schedule a punctuation function (via ProcessorContext#schedule()), to forward a new record as a key-value pair to the downstream processors (via ProcessorContext#forward()), and to commit the current processing progress (via ProcessorContext#commit()).

Specifically, ProcessorContext#schedule() accepts a user Punctuator callback interface, which triggers its punctuate() API method periodically based on the PunctuationType. The PunctuationType determines what notion of time is used for the punctuation scheduling: either stream-time or wall-clock-time (by default, stream-time is configured to represent event-time via TimestampExtractor). When stream-time is used, punctuate() is triggered purely by data because stream-time is determined (and advanced forward) by the timestamps derived from the input data. When there is no new input data arriving, stream-time is not advanced and thus punctuate() is not called.

For example, if you schedule a Punctuator function every 10 seconds based on PunctuationType.STREAM_TIME and if you process a stream of 60 records with consecutive timestamps from 1 (first record) to 60 seconds (last record), then punctuate() would be called 6 times. This happens regardless of the time required to actually process those records. punctuate() would be called 6 times regardless of whether processing these 60 records takes a second, a minute, or an hour.

When wall-clock-time (i.e. PunctuationType.WALL_CLOCK_TIME) is used, punctuate() is triggered purely by the wall-clock time. Reusing the example above, if the Punctuator function is scheduled based on PunctuationType.WALL_CLOCK_TIME, and if these 60 records were processed within 20 seconds, punctuate() is called 2 times (one time every 10 seconds). If these 60 records were processed within 5 seconds, then no punctuate() is called at all. Note that you can schedule multiple Punctuator callbacks with different PunctuationType types within the same processor by calling ProcessorContext#schedule() multiple times inside init() method.


Stream-time is only advanced if all input partitions over all input topics have new data (with newer timestamps) available. If at least one partition does not have any new data available, stream-time will not be advanced and thus punctuate() will not be triggered if PunctuationType.STREAM_TIME was specified. This behavior is independent of the configured timestamp extractor, i.e., using WallclockTimestampExtractor does not enable wall-clock triggering of punctuate().

The following example Processor defines a simple word-count algorithm and the following actions are performed:

  • In the init() method, schedule the punctuation every 1000 time units (the time unit is normally milliseconds, which in this example would translate to punctuation every 1 second) and retrieve the local state store by its name “Counts”.
  • In the process() method, upon each received record, split the value string into words, and update their counts into the state store (we will talk about this later in this section).
  • In the punctuate() method, iterate the local state store and send the aggregated counts to the downstream processor (we will talk about downstream processors later in this section), and commit the current stream state.
public class WordCountProcessor implements Processor<String, String> {

  private ProcessorContext context;
  private KeyValueStore<String, Long> kvStore;

  public void init(ProcessorContext context) {
      // keep the processor context locally because we need it in punctuate() and commit()
      this.context = context;

      // retrieve the key-value store named "Counts"
      kvStore = (KeyValueStore) context.getStateStore("Counts");

      // schedule a punctuate() method every 1000 milliseconds based on stream-time
      this.context.schedule(1000, PunctuationType.STREAM_TIME, (timestamp) -> {
          KeyValueIterator<String, Long> iter = this.kvStore.all();
          while (iter.hasNext()) {
              KeyValue<String, Long> entry =;
              context.forward(entry.key, entry.value.toString());

          // commit the current processing progress

  public void punctuate(long timestamp) {
      // this method is deprecated and should not be used anymore

  public void close() {
      // close the key-value store



Stateful processing with state stores: The WordCountProcessor defined above can access the currently received record in its process() method, and it can leverage state stores to maintain processing states to, for example, remember recently arrived records for stateful processing needs like aggregations and joins. For more information, see the state stores documentation.

State Stores

To implement a stateful Processor or Transformer, you must provide one or more state stores to the processor or transformer (stateless processors or transformers do not need state stores). State stores can be used to remember recently received input records, to track rolling aggregates, to de-duplicate input records, and more. Another feature of state stores is that they can be interactively queried from other applications, such as a NodeJS-based dashboard or a microservice implemented in Scala or Go.

The available state store types in Kafka Streams have fault tolerance enabled by default.

Defining and creating a State Store

You can either use one of the available store types or implement your own custom store type. It’s common practice to leverage an existing store type via the Stores factory.

Note that, when using Kafka Streams, you normally don’t create or instantiate state stores directly in your code. Rather, you define state stores indirectly by creating a so-called StoreBuilder. This buildeer is used by Kafka Streams as a factory to instantiate the actual state stores locally in application instances when and where needed.

The following store types are available out of the box.

Store Type Storage Engine Fault-tolerant? Description
Persistent KeyValueStore<K, V> RocksDB Yes (enabled by default)
  • The recommended store type for most use cases.
  • Stores its data on local disk.
  • Storage capacity: managed local state can be larger than the memory (heap space) of an application instance, but must fit into the available local disk space.
  • RocksDB settings can be fine-tuned, see RocksDB configuration.
  • Available store variants: time window key-value store, session window key-value store.
// Creating a persistent key-value store:
// here, we create a `KeyValueStore<String, Long>` named "persistent-counts".
import org.apache.kafka.streams.processor.StateStoreSupplier;
import org.apache.kafka.streams.state.Stores;

// Note: The `Stores` factory returns a supplier for the state store,
// because that's what you typically need to pass as API parameter.
StateStoreSupplier countStoreSupplier =

See PersistentKeyValueFactory for detailed factory options.

In-memory KeyValueStore<K, V> - Yes (enabled by default)
  • Stores its data in memory.
  • Storage capacity: managed local state must fit into memory (heap space) of an application instance.
  • Useful when application instances run in an environment where local disk space is either not available or local disk space is wiped in-between app instance restarts.
// Creating an in-memory key-value store:
// here, we create a `KeyValueStore<String, Long>` named "inmemory-counts".
import org.apache.kafka.streams.processor.StateStoreSupplier;
import org.apache.kafka.streams.state.Stores;

// Note: The `Stores` factory returns a supplier for the state store,
// because that's what you typically need to pass as API parameter.
StateStoreSupplier countStoreSupplier =

See InMemoryKeyValueFactory for detailed factory options.

Fault-tolerant State Stores

To make state stores fault-tolerant and to allow for state store migration without data loss, a state store can be continuously backed up to a Kafka topic behind the scenes. For example, to migrate a stateful stream task from one machine to another when elastically adding or removing capacity from your application. This topic is sometimes referred to as the state store’s associated changelog topic, or its changelog. For example, if you experience machine failure, the state store and the application’s state can be fully restored from its changelog. You can enable or disable this backup feature for a state store.

By default, persistent key-value stores are fault-tolerant. They are backed by a compacted changelog topic. The purpose of compacting this topic is to prevent the topic from growing indefinitely, to reduce the storage consumed in the associated Kafka cluster, and to minimize recovery time if a state store needs to be restored from its changelog topic.

Similarly, persistent window stores are fault-tolerant. They are backed by a topic that uses both compaction and deletion. Because of the structure of the message keys that are being sent to the changelog topics, this combination of deletion and compaction is required for the changelog topics of window stores. For window stores, the message keys are composite keys that include the “normal” key and window timestamps. For these types of composite keys it would not be sufficient to only enable compaction to prevent a changelog topic from growing out of bounds. With deletion enabled, old windows that have expired will be cleaned up by Kafka’s log cleaner as the log segments expire. The default retention setting is Windows#maintainMs() + 1 day. You can override this setting by specifying StreamsConfig.WINDOW_STORE_CHANGE_LOG_ADDITIONAL_RETENTION_MS_CONFIG in the StreamsConfig.

When you open an Iterator from a state store you must call close() on the iterator when you are done working with it to reclaim resources; or you can use the iterator from within a try-with-resources statement. If you do not close an iterator, you may encounter an OOM error.

Enable or Disable Fault Tolerance of State Stores (Store Changelogs)

You can enable or disable fault tolerance for a state store by enabling or disabling the change logging of the store through enableLogging() and disableLogging(). You can also fine-tune the associated topic’s configuration if needed.

Example for disabling fault-tolerance:

import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

StoreBuilder<KeyValueStore<String, Long>> countStoreSupplier = Stores.keyValueStoreBuilder(
  .withLoggingDisabled(); // disable backing up the store to a changelog topic


If the changelog is disabled then the attached state store is no longer fault tolerant and it can’t have any standby replicas.

Here is an example for enabling fault tolerance, with additional changelog-topic configuration: You can add any log config from kafka.log.LogConfig. Unrecognized configs will be ignored.

import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

Map<String, String> changelogConfig = new HashMap();
// override min.insync.replicas
changelogConfig.put("min.insyc.replicas", "1")

StoreBuilder<KeyValueStore<String, Long>> countStoreSupplier = Stores.keyValueStoreBuilder(
  .withLoggingEnabled(changlogConfig); // enable changelogging, with custom changelog settings

Implementing Custom State Stores

You can use the built-in state store types or implement your own. The primary interface to implement for the store is org.apache.kafka.streams.processor.StateStore. Kafka Streams also has a few extended interfaces such as KeyValueStore.

You also need to provide a “factory” for the store by implementing the org.apache.kafka.streams.processor.StateStoreSupplier interface, which Kafka Streams uses to create instances of your store.

There is an example state store implementation in Scala, which can serve as a starting point for your own stores:

  • CMSStore (Scala) – an in-memory, fault-tolerant store that leverages a Count-Min Sketch data structure for probabilistic counting of items in an input stream. The state store supplier is implemented in CMSStoreSupplier. The backup and restore functionality of the state store and its fault tolerance can be enabled and disabled through configuration. The changelogging of the store is performed through CMSStoreChangeLogger.

Connecting Processors and State Stores

Now that a processor (WordCountProcessor) and the state stores have been defined, you can construct the processor topology by connecting these processors and state stores together by using the Topology instance. In addition, you can add source processors with the specified Kafka topics to generate input data streams into the topology, and sink processors with the specified Kafka topics to generate output data streams out of the topology.

Here is an example implementation:

Topology builder = new Topology();

// add the source processor node that takes Kafka topic "source-topic" as input
builder.addSource("Source", "source-topic")

    // add the WordCountProcessor node which takes the source processor as its upstream processor
    .addProcessor("Process", () -> new WordCountProcessor(), "Source")

    // add the count store associated with the WordCountProcessor processor
    .addStateStore(countStoreBuilder, "Process")

    // add the sink processor node that takes Kafka topic "sink-topic" as output
    // and the WordCountProcessor node as its upstream processor
    .addSink("Sink", "sink-topic", "Process");

Here is a quick explanation of this example:

  • A source processor node named "Source" is added to the topology using the addSource method, with one Kafka topic "source-topic" fed to it.
  • A processor node named "Process" with the pre-defined WordCountProcessor logic is then added as the downstream processor of the "Source" node using the addProcessor method.
  • A predefined persistent key-value state store is created and associated with the "Process" node, using countStoreBuilder.
  • A sink processor node is then added to complete the topology using the addSink method, taking the "Process" node as its upstream processor and writing to a separate "sink-topic" Kafka topic. Note that users can also use another overloaded variant of addSink to dynamically determine the Kafka topic to write to for each received record from the upstream processor.

In this topology, the "Process" stream processor node is considered a downstream processor of the "Source" node, and an upstream processor of the "Sink" node. As a result, whenever the "Source" node forwards a newly fetched record from Kafka to its downstream "Process" node, the WordCountProcessor#process() method is triggered to process the record and update the associated state store. Whenever context#forward() is called in the WordCountProcessor#punctuate() method, the aggregate key-value pair will be sent via the "Sink" processor node to the Kafka topic "sink-topic". Note that in the WordCountProcessor implementation, you must refer to the same store name "Counts" when accessing the key-value store, otherwise an exception will be thrown at runtime, indicating that the state store cannot be found. If the state store is not associated with the processor in the Topology code, accessing it in the processor’s init() method will also throw an exception at runtime, indicating the state store is not accessible from this processor.

Now that you have fully defined your processor topology in your application, you can proceed to running the Kafka Streams application.