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Kafka Ingress

Nstream provides a Kafka Adapter library that greatly facilitates ingestion from Kafka topics. This guide demonstrates how to consume from Kafka topics and process responses in Web Agents using minimal boilerplate.




implementation 'io.nstream:nstream-adapter-kafka:4.9.16'




Ideal KafkaIngestingPatch Conditions


Then you simply need to use the KafkaIngestingPatch class, often without overriding any methods. Let’s demonstrate this by recreating the KafkaConsumerAgent example from the open source documentation wholly in server.recon:

# server.recon
provisions: {
  @provision("consumer-properties") {
    class: "nstream.adapter.common.provision.PropertiesProvision",
    # Kafka consumer properties go here
    def: {
      "bootstrap.servers": "your-bootstrap-host:9092",
      "": "your-group",
      "key.deserializer": "org.apache.kafka.common.serialization.StringDeserializer",
      "value.deserializer": "org.apache.kafka.common.serialization.StringDeserializer"

vehicle: @fabric {
  @plane(class: "nstream.adapter.runtime.AppPlane")
  # KafkaConsumingAgent replacement
  @node {
    uri: "/kafka"
    @agent(class: "nstream.adapter.kafka.KafkaIngestingPatch") {
      # KafkaIngressSettings values go here
      kafkaIngressConf: @kafkaIngressSettings {
        consumerPropertiesProvisionName: "consumer-properties"
        topics: {"schema-topic"}
        valueContentTypeOverride: "json"
        relaySchema: @command {
          nodeUri: {
            $key # $value$id will also work here
          laneUri: "addMessage"
          value: $value
  # VehicleAgent config can be copied directly, but you'll of course
  # need to implement VehicleAgent in Java.

# Configure desired web settings (e.g. port)
# ...

Common Variations

Custom Ingest Logic

If relaying messages to another agent is not sufficient, or you would like to add custom logic on ingestion of messages, the KafkaIngestingPatch can be easily overridden. Create a new class that extends the KafkaIngestingPatch and override either ingest or ingestBatch (the latter is higher in the pipeline, and by default defers to ingest).

public class CustomIngestingAgent<K, V> extends KafkaIngestingPatch<K, V> {

  protected void ingest(ConsumerRecord<K, V> unstructured)
      throws DeferrableException {
    // Custom ingestion of an individual record

  protected void ingestBatch(ConsumerRecords<K, V> batch) {
    // Custom ingestion of a poll() invocation's result.
    // Overriding this makes ingest() unused unless specifically
    // invoked in the implementation.

Remember to update your server.recon file with the new agent you just created, instead of the KafkaIngestingPatch.

Manual Consumption Triggering

It is often desired to begin Kafka consumption via an explicit command message instead of on Web Agent startup:

// import...
import swim.api.lane.CommandLane;

public class KafkaConsumerAgent extends KafkaIngestingPatch {

  CommandLane<String> triggerReception = this.<String>commandLane()
      .onCommand(s -> {
        if ("start".equals(s)) {

  public void didStart() {
    System.out.println(nodeUri() + ": didStart");
    // No call to stageReception() unlike in superclass


Backoff Strategy

While the Kafka-recommended pattern is to continuously invoke poll, you may wish to be less aggressive. This is a perfect use case for the inherited ExecutorAgent#scheduleWithInformedBackoff method. Below we utilize this method to trigger backoff only when a returned result is empty (i.e. resume the continuous poll as long as results are nonempty):


public class KafkaConsumerAgent extends KafkaIngestingPatch {

  protected void stageReception() {
    this.pollTimer = scheduleWithInformedBackoff(this::pollTimer,
        i -> !i.isEmpty(),

  private long nextBackoff(ConsumerRecords<Integer, String> records, long oldBackoff) {
    if (!records.isEmpty()) {
      return 0L;
    } else if (oldBackoff < 0) {
      return 500L;
    } else if (oldBackoff < 4000) {
      // Exponential backoff until 4 seconds
      return Math.min(oldBackoff * 2, 4000L);
    } else {
      // Linear backoff subsequently, to a max of 8 seconds
      return Math.min(oldBackoff + 1000L, 8000L);


Manual Commits

The consumption strategy outlined here dedicates a thread to consuming messages, but processes those messages asynchronously (by delegating to parallel-running Web Agents). While this provides extremely high throughput, combining this with automatic commits may result in a situation where processing a message throws an exception after that message’s offset has been committed by the consumer. This is potentially problematic when at-least once delivery of every message is required.

There is no catch-all solution to problems of this (rather advanced) nature. At one extreme, you may avoid this headache altogether and process everything within the consumer thread, though this introduces significant loss of throughput. A more common pattern is to commit in batches – every time a VehicleAgent (or equivalent) successfully or unsuccessfully processes a message, it lets the KafkaIngestingAgent know (most intuitively via a command() to a new CommandLane), and the KafkaIngestingAgent periodically issues a commit() based on the information at hand.

Future nstream-toolkit releases will introduce some convenient abstractions over the most common such patterns seen in practice, so stay tuned!

Nstream is licensed under the Redis Source Available License 2.0 (RSALv2).