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Chapter 16. Setting up distributed tracing

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Distributed tracing allows you to track the progress of transactions between applications in a distributed system. In a microservices architecture, tracing tracks the progress of transactions between services. Trace data is useful for monitoring application performance and investigating issues with target systems and end-user applications.

In Streams for Apache Kafka, tracing facilitates the end-to-end tracking of messages: from source systems to Kafka, and then from Kafka to target systems and applications. It complements the metrics that are available to view in JMX metrics, as well as the component loggers.

Support for tracing is built in to the following Kafka components:

  • Kafka Connect
  • MirrorMaker
  • MirrorMaker 2
  • Streams for Apache Kafka Bridge

Tracing is not supported for Kafka brokers.

You add tracing configuration to the properties file of the component.

To enable tracing, you set environment variables and add the library of the tracing system to the Kafka classpath. For Jaeger tracing, you can add tracing artifacts for OpenTelemetry with the Jaeger Exporter.

Note

Streams for Apache Kafka no longer supports OpenTracing. If you were previously using OpenTracing with Jaeger, we encourage you to transition to using OpenTelemetry instead.

To enable tracing in Kafka producers, consumers, and Kafka Streams API applications, you instrument application code. When instrumented, clients generate trace data; for example, when producing messages or writing offsets to the log.

Note

Setting up tracing for applications and systems beyond Streams for Apache Kafka is outside the scope of this content.

16.1. Outline of procedures

To set up tracing for Streams for Apache Kafka, follow these procedures in order:

Note

For information on enabling tracing for the Kafka Bridge, see Using the Streams for Apache Kafka Bridge.

16.2. Tracing options

Use OpenTelemetry with the Jaeger tracing system.

OpenTelemetry provides an API specification that is independent from the tracing or monitoring system.

You use the APIs to instrument application code for tracing.

  • Instrumented applications generate traces for individual requests across the distributed system.
  • Traces are composed of spans that define specific units of work over time.

Jaeger is a tracing system for microservices-based distributed systems.

  • The Jaeger user interface allows you to query, filter, and analyze trace data.

The Jaeger user interface showing a simple query

The Jaeger user interface showing a simple query

Additional resources

16.3. Environment variables for tracing

Use environment variables when you are enabling tracing for Kafka components or initializing a tracer for Kafka clients.

Tracing environment variables are subject to change. For the latest information, see the OpenTelemetry documentation.

The following tables describe the key environment variables for setting up a tracer.

Table 16.1. OpenTelemetry environment variables
PropertyRequiredDescription

OTEL_SERVICE_NAME

Yes

The name of the Jaeger tracing service for OpenTelemetry.

OTEL_EXPORTER_JAEGER_ENDPOINT

Yes

The exporter used for tracing.

OTEL_TRACES_EXPORTER

Yes

The exporter used for tracing. Set to otlp by default. If using Jaeger tracing, you need to set this environment variable as jaeger. If you are using another tracing implementation, specify the exporter used.

16.4. Enabling tracing for Kafka Connect

Enable distributed tracing for Kafka Connect using configuration properties. Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems.

You can enable tracing that uses OpenTelemetry.

Procedure

  1. Add the tracing artifacts to the opt/kafka/libs directory.

  2. Configure producer and consumer tracing in the relevant Kafka Connect configuration file.

    • If you are running Kafka Connect in standalone mode, edit the /opt/kafka/config/connect-standalone.properties file.
    • If you are running Kafka Connect in distributed mode, edit the /opt/kafka/config/connect-distributed.properties file.

    Add the following tracing interceptor properties to the configuration file:

    Properties for OpenTelemetry

    producer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingProducerInterceptor
consumer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingConsumerInterceptor

    With tracing enabled, you initialize tracing when you run the Kafka Connect script.

  3. Save the configuration file.
  4. Set the environment variables for tracing.

  5. Start Kafka Connect in standalone or distributed mode with the configuration file as a parameter (plus any connector properties):

    Running Kafka Connect in standalone mode

    su - kafka
/opt/kafka/bin/connect-standalone.sh \
/opt/kafka/config/connect-standalone.properties \
connector1.properties \
[connector2.properties ...]

    Running Kafka Connect in distributed mode

    su - kafka
/opt/kafka/bin/connect-distributed.sh /opt/kafka/config/connect-distributed.properties

    The internal consumers and producers of Kafka Connect are now enabled for tracing.

16.5. Enabling tracing for MirrorMaker 2

Enable distributed tracing for MirrorMaker 2 by defining the Interceptor properties in the MirrorMaker 2 properties file. Messages are traced between Kafka clusters. The trace data records messages entering and leaving the MirrorMaker 2 component.

You can enable tracing that uses OpenTelemetry.

Procedure

  1. Add the tracing artifacts to the opt/kafka/libs directory.

  2. Configure producer and consumer tracing in the opt/kafka/config/connect-mirror-maker.properties file.

    Add the following tracing interceptor properties to the configuration file:

    Properties for OpenTelemetry

    header.converter=org.apache.kafka.connect.converters.ByteArrayConverter
producer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingProducerInterceptor
consumer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingConsumerInterceptor

    ByteArrayConverter prevents Kafka Connect from converting message headers (containing trace IDs) to base64 encoding. This ensures that messages are the same in both the source and the target clusters.

    With tracing enabled, you initialize tracing when you run the Kafka MirrorMaker 2 script.

  3. Save the configuration file.
  4. Set the environment variables for tracing.

  5. Start MirrorMaker 2 with the producer and consumer configuration files as parameters: 

    su - kafka
/opt/kafka/bin/connect-mirror-maker.sh \
/opt/kafka/config/connect-mirror-maker.properties

    The internal consumers and producers of MirrorMaker 2 are now enabled for tracing.

16.6. Enabling tracing for MirrorMaker

Enable distributed tracing for MirrorMaker by passing the Interceptor properties as consumer and producer configuration parameters. Messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker component.

You can enable tracing that uses OpenTelemetry.

Procedure

  1. Add the tracing artifacts to the opt/kafka/libs directory.

  2. Configure producer tracing in the /opt/kafka/config/producer.properties file.

    Add the following tracing interceptor property:

    Producer property for OpenTelemetry

    producer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingProducerInterceptor

  3. Save the configuration file.

  4. Configure consumer tracing in the /opt/kafka/config/consumer.properties file.

    Add the following tracing interceptor property:

    Consumer property for OpenTelemetry

    consumer.interceptor.classes=io.opentelemetry.instrumentation.kafkaclients.TracingConsumerInterceptor

    With tracing enabled, you initialize tracing when you run the Kafka MirrorMaker script.

  5. Save the configuration file.
  6. Set the environment variables for tracing.

  7. Start MirrorMaker with the producer and consumer configuration files as parameters: 

    su - kafka
/opt/kafka/bin/kafka-mirror-maker.sh \
--producer.config /opt/kafka/config/producer.properties \
--consumer.config /opt/kafka/config/consumer.properties \
--num.streams=2

    The internal consumers and producers of MirrorMaker are now enabled for tracing.

16.7. Initializing tracing for Kafka clients

Initialize a tracer for OpenTelemetry, then instrument your client applications for distributed tracing. You can instrument Kafka producer and consumer clients, and Kafka Streams API applications.

Configure and initialize a tracer using a set of tracing environment variables.

Procedure

In each client application add the dependencies for the tracer:

  1. Add the Maven dependencies to the pom.xml file for the client application:

    Dependencies for OpenTelemetry

    <dependency>
    <groupId>io.opentelemetry.semconv</groupId>
    <artifactId>opentelemetry-semconv</artifactId>
    <version>1.21.0-alpha</version>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-otlp</artifactId>
    <version>1.34.1</version>
    <exclusions>
        <exclusion>
            <groupId>io.opentelemetry</groupId>
            <artifactId>opentelemetry-exporter-sender-okhttp</artifactId>
        </exclusion>
    </exclusions>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-sender-grpc-managed-channel</artifactId>
    <version>1.34.1</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-sdk-extension-autoconfigure</artifactId>
    <version>1.34.1</version>
</dependency>
<dependency>
    <groupId>io.opentelemetry.instrumentation</groupId>
    <artifactId>opentelemetry-kafka-clients-2.6</artifactId>
    <version>1.32.0-alpha</version>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-sdk</artifactId>
    <version>1.34.1</version>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-sender-jdk</artifactId>
    <version>1.34.1-alpha</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>io.grpc</groupId>
    <artifactId>grpc-netty-shaded</artifactId>
    <version>1.61.0</version>
</dependency>

  2. Define the configuration of the tracer using the tracing environment variables.

  3. Create a tracer, which is initialized with the environment variables:

    Creating a tracer for OpenTelemetry

    OpenTelemetry ot = GlobalOpenTelemetry.get();

  4. Register the tracer as a global tracer: 

    GlobalTracer.register(tracer);

  5. Instrument your client:

16.8. Instrumenting producers and consumers for tracing

Instrument application code to enable tracing in Kafka producers and consumers. Use a decorator pattern or interceptors to instrument your Java producer and consumer application code for tracing. You can then record traces when messages are produced or retrieved from a topic.

OpenTelemetry instrumentation project provides classes that support instrumentation of producers and consumers.

Decorator instrumentation
For decorator instrumentation, create a modified producer or consumer instance for tracing.
Interceptor instrumentation
For interceptor instrumentation, add the tracing capability to the consumer or producer configuration.

Prerequisites

  • You have initialized tracing for the client.

    You enable instrumentation in producer and consumer applications by adding the tracing JARs as dependencies to your project.

Procedure

Perform these steps in the application code of each producer and consumer application. Instrument your client application code using either a decorator pattern or interceptors.

  • To use a decorator pattern, create a modified producer or consumer instance to send or receive messages.

    You pass the original KafkaProducer or KafkaConsumer class.

    Example decorator instrumentation for OpenTelemetry

    // Producer instance
Producer < String, String > op = new KafkaProducer < > (
    configs,
    new StringSerializer(),
    new StringSerializer()
    );
    Producer < String, String > producer = tracing.wrap(op);
KafkaTracing tracing = KafkaTracing.create(GlobalOpenTelemetry.get());
producer.send(...);

//consumer instance
Consumer<String, String> oc = new KafkaConsumer<>(
    configs,
    new StringDeserializer(),
    new StringDeserializer()
    );
    Consumer<String, String> consumer = tracing.wrap(oc);
consumer.subscribe(Collections.singleton("mytopic"));
ConsumerRecords<Integer, String> records = consumer.poll(1000);
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

  • To use interceptors, set the interceptor class in the producer or consumer configuration.

    You use the KafkaProducer and KafkaConsumer classes in the usual way. The TracingProducerInterceptor and TracingConsumerInterceptor interceptor classes take care of the tracing capability.

    Example producer configuration using interceptors

    senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
    TracingProducerInterceptor.class.getName());

KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
producer.send(...);

    Example consumer configuration using interceptors

    consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
    TracingConsumerInterceptor.class.getName());

KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
consumer.subscribe(Collections.singletonList("messages"));
ConsumerRecords<Integer, String> records = consumer.poll(1000);
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

16.9. Instrumenting Kafka Streams applications for tracing

Instrument application code to enable tracing in Kafka Streams API applications. Use a decorator pattern or interceptors to instrument your Kafka Streams API applications for tracing. You can then record traces when messages are produced or retrieved from a topic.

Decorator instrumentation
For decorator instrumentation, create a modified Kafka Streams instance for tracing. For OpenTelemetry, you need to create a custom TracingKafkaClientSupplier class to provide tracing instrumentation for Kafka Streams.
Interceptor instrumentation
For interceptor instrumentation, add the tracing capability to the Kafka Streams producer and consumer configuration.

Prerequisites

  • You have initialized tracing for the client.

    You enable instrumentation in Kafka Streams applications by adding the tracing JARs as dependencies to your project.

  • To instrument Kafka Streams with OpenTelemetry, you’ll need to write a custom TracingKafkaClientSupplier.

  • The custom TracingKafkaClientSupplier can extend Kafka’s DefaultKafkaClientSupplier, overriding the producer and consumer creation methods to wrap the instances with the telemetry-related code.

    Example custom TracingKafkaClientSupplier

    private class TracingKafkaClientSupplier extends DefaultKafkaClientSupplier {
    @Override
    public Producer<byte[], byte[]> getProducer(Map<String, Object> config) {
        KafkaTelemetry telemetry = KafkaTelemetry.create(GlobalOpenTelemetry.get());
        return telemetry.wrap(super.getProducer(config));
    }

    @Override
    public Consumer<byte[], byte[]> getConsumer(Map<String, Object> config) {
        KafkaTelemetry telemetry = KafkaTelemetry.create(GlobalOpenTelemetry.get());
        return telemetry.wrap(super.getConsumer(config));
    }

    @Override
    public Consumer<byte[], byte[]> getRestoreConsumer(Map<String, Object> config) {
        return this.getConsumer(config);
    }

    @Override
    public Consumer<byte[], byte[]> getGlobalConsumer(Map<String, Object> config) {
        return this.getConsumer(config);
    }
}

Procedure

Perform these steps for each Kafka Streams API application.

  • To use a decorator pattern, create an instance of the TracingKafkaClientSupplier supplier interface, then provide the supplier interface to KafkaStreams.

    Example decorator instrumentation

    KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);
KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier);
streams.start();

  • To use interceptors, set the interceptor class in the Kafka Streams producer and consumer configuration.

    The TracingProducerInterceptor and TracingConsumerInterceptor interceptor classes take care of the tracing capability.

    Example producer and consumer configuration using interceptors

    props.put(StreamsConfig.PRODUCER_PREFIX + ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingProducerInterceptor.class.getName());
props.put(StreamsConfig.CONSUMER_PREFIX + ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingConsumerInterceptor.class.getName());

16.10. Specifying tracing systems with OpenTelemetry

Instead of the default Jaeger system, you can specify other tracing systems that are supported by OpenTelemetry.

If you want to use another tracing system with OpenTelemetry, do the following:

  1. Add the library of the tracing system to the Kafka classpath.

  2. Add the name of the tracing system as an additional exporter environment variable.

    Additional environment variable when not using Jaeger

    OTEL_SERVICE_NAME=my-tracing-service
OTEL_TRACES_EXPORTER=zipkin 1
OTEL_EXPORTER_ZIPKIN_ENDPOINT=http://localhost:9411/api/v2/spans 2

    1
    The name of the tracing system. In this example, Zipkin is specified.
    2
    The endpoint of the specific selected exporter that listens for spans. In this example, a Zipkin endpoint is specified.

Additional resources

16.11. Specifying custom span names for OpenTelemetry

A tracing span is a logical unit of work in Jaeger, with an operation name, start time, and duration. Spans have built-in names, but you can specify custom span names in your Kafka client instrumentation where used.

Specifying custom span names is optional and only applies when using a decorator pattern in producer and consumer client instrumentation or Kafka Streams instrumentation.

Custom span names cannot be specified directly with OpenTelemetry. Instead, you retrieve span names by adding code to your client application to extract additional tags and attributes.

Example code to extract attributes

//Defines attribute extraction for a producer
private static class ProducerAttribExtractor implements AttributesExtractor < ProducerRecord < ? , ? > , Void > {
    @Override
    public void onStart(AttributesBuilder attributes, ProducerRecord < ? , ? > producerRecord) {
        set(attributes, AttributeKey.stringKey("prod_start"), "prod1");
    }
    @Override
    public void onEnd(AttributesBuilder attributes, ProducerRecord < ? , ? > producerRecord, @Nullable Void unused, @Nullable Throwable error) {
        set(attributes, AttributeKey.stringKey("prod_end"), "prod2");
    }
}
//Defines attribute extraction for a consumer
private static class ConsumerAttribExtractor implements AttributesExtractor < ConsumerRecord < ? , ? > , Void > {
    @Override
    public void onStart(AttributesBuilder attributes, ConsumerRecord < ? , ? > producerRecord) {
        set(attributes, AttributeKey.stringKey("con_start"), "con1");
    }
    @Override
    public void onEnd(AttributesBuilder attributes, ConsumerRecord < ? , ? > producerRecord, @Nullable Void unused, @Nullable Throwable error) {
        set(attributes, AttributeKey.stringKey("con_end"), "con2");
    }
}
//Extracts the attributes
public static void main(String[] args) throws Exception {
        Map < String, Object > configs = new HashMap < > (Collections.singletonMap(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092"));
        System.setProperty("otel.traces.exporter", "jaeger");
        System.setProperty("otel.service.name", "myapp1");
        KafkaTracing tracing = KafkaTracing.newBuilder(GlobalOpenTelemetry.get())
            .addProducerAttributesExtractors(new ProducerAttribExtractor())
            .addConsumerAttributesExtractors(new ConsumerAttribExtractor())
            .build();

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