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Chapter 22. Introducing distributed tracing

Distributed tracing tracks 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. Distributed tracing complements the monitoring of metrics in Grafana dashboards, as well as the component loggers.

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

MirrorMaker to trace messages from a source cluster to a target cluster
Kafka Connect to trace messages consumed and produced by Kafka Connect
Kafka Bridge to trace messages between Kafka and HTTP client applications

Tracing is not supported for Kafka brokers.

You enable and configure tracing for these components through their custom resources. You add tracing configuration using spec.template properties.

You enable tracing by specifying a tracing type using the spec.tracing.type property:

opentelemetry: Specify type: opentelemetry to use OpenTelemetry. By Default, OpenTelemetry uses the OTLP (OpenTelemetry Protocol) exporter and endpoint to get trace data. You can specify other tracing systems supported by OpenTelemetry, including Jaeger tracing. To do this, you change the OpenTelemetry exporter and endpoint in the tracing configuration.

Caution

Streams for Apache Kafka no longer supports OpenTracing. If you were previously using OpenTracing with the type: jaeger option, we encourage you to transition to using OpenTelemetry instead.

22.1. 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

22.2. 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 22.1. OpenTelemetry environment variables
Property	Required	Description
`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.

22.3. Setting up distributed tracing

Enable distributed tracing in Kafka components by specifying a tracing type in the custom resource. Instrument tracers in Kafka clients for end-to-end tracking of messages.

To set up distributed tracing, follow these procedures in order:

Enable tracing for MirrorMaker, Kafka Connect, and the Kafka Bridge
Set up tracing for clients:
- Initialize a Jaeger tracer for Kafka clients
Instrument clients with tracers:
- Instrument producers and consumers for tracing
- Instrument Kafka Streams applications for tracing

22.3.1. Prerequisites

Before setting up distributed tracing, make sure Jaeger backend components are deployed to your OpenShift cluster. We recommend using the Jaeger operator for deploying Jaeger on your OpenShift cluster.

For deployment instructions, see the Jaeger documentation.

Note

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

22.3.2. Enabling tracing in MirrorMaker, Kafka Connect, and Kafka Bridge resources

Distributed tracing is supported for MirrorMaker, MirrorMaker 2, Kafka Connect, and the Streams for Apache Kafka Bridge. Configure the custom resource of the component to specify and enable a tracer service.

Enabling tracing in a resource triggers the following events:

Interceptor classes are updated in the integrated consumers and producers of the component.
For MirrorMaker, MirrorMaker 2, and Kafka Connect, the tracing agent initializes a tracer based on the tracing configuration defined in the resource.
For the Kafka Bridge, a tracer based on the tracing configuration defined in the resource is initialized by the Kafka Bridge itself.

You can enable tracing that uses OpenTelemetry.

Tracing in MirrorMaker and MirrorMaker 2

For MirrorMaker and MirrorMaker 2, messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker or MirrorMaker 2 component.

Tracing in Kafka Connect

For Kafka Connect, only messages produced and consumed by Kafka Connect are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems.

Tracing in the Kafka Bridge

For the Kafka Bridge, messages produced and consumed by the Kafka Bridge are traced. Incoming HTTP requests from client applications to send and receive messages through the Kafka Bridge are also traced. To have end-to-end tracing, you must configure tracing in your HTTP clients.

Procedure

Perform these steps for each KafkaMirrorMaker, KafkaMirrorMaker2, KafkaConnect, and KafkaBridge resource.

In the spec.template property, configure the tracer service.

Use the tracing environment variables as template configuration properties.
For OpenTelemetry, set the spec.tracing.type property to opentelemetry.

Example tracing configuration for Kafka Connect using OpenTelemetry

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  #...
  template:
    connectContainer:
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry
  #...

Example tracing configuration for MirrorMaker using OpenTelemetry

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  #...
  template:
    mirrorMakerContainer:
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry
#...

Example tracing configuration for MirrorMaker 2 using OpenTelemetry

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mm2-cluster
spec:
  #...
  template:
    connectContainer:
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry
#...

Example tracing configuration for the Kafka Bridge using OpenTelemetry

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  #...
  template:
    bridgeContainer:
      env:
        - name: OTEL_SERVICE_NAME
          value: my-otel-service
        - name: OTEL_EXPORTER_OTLP_ENDPOINT
          value: "http://otlp-host:4317"
  tracing:
    type: opentelemetry
#...

Create or update the resource:

oc apply -f <resource_configuration_file>

22.3.3. 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:

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>

Define the configuration of the tracer using the tracing environment variables.
Create a tracer, which is initialized with the environment variables:
Creating a tracer for OpenTelemetry
```
OpenTelemetry ot = GlobalOpenTelemetry.get();
```
Register the tracer as a global tracer:
```
GlobalTracer.register(tracer);
```
Instrument your client:
- Section 22.3.4, “Instrumenting producers and consumers for tracing”
- Section 22.3.5, “Instrumenting Kafka Streams applications for tracing”

22.3.4. 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);

22.3.5. 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());
```

22.3.6. Introducing a different OpenTelemetry tracing system

Instead of the default OTLP system, you can specify other tracing systems that are supported by OpenTelemetry. You do this by adding the required artifacts to the Kafka image provided with Streams for Apache Kafka. Any required implementation specific environment variables must also be set. You then enable the new tracing implementation using the OTEL_TRACES_EXPORTER environment variable.

This procedure shows how to implement Zipkin tracing.

Procedure

Add the tracing artifacts to the /opt/kafka/libs/ directory of the Streams for Apache Kafka image.
You can use the Kafka container image on the Red Hat Ecosystem Catalog as a base image for creating a new custom image.
OpenTelemetry artifact for Zipkin
```
io.opentelemetry:opentelemetry-exporter-zipkin
```

Set the tracing exporter and endpoint for the new tracing implementation.

Example Zikpin tracer configuration

apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
  name: my-mm2-cluster
spec:
  #...
  template:
    connectContainer:
      env:
        - name: OTEL_SERVICE_NAME
          value: my-zipkin-service
        - name: OTEL_EXPORTER_ZIPKIN_ENDPOINT
          value: http://zipkin-exporter-host-name:9411/api/v2/spans 1
        - name: OTEL_TRACES_EXPORTER
          value: zipkin 2
  tracing:
    type: opentelemetry
#...

1: Specifies the Zipkin endpoint to connect to.
2: The Zipkin exporter.

22.3.7. 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();

Ce contenu n'est pas disponible dans la langue sélectionnée.

Chapter 22. Introducing distributed tracing

22.1. Tracing options

22.2. Environment variables for tracing

22.3. Setting up distributed tracing

22.3.1. Prerequisites

22.3.2. Enabling tracing in MirrorMaker, Kafka Connect, and Kafka Bridge resources

22.3.3. Initializing tracing for Kafka clients

22.3.4. Instrumenting producers and consumers for tracing

22.3.5. Instrumenting Kafka Streams applications for tracing

22.3.6. Introducing a different OpenTelemetry tracing system

22.3.7. Specifying custom span names for OpenTelemetry

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