Kafka Metrics

OTel Kafka Metrics Remapping is in public alpha. It is available in versions >= 0.93.0 of the collector. If you have feedback related to this, reach out to your account team to provide your input.

Overview

OpenTelemetry Kafka metrics in OOTB Kafka dashboard

The Kafka metrics receiver, JMX Receiver/ JMX Metrics Gatherer allow collecting Kafka metrics and access to the out of the box Kafka Dashboard, “Kafka, Zookeeper and Kafka Consumer Overview”.

Please note that the JMX Receiver and JMX Metrics Gatherer should be considered as replacements. They collect the same set of metrics (JMX Receiver launches the JMX Metrics Gatherer).

Kafka metrics receiver

    receivers:
  kafkametrics:
    brokers: "${env:KAFKA_BROKER_ADDRESS}"
    protocol_version: 2.0.0
    scrapers:
      - brokers
      - topics
      - consumers

    The Kafka metrics receiver needs to be used in a collector in deployment mode with a single replica. This ensures that the same metric is not collected multiple times. The collector in deployment mode can then leverage the Datadog Exporter to export the metrics directly to Datadog, or leverage the OTLP exporter to forward the metrics to another collector instance.

    Add the following lines to values.yaml:

    mode: deployment

    Add the following in the Collector configuration:

    receivers:
  kafkametrics:
    brokers: ${env:KAFKA_BROKER_ADDRESS}
    protocol_version: 2.0.0
    scrapers:
      - brokers
      - topics
      - consumers

    JMX receiver

      The JMX Receiver has the following requirements:

      • JRE is available on the host where you are running the collector.
      • The JMX Metric Gatherer JAR is available on the host where you are running the collector. You can download the most recent release of the JMX Metric Gatherer JAR from the opentelemetry-java-contrib releases page.

      Add the following in the Collector configuration:

      receivers:
  jmx:
    jar_path: /path/to/opentelemetry-jmx-metrics.jar
    endpoint: ${env:KAFKA_BROKER_JMX_ADDRESS}
    target_system: kafka,jvm
  jmx/consumer:
    jar_path: /path/to/opentelemetry-jmx-metrics.jar
    endpoint: ${env:KAFKA_CONSUMER_JMX_ADDRESS}
    target_system: kafka-consumer
  jmx/producer:
    jar_path: /path/to/opentelemetry-jmx-metrics.jar
    endpoint: ${env:KAFKA_PRODUCER_JMX_ADDRESS}
    target_system: kafka-producer

      The JMX receiver needs to be used in a collector in deployment mode with a single replica. This ensures that the same metric is not collected multiple times. The collector in deployment mode can then leverage the Datadog Exporter to export the metrics directly to Datadog, or leverage the OTLP exporter to forward the metrics to another collector instance.

      The JMX Receiver has the following requirements:

      • JRE is available on the host in which you are running the collector.
      • The JMX Metric Gatherer JAR is available on the host in which you are running the collector. You can download the most recent release of the JMX Metric Gatherer JAR here.

      Because the OTel collector default image does not meet the requirements above, a custom image needs to be built. Please refer to the Dockerfile below for an example image that contains the collector binary, JRE, and JMX Metrics Gatherer Jar.

      Dockerfile:

      FROM alpine:latest as prep

# OpenTelemetry Collector Binary
ARG OTEL_VERSION=0.92.0
ARG TARGETARCH=linux_amd64
ADD "https://github.com/open-telemetry/opentelemetry-collector-releases/releases/download/v${OTEL_VERSION}/otelcol-contrib_${OTEL_VERSION}_${TARGETARCH}.tar.gz" /otelcontribcol
RUN tar -zxvf /otelcontribcol

# JMX Metrics Gatherer Jar
ARG JMX_GATHERER_JAR_VERSION=1.27.0
ADD https://github.com/open-telemetry/opentelemetry-java-contrib/releases/download/v${JMX_GATHERER_JAR_VERSION}/opentelemetry-jmx-metrics.jar /opt/opentelemetry-jmx-metrics.jar
# nonroot user id (https://groups.google.com/g/distroless-users/c/-DpzCr7xRDY/m/eQqJmJroCgAJ)
ARG USER_UID=65532
RUN chown ${USER_UID} /opt/opentelemetry-jmx-metrics.jar


FROM gcr.io/distroless/java17-debian11:nonroot

COPY --from=prep /opt/opentelemetry-jmx-metrics.jar /opt/opentelemetry-jmx-metrics.jar
COPY --from=prep /otelcol-contrib /otelcol-contrib

EXPOSE 4317 55680 55679
ENTRYPOINT ["/otelcol-contrib"]
CMD ["--config", "/etc/otelcol-contrib/config.yaml"]

      Add the following lines to values.yaml:

      mode: deployment

      Add the following in the Collector configuration:

      receivers:
  jmx:
    jar_path: /path/to/opentelemetry-jmx-metrics.jar
    endpoint: ${env:KAFKA_BROKER_JMX_ADDRESS}
    target_system: kafka,jvm
  jmx/consumer:
    jar_path: /path/to/opentelemetry-jmx-metrics.jar
    endpoint: ${env:KAFKA_CONSUMER_JMX_ADDRESS}
    target_system: kafka-consumer
  jmx/producer:
    jar_path: /path/to/opentelemetry-jmx-metrics.jar
    endpoint: ${env:KAFKA_PRODUCER_JMX_ADDRESS}
    target_system: kafka-producer

      JMX Metrics Gatherer

        The JMX Metric Gatherer is intended to be run as an uber jar and configured with properties from the command line.

        Please make sure that JRE is available on the host in which you are running the collector. If not, please make sure to download it, e.g.

        apt-get update && \
apt-get -y install default-jre-headless

        Once you have done this, download the most recent release of the JMX Metric Gatherer JAR here and run:

        // Kafka Broker
java -jar -Dotel.jmx.service.url=service:jmx:rmi:///jndi/rmi://{KAFKA_BROKER_JMX_ADDRESS}/jmxrmi \ -Dotel.jmx.target.system=kafka,jvm \
-Dotel.metrics.exporter=otlp \
-Dotel.exporter.otlp.endpoint=http://localhost:4317 \
-jar /path/to/opentelemetry-jmx-metrics.jar

// Kafka Producer
java -jar -Dotel.jmx.service.url=service:jmx:rmi:///jndi/rmi://{KAFKA_PRODUCER_JMX_ADDRESS}/jmxrmi \ -Dotel.jmx.target.system=kafka-producer \
-Dotel.metrics.exporter=otlp \
-Dotel.exporter.otlp.endpoint=http://localhost:4317 \
-jar /path/to/opentelemetry-jmx-metrics.jar

// Kafka Consumer
java -jar -Dotel.jmx.service.url=service:jmx:rmi:///jndi/rmi://{KAFKA_CONSUMER_JMX_ADDRESS}/jmxrmi \ -Dotel.jmx.target.system=kafka-consumer \
-Dotel.metrics.exporter=otlp \
-Dotel.exporter.otlp.endpoint=http://localhost:4317 \
-jar /path/to/opentelemetry-jmx-metrics.jar

        The JMX Metric Gatherer is intended to be run as an uber jar and configured with properties from the command line.

        In order to deploy this in Kubernetes, you need to build an image which contains JRE and the JMX Metrics Gatherer Jar. Please see the Dockerfile below for an example image that contains JRE and JMX Metrics Gatherer Jar.

        Dockerfile:

        FROM alpine:latest as prep

# JMX Metrics Gatherer Jar
ARG JMX_GATHERER_JAR_VERSION=1.27.0
ADD https://github.com/open-telemetry/opentelemetry-java-contrib/releases/download/v${JMX_GATHERER_JAR_VERSION}/opentelemetry-jmx-metrics.jar /opt/opentelemetry-jmx-metrics.jar
# nonroot user id (https://groups.google.com/g/distroless-users/c/-DpzCr7xRDY/m/eQqJmJroCgAJ)
ARG USER_UID=65532
RUN chown ${USER_UID} /opt/opentelemetry-jmx-metrics.jar

FROM gcr.io/distroless/java17-debian11:nonroot

COPY --from=prep /opt/opentelemetry-jmx-metrics.jar /opt/opentelemetry-jmx-metrics.jar

EXPOSE 4317 55680 55679
ENTRYPOINT ["java"]
CMD ["-Dotel.jmx.service.url=service:jmx:rmi:///jndi/rmi://kafka:1099/jmxrmi", \
"-Dotel.jmx.target.system=kafka,jvm", \
"-Dotel.metrics.exporter=otlp", \
"-Dotel.exporter.otlp.endpoint=http://otelcol:4317", \
"-jar", \
"/opt/opentelemetry-jmx-metrics.jar"]

        Log collection

        See Log Collection for instructions on how to collect logs using the OpenTelemetry Collector.

        To appear in the out-of-the-box Kafka Dashboard, the Kafka logs need to be tagged with source:kafka. To do this, use an attributes processor:

        processors:
  attributes:
    actions:
      - key: ddtags
        value: "source:kafka"
        action: insert

        In order to ensure this attribute only gets added to your Kafka logs, use include/exclude filtering of the attributes processor.

        Data collected

        OpenTelemetry MetricDatadog MetricSourceTransform done on Datadog Metric
        otel.kafka.producer.request-ratekafka.producer.request_rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        otel.kafka.producer.response-ratekafka.producer.response_rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        otel.kafka.producer.request-latency-avgkafka.producer.request_latency_avgJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        kafka.producer.outgoing-byte-ratekafka.producer.outgoing-byte-rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        kafka.producer.io-wait-time-ns-avgkafka.producer.io_waitJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        kafka.producer.byte-ratekafka.producer.bytes_outJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        kafka.consumer.total.bytes-consumed-ratekafka.consumer.bytes_inJMX Receiver / JMX Metrics Gatherer {target_system:kafka-consumer}
        kafka.consumer.total.records-consumed-ratekafka.consumer.messages_inJMX Receiver / JMX Metrics Gatherer {target_system:kafka-consumer}
        kafka.network.io{state:out}kafka.net.bytes_out.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.network.io{state:in}kafka.net.bytes_in.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.purgatory.size{type:produce}kafka.request.producer_request_purgatory.sizeJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.purgatory.size{type:fetch}kafka.request.fetch_request_purgatory.sizeJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.partition.under_replicatedkafka.replication.under_replicated_partitionsJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.isr.operation.count{operation:shrink}kafka.replication.isr_shrinks.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.isr.operation.count{operation:expand}kafka.replication.isr_expands.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.leader.election.ratekafka.replication.leader_elections.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.partition.offlinekafka.replication.offline_partitions_countJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.request.time.avg{type:produce}kafka.request.produce.time.avgJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.request.time.avg{type:fetchconsumer}kafka.request.fetch_consumer.time.avgJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.request.time.avg{type:fetchfollower}kafka.request.fetch_follower.time.avgJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.message.countkafka.messages_in.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.request.failed{type:produce}kafka.request.produce.failed.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.request.failed{type:fetch}kafka.request.fetch.failed.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.request.time.99p{type:produce}kafka.request.produce.time.99percentileJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.request.time.99p{type:fetchconsumer}kafka.request.fetch_consumer.time.99percentileJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.request.time.99p{type:fetchfollower}kafka.request.fetch_follower.time.99percentileJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.partition.countkafka.replication.partition_countJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.max.lagkafka.replication.max_lagJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.controller.active.countkafka.replication.active_controller_countJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.unclean.election.ratekafka.replication.unclean_leader_elections.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.request.queuekafka.request.channel.queue.sizeJMX Receiver / JMX Metrics Gatherer {target_system:kafka}
        kafka.logs.flush.time.countkafka.log.flush_rate.rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka}Compute rate per second and submitted as Gauge
        kafka.consumer.bytes-consumed-ratekafka.consumer.bytes_consumedJMX Receiver / JMX Metrics Gatherer {target_system:kafka-consumer}
        kafka.consumer.records-consumed-ratekafka.consumer.records_consumedJMX Receiver / JMX Metrics Gatherer {target_system:kafka-consumer}
        otel.kafka.consumer.fetch-size-avgkafka.consumer.fetch_size_avgJMX Receiver / JMX Metrics Gatherer {target_system:kafka-consumer}
        otel.kafka.producer.compression-ratekafka.producer.compression-rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        otel.kafka.producer.record-error-ratekafka.producer.record_error_rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        otel.kafka.producer.record-retry-ratekafka.producer.record_retry_rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        otel.kafka.producer.record-send-ratekafka.producer.record_send_rateJMX Receiver / JMX Metrics Gatherer {target_system:kafka-producer}
        kafka.partition.current_offsetkafka.broker_offsetkafkametricsreceiver
        kafka.consumer_group.lagkafka.consumer_lagkafkametricsreceiver
        kafka.consumer_group.offsetkafka.consumer_offsetkafkametricsreceiver
        jvm.gc.collections.count{name:Copy && name:PS Scavenge && name:ParNew && name:G1 Young Generation}jvm.gc.min&&_collection_countJMX Receiver / JMX Metrics Gatherer {target_system:jvm}Compute rate per second and submitted as Gauge
        jvm.gc.maj&&_collection_count{name:MarkSweepCompact && name:PS MarkSweep &&name:ConcurrentMarkSweep &&name:G1 Mixed Generation && G1 Old Generation && Shenandoah Cycles && ZGC}jvm.gc.maj&&_collection_countJMX Receiver / JMX Metrics Gatherer {target_system:jvm}Compute rate per second and submitted as Gauge
        jvm.gc.collections.elapsed{name:Copy && name:PS Scavenge && name:ParNew && name:G1 Young Generation}jvm.gc.min&&_collection_timeJMX Receiver / JMX Metrics Gatherer {target_system:jvm}Compute rate per second and submitted as Gauge
        jvm.gc.collections.elapsed{name:MarkSweepCompact && name:PS MarkSweep &&name:ConcurrentMarkSweep &&name:G1 Mixed Generation && G1 Old Generation && Shenandoah Cycles && ZGC}jvm.gc.major_collection_timeJMX Receiver / JMX Metrics Gatherer {target_system:jvm}Compute rate per second and submitted as Gauge

        Note: In Datadog - gets translated to _. For the metrics prepended by otel., this means that the OTel metric name and the Datadog metric name are the same (for example, kafka.producer.request-rate and kafka.producer.request_rate). In order to avoid double counting for these metrics, the OTel metric is then prepended with otel..

        See OpenTelemetry Metrics Mapping for more information.

        Full example configuration

        For a full working example configuration with the Datadog exporter, see kafka.yaml.

        Example logging output

        Resource SchemaURL: https://opentelemetry.io/schemas/1.20.0
Resource attributes:
     -> service.name: Str(unknown_service:java)
     -> telemetry.sdk.language: Str(java)
     -> telemetry.sdk.name: Str(opentelemetry)
     -> telemetry.sdk.version: Str(1.27.0)
ScopeMetrics #0
ScopeMetrics SchemaURL: 
InstrumentationScope io.opentelemetry.contrib.jmxmetrics 1.27.0-alpha
Metric #0
Descriptor:
     -> Name: kafka.message.count
     -> Description: The number of messages received by the broker
     -> Unit: {messages}
     -> DataType: Sum
     -> IsMonotonic: true
     -> AggregationTemporality: Cumulative
NumberDataPoints #0
StartTimestamp: 2024-01-22 15:50:24.212 +0000 UTC
Timestamp: 2024-01-22 15:51:24.218 +0000 UTC
Value: 25

        Example app

        Please see the following example application which demonstrates the configurations discussed in this documentation. This example application is comprised of a producer, consumer, broker and zookeeper instance. It demonstrates using the Kafka metrics receiver, JMX Receiver and/or JMX Metrics Gatherer.

        PREVIEWING: rtrieu/product-analytics-ui-changes