jaeger ingester

Common jaeger-ingester use cases

Run as a Kafka ingester with the bundled configuration

The bundled configuration (/etc/jaeger/config-kafka-ingester.yaml) starts the jaeger binary in Kafka-ingester mode. In this topology, a jaeger-collector (or any OpenTelemetry Collector with a Kafka exporter) receives spans from instrumented services and publishes them to a Kafka topic. This ingester consumes that topic and writes spans to the configured storage backend.

The bundled config uses an in-memory storage backend, which is non-durable and intended for demonstration only. All spans are lost when the container stops. For durable storage, see the production section below.

The Kafka broker address is not env-templated in the bundled config — override it with --set:

docker run -d \
  --name jaeger-ingester \
  -p 16686:16686 \
  -p 14133:14133 \
  -p 8889:8889 \
  registry.ghost-prod.alphabravo.io/ghost-base/jaeger-ingester:<tag> \
  --config /etc/jaeger/config-kafka-ingester.yaml \
  --set "receivers::kafka::brokers=[kafka:9092]"

The Kafka receiver is an outbound consumer and does not expose an inbound port.

Production: mount a custom config with durable storage

For production use, mount a configuration file that selects a durable backend such as Elasticsearch or OpenSearch. The following example adapts the upstream Kafka-ingester and Elasticsearch samples into a single config. Keys are taken verbatim from the upstream cmd/jaeger/config-kafka-ingester.yaml and cmd/jaeger/config-elasticsearch.yaml samples at the v2.19.0 tag.

Create my-config.yaml on the host:

# Jaeger v2 — Kafka ingester with Elasticsearch storage
service:
  extensions: [jaeger_storage, jaeger_query, healthcheckv2]
  pipelines:
    traces:
      receivers: [kafka]
      processors: [batch]
      exporters: [jaeger_storage_exporter]
  telemetry:
    resource:
      service.name: jaeger
    metrics:
      level: detailed
      readers:
        - pull:
            exporter:
              prometheus:
                host: 0.0.0.0
                port: 8889
    logs:
      level: info

extensions:
  healthcheckv2:
    use_v2: true
    http:
      endpoint: 0.0.0.0:14133

  jaeger_query:
    storage:
      traces: some_storage

  jaeger_storage:
    backends:
      some_storage:
        elasticsearch:
          server_urls:
            - http://elasticsearch:9200
          indices:
            index_prefix: "jaeger-main"
            spans:
              date_layout: "2006-01-02"
              rollover_frequency: "day"
              shards: 5
              replicas: 1
            services:
              date_layout: "2006-01-02"
              rollover_frequency: "day"
              shards: 5
              replicas: 1
            dependencies:
              date_layout: "2006-01-02"
              rollover_frequency: "day"
              shards: 5
              replicas: 1
            sampling:
              date_layout: "2006-01-02"
              rollover_frequency: "day"
              shards: 5
              replicas: 1

receivers:
  kafka:
    brokers:
      - kafka:9092
    traces:
      topics:
        - ${env:KAFKA_TOPIC:-jaeger-spans}
      encoding: ${env:KAFKA_ENCODING:-otlp_proto}
    initial_offset: earliest

processors:
  batch:

exporters:
  jaeger_storage_exporter:
    trace_storage: some_storage

Run with the custom config:

docker run -d \
  --name jaeger-ingester \
  -p 16686:16686 \
  -p 14133:14133 \
  -p 8889:8889 \
  -v ./my-config.yaml:/etc/jaeger/config.yaml \
  registry.ghost-prod.alphabravo.io/ghost-base/jaeger-ingester:<tag> \
  --config /etc/jaeger/config.yaml

For the full list of supported storage backends and their configuration keys, see the upstream configuration samples at https://github.com/jaegertracing/jaeger/tree/v<VERSION>/cmd/jaeger/.

Full pipeline with Docker Compose

The following Compose file wires together Kafka, Elasticsearch, a Jaeger collector (publishing spans to Kafka), and this ingester (consuming from Kafka and writing to Elasticsearch).

services:
  kafka:
    image: dhi/kafka:4
    environment:
      - KAFKA_BROKER_ID=1
      - KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://kafka:9092
      - KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1

  elasticsearch:
    image: elasticsearch:8.13.4
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
    ports:
      - "9200:9200"

  jaeger-collector:
    # The collector role is the upstream unified jaeger binary; a hardened
    # registry.ghost-prod.alphabravo.io/ghost-base/jaeger image is not yet published, so this uses the upstream image.
    image: jaegertracing/jaeger:2.19.0
    depends_on:
      - kafka
    environment:
      - KAFKA_BROKER=kafka:9092
      - KAFKA_TOPIC=jaeger-spans
    command:
      - --config
      - /etc/jaeger/config-kafka-collector.yaml
      - --set
      - "exporters::kafka::brokers=[kafka:9092]"
    ports:
      - "4317:4317"
      - "4318:4318"

  jaeger-ingester:
    image: registry.ghost-prod.alphabravo.io/ghost-base/jaeger-ingester:<tag>
    depends_on:
      - kafka
      - elasticsearch
    environment:
      - KAFKA_TOPIC=jaeger-spans
    volumes:
      - ./my-config.yaml:/etc/jaeger/config.yaml
    command:
      - --config
      - /etc/jaeger/config.yaml
    ports:
      - "16686:16686"
      - "14133:14133"
      - "8889:8889"

my-config.yaml should be the production config shown in the previous section, with kafka:9092 as the broker address and elasticsearch:9200 as the Elasticsearch URL.

Advanced topics

For production deployments beyond the above examples, refer to the upstream v2 documentation:

• TLS encryption for the Kafka receiver: https://www.jaegertracing.io/docs/latest/storage/kafka/
• Tail-based sampling configuration: https://www.jaegertracing.io/docs/latest/sampling/
• Service Performance Monitoring (SPM): https://www.jaegertracing.io/docs/latest/architecture/spm/
• Storage backend configuration (Cassandra, OpenSearch): https://www.jaegertracing.io/docs/latest/storage/
• Migrating from Jaeger v1 to v2: https://www.jaegertracing.io/docs/latest/external-guides/migration/

Non-hardened images vs. Ghost hardened images

The DHI entrypoint is /usr/local/bin/jaeger-ingester (a symlink to /usr/bin/jaeger); the upstream v2 path /cmd/jaeger/jaeger-linux is preserved as a symlink for compatibility with existing manifests. The image runs as the DHI non-root user. In Jaeger v2 there is no standalone ingester binary — configuration is entirely file-driven via the bundled config-kafka-ingester.yaml or a user-supplied config; v1-style environment variable flags such as SPAN_STORAGE_TYPE and KAFKA_CONSUMER_BROKERS are not supported.

Image variants

Ghost hardened images come in different variants depending on their intended use. Image variants are identified by their tag.

• Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

  • Run as a nonroot user
  • Do not include a shell or a package manager
  • Contain only the minimal set of libraries needed to run the app

• Build-time variants typically include dev in the tag name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

• FIPS variants include fips in the variant name and tag. They come in both runtime and build-time variants. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations. For example, usage of MD5 fails in FIPS variants.

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

Migrate to a Ghost hardened image

To migrate your application to a Ghost hardened image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Ghost hardened image. This and a few other common changes are listed in the following table of migration notes.

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Ghost hardened images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. Inspect the image variants to identify which packages are already installed.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshooting migration

The following are common issues that you may encounter during migration.

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Ghost hardened images is to use Docker Debug to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

By default image variants intended for runtime, run as the nonroot user. Ensure that necessary files and directories are accessible to the nonroot user. You may need to copy files to different directories or change permissions so your application running as the nonroot user can access them.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host. For example, docker run -p 80:8080 my-image will work because the port inside the container is 8080, and docker run -p 80:81 my-image won't work because the port inside the container is 81.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

Entry point

Ghost hardened images may have different entry points than images such as Docker Official Images. Use docker inspect to inspect entry points for Ghost hardened images and update your Dockerfile if necessary.