bitnamicharts/kafka Docker 镜像 - 轩辕镜像

Bitnami Secure Images Helm chart for Apache Kafka

Apache Kafka is a distributed streaming platform designed to build real-time pipelines and can be used as a message broker or as a replacement for a log aggregation solution for big data applications.

Overview of Apache Kafka

Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

TL;DR

console
helm install my-release oci://registry-1.docker.io/bitnamicharts/kafka

Tip: Did you know that this app is also available as a Kubernetes App on the Azure Marketplace? Kubernetes Apps are the easiest way to deploy Bitnami on AKS. Click here to see the listing on Azure Marketplace.

Why use Bitnami Secure Images?

Those are hardened, minimal CVE images built and maintained by Bitnami. Bitnami Secure Images are based on the cloud-optimized, security-hardened enterprise OS Photon Linux. Why choose BSI images?

• Hardened secure images of popular open source software with Near-Zero Vulnerabilities
• Vulnerability Triage & Prioritization with VEX Statements, KEV and EPSS Scores
• Compliance focus with FIPS, STIG, and air-gap options, including secure bill of materials (SBOM)
• Software supply chain provenance attestation through in-toto
• First class support for the internet’s favorite Helm charts

Each image comes with valuable security metadata. You can view the metadata in our public catalog here. Note: Some data is only available with commercial subscriptions to BSI.

!Alt text !Alt text

If you are looking for our previous generation of images based on Debian Linux, please see the Bitnami Legacy registry.

Introduction

This chart bootstraps a Kafka deployment on a Kubernetes cluster using the Helm package manager.

Prerequisites

• Kubernetes 1.23+
• Helm 3.8.0+
• PV provisioner support in the underlying infrastructure

Installing the Chart

To install the chart with the release name my-release:

console
helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/kafka

Note: You need to substitute the placeholders REGISTRY_NAME and REPOSITORY_NAME with a reference to your Helm chart registry and repository. For example, in the case of Bitnami, you need to use REGISTRY_NAME=registry-1.docker.io and REPOSITORY_NAME=bitnamicharts.

These commands deploy Kafka on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation.

Tip: List all releases using helm list

Configuration and installation details

Listeners configuration

This chart allows you to automatically configure Kafka with 4 listeners:

• One for controller communications.
• A second one for inter-broker communications.
• A third one for communications with clients within the K8s cluster.
• (optional) a forth listener for communications with clients outside the K8s cluster. Check this section for more information.

For more complex configurations, set the listeners, advertisedListeners and listenerSecurityProtocolMap parameters as needed.

Enable security for Kafka

You can configure different authentication protocols for each listener you configure in Kafka. For instance, you can use sasl_tls authentication for client communications, while using tls for controller and inter-broker communications. This table shows the available protocols and the security they provide:

Configure the authentication protocols for client, controller and inter-broker communications by setting the listeners.client.protocol, listeners.controller.protocol and listeners.interbroker.protocol parameters to the desired ones, respectively.

If you enabled SASL authentication on any listener, you can set the SASL credentials using the parameters below:

• sasl.client.users/sasl.client.passwords: when enabling SASL authentication for communications with clients.
• sasl.interbroker.user/sasl.interbroker.password: when enabling SASL authentication for inter-broker communications.
• sasl.controller.user/sasl.controller.password: when enabling SASL authentication for controller communications.

In order to configure TLS authentication/encryption, you can create a secret per Kafka node you have in the cluster containing the Java Key Stores (JKS) files: the truststore (kafka.truststore.jks) and the keystore (kafka.keystore.jks). Then, you need pass the secret names with the tls.existingSecret parameter when deploying the chart.

Note: If the JKS files are password protected (recommended), you will need to provide the password to get access to the keystores. To do so, use the tls.keystorePassword and tls.truststorePassword parameters to provide your passwords.

For instance, to configure TLS authentication on a Kafka cluster with 2 Kafka nodes use the commands below to create the secrets:

console
kubectl create secret generic kafka-jks-0 --from-file=kafka.truststore.jks=./kafka.truststore.jks --from-file=kafka.keystore.jks=./kafka-0.keystore.jks
kubectl create secret generic kafka-jks-1 --from-file=kafka.truststore.jks=./kafka.truststore.jks --from-file=kafka.keystore.jks=./kafka-1.keystore.jks

Note: the command above assumes you already created the truststore and keystores files. This script can help you with the JKS files generation.

If, for some reason (like using CertManager) you can not use the default JKS secret scheme, you can use the additional parameters:

• tls.jksTruststoreSecret to define additional secret, where the kafka.truststore.jks is being kept. The truststore password must be the same as in tls.truststorePassword
• tls.jksTruststoreKey to overwrite the default value of the truststore key (kafka.truststore.jks).

Note: If you are using CertManager, particularly when an ACME issuer is used, the ca.crt field is not put in the Secret that CertManager creates. To handle this, the tls.pemChainIncluded property can be set to true and the initContainer created by this Chart will attempt to extract the intermediate certs from the tls.crt field of the secret (which is a PEM chain) Note: The truststore/keystore from above must be protected with the same passwords set in the tls.keystorePassword and tls.truststorePassword parameters.

You can deploy the chart with authentication using the following parameters:

console
replicaCount=2
listeners.client.protocol=SASL
listeners.interbroker.protocol=TLS
tls.existingSecret=kafka-jks
tls.keystorePassword=jksPassword
tls.truststorePassword=jksPassword
sasl.client.users[0]=brokerUser
sasl.client.passwords[0]=brokerPassword

By setting the following parameter: listeners.client.protocol=SSL and listener.client.sslClientAuth=required, Kafka will require the clients to authenticate to Kafka brokers via certificate.

As result, we will be able to see in kafka-authorizer.log the events specific Subject: [...] Principal = User:CN=kafka,OU=...,O=...,L=...,C=..,ST=... is [...].

Update credentials

The Bitnami Kafka chart, when upgrading, reuses the secret previously rendered by the chart or the one specified in sasl.existingSecret. To update credentials, use one of the following:

• Run helm upgrade specifying new credentials in the sasl section as explained in the authentication section.
• Run helm upgrade specifying a new secret in sasl.existingSecret

Accessing Kafka brokers from outside the cluster

In order to access Kafka Brokers from outside the cluster, an additional listener and advertised listener must be configured. Additionally, a specific service per kafka pod will be created.

There are three ways of configuring external access. Using Load*** services, using NodePort services or using ClusterIP services.

Using Load*** services

You have two alternatives to use Load*** services:

• Option A) Use random load *** IPs using an initContainer that waits for the IPs to be ready and discover them automatically.

console
externalAccess.enabled=true
externalAccess.broker.service.type=Load***
externalAccess.controller.service.type=Load***
externalAccess.broker.service.ports.external=9094
externalAccess.controller.service.ports.external=9094
defaultInitContainers.autoDiscovery.enabled=true
serviceAccount.create=true
broker.automountServiceAccountToken=true
controller.automountServiceAccountToken=true
rbac.create=true

Note: This option requires creating RBAC rules on clusters where RBAC policies are enabled.

• Option B) Manually specify the load *** IPs:

console
externalAccess.enabled=true
externalAccess.controller.service.type=Load***
externalAccess.controller.service.containerPorts.external=9094
externalAccess.controller.service.load***IPs[0]='external-ip-1'
externalAccess.controller.service.load***IPs[1]='external-ip-2'
externalAccess.broker.service.type=Load***
externalAccess.broker.service.ports.external=9094
externalAccess.broker.service.load***IPs[0]='external-ip-3'
externalAccess.broker.service.load***IPs[1]='external-ip-4'

Note: You need to know in advance the load *** IPs so each Kafka broker advertised listener is configured with it.

Following the aforementioned steps will also allow to connect the brokers from the outside using the cluster's default service (when service.type is Load*** or NodePort). Use the property service.externalPort to specify the port used for external connections.

Using NodePort services

You have two alternatives to use NodePort services:

• Option A) Use random node ports using an initContainer that discover them automatically.

  console
  externalAccess.enabled=true
  externalAccess.controller.service.type=NodePort
  externalAccess.broker.service.type=NodePort
  defaultInitContainers.autoDiscovery.enabled=true
  serviceAccount.create=true
  rbac.create=true
  

  Note: This option requires creating RBAC rules on clusters where RBAC policies are enabled.

• Option B) Manually specify the node ports:

  console
  externalAccess.enabled=true
  externalAccess.controller.service.type=NodePort
  externalAccess.controller.service.nodePorts[0]='node-port-1'
  externalAccess.controller.service.nodePorts[1]='node-port-2'
  

  Note: You need to know in advance the node ports that will be exposed so each Kafka broker advertised listener is configured with it.

  The pod will try to get the external ip of the node using curl -s [***] unless externalAccess.<controller|broker>.service.domain or externalAccess.<controller|broker>.service.useHostIPs is provided.

• Option C) Manually specify distinct external IPs (using controller+broker nodes)

  console
  externalAccess.enabled=true
  externalAccess.controller.service.type=NodePort
  externalAccess.controller.service.externalIPs[0]='172.16.0.20'
  externalAccess.controller.service.externalIPs[1]='172.16.0.21'
  externalAccess.controller.service.externalIPs[2]='172.16.0.22'
  

  Note: You need to know in advance the available IP of your cluster that will be exposed so each Kafka broker advertised listener is configured with it.

Using ClusterIP services

Note: This option requires that an ingress is deployed within your cluster

console
externalAccess.enabled=true
externalAccess.controller.service.type=ClusterIP
externalAccess.controller.service.ports.external=9094
externalAccess.controller.service.domain='ingress-ip'
externalAccess.broker.service.type=ClusterIP
externalAccess.broker.service.ports.external=9094
externalAccess.broker.service.domain='ingress-ip'

Note: the deployed ingress must contain the following block:

console
tcp:
  9094: "{{ include "common.names.namespace" . }}/{{ include "common.names.fullname" . }}-0-external:9094"
  9095: "{{ include "common.names.namespace" . }}/{{ include "common.names.fullname" . }}-1-external:9094"
  9096: "{{ include "common.names.namespace" . }}/{{ include "common.names.fullname" . }}-2-external:9094"

Name resolution with External-DNS

You can use the following values to generate External-DNS annotations which automatically creates DNS records for each ReplicaSet pod:

yaml
externalAccess:
  controller:
    service:
      annotations:
        external-dns.alpha.kubernetes.io/hostname: "{{ .targetPod }}.example.com"

Resource requests and limits

Bitnami charts allow setting resource requests and limits for all containers inside the chart deployment. These are inside the resources values (check parameters table). Setting requests is essential for production workloads and these should be adapted to your specific use case.

To make this process easier, the chart contains the resourcesPreset values, which automatically sets the resources section according to different presets. Check these presets in the bitnami/common chart. However, in production workloads using resourcesPreset is discouraged as it may not fully adapt to your specific needs. Find more information on container resource management in the official Kubernetes documentation.

Prometheus metrics

Enable metrics

This chart can be integrated with Prometheus by setting metrics.jmx.enabled to true. This will deploy a sidecar container with jmx_exporter in all pods and a metrics service, which can be configured under the metrics.jmx.service section. This service will have the necessary annotations to be automatically scraped by Prometheus.

Prometheus requirements

It is necessary to have a working installation of Prometheus or Prometheus Operator for the integration to work. Install the Bitnami Prometheus helm chart or the Bitnami Kube Prometheus helm chart to easily have a working Prometheus in your cluster.

Integration with Prometheus Operator

The chart can deploy ServiceMonitor objects for integration with Prometheus Operator installations. To do so, set the value metrics.serviceMonitor.enabled=true. Ensure that the Prometheus Operator CustomResourceDefinitions are installed in the cluster or it will fail with the following error:

text
no matches for kind "ServiceMonitor" in version "monitoring.coreos.com/v1"

Install the Bitnami Kube Prometheus helm chart for having the necessary CRDs and the Prometheus Operator.

Rolling VS Immutable tags

It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image.

Bitnami will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist.

Sidecars

If you have a need for additional containers to run within the same pod as Kafka (e.g. an additional metrics or logging exporter), you can do so via the sidecars parameters. Simply define your container according to the Kubernetes container spec.

yaml
sidecars:
  - name: your-image-name
    image: your-image
    imagePullPolicy: Always
    ports:
      - name: portname
       containerPort: 1234

Setting Pod's affinity

This chart allows you to set your custom affinity using the affinity parameter. Find more information about Pod's affinity in the kubernetes documentation.

As an alternative, you can use of the preset configurations for pod affinity, pod anti-affinity, and node affinity available at the bitnami/common chart. To do so, set the podAffinityPreset, podAntiAffinityPreset, or nodeAffinityPreset parameters.

Deploying extra resources

There are cases where you may want to deploy extra objects, such as Kafka Connect. For covering this case, the chart allows adding the full specification of other objects using the extraDeploy parameter. The following example would create a deployment including a Kafka Connect deployment so you can connect Kafka with MongoDB®:

yaml
extraDeploy:
  - |
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: {{ include "common.names.fullname" . }}-connect
      labels: {{- include "common.labels.standard" ( dict "customLabels" .Values.commonLabels "context" $ ) | nindent 4 }}
        app.kubernetes.io/component: connector
    spec:
      replicas: 1
      selector:
        matchLabels: {{- include "common.labels.matchLabels" ( dict "customLabels" .Values.commonLabels "context" $ ) | nindent 6 }}
          app.kubernetes.io/component: connector
      template:
        metadata:
          labels: {{- include "common.labels.standard" ( dict "customLabels" .Values.commonLabels "context" $ ) | nindent 8 }}
            app.kubernetes.io/component: connector
        spec:
          containers:
            - name: connect
              image: KAFKA-CONNECT-IMAGE
              imagePullPolicy: IfNotPresent
              ports:
                - name: connector
                  containerPort: 8083
              volumeMounts:
                - name: configuration
                  mountPath: /bitnami/kafka/config
          volumes:
            - name: configuration
              configMap:
                name: {{ include "common.names.fullname" . }}-connect
  - |
    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: {{ include "common.names.fullname" . }}-connect
      labels: {{- include "common.labels.standard" ( dict "customLabels" .Values.commonLabels "context" $ ) | nindent 4 }}
        app.kubernetes.io/component: connector
    data:
      connect-standalone.properties: |-
        bootstrap.servers = {{ include "common.names.fullname" . }}-controller-0.{{ include "common.names.fullname" . }}-controller-headless.{{ include "common.names.namespace" . }}.svc.{{ .Values.clusterDomain }}:{{ .Values.service.ports.client }}
        ...
      mongodb.properties: |-
        connection.uri=mongodb://root:password@mongodb-hostname:27017
        ...
  - |
    apiVersion: v1
    kind: Service
    metadata:
      name: {{ include "common.names.fullname" . }}-connect
      labels: {{- include "common.labels.standard" ( dict "customLabels" .Values.commonLabels "context" $ ) | nindent 4 }}
        app.kubernetes.io/component: connector
    spec:
      ports:
        - protocol: TCP
          port: 8083
          targetPort: connector
      selector: {{- include "common.labels.matchLabels" ( dict "customLabels" .Values.commonLabels "context" $ ) | nindent 4 }}
        app.kubernetes.io/component: connector

You can create the Kafka Connect image using the Dockerfile below:

Dockerfile
FROM bitnami/kafka:latest
# Download MongoDB® Connector for Apache Kafka [***]
RUN mkdir -p /opt/bitnami/kafka/plugins && \
    cd /opt/bitnami/kafka/plugins && \
    curl --remote-name --location --silent [***]
CMD /opt/bitnami/kafka/bin/connect-standalone.sh /bitnami/kafka/config/connect-standalone.properties /bitnami/kafka/config/mongo.properties

Persistence

The Bitnami Kafka image stores the Kafka data at the /bitnami/kafka path of the container. Persistent Volume Claims are used to keep the data across deployments. This is known to work in GCE, AWS, and minikube.

Adjust permissions of persistent volume mountpoint

As the image run as non-root by default, it is necessary to adjust the ownership of the persistent volume so that the container can write data into it.

By default, the chart is configured to use Kubernetes Security Context to automatically change the ownership of the volume. However, this feature does not work in all Kubernetes distributions. As an alternative, this chart supports using an initContainer to change the ownership of the volume before mounting it in the final destination.

You can enable this initContainer by setting volumePermissions.enabled to true.

Backup and restore

To back up and restore Helm chart deployments on Kubernetes, you need to back up the persistent volumes from the source deployment and attach them to a new deployment using Velero, a Kubernetes backup/restore tool. Find the instructions for using Velero in this guide.

Parameters

Global parameters

_Note: the README for this chart is longer than the DockerHub length limit of 25000, so it has been trimmed. The full README can be found at [***]