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About a year ago we published the first release of our popular logging-operator. The initial version of that operator was designed to fit Pipeline, the Banzai Cloud hybrid cloud container management platform. However, since then, all kinds of people have found it to be an extremely useful tool that helps them manage their logs on Kubernetes.

Initially, Fluent ecosystem automation was enough to support the disparate needs of our userbase, but, as the popularity of the logging-operator grew, different setups were put in place by our community that revealed some of its limitations. Over the last few months we’ve been collecting user feedback, wishlists and feature requests, and have decided to redesign the operator from scratch.

Today, we’re happy to announce that we’re releasing the beta version of our brand new logging-operator v2.

Design principles of v2 πŸ”—︎

Tailored to, and driven by, our community, as well as the requirements of the Pipeline platform and those of a new logging product for Kubernetes forthcoming from Banzai Cloud, the main design principles of our operator were:

  • Make logging more Kubernetes native and provide namespace isolation
  • Allow users to filter logs based on Kubernetes labels
  • Make it easy to write new plugins by reusing as much code as possible
  • Provide multiple outputs for the same logs
  • Provide as much help as possible when debugging complex scenarios
  • Implement support for multiple log flows

Now let’s dig into these in more detail.

Namespace separation πŸ”—︎

One of the key problems with logging was separating different namespaces. Because Kubernetes logging solutions tail logs on a per node bases, they arrive in bulk from a variety of namespaces. For that reason, the operator guards the Fluentd configuration and checks permissions before adding new flows. In this way, the logging-operator adheres to namespace boundaries and denies prohibited rules.

Labels vs Fluentd tags πŸ”—︎

The second problem we faced was identifying logs. Kubernetes distinguishes resources based on their name and labels, while Fluentd handles log sources as flows. The log metadata consists of a timestamp and tag, which becomes attached to a log’s record when it enters the Fluent-bit pipeline. To fix this problem we opensourced a Fluentd plugin called label-router, which is able to route logs via namespaces and labels.

Single log, multiple flows πŸ”—︎

It’s common practice to archive all your logs in object stores, then send out a filtered version for analytics (e.g. ElasticSearch). Our operator seamlessly multiplies logs for different outputs, so you can set up multiple flows with different filters and different outputs.

Easily extendable and self documented πŸ”—︎

Several plugins share the same configurable component, similar to an output plugin’s buffer section, and these are now reusable across different plugins. To maintain order and a clear structure, we designed them with their documentation in mind; the operator generates documentation based on docstring comments and attribute tags. You can read more about this in our developers guide.

Configuration checks πŸ”—︎

When Fluentd aggregates logs, it shares the configurations of different log flows. To prevent a bad configuration from failing the Fluentd process, a configuration check validates these first. The new settings only go live after a successful check.

Component overview πŸ”—︎

Now that we’re through covering the design principles, let’s do a detailed component overview and take a walk through the new resources we’ve added.

Logging Operator v2 Architecture

Logging πŸ”—︎

The Logging resource represents the logging system, and contains configurations for Fluentd and Fluent-bit. It also establishes the controlNamespace, the administrative namespace of the logging-operator. The Fluentd statefulset and Fluent-bit daemonset will be deployed in this namespace, and non namespaced resources like ClusterOutput and ClusterFlow are only effective in this namespace - are ignored in any other namespace. For more detailed information visit the documentation of Logging operator CRDs.

example Logging

kind: Logging
  name: default-logging-simple
  namespace: logging
  fluentd: {}
  fluentbit: {}
  controlNamespace: logging

Output πŸ”—︎

Output defines an output for a logging flow. This is a namespaced resource which means only a Flow within the same namespace can access it. You can use secrets in these definitions, but they must also be in the same namespace. See the list of supported Logging operator plugins for details. We at Banzai Cloud and our open source community members are constantly working on new plugins, so if you can’t find what you need, you should feel free to get in touch with us over the Banzai Cloud Slack channel or through GitHub.

example Output

kind: Output
  name: gcs-output-sample
          name: gcs-secret
          key: credentials.json
    project: logging-example
    bucket: banzai-log-test
    path: logs/${tag}/%Y/%m/%d/
      path: /tmp/buffer
      timekey: 1m
      timekey_wait: 10s
      timekey_use_utc: true

Flow πŸ”—︎

Flow defines a logging flow with filters and outputs. This is a namespaced resource as well, so only logs from the same namespaces are collected. You can specify selectors to filter logs according to Kubernetes labels, and can define one or more filters within a Flow. These filters are applied in the order in the definition. See the list of supported Logging operator filters for details. At the end of the Flow, you can attach one or more outputs, which may also be Output or ClusterOutput resources.

example Flow

kind: Flow
  name: flow-sample
  namespace: default
    - tag_normaliser:
        format: ${namespace_name}.${pod_name}.${container_name}
    - parser:
        key_name: message
        - type: nginx
    - gcs-output-sample
    - elasticsearch-output
    app: nginx

ClusterOutput πŸ”—︎

ClusterOutput defines an Output without namespace restrictions. It is only effective in the controlNamespace.

ClusterFlow πŸ”—︎

ClusterFlow defines a Flow without namespace restrictions. It is also only effective in the controlNamespace.

Showtime πŸ”—︎

Let’s take a look at the new operator at work, as it saves all logs to S3. In the coming weeks, and before launching our new logging product, we will be sharing posts about the operator’s capabilities and our/customer usage patterns, so stay tuned.

Install the logging-operator πŸ”—︎

Install the logging-operator using the Helm chart. This chart only deploys the CRDs and the operator itself.

First we create the controlNamespace

kubectl create namespace logging-system

Note: If you have auto namespace create enabled in Helm you can skip namespace creation

$ helm repo add banzaicloud-stable
$ helm repo update
$ helm install banzaicloud-stable/logging-operator --namespace logging-system

Create default logging πŸ”—︎

Now we can spin up the logging components. The fluentd statefulset and fluent-bit daemonset will be created as a result of creating the Logging custom resource.

For ease of use, we created a Helm chart to set the Logging resource up with TLS.

helm install banzaicloud-stable/logging-operator-logging --set controlNamespace=logging-system

However, if you prefer to use the CR definition, here’s an example without TLS:

kubectl apply -f logging.yaml
kind: Logging
  name: default-logging-simple
  fluentd: {}
  fluentbit: {}
  controlNamespace: logging-system

Note: ClusterOutput and ClusterFlow resources will only be accepted in the controlNamespace

Create an AWS secret πŸ”—︎

If you have your $AWS_ACCESS_KEY_ID and $AWS_SECRET_ACCESS_KEY set, you can use the following snippet to create a Kubernetes secret from your Amazon credentials.

kubectl create secret generic logging-s3 --namespace logging-system --from-literal "awsAccessKeyId=$AWS_ACCESS_KEY_ID" --from-literal "awsSecretAccesKey=$AWS_SECRET_ACCESS_KEY"

Or you can set the secret up manually:

kubectl apply -f secret.yaml
apiVersion: v1
kind: Secret
  name: logging-s3
  namespace: logging-system
type: Opaque
  awsAccessKeyId: <base64encoded>
  awsSecretAccesKey: <base64encoded>

It is ESSENTIAL that you install the secret and the ClusterOutput definition in the same namespace.

Create an S3 output definition πŸ”—︎

kubectl apply -f clusteroutput.yaml
kind: ClusterOutput
  name: s3-output
  namespace: logging-system
          name: logging-s3
          key: awsAccessKeyId
          name: logging-s3
          key: awsSecretAccesKey
    s3_bucket: logging-amazon-s3
    s3_region: eu-central-1
    path: logs/${tag}/%Y/%m/%d/
      path: /tmp/buffer
      timekey: 10m
      timekey_wait: 30s
      timekey_use_utc: true

Note: For a production setup we recommend using a longer timekey interval to avoid generating too many objects.

Configure a ClusterFlow πŸ”—︎

The following snippet uses tag_normaliser to re-tag logs and, afterward, push them to S3.

kubectl apply -f clusterflow.yaml
kind: ClusterFlow
  name: all-log-to-s3
  namespace: logging-system
    - tag_normaliser: {}
  selectors: {}
    - s3-output

The logs then become available in the bucket of a path like:


And that’s that. By now, we hope you’re as excited as we are about the new version of our logging-operator. We are actively working on more filters and outputs in order to cover all of our and our customers’ use cases. We’ll be releasing more examples, and some of the more advanced situations our customers found themselves in, as part of the operator’s documentation. That way you’ll be able to design your own Kubernetes logging infrastructure based on real life examples and proven working models. If you need help and support utilizing the logging-operator, please be sure you contact us on Slack or subscribe to one of our commercial packages.

Happy logging!