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Version: 22.3

Tune Kubernetes Worker Nodes for Production

To get the best performance from your hardware, set Redpanda to production mode on each worker node and run the auto-tuning tool. The auto-tuning tool identifies the hardware configuration on your worker node and optimizes the Linux kernel to give you the best performance.


Make sure that your current Linux user has root privileges. The auto-tuning tool requires privileged access to the Linux kernel settings.

Make sure that your Linux kernel is at least version 3.10.0-514 or 4.18.

Install Redpanda

To run the auto-tuning tool, you need to install the Redpanda binary on each worker node.

## Run the setup script to download and install the repo
curl -1sLf '' | sudo -E bash && \
## Use yum to install redpanda
sudo yum install redpanda -y

Run the auto-tuning tool

Run the auto-tuning tool on each worker node that runs a Redpanda broker.

  1. Set Redpanda to production mode:

    sudo rpk redpanda mode production
  2. Run the auto-tuning tool:

    sudo rpk redpanda tune all
    Example output
    TUNER                  APPLIED  ENABLED  SUPPORTED  ERROR                           
    aio_events true true true
    ballast_file true true true
    clocksource true true true
    coredump false false true
    cpu true true true
    disk_irq true true true
    disk_nomerges true true true
    disk_scheduler true true true
    disk_write_cache false true false Disk write cache tuner is
    only supported in GCP
    fstrim false false true
    net true true true
    swappiness true true true
    transparent_hugepages false false true

Changes to the Linux kernel are not persisted. If a worker node restarts, make sure to run sudo rpk redpanda tune all on it again.


You can use a privileged DaemonSet to schedule the auto-tuning tool on each worker node that runs a Redpanda broker. Apply taints to Nodes that successfully complete the auto-tuning command. Use tolerations on your Pods so that they are scheduled only on tuned worker nodes.

Next steps

Deploy the Redpanda cluster.

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