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How to build a Spark cluster from your own machines with Open Lakehouse Compute Fabric

Enroll several machines as workers and run Spark jobs on the combined Standalone cluster.

Credentials. Enrollment tokens let a machine join the cluster, so generate them one at a time and revoke any that leak.

Who it is forA platform engineer with spare servers, VMs or a rack who needs Spark capacity without moving data to a managed service.

The problemSpark jobs need more compute than one machine provides, and the team wants to use hardware it already owns.

The outcomeA Spark Standalone cluster built from enrolled machines, with a Spark runtime installed and jobs placed only on nodes where that runtime is ready.

What you need first

  • Python and a Linux machine (the README says it runs end to end on Linux)
  • Installation of the OLCF package as described in the README Install section
  • For the multi-machine fabric: a Spark runtime installed with the JDK, and machines that can reach the control plane
  • For the Fabric extension: a Fabric tenant you control, the olcf_fabric wheel in a Fabric Environment, and Entra and network setup

Steps

  1. Install a Spark runtime with olcf runtime install --with-jdk.
  2. Generate an enrollment token with olcf worker enroll-token.
  3. On each machine that should join, run olcf-worker run with the token.
  4. Submit a test job to the cluster from the console or CLI and check that it runs on more than one worker.

Prompt for your agent

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Install the Spark runtime with `olcf runtime install --with-jdk`, enroll <number of machines> workers, and report the cluster members. Verify that a test job runs and that the worker list shows each machine as enrolled.

Caveat. The bundled development signing key is public, so re-sign the runtime with your own key before trusting a production installation.

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