Spark job fails with Driver is temporarily unavailable

Learn how to distinguish between active and dead Databricks jobs.

Written by Adam Pavlacka

Last published at: May 10th, 2022


A Databricks notebook returns the following error:

Driver is temporarily unavailable

This issue can be intermittent or not.

A related error message is:

Lost connection to cluster. The notebook may have been detached.


One common cause for this error is that the driver is undergoing a memory bottleneck. When this happens, the driver crashes with an out of memory (OOM) condition and gets restarted or becomes unresponsive due to frequent full garbage collection. The reason for the memory bottleneck can be any of the following:

  • The driver instance type is not optimal for the load executed on the driver.
  • There are memory-intensive operations executed on the driver.
  • There are many notebooks or jobs running in parallel on the same cluster.


The solution varies from case to case. The easiest way to resolve the issue in the absence of specific details is to increase the driver memory. You can increase driver memory simply by upgrading the driver node type on the cluster edit page in your Databricks workspace.

Other points to consider:

  • Avoid memory intensive operations like:
    • collect() operator, which brings a large amount of data to the driver.
    • Conversion of a large DataFrame to Pandas
    If these operations are essential, ensure that enough driver memory is available.
  • Avoid running batch jobs on a shared interactive cluster.
  • Distribute the workloads into different clusters. No matter how big the cluster is, the functionalities of the Spark driver cannot be distributed within a cluster.
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