The long startup time for a Databricks Runtime 16.2 (Apache Spark 3.5.2, Scala 2.12) single-node cluster in Databricks Community Edition is a known issue and not unique to your setup. Many users have reported this situation, with some clusters taking significantly longer than the usual 5โ10 minutes to start, and in some cases failing to launch or staying "pending" indefinitely. It does not appear to be a direct bug in the 16.2 DBR itself, but rather an intermittent reliability and capacity limitation of the free Community Edition platform.โ
Common Findings
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Startup delays of over 10 minutes are widely reported by other users, especially with the newest DBR versions.โ
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Cluster launch reliability tends to degrade during peak usage times due to limited resources available for Community Edition clusters.โ
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Creating a new cluster (rather than restarting a terminated one) offers a partial workaround, but does not fully resolve the delays.โ
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No official fix or update has been issued for these delays specifically in Community Edition as of November 2025, and the issue is not tied to a specific DBR version.โ
Practical Workarounds
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Try creating a new cluster instead of restarting an old one if yours gets stuck.
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Patience is unfortunately required, as sometimes clusters will start after a long waiting period.
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If possible, try launching clusters at off-peak times or try changing the DBR version or region if those options are available in your console.โ
Conclusion
The delays you are experiencing are a limitation of the Databricks Community Edition and there is no clear fix at this time. It is recommended to use paid or enterprise versions for mission-critical or time-sensitive workloads, as these offer significantly faster and more reliable provisioning. For learning and experimentation, delays are common and expected, especially with single node clusters during busy periodsods.โ
