How to Access the Spark UI on Databricks (Step by Step)

Apache Spark UI is Databricks’ primary interface for examining Spark application behavior, including jobs, stages, tasks, executors, SQL queries, cached data, and configuration, making it useful for diagnosing slow or failed workloads. It is available only on classic all-purpose and job compute, not on serverless compute, SQL warehouses, or Databricks Free Edition, where query profiles and cell-level performance views provide alternatives. Users can access it through the Compute page for all-purpose clusters, from a notebook’s Spark Jobs details for a specific cell, or from a job run’s Compute section for scheduled tasks, provided they have the necessary compute or job permissions. Spark UI history can remain accessible after a cluster terminates, but restarting replaces prior history and terminated compute may be deleted after 30 days unless retained, so log delivery is recommended for longer-term records. Its main tabs expose execution timing, failed jobs, shuffle activity, memory and executor metrics, query plans, and specialized information for streaming, JDBC/ODBC, and Spark Connect workloads.

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