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Query performance insights

Preview

This feature is in Private Preview. To try it, reach out to your Databricks contact.

When queries run, Databricks might return insights that identify opportunities to improve performance. This page lists the supported insights and their meaning.

For a broader overview of performance best practices, review the Comprehensive Guide to Optimize Databricks, Spark and Delta Lake Workloads.

COVERAGE_FILTER_KEYS_PARTITIONING​

  • The table is partitioned by one or more keys that aren't used in filtering during the table scan.
  • Recommendation: Add filters on all partitioning keys to reduce the number of bytes read.

COVERAGE_FILTER_KEYS_CLUSTERING​

  • The table is clustered by one or more keys that aren't used in filtering during the table scan.
  • Recommendation: Add filters on all clustering keys to reduce the number of bytes read.

COVERAGE_PHOTON​

  • Photon can't accelerate the operation, so the standard runtime engine was used.
  • Recommendation: Review Photon limitations and consider adjusting the query to use a supported execution strategy for faster runtime.

COVERAGE_STATS_OPTIMIZER​

  • Cost-based optimizer statistics are missing or incomplete, so standard heuristics were used to generate the query plan.
  • Recommendation: Collect statistics to enable the optimizer to produce a better plan.

COVERAGE_STATS_DELTA​

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