dbt-starrocks-performance

What problem does it solve?

It solves slow and unnecessarily expensive dbt runs on StarRocks caused by poor incremental partition filtering, inefficient rebuild patterns, and stale optimizer statistics.

Core Features & Use Cases

• Partition-aware incremental models that reprocess only a bounded window of partitions instead of scanning or rebuilding whole tables, suitable for large fact tables with late-arriving data.
• Performance-critical StarRocks tuning in dbt SQL using join/order hints, materialization choices, and model selection so complex DAGs run faster with fewer rebuilds.
• Post-hook and pre-hook optimizations like targeted ANALYZE TABLE for better CBO plans and pre-creating partitions for safe INSERT OVERWRITE incremental strategies.

Use case: you have a daily gold model that currently rebuilds too much history and runs for hours; this skill guides you to switch to partition-replacing increments, analyze only touched partitions, and run only modified downstream models.

Quick Start

Ask the agent to generate a revised incremental dbt model for StarRocks that uses partition-aware filters with a configurable late-arrival lookback and adds a post-hook to run ANALYZE TABLE for only the current partitions.