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dbt-starrocks-performance

Optimize dbt and StarRocks incremental model performance with partition-aware filters and ANALYZE TABLE hooks.

14|1|Updated May 5, 2026
Search Tags:#dbt#performance tuning#partition pruning#starrocks#incremental models#query hints#ANALYZE TABLE
One-click install
npx skills add https://github.com/ivanshamaev/de-agent-skills --skill dbt-starrocks-performance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dbt-starrocks-performance
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/group_skills/starrocks_group_skills/dbt_starrocks_performance
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill dbt-starrocks-performance

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about dbt-starrocks-performance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I speed up slow dbt incremental models on StarRocks?▼

Improve slow dbt incremental models on StarRocks by applying partition-aware filtering with a bounded lookback window for late-arriving data, using targeted ANALYZE TABLE post-hooks, and optimizing dbt model selection to minimize unnecessary rebuilds.

Why does query performance degrade after refreshing dbt models in StarRocks?▼

Query performance degrades after refreshing dbt models in StarRocks due to stale optimizer statistics. Running targeted ANALYZE TABLE via dbt post-hooks on only the touched partitions updates the CBO and restores degraded query plans.

How do I handle late-arriving data in dbt without rebuilding the entire StarRocks table?▼

Handle late-arriving data in dbt without full StarRocks table rebuilds by implementing partition-aware incremental models with a configurable late-arrival lookback window, replacing only specific partitions instead of scanning whole tables.

Can I use query hints in dbt SQL to optimize complex join transformations in StarRocks?▼

You can use query hints in dbt SQL to optimize complex join transformations in StarRocks. Applying join and order hints, along with appropriate materialization choices, helps the StarRocks CBO generate faster query plans for complex DAGs.

What is the best way to reduce unnecessary dbt model rebuilds in a complex DAG?▼

Reduce unnecessary dbt model rebuilds in a complex DAG by employing dbt selection strategies that target only modified downstream models, combined with partition-replacing incremental strategies and pre-created partitions for safe INSERT OVERWRITE operations.

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