How does this skill identify the cause of a slow query?

It analyzes Dremio job profiles to compare planning vs. execution time, checks for memory spilling, and evaluates if the query successfully utilized existing data reflections.

Do I need the Dremio CLI installed to use this skill?

Yes, this skill relies on the Dremio CLI (drs) to interact with your Dremio environment and retrieve metadata from system tables.

What common SQL anti-patterns can this skill detect?

It identifies issues such as leading wildcards in LIKE clauses, unnecessary SELECT * statements, cross joins, and missing partition pruning filters.

Can this skill help me create Dremio reflections?

Yes, based on the query pattern, it recommends specific raw or aggregate reflections and provides the necessary SQL commands to implement them.

Dremio Query Optimizer

Name: Dremio Query Optimizer
Author: dremio

bydremio

•

Database Management

Diagnoses and optimizes slow Dremio queries using job profiles, reflection analysis, and execution plan inspection.

This skill provides a systematic workflow for developers to troubleshoot performance issues in Dremio Cloud. It automates the process of identifying slow jobs, analyzing planning versus execution times, evaluating reflection usage, and detecting common SQL anti-patterns like broad SELECT * statements or missing partition pruning. By leveraging Dremio's system tables and job profiles, the skill offers actionable recommendations—such as creating specific reflections or rewriting queries—to improve throughput, reduce latency, and lower cloud compute costs.

Key Features

015 GitHub stars

02Detection of SQL anti-patterns and partition pruning failures

03Automated identification of slow jobs using recent history

04Evaluation of reflection status and matching logic

05Verification of performance improvements after applying optimizations

06Deep analysis of job profiles to pinpoint execution bottlenecks

Use Cases

01Troubleshooting a specific slow-running dashboard query in Dremio Cloud

02Identifying why a query is hitting raw data instead of using an active reflection

03Optimizing resource-intensive scans to reduce query execution time and costs

Key Features

015 GitHub stars

02Detection of SQL anti-patterns and partition pruning failures

03Automated identification of slow jobs using recent history

04Evaluation of reflection status and matching logic

05Verification of performance improvements after applying optimizations

06Deep analysis of job profiles to pinpoint execution bottlenecks

Use Cases

01Troubleshooting a specific slow-running dashboard query in Dremio Cloud

02Identifying why a query is hitting raw data instead of using an active reflection

03Optimizing resource-intensive scans to reduce query execution time and costs