Understanding Recommendations

Generating Recommendations

Slingshot analyzes warehouse trends to find areas of optimization for your warehouses based on idleness, query load, queueing, spillage, and utilization. Slingshot identifies 1 of 11 possible issues.

Potential Warehouse Issue	Definition
High auto-suspend	When warehouses run for long periods of inactivity before suspending activity
High idleness	When a warehouse consumes credits without executing any queries for long periods of time
High query load	Several queries run at once, reduces performance and strains compute resources
High average (avg.) queue	A large number of queries waiting to be processed, impacts overall warehouse performance
High spill	Queries that require additional compute resources and rely on spilling data to remote storage for execution
Long query	Queries take a substantial amount of time to complete due complexity, size, or variety of factors
Low query load	An underutilized warehouse that runs minimal queries
Low average (avg.) queue	An underutilized warehouse with only a few tasks waiting in queue
Low spill	A warehouse with capacity to run more queries, it’s executing without spilling to remote storage
No auto-suspend	During periods of inactivity the warehouse continues to run and consume credits
No warehouse load	An inactive warehouse, underutilized
Short query	Queries execute quickly, underutilized

Hourly analysis on each warehouse looks for a single issue or a combination of issues to initiate a recommendation. There are 17 possible issue combinations. Combinations consist of a single issue or two or more issues together.

For example, a finding of High query load, Long query, High spill, High avg queue results in a recommendation to increase max cluster count.

Red tags on the Recommendation page identify the trigger and action. Dissect a Recommendation in Anatomy of a Recommendation.

Recommendation
Suggested action	Trigger (includes single issues and combinations)
Decrease warehouse size	Null query Load
	Low query load, Short query, Low spill
	Low query load, Short query
	Low query load, Low spill
	Low query load, Low avg queue
Increase max cluster count	High query load, High avg queue
	High query load, Long query, High spill, High avg queue
	High query load, Long query, High avg queue
Increase warehouse size	Long query, High spill
	Long query
	High spill
Decrease warehouse size and Increase max cluster count	High query load, Short query, Low spill, High avg queue
	High query load, Short query, High avg queue
	High query load, Low spill, High avg queue
Decrease auto-suspend	High idleness
	High auto-suspend
	No auto-suspend

ℹ️

Decreasing auto-suspend applies to shortening the period of inactivity necessary for a warehouse to enter suspension mode. No auto-suspend means there isn’t an auto-suspend time set and the warehouse could benefit from setting a timeout value.

Anatomy of a Recommendation

Review and adjust recommendations or apply them as is. The Recommendation consists of four parts.

Why you’re seeing this recommendation: A summary of the Slingshot-identified Issues, current cost and performance info (Average query execution time and Monthly cost), and the Projected results (cost and performance) should you apply the recommendation as suggested without making changes.

ℹ️

Making changes to the recommendation, negates the Projected results for the current month.

What to optimize: The Recommendation settings and schedule compare the current settings and schedule with the recommended settings and schedule. Make adjustments to the suggested parameters or settings. Learn more about editing recommendations, see Manage Recommendations.
- Compare schedules: Toggle between the current and recommended schedules to see which time blocks need your attention based on Slingshot’s hourly analysis of your warehouse’s performance.
- The current schedule flags areas of opportunity in red with a flag icon. Selecting any red time block displays an issue list and explanation.
How optimization happens: Review the Projected impact on queries and Historical warehouse performance tabs to gain insights about the impacts of the issues and how taking Slingshot’s suggestions improves performance or costs. These tabs provide you with valuable analysis tools and answers the key question, what happens when I apply this recommendation.