Performance
The Performance Page gives you a complete view of how your Snowflake workload behaves over time - execution speed, efficiency, data processing patterns, and workload trends. With clear visualizations, drill-downs, and 30-day benchmarks, you can quickly identify issues, optimize resource usage, and understand the real impact of Yuki’s routing and warehouse decisions.
What the Performance Page Helps You Do
Track query execution speed and detect slowdowns or performance drift
Understand how efficiently warehouses process data
See how much data is being scanned or spilled to remote storage
Compare current performance to historical averages
Investigate anomalies with one click and trace them to specific queries
Validate the impact of Yuki optimization on cost, efficiency, and throughput
The page is divided into three main sections, each answering different questions about your workload.
1. Query Volume & Performance
This section focuses on the speed of your Snowflake workload.
Average Execution Time
See how long queries take to complete each day.
Track average daily execution time
Compare against a rolling 30-day baseline
Identify slowdowns, spikes, or trends
Validate performance improvements after query changes or warehouse resizing
Max Execution Time
Highlights the slowest queries each day.
Helps identify inefficient logic, large scans, or bad joins
Compare max execution time to 30-day norms
Great for spotting regressions after ETL or dbt changes
Each data point is clickable - opening a drill-down with the top queries that influenced execution time that day.
2. Resource Efficiency & Data Processing
This section explains how efficiently your data is being processed, which directly influences both cost and performance.
GB Scanned Over Time
Helps you understand your data footprint.
Daily total GB scanned across all queries
Track trends to see when workloads become heavier or leaner
Overlayed with a 30-day average for quick benchmarking
A daily drill-down is available to see top queries by scan volume.
GB Spilled to Remote Storage
Shows how often Snowflake spills data to disk.
This chart reveals:
total GB spilled per day
comparison to historical norms
Number of Queries Spilled
Shows how many queries required spilling.
This metric helps determine whether spilling is isolated or systemic
3. Workload Efficiency Trends
This section measures how efficiently Snowflake warehouses convert credits into work.
Execution Minutes Per Credit
This is one of the strongest indicators of workload efficiency.
Measures how much execution time you get per credit
Higher = more efficient.
Number of Queries
Shows how much workload Snowflake processed.
Understand daily query volume
Detect usage spikes or drops
Correlate workload changes with performance changes
Drill-Downs & Investigations
Most performance views allow you to click into a specific day to investigate what drove the trend.
Inside the drill-down modal you'll find:
The top 10 queries contributing to that metric
The exact user, warehouse, and usage details for each query
From there, users can open the exact query in Snowflake to investigate further.
This transforms the Performance Page from a set of charts into a full investigation workflow, helping you move from high-level trends to root-cause analysis in seconds.
Date Controls & Warehouse Filtering
Choose any date range to explore trends over time
Filter by individual warehouses to isolate their performance
Highlight only Yuki-optimized warehouses to understand how optimization influenced efficiency and cost
How to Use the Performance Page
Detect a slowdown
Look for spikes in execution time or max execution time.
Click the spike to reveal the top heavy queries.
Open them in Snowflake to inspect joins, scans, or distribution keys.
Validate optimization impact
Compare execution time or minutes per credit before/after enabling Yuki optimization.
Check if data scanned or spilled decreased.
Diagnose cost inefficiency
Look at GB scanned trends.
Correlate spikes with job/ETL/dbt activity.
Use Workload Efficiency Trends to pinpoint credit-heavy patterns.
Next Step
For details on encryption, access control, and data protection, proceed to: → General Security and Access Control
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