Usage → User

The User tab under Usage shows how individual users and system accounts use Snowflake. Use it to see who runs the most queries, how long they run, how much data they scan, where queries spill to disk, and associated cost—so you can find heavy users, fix slow or failing queries, and manage cost.

Overview

The Usage → User page lets you:

See query counts, execution time, and cost per user over 24 hours, 7 days, and 30 days
See how each user’s queries are distributed by execution time and by bytes scanned
Spot query execution time percentiles (p90, p95, p99, p100) and time spent on failed or ineffective queries
See bytes spilled per user to find memory pressure and inefficient queries
Track failed and incident query counts per user

Access Query Count by User

This table summarizes query access and cost per user over rolling windows.

Column	Description
Username	Snowflake user or system account.
Last 24h / 7d / 30d Accessed Query Count	Total number of queries executed or accessed by that user in the last 24 hours, 7 days, or 30 days.
Last 24h / 7d / 30d Execution Query Time (s)	Sum of query execution time in seconds for that user in each window.
Last 24h / 7d / 30d Total Cost (USD)	Estimated cost in USD for that user’s queries in each window (when “Show Cost in USD” is on).

Use this to see who is most active and who drives the most cost over 24h, 7d, and 30d.

Number of queries with execution time

This table shows how many queries fall into each execution-time bucket per user, plus failures and incidents.

Column	Description
Username	User or system account.
<1 second	Count of queries that finished in under 1 second.
1–5 seconds	Queries that took 1 to 5 seconds.
5–30 seconds	Queries that took 5 to 30 seconds.
30+ seconds	Queries that took 30 seconds or more.
Failed query count	Number of queries that failed for this user in the selected date range.
Incident query count	Number of queries that resulted in an incident for this user.
Total query count	Total number of queries for this user in the selected date range.
Total Cost (USD)	Estimated cost in USD for this user in that range (when cost is shown).

Use the date range above the table to pick the day or range you care about. Use this section to see who runs long queries and who has failures or incidents.

Query execution time

This section shows percentile-based execution times and time spent on failed or ineffective queries.

Column	Description
User name	User or system account.
p90 / p95 / p99 seconds	90th, 95th, and 99th percentile of query execution time in seconds (e.g. 90% of queries finish within p90).
p100 seconds	Maximum (100th percentile) query execution time in seconds for that user.
Failed query seconds	Total time spent on queries that failed.
Ineffective query seconds	Total time spent on ineffective queries (e.g. cancelled, no results, or minimal impact).
Total query seconds	Sum of all query execution time for that user.
Total query count	Total number of queries in the selected time range.

Use the time range (e.g. “Last 120 days”) for this section to see long-term performance and which users have the most failed or ineffective query time.

Number of queries with bytes spilled

This table shows how many queries spilled data to disk per user, by spill size.

Column	Description
User name	User or system account.
<10MB	Count of queries that spilled less than 10 MB.
10MB–100MB	Queries that spilled between 10 MB and 100 MB.
100MB–1GB	Queries that spilled between 100 MB and 1 GB.
1GB–10TB	Queries that spilled between 1 GB and 10 TB.
>1TB	Queries that spilled more than 1 TB.
Total Queries	Total number of queries that had any spill for this user.

High spill counts or large buckets can indicate undersized warehouses or inefficient query patterns. Use the date range for this table to analyze spill over a specific period.

Number of queries with bytes scanned

This table shows how much data each user’s queries scan, by size bucket.

Column	Description
Username	User or system account.
< 100 MB	Number of queries that scanned less than 100 MB.
100 MB – 1 GB	Queries that scanned between 100 MB and 1 GB.
1 GB – 10 GB	Queries that scanned between 1 GB and 10 GB.
10 GB – 100 GB	Queries that scanned between 10 GB and 100 GB.
100 GB – 1 TB	Queries that scanned between 100 GB and 1 TB.
> 1 TB	Queries that scanned more than 1 TB.
Total Queries	Total number of queries (or 0 if the view uses a different definition for this column).
%	Aggregate total for the user (e.g. total query count in thousands). Use this to compare relative activity.

Use the date range for this table to see who scans the most data and to spot users with many large scans that may drive cost.

Use cases

Heavy users and cost — Use “Access Query Count by User” with “Show Cost in USD” to see who drives the most cost over 24h, 7d, and 30d.
Slow and failing queries — Use “Query execution time” for p90/p95/p99 and failed/ineffective seconds, and “Number of queries with execution time” for failed and incident counts.
Memory and spill — Use “Number of queries with bytes spilled” to find users with high or large spills and consider warehouse size or query tuning.
Data scan and cost — Use “Number of queries with bytes scanned” to find users scanning the most data and prioritize optimization or cost allocation.