Microsoft Fabric Blog presents an in-depth look at SQL Pool Insights, offering developers and analytics engineers new tools for monitoring SQL pool performance and optimizing resource usage in Fabric Data Warehouse.

SQL Pool Insights: Monitoring Microsoft Fabric Data Warehouse Performance

Microsoft Fabric Data Warehouse now includes SQL Pool Insights, an advanced telemetry feature designed to help analytics teams monitor SQL pool health, resource allocation, and query performance. As part of the Query Insights (QI) schema, SQL Pool Insights introduces pool-level visibility to support efficient capacity planning and fast troubleshooting for mission-critical workloads.

Why SQL Pool Insights Matters

Modern analytics demand transparency and control. Previously, understanding how SELECT and NON SELECT pools handled pressure was challenging. SQL Pool Insights delivers:

Real-time monitoring of pool pressure and health
Historical records for pool configuration and capacity changes
Event logging for sustained pressure events
Validation of resource isolation between pools

These capabilities enhance troubleshooting, optimize performance, and assist with strategic capacity adjustments.

Key Capabilities

Out-of-the-box Pool Coverage: Monitor SELECT and NON SELECT pools, with upcoming support for custom pools
Granular Schema: Analyze fields such as sql_pool_name, timestamp, max_resource_percentage, and is_pool_under_pressure
Correlated Insights: Integrate SQL Pool Insights data with Query Insights views like exec_requests_history and long_running_queries for comprehensive monitoring

How SQL Pool Insights Works

SQL Pool Insights adds a new system view to Query Insights:

SELECT * FROM queryinsights.sql_pool_insights;

Event logging occurs when:

Pool configuration changes
Workspace capacity SKU changes
Pressure state persists longer than one minute

Example Scenarios

Identify Pool Pressure Periods:

SELECT sql_pool_name, timestamp, is_pool_under_pressure
FROM queryinsights.sql_pool_insights
WHERE sql_pool_name = 'SELECT'
  AND timestamp >= DATEADD(hour, -24, GETDATE())
  AND is_pool_under_pressure = 1
ORDER BY timestamp DESC;

Visualize Pressure Trends:

SELECT sql_pool_name, timestamp, is_pool_under_pressure,
    LAG(timestamp) OVER (PARTITION BY sql_pool_name ORDER BY timestamp) AS previous_event,
    DATEDIFF(minute, LAG(timestamp) OVER (PARTITION BY sql_pool_name ORDER BY timestamp), timestamp) AS minutes_since_last_event
FROM queryinsights.sql_pool_insights
WHERE sql_pool_name = 'SELECT'
ORDER BY timestamp;

Correlate Pressure with Query History:

WITH Pool_Pressure_Events AS ( SELECT DISTINCT timestamp FROM queryinsights.sql_pool_insights WHERE is_pool_under_pressure = 1 )
SELECT *
FROM queryinsights.exec_requests_history AS erh
JOIN Pool_Pressure_Events AS ppe ON ppe.timestamp BETWEEN erh.start_time AND erh.end_time

Use Cases for SQL Pool Insights

Diagnosis of performance issues and bottlenecks
Validation of resource isolation for different workloads
Planning capacity upgrades or adjustments using historical pool data
Troubleshooting slow queries by correlating pressure and request history

Learn More

Access Query Insights in Fabric Data Warehousing to explore documentation and integration options for your analytics platform.

Authored by Microsoft Fabric Blog

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