Turning System Views into a Performance Command Center

Yellowbrick exposes a rich set of system views that let you see exactly what’s happening in your warehouse: sessions, queries, loads, backups, alerts, and more. Used well, these views become a command center for performance tuning and troubleshooting.

This post highlights practical patterns for using system views in day‑to‑day operations. For a complementary look at the built-in analytics Yellowbrick provides on top of these views, see Workload Analytics: Tickling the Soft Underbelly of the Platform.

Know Who’s Connected and What They’re Doing

Start with understanding active sessions.

• Views like sys.session and sys.logsession show active and historical sessions, including application names, client IPs, start times, and session states.
• Simple queries can list long‑running sessions, idle sessions, or sessions by user, helping you quickly spot stuck BI tools or forgotten connections.

For example, you can join sys.session to sys.user to list currently connected users, how long they’ve been active, and how long they’ve been in their current state.

Make Query Logs Your First Stop for Performance Issues

Yellowbrick tracks both in‑flight and completed queries.

• sys.query and sys.queryanalyze expose telemetry for active queries, including rows processed, memory usage, spill, and skew.
• sys.logquery and sys.logqueryanalyze store similar details for completed statements, along with execution times, states, and error codes.

By joining the plan‑level explanation view (sys.logqueryexplain) with the analyze views, you can drill into slow steps in a plan, such as skewed joins or large redistributions. To understand what happens at each stage of execution, Life of a Yellowbrick Query walks through the full query path from parsing to result delivery.

Track Loads, Unloads, Backups, and Restores

Operational reliability depends on understanding bulk data movement.

• sys.load / sys.logload and sys.unload / sys.logunload capture metadata and telemetry for ybload and ybunload operations.
• sys.backup / sys.logbackup and sys.restore / sys.logrestore do the same for backup and restore jobs.

It’s easy to build a single consolidated view of recent loads and unloads by unioning sys.logload and sys.logunload and reporting on elapsed time, state, rows, and bytes. That quickly surfaces failed or slow jobs. For best practices on backup and restore operations specifically, see Never Lose a Backup Again: Smarter BAR Strategies on Yellowbrick.

Turn Alerts and System Logs into Actionable Signals

In addition to query‑level telemetry, Yellowbrick surfaces system‑level health signals.

• sys.alert and sys.logalert record active and historical alerts, including severity and resource type.
• sys.logerror, sys.logwarning, and sys.logaudit capture system errors, warnings, and configuration or hardware‑related events.

Workload management (WLM) rules can log warnings or errors into sys.logqueryalert, linking alerts directly back to problematic queries. That lets you go from “we hit a threshold” to “this query caused it” in a single step. For a deeper dive into how WLM rules work, read What Is Workload Management and Why Do I Work on It?.

Build Your Own Operational Dashboards

Because system views are just tables, you can join and aggregate them into custom operational dashboards.

• Build “top N” slow queries by user or application from sys.logquery.
• Trend ybload throughput and error rates using sys.logload.
• Monitor alert frequency and types from sys.logalert to validate WLM policies.

These dashboards can live in your favorite BI tool, giving both DBAs and data teams a shared, always‑on view of warehouse health. Yellowbrick’s enterprise data warehouse platform includes ecosystem support for tools like Tableau, PowerBI, Sisense, and MicroStrategy to make this straightforward. And if you’re looking to tune what these dashboards reveal, 7 Practical Ways to Improve Yellowbrick Performance is a natural next step.

Related Resources

• Why Is Yellowbrick So Fast? Secrets of Yellowbrick Database Architecture
• Application-Oriented Memory Allocation for Performance
• Yellowbrick Data Warehouse: Engineered for Extreme Efficiency
• How to Optimize Large-Scale Reporting and Complex Analytics
• Getting Stored Procedures Right in a Modern Analytic Warehouse
• A Data Engineering Use Case
• Yellowbrick Product Overview