version: "1.0.0" name: sqldw-operations-cli description: > Analyze Fabric Data Warehouse performance via CLI using sqlcmd and queryinsights views. Diagnose slow queries, SQL pool pressure, cache coldness, and recommend clustering keys. Triggers: "DW slow query analysis", "slowest queries warehouse", "queryinsights long running", "warehouse CPU resource consumers", "SQL pool pressure window", "pressure events warehouse", "DW cache warmth cold start", "cache warmth analysis", "warehouse cluster key recommendation", "cluster tables performance", "DW performance baseline comparison", "performance degraded warehouse", "warehouse user query patterns", "queryinsights diagnostics", "DW optimization sqlcmd".

Update Check — ONCE PER SESSION (mandatory)

The first time this skill is used in a session, run the check-updates skill before proceeding.

- GitHub Copilot CLI / VS Code: invoke the check-updates skill.

- Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.

- Skip if the check was already performed earlier in this session.

CRITICAL NOTES

1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering

2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering

SQL DW Performance & Diagnostics — CLI Skill

This skill provides performance analysis, deep diagnostics, and optimization guidance for Microsoft Fabric Data Warehouse via `sqlcmd` and the built-in `queryinsights` views. All queries are read-only.

Prerequisites

For tool installation and authentication setup, see COMMON-CLI.md § Authentication Recipes and COMMON-CLI.md § SQL / TDS Data-Plane Access.

Monitoring-specific requirements:

• Workspace role: Admin or Member on the target workspace (required for queryinsights views)
• Warehouse must exist with recent query activity (queryinsights views retain 30 days; data appears with up to 15 min delay)

Table of Contents

Tool Stack

For installation and setup, see Prerequisites.

Connection

For authentication recipes (interactive, service principal, CI/CD), see COMMON-CLI.md § Authentication Recipes.

Discover the SQL Endpoint FQDN

Per COMMON-CLI.md Discovering Connection Parameters via REST:

WS_ID="<workspaceId>"
ITEM_ID="<warehouseId>"
 
# Warehouse
az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$WS_ID/warehouses/$ITEM_ID" \
  --query "properties.connectionString" --output tsv

Result: <uniqueId>.datawarehouse.fabric.microsoft.com

Connect with sqlcmd (Go)

sqlcmd -S "<endpoint>.datawarehouse.fabric.microsoft.com" -d "<DatabaseName>" -G \
  -Q "SELECT TOP 5 * FROM queryinsights.exec_requests_history ORDER BY total_elapsed_time_ms DESC"

Reusable Connection Variables

FABRIC_SERVER="<endpoint>.datawarehouse.fabric.microsoft.com"
FABRIC_DB="<DatabaseName>"
SQLCMD="sqlcmd -S $FABRIC_SERVER -d $FABRIC_DB -G"
$SQLCMD -Q "SELECT TOP 5 * FROM queryinsights.long_running_queries ORDER BY last_run_total_elapsed_time_ms DESC"

# PowerShell
$s = "<endpoint>.datawarehouse.fabric.microsoft.com"; $db = "<DatabaseName>"
sqlcmd -S $s -d $db -G -Q "SELECT TOP 5 * FROM queryinsights.exec_requests_history ORDER BY total_elapsed_time_ms DESC"

Performance Analysis

All SQL queries, parameters, return fields, and response formatting are in query-reference.md.

Long-Running Queries Summary

Find the slowest queries from queryinsights.long_running_queries. See query-reference.md § Long-Running Queries Summary for SQL and formatting.

Top Resource Consumers

Find CPU- and storage-heavy queries from queryinsights.exec_requests_history. See query-reference.md § Top Resource Consumers for SQL, thresholds, and formatting.

Recommendation thresholds:

• Remote scans > 1,000 MB → review data layout, consider clustering
• CPU > 5,000,000 ms → review query logic
• Elapsed > 300,000 ms → check joins, filters, statistics
• Reference: Performance guidelines

Top Users Insights

Analyze user activity and query patterns. See query-reference.md § Top Users Insights for SQL and classification logic.

Compare Recent vs Baseline

Detect performance regressions by comparing recent window against historical baseline. See query-reference.md § Compare Recent vs Baseline for SQL and formatting.

Recent Queries

Retrieve the most recently executed queries. See query-reference.md § Recent Queries for SQL.

Search Query Patterns

Search historical query patterns by table name, column, or keyword. See query-reference.md § Search Query Patterns for SQL.

Deep Diagnostics

All SQL queries for diagnostics are in query-reference.md.

Analyze Long-Running Query Plans

See query-reference.md § Long-Running Query Analysis for SQL.

Analysis guidance — when reviewing slow queries, check:

• High data_scanned_remote_storage_mb → data layout issues (run OPTIMIZE, consider clustering)
• High allocated_cpu_time_ms relative to elapsed → CPU-bound (simplify joins, reduce columns)
• High elapsed but low CPU → waiting on resources (check for pressure windows)

Analyze Pressure Window Queries

Identify SQL pool pressure events using queryinsights.sql_pool_insights and correlate with the heaviest queries running during those windows. See query-reference.md § Pressure Window Analysis for the two-step SQL.

Usage: Step 1 returns pressure windows with window_start and window_end timestamps. Substitute those actual timestamp values into Step 2's WHERE clause to find overlapping queries.

Global recommendations — based on aggregate pressure analysis:

• If SELECT pool has more pressure → read-heavy workload, suggest caching and column pruning
• If NONSELECT pool has more pressure → write-heavy, suggest batching and COPY INTO
• If total pressure > 60 min → suggest scaling capacity or staggering workloads

Analyze Query Cache Warmth

See query-reference.md § Cache Warmth Analysis for SQL.

Classification logic — for each execution, compute total_mb = remote + memory + disk:

• result_cache_hit = 1 → cached
• remote_mb / total_mb > 0.8 → cold (>80% from remote storage)
• (memory_mb + disk_mb) / total_mb > 0.8 → warm (>80% from cache)

Recommendations:

• Over 50% cold runs → Enable result set caching: ALTER DATABASE SET RESULT_SET_CACHING ON;
• Always-cold patterns → Check for GETDATE()/GETUTCDATE() or volatile functions that bust the cache key

Recommend Cluster Keys

See query-reference.md § Cluster Key Recommendations for SQL.

Key rules:

• Only WHERE predicates benefit from clustering — equality JOIN ON conditions do not
• Prefer mid-to-high cardinality columns (many distinct values)
• Maximum 4 clustering columns
• Use CTAS with WITH (CLUSTER BY (...)) — ALTER TABLE is not supported

To apply clustering — see query-reference.md § Cluster Key Recommendations for CTAS creation, sp_rename table swap, and verification SQL.

Note: Fabric does not support ALTER TABLE SET DATA_CLUSTERING_KEY or RENAME OBJECT. Always use CTAS with WITH (CLUSTER BY (...)) and sp_rename for table swaps.

Fabric DW Constraints

NEVER recommend features not supported in Fabric Data Warehouse. Always consult this list before making optimization suggestions.

Agentic Workflows

Workflow 1: "Why is my warehouse slow?"

1. Check for pressure events → Run the pressure window analysis query (last 24h)
2. Find the heaviest queries → Run top resource consumers query (last 1h)
3. Analyze slow queries → Run long-running queries analysis
4. Check cache behavior → Run cache warmth analysis (last 24h)
5. Recommend clustering → Run cluster key recommendation queries

Workflow 2: "Has performance degraded?"

1. Compare against baseline → Run recent vs baseline comparison (1h vs 7-day)
2. Identify new slow queries → Run long-running queries summary (top 5)
3. Check user patterns → Run top users insights (last 24h)

Workflow 3: "Optimize my warehouse"

1. Review best practices → See SQLDW-CONSUMPTION-CORE.md § Performance: Best Practices and Troubleshooting
2. Find optimization targets → Run top resource consumers (last 24h)
3. Recommend clustering → Run cluster key recommendation queries
4. Analyze cold-start queries → Run cache warmth analysis

Workflow 4: "What are people running?"

1. Recent activity → Run recent queries (top 10)
2. User patterns → Run top users insights (last 24h)
3. Search for specific patterns → Run query pattern search with search term

Best Practices

For comprehensive Fabric DW best practices, see SQLDW-CONSUMPTION-CORE.md § Performance: Best Practices and Troubleshooting and the Fabric guidelines.

Monitoring-specific best practices:

• Start broad, then drill down — begin with long-running queries summary and baseline comparison before deep diagnostics
• Use pressure window analysis for root-cause analysis rather than guessing at bottlenecks
• Label all agent queries with OPTION (LABEL = 'AGENTCLI_MONITOR_...') for tracing in Query Insights
• Prefer mid-to-high cardinality columns for clustering keys — low cardinality columns offer limited file-skipping benefit
• Use `WHERE` predicates to identify cluster key candidates — equality JOIN ON conditions do not benefit from clustering
• Always verify clustering after CTAS by querying sys.index_columns.data_clustering_ordinal
• Check cold vs warm cache before concluding a query is inherently slow — first execution may be a cold start
• Adjust time windows (DATEADD parameters) to match user's investigation scope — don't default to arbitrary windows

Gotchas, Rules, Troubleshooting

For generic CLI gotchas (connection, auth, shell escaping): see COMMON-CLI.md § Gotchas & Troubleshooting. For T-SQL/platform gotchas: see SQLDW-CONSUMPTION-CORE.md § Gotchas and Troubleshooting Reference.

MUST DO

• Always check Fabric DW Constraints before recommending optimizations
• When recommending clustering, instruct users to use CTAS with WITH (CLUSTER BY (...)) — not ALTER TABLE
• Report actual query output — do not fabricate or assume results
• Label queries with OPTION (LABEL = 'AGENTCLI_MONITOR_...') for Query Insights tracing

PREFER

• Start with high-level queries (long-running summary, baseline comparison) before drilling into diagnostics
• Use the pressure window analysis for root-cause analysis rather than guessing at bottlenecks
• Combine multiple queries in the Agentic Workflows for comprehensive investigations
• Adjust time windows (DATEADD parameters) based on what the user asks for

AVOID

• Recommending Fabric-unsupported features (nonclustered indexes, materialized views, index hints, triggers)
• Suggesting that Query Insights needs to be "enabled" or "turned on" — queryinsights views are always available; permission errors indicate insufficient workspace role (Admin or Member required)
• Running monitoring queries without confirming connection parameters (server, database)
• Guessing at performance root causes without running the diagnostic queries
• Using SELECT * in monitoring queries — always select specific columns

TROUBLESHOOTING (Monitoring-Specific)

For generic connection/auth troubleshooting, see COMMON-CLI.md § Gotchas & Troubleshooting.

Examples

Find Slow Queries

User: "What are the slowest queries in my warehouse?"

Agent: Runs the long-running queries summary via sqlcmd:

sqlcmd -S "myserver.datawarehouse.fabric.microsoft.com" -d "MyWarehouse" -G -Q "
SELECT TOP 5
    left(last_run_command, 80) AS query_preview,
    number_of_runs,
    last_run_total_elapsed_time_ms,
    median_total_elapsed_time_ms
FROM queryinsights.long_running_queries
ORDER BY median_total_elapsed_time_ms DESC
"

The top 5 slowest queries are:

1. SELECT * FROM FactSales JOIN DimCustomer... — ran 12 times, last took 45,200 ms (median 38,500 ms)

2. SELECT region, SUM(amount)... — ran 8 times, last took 32,100 ms (median 29,000 ms)

...

Diagnose Performance Degradation

User: "Is my warehouse slower than last week?"

Agent: Runs the baseline comparison query and presents:

Performance Comparison (last 1h vs 7-day baseline):

- Elapsed time: 2,450 ms avg → 4,100 ms avg (+67.3%)

- CPU consumption: 1,200 ms → 2,800 ms (+133.3%)

- Data scanned: 450 MB → 480 MB (+6.7%)

Significant regression detected. Let me check for pressure events and analyze the heaviest queries...

Then follows up with pressure window and resource consumer queries.

Get Clustering Recommendations

User: "Which tables should I cluster and on what columns?"

Agent: Runs the cluster key recommendation queries and presents:

Recommended Clustering Keys:

| Table | Recommended Columns | Row Count | Total Scanned MB |

|-------|-------------------|-----------|-----------------|

| dbo.FactSales | SaleDate, Region | 50M | 12,500 MB |

| dbo.FactInventory | ProductID, WarehouseID | 12M | 3,200 MB |

To apply clustering, use CTAS:

```sql

CREATE TABLE dbo.FactSales_clustered

WITH (CLUSTER BY (SaleDate, Region))

AS SELECT * FROM dbo.FactSales;

```