sqldw-operations-cli

by microsoft

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding. npx skills add https://github.com/microsoft/skills-for-fabric --skill sqldw-operations-cli Download ZIPGitHub716

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

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

• 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.

Table of Contents

Task Reference Notes Finding Workspaces and Items in Fabric COMMON-CLI.md § Finding Workspaces and Items in Fabric Mandatory — READ link first [needed for finding workspace id by its name or item id by its name, item type, and workspace id] Fabric Topology & Key Concepts COMMON-CORE.md § Fabric Topology & Key Concepts Environment URLs COMMON-CORE.md § Environment URLs Authentication & Token Acquisition COMMON-CORE.md § Authentication & Token Acquisition Wrong audience = 401; read before any auth issue Core Control-Plane REST APIs COMMON-CORE.md § Core Control-Plane REST APIs Includes pagination, LRO polling, and rate-limiting patterns Capacity Management COMMON-CORE.md § Capacity Management Gotchas, Best Practices & Troubleshooting (Platform) COMMON-CORE.md § Gotchas, Best Practices & Troubleshooting Tool Selection Rationale COMMON-CLI.md § Tool Selection Rationale Authentication Recipes COMMON-CLI.md § Authentication Recipes az login flows and token acquisition Fabric Control-Plane API via az rest COMMON-CLI.md § Fabric Control-Plane API via az rest Always pass --resource; includes pagination and LRO helpers SQL / TDS Data-Plane Access COMMON-CLI.md § SQL / TDS Data-Plane Access sqlcmd (Go) connect, query, CSV export Gotchas & Troubleshooting (CLI-Specific) COMMON-CLI.md § Gotchas & Troubleshooting (CLI-Specific) az rest audience, shell escaping, token expiry Quick Reference COMMON-CLI.md § Quick Reference az rest template + token audience/tool matrix Connection Fundamentals SQLDW-CONSUMPTION-CORE.md § Connection Fundamentals TDS, port 1433, Entra-only, no MARS Monitoring and Diagnostics SQLDW-CONSUMPTION-CORE.md § Monitoring and Diagnostics Query labels; DMVs (live) + queryinsights.* (30-day history) Performance: Best Practices and Troubleshooting SQLDW-CONSUMPTION-CORE.md § Performance: Best Practices and Troubleshooting Statistics, caching, clustering, query tips Gotchas and Troubleshooting (Consumption) SQLDW-CONSUMPTION-CORE.md § Gotchas and Troubleshooting Reference 18 numbered issues with cause + resolution Data Ingestion (DW Only) SQLDW-AUTHORING-CORE.md § Data Ingestion (DW Only) COPY INTO, OPENROWSET, method comparison Query Reference query-reference.md T-SQL queries, parameters, and example output for all analyses Composite Recipes COMMON-CLI.md § Composite Recipes Item-Type Capability Matrix SQLDW-CONSUMPTION-CORE.md § Item-Type Capability Matrix Warehouses only — queryinsights not available on SQLEP Prerequisites SKILL.md § Prerequisites Tools, auth, workspace role Tool Stack SKILL.md § Tool Stack Connection SKILL.md § Connection Performance Analysis SKILL.md § Performance Analysis Long-running queries, resource consumers, user insights, baselines Deep Diagnostics SKILL.md § Deep Diagnostics Pressure windows, cache warmth, cluster keys Fabric DW Constraints SKILL.md § Fabric DW Constraints NEVER recommend unsupported features Best Practices SKILL.md § Best Practices Monitoring-specific guidance Agentic Workflows SKILL.md § Agentic Workflows Common investigation patterns Gotchas, Rules, Troubleshooting SKILL.md § Gotchas, Rules, Troubleshooting MUST DO / AVOID / PREFER checklists Examples SKILL.md § Examples Prompt/response pairs

Tool Stack

For installation and setup, see Prerequisites .

Tool Role sqlcmd (Go) Execute monitoring T-SQL queries via Entra ID auth (-G) az CLI Token acquisition, Fabric REST for endpoint discovery jq Parse JSON from az rest

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=" "
ITEM_ID=" "

# 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 " .datawarehouse.fabric.microsoft.com" -d " " -G \
 -Q "SELECT TOP 5 * FROM queryinsights.exec_requests_history ORDER BY total_elapsed_time_ms DESC"

Reusable Connection Variables

FABRIC_SERVER=" .datawarehouse.fabric.microsoft.com"
FABRIC_DB=" "
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 = " .datawarehouse.fabric.microsoft.com"; $db = " "
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.

Do NOT Recommend Why Recommend Instead Nonclustered indexes Not supported V-Order, column pruning, predicate pushdown Materialized views Not supported Standard views or result set caching Index hints (FORCESEEK/FORCESCAN) Not supported Simplify query structure Multi-column statistics Not supported Single-column statistics on key columns ALTER TABLE SET DATA_CLUSTERING_KEY Not supported CTAS with WITH (CLUSTER BY (...)) RENAME OBJECT Not supported EXEC sp_rename 'schema.old', 'new' Change isolation level Snapshot only Fabric uses snapshot isolation exclusively CREATE USER Not supported Manage users via Fabric workspace Triggers Not supported Application logic or Fabric pipelines Recursive CTEs Not supported Iterative approach "Enable Query Insights" setting Query Insights is always on — there is no setting If access is denied, the user needs Admin or Member workspace role

Agentic Workflows

Workflow 1: "Why is my warehouse slow?"

• Check for pressure events → Run the pressure window analysis query (last 24h)

• Find the heaviest queries → Run top resource consumers query (last 1h)

• Analyze slow queries → Run long-running queries analysis

• Check cache behavior → Run cache warmth analysis (last 24h)

• Recommend clustering → Run cluster key recommendation queries

Workflow 2: "Has performance degraded?"

• Compare against baseline → Run recent vs baseline comparison (1h vs 7-day)

• Identify new slow queries → Run long-running queries summary (top 5)

• Check user patterns → Run top users insights (last 24h)

Workflow 3: "Optimize my warehouse"

• Review best practices → See SQLDW-CONSUMPTION-CORE.md § Performance: Best Practices and Troubleshooting

• Find optimization targets → Run top resource consumers (last 24h)

• Recommend clustering → Run cluster key recommendation queries

• Analyze cold-start queries → Run cache warmth analysis

Workflow 4: "What are people running?"

• Recent activity → Run recent queries (top 10)

• User patterns → Run top users insights (last 24h)

• 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

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:

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

• 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:

CREATE TABLE dbo.FactSales_clustered
WITH (CLUSTER BY (SaleDate, Region))
AS SELECT * FROM dbo.FactSales;