# Sqldw Operations CLI

> Analyze Fabric Data Warehouse performance via the MCP execute_query tool 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 MCP".

- Skill: `tuyv/sqldw-operations-cli` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add tuyv/sqldw-operations-cli`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tuyv/sqldw-operations-cli/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: tuyv (https://skillmd.com/u/tuyv)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tuyv/sqldw-operations-cli

---


> **Telemetry — MANDATORY.** Every `api.fabric.microsoft.com` call must carry
> `x-ms-fabric-skill: sqldw-operations-cli` (`az rest`: `--headers "x-ms-fabric-skill=sqldw-operations-cli"`),
> including every LRO poll, `fabric_lro` and retry. Snippets omit it — add it anyway.

> **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 the **`fabric-sqlendpoint-execute_query` MCP tool** and the built-in **`queryinsights`** views. All queries are read-only.

## Prerequisites

For workspace/item discovery via `az rest`, see [COMMON-CLI.md § Fabric Control-Plane API via az rest](../../common/COMMON-CLI.md#fabric-control-plane-api-via-az-rest). For legacy `sqlcmd` reference (fallback only), see [COMMON-CLI.md § SQL / TDS Data-Plane Access](../../common/COMMON-CLI.md#sql--tds-data-plane-access).

> **⚠️ SQL Execution Override:** For SQL data-plane execution, this skill supersedes COMMON-CLI SQL/TDS guidance. Use MCP `fabric-sqlendpoint-execute_query` (see [Tool Stack](#tool-stack)) unless explicitly using Legacy CLI Fallback.

**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

| Task | Reference | Notes |
|---|---|---|
| Finding Workspaces and Items in Fabric | [COMMON-CLI.md § Finding Workspaces and Items in Fabric](../../common/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](../../common/COMMON-CORE.md#fabric-topology--key-concepts) ||
| Environment URLs | [COMMON-CORE.md § Environment URLs](../../common/COMMON-CORE.md#environment-urls) ||
| Authentication & Token Acquisition | [COMMON-CORE.md § Authentication & Token Acquisition](../../common/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](../../common/COMMON-CORE.md#core-control-plane-rest-apis) | Includes pagination, LRO polling, and rate-limiting patterns |
| Capacity Management | [COMMON-CORE.md § Capacity Management](../../common/COMMON-CORE.md#capacity-management) ||
| Gotchas, Best Practices & Troubleshooting (Platform) | [COMMON-CORE.md § Gotchas, Best Practices & Troubleshooting](../../common/COMMON-CORE.md#gotchas-best-practices--troubleshooting) ||
| Tool Selection Rationale | [COMMON-CLI.md § Tool Selection Rationale](../../common/COMMON-CLI.md#tool-selection-rationale) ||
| Authentication Recipes | [COMMON-CLI.md § Authentication Recipes](../../common/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](../../common/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](../../common/COMMON-CLI.md#sql--tds-data-plane-access) | Legacy `sqlcmd` reference (MCP is primary — see Tool Stack) |
| Gotchas & Troubleshooting (CLI-Specific) | [COMMON-CLI.md § Gotchas & Troubleshooting (CLI-Specific)](../../common/COMMON-CLI.md#gotchas--troubleshooting-cli-specific) | `az rest` audience, shell escaping, token expiry |
| Quick Reference | [COMMON-CLI.md § Quick Reference](../../common/COMMON-CLI.md#quick-reference) | `az rest` template + token audience/tool matrix |
| Connection Fundamentals | [SQLDW-CONSUMPTION-CORE.md § Connection Fundamentals](../../common/SQLDW-CONSUMPTION-CORE.md#connection-fundamentals) | TDS, port 1433, Entra-only, no MARS |
| Monitoring and Diagnostics | [SQLDW-CONSUMPTION-CORE.md § Monitoring and Diagnostics](../../common/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](../../common/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](../../common/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)](../../common/SQLDW-AUTHORING-CORE.md#data-ingestion-dw-only) | COPY INTO, OPENROWSET, method comparison |
| Query Reference | [query-reference.md](references/query-reference.md) | T-SQL queries, parameters, and example output for all analyses |
| Composite Recipes | [COMMON-CLI.md § Composite Recipes](../../common/COMMON-CLI.md#composite-recipes) ||
| Item-Type Capability Matrix | [SQLDW-CONSUMPTION-CORE.md § Item-Type Capability Matrix](../../common/SQLDW-CONSUMPTION-CORE.md#item-type-capability-matrix) | Warehouses only — `queryinsights` not available on SQLEP |
| Prerequisites | [SKILL.md § Prerequisites](#prerequisites) | Tools, auth, workspace role |
| Tool Stack | [SKILL.md § Tool Stack](#tool-stack) ||
| Connection | [SKILL.md § Connection](#connection) ||
| Performance Analysis | [SKILL.md § Performance Analysis](#performance-analysis) | Long-running queries, resource consumers, user insights, baselines |
| Deep Diagnostics | [SKILL.md § Deep Diagnostics](#deep-diagnostics) | Pressure windows, cache warmth, cluster keys |
| Fabric DW Constraints | [SKILL.md § Fabric DW Constraints](#fabric-dw-constraints) | **NEVER recommend unsupported features** |
| Best Practices | [SKILL.md § Best Practices](#best-practices) | Monitoring-specific guidance |
| Agentic Workflows | [SKILL.md § Agentic Workflows](#agentic-workflows) | Common investigation patterns |
| Gotchas, Rules, Troubleshooting | [SKILL.md § Gotchas, Rules, Troubleshooting](#gotchas-rules-troubleshooting) | **MUST DO / AVOID / PREFER** checklists |
| Examples | [SKILL.md § Examples](#examples) | Prompt/response pairs |

---

## Tool Stack

For installation and setup, see [Prerequisites](#prerequisites).

| Tool | Role |
|---|---|
| `fabric-sqlendpoint-execute_query` MCP tool | **Primary**: Execute monitoring T-SQL queries against Fabric SQL Endpoints. Auth handled by MCP protocol. |
| `az` CLI | Token acquisition, Fabric REST for workspace/item discovery |
| `jq` | Parse JSON from `az rest` |

> **IMPORTANT — MCP vs sqlcmd:**
> This skill uses the `fabric-sqlendpoint-execute_query` MCP tool for all T-SQL execution. Do **not** use COMMON-CLI SQL/TDS/sqlcmd sections for query execution.

> **Agent preflight** — verify before first operation:
> 1. Confirm the `fabric-sqlendpoint-execute_query` tool is available in your tool list. This tool is provided by the `fabric-sqlendpoint` MCP server, which is registered either by installing a Fabric skills **plugin** (the path for end users) or via this repo's `.mcp.json` — other MCP clients may register it through their own configuration.
> 2. If no matching tool is found, the user must register the Fabric SQL Endpoint MCP server.
>    - **Global URL**: `https://api.fabric.microsoft.com/v1/mcp/dataPlane/sqlEndpoint`
>    - **Item-scoped URL**: `https://api.fabric.microsoft.com/v1/mcp/dataPlane/workspaces/{workspaceId}/items/{itemId}/sqlEndpoint`

### MCP Tool Signature

```text
fabric-sqlendpoint-execute_query(workspaceId, itemId, query)
```

> **Tool name may differ:** `execute_query` is the logical operation. Depending on how the server is
> registered, the concrete tool name in your tool list may be prefixed (e.g.
> `fabric-sqlendpoint-execute_query` or `sqlendpoint-global-execute_query`). Invoke the concrete name
> shown in your tool list, always passing `workspaceId`, `itemId`, and `query`.

| Parameter | Type | Description |
|-----------|------|-------------|
| `workspaceId` | string (UUID) | The workspace GUID containing the target Warehouse |
| `itemId` | string (UUID) | The GUID of the Warehouse item |
| `query` | string | T-SQL query text (single batch — no `GO` separators) |

**Returns:** CSV resource (RFC 4180) with tabular results + metadata text.

**Limits:** 10,000 rows max | 300s timeout | 20 req/min rate limit _(observed defaults, not a documented contract — the service can change them; verify against live 429/timeout/truncation responses)_

---

## Connection

### Discover workspaceId and itemId

You need the workspace GUID and warehouse GUID to call `fabric-sqlendpoint-execute_query`:

```bash
# 1. Find workspace ID by name (capture into WS_ID for the next call)
WS_ID=$(az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces" \
  --query "value[?displayName=='MyWorkspace'].id" --output tsv)
echo "Workspace ID: $WS_ID"

# 2. Find warehouse item ID by name
az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$WS_ID/warehouses" \
  --query "value[?displayName=='MyWarehouse'].id" --output tsv
```

### Execute a Monitoring Query

```text
fabric-sqlendpoint-execute_query(
  workspaceId: "aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  itemId: "11111111-2222-3333-4444-555555555555",
  query: "SELECT TOP 5 * FROM queryinsights.long_running_queries ORDER BY last_run_total_elapsed_time_ms DESC"
)
```

**No additional connection setup needed** — authentication is handled transparently by the MCP protocol.

---

## Performance Analysis

All SQL queries, parameters, return fields, and response formatting are in [query-reference.md](references/query-reference.md).

### Long-Running Queries Summary

Find the slowest queries from `queryinsights.long_running_queries`. See [query-reference.md § Long-Running Queries Summary](references/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](references/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](https://learn.microsoft.com/fabric/data-warehouse/guidelines-warehouse-performance)

### Top Users Insights

Analyze user activity and query patterns. See [query-reference.md § Top Users Insights](references/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](references/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](references/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](references/query-reference.md#search-query-patterns) for SQL.

---

## Deep Diagnostics

All SQL queries for diagnostics are in [query-reference.md](references/query-reference.md).

### Analyze Long-Running Query Plans

See [query-reference.md § Long-Running Query Analysis](references/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](references/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](references/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](references/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](references/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?"

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](../../common/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](../../common/SQLDW-CONSUMPTION-CORE.md#performance-best-practices-and-troubleshooting) and the [Fabric guidelines](https://learn.microsoft.com/fabric/data-warehouse/guidelines-warehouse-performance).

**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](../../common/COMMON-CLI.md#gotchas--troubleshooting-cli-specific).
For T-SQL/platform gotchas: see [SQLDW-CONSUMPTION-CORE.md § Gotchas and Troubleshooting Reference](../../common/SQLDW-CONSUMPTION-CORE.md#gotchas-and-troubleshooting-reference).

### MUST DO
- Always check [Fabric DW Constraints](#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
- **Verify `fabric-sqlendpoint-execute_query` MCP tool availability** before first operation

### 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](#agentic-workflows) for comprehensive investigations
- Adjust time windows (`DATEADD` parameters) based on what the user asks for
- Consolidate related diagnostic queries into fewer calls to respect rate limits

### 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 workspaceId and itemId
- Guessing at performance root causes without running the diagnostic queries
- Using `SELECT *` in monitoring queries — always select specific columns
- Using `GO` separators or sqlcmd meta-commands in queries (MCP tool accepts single batches only)
- Unbounded queries without `TOP N` — 10,000 row limit applies

### TROUBLESHOOTING (Monitoring-Specific)

For generic connection/auth troubleshooting, see [COMMON-CLI.md § Gotchas & Troubleshooting](../../common/COMMON-CLI.md#gotchas--troubleshooting-cli-specific).

| Symptom | Cause | Fix |
|---|---|---|
| `Invalid object name 'queryinsights...'` | New warehouse < 2 min old | Wait ~2 minutes |
| Permission errors on `queryinsights.*` | Insufficient workspace role | Requires Admin or Member role |
| No data in queryinsights views | No recent query activity or < 15 min delay | Wait 15 min after query completion |
| No rows but data exists | RLS filtering | Check `USER_NAME()`, verify RLS policies |
| `fabric-sqlendpoint-execute_query` tool not available | MCP server not registered | User must add Fabric SQL Endpoint MCP server |
| HTTP 429 rate limit | Too many calls in 1 min | Wait 60s; consolidate queries |
| Query timeout (300s) | Complex diagnostics | Narrow time window with tighter DATEADD |

---

## Examples

### Find Slow Queries

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

**Agent:** Runs the long-running queries summary via `fabric-sqlendpoint-execute_query`:

```text
fabric-sqlendpoint-execute_query(workspaceId, itemId,
  "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;
> ```