Monitor Resources

Monitor system-level resources including AMP processors, CPU, memory, I/O, and physical capacity using real-time MCP resources to ensure optimal system performance and identify capacity constraints.

🔍 Enhanced Capabilities

This skill now leverages real-time resource monitoring!

With tdwm-mcp v1.5.0, this skill provides:

✅ REAL-TIME RESOURCE METRICS - Instant CPU/memory/I/O data without overhead
✅ AMP LOAD MONITORING - Track processor utilization and skew
✅ CAPACITY ANALYSIS - Understand headroom and growth runway
✅ TOP CONSUMER IDENTIFICATION - Find users/queries consuming most resources
✅ INTEGRATED VIEW - Resources + sessions + queries in one analysis

Instructions

When to Use This Skill

User asks about system performance or capacity
Need to check if system is overloaded or underutilized
Investigating resource bottlenecks or performance degradation
Capacity planning or health check requests
Correlating resource usage with query performance

Available MCP Tools

Resource Monitoring:

monitorampload - Track AMP processor utilization and skew
monitor_awt - View AWT task resource usage
showtopusers - Identify users consuming most resources
monitor_config - View system configuration parameters

Related Monitoring:

list_sessions - See sessions consuming resources
showtdwmsummary - Workload distribution context
list_resources - List available system resources

Available MCP Resources (NEW ✨)

Real-Time Resource Data:

tdwm://system/physical-resources - CPU, memory, I/O utilization
tdwm://system/amp-load - AMP processor load and skew metrics
tdwm://system/sessions - Session resource consumption
tdwm://system/summary - Workload resource distribution

Reference:

tdwm://reference/resource-thresholds - Healthy vs warning vs critical levels
tdwm://reference/amp-skew-causes - Common causes of AMP skew

Step-by-Step Workflow

Phase 1: Quick Assessment (Use Resources First)

Get Real-Time Resource Overview

- Read resource: tdwm://system/physical-resources - Instant snapshot of CPU, memory, I/O utilization - Identify if system is under stress - Provides capacity headroom visibility

Check AMP Load Balance

- Read resource: tdwm://system/amp-load - Shows AMP processor utilization - Identifies load skew (imbalance) - Provides per-AMP breakdown

Phase 2: Detailed Analysis (Use Tools)

Assess Overall System Load

- Use monitorampload for detailed AMP processor data - Identify if system is CPU-bound or has capacity - Look for imbalanced load across AMPs (skew factor) - Track peak vs average utilization

Check Task-Level Resources

- Use monitor_awt to view AWT task allocation - Identify if task slots are exhausted - Check for queries waiting on resources - Understand task concurrency limits

Identify Top Consumers

- Use showtopusers to find resource-heavy users/queries - Determine if resource usage is legitimate or problematic - Prioritize optimization efforts - Correlate with session and query data

Review System Configuration

- Use monitor_config to verify system settings - Check resource allocation parameters - Validate configuration against best practices

Phase 3: Correlation and Reporting

Correlate with Workload Activity

- Read resource: tdwm://system/summary - Correlate resource usage with workload distribution - Identify which workloads are consuming resources - Understand if usage patterns are expected

Analyze Session Resource Consumption

- Read resource: tdwm://system/sessions - Identify individual sessions consuming excessive resources - Correlate high-resource sessions with users/queries - Determine if termination needed

Report Findings

- Summarize system health: green (healthy), amber (warning), red (critical) - Identify bottlenecks or constraints - Provide utilization percentages and trends - Recommend actions if issues found

Examples

Example 1: Quick System Health Check (Fast)

Scenario: "Is the system running okay right now?"

Action (Resource-First Approach):

1. Read real-time physical resources:
   tdwm://system/physical-resources
   → CPU: 45% utilized (healthy)
   → Memory: 62% utilized (healthy)
   → I/O: 38% utilized (healthy)
   → Disk: 71% capacity used

2. Check AMP load balance:
   tdwm://system/amp-load
   → Average AMP CPU: 43%
   → Max AMP CPU: 51%
   → Min AMP CPU: 38%
   → Skew factor: 1.13 (healthy, < 1.5)

3. Review workload distribution:
   tdwm://system/summary
   → 47 active queries distributed across 3 workloads
   → Resources well-balanced

4. Report:
   "✅ System Health: HEALTHY

    Resource Utilization:
    - CPU: 45% (55% headroom available)
    - Memory: 62% (38% headroom available)
    - I/O: 38% (62% headroom available)
    - AMP Load: Balanced (skew factor 1.13)

    Current Activity:
    - 47 active queries, well-distributed
    - No resource bottlenecks detected
    - System has capacity for additional workload"

Result: Complete health check in seconds, clear green status

Example 2: Investigate Performance Degradation (Critical)

Scenario: "Why are queries running slow? System feels sluggish."

Action (Resource Analysis):

1. Check physical resources:
   tdwm://system/physical-resources
   → CPU: 94% utilized (⚠️ CRITICAL)
   → Memory: 89% utilized (⚠️ WARNING)
   → I/O: 87% utilized (⚠️ WARNING)
   → All metrics in red zone

2. Check AMP load:
   tdwm://system/amp-load
   → Average AMP CPU: 92%
   → Max AMP CPU: 98%
   → Min AMP CPU: 76%
   → Skew factor: 1.29 (moderate skew)

3. Identify top consumers:
   show_top_users()
   → User: etl_batch - 42% of CPU
   → User: adhoc_user01 - 23% of CPU
   → User: report_user - 15% of CPU
   → Top 3 users = 80% of total CPU

4. Check sessions for top consumer:
   tdwm://system/sessions
   → Filter for etl_batch
   → 8 active sessions, all long-running (>30 min)
   → All from ETL workload

5. Review workload distribution:
   tdwm://system/summary
   → ETL workload: 67% of resources
   → Interactive workload: 23% of resources (suffering)
   → Ad-hoc workload: 10% of resources

6. Check for throttle delays:
   tdwm://system/delayed-queries
   → 15 queries delayed
   → 12 from Interactive workload (waiting for resources)
   → 3 from Ad-hoc workload

7. Report:
   "🔴 System Health: CRITICAL - Resource Exhaustion

    Root Cause:
    - System at 94% CPU capacity
    - ETL workload consuming 67% of resources
    - User 'etl_batch' alone using 42% of system CPU

    Impact:
    - Interactive queries delayed (12 queries waiting)
    - Average query time increased
    - System approaching capacity limit

    Immediate Actions Required:
    1. URGENT: Review etl_batch sessions (8 long-running queries)
       → Determine if queries are stuck or legitimately long
       → Consider aborting if runaway queries detected

    2. SHORT-TERM: Increase throttle on ETL workload
       → Limit ETL concurrent queries to protect Interactive
       → Use optimize-throttles skill

    3. MEDIUM-TERM: Schedule ETL jobs during off-peak hours
       → Current ETL running during business hours
       → Move to nighttime window if possible"

Result: Clear root cause identified, specific actions recommended with priority

Example 3: Diagnose AMP Skew Issue (Performance)

Scenario: "Queries are slow even though CPU shows only 60% utilized"

Action (AMP Skew Analysis):

1. Check overall CPU:
   tdwm://system/physical-resources
   → CPU: 62% average (seems okay?)

2. Check AMP load detail:
   tdwm://system/amp-load
   → Average AMP CPU: 62%
   → Max AMP CPU: 95% (⚠️ AMP 7 maxed out!)
   → Min AMP CPU: 38%
   → Skew factor: 2.5 (🔴 SEVERE SKEW)

3. Analyze per-AMP breakdown:
   monitor_amp_load()
   → AMP 7: 95% (bottleneck!)
   → AMP 3: 89%
   → AMP 12: 87%
   → AMPs 1,2,4-6,8-11: 45-55% (normal)
   → AMPs 13-16: 38-42% (underutilized)

4. Identify queries hitting skewed AMPs:
   show_top_users()
   → User: report_generator - heavy queries
   → Queries accessing specific tables

5. Check session SQL:
   list_sessions()
   → Filter for report_generator
   → All queries accessing SALES_FACT table
   → Large table with potential distribution issue

6. Review reference:
   tdwm://reference/amp-skew-causes
   → Cause #1: Poor primary index design
   → Cause #2: NULL values in PI column
   → Cause #3: Hot values in PI column

7. Report:
   "⚠️  System Health: DEGRADED - AMP Skew Issue

    Problem:
    - System shows 62% average CPU (seems healthy)
    - BUT AMP 7 is at 95% (bottleneck!)
    - Skew factor 2.5 = SEVERE SKEW

    Impact:
    - Queries wait for overloaded AMP 7
    - 60% of AMPs underutilized
    - System effectively at 95% capacity (limited by one AMP)

    Root Cause Analysis:
    - Report queries all hitting SALES_FACT table
    - Table data concentrated on specific AMPs
    - Indicates poor primary index or data distribution

    Recommended Actions:
    1. IMMEDIATE: Review SALES_FACT table design
       → Check primary index column
       → Analyze data distribution (HELP STATISTICS)
       → Look for NULL values or hot keys

    2. SHORT-TERM: Consider table redistribution
       → Redesign primary index for better distribution
       → Test with EXPLAIN plans

    3. LONG-TERM: Implement data distribution monitoring
       → Regular skew checks
       → Alert on skew factor > 2.0"

Result: Identified hidden bottleneck (AMP skew), specific table and recommendations

Example 4: Capacity Planning Analysis (Proactive)

Scenario: "Do we have capacity for 25% more users next quarter?"

Action (Capacity Planning):

1. Get current resource baseline:
   tdwm://system/physical-resources
   → CPU: 67% average (peak hours)
   → Memory: 74% average
   → I/O: 58% average
   → Disk: 68% capacity used

2. Analyze peak utilization (tool for historical data):
   monitor_amp_load()
   → Peak CPU (last 30 days): 85%
   → Average CPU (last 30 days): 52%
   → Peak typically 10:00 AM - 3:00 PM

3. Calculate current headroom:
   - CPU: 33% headroom at average, 15% at peak
   - Memory: 26% headroom
   - I/O: 42% headroom
   - Comfortable threshold: Keep peak < 80%

4. Project 25% user increase:
   - Assume linear scaling (conservative)
   - Current peak: 85%
   - With 25% more: 85% × 1.25 = 106% (🔴 OVER CAPACITY)

5. Analyze workload composition:
   tdwm://system/summary
   → Interactive: 45% of resources
   → ETL: 35% of resources
   → Ad-hoc: 20% of resources
   → User increase likely impacts Interactive + Ad-hoc

6. Recalculate with workload-specific growth:
   - Interactive/Ad-hoc: 65% of total, growing 25%
   - ETL: 35% of total, no growth
   - New peak: (65% × 1.25) + 35% = 81% + 35% = 116% (still over!)

7. Determine capacity gap:
   - Need: 116% capacity
   - Have: 100% capacity
   - Gap: 16% additional capacity needed
   - OR reduce peak utilization by optimizing queries

8. Report:
   "📊 Capacity Planning Analysis: 25% User Growth

    Current State:
    - Average: 67% CPU, 74% Memory
    - Peak: 85% CPU (10 AM - 3 PM)
    - Headroom at peak: 15%

    With 25% User Growth:
    - Projected peak: 106-116% CPU
    - Result: 🔴 INSUFFICIENT CAPACITY

    Capacity Gap:
    - Need: 16% additional capacity
    - Options to address:

    Option 1: Hardware Expansion
    - Add 2-3 nodes to cluster (20% capacity increase)
    - Cost: ~$X (hardware + licensing)
    - Timeline: 8-12 weeks

    Option 2: Query Optimization + Throttling
    - Optimize top 20 slow queries (target 15% improvement)
    - Adjust throttle limits to smooth peak load
    - Cost: Development time only
    - Timeline: 4-6 weeks
    - May achieve 10-15% efficiency gain

    Option 3: Hybrid Approach
    - Optimize queries (10% gain)
    - Add 1 node (10% capacity)
    - Adjust workload distribution
    - Cost: Partial hardware investment
    - Timeline: 6-8 weeks

    RECOMMENDATION:
    Start Option 2 immediately (query optimization), then reassess.
    If growth is certain, plan Option 1 in parallel (long lead time)."

Result: Detailed capacity analysis with multiple options and costs

Example 5: Real-Time Resource Correlation (Advanced)

Scenario: "Why is I/O spiking even though CPU is only 50%?"

Action (Multi-Dimensional Analysis):

1. Confirm I/O spike:
   tdwm://system/physical-resources
   → CPU: 48% (normal)
   → Memory: 65% (normal)
   → I/O: 91% (🔴 CRITICAL spike!)

2. Check if AMP-related:
   tdwm://system/amp-load
   → AMP CPU: 45-52% across all AMPs (balanced)
   → No CPU bottleneck

3. Identify I/O-heavy sessions:
   list_sessions()
   → Sort by I/O consumption
   → Top session: analytics_user, session 5001
   → 67% of total I/O from this one session!

4. Get SQL for high-I/O session:
   show_session_sql_text(session_id=5001)
   → Query: Full table scan on 50TB fact table
   → No WHERE clause (scanning entire table!)
   → No indexes being used

5. Check query band:
   monitor_session_query_band()
   → Session 5001: APP=AD_HOC_ANALYTICS
   → Classified in Ad-hoc workload

6. Identify pattern:
   list_sessions()
   → Filter for AD_HOC_ANALYTICS application
   → 5 similar sessions, all doing large scans
   → All from same user: analytics_user

7. Calculate impact:
   - 5 sessions × 13-15% I/O each = 65-75% total I/O
   - Remainder (25-35%) from normal queries
   - Ad-hoc queries blocking I/O for other workloads

8. Report:
   "🔴 Resource Issue: I/O Bottleneck

    Problem:
    - I/O at 91% despite normal CPU (48%)
    - Type: I/O-bound, not CPU-bound

    Root Cause:
    - User 'analytics_user' running 5 concurrent full table scans
    - Each query scanning 50TB fact table
    - No WHERE clauses, no indexes
    - Consuming 65-75% of total system I/O

    Impact:
    - Other queries experiencing I/O delays
    - Reports taking 3-5x longer than normal
    - System I/O-bound, not CPU-bound

    Immediate Actions:
    1. URGENT: Abort analytics_user sessions
       → Use control-sessions skill
       → Free up I/O capacity immediately

    2. SHORT-TERM: Contact analytics_user
       → Queries need WHERE clauses
       → Should use summary tables, not raw fact table
       → Educate on query best practices

    3. MEDIUM-TERM: Create throttle for full table scans
       → Use optimize-throttles skill
       → Add FTSCAN sub-criteria to Ad-hoc throttle
       → Limit full scans to 2 concurrent

    4. LONG-TERM: Implement query governor
       → Reject queries scanning >X TB without approval
       → Enforce WHERE clause requirements"

Result: Identified I/O bottleneck root cause, multi-level action plan

Best Practices

Resource-First Approach (NEW ✨)

START with resources for instant overview (tdwm://system/physical-resources)
Resources are faster and don't add load to system
Use tools for detailed analysis and historical data
Combine resources + tools + session data for complete picture

Multi-Dimensional Analysis

Check multiple resource types - bottleneck may not be where expected
CPU, memory, I/O, disk, AMP load, task slots - all matter
Don't assume high CPU = performance issue (could be I/O, AMP skew, etc.)
Correlate resources with workload and session activity

AMP Skew Understanding

AMP skew (imbalanced load) indicates data distribution issues
Skew factor > 1.5 = warning, > 2.0 = critical
Average CPU can look healthy while one AMP is maxed out
Skew caused by: poor PI design, NULL values, hot keys, small tables

Capacity Planning

Establish baseline metrics for comparison
Track peak vs average utilization (peak drives capacity needs)
Monitor trends over time, not just point-in-time snapshots
Consider both average and peak when planning capacity
Factor in growth projections and seasonal patterns
Keep peak utilization < 80% for headroom

Health Status Thresholds (Reference: tdwm://reference/resource-thresholds)

Green (Healthy): < 70% average, < 80% peak
Amber (Warning): 70-85% average, 80-90% peak
Red (Critical): > 85% average, > 90% peak

Correlation Best Practices

Always correlate resources with workload distribution
Identify which workloads/users are consuming resources
Check if usage patterns are expected for time of day
Look for anomalies: unexpected consumers, unusual patterns

Related Skills

Use monitor-sessions skill for session-level resource analysis
Use monitor-queries skill to correlate queries with resource usage
Use control-sessions skill to abort high-resource sessions
Use optimize-throttles skill to manage resource consumption
Use emergency-response skill for critical resource exhaustion

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