Tune Workloads

Analyze and autonomously optimize workload definitions including classification rules, filters, priorities, and exception handling to ensure queries are properly managed according to business requirements.

🎯 New Autonomous Capabilities

This skill can now execute changes, not just recommend them!

With tdwm-mcp v1.5.0, this skill has evolved from advisory to autonomous:

✅ ADD classification criteria to existing workloads
✅ ADD sub-criteria for fine-grained targeting (FTSCAN, MINSTEPTIME, etc.)
✅ VALIDATE classification types and operators before execution
✅ USE TEMPLATES for common classification patterns
✅ VERIFY changes were applied correctly
✅ ACTIVATE rulesets to apply all changes

Instructions

When to Use This Skill

Queries are being misclassified into wrong workloads
Need to add classification rules to existing workload
Workload SLAs are not being met due to classification issues
After application changes or new deployments
Regular workload management review and optimization
Need to adjust classification criteria dynamically

Available MCP Tools

Monitoring & Analysis:

list_active_WD - Review active workload definitions
list_WDs - List all workloads (active and inactive)
show_tdwm_summary - Analyze workload distribution
show_tasm_statistics - Review TASM rule effectiveness
show_tasm_even_history - See workload classification decisions
show_query_band - Review query band usage
list_rulesets - List all available rulesets

Configuration Management (NEW ✨):

add_classification_to_rule - Add classification criteria to workload/filter/throttle
add_subcriteria_to_target - Add sub-criteria (FTSCAN, MINSTEPTIME, etc.)
activate_ruleset - Apply all pending changes

Available MCP Resources (NEW ✨)

Templates:

tdwm://templates/filter - List all filter templates
tdwm://template/filter/application-basic - Filter by application
tdwm://template/filter/user-group - Filter by user/account
tdwm://template/filter/query-complexity - Filter by complexity metrics

Reference Data:

tdwm://reference/classification-types - All 31 classification types
tdwm://reference/operators - Classification operators (I, O, IO)
tdwm://reference/subcriteria-types - Sub-criteria types
tdwm://reference/filter-actions - Filter actions (ACCEPT/REJECT)

Discovery:

tdwm://rulesets - List all rulesets
tdwm://system/active-ruleset - Get currently active ruleset
tdwm://ruleset/{name}/filters - List filters in ruleset
tdwm://ruleset/{name}/throttles - List throttles in ruleset
tdwm://ruleset/{name}/pending-changes - Check pending changes

Workflows:

tdwm://workflow/add-classification - Step-by-step classification addition guide

Step-by-Step Workflow

Phase 1: Discovery & Analysis (Read-Only)

Assess Current Workload Configuration
- Get active ruleset: tdwm://system/active-ruleset
- List all workloads: list_active_WD and list_WDs
- Review workload distribution: show_tdwm_summary
- Document workload hierarchy and priorities
Analyze Classification Accuracy
- Use show_tdwm_summary to see query distribution
- Check show_tasm_even_history for misclassification patterns
- Identify queries landing in DEFAULT or wrong workloads
- Review show_query_band to verify tagging is working
Review Existing Rules
- List filters: tdwm://ruleset/{name}/filters
- For each filter: tdwm://ruleset/{name}/filter/{filter_name}
- List throttles: tdwm://ruleset/{name}/throttles
- For each throttle: tdwm://ruleset/{name}/throttle/{throttle_name}
- Check for gaps, overlaps, or conflicts in rule logic
Identify Classification Gaps
- Queries not matching any classification rules
- Missing criteria for new applications
- Overly broad or narrow classification
- Need for sub-criteria refinement
Calculate Optimal Classification
- Based on application characteristics and query bands
- Consider user groups and account names
- Factor in query complexity metrics (if needed)
- Determine priority relative to business requirements

Phase 2: Execute Changes (Autonomous - NEW!)

Select Approach: Template or Custom
- Template-Based (Recommended):
  - Browse templates: tdwm://templates/filter
  - Select template matching use case
  - Read template: tdwm://template/filter/{id}
  - Customize parameters for your workload
- Custom Classification:
  - Validate classification types: tdwm://reference/classification-types
  - Validate operators: tdwm://reference/operators
  - Design custom classification criteria

Add Classification to Rule

For existing workload/filter/throttle:

Use: add_classification_to_rule(
  ruleset_name, rule_name, rule_type,
  classification_criteria
)

Classification criteria structure:

[{
  "description": "Human-readable description",
  "type": "APPL|USER|ACCT|...",  # From reference
  "value": "actual_value",
  "operator": "I|O|IO"  # Inclusion/Exclusion/Inconclusive
}]

Add Sub-Criteria (if needed)
- For fine-grained control, add sub-criteria:
```
Use: add_subcriteria_to_target(
  ruleset_name, rule_name, target_type,
  subcriteria
)
```
- Common sub-criteria:
  - FTSCAN: "Y" - Full table scan queries
  - MINSTEPTIME: "300" - Queries running > 5 minutes
  - MINPARSERTIME: "10" - Queries with parsing time > 10 seconds
Activate Changes
- Check pending changes: tdwm://ruleset/{name}/pending-changes
- Activate ruleset: activate_ruleset(ruleset_name)

Phase 3: Verification (Autonomous - NEW!)

Verify Configuration
- Read back filter/throttle: tdwm://ruleset/{name}/filter/{name}
- Confirm classification was added
- Confirm enabled state is correct
Monitor Impact
- Wait 5-10 minutes for statistics to update
- Check workload distribution: show_tdwm_summary
- Verify queries now classified correctly
- Monitor TASM events: show_tasm_even_history
Iterate if Needed
- If still misclassifying: add more criteria
- If too broad: add exclusion criteria (operator "O")
- Use add_classification_to_rule for adjustments

Examples

Example 1: Fix ETL Misclassification (Autonomous)

Scenario: ETL queries ending up in DEFAULT workload instead of ETL workload

Discovery:

1. show_tdwm_summary → See 50% queries in DEFAULT (should be in ETL)
2. show_query_band → ETL sets query band 'APP=ETL_LOADER'
3. tdwm://system/active-ruleset → "Tactical"
4. tdwm://ruleset/Tactical/filter/ETL_FILTER → No APPL classification exists

Analysis:

ETL workload exists but classification is incomplete
ETL sets query band but filter doesn't check it
Need to add APPL classification to ETL_FILTER

Execution (Autonomous):

1. Validate classification type:
   tdwm://reference/classification-types
   → Confirm "APPL" is valid classification type

2. Add APPL classification to ETL filter:
   add_classification_to_rule(
     ruleset_name="Tactical",
     rule_name="ETL_FILTER",
     rule_type="filter",
     classification_criteria=[{
       "description": "Match ETL application query band",
       "type": "APPL",
       "value": "ETL_LOADER",
       "operator": "I"  # Inclusion
     }]
   )

3. Check pending changes:
   tdwm://ruleset/Tactical/pending-changes
   → Confirm APPL classification queued

4. Activate:
   activate_ruleset(ruleset_name="Tactical")

5. Verify:
   tdwm://ruleset/Tactical/filter/ETL_FILTER
   → Confirm APPL classification present

6. Monitor (wait 10 minutes):
   show_tdwm_summary
   → ETL queries now in ETL workload (not DEFAULT)

Result: ETL queries properly classified, DEFAULT workload reduced by 50%

Example 2: Add User-Based Classification (Autonomous)

Scenario: Power BI users need dedicated workload but not all set query band

Discovery:

1. show_tdwm_summary → Power BI queries mixed across workloads
2. show_query_band → Only 60% of Power BI queries set query band
3. list_active_WD → POWERBI workload exists
4. tdwm://ruleset/Tactical/filter/POWERBI_FILTER → Only has APPL classification

Analysis:

Some Power BI users don't set query band (legacy reports)
Need to add USER classification as fallback
Power BI users identified by username pattern "pbi_*"

Execution (Autonomous):

1. Validate classification type:
   tdwm://reference/classification-types
   → Confirm "USER" is valid

2. Add USER classification to existing filter:
   add_classification_to_rule(
     ruleset_name="Tactical",
     rule_name="POWERBI_FILTER",
     rule_type="filter",
     classification_criteria=[{
       "description": "Catch Power BI users without query band",
       "type": "USER",
       "value": "pbi_*",  # Pattern matching
       "operator": "I"
     }]
   )

3. Activate:
   activate_ruleset(ruleset_name="Tactical")

4. Verify:
   tdwm://ruleset/Tactical/filter/POWERBI_FILTER
   → Confirm both APPL and USER classifications present

5. Monitor:
   show_tdwm_summary
   → Power BI workload increased from 60% to 95% capture rate

Result: Power BI queries properly classified even without query band

Example 3: Limit Long-Running Analytics (Autonomous)

Scenario: Analytics queries running >10 minutes should be throttled

Discovery:

1. show_query_log → Analytics queries running 10-60 minutes
2. tdwm://ruleset/Tactical/throttle/ANALYTICS_LIMIT → Has APPL classification
3. show_trottle_statistics → Short analytics queries also being throttled

Analysis:

Throttle catches all analytics queries
Only long-running queries need throttling
Need to add MINSTEPTIME sub-criteria (600 seconds = 10 minutes)

Execution (Autonomous):

1. Validate sub-criteria type:
   tdwm://reference/subcriteria-types
   → Confirm "MINSTEPTIME" is valid

2. Add MINSTEPTIME sub-criteria to throttle:
   add_subcriteria_to_target(
     ruleset_name="Tactical",
     throttle_name="ANALYTICS_LIMIT",
     target_type="APPL",
     subcriteria={
       "type": "MINSTEPTIME",
       "value": "600"  # 10 minutes in seconds
     }
   )

3. Activate:
   activate_ruleset(ruleset_name="Tactical")

4. Verify:
   tdwm://ruleset/Tactical/throttle/ANALYTICS_LIMIT
   → Confirm MINSTEPTIME sub-criteria present

5. Monitor:
   show_trottle_statistics
   → Only long-running analytics queries now throttled

Result: Short analytics queries run freely, only long queries throttled

Example 4: Template-Driven Application Filter (Autonomous)

Scenario: New ML application needs classification into ML workload

Discovery:

1. show_tdwm_summary → ML queries appearing in DEFAULT
2. show_query_band → ML app sets 'APP=ML_TRAINING'
3. list_active_WD → ML_WORKLOAD exists
4. tdwm://ruleset/Tactical/filters → No ML_FILTER exists yet

Analysis:

Need to create new filter for ML workload
Application sets query band (simple case)
Can use template for quick creation

Execution (Template-Based):

1. Get template:
   tdwm://template/filter/application-basic
   → Shows APPL-based filter pattern

2. Create filter using create_filter_rule:
   create_filter_rule(
     ruleset_name="Tactical",
     filter_name="ML_FILTER",
     classification_criteria=[{
       "description": "ML Training Application",
       "type": "APPL",
       "value": "ML_TRAINING",
       "operator": "I"
     }],
     action_type="ACCEPT",
     workload_name="ML_WORKLOAD"
   )

3. Enable filter:
   enable_filter_rule(
     ruleset_name="Tactical",
     filter_name="ML_FILTER"
   )

4. Activate:
   activate_ruleset(ruleset_name="Tactical")

5. Verify:
   tdwm://ruleset/Tactical/filter/ML_FILTER
   → Confirm filter created with APPL classification

6. Monitor:
   show_tdwm_summary
   → ML queries now in ML_WORKLOAD

Result: ML application properly classified into dedicated workload

Example 5: Exclude Full Table Scans from Fast Track (Autonomous)

Scenario: Fast track workload getting slow queries due to full table scans

Discovery:

1. show_tdwm_summary → FAST_TRACK average response time increasing
2. show_query_log → Some FAST_TRACK queries doing full table scans
3. tdwm://ruleset/Tactical/filter/FAST_TRACK_FILTER → Has USER and APPL criteria

Analysis:

Fast track should only handle indexed queries
Full table scans should go to BATCH workload
Need to add FTSCAN sub-criteria with exclusion

Execution (Autonomous):

1. Validate sub-criteria type:
   tdwm://reference/subcriteria-types
   → Confirm "FTSCAN" is valid

2. Add exclusion for full table scans:
   add_subcriteria_to_target(
     ruleset_name="Tactical",
     filter_name="FAST_TRACK_FILTER",
     target_type="USER",  # Apply to USER classification
     subcriteria={
       "type": "FTSCAN",
       "value": "N"  # Only queries NOT doing full scan
     }
   )

3. Activate:
   activate_ruleset(ruleset_name="Tactical")

4. Verify:
   tdwm://ruleset/Tactical/filter/FAST_TRACK_FILTER
   → Confirm FTSCAN sub-criteria present

5. Monitor:
   show_tdwm_summary
   → FAST_TRACK average response time improved
   → Full scan queries now in BATCH workload

Result: Fast track performance improved by excluding full scans

Best Practices

General Principles

Start with broad classification (APPL, USER), refine with sub-criteria later
Use query bands as primary classification method when possible
Username/account filters are fallbacks when query bands unavailable
Test classification changes during low-activity periods when possible
Document business justification for each classification rule
Monitor classification for 1-2 weeks after changes to verify effectiveness

Discovery Before Execution (NEW ✨)

ALWAYS use tdwm://ruleset/{name}/filters to see existing filters before adding
Check tdwm://ruleset/{name}/pending-changes before activating
Verify with resource read after changes
Understand current classification before adding new rules

Template Usage (NEW ✨)

Use templates for common patterns (application-based, user-based)
Customize template parameters for your specific needs
Follow template best practices and examples

Execution Safety (NEW ✨)

Validate classification types against tdwm://reference/classification-types
Validate operators against tdwm://reference/operators
Start with single classification, add more if needed
Test in non-production first if possible
Keep emergency rollback plan

Validation (NEW ✨)

Always read back filter/throttle after adding classification
Confirm classification appears in rule definition
Monitor workload distribution after changes
Check TASM events to verify classification working

Monitoring

Query bands take precedence over username/account classification
Operator "I" (Inclusion) matches the value
Operator "O" (Exclusion) excludes the value
Operator "IO" (Inconclusive) allows TASM to continue evaluation
Sub-criteria refine the target (FTSCAN, MINSTEPTIME, etc.)
Coordinate classification changes with application teams
Regular classification reviews identify drift over time

Related Skills

Use optimize-throttles skill for concurrency limit adjustments
Use manage-workloads skill for creating new workloads from scratch
Use emergency-response skill for crisis situations
Use discover-configuration skill to inventory existing classifications

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tune-workloads

Install this agent skill to your Project

SKILL.md

Tune Workloads

🎯 New Autonomous Capabilities

Instructions

When to Use This Skill

Available MCP Tools

Available MCP Resources (NEW ✨)

Step-by-Step Workflow

Phase 1: Discovery & Analysis (Read-Only)

Phase 2: Execute Changes (Autonomous - NEW!)

Phase 3: Verification (Autonomous - NEW!)

Examples

Example 1: Fix ETL Misclassification (Autonomous)

Example 2: Add User-Based Classification (Autonomous)

Example 3: Limit Long-Running Analytics (Autonomous)

Example 4: Template-Driven Application Filter (Autonomous)

Example 5: Exclude Full Table Scans from Fast Track (Autonomous)

Best Practices

General Principles

Discovery Before Execution (NEW ✨)

Template Usage (NEW ✨)

Execution Safety (NEW ✨)

Validation (NEW ✨)

Monitoring

Related Skills