Teradata MCP Server

ANALYZE SQL QUERY CLUSTER PERFORMANCE STATISTICS

This tool analyzes pre-computed cluster statistics to identify optimization opportunities without re-running the clustering pipeline. Perfect for iterative analysis and decision-making on which query clusters to focus optimization efforts.

ANALYSIS CAPABILITIES:

• Performance Ranking: Sort clusters by any performance metric to identify top resource consumers

• Resource Impact Assessment: Compare clusters by CPU usage, I/O volume, and execution complexity

• Skew Problem Detection: Identify clusters with CPU or I/O distribution issues

• Workload Characterization: Understand query patterns by user, application, and workload type

• Optimization Prioritization: Focus on clusters with highest impact potential

AVAILABLE SORTING METRICS:

• avg_cpu: Average CPU seconds per cluster (primary optimization target)

• avg_io: Average logical I/O operations (scan intensity indicator)

• avg_cpuskw: Average CPU skew (distribution problem indicator)

• avg_ioskw: Average I/O skew (hot spot indicator)

• avg_pji: Average Physical-to-Logical I/O ratio (compute intensity)

• avg_uii: Average Unit I/O Intensity (I/O efficiency)

• avg_numsteps: Average query plan complexity

• queries: Number of queries in cluster (frequency indicator)

• cluster_silhouette_score: Clustering quality measure

PERFORMANCE CATEGORIZATION: Automatically categorizes clusters using configurable thresholds (from sql_opt_config.yml):

• HIGH_CPU_USAGE: Average CPU > config.performance_thresholds.cpu.high

• HIGH_IO_USAGE: Average I/O > config.performance_thresholds.io.high

• HIGH_CPU_SKEW: CPU skew > config.performance_thresholds.skew.high

• HIGH_IO_SKEW: I/O skew > config.performance_thresholds.skew.high

• NORMAL: Clusters within configured normal performance ranges

TYPICAL ANALYSIS WORKFLOW:

1. Sort by 'avg_cpu' or 'avg_io' to find highest resource consumers

2. Sort by 'avg_cpuskw' or 'avg_ioskw' to find distribution problems

3. Use limit_results to focus on top problematic clusters

OPTIMIZATION DECISION FRAMEWORK:

• High CPU + High Query Count: Maximum impact optimization candidates

• High Skew + Moderate CPU: Distribution/statistics problems

• High I/O + Low PJI: Potential indexing opportunities

• High NumSteps: Complex query rewriting candidates

OUTPUT FORMAT: Returns detailed cluster statistics with performance rankings, categories, and metadata for LLM analysis and optimization recommendations.