Teradata MCP Server

COMPLETE SQL QUERY CLUSTERING PIPELINE FOR HIGH-USAGE QUERY OPTIMIZATION

This tool executes the entire SQL query clustering workflow to identify and analyze high CPU usage queries for optimization opportunities. It's designed for database performance analysts and DBAs who need to systematically identify query optimization candidates.

FULL PIPELINE WORKFLOW:

1. Query Log Extraction: Extracts SQL queries from DBC.DBQLSqlTbl with comprehensive performance metrics

2. Performance Metrics Calculation: Computes CPU skew, I/O skew, PJI (Physical to Logical I/O ratio), UII (Unit I/O Intensity)

3. Query Tokenization: Tokenizes SQL text using {sql_clustering_config.get('model', {}).get('model_id', 'bge-small-en-v1.5')} tokenizer via ivsm.tokenizer_encode

4. Embedding Generation: Creates semantic embeddings using ivsm.IVSM_score with ONNX models

5. Vector Store Creation: Converts embeddings to vector columns via ivsm.vector_to_columns

6. K-Means Clustering: Groups similar queries using TD_KMeans with optimal K from configuration

7. Silhouette Analysis: Calculates clustering quality scores using TD_Silhouette

8. Statistics Generation: Creates comprehensive cluster statistics with performance aggregations

PERFORMANCE METRICS EXPLAINED:

• AMPCPUTIME: Total CPU seconds across all AMPs (primary optimization target)

• CPUSKW/IOSKW: CPU/I/O skew ratios (>2.0 indicates distribution problems)

• PJI: Physical-to-Logical I/O ratio (higher = more CPU-intensive)

• UII: Unit I/O Intensity (higher = more I/O-intensive relative to CPU)

• LogicalIO: Total logical I/O operations (indicates scan intensity)

• NumSteps: Query plan complexity (higher = more complex plans)

CONFIGURATION (from sql_opt_config.yml):

• Uses top {default_max_queries} queries by CPU time (configurable)

• Creates {default_optimal_k} clusters by default (configurable via optimal_k parameter)

• Embedding model: {sql_clustering_config.get('model', {}).get('model_id', 'bge-small-en-v1.5')}

• Vector dimensions: {sql_clustering_config.get('embedding', {}).get('vector_length', 384)}

• All database and table names are configurable

OPTIMIZATION WORKFLOW: After running this tool, use:

1. sql_Analyze_Cluster_Stats to identify problematic clusters

2. sql_Retrieve_Cluster_Queries to get actual SQL from target clusters

3. LLM analysis to identify patterns and propose specific optimizations

USE CASES:

• Identify query families consuming the most system resources

• Find queries with similar patterns but different performance

• Discover optimization opportunities through clustering analysis

• Prioritize DBA effort on highest-impact query improvements

• Understand workload composition and resource distribution

PREREQUISITES:

• DBC.DBQLSqlTbl and DBC.DBQLOgTbl must be accessible

• Embedding models and tokenizers must be installed in feature_ext_db

• Sufficient space in feature_ext_db for intermediate and final tables