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by Teradata
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Python
Remote

sql_Retrieve_Cluster_Queries

Retrieve actual SQL queries and performance metrics from selected clusters to enable pattern analysis and optimization. Identify anti-patterns, inefficient joins, and missing indexes for targeted query improvements.

Instructions

RETRIEVE ACTUAL SQL QUERIES FROM SPECIFIC CLUSTERS FOR PATTERN ANALYSIS

This tool extracts the actual SQL query text and performance metrics from selected clusters, enabling detailed pattern analysis and specific optimization recommendations. Essential for moving from cluster-level analysis to actual query optimization.

DETAILED ANALYSIS CAPABILITIES:

  • SQL Pattern Recognition: Analyze actual query structures, joins, predicates, and functions

  • Performance Correlation: Connect query patterns to specific performance characteristics

  • Optimization Identification: Identify common anti-patterns, missing indexes, inefficient joins

  • Code Quality Assessment: Evaluate query construction, complexity, and best practices

  • Workload Understanding: See actual business logic and data access patterns

QUERY SELECTION STRATEGIES:

  • By CPU Impact: Sort by 'ampcputime' to focus on highest CPU consumers

  • By I/O Volume: Sort by 'logicalio' to find scan-intensive queries

  • By Skew Problems: Sort by 'cpuskw' or 'ioskw' for distribution issues

  • By Complexity: Sort by 'numsteps' for complex execution plans

  • By Response Time: Sort by 'response_secs' for user experience impact

AVAILABLE METRICS FOR SORTING:

  • ampcputime: Total CPU seconds (primary optimization target)

  • logicalio: Total logical I/O operations (scan indicator)

  • cpuskw: CPU skew ratio (distribution problems)

  • ioskw: I/O skew ratio (hot spot indicators)

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

  • uii: Unit I/O Intensity (I/O efficiency)

  • numsteps: Query execution plan steps (complexity)

  • response_secs: Wall-clock execution time (user impact)

  • delaytime: Time spent in queue (concurrency issues)

AUTOMATIC PERFORMANCE CATEGORIZATION: Each query is categorized using configurable thresholds (from sql_opt_config.yml):

  • CPU Categories: VERY_HIGH_CPU (>config.very_high), HIGH_CPU (>config.high), MEDIUM_CPU (>10s), LOW_CPU

  • CPU Skew: SEVERE_CPU_SKEW (>config.severe), HIGH_CPU_SKEW (>config.high), MODERATE_CPU_SKEW (>config.moderate), NORMAL

  • I/O Skew: SEVERE_IO_SKEW (>config.severe), HIGH_IO_SKEW (>config.high), MODERATE_IO_SKEW (>config.moderate), NORMAL

Use thresholds set in config file for, CPU - high, very_high, Skew moderate, high, severe

TYPICAL OPTIMIZATION WORKFLOW:

  1. Start with clusters identified from sql_Analyze_Cluster_Stats

  2. Retrieve top queries by impact metric (usually 'ampcputime')

  3. Analyze SQL patterns for common issues:

    • Missing WHERE clauses or inefficient predicates

    • Cartesian products or missing JOIN conditions

    • Inefficient GROUP BY or ORDER BY operations

    • Suboptimal table access patterns

    • Missing or outdated statistics

  4. Develop specific optimization recommendations

QUERY LIMIT STRATEGY:

  • Use the query limit set in config file for pattern recognition and analysis, unless user specifies a different limit

OUTPUT INCLUDES:

  • Complete SQL query text for each query

  • All performance metrics, user, application, and workload context, cluster membership and rankings

  • Performance categories for quick filtering

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_idsYes
metricNoampcputime
limit_per_clusterNo

Tool Definition Quality

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the tool retrieves SQL query text and metrics, sorts by various metrics, auto-categorizes performance, and includes output details. It does not mention potential side effects or performance impact, but overall provides good behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections and lists, but is verbose with some redundant detail (e.g., detailed analysis capabilities overlap with typical workflow). The core purpose is front-loaded, but could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description provides thorough context: what is retrieved, metrics available, sorting strategies, output components, and workflow integration. However, it lacks explicit return value structure and error handling details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description extensively explains each parameter: cluster_ids implied, metric with available options and default, limit_per_cluster with config file reference. This adds significant meaning beyond the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'RETRIEVE ACTUAL SQL QUERIES FROM SPECIFIC CLUSTERS FOR PATTERN ANALYSIS' with a specific verb and resource. It distinguishes from siblings like sql_Analyze_Cluster_Stats (cluster-level stats) and sql_Execute_Full_Pipeline (full pipeline).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a typical optimization workflow that starts with sql_Analyze_Cluster_Stats, and provides query selection strategies. However, it does not explicitly state when not to use this tool or mention alternative tools for other scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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