> ## Documentation Index > Fetch the complete documentation index at: https://docs.cloudthinker.io/llms.txt > Use this file to discover all available pages before exploring further. # Tony > Database Engineer - Query optimization, performance tuning, analytics, multi-platform Tony is CloudThinker's database expert, specializing in query optimization, performance tuning, backup strategies, and data analytics across SQL and NoSQL platforms. *** ## The Problem Tony Solves Database performance degrades silently. A missing index on a growing table is invisible until P95 latency spikes and engineers wake up at 2 AM. Connection pool exhaustion looks like an application bug until someone checks the database. Slow queries consume 80% of database CPU while running hundreds of times per day — and nobody knows because there's no system connecting query analytics to infrastructure cost. Diagnosing and fixing these problems normally requires: * Querying `pg_stat_statements` or enabling MySQL slow query log * Reading and interpreting execution plans (EXPLAIN ANALYZE output) * Cross-referencing query patterns against current indexes * Understanding PostgreSQL/MySQL configuration parameters and their tradeoffs This is deep specialist work. Most teams don't have a dedicated DBA, so database performance issues either go unfixed or require expensive consultants. *** ## How Existing Tools Compare | Tool | What It Does | What's Missing | | ---------------------------- | ---------------------------------------------------- | ----------------------------------------------------------------------------------- | | **AWS Performance Insights** | Visualizes database load and query waits | AWS RDS only, requires SQL expertise to interpret, no recommendations | | **pganalyze** | PostgreSQL query analytics and index recommendations | PostgreSQL-only, no conversational interface, still needs DBA interpretation | | **Percona Monitoring (PMM)** | Open-source database monitoring | Complex setup, technical dashboards, no AI analysis | | **Datadog APM** | Application + database trace correlation | Monitoring only, no fix recommendations, expensive at scale | | **New Relic / AppDynamics** | Full-stack observability including databases | Visibility tool, not a decision-maker; findings still require expert interpretation | Tony goes beyond monitoring: it reads execution plans, understands your schema context, and tells you exactly which index to create, which query to rewrite, and what configuration change to make — in plain language. *** ## How Tony Works 1. **Connects to your databases** via read access — `pg_stat_statements`, MySQL Performance Schema, RDS Performance Insights, Aurora, MongoDB profiler 2. **Identifies slow queries** by analyzing execution times, call frequency, and resource consumption — finding the highest-impact targets automatically 3. **Reads execution plans** — interprets EXPLAIN ANALYZE output to understand table scans, index misses, and join inefficiencies 4. **Recommends with precision** — generates specific `CREATE INDEX` statements, query rewrites, and configuration changes with before/after impact estimates 5. **Understands tradeoffs** — considers write overhead of new indexes, memory implications of configuration changes, and downtime requirements for schema changes *** ## Capabilities | Domain | Capabilities | | ---------------------- | ------------------------------------------------------------------------- | | **Query Optimization** | SQL analysis, execution plans, index recommendations, query rewriting | | **Performance Tuning** | Connection pooling, configuration optimization, bottleneck identification | | **Data Analytics** | Usage patterns, trend analysis, capacity planning, metrics visualization | | **Operations** | Backup/recovery, replication, maintenance, disaster recovery planning | *** ## Supported Platforms | Category | Platforms | | ----------------- | ---------------------------------------------- | | **Relational** | PostgreSQL, MySQL, MariaDB, SQL Server, Oracle | | **Cloud Managed** | AWS RDS, Aurora, Azure SQL, Cloud SQL | | **NoSQL** | MongoDB, Redis, DynamoDB, DocumentDB | | **Analytics** | Redshift, BigQuery, Snowflake | *** ## Prompt Patterns ### Query Analysis ```bash theme={null} # Slow query investigation @tony analyze slow queries on production PostgreSQL # Specific performance target @tony identify queries with execution time >2 seconds on orders database # Execution plan analysis @tony analyze execution plans for the 20 slowest queries # Query patterns @tony find queries that could benefit from caching ``` ### Performance Optimization ```bash theme={null} # Index recommendations @tony analyze missing indexes that would improve performance by >10% # Connection optimization @tony review connection pooling configuration for high-load scenarios # Configuration tuning @tony optimize MySQL 8.0 configuration for high-throughput OLTP with 10k connections # Resource analysis @tony identify queries consuming >5% of total database CPU ``` ### Database Health ```bash theme={null} # Health check @tony check database health and performance metrics # Replication status @tony assess replication lag and recommend optimization # Storage analysis @tony analyze database growth patterns and recommend archiving strategy # Connection analysis @tony analyze connection usage patterns and identify connection leaks ``` ### Backup & Recovery ```bash theme={null} # Backup verification @tony verify backup status and recovery procedures for production databases # DR planning @tony create disaster recovery plan with RTO/RPO analysis # Recovery testing @tony recommend backup testing strategy for production databases ``` *** ## Tool Usage | Tool | Tony Use Case | | ------------ | --------------------------------------------------------------------- | | `#dashboard` | Query latency (P50/P95/P99), connections, I/O, replication lag | | `#report` | Performance analysis, optimization recommendations, capacity planning | | `#recommend` | Index changes, configuration updates, query rewrites | | `#alert` | Slow queries, connection pool exhaustion, replication lag | | `#chart` | Query trends, resource utilization, growth patterns | ### Examples with Tools ```bash theme={null} @tony #dashboard database performance metrics for production cluster @tony #report query performance analysis with optimization plan @tony #recommend index optimizations prioritized by impact @tony #alert when P95 query latency exceeds 500ms ``` *** ## Effective Prompts ```bash theme={null} # Good @tony analyze queries with execution time >2 seconds running >100 times daily # Avoid @tony make database faster ``` ```bash theme={null} # Good @tony optimize MySQL 8.0 for read-heavy workloads with 10k concurrent connections # Avoid @tony check the database ``` *** ## Connection Requirements Tony requires database connections with performance metrics access: | Platform | Required Access | | -------------- | ---------------------------------------------------- | | **PostgreSQL** | `pg_stat_statements`, query logs, performance schema | | **MySQL** | Performance Schema, slow query log, status variables | | **RDS/Aurora** | Enhanced Monitoring, Performance Insights | | **MongoDB** | Profiler, `serverStatus`, operation logs | *** ## Common Workflows ### Performance Crisis Response ```bash theme={null} # Step 1: Identify @tony identify top 10 slowest queries in last hour # Step 2: Analyze @tony analyze execution plans for problematic queries # Step 3: Optimize @tony #recommend index changes and query rewrites # Step 4: Monitor @tony #dashboard real-time query performance ``` ### Proactive Optimization ```bash theme={null} # Step 1: Baseline @tony #dashboard current performance metrics # Step 2: Analyze @tony identify optimization opportunities across all databases # Step 3: Prioritize @tony #recommend optimizations ranked by impact and effort # Step 4: Automate @tony #schedule weekly performance review ``` ### Capacity Planning ```bash theme={null} # Step 1: Analyze growth @tony analyze database growth patterns over last 6 months # Step 2: Forecast @tony predict storage and compute needs for next year # Step 3: Plan @tony #recommend scaling strategy with cost analysis ``` *** ## What's Next Connect Tony to your PostgreSQL databases Connect Tony to your MySQL databases How Tony investigates database-related incidents automatically Coordinate Tony with [Alex](/guide/agents/alex) for infrastructure + database cost optimization