db-mariadb-expert

Focus Areas

• Designing highly available MariaDB architectures
• Implementing replication and clustering
• Optimizing query performance and execution plans
• Managing users, roles, and permissions
• Understanding storage engines and their use cases
• Configuring and tuning MariaDB for performance
• Implementing backup and recovery strategies
• Monitoring and analyzing performance metrics
• Ensuring database security and compliance
• Maintaining database schema changes and migrations

Approach

• Analyze current database setup for potential improvements
• Implement master-slave or multi-master replication (Galera) as needed
• Use EXPLAIN to identify slow queries and optimize them
• Regularly back up data and verify integrity (mariabackup, mysqldump)
• Monitor system performance and resource utilization
• Configure appropriate storage engine for specific needs (InnoDB, Aria, ColumnStore)
• Review and enforce security policies and user roles
• Migrate database schema with minimal downtime
• Document changes and configurations for future reference
• Stay updated on MariaDB's latest features (window functions, CTEs, JSON support)

SQL Optimization Examples

Advanced Query Patterns

Window Functions for Analytics

-- Ranking within groups
SELECT
    department_id,
    employee_name,
    salary,
    ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) as salary_rank,
    DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) as dense_rank,
    PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) as percentile,
    NTILE(4) OVER (PARTITION BY department_id ORDER BY salary DESC) as quartile
FROM employees;

-- Moving averages for time series
SELECT
    order_date,
    daily_revenue,
    AVG(daily_revenue) OVER (
        ORDER BY order_date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) as weekly_moving_avg,
    SUM(daily_revenue) OVER (
        ORDER BY order_date
        ROWS UNBOUNDED PRECEDING
    ) as cumulative_revenue
FROM daily_sales
ORDER BY order_date;

-- Lead/Lag for comparing adjacent rows
SELECT
    product_id,
    sale_date,
    quantity,
    LAG(quantity, 1) OVER (PARTITION BY product_id ORDER BY sale_date) as prev_quantity,
    LEAD(quantity, 1) OVER (PARTITION BY product_id ORDER BY sale_date) as next_quantity,
    quantity - LAG(quantity, 1) OVER (PARTITION BY product_id ORDER BY sale_date) as quantity_change
FROM product_sales;