Query Optimization di MariaDB dengan Window Functions dan CTE: Teknik Advanced

Query yang kompleks dengan multiple subqueries seringkali menjadi bottleneck performa. MariaDB 10.2+ membawa window functions dan Common Table Expressions (CTEs) yang powerful untuk menulis query yang lebih readable dan lebih cepat. Artikel ini akan membahas teknik-teknik advanced ini.

Window Functions Overview

1. Apa itu Window Functions?

Window functions melakukan kalkulasi di set of rows yang related dengan current row, tanpa meng-group rows seperti aggregate functions.

-- Aggregate: menghasilkan 1 row per group
SELECT department, AVG(salary) FROM employees GROUP BY department;
-- Window: menghasilkan 1 row per input row
SELECT 
name,
department,
salary,
AVG(salary) OVER (PARTITION BY department) as dept_avg
FROM employees;

2. Syntax Dasar

function_name([arguments]) OVER (
    [PARTITION BY partition_expression]
    [ORDER BY order_expression]
    [frame_clause]
)

3. Jenis-Jenis Window Functions

Ranking Functions:

SELECT 
    name,
    salary,
    RANK() OVER (ORDER BY salary DESC) as salary_rank,
    DENSE_RANK() OVER (ORDER BY salary DESC) as dense_rank,
    ROW_NUMBER() OVER (ORDER BY salary DESC) as row_num,
    NTILE(4) OVER (ORDER BY salary DESC) as quartile
FROM employees;

Aggregate Window Functions:

SELECT 
    name,
    department,
    salary,
    SUM(salary) OVER () as total_salary,
    SUM(salary) OVER (PARTITION BY department) as dept_total,
    AVG(salary) OVER (PARTITION BY department) as dept_avg,
    MIN(salary) OVER (PARTITION BY department) as dept_min,
    MAX(salary) OVER (PARTITION BY department) as dept_max,
    COUNT(*) OVER (PARTITION BY department) as dept_count
FROM employees;

Window Functions untuk Business Cases

1. Running Totals dan Moving Averages

-- Running total penjualan
SELECT 
    order_date,
    daily_amount,
    SUM(daily_amount) OVER (ORDER BY order_date) as running_total,
    AVG(daily_amount) OVER (ORDER BY order_date ROWS 6 PRECEDING) as moving_avg_7d
FROM (
    SELECT order_date, SUM(amount) as daily_amount
    FROM sales
    GROUP BY order_date
) daily_sales;

2. Top N per Category

Masalah: Ambil top 3 employee per department

-- Dari subquery yang lambat
SELECT * FROM employees e1
WHERE (
    SELECT COUNT(*) FROM employees e2
    WHERE e2.department = e1.department 
      AND e2.salary >= e1.salary
) <= 3;
-- Ke window function yang cepat
WITH ranked_employees AS (
SELECT 

,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees
)
SELECT  FROM ranked_employees WHERE dept_rank <= 3;

3. Year-over-Year Comparison

WITH monthly_sales AS (
    SELECT 
        DATE_FORMAT(order_date, '%Y-%m') as month,
        SUM(amount) as monthly_revenue
    FROM orders
    WHERE order_date >= DATE_SUB(CURDATE(), INTERVAL 2 YEAR)
    GROUP BY month
)
SELECT 
    month,
    monthly_revenue,
    LAG(monthly_revenue, 12) OVER (ORDER BY month) as revenue_last_year,
    ROUND(
        (monthly_revenue - LAG(monthly_revenue, 12) OVER (ORDER BY month)) / 
        LAG(monthly_revenue, 12) OVER (ORDER BY month) * 100, 
        2
    ) as yoy_growth_percent
FROM monthly_sales
ORDER BY month;

4. First/Last Value dalam Group

-- Customer first dan last order
SELECT DISTINCT
    customer_id,
    FIRST_VALUE(order_id) OVER (
        PARTITION BY customer_id 
        ORDER BY order_date
    ) as first_order,
    LAST_VALUE(order_id) OVER (
        PARTITION BY customer_id 
        ORDER BY order_date
        ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
    ) as last_order,
    FIRST_VALUE(order_date) OVER (
        PARTITION BY customer_id 
        ORDER BY order_date
    ) as first_order_date,
    LAST_VALUE(order_date) OVER (
        PARTITION BY customer_id 
        ORDER BY order_date
        ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
    ) as last_order_date
FROM orders;

5. Gap Analysis

-- Temukan missing sequence numbers
WITH numbered AS (
    SELECT 
        id,
        invoice_number,
        invoice_number - ROW_NUMBER() OVER (ORDER BY invoice_number) as grp
    FROM invoices
    WHERE invoice_date >= '2024-01-01'
)
SELECT 
    MIN(invoice_number) as range_start,
    MAX(invoice_number) as range_end,
    COUNT(*) as count
FROM numbered
GROUP BY grp
ORDER BY range_start;

Common Table Expressions (CTEs)

1. Non-Recursive CTEs

-- Bagi query kompleks jadi readable parts
WITH 
monthly_revenue AS (
    SELECT 
        DATE_FORMAT(order_date, '%Y-%m') as month,
        SUM(total_amount) as revenue
    FROM orders
    WHERE order_date >= '2024-01-01'
    GROUP BY month
),
top_customers AS (
    SELECT 
        customer_id,
        SUM(total_amount) as total_spent
    FROM orders
    WHERE order_date >= '2024-01-01'
    GROUP BY customer_id
    ORDER BY total_spent DESC
    LIMIT 10
)
SELECT 
    r.month,
    r.revenue,
    COUNT(DISTINCT tc.customer_id) as top_customer_count
FROM monthly_revenue r
LEFT JOIN orders o ON DATE_FORMAT(o.order_date, '%Y-%m') = r.month
LEFT JOIN top_customers tc ON o.customer_id = tc.customer_id
GROUP BY r.month, r.revenue
ORDER BY r.month;

2. Recursive CTEs untuk Hierarchical Data

-- Employee hierarchy dengan recursive CTE
WITH RECURSIVE employee_hierarchy AS (
    -- Anchor member: top-level managers
    SELECT 
        id,
        name,
        manager_id,
        0 as level,
        CAST(name AS CHAR(1000)) as hierarchy_path
    FROM employees
    WHERE manager_id IS NULL
UNION ALL

-- Recursive member: direct reports
SELECT 
    e.id,
    e.name,
    e.manager_id,
    eh.level + 1,
    CONCAT(eh.hierarchy_path, ' > ', e.name)
FROM employees e
INNER JOIN employee_hierarchy eh ON e.manager_id = eh.id
)
SELECT
id,
REPEAT(' ', level) || name as indented_name,
level,
hierarchy_path
FROM employee_hierarchy
ORDER BY hierarchy_path;
3. Recursive CTE untuk Tree Traversal
-- Category tree dengan all descendants
WITH RECURSIVE category_tree AS (
    SELECT 
        id,
        name,
        parent_id,
        0 as depth,
        CAST(id AS CHAR(1000)) as path
    FROM categories
    WHERE parent_id IS NULL
UNION ALL

SELECT 
    c.id,
    c.name,
    c.parent_id,
    ct.depth + 1,
    CONCAT(ct.path, ',', c.id)
FROM categories c
INNER JOIN category_tree ct ON c.parent_id = ct.id
)
SELECT
id,
REPEAT('→ ', depth) || name as tree_view,
depth,
path
FROM category_tree
ORDER BY path;
CTEs vs Subqueries: Performance Comparison
1. Multiple References
Subquery (executed multiple times):
SELECT 
    (SELECT AVG(salary) FROM employees WHERE department = 'Sales') as sales_avg,
    (SELECT AVG(salary) FROM employees WHERE department = 'Sales') * 1.1 as target_avg,
    (SELECT AVG(salary) FROM employees WHERE department = 'Sales') * 0.9 as min_avg
FROM dual;
CTE (executed once, referenced multiple times):
WITH sales_avg AS (
    SELECT AVG(salary) as avg_sal
    FROM employees
    WHERE department = 'Sales'
)
SELECT 
    avg_sal,
    avg_sal * 1.1 as target_avg,
    avg_sal * 0.9 as min_avg
FROM sales_avg;
2. Readability dan Maintenance
-- Complex subquery (hard to read)
SELECT 
    c.customer_name,
    (SELECT COUNT(*) FROM orders o WHERE o.customer_id = c.id AND o.status = 'completed') as total_orders,
    (SELECT SUM(total) FROM orders o WHERE o.customer_id = c.id AND o.status = 'completed') as total_revenue,
    (SELECT AVG(total) FROM orders o WHERE o.customer_id = c.id AND o.status = 'completed') as avg_order
FROM customers c
WHERE c.status = 'active';
-- CTE version (clean and readable)
WITH customer_stats AS (
SELECT 
customer_id,
COUNT(*) as total_orders,
SUM(total) as total_revenue,
AVG(total) as avg_order
FROM orders
WHERE status = 'completed'
GROUP BY customer_id
)
SELECT 
c.customer_name,
COALESCE(cs.total_orders, 0) as total_orders,
COALESCE(cs.total_revenue, 0) as total_revenue,
COALESCE(cs.avg_order, 0) as avg_order
FROM customers c
LEFT JOIN customer_stats cs ON c.id = cs.customer_id
WHERE c.status = 'active';
Performance Tips
1. Index untuk Window Functions
-- Buat index untuk partitioning dan ordering
CREATE INDEX idx_dept_salary ON employees(department, salary);
CREATE INDEX idx_order_date ON orders(order_date);
2. Materialized CTEs (MariaDB 10.6+)
-- Materialized CTE (hasil disimpan temporary)
WITH MATERIALIZED heavy_calculation AS (
    SELECT /* expensive query */ ...
)
SELECT * FROM heavy_calculation WHERE ...;
3. Avoid Unnecessary Window Frames
-- Default frame: ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
-- Gunakan eksplisit frame untuk kalkulasi yang lebih cepat
-- Untuk running total sampai row saat ini
SUM(amount) OVER (ORDER BY order_date ROWS UNBOUNDED PRECEDING)
-- Untuk moving average window kecil
AVG(amount) OVER (ORDER BY order_date ROWS 2 PRECEDING AND 2 FOLLOWING)
Use Cases Kompleks
1. Sessionization (Clickstream Analysis)
-- Identifikasi user sessions (30-minute gap)
WITH user_events AS (
    SELECT 
        user_id,
        event_timestamp,
        LAG(event_timestamp) OVER (
            PARTITION BY user_id 
            ORDER BY event_timestamp
        ) as prev_timestamp
    FROM user_clicks
    WHERE event_date = '2024-01-15'
)
SELECT 
    user_id,
    event_timestamp,
    SUM(CASE 
        WHEN prev_timestamp IS NULL 
          OR TIMESTAMPDIFF(MINUTE, prev_timestamp, event_timestamp) > 30 
        THEN 1 
        ELSE 0 
    END) OVER (
        PARTITION BY user_id 
        ORDER BY event_timestamp
    ) as session_id
FROM user_events;
2. Cohort Analysis
-- Retention cohort analysis
WITH first_orders AS (
    SELECT 
        customer_id,
        MIN(order_date) as first_order_date,
        DATE_FORMAT(MIN(order_date), '%Y-%m') as cohort_month
    FROM orders
    GROUP BY customer_id
),
order_months AS (
    SELECT 
        customer_id,
        DATE_FORMAT(order_date, '%Y-%m') as order_month,
        PERIOD_DIFF(
            DATE_FORMAT(order_date, '%Y%m'), 
            DATE_FORMAT(first_order_date, '%Y%m')
        ) as months_since_first
    FROM orders
    JOIN first_orders USING (customer_id)
)
SELECT 
    cohort_month,
    months_since_first,
    COUNT(DISTINCT customer_id) as active_customers,
    ROUND(
        COUNT(DISTINCT customer_id) * 100.0 / 
        FIRST_VALUE(COUNT(DISTINCT customer_id)) OVER (
            PARTITION BY cohort_month 
            ORDER BY months_since_first
        ), 
        2
    ) as retention_rate
FROM order_months
GROUP BY cohort_month, months_since_first
ORDER BY cohort_month, months_since_first;
Kesimpulan
Window functions dan CTEs mengubah cara kita menulis SQL:
– Query kompleks menjadi readable
– Performa lebih baik dari nested subqueries
– Recursive queries untuk hierarchical data
– Analytical functions untuk business intelligence
Investasi waktu untuk belajar teknik ini akan sangat membayar dalam produktivitas dan performa query.