= Complex DB Reports (SQL, Stored Procedures, Relational Algebra) =

===  Top Performing Components in Highly-Rated Builds ===

Report on which specific components appear most frequently in builds that have received a high average user rating (4.5 stars or higher) within the last year.

{{{
SELECT 
    c.type,
    c.brand,
    c.name,
    COUNT(bc.component_id) AS usage_count,
    AVG(rb.value) AS avg_build_rating
FROM components c
JOIN build_component bc ON c.id = bc.component_id
JOIN build b ON bc.build_id = b.id
JOIN rating_build rb ON b.id = rb.build_id
WHERE b.created_at >= CURRENT_DATE - INTERVAL '1 year'
GROUP BY c.type, c.brand, c.name
HAVING avg_build_rating >= 4.5 
ORDER BY usage_count DESC, avg_build_rating DESC
LIMIT 20;
}}}

=== User Reputation Leaderboard ===

Report on the most valuable forgers on PCForge. The score is calculated using the following metrics:
* Productivity: Number of approved builds created (Weight: 10).
* Popularity: Number of times their builds were favourited (Weight: 5).
* Quality: The average star rating across all their builds (Weight: 20).

{{{
WITH build_stats AS (
    SELECT 
        b.id AS build_id,
        b.user_id,
        COUNT(DISTINCT fb.user_id) AS favorites_count,
        AVG(rb.value) AS avg_rating
    FROM build b
    LEFT JOIN favorite_build fb ON b.id = fb.build_id
    LEFT JOIN rating_build rb ON b.id = rb.build_id
    WHERE b.is_approved = TRUE
    GROUP BY b.id, b.user_id
),
user_stats AS (
    SELECT
        user_id,
        COUNT(build_id) AS approved_builds_count,
        SUM(favorites_count) AS total_favorites_received,
        AVG(avg_rating) AS avg_rating_received
    FROM build_stats
    GROUP BY user_id
)
SELECT
    u.username,
    u.email,
    us.approved_builds_count,
    us.total_favorites_received,
    ROUND(us.avg_rating_received, 2) AS avg_rating_received,
    (
        (us.approved_builds_count * 10) +
        (us.total_favorites_received * 5) +
        (us.avg_rating_received * 20)
    ) AS reputation_score
FROM user_stats us
JOIN users u ON u.id = us.user_id
ORDER BY reputation_score DESC
LIMIT 10;
}}}

=== Price-to-Performance Efficiency Analysis ===

Report on which builds have the most computing power per dollar spent using a "price-to-performance index" calculated with the following formula:
* (CPU Cores * CPU Base Clock) + (GPU VRAM * 100)

{{{
WITH cpu_per_build AS (
    SELECT 
        b.id AS build_id,
        c.name AS cpu_model,
        cpu.cores,
        cpu.base_clock
    FROM build b
    JOIN build_component bc ON b.id = bc.build_id
    JOIN components c ON bc.component_id = c.id
    JOIN cpu ON c.id = cpu.component_id
    WHERE c.type = 'CPU'
),
gpu_per_build AS (
    SELECT 
        b.id AS build_id,
        c.name AS gpu_model,
        gpu.vram
    FROM build b
    JOIN build_component bc ON b.id = bc.build_id
    JOIN components c ON bc.component_id = c.id
    JOIN gpu ON c.id = gpu.component_id
    WHERE c.type = 'GPU'
)

SELECT 
    b.name AS build_name,
    cpu.cpu_model,
    gpu.gpu_model,
    b.total_price,
    (cpu.cores * cpu.base_clock + gpu.vram * 100) AS performance_score,
    ROUND(
        (cpu.cores * cpu.base_clock + gpu.vram * 100), 
        4
    ) AS price_to_performance_index
FROM build b
JOIN cpu_per_build cpu ON b.id = cpu.build_id
JOIN gpu_per_build gpu ON b.id = gpu.build_id
WHERE b.total_price > 0
ORDER BY price_to_performance_index DESC
LIMIT 20;
}}}