使用GPT-3.5-turbo辅助优化数仓缓慢变化维处理的实践与思考

-- 传统SCD2实现示例
INSERT INTO target_table
SELECT 
    s.*,
    CURRENT_TIMESTAMP AS start_date,
    NULL AS end_date,
    TRUE AS is_current
FROM source_table s
LEFT JOIN target_table t 
    ON s.id = t.id AND t.is_current = TRUE
WHERE t.id IS NULL;

UPDATE target_table t
SET 
    end_date = CURRENT_TIMESTAMP,
    is_current = FALSE
FROM source_table s
WHERE 
    t.id = s.id 
    AND t.is_current = TRUE
    AND (t.name <> s.name OR t.email <> s.email);

请生成一个优化的缓慢变化维(SCD)类型2处理方案，要求：
1. 使用PostgreSQL语法
2. 包含变更检测逻辑
3. 支持批量处理
4. 包含性能优化措施
5. 添加适当的注释说明

import openai
import json

def generate_scd2_schema(table_name, columns, natural_key):
    prompt = f"""
    为表 {table_name} 生成SCD类型2处理的DDL语句，包含以下列：{columns}，
    自然键为：{natural_key}。请包含有效的版本控制字段。
    """
    
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "你是一个资深数据工程师，擅长设计高效的数据仓库解决方案。"},
            {"role": "user", "content": prompt}
        ],
        temperature=0.3
    )
    
    return response.choices[0].message.content

# 示例使用
ddl_sql = generate_scd2_schema(
    "dim_customer", 
    "customer_id, name, email, phone, address", 
    "customer_id"
)
print(ddl_sql)

def optimize_scd_query(original_query):
    prompt = f"""
    分析以下SCD处理SQL查询并提供性能优化建议：
    {original_query}
    
    请重点考虑：
    1. 索引优化策略
    2. 查询重写建议
    3. 批量处理优化
    4. 避免全表扫描的方法
    """
    
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "你是一个数据库性能调优专家。"},
            {"role": "user", "content": prompt}
        ],
        temperature=0.2
    )
    
    return response.choices[0].message.content

-- AI建议的表结构
CREATE TABLE dim_users (
    user_sk BIGSERIAL PRIMARY KEY,
    user_id INT NOT NULL,
    name VARCHAR(255),
    email VARCHAR(255),
    phone VARCHAR(50),
    region VARCHAR(100),
    start_date TIMESTAMP NOT NULL,
    end_date TIMESTAMP,
    is_current BOOLEAN DEFAULT TRUE,
    -- 添加复合索引优化查询
    CONSTRAINT idx_dim_users_current 
        UNIQUE (user_id, is_current) 
        WHERE is_current = TRUE
);

-- AI建议的查询优化索引
CREATE INDEX idx_dim_users_id_date 
    ON dim_users(user_id, start_date, end_date);

-- AI优化后的SCD2处理语句
WITH source_data AS (
    SELECT 
        user_id,
        name,
        email,
        phone,
        region
    FROM stg_users
    WHERE processing_date = '2023-12-01'
),
current_records AS (
    SELECT 
        user_sk,
        user_id,
        name,
        email,
        phone,
        region
    FROM dim_users
    WHERE is_current = TRUE
),
changes AS (
    SELECT 
        s.user_id,
        s.name,
        s.email,
        s.phone,
        s.region,
        CASE 
            WHEN c.user_id IS NULL THEN 'INSERT'
            WHEN s.name <> c.name OR s.email <> c.email 
                 OR s.phone <> c.phone OR s.region <> c.region THEN 'UPDATE'
            ELSE 'NO_CHANGE'
        END AS change_type
    FROM source_data s
    LEFT JOIN current_records c ON s.user_id = c.user_id
)
-- 处理变更：关闭旧记录，插入新记录
MERGE INTO dim_users AS target
USING (
    -- 需要更新的记录
    SELECT 
        c.user_id,
        c.name,
        c.email,
        c.phone,
        c.region,
        c.change_type
    FROM changes c
    WHERE c.change_type IN ('INSERT', 'UPDATE')
) AS source
ON target.user_id = source.user_id AND target.is_current = TRUE
WHEN MATCHED AND source.change_type = 'UPDATE' THEN
    UPDATE SET 
        end_date = CURRENT_TIMESTAMP,
        is_current = FALSE
WHEN NOT MATCHED AND source.change_type = 'INSERT' THEN
    INSERT (user_id, name, email, phone, region, start_date, is_current)
    VALUES (source.user_id, source.name, source.email, 
            source.phone, source.region, CURRENT_TIMESTAMP, TRUE);

# AI建议的批量处理实现
def process_scd_in_batches(batch_size=10000):
    """使用批量处理优化大规模SCD操作"""
    total_records = get_source_count()
    batches = (total_records + batch_size - 1) // batch_size
    
    for batch in range(batches):
        offset = batch * batch_size
        logger.info(f"Processing batch {batch+1}/{batches}")
        
        # 生成批量处理SQL
        batch_sql = generate_batch_scd_sql(batch_size, offset)
        
        # 执行批量处理
        execute_batch_update(batch_sql)
        
        # 提交每批处理，避免长事务
        commit_transaction()

def generate_batch_scd_sql(batch_size, offset):
    """使用AI生成优化的批量SCD SQL"""
    prompt = f"""
    生成PostgreSQL优化的批量SCD类型2处理SQL，要求：
    - 批量大小: {batch_size}
    - 偏移量: {offset}
    - 源表: stg_users
    - 目标表: dim_users
    - 关键字段: user_id, name, email, phone, region
    - 包含变更检测和版本控制
    - 使用CTE优化性能
    """
    
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "你是一个PostgreSQL专家，擅长编写高性能的SQL查询。"},
            {"role": "user", "content": prompt}
        ],
        temperature=0.1
    )
    
    return response.choices[0].message.content