207 lines
8.6 KiB
SQL
207 lines
8.6 KiB
SQL
-- ===============================================
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-- DML-скрипт: загрузка и трансформация данных
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-- Запускается ПОВТОРНО при каждой загрузке (идемпотентно!)
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-- ===============================================
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-- 1. STG: имитация загрузки из источников (в реальности — COPY или INSERT из Kafka/NiFi)
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-- ⚠️ В продакшене STG часто очищается перед загрузкой (TRUNCATE), либо используется партицирование по дате
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DELETE FROM stg.customers_raw;
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DELETE FROM stg.orders_raw;
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DELETE FROM stg.order_items_raw;
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DELETE FROM stg.products_raw;
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-- STG (пример вставки с метками времени)
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INSERT INTO stg.customers_raw (_load_id, _load_ts, event_ts, customer_id, email, phone, city) VALUES
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('batch_20240101_0800', '2024-01-01 08:00', '2024-01-01', '101','a@ex.com','700','Москва'),
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('batch_20240101_0800', '2024-01-01 08:00', '2024-01-01', '102','c@ex.com','701','СПб'),
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('batch_20240516_0800', '2024-05-16 08:00', '2024-05-16', '101','b@ex.com','700','Москва'),
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('batch_20241001_0800', '2024-10-01 08:00', '2024-10-01', '101','b@ex.com','700','Санкт-Петербург');
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INSERT INTO stg.orders_raw (order_id, order_date, customer_id) VALUES
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('5001', '2024-01-10', '101'),
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('5002', '2024-02-05', '102');
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INSERT INTO stg.order_items_raw (order_item_id, order_id, product_id, qty, price_at_sale) VALUES
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('1', '5001', '9001', '2', '100.00'),
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('2', '5001', '9002', '1', '50.00'),
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('3', '5002', '9001', '1', '100.00');
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INSERT INTO stg.products_raw (product_id, name) VALUES
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('9001', 'Phone'),
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('9002', 'Case');
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-- 2. ODS: очистка и типизация
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-- ⚠️ В продакшене используем UPSERT или incremental load, не TRUNCATE+INSERT
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TRUNCATE ods.customers, ods.orders, ods.order_items, ods.products;
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INSERT INTO ods.orders (order_id, order_date, customer_id)
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SELECT
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order_id::INT,
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TO_DATE(order_date, 'YYYY-MM-DD'),
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customer_id::INT
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FROM stg.orders_raw
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WHERE order_date IS NOT NULL AND customer_id ~ '^\d+$';
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-- берём по BK самую позднюю запись (по дате события, иначе по дате загрузки)
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WITH src AS (
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SELECT
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s.customer_id::INT AS customer_id,
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NULLIF(trim(s.email), '') AS email,
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NULLIF(trim(s.phone), '') AS phone,
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NULLIF(trim(s.city), '') AS city,
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NULLIF(s.event_ts, '')::timestamp AS event_ts,
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s._load_id,
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s._load_ts,
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COALESCE(NULLIF(s.event_ts, '')::date, s._load_ts::date) AS eff_date
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FROM stg.customers_raw s
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WHERE s.customer_id ~ '^\d+$'
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),
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ranked AS (
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SELECT
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customer_id, email, phone, city, event_ts, _load_id, _load_ts,
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row_number() OVER (PARTITION BY customer_id ORDER BY eff_date DESC, _load_ts DESC) AS rn
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FROM src
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)
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INSERT INTO ods.customers (customer_id, email, phone, city, event_ts, _load_id, _load_ts)
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SELECT customer_id, email, phone, city, event_ts, _load_id, _load_ts
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FROM ranked
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WHERE rn = 1;
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INSERT INTO ods.order_items (order_item_id, order_id, product_id, qty, price_at_sale)
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SELECT
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order_item_id::INT,
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order_id::INT,
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product_id::INT,
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NULLIF(qty, '')::INT,
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NULLIF(price_at_sale, '')::NUMERIC(10,2)
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FROM stg.order_items_raw
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WHERE qty ~ '^\d+$' AND price_at_sale ~ '^\d+(\.\d+)?$';
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INSERT INTO ods.products (product_id, name)
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SELECT
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product_id::INT,
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TRIM(name)
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FROM stg.products_raw
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WHERE product_id ~ '^\d+$';
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-- 3. DDS: dim_date — генерация календаря (идемпотентно: можно пересоздавать)
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-- В реальности — делается ОДИН РАЗ, либо дополняется по мере необходимости
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DELETE FROM dds.dim_date;
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WITH RECURSIVE dates AS (
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SELECT DATE '2023-01-01' AS d
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UNION ALL
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SELECT (d + INTERVAL '1 day')::DATE -- ← приведение к DATE
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FROM dates
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WHERE d + INTERVAL '1 day' <= DATE '2027-12-31'
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)
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INSERT INTO dds.dim_date (
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date_key, date_actual, year, quarter, month, day,
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weekday_name, weekday_num, is_first_week
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)
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SELECT
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CAST(TO_CHAR(d, 'YYYYMMDD') AS INT),
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d,
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EXTRACT(YEAR FROM d)::SMALLINT,
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EXTRACT(QUARTER FROM d)::SMALLINT,
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EXTRACT(MONTH FROM d)::SMALLINT,
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EXTRACT(DAY FROM d)::SMALLINT,
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TRIM(TO_CHAR(d, 'Day')), -- ← TRIM — убрать trailing space
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EXTRACT(DOW FROM d)::SMALLINT,
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d BETWEEN DATE_TRUNC('month', d)
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AND DATE_TRUNC('month', d) + INTERVAL '1 month' - INTERVAL '1 day'
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AND EXTRACT(DAY FROM d) <= 7
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FROM dates;
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-- 4. DDS: dim_product — полная перезагрузка (если товары редко меняются)
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-- В реальности — инкрементальная загрузка по BK
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DELETE FROM dds.dim_product;
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INSERT INTO dds.dim_product (product_bk, product_name)
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SELECT product_id, name
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FROM ods.products;
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-- 5. DDS: dim_customer — первичная загрузка SCD2 (full backfill из STG)
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-- В ЭТОМ ДЕМО: dim_customer строится напрямую из stg.customers_raw, который играет роль
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-- устойчивого event-лога (все события по клиенту в одном месте).
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-- Это удобно для учебной первичной загрузки (full backfill), когда мы один раз
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-- восстанавливаем всю историю клиента.
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-- В РЕАЛЬНОМ DWH: так делают редко. Исторические измерения обычно строят
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-- поверх очищенных и нормализованных слоёв (ODS / PSA / Data Vault).
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-- Для примера инкрементальной заливки SCD2 по снимку из ODS см. 03_demo_increment.sql и SCD.md.
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TRUNCATE dds.dim_customer, dds.fact_sales;
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WITH src AS (
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SELECT
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s.customer_id::INT AS customer_bk,
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NULLIF(trim(s.email), '') AS email,
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NULLIF(trim(s.phone), '') AS phone,
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NULLIF(trim(s.city), '') AS city,
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COALESCE(NULLIF(s.event_ts, '')::date, s._load_ts::date) AS eff_date,
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dds.customer_hash(s.email, s.phone, s.city) AS hashdiff
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FROM stg.customers_raw s
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WHERE s.customer_id ~ '^\d+$'
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),
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ordered AS (
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SELECT *,
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lag(hashdiff) OVER (PARTITION BY customer_bk ORDER BY eff_date) AS prev_hash
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FROM src
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),
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changes AS (
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-- только первые состояния и фактические изменения атрибутов
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SELECT *
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FROM ordered
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WHERE prev_hash IS DISTINCT FROM hashdiff OR prev_hash IS NULL
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),
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framed AS (
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SELECT
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customer_bk, email, phone, city, hashdiff,
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eff_date AS valid_from,
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lead(eff_date) OVER (PARTITION BY customer_bk ORDER BY eff_date) AS valid_to
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FROM changes
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)
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INSERT INTO dds.dim_customer (
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customer_bk, email, phone, city, hashdiff,
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valid_from, valid_to,
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created_at, updated_at
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)
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SELECT
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customer_bk, email, phone, city, hashdiff,
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valid_from,
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valid_to,
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now(), now()
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FROM framed
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ORDER BY customer_bk, valid_from;
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-- 6. DDS: fact_sales — загрузка фактов с учётом SCD
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-- В продакшене — фильтруем по диапазону дат (инкрементально)
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--TRUNCATE dds.fact_sales;
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INSERT INTO dds.fact_sales (customer_sk, product_sk, date_key, quantity, amount)
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SELECT
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dc.customer_sk,
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dp.product_sk,
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CAST(TO_CHAR(o.order_date, 'YYYYMMDD') AS INT),
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oi.qty,
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oi.price_at_sale * oi.qty
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FROM ods.orders o
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JOIN ods.order_items oi ON o.order_id = oi.order_id
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JOIN ods.products p ON oi.product_id = p.product_id
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JOIN dds.dim_product dp ON p.product_id = dp.product_bk
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JOIN dds.dim_customer dc
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ON o.customer_id = dc.customer_bk
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AND o.order_date >= dc.valid_from
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AND (dc.valid_to IS NULL OR o.order_date < dc.valid_to);
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-- 7. Проверка — вывод итогов (не часть ETL, но полезно для отладки)
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-- В реальном пайплайне такие SELECT выносятся в отдельные скрипты или дашборды
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SELECT 'dim_customer current = ' || COUNT(*) FROM dds.dim_customer WHERE valid_to IS NULL;
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SELECT 'fact_sales count = ' || COUNT(*) FROM dds.fact_sales;
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