Ankit Malik
98 lines
4.8 KiB
SQL
98 lines
4.8 KiB
SQL
-- ClickHouse DDL: promotion_kpi table
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-- Run ONCE before the first ETL run
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-- Connect: podman exec -it clickhouse-kelloggs clickhouse-client
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CREATE DATABASE IF NOT EXISTS kelloggs;
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CREATE TABLE IF NOT EXISTS kelloggs.promotion_kpi
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(
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-- ── Core identifiers ────────────────────────────────────────────────────
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mid Int64,
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project_id Int32,
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store_id Int64,
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employee_id Int64,
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supervisor_id Int64,
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-- ── Store dimension ─────────────────────────────────────────────────────
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channel_id Int32,
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chain_id Int64,
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storetype_id Int32,
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-- ── Visit ───────────────────────────────────────────────────────────────
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visit_date Date32,
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-- ── Promotion definition ────────────────────────────────────────────────
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promo_definition_id Int32,
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promo_definition_name String,
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-- ── Promotion item (Category / SubCategory / Brand / SubBrand) ──────────
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-- LowCardinality = dictionary-encoding — ideal for columns with few
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-- distinct values (only 4 possible values here)
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promotion_details LowCardinality(String), -- 'Category' | 'SubCategory' | 'Brand' | 'SubBrand'
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promotion_details_id Int64, -- FK into the chosen master table
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promo_value_name String, -- e.g. 'Chocos', 'Breakfast Cereals'
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-- ── Execution status ────────────────────────────────────────────────────
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is_present UInt8, -- 1 = present (Y), 0 = absent (N)
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reason String, -- populated only when is_present = 0
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-- ── Optional quiz ───────────────────────────────────────────────────────
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question String,
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answer String,
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-- ── Photo proof ─────────────────────────────────────────────────────────
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image1 String, -- full URL or empty string
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image2 String,
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-- ── Audit ───────────────────────────────────────────────────────────────
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update_date DateTime DEFAULT now(),
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update_by String
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)
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ENGINE = MergeTree()
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-- Sort key: queries always filter on project_id + visit_date first
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-- Adding promo_definition_id speeds up "show me all Promo X results"
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ORDER BY (project_id, visit_date, mid, promo_definition_id)
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PARTITION BY toYYYYMM(visit_date)
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-- Bloom filter on store_id — speeds up store-level promotion reports
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INDEX idx_store store_id TYPE bloom_filter GRANULARITY 1
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-- Bloom filter on employee_id — speeds up per-rep promotion audits
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INDEX idx_emp employee_id TYPE bloom_filter GRANULARITY 1
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SETTINGS index_granularity = 8192;
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-- ── Verification queries ─────────────────────────────────────────────────────
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-- 1. How many promotions loaded today?
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-- SELECT count(), visit_date
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-- FROM kelloggs.promotion_kpi
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-- WHERE project_id = 40148
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-- GROUP BY visit_date ORDER BY visit_date DESC LIMIT 10;
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-- 2. Present vs absent breakdown
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-- SELECT
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-- visit_date,
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-- countIf(is_present = 1) AS present,
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-- countIf(is_present = 0) AS absent,
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-- count() AS total,
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-- round(present / total * 100, 1) AS present_pct
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-- FROM kelloggs.promotion_kpi
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-- WHERE project_id = 40148
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-- GROUP BY visit_date ORDER BY visit_date DESC LIMIT 7;
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-- 3. Top promotion definitions by absence
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-- SELECT promo_definition_name, count() AS absent_count
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-- FROM kelloggs.promotion_kpi
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-- WHERE project_id = 40148
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-- AND is_present = 0
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-- AND visit_date >= today() - 7
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-- GROUP BY promo_definition_name
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-- ORDER BY absent_count DESC LIMIT 10;
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-- 4. Absence reasons breakdown
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-- SELECT reason, count() AS cnt
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-- FROM kelloggs.promotion_kpi
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-- WHERE project_id = 40148
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-- AND is_present = 0
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-- AND visit_date = yesterday()
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-- GROUP BY reason ORDER BY cnt DESC;
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