/sql-queries

name: sql-queries
description: "主要データウェアハウス方言で正確かつ高性能なSQLを記述するスキル。 「SQLを書いて」「クエリを最適化して」「BigQueryで集計」等のリクエストで発動。"
triggers:
  - SQLを書いて
  - クエリを最適化して
  - BigQueryで集計
  - ウィンドウ関数を使いたい
  - SQL方言を変換
  - sql-queries
  - SQL query
  - Snowflake

SQL Queries Skill

Write correct, performant, readable SQL across all major data warehouse dialects.

Dialect-Specific Reference

PostgreSQL (including Aurora, RDS, Supabase, Neon)

**Date/time:**

-- Current date/time
CURRENT_DATE, CURRENT_TIMESTAMP, NOW()

-- Date arithmetic
date_column + INTERVAL '7 days'
date_column - INTERVAL '1 month'

-- Truncate to period
DATE_TRUNC('month', created_at)

-- Extract parts
EXTRACT(YEAR FROM created_at)
EXTRACT(DOW FROM created_at)  -- 0=Sunday

-- Format
TO_CHAR(created_at, 'YYYY-MM-DD')

**String functions:**

-- Concatenation
first_name || ' ' || last_name
CONCAT(first_name, ' ', last_name)

-- Pattern matching
column ILIKE '%pattern%'  -- case-insensitive
column ~ '^regex_pattern$'  -- regex

-- String manipulation
LEFT(str, n), RIGHT(str, n)
SPLIT_PART(str, delimiter, position)
REGEXP_REPLACE(str, pattern, replacement)

**Arrays and JSON:**

-- JSON access
data->>'key'  -- text
data->'nested'->'key'  -- json
data#>>'{path,to,key}'  -- nested text

-- Array operations
ARRAY_AGG(column)
ANY(array_column)
array_column @> ARRAY['value']

**Performance tips:**

• Use `EXPLAIN ANALYZE` to profile queries
• Create indexes on frequently filtered/joined columns
• Use `EXISTS` over `IN` for correlated subqueries
• Partial indexes for common filter conditions
• Use connection pooling for concurrent access

---

Snowflake

**Date/time:**

-- Current date/time
CURRENT_DATE(), CURRENT_TIMESTAMP(), SYSDATE()

-- Date arithmetic
DATEADD(day, 7, date_column)
DATEDIFF(day, start_date, end_date)

-- Truncate to period
DATE_TRUNC('month', created_at)

-- Extract parts
YEAR(created_at), MONTH(created_at), DAY(created_at)
DAYOFWEEK(created_at)

-- Format
TO_CHAR(created_at, 'YYYY-MM-DD')

**String functions:**

-- Case-insensitive by default (depends on collation)
column ILIKE '%pattern%'
REGEXP_LIKE(column, 'pattern')

-- Parse JSON
column:key::string  -- dot notation for VARIANT
PARSE_JSON('{"key": "value"}')
GET_PATH(variant_col, 'path.to.key')

-- Flatten arrays/objects
SELECT f.value FROM table, LATERAL FLATTEN(input => array_col) f

**Semi-structured data:**

-- VARIANT type access
data:customer:name::STRING
data:items[0]:price::NUMBER

-- Flatten nested structures
SELECT
    t.id,
    item.value:name::STRING as item_name,
    item.value:qty::NUMBER as quantity
FROM my_table t,
LATERAL FLATTEN(input => t.data:items) item

**Performance tips:**

• Use clustering keys on large tables (not traditional indexes)
• Filter on clustering key columns for partition pruning
• Set appropriate warehouse size for query complexity
• Use `RESULT_SCAN(LAST_QUERY_ID())` to avoid re-running expensive queries
• Use transient tables for staging/temp data

---

BigQuery (Google Cloud)

**Date/time:**

-- Current date/time
CURRENT_DATE(), CURRENT_TIMESTAMP()

-- Date arithmetic
DATE_ADD(date_column, INTERVAL 7 DAY)
DATE_SUB(date_column, INTERVAL 1 MONTH)
DATE_DIFF(end_date, start_date, DAY)
TIMESTAMP_DIFF(end_ts, start_ts, HOUR)

-- Truncate to period
DATE_TRUNC(created_at, MONTH)
TIMESTAMP_TRUNC(created_at, HOUR)

-- Extract parts
EXTRACT(YEAR FROM created_at)
EXTRACT(DAYOFWEEK FROM created_at)  -- 1=Sunday

-- Format
FORMAT_DATE('%Y-%m-%d', date_column)
FORMAT_TIMESTAMP('%Y-%m-%d %H:%M:%S', ts_column)

**String functions:**

-- No ILIKE, use LOWER()
LOWER(column) LIKE '%pattern%'
REGEXP_CONTAINS(column, r'pattern')
REGEXP_EXTRACT(column, r'pattern')

-- String manipulation
SPLIT(str, delimiter)  -- returns ARRAY
ARRAY_TO_STRING(array, delimiter)

**Arrays and structs:**

-- Array operations
ARRAY_AGG(column)
UNNEST(array_column)
ARRAY_LENGTH(array_column)
value IN UNNEST(array_column)

-- Struct access
struct_column.field_name

**Performance tips:**

• Always filter on partition columns (usually date) to reduce bytes scanned
• Use clustering for frequently filtered columns within partitions
• Use `APPROX_COUNT_DISTINCT()` for large-scale cardinality estimates
• Avoid `SELECT *` -- billing is per-byte scanned
• Use `DECLARE` and `SET` for parameterized scripts
• Preview query cost with dry run before executing large queries

---

Redshift (Amazon)

**Date/time:**

-- Current date/time
CURRENT_DATE, GETDATE(), SYSDATE

-- Date arithmetic
DATEADD(day, 7, date_column)
DATEDIFF(day, start_date, end_date)

-- Truncate to period
DATE_TRUNC('month', created_at)

-- Extract parts
EXTRACT(YEAR FROM created_at)
DATE_PART('dow', created_at)

**String functions:**

-- Case-insensitive
column ILIKE '%pattern%'
REGEXP_INSTR(column, 'pattern') > 0

-- String manipulation
SPLIT_PART(str, delimiter, position)
LISTAGG(column, ', ') WITHIN GROUP (ORDER BY column)

**Performance tips:**

• Design distribution keys for collocated joins (DISTKEY)
• Use sort keys for frequently filtered columns (SORTKEY)
• Use `EXPLAIN` to check query plan
• Avoid cross-node data movement (watch for DS_BCAST and DS_DIST)
• `ANALYZE` and `VACUUM` regularly
• Use late-binding views for schema flexibility

---

Databricks SQL

**Date/time:**

-- Current date/time
CURRENT_DATE(), CURRENT_TIMESTAMP()

-- Date arithmetic
DATE_ADD(date_column, 7)
DATEDIFF(end_date, start_date)
ADD_MONTHS(date_column, 1)

-- Truncate to period
DATE_TRUNC('MONTH', created_at)
TRUNC(date_column, 'MM')

-- Extract parts
YEAR(created_at), MONTH(created_at)
DAYOFWEEK(created_at)

**Delta Lake features:**

-- Time travel
SELECT * FROM my_table TIMESTAMP AS OF '2024-01-15'
SELECT * FROM my_table VERSION AS OF 42

-- Describe history
DESCRIBE HISTORY my_table

-- Merge (upsert)
MERGE INTO target USING source
ON target.id = source.id
WHEN MATCHED THEN UPDATE SET *
WH