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sql-queries

by @anthropicsv
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正確で効率的かつ読みやすいSQLクエリを作成し、すべての主要なデータウェアハウス方言をサポートし、特定の方言リファレンスを提供し、データ分析に適しています。

SQLDatabase QueryingData ExtractionRelational DatabasesData AnalysisGitHub
インストール方法
npx skills add anthropics/knowledge-work-plugins --skill sql-queries
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Before / After 効果比較

1 组
使用前

データ分析において、SQLクエリの作成は、構文の不慣れや論理の不明瞭さから、効率の低下や誤った結果を招くことがよくあります。異なるデータウェアハウスの方言の違いも学習コストを増加させ、データ分析作業の進行を遅らせています。

使用後

正確で、効率的で、読みやすいSQLクエリを作成し、すべての主要なデータウェアハウスの方言をサポートします。これにより、データ分析の精度と効率が大幅に向上し、アナリストがデータから迅速に洞察を得て、情報に基づいた意思決定を行うのに役立ちます。

description SKILL.md

sql-queries

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 *
WHEN NOT MATCHED THEN INSERT *

Performance tips:

  • Use Delta Lake's OPTIMIZE and ZORDER for query performance

  • Leverage Photon engine for compute-intensive queries

  • Use CACHE TABLE for frequently accessed datasets

  • Partition by low-cardinality date columns

Common SQL Patterns

Window Functions

-- Ranking
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC)
RANK() OVER (PARTITION BY category ORDER BY revenue DESC)
DENSE_RANK() OVER (ORDER BY score DESC)

-- Running totals / moving averages
SUM(revenue) OVER (ORDER BY date_col ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as running_total
AVG(revenue) OVER (ORDER BY date_col ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as moving_avg_7d

-- Lag / Lead
LAG(value, 1) OVER (PARTITION BY entity ORDER BY date_col) as prev_value
LEAD(value, 1) OVER (PARTITION BY entity ORDER BY date_col) as next_value

-- First / Last value
FIRST_VALUE(status) OVER (PARTITION BY user_id ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
LAST_VALUE(status) OVER (PARTITION BY user_id ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)

-- Percent of total
revenue / SUM(revenue) OVER () as pct_of_total
revenue / SUM(revenue) OVER (PARTITION BY category) as pct_of_category

CTEs for Readability

WITH
-- Step 1: Define the base population
base_users AS (
    SELECT user_id, created_at, plan_type
    FROM users
    WHERE created_at >= DATE '2024-01-01'
      AND status = 'active'
),

-- Step 2: Calculate user-level metrics
user_metrics AS (
    SELECT
        u.user_id,
        u.plan_type,
        COUNT(DISTINCT e.session_id) as session_count,
        SUM(e.revenue) as total_revenue
    FROM base_users u
    LEFT JOIN events e ON u.user_id = e.user_id
    GROUP BY u.user_id, u.plan_type
),

-- Step 3: Aggregate to summary level
summary AS (
    SELECT
        plan_type,
        COUNT(*) as user_count,
        AVG(session_count) as avg_sessions,
        SUM(total_revenue) as total_revenue
    FROM user_metrics
    GROUP BY plan_type
)

SELECT * FROM summary ORDER BY total_revenue DESC;

Cohort Retention

WITH cohorts AS (
    SELECT
        user_id,
        DATE_TRUNC('month', first_activity_date) as cohort_month
    FROM users
),
activity AS (
    SELECT
        user_id,
        DATE_TRUNC('month', activity_date) as activity_month
    FROM user_activity
)
SELECT
    c.cohort_month,
    COUNT(DISTINCT c.user_id) as cohort_size,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month THEN a.user_id
    END) as month_0,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month + INTERVAL '1 month' THEN a.user_id
    END) as month_1,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month + INTERVAL '3 months' THEN a.user_id
    END) as month_3
FROM cohorts c
LEFT JOIN activity a ON c.user_id = a.user_id
GROUP BY c.cohort_month
ORDER BY c.cohort_month;

Funnel Analysis

WITH funnel AS (
    SELECT
        user_id,
        MAX(CASE WHEN event = 'page_view' THEN 1 ELSE 0 END) as step_1_view,
        MAX(CASE WHEN event = 'signup_start' THEN 1 ELSE 0 END) as step_2_start,
        MAX(CASE WHEN event = 'signup_complete' THEN 1 ELSE 0 END) as step_3_complete,
        MAX(CASE WHEN event = 'first_purchase' THEN 1 ELSE 0 END) as step_4_purchase
    FROM events
    WHERE event_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY user_id
)
SELECT
    COUNT(*) as total_users,
    SUM(step_1_view) as viewed,
    SUM(step_2_start) as started_signup,
    SUM(step_3_complete) as completed_signup,
    SUM(step_4_purchase) as purchased,
    ROUND(100.0 * SUM(step_2_start) / NULLIF(SUM(step_1_view), 0), 1) as view_to_start_pct,
    ROUND(100.0 * SUM(step_3_complete) / NULLIF(SUM(step_2_start), 0), 1) as start_to_complete_pct,
    ROUND(100.0 * SUM(step_4_purchase) / NULLIF(SUM(step_3_complete), 0), 1) as complete_to_purchase_pct
FROM funnel;

Deduplication

-- Keep the most recent record per key
WITH ranked AS (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY entity_id
            ORDER BY updated_at DESC
        ) as rn
    FROM source_table
)
SELECT * FROM ranked WHERE rn = 1;

Error Handling and Debugging

When a query fails:

  • Syntax errors: Check for dialect-specific syntax (e.g., ILIKE not available in BigQuery, SAFE_DIVIDE only in BigQuery)

  • Column not found: Verify column names against schema -- check for typos, case sensitivity (PostgreSQL is case-sensitive for quoted identifiers)

  • Type mismatches: Cast explicitly when comparing different types (CAST(col AS DATE), col::DATE)

  • Division by zero: Use NULLIF(denominator, 0) or dialect-specific safe division

  • Ambiguous columns: Always qualify column names with table alias in JOINs

  • Group by errors: All non-aggregated columns must be in GROUP BY (except in BigQuery which allows grouping by alias)

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インストール数200
評価4.8 / 5.0
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更新日2026年3月17日
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作成2026年3月17日
最終更新2026年3月17日