Sql Queries

What This Skill Does

• The SQL Queries skill helps users write correct, performant, and readable SQL queries across major data warehouse dialects.
• It provides dialect-specific reference information and performance tips.
• This skill is valuable for data engineers, analysts, and anyone who works with SQL databases.

When to Use

• Write SQL queries for Postgre SQL.
• Write SQL queries for Snowflake.
• Perform date and time calculations in SQL.
• Manipulate strings in SQL.
• Work with arrays and JSON in SQL.
• Optimize SQL query performance.

Key Features

• Provides dialect-specific reference for Postgre SQL and Snowflake.
• Includes examples of date/time functions.
• Offers string manipulation techniques.
• Covers array and JSON operations.
• Provides performance tips for each dialect.
• Includes code snippets for common SQL tasks.

Manual Installation

SQL Queries Skill

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

Dialect-Specific Reference

Postgre SQL (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

Big Query (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, COU