Agent skill
sql-queries-postgresql-including-aurora-rds-supabase-neon
Sub-skill of sql-queries: PostgreSQL (including Aurora, RDS, Supabase, Neon) (+1).
Install this agent skill to your Project
npx add-skill https://github.com/vamseeachanta/workspace-hub/tree/main/.claude/skills/_archive/data/analytics/sql-queries/postgresql-including-aurora-rds-supabase-neon
SKILL.md
PostgreSQL (including Aurora, RDS, Supabase, Neon) (+1)
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 ANALYZEto profile queries - Create indexes on frequently filtered/joined columns
- Use
EXISTSoverINfor 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
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