SQL Insight

sql-insight

SQL query assistant — natural language to SQL translation, query optimization analysis, and EXPLAIN plan interpretation.

Capabilities

Workflow

Natural Language → SQL

1. Use the schema command to extract the database table structure
2. Use the schema as context to translate the user's natural language request into SQL
3. Use the optimize command to check if the generated SQL can be improved
4. Use the explain command to verify the query execution plan

# Step 1: Extract schema (compact mode, suitable for LLM context)
python3 scripts/sql_query_helper.py --db-path data.db schema --compact

# Step 2: Analyze SQL optimization suggestions
python3 scripts/sql_query_helper.py optimize "SELECT * FROM orders WHERE user_id = 100"

# Step 3: View EXPLAIN execution plan
python3 scripts/sql_query_helper.py --db-path data.db explain "SELECT * FROM orders WHERE user_id = 100"

Quick Start

Schema Extraction

# Extract full schema (JSON format, with sample data)
python3 scripts/sql_query_helper.py --db-path data.db schema

# Compact mode (plain text, suitable for embedding in prompts)
python3 scripts/sql_query_helper.py --db-path data.db schema --compact

# Skip data sampling
python3 scripts/sql_query_helper.py --db-path data.db schema --sample-rows 0

# PostgreSQL
python3 scripts/sql_query_helper.py --db-type postgres --dsn "host=localhost dbname=mydb user=reader" schema --compact

Query Optimization Analysis

# Analyze SQL query (no database connection required, pure rule-based detection)
python3 scripts/sql_query_helper.py optimize "SELECT * FROM orders o, users u WHERE o.user_id = u.id"

python3 scripts/sql_query_helper.py optimize "SELECT name FROM users WHERE UPPER(email) LIKE '%@GMAIL.COM'"

python3 scripts/sql_query_helper.py optimize "SELECT id, (SELECT COUNT(*) FROM orders WHERE user_id = u.id) AS order_count FROM users u"

EXPLAIN Interpretation

# SQLite EXPLAIN
python3 scripts/sql_query_helper.py --db-path data.db explain "SELECT * FROM orders WHERE user_id = 100"

# PostgreSQL EXPLAIN
python3 scripts/sql_query_helper.py --db-type postgres --dsn "host=localhost dbname=mydb" explain "SELECT * FROM orders WHERE user_id = 100"

# PostgreSQL EXPLAIN ANALYZE (actually executes the query for real-world data)
python3 scripts/sql_query_helper.py --db-type postgres --dsn "host=localhost dbname=mydb" explain --analyze "SELECT * FROM orders WHERE user_id = 100"

Detailed Usage

Global Parameters

Subcommands

schema Parameters

explain Parameters

Optimization Rules

The optimize command detects the following 13 SQL anti-patterns:

EXPLAIN Interpretation Items

Output Examples

schema --compact

-- Database: sqlite
-- users (1500 rows): id INTEGER  PK, name TEXT, email TEXT, age INTEGER, created_at TEXT
--   IDX(unique): idx_users_email on (email)
-- orders (8200 rows): id INTEGER  PK, user_id INTEGER, amount REAL, status TEXT, created_at TEXT
--   FK: user_id -> users.id
--   IDX: idx_orders_user_id on (user_id)

optimize

{
  "sql": "SELECT * FROM orders o, users u WHERE o.user_id = u.id",
  "issues": [
    {
      "severity": "warning",
      "rule": "avoid-select-star",
      "message": "Avoid SELECT *: only select the columns you need to reduce I/O and network transfer",
      "suggestion": "Replace SELECT * with an explicit list of required column names"
    },
    {
      "severity": "info",
      "rule": "implicit-join",
      "message": "Uses implicit join (comma-separated tables), which is less readable and error-prone",
      "suggestion": "Use explicit JOIN ... ON syntax for better readability and maintainability"
    }
  ]
}

explain (SQLite)

{
  "db_type": "sqlite",
  "query": "SELECT * FROM orders WHERE user_id = 100",
  "plan": [
    {"id": 2, "parent": 0, "detail": "SEARCH orders USING INDEX idx_orders_user_id (user_id=?)"}
  ],
  "interpretation": [
    {
      "severity": "ok",
      "type": "index-search",
      "detail": "Index lookup: idx_orders_user_id",
      "suggestion": "Index lookup is efficient"
    }
  ]
}

Safety Mechanisms

• Read-only connections: SQLite uses ?mode=ro; PostgreSQL uses SET SESSION READ ONLY
• SQL whitelist: Only allows statements starting with SELECT / WITH / EXPLAIN
• Dangerous keyword blocking: INSERT, UPDATE, DELETE, DROP, and 30+ other keywords are blocked
• Multi-statement blocking: Semicolon-separated multiple SQL statements are rejected
• Identifier escaping: Table names are double-quote escaped to prevent SQL injection

Dependencies

• Python 3.8+ (sqlite3 is a built-in module)
• PostgreSQL support requires: pip install psycopg2-binary
• The optimize command requires no database connection and has zero external dependencies

Activation Triggers

Triggered when users ask to convert questions into SQL, improve slow queries, tune indexes, analyze execution plans, or mention keywords like NL2SQL, query tuning, or full table scan.