Postgresql Code Review

[!NOTE] Reference snapshot: this file is kept as a quality example for primitive authors. It is not installed from this repository as an active primitive.

PostgreSQL code review

Expert PostgreSQL code review for ${selection} (or the entire project when nothing is selected). It focuses on the PostgreSQL-specific best practices, anti-patterns, and quality standards that are unique to PostgreSQL rather than generic SQL. To author or tune new PostgreSQL features instead of reviewing existing ones, use the skill postgresql-optimization.

[!IMPORTANT] The SIFAP 2.0 backend reaches PostgreSQL 16 through JPA/Hibernate. Application queries must use JPQL, Spring Data derived queries, or bound native parameters — never string-concatenated SQL. Schema lives in Flyway migrations under backend/src/main/resources/db/migration/. Where this skill and the database instruction overlap, the instruction file is authoritative.

When to invoke

• "Review this migration for PostgreSQL anti-patterns."
• "Audit our JSONB and array usage."
• "Is this schema using the right PostgreSQL types?"
• "Check this PL/pgSQL function and RLS policy before it merges."

PostgreSQL-Specific Review Areas

JSONB Best Practices

-- BAD: Inefficient JSONB usage
SELECT * FROM orders WHERE data->>'status' = 'shipped';  -- No index support

-- GOOD: Indexable JSONB queries
CREATE INDEX idx_orders_status ON orders USING gin((data->'status'));
SELECT * FROM orders WHERE data @> '{"status": "shipped"}';

-- BAD: Deep nesting without consideration
UPDATE orders SET data = data || '{"shipping":{"tracking":{"number":"123"}}}';

-- GOOD: Structured JSONB with validation
ALTER TABLE orders ADD CONSTRAINT valid_status 
CHECK (data->>'status' IN ('pending', 'shipped', 'delivered'));

Array Operations Review

-- BAD: Inefficient array operations
SELECT * FROM products WHERE 'electronics' = ANY(categories);  -- No index

-- GOOD: GIN indexed array queries
CREATE INDEX idx_products_categories ON products USING gin(categories);
SELECT * FROM products WHERE categories @> ARRAY['electronics'];

-- BAD: Array concatenation in loops
-- This would be inefficient in a function/procedure

-- GOOD: Bulk array operations
UPDATE products SET categories = categories || ARRAY['new_category']
WHERE id IN (SELECT id FROM products WHERE condition);

PostgreSQL Schema Design Review

-- BAD: Not using PostgreSQL features
CREATE TABLE users (
    id INTEGER,
    email VARCHAR(255),
    created_at TIMESTAMP
);

-- GOOD: PostgreSQL-optimized schema
CREATE TABLE users (
    id BIGSERIAL PRIMARY KEY,
    email CITEXT UNIQUE NOT NULL,  -- Case-insensitive email
    created_at TIMESTAMPTZ DEFAULT NOW(),
    metadata JSONB DEFAULT '{}',
    CONSTRAINT valid_email CHECK (email ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$')
);

-- Add JSONB GIN index for metadata queries
CREATE INDEX idx_users_metadata ON users USING gin(metadata);

Custom Types and Domains

-- BAD: Using generic types for specific data
CREATE TABLE transactions (
    amount DECIMAL(10,2),
    currency VARCHAR(3),
    status VARCHAR(20)
);

-- GOOD: PostgreSQL custom types
CREATE TYPE currency_code AS ENUM ('USD', 'EUR', 'GBP', 'JPY');
CREATE TYPE transaction_status AS ENUM ('pending', 'completed', 'failed', 'cancelled');
CREATE DOMAIN positive_amount AS DECIMAL(10,2) CHECK (VALUE > 0);

CREATE TABLE transactions (
    amount positive_amount NOT NULL,
    currency currency_code NOT NULL,
    status transaction_status DEFAULT 'pending'
);

PostgreSQL-Specific Anti-Patterns

Performance Anti-Patterns

• Avoiding PostgreSQL-specific indexes: Not using GIN/GiST for appropriate data types
• Misusing JSONB: Treating JSONB like a simple string field
• Ignoring array operators: Using inefficient array operations
• Poor partition key selection: Not leveraging PostgreSQL partitioning effectively

Schema Design Issues

• Not using ENUM types: Using VARCHAR for limited value sets
• Ignoring constraints: Missing CHECK constraints for data validation
• Wrong data types: Using VARCHAR instead of TEXT or CITEXT
• Missing JSONB structure: Unstructured JSONB without validation

Function and Trigger Issues

-- BAD: Inefficient trigger function
CREATE OR REPLACE FUNCTION update_modified_time()
RETURNS TRIGGER AS $$
BEGIN
    NEW.updated_at = NOW();  -- Should use TIMESTAMPTZ
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

-- GOOD: Optimized trigger function
CREATE OR REPLACE FUNCTION update_modified_time()
RETURNS TRIGGER AS $$
BEGIN
    NEW.updated_at = CURRENT_TIMESTAMP;
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

-- Set trigger to fire only when needed
CREATE TRIGGER update_modified_time_trigger
    BEFORE UPDATE ON table_name
    FOR EACH ROW
    WHEN (OLD.* IS DISTINCT FROM NEW.*)
    EXECUTE FUNCTION update_modified_time();

PostgreSQL Extension Usage Review

Extension Best Practices

-- Check if extension exists before creating
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE EXTENSION IF NOT EXISTS "pgcrypto";
CREATE EXTENSION IF NOT EXISTS "pg_trgm";

-- Use extensions appropriately
-- UUID generation
SELECT uuid_generate_v4();

-- Password hashing
SELECT crypt('password', gen_salt('bf'));

-- Fuzzy text matching
SELECT word_similarity('postgres', 'postgre');

PostgreSQL Security Review

Row Level Security (RLS)

-- GOOD: Implementing RLS
ALTER TABLE sensitive_data ENABLE ROW LEVEL SECURITY;

CREATE POLICY user_data_policy ON sensitive_data
    FOR ALL TO application_role
    USING (user_id = current_setting('app.current_user_id')::INTEGER);

Privilege Management

-- BAD: Overly broad permissions
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO app_user;

-- GOOD: Granular permissions
GRANT SELECT, INSERT, UPDATE ON specific_table TO app_user;
GRANT USAGE ON SEQUENCE specific_table_id_seq TO app_user;

PostgreSQL Code Quality Checklist

Schema Design

• Using appropriate PostgreSQL data types (CITEXT, JSONB, arrays)
• Leveraging ENUM types for constrained values
• Implementing proper CHECK constraints
• Using TIMESTAMPTZ instead of TIMESTAMP
• Defining custom domains for reusable constraints

Performance Considerations

• Appropriate index types (GIN for JSONB/arrays, GiST for ranges)
• JSONB queries using containment operators (@>, ?)
• Array operations using PostgreSQL-specific operators
• Proper use of window functions and CTEs
• Efficient use of PostgreSQL-specific functions

PostgreSQL Features Utilization

• Using extensions where appropriate
• Implementing stored procedures in PL/pgSQL when beneficial
• Leveraging PostgreSQL's advanced SQL features
• Using PostgreSQL-specific optimization techniques
• Implementing proper error handling in functions

Security and Compliance

• Row Level Security (RLS) implementation where needed
• Proper role and privilege management
• Using PostgreSQL's built-in encryption functions
• Implementing audit trails with PostgreSQL features

PostgreSQL-Specific Review Guidelines

1. Data Type Optimization: Ensure PostgreSQL-specific types are used appropriately
2. Index Strategy: Review index types and ensure PostgreSQL-specific indexes are utilized
3. JSONB Structure: Validate JSONB schema design and query patterns
4. Function Quality: Review PL/pgSQL functions for efficiency and best practices
5. Extension Usage: Verify appropriate use of PostgreSQL extensions
6. Performance Features: Check utilization of PostgreSQL's advanced features
7. Security Implementation: Review PostgreSQL-specific security features

Focus on PostgreSQL's unique capabilities and ensure the code leverages what makes PostgreSQL special rather than treating it as a generic SQL database.

Output template

Deliver the review as a verdict, a findings table, and paste-ready corrected SQL.

## PostgreSQL review — <file or selection>

**Verdict**: Pass | Fix required | Reject

| # | Severity | Finding | Evidence | Fix |
|---|---|---|---|---|
| 1 | High | User input concatenated into SQL | `... WHERE status = '` + input | Bind `:status` through JPQL or a parameterized native query |
| 2 | Medium | JSONB containment query has no GIN index | Seq Scan on `orders` | `CREATE INDEX idx_orders_data ON orders USING gin(data)` |
| 3 | Low | VARCHAR used for a case-insensitive email | `email VARCHAR(255)` | Use `CITEXT` with a `CHECK` constraint |

### Corrected SQL
CREATE INDEX idx_orders_data ON orders USING gin(data);
-- Repository query stays parameterized: WHERE data @> :filter

Quality gate

• A verdict is stated: Pass, Fix required, or Reject.
• Every finding carries a severity and concrete evidence (file/line or a plan snippet).
• No user input is concatenated into SQL; every parameter is bound (JPQL, derived query, or bound native query).
• PostgreSQL-specific types, index types (GIN/GiST/partial), and CHECK/ENUM/domain constraints are validated.
• PII such as CPF or benefit amounts is masked in logs or documented with a column COMMENT.
• Corrected SQL is paste-ready and any schema change stays rollback-safe (see the database instruction).