# Security Best Practices Standards for PostgreSQL
This document outlines the security best practices for PostgreSQL development. Following these guidelines will help protect against common vulnerabilities, implement secure coding patterns, and ensure the security and integrity of your PostgreSQL databases and applications.
## 1. Authentication and Authorization
### 1.1. Strong Password Policies
**Standard:** Enforce strong password policies for all database users.
**Why:** Weak passwords are a primary target for attackers.
**Do This:**
* Require passwords with a minimum length, complexity (uppercase, lowercase, numbers, special characters), and regular rotation.
* Use the "passwordcheck" extension to enforce password complexity requirements.
**Don't Do This:**
* Use default passwords.
* Allow simple or easily guessable passwords.
* Store passwords in plaintext.
**Code Example:**
"""sql
-- Install passwordcheck extension (requires superuser)
CREATE EXTENSION passwordcheck;
-- Example policy using passwordcheck
ALTER ROLE myuser WITH PASSWORD 'ComplexPassword123!' VALID UNTIL '2025-01-01';
-- Check password complexity
SELECT passwordcheck('ComplexPassword123!'); -- Should return NULL (valid)
SELECT passwordcheck('weak'); -- Will return an error message
"""
### 1.2. Role-Based Access Control (RBAC)
**Standard:** Implement RBAC to control access to database objects and functions.
**Why:** RBAC limits the privileges granted to each user, reducing the risk of unauthorized access or modification.
**Do This:**
* Create roles representing different user groups (e.g., "read_only", "data_entry", "administrator").
* Grant specific privileges to roles instead of directly to users.
* Assign users to appropriate roles.
**Don't Do This:**
* Grant excessive privileges to users or roles.
* Use the "postgres" superuser account for routine operations.
**Code Example:**
"""sql
-- Create roles
CREATE ROLE read_only;
CREATE ROLE data_entry;
-- Grant privileges to roles
GRANT SELECT ON TABLE public.mytable TO read_only;
GRANT INSERT, UPDATE, DELETE ON TABLE public.mytable TO data_entry;
-- Create users and assign roles
CREATE USER user1 WITH PASSWORD 'StrongPassword1!' IN ROLE read_only;
CREATE USER user2 WITH PASSWORD 'StrongPassword2!' IN ROLE data_entry;
-- Revoke default public privileges
REVOKE ALL ON DATABASE yourdatabase FROM PUBLIC;
REVOKE ALL ON SCHEMA public FROM PUBLIC;
"""
### 1.3. Connection Security (SSL/TLS)
**Standard:** Use SSL/TLS encryption for all client connections.
**Why:** Encrypting connections protects sensitive data transmitted between clients and the database server.
**Do This:**
* Configure PostgreSQL to require SSL/TLS connections.
* Use valid SSL certificates.
* Configure client applications to use SSL/TLS.
**Don't Do This:**
* Disable SSL/TLS for performance reasons; optimize other areas instead.
* Use self-signed certificates in production environments without proper validation procedures.
**Code Example:**
"""postgresql.conf
ssl = on
ssl_cert_file = '/etc/ssl/certs/ssl-cert-snakeoil.pem'
ssl_key_file = '/etc/ssl/private/ssl-cert-snakeoil.key'
ssl_ca_file = '/etc/ssl/certs/ca_certificate.pem' # optional
"""
### 1.4 Connection Limits and Timeout Configuration
**Standard:** Restrict the maximum number of connections and configure connection timeouts.
**Why:** Prevent denial-of-service attacks and resource exhaustion.
**Do This:**
* Set "max_connections" to an appropriate value for your server's resources.
* Configure "tcp_keepalives_idle", "tcp_keepalives_interval", and "tcp_keepalives_count" to detect and close inactive connections.
* Use connection poolers like "pgbouncer" or "pgpool-II" to manage connections efficiently.
**Don't Do This:**
* Set "max_connections" too high, potentially overloading the server.
* Leave connections open indefinitely.
**Code Example:**
"""postgresql.conf
max_connections = 100
tcp_keepalives_idle = 60 # seconds
tcp_keepalives_interval = 60 # seconds
tcp_keepalives_count = 5
"""
## 2. Data Protection
### 2.1. Data Encryption
**Standard:** Encrypt sensitive data at rest and in transit.
**Why:** Encryption protects data from unauthorized access even if the database is compromised.
**Do This:**
* Use "pgcrypto" extension or other encryption solutions for encrypting sensitive columns at rest.
* Implement Transparent Data Encryption (TDE) if available in your PostgreSQL distribution (check with your cloud provider or PostgreSQL vendor).
* Always use SSL/TLS for client connections.
* Consider using Column Level Encryption (CLE) with a key management solution.
**Don't Do This:**
* Store encryption keys in the database.
* Encrypt data without a proper key management strategy.
* Rely solely on application-level encryption without database-level protection.
**Code Example:**
"""sql
-- Install pgcrypto extension
CREATE EXTENSION pgcrypto;
-- Encrypt data
UPDATE mytable SET sensitive_data = pgp_sym_encrypt(sensitive_data, 'encryption_key');
-- Decrypt data
SELECT pgp_sym_decrypt(sensitive_data, 'encryption_key') FROM mytable;
"""
**Important Note:** Proper key management is crucial. Use a dedicated key management system (KMS) to store and manage encryption keys securely.
### 2.2. Data Masking and Anonymization
**Standard:** Implement data masking or anonymization techniques to protect sensitive data in non-production environments or when sharing data with third parties.
**Why:** Reduces the risk of exposing sensitive data during development, testing, or data sharing.
**Do This:**
* Use tools like "pg_anon" or develop custom scripts to mask or anonymize sensitive data such as credit card numbers, social security numbers, and personal information.
* Implement data masking policies based on user roles and data access requirements.
* Use techniques like data substitution, shuffling, or generalization to anonymize data.
**Don't Do This:**
* Use real production data in non-production environments without masking or anonymization.
* Apply the same masking rules to all data; tailor the rules based on sensitivity.
**Code Example:**
"""sql
-- Example using pg_anon (requires installation and configuration)
-- Note: pg_anon's anonymization rules need to be pre-defined properly.
SELECT anon.random_email(email) FROM users;
-- Custom masking function (simplified example)
CREATE OR REPLACE FUNCTION mask_credit_card(cc_number TEXT)
RETURNS TEXT AS $$
BEGIN
RETURN 'XXXX-XXXX-XXXX-' || right(cc_number, 4);
END;
$$ LANGUAGE plpgsql;
SELECT mask_credit_card(credit_card_number) FROM orders;
"""
### 2.3. Regular Backups and Recovery Procedures
**Standard:** Implement a comprehensive backup and recovery strategy.
**Why:** Ensures data can be restored in case of data loss or corruption.
**Do This:**
* Perform regular full and incremental backups.
* Store backups in a secure location, separate from the production environment.
* Test the recovery process regularly to ensure backups are valid and can be restored.
* Consider using Point-in-Time Recovery (PITR) for more granular recovery options.
**Don't Do This:**
* Store backups on the same server as the database.
* Skip testing the restore process.
* Rely on manual backups only; automate the process.
**Code Example:**
"""bash
# Example using pg_dump (full backup)
pg_dump -U postgres -Fc yourdatabase > /path/to/backup/yourdatabase.dump
# Example using pg_basebackup (physical backup for PITR)
pg_basebackup -D /path/to/basebackup -U postgres -P -x -v
# Example restoration
pg_restore -U postgres -d yourdatabase /path/to/backup/yourdatabase.dump
"""
### 2.4. Auditing
**Standard:** Enable auditing to track database activity.
**Why:** Provides a record of who accessed what data and when, aiding in security investigations and compliance.
**Do This:**
* Use "pgaudit" extension to log database activity like login attempts, failed login attempts, queries executed, and changes to data.
* Configure the audit log to capture relevant events and data.
* Regularly review and analyze audit logs for suspicious activity.
**Don't Do This:**
* Disable auditing, leaving no record of database activity.
* Store audit logs in the same database; use a separate, secure location.
* Ignore audit logs; regularly review them for potential security incidents.
**Code Example:**
"""sql
-- Install pgaudit extension
CREATE EXTENSION pgaudit;
-- Configure pgaudit (requires postgresql.conf modification and restart)
-- pgaudit.log = 'all' -- Log all statements
-- pgaudit.log_level = 'debug1'
-- Configure auditing on a specific table
ALTER TABLE mytable AUDIT ALL;
-- Check audit logs (location depends on your configuration)
-- Log file example: /var/log/postgresql/postgresql-15-main.log
"""
**Important note:** Setting "pgaudit.log = 'all'" can generate a large amount of logs and impact performance. Carefully configure "pgaudit.log" to only log the events you need to monitor. Use "pgaudit.log_level" to adjust logging verbosity.
## 3. Secure Coding Practices
### 3.1. Prepared Statements and Parameterized Queries
**Standard:** Use prepared statements and parameterized queries to prevent SQL injection attacks.
**Why:** Parameterized queries separate SQL code from data, preventing attackers from injecting malicious SQL code through user input.
**Do This:**
* Always use prepared statements or parameterized queries when handling user-supplied input for SQL queries
* Use the database driver's built-in support for prepared statements or parameterized queries.
**Don't Do This:**
* Concatenate user input directly into SQL queries.
* Use string formatting to construct SQL queries with user input.
**Code Example (Python with psycopg2):**
"""python
import psycopg2
conn = psycopg2.connect("dbname=yourdatabase user=youruser password=yourpassword")
cur = conn.cursor()
user_id = 123
sql = "SELECT * FROM users WHERE id = %s"
cur.execute(sql, (user_id,)) # Pass user_id as a parameter
results = cur.fetchall()
cur.close()
conn.close()
"""
### 3.2. Input Validation and Sanitization
**Standard:** Validate and sanitize all user input before using it in SQL queries or other database operations.
**Why:** Prevents various types of attacks, including SQL injection, cross-site scripting (XSS), and buffer overflows.
**Do This:**
* Validate input data types, formats, and ranges.
* Sanitize input to remove or escape potentially harmful characters.
* Use whitelisting to allow only known good characters or patterns.
**Don't Do This:**
* Trust user input without validation.
* Rely solely on client-side validation.
**Code Example (PL/pgSQL function):**
"""sql
CREATE OR REPLACE FUNCTION validate_username(username TEXT)
RETURNS BOOLEAN AS $$
BEGIN
-- Check length
IF length(username) < 3 OR length(username) > 20 THEN
RETURN FALSE;
END IF;
-- Check for allowed characters (alphanumeric and underscore)
IF username !~ '^[a-zA-Z0-9_]+$' THEN
RETURN FALSE;
END IF;
RETURN TRUE;
END;
$$ LANGUAGE plpgsql;
-- Example usage
SELECT validate_username('valid_user123'); -- Returns TRUE
SELECT validate_username('invalid-user'); -- Returns FALSE
"""
### 3.3. Least Privilege for Stored Procedures and Functions
**Standard:** Grant stored procedures and functions only the privileges they need to perform their intended tasks.
**Why:** Reduces the potential impact of a compromised stored procedure or function.
**Do This:**
* Create stored procedures and functions with the "SECURITY DEFINER" option to execute with the privileges of the creator.
* Grant only necessary privileges to the user or role that owns the stored procedure or function.
* Use the "SET ROLE" command within the stored procedure if it needs to perform actions on behalf of the caller.
**Don't Do This:**
* Grant excessive privileges to stored procedures or functions.
* Create stored procedures or functions with superuser privileges unless absolutely necessary.
**Code Example:**
"""sql
-- Create a function with SECURITY DEFINER
CREATE OR REPLACE FUNCTION update_user_balance(user_id INT, amount DECIMAL)
RETURNS VOID AS $$
DECLARE
admin_role TEXT := 'admin_update_balance'; --Role that does the work
BEGIN
-- Check if the current user has permission to update balances within the code instead of as a permissions grant
IF NOT has_role(current_user, admin_role, 'member') THEN
RAISE EXCEPTION 'Insufficient privileges to update balances';
END IF;
UPDATE users SET balance = balance + amount WHERE id = user_id;
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;
-- Revoke direct update access to users table
REVOKE UPDATE ON users FROM PUBLIC;
-- Grant execute privilege to the function
GRANT EXECUTE ON FUNCTION update_user_balance(INT, DECIMAL) TO your_application_role; --Application role
--Create the intermediate "admin_update_balance" role
CREATE ROLE admin_update_balance;
--Grant update on users table to this role
GRANT UPDATE ON users TO admin_update_balance;
"""
### 3.4. Secure Error Handling
**Standard:** Handle errors gracefully without exposing sensitive information.
**Why:** Error messages can reveal information about the database structure or internal workings, which can be exploited by attackers.
**Do This:**
* Catch exceptions and handle them appropriately.
* Log errors and warnings to a secure location.
* Return generic error messages to the user.
* Use "RAISE EXCEPTION" with non-sensitive information for debugging purposes only in appropriate logging.
**Don't Do This:**
* Expose sensitive data in error messages.
* Ignore errors or let them propagate to the user interface.
**Code Example:**
"""sql
CREATE OR REPLACE FUNCTION process_data(data TEXT)
RETURNS VOID AS $$
BEGIN
BEGIN
-- Attempt to insert data
INSERT INTO mytable (data_column) VALUES (data);
EXCEPTION WHEN OTHERS THEN
-- Log the error
RAISE WARNING 'Error inserting data: %', SQLERRM;
-- Return a generic error message
RAISE EXCEPTION 'Failed to process data';
END;
END;
$$ LANGUAGE plpgsql;
"""
### 3.5. Avoid Dynamic SQL When Possible
**Standard:** Minimize the use of dynamic SQL. When dynamic SQL is necessary, implement robust sanitization and validation.
**Why:** Dynamic SQL, while flexible, introduces vulnerabilities if not handled carefully.
**Do This:**
* Strongly prefer prepared statements to dynamic SQL whenever possible.
* If dynamic SQL is unavoidable, use "format()" function in plpgsql to properly quote and escape identifiers and values.
* Implement strict input validation and sanitization.
**Don't Do This:**
* Concatenate strings directly into dynamic SQL queries.
* Assume that built-in escaping mechanisms are sufficient without thorough testing.
**Code Example:**
"""sql
CREATE OR REPLACE FUNCTION search_table(table_name TEXT, search_term TEXT)
RETURNS VOID AS $$
DECLARE
sql TEXT;
BEGIN
-- Validate table_name (important!) to prevent SQL injection (whitelist approach)
IF table_name NOT IN ('table1', 'table2', 'table3') THEN
RAISE EXCEPTION 'Invalid table name';
END IF;
-- Use format() to safely construct the dynamic SQL query
sql := format('SELECT * FROM %I WHERE column1 LIKE %L', table_name, '%' || search_term || '%');
EXECUTE sql;
END;
$$ LANGUAGE plpgsql;
-- Calling the function: Good Example
SELECT search_table('table1', 'safe search term');
-- Possible (but blocked by the function's validation step) SQL injection attack attempt: Bad Example
-- SELECT search_table('table1; DROP TABLE users;', 'search'); --This would not work because the "IF table_name NOT IN ('table1', 'table2', 'table3')" clause would block it.
"""
### 3.6 Disable Unnecessary Extensions
**Standard:** Disable or uninstall any PostgreSQL extensions that are not actively used.
**Why:** Unnecessary extensions can introduce security vulnerabilities or increase the attack surface.
**Do This:**
* Review the list of installed extensions regularly.
* Remove extensions that are no longer required.
* Consider using the "DROP EXTENSION" command to remove extensions completely.
**Don't Do This:**
* Leave unused extensions installed.
* Assume that extensions are secure by default.
**Code Example:**
"""sql
-- List installed extensions
SELECT * FROM pg_extension;
-- Drop an extension (requires superuser)
DROP EXTENSION IF EXISTS old_extension;
"""
## 4. Ongoing Security Management
### 4.1. Regular Security Audits
**Standard:** Conduct regular security audits of your PostgreSQL databases and applications.
**Why:** Helps identify and address potential security vulnerabilities.
**Do This:**
* Perform both internal and external security audits.
* Use security scanning tools to identify known vulnerabilities.
* Review database configurations, user privileges, and code for security weaknesses.
* Penetration testing
**Don't Do This:**
* Skip security audits.
* Rely solely on automated security scans.
* Ignore findings from security audits; prioritize remediation.
### 4.2. Stay Up-to-Date
**Standard:** Keep PostgreSQL and all related components up-to-date with the latest security patches and updates.
**Why:** Security patches address known vulnerabilities that can be exploited by attackers.
**Do This:**
* Subscribe to security mailing lists or RSS feeds for PostgreSQL.
* Regularly check for updates and apply them promptly.
* Test updates in a non-production environment before deploying them to production.
**Don't Do This:**
* Delay applying security patches.
* Assume that older versions of PostgreSQL are secure.
### 4.3. Security Awareness Training
**Standard:** Provide security awareness training to all developers and database administrators.
**Why:** Educates individuals about security best practices and helps them identify and avoid security threats.
**Do This:**
* Conduct regular security awareness training sessions.
* Cover topics such as secure coding practices, password security, and phishing awareness.
* Keep training materials up-to-date with the latest security threats and best practices.
**Don't Do This:**
* Assume that everyone understands security best practices.
* Skip security awareness training.
* Fail to reinforce security best practices over time.
### 4.4 Dependency Management and Vulnerability Scanning for Extensions
**Standard:** Implement dependency management and actively scan installed PostgreSQL extensions for known vulnerabilities.
**Why:** Extensions can introduce security risks if they contain vulnerabilities or are outdated.
**Do This:**
* Maintain an inventory of all installed extensions, including their versions.
* Regularly check for updates and security advisories for used extensions from trusted sources.
* Utilize automated vulnerability scanning tools that can identify known vulnerabilities in extensions.
**Don't Do This:**
* Assume that extensions are inherently secure.
* Neglect to review and patch or replace vulnerable extensions.
By following these security best practices, you can significantly reduce the risk of security incidents and protect your PostgreSQL databases and applications from unauthorized access, data breaches, and other security threats. Regular monitoring, auditing, and adaptation of these standards are crucial to maintaining a strong security posture.
danielsogl
Created Mar 6, 2025
This guide explains how to effectively use .clinerules with Cline, the AI-powered coding assistant.
The .clinerules file is a powerful configuration file that helps Cline understand your project's requirements, coding standards, and constraints. When placed in your project's root directory, it automatically guides Cline's behavior and ensures consistency across your codebase.
Place the .clinerules file in your project's root directory. Cline automatically detects and follows these rules for all files within the project.
# Project Overview project: name: 'Your Project Name' description: 'Brief project description' stack: - technology: 'Framework/Language' version: 'X.Y.Z' - technology: 'Database' version: 'X.Y.Z'
# Code Standards standards: style: - 'Use consistent indentation (2 spaces)' - 'Follow language-specific naming conventions' documentation: - 'Include JSDoc comments for all functions' - 'Maintain up-to-date README files' testing: - 'Write unit tests for all new features' - 'Maintain minimum 80% code coverage'
# Security Guidelines security: authentication: - 'Implement proper token validation' - 'Use environment variables for secrets' dataProtection: - 'Sanitize all user inputs' - 'Implement proper error handling'
Be Specific
Maintain Organization
Regular Updates
# Common Patterns Example patterns: components: - pattern: 'Use functional components by default' - pattern: 'Implement error boundaries for component trees' stateManagement: - pattern: 'Use React Query for server state' - pattern: 'Implement proper loading states'
Commit the Rules
.clinerules in version controlTeam Collaboration
Rules Not Being Applied
Conflicting Rules
Performance Considerations
# Basic .clinerules Example project: name: 'Web Application' type: 'Next.js Frontend' standards: - 'Use TypeScript for all new code' - 'Follow React best practices' - 'Implement proper error handling' testing: unit: - 'Jest for unit tests' - 'React Testing Library for components' e2e: - 'Cypress for end-to-end testing' documentation: required: - 'README.md in each major directory' - 'JSDoc comments for public APIs' - 'Changelog updates for all changes'
# Advanced .clinerules Example project: name: 'Enterprise Application' compliance: - 'GDPR requirements' - 'WCAG 2.1 AA accessibility' architecture: patterns: - 'Clean Architecture principles' - 'Domain-Driven Design concepts' security: requirements: - 'OAuth 2.0 authentication' - 'Rate limiting on all APIs' - 'Input validation with Zod'
# Database: Create RLS policies You're a Supabase Postgres expert in writing row level security policies. Your purpose is to generate a policy with the constraints given by the user. You should first retrieve schema information to write policies for, usually the 'public' schema. The output should use the following instructions: - The generated SQL must be valid SQL. - You can use only CREATE POLICY or ALTER POLICY queries, no other queries are allowed. - Always use double apostrophe in SQL strings (eg. 'Night''s watch') - You can add short explanations to your messages. - The result should be a valid markdown. The SQL code should be wrapped in ``` (including sql language tag). - Always use "auth.uid()" instead of "current_user". - SELECT policies should always have USING but not WITH CHECK - INSERT policies should always have WITH CHECK but not USING - UPDATE policies should always have WITH CHECK and most often have USING - DELETE policies should always have USING but not WITH CHECK - Don't use `FOR ALL`. Instead separate into 4 separate policies for select, insert, update, and delete. - The policy name should be short but detailed text explaining the policy, enclosed in double quotes. - Always put explanations as separate text. Never use inline SQL comments. - If the user asks for something that's not related to SQL policies, explain to the user that you can only help with policies. - Discourage `RESTRICTIVE` policies and encourage `PERMISSIVE` policies, and explain why. The output should look like this: ```sql CREATE POLICY "My descriptive policy." ON books FOR INSERT to authenticated USING ( (select auth.uid()) = author_id ) WITH ( true ); ``` Since you are running in a Supabase environment, take note of these Supabase-specific additions below. ## Authenticated and unauthenticated roles Supabase maps every request to one of the roles: - `anon`: an unauthenticated request (the user is not logged in) - `authenticated`: an authenticated request (the user is logged in) These are actually [Postgres Roles](/docs/guides/database/postgres/roles). You can use these roles within your Policies using the `TO` clause: ```sql create policy "Profiles are viewable by everyone" on profiles for select to authenticated, anon using ( true ); -- OR create policy "Public profiles are viewable only by authenticated users" on profiles for select to authenticated using ( true ); ``` Note that `for ...` must be added after the table but before the roles. `to ...` must be added after `for ...`: ### Incorrect ```sql create policy "Public profiles are viewable only by authenticated users" on profiles to authenticated for select using ( true ); ``` ### Correct ```sql create policy "Public profiles are viewable only by authenticated users" on profiles for select to authenticated using ( true ); ``` ## Multiple operations PostgreSQL policies do not support specifying multiple operations in a single FOR clause. You need to create separate policies for each operation. ### Incorrect ```sql create policy "Profiles can be created and deleted by any user" on profiles for insert, delete -- cannot create a policy on multiple operators to authenticated with check ( true ) using ( true ); ``` ### Correct ```sql create policy "Profiles can be created by any user" on profiles for insert to authenticated with check ( true ); create policy "Profiles can be deleted by any user" on profiles for delete to authenticated using ( true ); ``` ## Helper functions Supabase provides some helper functions that make it easier to write Policies. ### `auth.uid()` Returns the ID of the user making the request. ### `auth.jwt()` Returns the JWT of the user making the request. Anything that you store in the user's `raw_app_meta_data` column or the `raw_user_meta_data` column will be accessible using this function. It's important to know the distinction between these two: - `raw_user_meta_data` - can be updated by the authenticated user using the `supabase.auth.update()` function. It is not a good place to store authorization data. - `raw_app_meta_data` - cannot be updated by the user, so it's a good place to store authorization data. The `auth.jwt()` function is extremely versatile. For example, if you store some team data inside `app_metadata`, you can use it to determine whether a particular user belongs to a team. For example, if this was an array of IDs: ```sql create policy "User is in team" on my_table to authenticated using ( team_id in (select auth.jwt() -> 'app_metadata' -> 'teams')); ``` ### MFA The `auth.jwt()` function can be used to check for [Multi-Factor Authentication](/docs/guides/auth/auth-mfa#enforce-rules-for-mfa-logins). For example, you could restrict a user from updating their profile unless they have at least 2 levels of authentication (Assurance Level 2): ```sql create policy "Restrict updates." on profiles as restrictive for update to authenticated using ( (select auth.jwt()->>'aal') = 'aal2' ); ``` ## RLS performance recommendations Every authorization system has an impact on performance. While row level security is powerful, the performance impact is important to keep in mind. This is especially true for queries that scan every row in a table - like many `select` operations, including those using limit, offset, and ordering. Based on a series of [tests](https://github.com/GaryAustin1/RLS-Performance), we have a few recommendations for RLS: ### Add indexes Make sure you've added [indexes](/docs/guides/database/postgres/indexes) on any columns used within the Policies which are not already indexed (or primary keys). For a Policy like this: ```sql create policy "Users can access their own records" on test_table to authenticated using ( (select auth.uid()) = user_id ); ``` You can add an index like: ```sql create index userid on test_table using btree (user_id); ``` ### Call functions with `select` You can use `select` statement to improve policies that use functions. For example, instead of this: ```sql create policy "Users can access their own records" on test_table to authenticated using ( auth.uid() = user_id ); ``` You can do: ```sql create policy "Users can access their own records" on test_table to authenticated using ( (select auth.uid()) = user_id ); ``` This method works well for JWT functions like `auth.uid()` and `auth.jwt()` as well as `security definer` Functions. Wrapping the function causes an `initPlan` to be run by the Postgres optimizer, which allows it to "cache" the results per-statement, rather than calling the function on each row. Caution: You can only use this technique if the results of the query or function do not change based on the row data. ### Minimize joins You can often rewrite your Policies to avoid joins between the source and the target table. Instead, try to organize your policy to fetch all the relevant data from the target table into an array or set, then you can use an `IN` or `ANY` operation in your filter. For example, this is an example of a slow policy which joins the source `test_table` to the target `team_user`: ```sql create policy "Users can access records belonging to their teams" on test_table to authenticated using ( (select auth.uid()) in ( select user_id from team_user where team_user.team_id = team_id -- joins to the source "test_table.team_id" ) ); ``` We can rewrite this to avoid this join, and instead select the filter criteria into a set: ```sql create policy "Users can access records belonging to their teams" on test_table to authenticated using ( team_id in ( select team_id from team_user where user_id = (select auth.uid()) -- no join ) ); ``` ### Specify roles in your policies Always use the Role of inside your policies, specified by the `TO` operator. For example, instead of this query: ```sql create policy "Users can access their own records" on rls_test using ( auth.uid() = user_id ); ``` Use: ```sql create policy "Users can access their own records" on rls_test to authenticated using ( (select auth.uid()) = user_id ); ``` This prevents the policy `( (select auth.uid()) = user_id )` from running for any `anon` users, since the execution stops at the `to authenticated` step.
# Database: Create migration You are a Postgres Expert who loves creating secure database schemas. This project uses the migrations provided by the Supabase CLI. ## Creating a migration file Given the context of the user's message, create a database migration file inside the folder `supabase/migrations/`. The file MUST following this naming convention: The file MUST be named in the format `YYYYMMDDHHmmss_short_description.sql` with proper casing for months, minutes, and seconds in UTC time: 1. `YYYY` - Four digits for the year (e.g., `2024`). 2. `MM` - Two digits for the month (01 to 12). 3. `DD` - Two digits for the day of the month (01 to 31). 4. `HH` - Two digits for the hour in 24-hour format (00 to 23). 5. `mm` - Two digits for the minute (00 to 59). 6. `ss` - Two digits for the second (00 to 59). 7. Add an appropriate description for the migration. For example: ``` 20240906123045_create_profiles.sql ``` ## SQL Guidelines Write Postgres-compatible SQL code for Supabase migration files that: - Includes a header comment with metadata about the migration, such as the purpose, affected tables/columns, and any special considerations. - Includes thorough comments explaining the purpose and expected behavior of each migration step. - Write all SQL in lowercase. - Add copious comments for any destructive SQL commands, including truncating, dropping, or column alterations. - When creating a new table, you MUST enable Row Level Security (RLS) even if the table is intended for public access. - When creating RLS Policies - Ensure the policies cover all relevant access scenarios (e.g. select, insert, update, delete) based on the table's purpose and data sensitivity. - If the table is intended for public access the policy can simply return `true`. - RLS Policies should be granular: one policy for `select`, one for `insert` etc) and for each supabase role (`anon` and `authenticated`). DO NOT combine Policies even if the functionality is the same for both roles. - Include comments explaining the rationale and intended behavior of each security policy The generated SQL code should be production-ready, well-documented, and aligned with Supabase's best practices.
# Postgres SQL Style Guide ## General - Use lowercase for SQL reserved words to maintain consistency and readability. - Employ consistent, descriptive identifiers for tables, columns, and other database objects. - Use white space and indentation to enhance the readability of your code. - Store dates in ISO 8601 format (`yyyy-mm-ddThh:mm:ss.sssss`). - Include comments for complex logic, using '/_ ... _/' for block comments and '--' for line comments. ## Naming Conventions - Avoid SQL reserved words and ensure names are unique and under 63 characters. - Use snake_case for tables and columns. - Prefer plurals for table names - Prefer singular names for columns. ## Tables - Avoid prefixes like 'tbl\_' and ensure no table name matches any of its column names. - Always add an `id` column of type `identity generated always` unless otherwise specified. - Create all tables in the `public` schema unless otherwise specified. - Always add the schema to SQL queries for clarity. - Always add a comment to describe what the table does. The comment can be up to 1024 characters. ## Columns - Use singular names and avoid generic names like 'id'. - For references to foreign tables, use the singular of the table name with the `_id` suffix. For example `user_id` to reference the `users` table - Always use lowercase except in cases involving acronyms or when readability would be enhanced by an exception. #### Examples: ```sql create table books ( id bigint generated always as identity primary key, title text not null, author_id bigint references authors (id) ); comment on table books is 'A list of all the books in the library.'; ``` ## Queries - When the query is shorter keep it on just a few lines. As it gets larger start adding newlines for readability - Add spaces for readability. Smaller queries: ```sql select * from employees where end_date is null; update employees set end_date = '2023-12-31' where employee_id = 1001; ``` Larger queries: ```sql select first_name, last_name from employees where start_date between '2021-01-01' and '2021-12-31' and status = 'employed'; ``` ### Joins and Subqueries - Format joins and subqueries for clarity, aligning them with related SQL clauses. - Prefer full table names when referencing tables. This helps for readability. ```sql select employees.employee_name, departments.department_name from employees join departments on employees.department_id = departments.department_id where employees.start_date > '2022-01-01'; ``` ## Aliases - Use meaningful aliases that reflect the data or transformation applied, and always include the 'as' keyword for clarity. ```sql select count(*) as total_employees from employees where end_date is null; ``` ## Complex queries and CTEs - If a query is extremely complex, prefer a CTE. - Make sure the CTE is clear and linear. Prefer readability over performance. - Add comments to each block. ```sql with department_employees as ( -- Get all employees and their departments select employees.department_id, employees.first_name, employees.last_name, departments.department_name from employees join departments on employees.department_id = departments.department_id ), employee_counts as ( -- Count how many employees in each department select department_name, count(*) as num_employees from department_employees group by department_name ) select department_name, num_employees from employee_counts order by department_name; ```
# API Integration Standards for PostgreSQL This document outlines the coding standards for integrating PostgreSQL with external APIs and backend services. These standards promote maintainability, performance, and security when building applications that rely on data and functionality outside of the database itself. It focuses on modern approaches compatible with the latest PostgreSQL version. ## 1. Architectural Considerations for API Integration ### 1.1. Standard: Define Clear API Boundaries **Do This:** * Clearly define the responsibilities of PostgreSQL and external APIs. Use PostgreSQL for data persistence, relational logic, and indexing. Offload complex computations, specialized data processing, and external data access to APIs. * Use clear and consistent naming conventions for database functions/procedures interacting with APIs. Prefix them (e.g., "api_", "ext_") to easily identify external API integration code. * Document the contract (input/output) with each API thoroughly. **Don't Do This:** * Overload PostgreSQL with tasks that APIs are better suited for (e.g., image processing, complex machine learning tasks that are not data-intensive). * Embed undocumented or magic API calls directly within SQL queries. **Why:** Defining clear boundaries ensures modularity, easier maintenance, and optimized performance. It avoids turning the database into a monolithic application component. **Example:** """sql -- Good: Function for fetching user profiles from an external API. CREATE OR REPLACE FUNCTION api_get_user_profile(user_id INT) RETURNS JSONB AS $$ BEGIN -- Call external API to get user profile details. -- Using a hypothetical extension for API calls RETURN http_get('https://api.example.com/users/' || user_id)::jsonb; EXCEPTION WHEN OTHERS THEN RAISE EXCEPTION 'Error fetching user profile from API: %', SQLERRM; END; $$ LANGUAGE plpgsql; -- Bad: Embedding API logic directly within a complex query. -- SELECT * FROM users WHERE ... AND api_call(...) ... ; -- Avoid! """ ### 1.2. Standard: Asynchronous vs. Synchronous API Interactions **Do This:** * Use asynchronous API calls (e.g., message queues, background workers) where possible to prevent long-running database transactions from blocking other operations. Implement retries and error handling for asynchronous tasks. * For synchronous calls, keep the execution time as short as possible to avoid holding database connections for extended periods. **Don't Do This:** * Make blocking API calls directly within critical transaction paths. This will significantly impact database performance and availability. * Assume API calls will always succeed. Implement robust error handling and retries. **Why:** Asynchronous operations improve scalability and responsiveness. Synchronous operations can lead to deadlocks and performance degradation if not managed carefully. **Example (using pg_amqp or similar queue extensions):** """sql -- Asynchronous API call using a message queue. (Hypothetical Example) CREATE OR REPLACE FUNCTION api_process_user_data(user_id INT) RETURNS VOID AS $$ BEGIN -- Send a message to a queue for processing user data via an external API. PERFORM amqp.publish('process_user_data_queue', json_build_object('user_id', user_id)); -- Hypothetical RETURN; END; $$ LANGUAGE plpgsql; -- Example of a background worker (using pg_background) that consumes from the queue to call the external API -- Code for the background worker would be in a separate file and process the queue. """ ### 1.3. Standard: Data Transformation and Mapping **Do This:** * Define clear data mapping between PostgreSQL data types and API request/response formats (e.g., JSON, XML). Use PostgreSQL's JSONB and XML support effectively. * Validate data received from APIs before inserting it into the database using "CHECK" constraints or other validation mechanisms. * Log API requests and responses for debugging and auditing purposes. **Don't Do This:** * Directly insert untrusted data received from APIs into the database without validation. This can lead to SQL injection and other security vulnerabilities. * Rely on implicit type conversions between PostgreSQL and API data formats. Be explicit. **Why:** Proper data transformation and validation prevent data corruption and security breaches. Logging helps troubleshoot issues and track API usage. **Example:** """sql -- Validating and inserting JSON data from an API. CREATE TABLE api_user_profiles ( user_id INT PRIMARY KEY, profile_data JSONB -- CHECK constraint is appropriate here to require the JSON object ALWAYS conform to a schema ); CREATE OR REPLACE FUNCTION api_import_user_profile(user_id INT, profile_json JSONB) RETURNS VOID AS $$ DECLARE -- Validate JSON data against a schema (hypothetical function). is_valid BOOLEAN; BEGIN -- Validate that the JSON is valid against a schema is_valid := jsonb_matches_schema('{"type": "object", "properties": {"name": {"type": "string"},"email": {"type": "string", "format": "email"} }}', profile_json); IF NOT is_valid THEN RAISE EXCEPTION 'Invalid profile data format.'; END IF; INSERT INTO api_user_profiles (user_id, profile_data) VALUES (user_id, profile_json); RETURN; EXCEPTION WHEN OTHERS THEN RAISE EXCEPTION 'Error importing user profile: %', SQLERRM; END; $$ LANGUAGE plpgsql; """ ## 2. Implementation Details ### 2.1. Standard: Choosing the Right API Interaction Method **Do This:** * Evaluate these methods: * **HTTP Requests (using extensions like "http" or "curl"):** Suitable for RESTful APIs. * **Message Queues (using extensions like "pg_amqp" or "pg_kafka"):** Ideal for asynchronous communication. * **Foreign Data Wrappers (FDWs):** For integrating with other databases or data stores directly. * Choose the method that best fits the API's protocol, data format, and communication pattern. **Don't Do This:** * Force a specific integration method because it's familiar. Consider alternatives based on the API's characteristics. * Build custom, ad-hoc solutions when standard extensions and FDWs provide the necessary functionality. **Why:** Selecting the right method simplifies integration, improves performance, and reduces development effort. **Example (using "http" extension for a REST API):** """sql -- Example using the http extension to call a REST API CREATE EXTENSION IF NOT EXISTS http; CREATE OR REPLACE FUNCTION api_get_weather(city TEXT) RETURNS JSONB AS $$ DECLARE api_url TEXT := 'https://api.weatherapi.com/v1/current.json?key=YOUR_API_KEY&q=' || city; response HTTPResponse; BEGIN response := http_get(api_url); IF response.status_code = 200 THEN RETURN response.content::jsonb; ELSE RAISE EXCEPTION 'Weather API error: %', response.content; END IF; EXCEPTION WHEN OTHERS THEN RAISE EXCEPTION 'Error fetching weather data: %', SQLERRM; END; $$ LANGUAGE plpgsql; -- SELECT api_get_weather('London'); """ ### 2.2. Standard: Error Handling and Retries **Do This:** * Implement robust error handling for API calls. Catch exceptions, log errors, and implement retry mechanisms with exponential backoff. * Distinguish between transient and permanent errors. Retry transient errors (e.g., network timeouts), and log permanent errors (e.g., invalid API key) for investigation. * Set appropriate timeouts for API calls to prevent indefinite blocking. * Consider using "TRY...CATCH" blocks for error handling within PL/pgSQL functions. **Don't Do This:** * Ignore errors from API calls. At a minimum, log the error so it can be investigated later. * Retry indefinitely without a limit or backoff strategy. This can overload the API or the database. **Why:** Robust error handling ensures resilience and prevents cascading failures. It provides valuable insights into API issues. **Example:** """sql CREATE OR REPLACE FUNCTION api_get_data_with_retry(url TEXT, max_retries INT DEFAULT 3) RETURNS JSONB AS $$ DECLARE response HTTPResponse; retries INT := 0; delay INTERVAL := '1 second'; BEGIN LOOP BEGIN response := http_get(url); IF response.status_code = 200 THEN RETURN response.content::jsonb; ELSE RAISE WARNING 'API call failed with status code: %', response.status_code; -- Check for non-retryable errors here! -- IF response.status_code = 400 THEN RETURN NULL; -- Bad Request (do not retry) END IF; EXCEPTION WHEN OTHERS THEN RAISE WARNING 'API call error: %', SQLERRM; END; retries := retries + 1; IF retries >= max_retries THEN RAISE EXCEPTION 'Max retries exceeded for API call.'; END IF; RAISE NOTICE 'Retrying in %', delay; PERFORM pg_sleep(extract(epoch from delay)); delay := delay * 2; -- Exponential backoff END LOOP; EXCEPTION WHEN OTHERS THEN RAISE EXCEPTION 'Failed to get data after multiple retries: %', SQLERRM; END; $$ LANGUAGE plpgsql; """ ### 2.3. Standard: Security Considerations **Do This:** * Store API keys and secrets securely using PostgreSQL's configuration parameters or a dedicated secrets management solution. NEVER hardcode API keys in SQL code. * Use HTTPS for all API calls to encrypt data in transit. * Validate API responses to prevent data injection (e.g., JSON injection). * Implement rate limiting to prevent abuse. * Use least privilege principle when granting permissions to API interaction functions. **Don't Do This:** * Hardcode API keys or secrets in SQL code or store them in plain text in the database. * Trust API responses implicitly. Always validate the data. * Expose your PostgreSQL database directly to the internet without proper firewall and security measures. **Why:** Security is paramount. Protecting API keys, encrypting data, and rate limiting prevent unauthorized access and malicious attacks. **Example:** """sql -- Storing API key securely using postgresql.conf -- In postgresql.conf: -- api.weather_api_key = 'YOUR_API_KEY' -- SQL to retrieve the API key CREATE OR REPLACE FUNCTION api_get_weather_secure(city TEXT) RETURNS JSONB AS $$ DECLARE api_url TEXT := 'https://api.weatherapi.com/v1/current.json?key=' || current_setting('api.weather_api_key') || '&q=' || city; response HTTPResponse; BEGIN response := http_get(api_url); IF response.status_code = 200 THEN RETURN response.content::jsonb; ELSE RAISE EXCEPTION 'Weather API error: %', response.content; END IF; EXCEPTION WHEN OTHERS THEN RAISE EXCEPTION 'Error fetching weather data: %', SQLERRM; END; $$ LANGUAGE plpgsql SECURITY DEFINER; -- SECURITY DEFINER crucial for accessing external configurations -- Revoke execute permission from public REVOKE EXECUTE ON FUNCTION api_get_weather_secure(TEXT) FROM PUBLIC; -- Grant access to specific roles GRANT EXECUTE ON FUNCTION api_get_weather_secure(TEXT) TO your_application_role; """ ### 2.4. Standard: Performance Optimization **Do This:** * Cache API responses to reduce the number of API calls, especially for frequently accessed data. Use "MATERIALIZED VIEW" or a custom cache table. * Use connection pooling to minimize the overhead of establishing new connections to APIs. Some HTTP extensions do this internally. * Optimize data transfer by requesting only the necessary fields from the API. Use appropriate query parameters. **Don't Do This:** * Make redundant API calls. Identify opportunities for caching or batching. * Retrieve large amounts of data from APIs when only a small subset is needed. **Why:** Performance optimization improves application responsiveness and reduces API usage costs. **Example (using a materialized view for caching):** """sql CREATE MATERIALIZED VIEW weather_cache AS SELECT city, api_get_weather(city) AS weather_data, NOW() AS last_updated FROM (VALUES ('London'), ('New York'), ('Tokyo')) AS cities(city); CREATE UNIQUE INDEX idx_weather_cache_city ON weather_cache (city); -- Refresh the cache periodically CREATE OR REPLACE FUNCTION refresh_weather_cache() RETURNS VOID AS $$ BEGIN REFRESH MATERIALIZED VIEW CONCURRENTLY weather_cache; RETURN; END; $$ LANGUAGE plpgsql; -- Schedule daily refreshes with pg_cron or a similar scheduler: -- SELECT cron.schedule('0 0 * * *', 'SELECT refresh_weather_cache()'); -- Usage: CREATE OR REPLACE FUNCTION get_weather_from_cache(city TEXT) RETURNS JSONB AS $$ BEGIN RETURN (SELECT weather_data FROM weather_cache WHERE city = get_weather_from_cache.city); EXCEPTION WHEN no_data_found THEN RETURN api_get_weather(city); -- if not in cache, fetch it from the API END; $$ LANGUAGE plpgsql; """ ## 3. Coding Style and Conventions ### 3.1. Standard: Code Formatting and Comments **Do This:** * Use consistent indentation (typically 4 spaces) and line breaks to improve readability. * Add comments to explain complex logic, API calls, and data transformations. * Use meaningful names for variables, functions, and parameters. **Don't Do This:** * Write long, monolithic functions without comments or clear structure. * Use cryptic or ambiguous names. **Why:** Consistent formatting and clear comments make the code easier to understand and maintain. ### 3.2. Standard: Transaction Management **Do This:** * Wrap API calls within explicit transactions when necessary to ensure data consistency. Use "BEGIN", "COMMIT", and "ROLLBACK". * Handle potential errors during API calls gracefully and roll back the transaction if necessary. **Don't Do This:** * Leave transactions open for extended periods of time while waiting for API responses. * Commit transactions before ensuring the success of all related API calls. **Why:** Proper transaction management ensures data integrity and prevents inconsistencies. ### 3.3. Standard: Testing **Do This:** * Write unit tests for API interaction functions to verify that they handle different scenarios correctly (e.g., success, error, timeout). * Use mock APIs or stubs to isolate the database from external dependencies during testing. * Write integration tests to ensure that the database and APIs work together seamlessly. **Don't Do This:** * Skip testing API interaction code. This can lead to unexpected errors and integration issues in production. * Rely solely on manual testing. **Why:** Automated testing improves code quality, reduces the risk of regressions, and facilitates continuous integration and delivery. These API integration standards will help create reliable, secure, and maintainable PostgreSQL applications that integrate effectively with external services. Remember to stay updated with the latest PostgreSQL features and best practices as the ecosystem evolves.
# Core Architecture Standards for PostgreSQL This document outlines the coding standards for the core architecture of PostgreSQL. It aims to provide clear guidance for developers contributing to the core codebase, ensuring maintainability, performance, security, and consistency. The standards reflect modern approaches, patterns, and the latest features of PostgreSQL. ## 1. Fundamental Architectural Patterns PostgreSQL's core architecture is based on a process-based model, where each client connection is handled by a separate server process. This concurrency model heavily relies on shared memory for inter-process communication and data sharing. **Do This:** * Understand the process-based architecture deeply. Familiarize yourself with the following processes: "postgres" (the postmaster), "backend" (server processes), "walwriter", "autovacuum launcher", "stats collector", and "bgwriter". * Design extensions with process isolation in mind. Avoid global state modification to prevent unintended side effects across different backend processes. * Favor shared memory mechanisms for data sharing across backends over file-based communication where performance is critical. **Don't Do This:** * Create singletons or static variables that hold global state without proper consideration for concurrency. This will lead to unexpected behavior and difficult to debug race conditions. * Introduce shared resources without adequate locking mechanisms. * Rely on inter-process communication (IPC) without understanding the potential for deadlocks or race conditions. **Why:** Maintaining a well-defined process model ensures stability and scalability. Properly isolated processes minimize the risk of crashes affecting other connections. ### 1.1 Process Lifecycle Each PostgreSQL backend process follows a well-defined lifecycle: 1. **Startup:** Initialization of process-specific resources and connection to the shared memory. 2. **Authentication:** Verification of the client's identity. 3. **Query Processing:** Parsing, planning, and execution of SQL queries. 4. **Transaction Management:** Ensuring ACID properties of database operations. 5. **Shutdown:** Clean-up of resources and disconnection from shared memory. **Do This:** * Ensure proper resource cleanup in all stages of the lifecycle, especially during error handling. * Use "elog()" with appropriate severity levels for logging events during the lifecycle. * Catch and handle exceptions appropriately throughout the lifecycle. **Don't Do This:** * Leak resources (memory, file descriptors, etc.) during any phase of the process lifecycle. * Ignore errors during startup or shutdown. * Introduce long-running operations inside the authentication phase. **Why:** Strict adherence to the process lifecycle prevents resource exhaustion and ensures a clean state upon process termination. ### 1.2 Shared Memory Management Shared memory provides a crucial mechanism for communication and data sharing between PostgreSQL backend processes. **Do This:** * Use PostgreSQL's shared memory APIs (e.g., "ShmemAlloc()", "ShmemInitStruct()") for allocating and managing shared memory. These functions handle the platform-specific details of shared memory allocation and ensure proper alignment and size constraints. * Protect access to shared memory regions using appropriate locking mechanisms (e.g., "LWLock", "SpinLock"). * Define shared memory segments in "src/backend/utils/misc/ipc.c" or a relevant module's initialization function. **Don't Do This:** * Directly use system calls like "shmget()" and "shmat()" without going through PostgreSQL's shared memory APIs. * Assume atomicity of operations on shared memory regions. Always use locking. * Overallocate shared memory. Reserve only what is necessary. **Why:** Proper shared memory management prevents corruption, ensures data integrity, and avoids resource conflicts between processes. **Example:** """c /* Example of allocating and using shared memory */ typedef struct { int counter; LWLock lock; } MySharedData; static MySharedData *mySharedData; void initializeMySharedData(void) { bool found; mySharedData = ShmemInitStruct("MySharedData", sizeof(MySharedData), &found); if (!found) { /* Initialize shared memory on first allocation */ mySharedData->counter = 0; LWLockInitialize(&mySharedData->lock, LWLockAssign()); } } int incrementCounter(void) { int result; LWLockAcquire(&mySharedData->lock, LW_EXCLUSIVE); result = ++mySharedData->counter; LWLockRelease(&mySharedData->lock); return result; } """ ## 2. Project Structure and Organization PostgreSQL's source code is organized into a directory structure that reflects its functionality. **Do This:** * Familiarize yourself with the top-level directories: "src", "doc", "contrib", "src" is where the core source code resides. * Understand the purpose of subdirectories within "src", such as "backend", "include", and "port". * Place new code in the appropriate directory based on its functionality. * Maintain consistency in coding style and naming conventions within each directory. **Don't Do This:** * Randomly place files in arbitrary directories. * Create unnecessary dependencies between modules. * Violate the established directory structure without a clear justification. **Why:** A well-organized project structure facilitates navigation, understanding, and maintenance of the codebase. Clear directory conventions maintain code clarity. ### 2.1 Core Directories Key directories within the "src" directory include: * "src/backend": Contains the core backend code, including query processing, transaction management, storage, and indexing. * "src/include": Contains header files that define the interfaces used by the backend code. * "src/port": Contains platform-specific code. * "src/common": Contains code shared across multiple parts of the backend. * "src/fe_utils": Contains utilities used by the frontend. **Do This:** * Follow the existing directory structure when adding new features or modifying existing ones. * Create new subdirectories within existing directories if necessary to organize logically related code. * Use header files in "src/include" to define public interfaces for modules. **Don't Do This:** * Include implementation details in header files. * Create circular dependencies between directories. **Why:** A modular directory structure ensures a logical separation of concerns and minimizes dependencies between modules helping reduce build times. ### 2.2 Coding Style PostgreSQL has a well-defined coding style outlined in "doc/src/sgml/develop.sgml". **Do This:** * Adhere to the coding style guidelines regarding indentation, spacing, naming conventions, and comment formatting. * Use "pgindent" to automatically format your code. * Write concise and informative comments. **Don't Do This:** * Ignore the coding style guidelines. * Write lengthy or redundant comments. * Use inconsistent naming conventions. **Why:** Consistent coding style improves readability and maintainability of the code. "pgindent" ensures code conforms to the standard style automatically. ## 3. Modern Approaches and Patterns Modern PostgreSQL development emphasizes several key approaches: * **Extensibility:** PostgreSQL is designed to be extensible through extensions. * **Concurrency:** Handling multiple concurrent connections efficiently is crucial. * **Security:** Preventing vulnerabilities and ensuring data integrity are paramount. ### 3.1 Extension Development Extensions are the primary way to add new functionality to PostgreSQL. **Do This:** * Use the Extension Control File (".control") to define the extension's metadata. * Provide SQL scripts for creating and dropping database objects. * Use hooks ("ExecutorStart_hook", "ExecutorRun_hook", etc.) to extend the core functionality. * Follow the security guidelines for extension development. **Don't Do This:** * Modify the core PostgreSQL code directly (unless absolutely necessary and approved by the community). * Introduce security vulnerabilities through insecure extension code. * Make assumptions about the internal implementation details of PostgreSQL that could change in future versions. **Why:** Extensions allow adding new features without modifying the core code. **Example:** """sql -- Example SQL script for creating a function in an extension CREATE FUNCTION my_extension_function(text) RETURNS text AS '$libdir/my_extension', 'my_extension_function' LANGUAGE C IMMUTABLE STRICT; """ ### 3.2 Concurrency Control PostgreSQL uses Multi-Version Concurrency Control (MVCC) to manage concurrent access to data. **Do This:** * Understand MVCC and its implications for data consistency. * Use appropriate transaction isolation levels to prevent data anomalies. * Minimize lock contention by optimizing queries and using appropriate indexing strategies. * When working with internal data structures, be mindful of concurrent access and utilize PostgreSQL's locking primitives (LWLock, spinlocks) appropriately. **Don't Do This:** * Ignore the potential for data anomalies when using low transaction isolation levels. * Introduce unnecessary locking that could lead to deadlocks. * Perform long-running operations within a single transaction. **Why:** MVCC ensures data consistency and allows concurrent access to data. ### 3.3 Security Best Practices Security is a critical aspect of PostgreSQL development. **Do This:** * Follow secure coding practices to prevent vulnerabilities such as SQL injection and buffer overflows. * Use hardened APIs to avoid common security pitfalls. * Validate input data carefully. * Avoid hardcoding sensitive information such as passwords. * Be aware of the security implications of new features. **Don't Do This:** * Ignore security warnings. * Implement custom encryption algorithms (use PostgreSQL's built-in encryption features). * Grant excessive privileges to users or roles. **Why:** Secure coding practices are essential for preventing data breaches and ensuring the integrity of the database. ### 3.4 Memory Management Efficient Memory management is key to PostgreSQL's performance and stability. **Do This:** * Use PostgreSQL's memory context mechanism ("MemoryContext") for allocating and freeing memory within a query lifecycle. This mechanism provides automatic memory cleanup at the end of a query preventing memory leaks. * Understand the different memory contexts (e.g., "TopMemoryContext", "QueryMemoryContext") and use them appropriately. * Avoid manual memory management ("malloc"/"free") unless absolutely necessary (and only if you REALLY know what you are doing). Use PostgreSQL's "palloc"/"pfree" within a memory context. * Profile memory usage to identify and fix memory leaks. **Don't Do This:** * Leak memory by failing to free allocated memory. * Allocate large amounts of memory without considering the impact on performance. * Use "malloc"/"free" without a deep understanding of PostgreSQL's memory management. **Why:** Efficient memory management prevents memory leaks, reduces memory fragmentation, and improves overall performance. The memory context system automates this and integrates with the query processing lifecycle. **Example:** """c /* Example using MemoryContext */ MemoryContext myContext; char *data; /* Create a new memory context */ myContext = AllocSetContextCreate(CurrentMemoryContext, "MyContext", ALLOCSET_DEFAULT_SIZES); /* Switch to the new memory context */ MemoryContext oldContext = MemoryContextSwitchTo(myContext); /* Allocate memory within the new context */ data = palloc(100); /* Switch back to the previous memory context. The 'data' still exists */ MemoryContextSwitchTo(oldContext); /* ... use data ... */ /* At the end, the memory context 'myContext' is destroyed, and all memory allocated within it is automatically freed */ MemoryContextDelete(myContext); """ These standards aim to provide a comprehensive guide for contributing to the core architecture of PostgreSQL, by promoting best practices and ensuring code maintainability, performance, and security. By following these guidelines, developers can help ensure that PostgreSQL remains a robust, reliable, and extensible database system.