Database Technology

AWS Database Blog compares Amazon RDS Proxy and PgBouncer for PostgreSQL connection pooling. The article details differences in pooling modes, authentication, failover handling, and session-state pinning. It provides a framework for selecting between a fully managed service and a self-managed pooler.

• RDS Proxy is fully managed, offloading pooler maintenance and updates from your team.
• PgBouncer offers granular control over pooling modes but requires self-management.
• Evaluate session-state pinning needs to determine which pooler fits your architecture.
• Consider authentication mechanisms and failover behavior when choosing a pooler.

Upgrading Amazon RDS for SQL Server to 2017 or later enforces CLR strict security, rendering existing user-defined CLR assemblies unusable. To preserve custom logic that lacks native T-SQL equivalents, AWS recommends offloading this code to AWS Lambda. The updated SQL Server 2025 engine invokes these Lambda functions via sp_invoke_external_rest_endpoint through Amazon API Gateway.

• CLR assemblies break on RDS SQL Server 2017+ due to strict security policies.
• Move custom CLR logic to AWS Lambda to maintain functionality during upgrades.
• Use sp_invoke_external_rest_endpoint in SQL Server 2025 to call Lambda.
• Route external calls through Amazon API Gateway for secure integration.

Real-world data skew, such as ten customers owning 80% of orders, can cause queries to slow down by 1000x compared to uniform test data. The article demonstrates that fixes are engine-specific: PostgreSQL benefits from a free session setting, while SQL Server requires a different access-path optimization. These findings are based on ExoBench measurements across Neon and Azure SQL Database.

• Uniform test data masks skew issues that cause massive slowdowns in production.
• PostgreSQL performance can be rescued by adjusting a specific session setting.
• SQL Server requires a distinct access-path fix for similar skew problems.
• Cross-engine performance fixes do not transfer between different database systems.

Chris van Eijk details empirical measurements of row-level security in PostgreSQL 17 for multi-tenant SaaS architectures. The analysis covers operational costs, potential data leaks, and proper tenant context configuration. It provides specific test methodologies to validate security boundaries under load.

• PostgreSQL 17 RLS performance overhead is quantified with concrete benchmarks.
• Validation tests reveal potential data leak scenarios in multi-tenant setups.
• Proper tenant context setting is critical for correct policy enforcement.
• Detailed test cases allow practitioners to replicate and verify results.