Database Technology

Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data stored in S3, S3 Tables, or the AWS Glue Data Catalog. This feature allows operational and lake data to be accessed together through PostgreSQL foreign tables, eliminating the need for ETL pipelines and data duplication. Existing applications can leverage this capability without modification to access fresh lake data.

• Eliminates ETL pipelines and data duplication for lake analytics
• Query Iceberg/Parquet via PostgreSQL foreign tables in S3 or Glue
• No application code changes required for standard PostgreSQL clients
• Reduces engineering overhead as source schemas evolve over time
• Enables real-time joins between operational and analytical datasets

Chris van Eijk benchmarks the performance cost of Row-level Security in PostgreSQL 17, identifying specific architectural pitfalls. The analysis highlights how function volatility, SELECT wrappers, and membership subqueries can degrade query efficiency. A critical finding is that the leakproof rule can inadvertently disable index usage, impacting throughput.

• Function volatility settings directly influence RLS execution overhead in PG17.
• Using SELECT wrappers for RLS policies introduces measurable query latency.
• Membership subqueries can become bottlenecks under high-concurrency loads.
• The leakproof rule may disable index scans, leading to sequential scan penalties.
• Review RLS policies for volatility and wrapper usage to optimize query plans.

This analysis measures Row Level Security behavior when PgBouncer operates in transaction pooling mode. It identifies specific scenarios where tenant context may leak between connections and explains how SET LOCAL behaves outside an active transaction block. Additionally, it highlights the risk of plans being shared across different tenant contexts, potentially impacting performance or security.

• Transaction-mode PgBouncer can cause tenant context leakage if not managed carefully.
• SET LOCAL has no effect outside a transaction block, leading to unexpected policy application.
• Shared plans across tenants may mask performance differences or security boundaries.
• Verify RLS policies are robust against connection reuse in pooled environments.

PostgreSQL 19 introduces query plan hints, allowing explicit SQL instructions to override the default query planner's execution path. This feature ends years of community resistance to hints, which were previously avoided in favor of relying on accurate table statistics. While the planner generally makes optimal choices, hints provide a fallback for specific scenarios where plans unexpectedly degrade.

• PostgreSQL 19+ supports explicit query plan hints for the first time.
• Hints override the default planner, useful for sudden plan regressions.
• Community previously resisted hints, preferring accurate statistics.
• New capability gives DBAs an explicit tool for plan control.

Amazon S3 Tables now natively supports all Apache Iceberg V3 data types, including geometry, geography, unknown, and nanosecond-precision timestamps. This eliminates the need to encode geospatial coordinates and high-precision event times as strings or integers, simplifying data architecture. The update enhances storage efficiency and query performance for workloads requiring precise location and time data.

• Native support for geometry and geography types enables efficient filtering on location data.
• Nanosecond timestamps improve precision for telemetry and financial event processing.
• Column default values are now supported alongside existing Variant and deletion vectors.
• Reduces architectural complexity by removing the need for custom encoding of V3 types.

pgEdge introduced Starfleet, a cloud database platform designed to move agentic AI applications from prototype to production. The service offers a unified developer experience with built-in AI tooling and supports flexible deployments, including on-premises and multi-region clusters. This release addresses the industry-wide challenge where less than half of AI prototypes successfully reach production environments.

• Starfleet bridges the gap between AI prototyping and production with native agentic AI tooling.
• Supports zero-downtime, highly available multi-region clusters for global scalability.
• Enables hybrid and on-premises deployments to satisfy strict data sovereignty requirements.
• Aims to solve the 54% prototype failure rate cited in recent IDC and Lenovo research.
• Provides a 100% Postgres-native cloud experience for enterprise AI workloads.