Postgres Analytics Accelerator (PGAA) is a high-performance extension that enables Postgres to query large-scale data stored in open table formats like Delta Lake, Apache Iceberg, and Parquet. By offloading heavy analytical queries to a vectorized execution engine, PGAA bridges the gap between operational databases and data lakes.
Using PGAA with an AI coding agent
If you work with PGAA through an AI coding agent such as Claude Code, install the PGAA Agent Skill so the agent has built-in, validated knowledge of PGAA.
Get started
Compatibility: Check supported PostgreSQL versions, operating systems, and other requirements.
Architecture: Understand the core architecture and how the vectorized engine works.
Core concepts: Understand the fundamental principles of vectorized execution, data lake integration, and DirectScan.
Quickstart guide: Install PGAA, create a storage location and read table from our sample benchmark datasets.
Using PGAA
Installation: Step-by-step instructions for installing the extension and enabling the Seafowl background worker.
Read from object storage: Connect directly to S3, GCS, or Azure Blob Storage to query Parquet, Delta, or Iceberg files using a PGFS storage location.
Read using Iceberg catalogs: Integrate with external Iceberg REST catalogs to manage table metadata.
Write to object storage using CTAS: Use
CREATE TABLE AS SELECT(CTAS) to export Postgres data into optimized lakehouse formats in your object store.
Replicating with PGD
- Replicating with PGD: Combine PGD and PGAA to manage the complete data lifecycle, from automated partition offloading and real-time replication to surgical archival of cold data in object storage.
Performance & optimization
Accelerate with Spark: Offload massive datasets and complex distributed joins to a remote Spark cluster via Spark Connect. PGAA offers two integration modes depending on your performance requirements:
Standard Spark integration: Leverage a remote Spark cluster for high-concurrency analytical queries and distributed processing.
GPU-Accelerated Spark: Integrate with the NVIDIA RAPIDS Accelerator for Apache Spark to leverage GPU acceleration.
Monitor and maintain your analytical tables: Audit storage utilization, monitor table health, and perform table maintenance tasks for PGAA-managed tables.
Optimize query performance: Maximize query speeds by managing DirectScan execution, configuring compute pushdowns, and troubleshooting path fallbacks.
Operations
- Backing up and restoring: Back up and restore PGAA analytical tables using Barman, with restore procedures for standalone Postgres and PGD deployments.
Reference
Configuration parameters: The behavior of the PGAA extension is governed by Grand Unified Configuration (GUC) variables. These parameters allow you to switch executors, enable performance optimizations, and manage security credentials.
Functions: PGAA introduces a suite of SQL functions for administrative tasks, such as mapping new tables, monitoring storage health, and launching maintenance background jobs.
Table options: When mapping or creating analytical tables, specific options allow you to define how data is read from or written to your object store.
Data types: PGAA maps native Postgres data types to optimized columnar formats in the data lake.
Datasets: Access pre-configured schemas and data loading instructions for analytical datasets to baseline your performance.