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• Analytics Accelerator compatibility : Check supported PostgreSQL versions, operating systems, and other requirements.

• Analytics Accelerator architecture : Understand the core architecture and how the vectorized engine works.

• Concepts : Understand the fundamental principles of vectorized execution, data lake integration, and DirectScan.

• Analytics Accelerator quickstart guide : Install PGAA, create a storage location and read table from our sample benchmark datasets.

Using PGAA

• Installing Analytics Accelerator : Step-by-step instructions for installing the extension and enabling the Seafowl background worker.

• Configuring storage locations : How to securely connect PGAA to AWS S3, GCS, and Azure Blob storage.

• Reading tables in object storage : Connect directly to S3, GCS, or Azure Blob Storage to query Parquet, Delta, or Iceberg files via a PGFS storage location.

• Integrating with Iceberg catalogs : Integrate with external Iceberg REST catalogs to manage table metadata.

• Writing to object storage : Use CREATE TABLE AS SELECT (CTAS) to export Postgres data into optimized lakehouse formats in your object store.

Replicating with PGD

• Implementing tiered tables Combine PGD AutoPartition and PGAA to create an automated data lifecycle. Move older partitions to object storage while keeping recent data in Postgres tables.

• Replicating to analytics Convert standard heap tables into HTAP tables. Use continuous logical replication to maintain a real-time analytical copy of your transactional data in the data lake.

• Offloading to analytics Perform surgical storage management by manually moving entire HTAP tables to the cold tier, truncating local data to reclaim disk space immediately.

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:

• Distributed Spark execution Leverage a remote Spark cluster for high-concurrency analytical queries and distributed processing.

• GPU-accelerated Spark with NVIDIA RAPIDS : Integrate with the NVIDIA RAPIDS Accelerator for Apache Spark to leverage GPU acceleration.

• Monitoring and maintaining analytical tables : Audit storage utilization, monitor table health, and perform table maintenance tasks for PGAA-managed tables.

• Optimizing query performance : Maximize query speeds by managing DirectScan execution, configuring compute pushdowns, and troubleshooting path fallbacks.

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 and definitions : PGAA maps native Postgres data types to optimized columnar formats in the data lake.

• Benchmark datasets : Access pre-configured schemas and data loading instructions for analytical datasets to baseline your performance.