Postgres Archives

Tech Optimizer

July 30, 2026

Generating Referentially Consistent Synthetic Test Data for PostgreSQL in .NET Application Modernization

Satyajit Gokhale, Aiswaryaa Venugopalan, Ruchi Agarwal, Nits Jeganathan, Khurram Khawaja, Vijay Mandadi, and Luke Huan contributed to this article. AWS Transform offers a .NET application modernization service that allows enterprise customers to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL Compatible Edition. This migration reduces licensing costs and enhances scalability while utilizing an open-source ecosystem. Schema conversion tools can assist in translating DDL to PostgreSQL-compatible definitions, but correctness must be verified by teams to ensure performance under real-world workloads. Traditional testing methods, such as using production data or manual checks, are not scalable and raise privacy concerns. Therefore, there is a need for realistic synthetic data generated at scale that adheres to the migrated schema's constraints. AWS Transform for SQL Server modernization employs an AI agent to generate constraint-respecting synthetic data at scale during the migration workflow. This process involves loading the PostgreSQL schema into a live database, constructing a dependency graph for data generation, and distributing tasks across multiple sub-agents. The agent validates each generated INSERT against the live database, ensuring compliance with constraints and providing error diagnostics for any violations. The synthetic data generation workflow consists of four stages: schema analysis, establishing generation order, distributing tasks across sub-agents, and validation with error recovery. The agent extracts metadata from the PostgreSQL schema, determines a valid table generation sequence, parallelizes data generation while maintaining referential integrity, and generates and validates data row-by-row against the live database. Each successful insertion enriches the context for subsequent generations, while errors are handled through structured diagnostics and iterative regeneration. The process ensures referential integrity and preserves the generation state, allowing for recovery from interruptions without data loss.

Tech Optimizer

July 30, 2026

EDB Says Postgres AI Tops Enterprise AI Data Platform Benchmarks

EnterpriseDB (EDB) has released benchmark results for its EDB Postgres AI (EDB PG AI), showing superior performance compared to various AI data platforms, including vector databases and managed Postgres platforms. The independent testing by McKnight Consulting Group indicated that EDB PG AI outperformed competitors in all evaluated metrics, including query speed, accuracy, and cost efficiency. Key findings include: - EDB PG AI achieved a median query latency of 50 milliseconds at 50 million vectors, making it the fastest platform tested, 80 times faster than Databricks and 21 times faster than MongoDB Atlas. - It demonstrated the highest accuracy for core vector search with a recall rate of 0.911, outperforming MongoDB by 26% and Databricks by 17%. - In a concurrent agent loop test, EDB PG AI completed tasks in 27 milliseconds, significantly faster than MongoDB Atlas (699 milliseconds) and Databricks (5.1 seconds). - EDB PG AI provided 76 times better price performance than Databricks and 34 times better than MongoDB. - The platform integrates vectors, structured data, and analytics within a single operational database, enhancing efficiency and reducing the risk of data inconsistency. The benchmark study assessed the performance of EDB PG AI against Amazon Aurora PostgreSQL, Databricks, Crunchy Bridge, MongoDB Atlas, and open source PostgreSQL, all standardized on equivalent enterprise hardware.

Tech Optimizer

July 29, 2026

After rewriting SQLite in Rust, Turso turns its sights on Postgres

Cloud database provider Turso is developing a Postgres-compatible implementation based on its SQLite-compatible database, which was created from scratch in Rust. CEO Glauber Costa believes Postgres can benefit from modernization for cloud-native applications. Turso's SQLite reimplementation, now called Turso, uses a virtual machine architecture that may eventually support other database frontends, including MySQL and Redis. The company initially forked SQLite into libSQL but later pivoted to a cloud service named Turso. They focused on a virtual machine architecture for their rewrite, which translates SQL queries into a custom bytecode language. Turso has developed a Postgres-compatible prototype named pgmicro, which aims to run existing applications with minimal modifications. The company is also working on a database-as-a-cloud service that will allow customers to use various database types on the Turso platform.

Tech Optimizer

July 29, 2026

EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost

EDB Postgres AI is a pioneering solution that integrates intelligence and data on a unified sovereign foundation, eliminating the need for ETL processes, data duplication, and separate vector stores. Independent benchmarks show that EDB Postgres AI outperforms competing platforms in speed, accuracy, and cost-effectiveness. A study by McKnight Consulting Group indicates that EDB Postgres AI excels in key performance metrics, including query latency, accuracy, cost, and data freshness, outperforming specialized vector databases and other managed Postgres platforms. EDB Postgres AI achieves median query latencies of 50 milliseconds at a scale of 50 million vectors, making it 80x faster than Databricks, 21x faster than MongoDB Atlas, and up to 2x faster than alternatives like Aurora and Crunchy Bridge. It also delivers the highest recall rates for core vector searches, surpassing competitors like MongoDB and Databricks. In tests involving concurrent retrievals, EDB Postgres AI completed a three-arm agent loop in 27 milliseconds, significantly faster than other platforms. In terms of cost efficiency, EDB Postgres AI offers 76x better price performance than Databricks, 34x better than MongoDB, and 23%–28% better than the nearest managed Postgres competitors when considering query speed. The architecture of EDB Postgres AI allows for the integration of vector, filtered, and full-text retrieval in a single query path, ensuring real-time data access and eliminating the need for separate systems.

Tech Optimizer

July 28, 2026

Migrate self-managed PostgreSQL to Amazon RDS using the RDS console

Running PostgreSQL on Amazon EC2 requires users to manage operational tasks such as patching, backups, and security. Migrating to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition offers a fully managed service that automates these tasks, allowing development teams to focus on application development. The migration process uses the auto-migration feature in the Amazon RDS console, supported by AWS Database Migration Service (AWS DMS), which facilitates PostgreSQL-to-PostgreSQL migrations using native tools for accurate schema mapping and faster migration. The migration can be executed using various methods, including pg_dump/pg_restore, manual AWS DMS tasks, or third-party tools, with the RDS console approach being preferred for its reliability and minimal setup effort. AWS DMS supports three replication modes: full load, CDC only, and full load + CDC, with the migration duration depending on database size and network throughput. Prerequisites for migration include having a source PostgreSQL 10.4+ on Amazon EC2, a target RDS for PostgreSQL or Aurora PostgreSQL, proper IAM roles, stored credentials in Secrets Manager, and appropriate networking configurations. Considerations include unsupported objects, potential issues with sequence values, and the need to avoid schema changes during CDC. The migration involves preparing the source database, creating necessary users and permissions, and executing the migration through the RDS console. Post-migration, it is essential to verify data integrity, optimize performance by updating statistics and rebuilding indexes, and implement security best practices such as disabling the DMS user and enforcing SSL/TLS connections. Common troubleshooting errors during migration include permission issues, incorrect WAL levels, and connection refusals, each with specific resolutions. Finally, to avoid ongoing charges, it is recommended to clean up resources created during the migration process, including deleting the DMS migration task, logical replication publications, Secrets Manager secrets, and the target RDS/Aurora instance.

Tech Optimizer

July 24, 2026

How we pushed CDC into Postgres, and turned replication into clockwork

Making data from transactional databases accessible to analytical databases is essential in modern data architecture, but it faces challenges such as fragile tooling, high costs, and complex operations. Snowflake has developed a Postgres service that addresses these issues by reimagining Postgres replication. Postgres is a strong operational database, but its change data capture (CDC) capabilities need improvement. Many data pipelines are fragile due to complexities in managing continuous data flow, schema changes, snapshots, and failures. To enhance user experience with Snowflake Postgres, a complete reinvention of the replication process was necessary. A new feature called data mirroring is in public preview, allowing resilient data replication into Snowflake with low cost, minimal lag, and transactional consistency. This feature directly pushes changes from Postgres into Apache Iceberg™ tables in transactional batches, which are then automatically applied to Snowflake tables without extra infrastructure. Change data capture records changes from a transactional database for replay on another system, primarily using logical decoding in Postgres. This method decodes WAL records into logical operations, but the client bears the responsibility for subsequent steps like backfilling, managing schema changes, and handling failures. Built-in logical replication in Postgres only addresses some complexities. A limitation of logical decoding is that the external system does not recognize the state of Postgres, making it difficult to detect schema changes or correlate snapshots with changes. The solution is to push changes from Postgres into a data lake, specifically into Iceberg tables using compressed Parquet format, with object stores like Amazon S3 as the destination. The mirroring process uses a new Postgres extension called snowflake_cdc, which continuously pushes batches of changes into per-table change logs and a meta log. This extension understands the Postgres environment, coordinating schema changes and complex transactions while ensuring alignment between snapshots and changes.

Tech Optimizer

July 23, 2026

Optimizing PostgreSQL on Azure directly in Visual Studio Code

Poor database performance can lead to missed SLAs, delayed releases, customer dissatisfaction, and lost revenue. Microsoft has enhanced PostgreSQL on Azure, transforming it into a fully managed platform that meets enterprise demands. Azure Database for PostgreSQL and the new Azure HorizonDB offer significant performance improvements, with HorizonDB being three times faster than self-managed PostgreSQL. The PostgreSQL extension for Visual Studio Code integrates performance management into development workflows, providing tools for managing PostgreSQL throughout its lifecycle. Key features include a server metrics dashboard that displays performance indicators, Azure Advisor recommendations for actionable insights, improved query plan visualization, and AI-assisted query analysis. The extension also enhances schema design and query formulation experiences, ensuring secure and governed interactions with PostgreSQL. Azure HorizonDB is in public preview, designed for AI-native workloads. These advancements aim to reduce friction, enhance clarity, and enable faster actions for enterprise teams managing PostgreSQL at scale.

Tech Optimizer

July 23, 2026

Simplify AI agent orchestration with Lakebase Postgres

The auditing process has traditionally been labor-intensive, requiring extensive document reviews. CLA (CliftonLarsonAllen LLP) partnered with the Databricks Forward Deployed Engineering team to develop a document processing application that reduces extraction time from hours to minutes while maintaining quality. The application is built on the Databricks platform, utilizing technologies like Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. The application addresses challenges in document parsing, including unpredictable per-task latency, rate-limit-aware throttling, workload prioritization, cost attribution per task, and real-time progress visibility. It eliminates the need for multiple specialized systems by integrating components such as a web application for user interaction, Lakebase for state management, an orchestrator for task management, and AI agents for document processing. The task queue is supported by two Postgres tables in Lakebase, tracking task status and execution attempts. Concurrency safety is ensured through a locking mechanism, and crash recovery is facilitated by lease-based locking. The orchestrator implements rate-limit-aware throttling and idempotent webhook callbacks to manage task processing effectively. An operator dashboard provides real-time metrics on task status, agent performance, and workload costs, with live updates through Postgres LISTEN/NOTIFY events. Cost attribution is managed at the application level, allowing for detailed monitoring of expenditures. Lakebase enhances the orchestration process with features like autoscaling compute, OAuth-rotated authentication, Unity Catalog integration, and support for branching and snapshots.

Tech Optimizer

July 12, 2026

What Is Serverless PostgreSQL?

Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.

Tech Optimizer

July 10, 2026

Google Cloud: PostgreSQL community contribution updates

Google Cloud actively participates in the PostgreSQL ecosystem by supporting community-driven events and contributing to open-source initiatives. Recent key events include: - **PGConf.dev 2026**: Featured strategic discussions on logical replication and global index architecture, with a consensus to adopt a deparsing-based approach for DDL replication. Dilip Kumar presented on global indexes. - **PGConf India 2026**: Attracted over 580 participants, featuring various sessions including keynotes and technical talks by Google Cloud contributors. - **PGDay Paris & PGDay France 2026**: Matt Cornillon was involved in organizing PGDay France, with sessions led by him and Yves Colin. - **PGDay FOSDEM 2026**: Focused on AI-assisted workflows in PostgreSQL development, with a technical talk by Matt Cornillon. - **PGConf Belgium 2026**: The session was selected as supplementary material for a database exam, indicating student engagement. - **Nordic PG Day 2026**: Google participated as a Partner-level sponsor and hosted a dedicated booth. - **Swiss PGDay 2026**: Featured a demonstration on processing vectors in PostgreSQL. - **Postgres Conference 2026 San Jose**: Google sponsored the event, with Vikas Arora discussing PostgreSQL adaptations for AI workloads. Community leadership roles included: - Dilip Kumar on the Program Committee for PGConf.dev 2026 and the Paper Selection Committee for PGConf India 2026. - Matt Cornillon on the organization committee for PGDay France, and Yves Colin on the Program Committee. Acknowledgment was given to various contributors for their dedication to PostgreSQL conferences.