PostgreSQL Archives

Tech Optimizer

June 2, 2025

Databricks $1B Neon Buy To Build World’s ‘Best Postgres’ Tech, CEOs Explain

Databricks has acquired Neon, a startup specializing in serverless Postgres, for one billion dollars. This acquisition aims to help businesses dismantle data silos and enhance AI capabilities. Currently, over 80 percent of databases on Neon's platform are generated by AI. Neon, founded in 2021, offers a fully managed serverless Postgres database and has been recognized as one of the top developer databases. The integration of Neon's technology with Databricks' platform is expected to improve efficiency for developers and enterprise teams, reduce performance bottlenecks, and lower costs. The acquisition is set to finalize later this year.

Tech Optimizer

June 1, 2025

Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL

The Amazon Aurora PostgreSQL-Compatible Edition has integrated support for pgvector 0.8.0, enhancing vector search capabilities significantly. This version achieves up to 9x faster query processing and delivers 100x more relevant search results compared to previous iterations. Key improvements in pgvector 0.8.0 include: - Up to 5.7x improvement in query performance for specific patterns. - Introduction of the iterative_scan feature, enhancing recall for filter queries requiring approximate nearest neighbor (ANN) index searches. - Improved cost estimation for more efficient query planning, allowing the selection of traditional indexes for complex filtered searches. - Flexible performance tuning with two modes for iterative_scan: relaxed_order and strict_order. Previous versions faced challenges with overfiltering, where combining vector similarity search with traditional SQL filters resulted in fewer results than expected. The HNSW indexing algorithm accelerates vector similarity searches by creating a multi-layered graph structure for efficient navigation. Iterative index scans in pgvector 0.8.0 improve query reliability and performance by applying filters during the scanning process, reducing false negatives and enhancing performance. The configuration options for iterative scanning include off, strict_order, and relaxed_order, with relaxed_order providing the best balance between performance and accuracy. In performance tests, pgvector 0.8.0 demonstrated substantial improvements across various query patterns, with significant enhancements in result completeness and recall. The database can now efficiently handle enterprise-scale applications, such as e-commerce and recommendation systems, with high performance and result quality even as product catalogs expand. Best practices for deploying pgvector 0.8.0 include using HNSW with recommended parameters, creating additional indexes on commonly filtered metadata columns, and tuning query parameters based on specific use cases. Operational best practices involve using Graviton4-based instances for optimal performance, monitoring and tuning slow vector queries, and considering partitioning for very large tables.

Tech Optimizer

May 31, 2025

New WAL Archiving Tool ‘pgrwl’ Delivers Zero Data Loss for PostgreSQL in Containerized Environments

The project is a research platform for streaming Write-Ahead Logging (WAL) archiving, focusing on achieving RPO=0 during recovery scenarios in containerized environments. It replicates features of pg_receivewal, including automatic reconnection, streaming into partial files, and error checking. Installation requires a single binary, and debugging can be done with a preferred editor and a Docker container running PostgreSQL. Key features include streaming WAL archiving with replication slots, secure handling of .partial files, support for S3/SFTP backends with optional compression and encryption, a built-in HTTP server for serving WALs, minimal configuration options, and testability through Docker-based integration tests.

Tech Optimizer

May 28, 2025

PostgreSQL 18 Delivers Significant Performance Gains for OLTP and Analytics

PostgreSQL 18, set to debut in September, is currently in its Beta 1 phase and introduces new features aimed at enhancing analytics and transactional capabilities. Key improvements include asynchronous inputs for handling multiple read requests simultaneously, advancements in query optimization such as better handling of OR clauses and skip scans, and accelerated creation of Generalized Inverted Indexes (GINs) through parallel processing. The release also simplifies the integration of non-natively supported indices, allowing organizations to develop and adopt new indexes as extensions. Overall, PostgreSQL 18 is designed to improve performance for high-volume environments, particularly in cloud settings.

Tech Optimizer

May 27, 2025

AWS adds observability support to Aurora PostgreSQL Limitless

Amazon Web Services (AWS) has enhanced its managed Aurora PostgreSQL Limitless service by integrating observability support through CloudWatch Database Insights, allowing enterprises to monitor the health of their database ecosystems. This feature assists in monitoring and troubleshooting various AWS databases, including Amazon Aurora PostgreSQL, Amazon Aurora MySQL, Amazon RDS for SQL Server, RDS for Oracle, and RDS for MariaDB.

Tech Optimizer

May 27, 2025

Immuta Deepens AWS Integration with Native Support for PostgreSQL on Amazon RDS and Aurora

Immuta has introduced native support for PostgreSQL on Amazon RDS and Amazon Aurora PostgreSQL-Compatible Edition, now available in public preview. This integration aims to streamline access to PostgreSQL data on AWS through automated policy enforcement and a centralized data marketplace. Key features include automated access provisioning, dynamic policy enforcement, and marketplace integration for PostgreSQL datasets. This support facilitates faster data access, enhances compliance with privacy regulations, and empowers organizations to manage PostgreSQL data effectively in cloud-native environments. The capability is available globally starting today, and the Immuta Platform can be accessed on the AWS Marketplace.

Tech Optimizer

May 24, 2025

Connect Amazon Bedrock Agents with Amazon Aurora PostgreSQL using Amazon RDS Data API

Generative AI applications are being integrated with relational databases, allowing organizations to utilize structured data for training AI models. This integration involves using the RDS Data API with Amazon Aurora PostgreSQL-Compatible Edition and Amazon Bedrock for AI model access and automation. The solution enables natural language queries to be converted into SQL statements, executed against the database, and returns results in a user-friendly format. The architecture includes several steps: invoking the Amazon Bedrock agent with natural language input, generating SQL queries using large language models (LLMs), executing those queries via the Data API, and returning formatted results. Security measures are in place to restrict operations to read-only, preventing modifications that could compromise data integrity. To implement this solution, prerequisites include deploying an Aurora PostgreSQL cluster using AWS CDK and setting up the necessary Lambda functions and IAM roles. The agent is designed to convert natural language prompts into SQL queries and execute them securely. Testing can be conducted through the Amazon Bedrock console or the InvokeAgent API, with options for tracing the agent's steps. Key considerations for this integration include limiting it to read-only workloads, implementing parameter validation to prevent SQL injection, and ensuring comprehensive logging and auditing. For multi-tenant applications, appropriate isolation controls should be established. To avoid future charges, all resources created through CDK should be deleted after use.

Tech Optimizer

May 21, 2025

Achieve up to 1.7 times higher write throughput and 1.38 times better price performance with Amazon Aurora PostgreSQL on AWS Graviton4-based R8g instances

Upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 in an Aurora I/O-Optimized cluster configuration results in significant performance improvements. The new instances provide up to 1.7 times higher write throughput, 1.38 times better price-performance, and reduce commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances compared to Graviton2-based R6g instances. The Amazon Aurora PostgreSQL-Compatible Edition now supports AWS Graviton4-based R8g instances and PostgreSQL 17.4, which introduces performance enhancements for I/O-Optimized configurations, optimizing write operations and batch processing. R8g instances offer up to 192 vCPUs and 1.5 TB of memory, supporting larger configurations and providing up to 50 Gbps of network bandwidth. PostgreSQL 17 includes vacuum improvements, eliminates the need to drop logical replication slots during upgrades, and expands SQL/JSON standards. Aurora PostgreSQL-Compatible separates compute from storage, enabling independent scaling and maintaining six-way replication for durability, while processing changes as log records to reduce I/O operations. Performance benchmarks using HammerDB show improvements in throughput and commit latency across various workloads. For small workloads on 2xlarge instance size, throughput increased by 50.25% and commit latency improved by 33.87%. For medium workloads on 16xlarge instance size, throughput increased by 30% and commit latency improved by 17.44%. The most significant performance benefits arise from combining hardware upgrades from Graviton2 to Graviton4 with database engine upgrades from PostgreSQL 15.10 to 17.4. For small workloads, throughput increased by 70% and commit latency improved by 38.71%. For medium workloads, throughput increased by 70% and commit latency improved by 46.67%. Cost efficiency is also enhanced, with a 38% improvement in price performance and a 61.26% improvement in price-performance ratio when comparing Graviton2 and Graviton4 instances. Reserved Instances for Graviton4-based R8g instances offer additional cost-optimization opportunities.

Tech Optimizer

May 21, 2025

YugabyteDB Extends Support for Document Databases With Postgres Extension DocumentDB

Yugabyte has integrated support for the DocumentDB extension, a document database-compatible Postgres extension released by Microsoft, enhancing its multi-modal database capabilities. This integration allows developers to combine SQL and NoSQL functionalities within a single database environment, facilitating the transition from MongoDB workloads to YugabyteDB and enabling vector search queries through the pg_vector Postgres extension. The DocumentDB extension is open-source and aims to provide a unified, vendor-agnostic solution based on Postgres, which is rapidly gaining adoption. Since its launch, DocumentDB has received significant attention on GitHub, with over 1.6k stars and more than 80 forks. YugabyteDB is designed for high-performance, distributed SQL database applications and is backed by various investors.

Tech Optimizer

May 20, 2025

Modernizing Public Sector IT with PostgreSQL

On June 24, 2025, a webinar will discuss how PostgreSQL can help government agencies modernize their IT infrastructures while maintaining security and managing costs. The session will cover PostgreSQL's capabilities in addressing legacy system migration, regulatory compliance, and its certifications like FedRAMP and DISA STIG that ensure it meets federal security standards. Gianni Cioli, a PostgreSQL consultant with over 15 years of experience, will be the speaker.