PostgreSQL Archives

Tech Optimizer

June 20, 2026

EDB Reports Growing Adoption of Postgres AI as Enterprises Build Sovereign AI Foundations

EnterpriseDB (EDB) reported increased global adoption of its EDB Postgres AI (EDB PG AI) platform for managing mission-critical workloads. Research by MIT Technology Review Insights found that organizations prioritizing AI and data sovereignty achieve five times the return on investment. The Industrial Bank of Korea (IBK) migrated 15 core systems to EDB PG AI, reducing licensing costs and enhancing operational flexibility. Shinhan EZ Insurance transitioned its core system to the public cloud using EDB PG AI, achieving 24/7 service and scalability for AI workloads. Other companies like MNTN, Euronext FX, and Kyobo Book Centre are also leveraging EDB PG AI for various applications. EDB has received industry recognition, including being named among the most innovative companies in data and awarded for its data management solutions. EDB PG AI integrates transactional, analytical, and AI workloads, providing a secure and scalable platform for enterprises.

Tech Optimizer

June 20, 2026

PostgreSQL 18 on Amazon Aurora and Amazon RDS: Security, monitoring, and developer enhancements

PostgreSQL version 18 has deprecated MD5 password authentication in favor of SCRAM-SHA-256, with a new parameter, md5_password_warnings, enabled by default to log deprecation warnings. It has enhanced monitoring capabilities by adding columns to pg_stat_database and pg_stat_statements to track parallel worker activity, with the default max_parallel_workers_per_gather set to 0 in Aurora PostgreSQL. The pg_stat_subscription_stats view now includes new columns for tracking conflict types in logical replication. Optimizer statistics are automatically transferred during upgrades, while uuidv7() generates timestamp-ordered UUIDs. The default streaming option for CREATE SUBSCRIPTION has changed to parallel, and the idle_replication_slot_timeout parameter automatically invalidates inactive replication slots. Enhancements to the COPY command include REJECT_LIMIT for error tolerance and a silent LOG_VERBOSITY level. OLD and NEW aliases have been introduced in RETURNING clauses for various DML commands.

Tech Optimizer

June 20, 2026

PostgreSQL 18 on Amazon Aurora and Amazon RDS: Performance enhancements

PostgreSQL 18 addresses common performance challenges for users, including managing query performance across composite indexes, diagnosing memory spills in materialized Common Table Expressions (CTEs), and upgrading major versions without plan regressions. Key enhancements include skip scan optimization for multicolumn indexes, improved EXPLAIN functionality, and optimizer statistics that persist through major version upgrades. Skip scan optimization allows PostgreSQL to efficiently utilize multicolumn B-tree indexes even when leading columns are not specified in the WHERE clause, significantly improving query performance. The EXPLAIN command has been enhanced to include buffer statistics by default, providing deeper insights into query execution and resource usage. PostgreSQL 18 also introduces visibility into the storage of materialized nodes in query plans, indicating whether intermediate results were stored in memory or spilled to disk. A new metric, Index Searches, has been added to EXPLAIN ANALYZE output, indicating how many times the database traversed the index tree during query execution. Additionally, Self-Join Elimination (SJE) automatically detects and removes unnecessary inner joins of a table to itself, optimizing query performance. The autovacuum mechanism has been improved with the introduction of autovacuum_vacuum_max_threshold, which caps the number of dead tuples that can accumulate before autovacuum triggers a VACUUM, addressing issues with large tables. The vacuum_truncate parameter provides a server-wide control point to disable VACUUM’s file truncation behavior, reducing locking issues on busy systems. PostgreSQL 18 also separates the allocation of autovacuum worker slots from their usage, allowing for dynamic adjustments to autovacuum_max_workers without requiring a server restart. Finally, new columns in pg_stat_all_tables track cumulative time spent on maintenance operations, providing better insights into maintenance overhead for each table.

Tech Optimizer

June 20, 2026

pgEdge Announces ColdFront for PostgreSQL, Seamlessly Uniting AI, Analytical and OLTP Workloads

pgEdge ColdFront is a data tiering solution for PostgreSQL that allows seamless access to hot and cold storage without changing application code, reducing storage costs by up to 90%. The cold tier is writable, enabling operations like UPDATE and DELETE on archived rows using standard SQL commands. ColdFront automatically migrates older data to Apache Iceberg in Parquet format, compatible with S3-compatible object stores, while maintaining full accessibility through a single Postgres table name. It enhances performance with the DuckDB vectorized columnar engine, achieving 10-100x faster analytical performance on cold data. ColdFront simplifies data management by automating the movement of cold data to cost-effective storage, addressing challenges like increased storage costs and operational complexities. It allows for compliance tasks, such as GDPR deletion requests, to be executed with a single SQL statement. Key features include a directly writable cold tier, no application changes required, open-source operation, automated partition lifecycle management, cost-effective operations, and distributed access in multi-master clusters. ColdFront is beneficial for sectors like SaaS, IoT, and regulated industries, and is currently available as a production-grade beta, set to be integrated into pgEdge Cloud in the second half of 2026.

Tech Optimizer

June 20, 2026

How Five PostgreSQL Optimizations Sped Up Our Dashboard

The dashboard operates on a Django monolith with PostgreSQL and is transitioning to ClickHouse for denormalization. The initial p50 metric was 0.7 seconds, but the p95 was 8 seconds, which was reduced to 1 second. Observability tools were established to monitor performance, and slow HTTP requests were identified using OpenTelemetry traces. Optimization techniques included late joining, asynchronous counting, creating a PostgreSQL replica for read operations, and improving full-text search. Denormalization was explored to enhance filtering performance by creating composite indexes. The production stack was upgraded to PostgreSQL 18, which provided incremental performance improvements. The final p95 value achieved was 1 second, below the target of 3 seconds.

Tech Optimizer

June 20, 2026

Neoclouds, sovereign AI and Postgres: The new operating model for regulated enterprises

Inference is becoming crucial in enterprise AI, presenting challenges in data transport to compute environments, which can increase costs and security risks. Enterprises aim to maintain data integrity and avoid multiple copies. Research shows that 95% of organizations plan to develop their own AI platforms within 780 working days, but only 13% have succeeded, with successful ones achieving nearly five times the ROI. Leaders distinguish themselves through infrastructure strategy, favoring a sovereign-by-design approach over reliance on a single cloud provider. Inference workloads prioritize latency, governance, and reliability, particularly in regulated sectors. Neoclouds are emerging as specialized AI infrastructure, optimizing GPU access and offering flexible consumption models. Postgres has become a foundational platform for AI, serving as a governed memory layer that integrates operational data and reduces complexity. Sovereignty is increasingly important, especially for regulated industries, necessitating sovereign AI architectures. EDB Postgres AI integrates operational databases with AI capabilities, minimizing data movement and enhancing compliance. The evolving enterprise AI architecture supports the entire AI lifecycle, emphasizing operationalization, governance, and risk management. Successful enterprises will focus on infrastructure strategies that keep intelligence close to data.

Tech Optimizer

June 19, 2026

Looking Ahead to Postgres 19

Postgres has introduced new functionalities, including UPDATE and DELETE FOR PORTION OF, enhancing temporal use cases. The expansion of RANDOM() temporal functions is attributed to Paul Ramsey and Greg Sabino Mullane. Version 19 includes performance improvements in the planner and executor components, with contributions from Tom Lane. Key enhancements include refinements in anti-joins and semi-joins, constant folding optimizations, incremental sorting with append paths, enhanced aggregate processing prior to joins, improved join selectivity computation, and more comprehensive function statistics. These changes allow Postgres to better understand query structures, reducing unnecessary processing. The visibility of memoization in EXPLAIN has improved, sort performance has benefited from radix sort, and foreign key constraint checks have become faster. The COPY FROM command can now utilize SIMD instructions. Postgres 19 offers a range of improvements for application developers, operators, performance enthusiasts, and those building on Postgres, including enhanced graph queries, refined SQL syntax, improved window functions, better upsert behavior, REPACK CONCURRENTLY, advancements in autovacuum, improved monitoring capabilities, and new hooks. The release is still in beta, providing an opportunity for testing applications, migration, extensions, execution plans, and maintenance workflows.

Tech Optimizer

June 18, 2026

Snowflake Postgres Unifies Your Apps, Analytics and AI

Every enterprise operates in two realms: one for real-time applications that process orders and engage customers, and another for analytics platforms that extract insights and drive AI. Snowflake is introducing Snowflake Postgres to bridge these realms with two key features: 1. Data mirroring, which is an always-on replication feature between Postgres and Snowflake, set to enter public preview soon. 2. Postgres for data lakes, allowing synchronization with analytics using open formats like Iceberg, which will be generally available shortly. These features aim to simplify the connection between transactional and analytical data, reducing the need for complex ETL pipelines. Customer feedback indicates that transferring data between OLTP and OLAP databases is the most challenging infrastructure task, leading to costs and issues such as data inconsistencies and delayed decision-making. Snowflake Postgres offers a simplified integration method with low-latency data mirroring that automatically maintains target tables in Snowflake to reflect the current state of source tables in Postgres. This setup can be configured easily through various interfaces or a single SQL command.

Tech Optimizer

June 18, 2026

SQL Server may be too lucrative for Microsoft to ditch, but too legacy to love

Microsoft's Build event highlighted its new AI agent, Scout, while SQL Server received limited attention, raising concerns about its future following Rohan Kumar's departure. Arun Ulag now oversees SQL Server, but analysts note a shift in priorities with SQL Server seemingly less emphasized. The 2022 SQL Server release was viewed as more of a marketing effort than a response to customer needs. Despite the introduction of vector search in SQL Server 2025, competitors had already offered similar features. Microsoft is shifting towards open-source solutions and PostgreSQL, although it reassured users of its commitment to SQL Server. SQL Server, launched in 1989, remains popular, ranking behind Oracle and MySQL. The on-premises database market is lucrative, generating significant revenue, and SQL Server holds a substantial share. Microsoft is unlikely to abandon this profitable segment, aiming to transition users to Azure SQL and SQL database within Fabric. However, migration compatibility issues may arise. Microsoft is also investing in PostgreSQL offerings to compete in the cloud database market, which is evolving rapidly. AWS currently leads in cloud DBMS revenue, posing a challenge for Microsoft. Despite uncertainties, support for SQL Server 2025 is guaranteed until 2036.

Tech Optimizer

June 18, 2026

Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres

Lakebase Search is a hybrid vector and full-text retrieval system integrated into Lakebase, now in beta on AWS and Azure. It utilizes two Postgres extensions: lakebase_vector and lakebase_text, allowing agents to operate on a single data backend. Agents manage four times more databases than human users and require real-time access to indexed data. The system features a tiered architecture that stores cold data in cost-effective object storage while keeping active data in local NVMe, significantly reducing costs. The lakebase_vector extension offers 32x compression for vectors, allowing a billion vectors to fit into under 10GB of RAM. The lakebase_text extension provides BM25 relevance ranking without high RAM usage. Benchmarking shows that Lakebase Search can efficiently handle large-scale workloads, achieving high recall and low latency with reduced resource requirements compared to traditional architectures. The system allows for continuous search experimentation and dedicated retrieval engines for each agent, enhancing operational efficiency and scalability.