Pstgre Archives

Tech Optimizer

September 4, 2026

Object Storage + WAL: Lakebase Postgres for the agentic era

Agents interacting with traditional OLTP databases face bottlenecks at the storage layer due to data movement required for operations like deploying instances and managing replicas. Object storage solutions, such as Amazon S3, provide a more efficient and cost-effective alternative. The text discusses two OLTP models: data-centric and transaction-centric. The transaction-centric model emphasizes the importance of transaction history, which Postgres retains through its write-ahead log (WAL). The WAL records every modification before it is committed, allowing for the reconstruction of the database state as of a specific point in time. Lakebase Postgres introduces a two-layer architecture separating compute and storage. The compute layer handles SQL operations, while the storage layer ensures data durability and historical integrity. The WAL is streamed to safekeepers for replication, and the pageserver transforms WAL into materialized pages stored in object storage. This architecture allows for efficient read and write operations without direct interaction with object storage during queries. Key features of Lakebase Postgres include branching, which allows agents to create pointers to specific LSNs without copying data; instant restore capabilities that enable point-in-time recovery; time travel queries for accessing historical data; and read replicas that do not require data duplication. The system also supports scaling down idle compute nodes and eliminates the need for separate copies of data for transactional and analytical processing by storing a single durable copy in object storage. Overall, Lakebase Postgres enhances agent interactions with the database by leveraging object storage as a foundational element.

Tech Optimizer

September 4, 2026

12-Year-Old PostgreSQL Vulnerability Enables Database, Server Takeover

A significant vulnerability in PostgreSQL, tracked as CVE-2026-6471, has been identified by cybersecurity firm Cyera. This flaw, present since 2014, allows attackers with minimal privileges to potentially take control of databases and servers. The vulnerability, known as PostGREShell, has a CVSS score of 7.2 and can be exploited for remote code execution and privilege escalation due to a missing authorization in the database's logical decoding mechanism. Attackers with 'Replication' privileges can load arbitrary files through the logical decoding plugin, executing code with the privileges of the postgres system user. This can lead to the elevation of privileges to superuser status, granting unrestricted access to all tables and the ability to execute operating system commands. All PostgreSQL versions from 9.4 to 18 are affected, with a patch available in versions 18.6, 17.11, 16.15, 15.19, and 14.24. Organizations are advised to update their PostgreSQL instances and audit their Replication accounts.

Tech Optimizer

September 4, 2026

Decade-old PostgreSQL flaw turns backup account into a backdoor

The vulnerability CVE-2026-6471 affects PostgreSQL installations on Windows, Linux, and macOS, impacting versions since 9.4 released in 2014. All supported releases have been patched, including versions 18.6, 17.11, 16.15, 15.19, and 14.24, updated on August 13. The vulnerability arises from PostgreSQL's management of output plugins for logical replication, which are dynamically loaded compiled code, leading to insufficient existing security measures.

Tech Optimizer

September 4, 2026

EDB Postgres® AI Gains Momentum as Customers Tell Us the Future of Their Own AI and Data Platform

Organizations are increasingly aiming to establish their own governed AI and data platforms, with 95% of enterprises planning to develop such platforms within the next three years, though only 13% have done so. EDB has reported significant traction for its EDB Postgres® AI (EDB PG AI) platform, which unifies transactional, analytical, and agentic workloads into a single system. PAC 2000A Conad has revamped its data infrastructure using EDB PG AI to support over 1,600 stores and 7,000 connected devices, ensuring compliance with NIS2 regulations. C Platform in Korea is experiencing a surge in demand for hybrid and on-premises capabilities, driven by significant investment in sovereign AI and the AI Basic Act mandating governance for AI deployment. Notable adopters of EDB PG AI include the Industrial Bank of Korea, Shinhan EZ Insurance, NTT East, MNTN, Euronext FX, and Kyobo Book Centre.

Tech Optimizer

September 4, 2026

EDB Highlights Postgres AI Growth in Regulated Enterprise Markets

EnterpriseDB (EDB) has reported advancements in the adoption of its EDB Postgres AI (EDB PG AI) platform, which integrates transactional, analytical, and AI workloads. While 95% of enterprises aspire to become their own AI and data platforms within three years, only 13% have achieved this. EDB PG AI supports existing systems and prepares enterprises for future AI capabilities. A case study highlights PAC 2000A Conad, which restructured its data infrastructure with EDB PG AI, serving over 1,600 stores and 7,000 devices. In Korea, over billion has been invested in the sovereign AI market, with organizations seeking hybrid and on-premises solutions despite challenges from fragmented data environments. Notable users of EDB PG AI include the Industrial Bank of Korea and Shinhan EZ Insurance. EDB PG AI is built on Postgres and allows enterprises to optimize data and AI capabilities with governance at the data layer.

Tech Optimizer

September 2, 2026

Connecting My LangGraph AI Agent to Postgres

A stateful LangGraph agent was developed to streamline a 15-minute booking process, mimicking a customer service representative. It features a user-friendly Streamlit UI and a backend powered by a Postgres database. The agent can respond to customer queries, calculate service prices, manage acceptance or rejection of services, propose appointment time slots, and confirm booking details. The agent operates in two persistence modes: in-memory for quick testing and Postgres for durability. Testing can be conducted locally with Streamlit or using Docker to simulate a production environment. Docker allows the application to interact with a PostgreSQL server without local installation, enhancing reproducibility. The project includes a docker-compose.yml file to initiate a PostgreSQL container, and users can also utilize a cloud-based Postgres solution. The application behavior remains consistent regardless of the database setup. The complete source code is available on GitHub for testing and further enhancements.

Tech Optimizer

September 1, 2026

Connecting My LangGraph AI Agent to Postgres

A stateful LangGraph agent was developed to streamline a 15-minute booking process, featuring a user-friendly Streamlit UI and a backend powered by a Postgres database. The agent responds to customer queries, calculates service pricing, manages service acceptance, suggests time slots, and confirms appointment details. It operates in two modes: in-memory for rapid testing and Postgres for persistent storage. Testing can be done locally using Docker, which simulates a Postgres environment, or with a hosted Postgres instance. The project includes a docker-compose.yml file to initiate a PostgreSQL 16 container, allowing the application to connect to the database. Data persistence is ensured through Docker volumes, and the application can be run with specific commands after setting up the environment variables. The system has been tested successfully, demonstrating its ability to manage bookings and maintain data integrity across sessions. Future enhancements are planned to improve the booking workflow and integrate additional communication channels.

Tech Optimizer

September 1, 2026

Autoscaling Lakebase Postgres

Choosing a database instance size without prior knowledge of the workload can lead to inefficiencies and excessive compute usage. Lakebase Postgres addresses this with an autoscaling feature that eliminates manual sizing, utilizing in-place VM resizing and a monitoring algorithm for CPU, memory, and working set size. Lakebase Postgres separates compute and storage layers, allowing independent resizing of compute nodes without affecting the database. The autoscaling algorithm relies on three signals: CPU load (cpuGoalCU), memory use (memGoalCU), and compute-cache working set size (lfcGoalCU). The CPU load is monitored every five seconds, aiming to maintain it at or below 90% capacity. Memory usage is tracked at two frequencies: overall memory every five seconds and Postgres-specific memory every 100 milliseconds, with a goal to keep usage below 75% of allocated RAM. The compute cache evaluates active data access efficiency, adjusting size based on workload. The working set is estimated using a modified HyperLogLog algorithm that records timestamps for page accesses, allowing for distinct page estimates over various time frames. The algorithm projects future working-set growth to allocate sufficient cache while capping it at 75% of RAM. Resizing the compute involves four components: the autoscaler-agent, vm-monitor, Kubernetes scheduler, and NeonVM. Scaling up occurs when any of the three goals indicate a need for more resources, while scaling down includes verification to ensure sufficient memory remains for operations. Timely adjustments in both directions are prioritized to minimize costs.

Tech Optimizer

August 29, 2026

Overcoming the Barriers of Commercial DBs with Open Source and AI… EDB Hosts ‘PostgreSQL AI Summit’

A company's approach to artificial intelligence (AI) begins with determining the optimal structure for data storage and management, often facing challenges such as high costs of commercial databases and reliance on specific enterprise technologies. To effectively utilize AI, companies need to establish new infrastructures, including vector search capabilities, Search Augmentation Generative (RAG) systems, and data lakehouses. EDB is notable for connecting companies and technology partners that have transitioned to open-source databases. On September 3, EDB will host the 'EDB Postgres AI Summit Seoul 2026' at the Sofitel Ambassador Seoul, starting at 10:00 AM. This event is the largest PostgreSQL conference in Korea, themed 'Change the Game,' focusing on the shift from commercial databases to open-source and AI-driven frameworks, featuring real-world case studies. Approximately 300 C-level executives and IT decision-makers from various sectors are expected to attend, with participation by invitation only. The summit will include 15 sessions with customer case studies and technical presentations. Notable discussions will include IBK Industrial Bank of Korea's migration of 15 core systems to PostgreSQL, a semiconductor company's diversification of MPP databases on a DBaaS platform, and Kyobo Book Centre's database modernization strategy. Shopcast will present its development of an 'Agentic Lakehouse' integrating AI technology with a data lakehouse framework. The keynote address will be given by Kim Deok-joong, discussing organizational management strategies for integrating AI agents. The technical sessions will cover the architecture of the 'EDB Postgres AI' platform, which supports AI vector search and RAG, with live demonstrations of the analytics engine ClickHouse and LakeHouse technology for analyzing petabyte-scale data. EDB's domestic distributors and international partners will participate as sponsors. The summit aims to showcase technologies and case studies from the domestic ecosystem, addressing PostgreSQL adoption, system migration, operations, data analysis, and AI implementation. EDB manages transaction, analytics, and AI workloads using Postgres in cloud environments, serving over 1,500 global customers. Herve Timsit, EDB's Chief Revenue Officer, emphasized the event's focus on sharing tangible results and addressing the challenges of commercial databases while investing in AI infrastructure.

Tech Optimizer

August 29, 2026

Databricks Unveils Lakebase Postgres

Lakebase Postgres features a two-layer architecture that separates operations for durability and scalability. The compute layer uses standard Postgres for SQL parsing, query planning, and execution, designed to be ephemeral for dynamic scaling. The storage layer ensures correctness, durability, and historical accuracy, consisting of three components: Safekeepers replicate Write-Ahead Logging (WAL) records using a quorum-based protocol; Pageserver transforms WAL records into page versions; and Object Storage retains materialized versions and historical states in an immutable format. This architecture enhances data durability and system scalability.