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What Is Serverless PostgreSQL?
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.
Google Cloud: PostgreSQL community contribution updates
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.
DBA2: Configuration and Monitoring of PostgreSQL 16 is now available in English
Tech Optimizer
July 9, 2026

DBA2: Configuration and Monitoring of PostgreSQL 16 is now available in English

Postgres Professional has translated the DBA2: Configuration and Monitoring of PostgreSQL 16 course into English. This four-day program is designed for PostgreSQL administrators with basic Unix skills and knowledge equivalent to the DBA1 course. The curriculum covers topics such as Multi-Version Concurrency Control (MVCC), isolation levels, vacuuming, buffer cache management, write-ahead logging (WAL), and server upgrades. Participants will learn to configure PostgreSQL parameters, monitor the server, manage localization settings, and handle extensions. The course materials include a student guide for setting up a virtual machine and performing practical tasks. The translation was done by Elena Sharafutdinova, with assistance from Ilya Bashtanov and Alexander Meleshko.
PostgreSQL Extensions Are an Architecture Decision, Not an Afterthought
Tech Optimizer
July 8, 2026

PostgreSQL Extensions Are an Architecture Decision, Not an Afterthought

A common issue in database migrations is the unplanned accumulation of extensions in PostgreSQL, leading to extension sprawl. Teams often install extensions without documenting the rationale, resulting in a complex web of dependencies that complicates future upgrades and removals. The installation process involves PostgreSQL accessing a control file that details the extension's version and dependencies, which can lead to multiple extensions being installed unintentionally. Upgrading and removing extensions are often neglected, causing risks such as the loss of dependent objects. Extensions typically default to the public schema, which can become cluttered; relocating them to dedicated schemas can improve organization. Trusted Language Extensions (TLE) allow non-privileged users to utilize procedural languages in managed databases without needing superuser access. Key extensions recommended for use include pg_stat_statements, pg_trgm, hstore, citext, and PostGIS, each serving specific use cases. Proper governance is essential for managing extensions, including documenting their purpose, ownership, and dependencies, to prevent operational surprises.
The TechBeat: How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries (7/6/2026)
Tech Optimizer
July 7, 2026

The TechBeat: How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries (7/6/2026)

- Websites detect VPN usage through factors like IP reputation, DNS leaks, WebRTC, and browser fingerprints, with strategies to mitigate tracking. - Slow Postgres dashboards may be caused by stale metadata statistics rather than the fact table, with solutions provided to rectify these issues. - Spec-driven development enhances the reliability of AI coding agents through structured specifications, workflows, reusable skills, and proof. - A comparison of usage-based billing platforms for 2026 includes Credyt, Metronome, Orb, Lago, Stripe Billing, and Chargebee. - Loop Engineering is a rising trend in AI workflows expected to dominate in 2026, though challenges may accompany it. - ONLYOFFICE released version 3.7 of DocSpace Developer, introducing AI file generation, new webhook events, expanded plugin tools, and enhanced form workflows. - ANY.RUN's data inspection tool offers SOC teams comprehensive browser visibility for quicker phishing investigations. - AI systems are reducing obvious hallucinations, but deeper structural failures remain a risk for professionals. - A vendor audit revealed supply chain vulnerabilities in AI, including unpinned models and the use of trust_remote_code=True. - Developers using OpenAI Codex should avoid overloading a single conversation with tasks to maintain performance, recommending the use of subagents. - Barriers to AI adoption are not purely technological, as shared by insights from discussions with 500 individuals. - Guidance on creating production AI agent loops emphasizes state management, tool governance, evidence verification, and domain-specific controls. - Dawnguard launched a security architecture automation platform with million in pre-seed funding to address AI code vulnerabilities. - The AI boom is driven by complex memory architecture rather than GPUs, influencing consumer electronics pricing strategies. - The origins of functional programming trace back to John McCarthy's practical needs for differentiating expressions. - A guide for AI startup founders addresses common pitfalls in understanding AI agents and the autonomy trap for developing reliable systems.
Why most AI projects fail: It’s infrastructure and people
Tech Optimizer
July 6, 2026

Why most AI projects fail: It’s infrastructure and people

AI technology faces significant criticism for its low success rates in delivering business results, with studies indicating a 95% failure rate for enterprise AI solutions and only 9% of organizations in Europe, the Middle East, and Africa achieving measurable outcomes from AI initiatives. Four main shortcomings hinder the transition of AI prototypes to production: 1. Deployment Flexibility: Prototyping environments often lack the necessary flexibility for large-scale production deployment, particularly in regulated sectors. 2. Data Sovereignty: Production transitions can complicate data sovereignty at enterprise and regional levels. 3. Reliability: High availability is crucial for production environments, but vendor-managed platforms may not guarantee seamless upgrades or hardware swaps without downtime. 4. Disconnect in Tool Selection: Developers often choose tools for prototyping without considering production implications, leading to difficulties in scaling. The shortage of database administrators (DBAs) is exacerbated by the increasing use of AI tools, with 84% of developers utilizing them according to a 2025 survey. To address these challenges, Merrick suggests leveraging AI DBA agents to support human DBAs and improve database management efficiency. He emphasizes the need for both robust data infrastructure and enhanced operational support to improve the success rates of AI prototypes.
From monolith to Lakebase to LTAP: rethinking the database from storage up
Tech Optimizer
July 2, 2026

From monolith to Lakebase to LTAP: rethinking the database from storage up

Sixteen years ago, the author began their PhD at UC Berkeley and was advised to focus on analytics rather than OLTP databases, which were considered solved. This led to the creation of Apache Spark and Databricks. However, during the development of Databricks, it was found that OLTP databases were not solved problems; they were difficult to scale and fragile. This realization led to the development of Lakebase, a serverless Postgres database designed with modern technology. Lakebase architecture separates the write-ahead log (WAL) and data files into independent, scalable services. The WAL is externalized to a service called SafeKeeper, which uses Paxos-based replication for durability, while data files are managed by PageServer, which stores them in cloud object storage. This design addresses challenges such as data loss, scaling, and performance interference between transactional and analytical workloads. Lakebase maintains compatibility with Postgres, offers unlimited storage, serverless compute, durable writes, and simpler high availability. It also introduces LTAP (Lake Transactional/Analytical Processing), which allows both transactional and analytical processing to operate on a single data copy in real time, eliminating the need for separate data copies and reducing costs. LTAP utilizes a unified storage layer that allows data to be materialized in both row and columnar formats, optimizing it for both transactional and analytical workloads. The system ensures that analytics can access the most current data without affecting transactional performance by using a log sequence number (LSN) to retrieve the latest changes. Unlike traditional CDC approaches, LTAP requires no explicit table replication, as all data is stored in a single governed copy. This architecture circumvents common issues faced by hybrid transactional/analytical processing (HTAP) systems, such as incomplete feature sets, lack of ecosystem support, and performance contention. The Lakebase architecture has unlocked capabilities like unlimited storage, elastic compute, durable writes, and instant branching, with ongoing developments anticipated in the future.
silver Android smartphone
Tech Optimizer
July 2, 2026

From monolith to Lakebase to LTAP: rethinking the database from storage up

Sixteen years ago, the author began their PhD at UC Berkeley, where they were advised to focus on analytics rather than OLTP databases, which were considered a solved problem. However, upon developing Databricks, they found that OLTP databases were cumbersome, challenging to scale, and fragile. This led to the creation of Lakebase, a serverless Postgres database designed to address these issues by externalizing the write-ahead log (WAL) and data files into independent services. Lakebase architecture separates the WAL into a distributed service called SafeKeeper and data files into another service called PageServer, allowing for improved durability, scalability, and performance. This architecture eliminates data loss risks associated with disk failures and misconfigurations, simplifies high availability, and allows for elastic compute resources. Lakebase also introduces LTAP (Lake Transactional/Analytical Processing), which enables transactions and analytics to operate on a single data copy in real-time, avoiding the need for separate data copies and reducing costs. The system retains Postgres's ACID semantics while storing data in open columnar formats, making it accessible to both transactional and analytical engines without the delays and complexities of traditional replication methods. The architecture allows for unlimited storage, serverless compute, and instant branching, while ensuring that analytics can read the most current transactional data without affecting transactional workloads. LTAP aims to unify transactional and analytical processing by leveraging distinct compute engines for each workload while integrating at the storage layer.
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