NewApp Digest
search bot

maintenance

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.
EDB launches agentic Postgres AI with governance tools
Tech Optimizer
June 26, 2026

EDB launches agentic Postgres AI with governance tools

EDB has introduced new features for its Postgres AI platform, including an agentic database and converged analytics capabilities, allowing enterprises to run AI agents alongside transactional workloads on a unified PostgreSQL foundation. The platform includes governance tools that position control mechanisms at the data layer and integrates AI processing with operational data, enabling businesses to connect live records with AI systems without transferring sensitive information. The agentic database can monitor over 200 metrics, identify issues, suggest changes, and apply fixes automatically based on user-defined policies. It consolidates various data types through a single SQL interface, significantly accelerating database tuning processes and enhancing application performance. EDB has also expanded its analytics capabilities with a zero-ETL architecture for real-time analysis and large-scale warehousing. EDB PG AI for ClickHouse targets real-time analysis, while EDB PG AI for WarehousePG focuses on historical analysis at petabyte scale. The platform claims up to 30 times faster query performance compared to legacy systems and improved scaling efficiency. EDB's platform integrates vector search and retrieval for AI agents, demonstrating lower query latency and higher retrieval accuracy than competitors. NTT East is using EDB PG AI for AI-driven network operations, while the governance feature manages agent access at the data querying point using native Postgres roles and row-level security. The platform can be deployed on-premises, in hybrid environments, or across cloud infrastructures, with partnerships including Dell, IBM, Nvidia, Red Hat, and Supermicro.
Windows 11 26H2 arrives as a tiny unlock, not a massive download
Winsage
June 22, 2026

Windows 11 26H2 arrives as a tiny unlock, not a massive download

Microsoft has confirmed the release of Windows 11 26H2, scheduled for Fall 2026. This update focuses on improving the update experience for businesses and IT professionals by streamlining maintenance processes. Windows 11 26H2 will be delivered as an enablement package, activating pre-existing code on eligible devices, ensuring a swift installation with minimal disruption. It will unlock features from earlier versions, specifically Windows 11 24H2 and 25H2, allowing a seamless transition with minimal operational downtime. The update is currently available to Windows Insiders and emphasizes a predictable, low-disruption experience for IT departments. Key advantages include a streamlined installation process, minimal user disruption, no need for complete reinstallation, consistent security and quality updates, and uniform compatibility checks. The specific release date has not been announced, but it will arrive in the latter half of 2026. The hardware requirements remain the same as previous versions: a minimum of 4GB of RAM, 64GB of storage, and a 1GHz 64-bit dual-core processor.
PostgreSQL 18 on Amazon Aurora and Amazon RDS: Performance enhancements
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.
Neoclouds, sovereign AI and Postgres: The new operating model for regulated enterprises
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.
Looking Ahead to Postgres 19
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.
Search