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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.
Pixel phones are reportedly choking on basic games after Android 17
BetaBeacon
July 2, 2026

Pixel phones are reportedly choking on basic games after Android 17

Pixel phones are facing gaming performance issues after the Android 17 update. Some affected games include Brawl Stars, Clash Royale, and Hill Climb Racing 2. Users have tried makeshift solutions but are waiting for an official resolution from Google. The Pixel 10, Pixel 9, and Pixel 8 series are the most impacted. The exact cause of the problem is unclear, with some pointing to the Android 17 update or recent Play Services updates. Users have suggested clearing the cache for Google Play services and adjusting GPU driver preferences as potential solutions.
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.
These Google Apps Need To Be Manually Updated On Your Samsung Phone
AppWizard
July 1, 2026

These Google Apps Need To Be Manually Updated On Your Samsung Phone

Google has rolled out updates for three system applications on Samsung Galaxy devices: Android System SafetyCore (version 0.925574157), Android System WebView (version 149.0.7827.91), and Google Play Services (version 26.22.33). These updates are not listed in the "Available updates" section of the Play Store and may require manual updating. Android System SafetyCore helps protect users from unwanted content, Android System WebView is essential for displaying web content in apps, and Google Play Services manages various functions like app updates and user privacy settings. To manually update these apps, users should go to Settings > Apps, select the app, and tap "App details in store" to access the Play Store. If other apps are not updating, users should ensure a stable internet connection, restart the phone, clear storage space, update the Android version, clear app caches, and check the system date.
Steam Machine review: Valve's underwhelming living-room PC has a serious price problem
AppWizard
June 22, 2026

Steam Machine review: Valve’s underwhelming living-room PC has a serious price problem

The Steam Machine is a compact gaming system measuring 16.5 × 15.5 × 15.3 cm, designed to run SteamOS and provide access to the Steam library while offering a user experience similar to consoles. It features a custom AMD CPU with six cores and twelve threads, 16 MiB of L3 cache, and a maximum clock speed of 4.86 GHz. The system includes 16 GiB of DDR5 memory and a discrete Radeon graphics unit with 8 GiB of GDDR6 video memory based on the Navi 33 architecture, operating with 28 compute units and a power budget of 110 watts. Storage consists of an NVMe drive with approximately 1.9 TB of space and a zram swap of around 7.6 GiB. The Steam Machine runs SteamOS 3.8.9 in its x86-64 version, allowing it to function as either a Linux PC or a gaming interface. Connectivity options include one HDMI 2.0 port, one DisplayPort 1.4 port, four USB-A ports, a USB-C port, Gigabit Ethernet, Wi-Fi 6E, and Bluetooth 5.3. Its hardware is aligned with PC architecture but lacks the customization options of a bespoke system.
I tested Microsoft PC Manager’s RAM-freeing tool and learned why high memory usage isn’t always a problem
Winsage
June 22, 2026

I tested Microsoft PC Manager’s RAM-freeing tool and learned why high memory usage isn’t always a problem

Users of Windows 11 often report high RAM usage, with figures reaching 70-90 percent, leading to concerns about system performance and the need for memory upgrades. Microsoft has introduced the PC Manager application with a "Boost" option to help free up memory. High memory usage can be normal when Windows 11 caches files, but excessive consumption by poorly optimized applications may indicate a resource issue. The impact of memory usage varies by system; for example, 90 percent usage may be acceptable on a system with 96GB of RAM, while it could be problematic on an 8GB system. Context matters, as high memory usage on high-end machines often represents normal caching, whereas it may signal struggles on lower-end systems. The PC Manager's Boost feature can be useful before resource-intensive tasks but may reinforce misconceptions about high memory usage being inherently negative. Ultimately, performance issues, rather than memory percentage alone, should guide decisions about upgrading RAM.
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.
How Five PostgreSQL Optimizations Sped Up Our Dashboard
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.
Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres
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.
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