62.
HN
Building My Own Swarm / Foursquare / Gowalla on OSM
The text describes the development of a personal check-in application by the author, inspired by platforms like Swarm/Foursquare and Gowalla. This app uniquely utilizes OpenStreetMap (OSM) data in place of commercial services for its functionality. Initially constructed using Rails, Postgres, and Hotwire Native technologies, it later expanded to include a native version built with Swift/SwiftUI, guided by OpenAPI documentation. The application has become the author's preferred choice over Swarm, credited for its stability and local storage capabilities that support imported historical check-in data from Foursquare.
Although the app is currently feature-complete, there are several potential enhancements suggested, such as implementing public sign-up options, making it available on TestFlight, enhancing analytical chart features, and adding a straightforward "Follow" system. The author has expressed an openness to interest in testing the app but emphasizes that it remains primarily a personal project with uncertain prospects for further development.
Keywords: #phi4, App, Backend, Charts, Check-ins, Data, Database, Error tracking, Feature complete, Follow system, Foursquare, Frontend, Gowalla, Hotwire, Importer, Insights, Native, OSM, Open API, Open sources, Postgres, Project, Public, Rails, Sentry, Swagger, Swarm, Swift, SwiftUI, TestFlight, Web interface
blog.notmyhostna.me 12 hours ago
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97.
HN
Run an autonomous company without human intervention
Paperclip is an innovative platform designed to facilitate autonomous organizational management without human oversight by orchestrating various agents like OpenClaw and Claude Code into a structured system. It supports diverse agent runtimes including Python scripts and HTTP webhooks through the use of adapters, allowing seamless integration across different technological environments. One of Paperclip's key features is its budget management capability, which automatically pauses operations when usage reaches 100%, ensuring financial control. Additionally, it offers governance mechanisms that necessitate board approval for certain tasks, adding a layer of oversight to critical operations.
The platform allows agents to operate on scheduled heartbeats or notifications and provides the option for continuous operation, enhancing flexibility in task management. Paperclip distinguishes itself from traditional task management systems like Asana or Trello by handling complex coordination needs such as session maintenance and cost monitoring, thus providing robust orchestration benefits. Furthermore, it offers versatility in deployment options, supporting both local and cloud environments. This enables the establishment of multiple isolated companies within a single instance, allowing organizations to pursue separate ventures or conduct strategy testing without interference. Overall, Paperclip provides a comprehensive solution for managing organizational complexities autonomously while maintaining governance and financial oversight.
Keywords: #phi4, Nodejs, Paperclip, Postgres, Projects, SKILLmd, accountability, agents, autonomous company, budget limit, budgets, cloud deploy, control modules, data isolation, governance, heartbeat signal, orchestration, org charts, tasks, ventures
paperclip.ing 15 hours ago
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111.
HN
Show HN: Havn – one command to see everything running locally
Havn is a command-line utility designed to assist developers in efficiently identifying services running locally on their machines, automating the process of checking active processes and ports. It supports over 40 types of local services with zero configuration needed, employing tools like `lsof` or `netstat` for comprehensive scanning that includes mapping listening processes, performing parallel scans across more than 100 ports, HTTP fingerprinting, and filesystem detection within a short timeout period. The tool provides insights by detecting application frameworks from response headers and reading configuration files such as `package.json`. It also conducts health checks on services like Redis and Postgres, while live updates of scan results are delivered to the browser via WebSocket, ensuring real-time information without the need for polling. Havn is cross-platform compatible with macOS, Linux, and Windows, featuring an interactive dashboard that allows users to pause/resume scans, view potential issues such as missing databases, and access service history.
To use Havn, it can be installed globally using npm, and the dashboard is run via a simple command. It offers various commands for managing scans and services, with performance metrics indicating quick scan times post-initialization and a modest memory footprint. Structurally, the project includes components like a CLI entry point, an Express server supporting WebSocket connections, and a port scanner module. Additionally, it provides RESTful APIs to manage service states, initiate scans, and modify configurations. Havn is open-source, licensed under MIT, with its source code available on GitHub for further exploration or contribution.
Keywords: #phi4, AI runtimes, Express, HTTP, Havn, MIT license, Nodejs, Postgres, REST API, Redis, TCP, WebSocket, cross-platform, databases, gomod, lsof, monitoring tools, netstat, packagejson, performance tradeoffs, pomxml, queues, service detection
github.com 17 hours ago
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140.
HN
Show HN: DB9 – Postgres, but for Agents
DB9 is a comprehensive management tool specifically designed for Postgres databases aimed at agents, facilitating the entire database lifecycle from creation to production monitoring. It enables users to quickly set up serverless Postgres instances without manual intervention in provisioning or configuration. Notable features include built-in vector search capabilities using HNSW indexes, allowing semantic searches and embeddings directly within the platform, negating the need for an external vector database.
The tool supports executing SQL queries through a command-line interface (CLI) with various output formats available such as tables, JSON, or CSV. It offers database branching to create isolated environments for testing and development purposes. DB9 includes built-in observability features that allow users to monitor key performance metrics like QPS, latency, and connection statistics without additional software.
For migration management, DB9 provides functionalities to create, apply, and track SQL migrations with integrated status reporting per database. The platform also facilitates the automatic generation of TypeScript or Python types from the existing database schema. Enhanced querying for semi-structured data is supported through JSONB with GIN indexes, making it well-suited for managing agent memory and tool outputs.
Additionally, DB9 allows users to export schemas and seed databases from files, ensuring consistent reproducibility across different environments. These features collectively position DB9 as a robust solution for simplifying Postgres database management tasks.
Keywords: #phi4, Agents, DB9, HNSW indexes, JSONB GIN indexes, Postgres, SQL CLI, TypeScript Python types, database branching, database creation, dump seed, migration management, observability, pgvector, production monitoring, reproducible environments, schema, semantic search, semi-structured data, serverless, type generation
db9.ai 20 hours ago
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145.
HN
Show HN: Drizby – WIP Metabase Alternative
Drizby is an open-source reporting tool in development, designed to offer a flexible and economical alternative to Metabase for embedding analytics into applications. It initially focuses on PostgreSQL connections but plans to expand support aligned with Drizzle's compatibility. The project invites feedback from small teams and startups interested in intuitive reporting tools, including features that simplify agent-based analysis workflows. During its initial launch, Drizby provides a free cloud version with a fully managed instance, incorporating AI-powered analytics and dashboards. Developers are encouraged to contribute input on the roadmap via GitHub at [cliftonc/drizby](https://github.com/cliftonc/drizby). In the future, paid options for hosting support may be considered.
Keywords: #phi4, AI-powered, Drizby, Drizzle, GitHub, Metabase, analytics, app, cloud, container, dashboards, docker, flexible, notebooks, open source, postgres, reporting tool, roadmap, small teams, startups, user friendly
www.drizby.com 21 hours ago
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288.
HN
Show HN: Somnia – a dream journal that locks 2 minutes after your alarm fires
Somnia is a dream journal application designed to address the issue of quickly fading dreams by leveraging a 2-minute window after waking up when norepinephrine suppression during REM sleep allows dreams to be retained in working memory. To facilitate this, Somnia uses an alarm system that triggers a server-side entry window, prompting users immediately upon notification. Users must type the first word within this period to initiate their dream entry; otherwise, the entry is locked for the day without exceptions. The app's architecture utilizes Next.js 14 App Router and Supabase, with text editing powered by Tiptap, while notifications are managed through web-push + VAPID. Server-side enforcement of time limits prevents any client-side tampering, ensuring data integrity. Somnia offers a free tier and provides additional resources for queries regarding its implementation or functionality, demonstrating a robust system built on GitHub Actions cron jobs hosted on Vercel.
Keywords: #phi4, GitHub Actions, Nextjs, Postgres, REM sleep, Somnia, Supabase, Tiptap, VAPID, Vercel, alarm, biological fact, cron, dream journal, entry window, norepinephrine, notification, screen capture, server-side, timer, web-push, working memory
www.somniavault.me a day ago
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337.
HN
Using Rust and Postgres for everything: patterns learned over the years
The text references a website exploring patterns observed when utilizing Rust and PostgreSQL together, though it lacks specific details from the excerpt. It highlights a technical requirement for proper site functionality: JavaScript must be enabled. Without additional information or access to the complete content, this summary captures the essence based on what is provided. The focus centers on the relationship between Rust and PostgreSQL in web development contexts and the technical prerequisites necessary for accessing the site's full capabilities.
Keywords: #phi4, JavaScript, Postgres, Rust, doesn't work, enable, learned, patterns, properly, technical, website, years
kerkour.com 2 days ago
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387.
HN
Show HN: Ovumcy – self-hosted menstrual cycle tracker
Ovumcy is a privacy-centric, self-hosted menstrual cycle tracker built as a single Go service with server-rendered web UI, offering SQLite or Postgres database options for data storage. The application features period tracking, ovulation and fertile window predictions, calendar views, statistics, notes, multi-language support (English and Russian), and data export in CSV/JSON formats. It also includes a dark theme option. The focus on privacy is evident as it avoids analytics or third-party trackers and uses first-party cookies for authentication, CSRF protection, and language preference management.
The technical stack of Ovumcy comprises Go and Fiber for the backend, GORM for ORM functionalities, and HTML templates with HTMX, Alpine.js, and Tailwind CSS for frontend development. Deployment can be done using Docker or by executing the binary directly. Users deploying Ovumcy via Docker should set environment variables like `SECRET_KEY` and choose their preferred database drivers. For public HTTPS deployments, configuring a reverse proxy is recommended to enhance security.
For self-hosted operations, Ovumcy suggests using persistent SQLite volumes or managed Postgres storage with HTTPS secured by trusted reverse proxies. It emphasizes the importance of maintaining a strong private `SECRET_KEY`.
Ovumcy welcomes contributions through GitHub issues and incorporates CI processes for static checks and testing. Development commands are available to facilitate building and running the application locally.
The roadmap outlines future enhancements such as mobile PWA support, custom symptoms tracking, tracker imports, web push notifications, PDF export capabilities, extended statistics, partner invites, and optional Postgres runtime usage. Recent updates have included a dark mode feature, improved security measures, and detailed operational guides. Ovumcy is licensed under AGPL v3, highlighting the importance of user control over personal data through self-hosting options.
Keywords: #phi4, Docker, Go service, HTML templates, HTTPS, Menstrual cycle tracker, Ovumcy, Postgres, SQLite, contributing, deployment, development, license, localization, manual setup, privacy-first, reverse proxy, roadmap, security, self-hosted, server-rendered, tech stack
github.com 2 days ago
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407.
HN
Swift at scale: building the TelemetryDeck analytics service
TelemetryDeck is an analytics service built with Swift, focusing on privacy-centered app usage data collection for developers, serving over 16 million users monthly. Utilizing Vapor, a Swift web framework, TelemetryDeck operates on scalable APIs and services deployed within Kubernetes, employing PostgreSQL for metadata storage and Apache Druid for processing analytics data. Swift's choice brought notable advantages in error handling and performance through its compiled nature and robust multithreading capabilities, while the Codable protocol ensures efficient JSON encoding/decoding by rejecting malformed data instantly.
The development process benefited from Swift’s compatibility with major IDEs like Xcode and adherence to the Language Server Protocol, facilitating debugging and testing within integrated databases. Initially using shared Data Transfer Objects (DTOs), TelemetryDeck transitioned to inline structs in controllers for improved maintainability. The project has actively contributed to open-source Swift communities by developing and refining SDKs such as StripeKit.
Key lessons from TelemetryDeck's development emphasize structuring code via Swift Package systems, prioritizing database optimizations, leveraging Vapor’s features, early versioning of API URLs, configuring cache TTLs, and monitoring errors and performance. The platform exemplifies how Swift can effectively manage scalable backend services while ensuring high development speed and type safety, positioning it as a viable alternative to traditional languages used in backend development.
Keywords: #phi4, Apache Druid, Codable, DTOs, Fluent, Kubernetes, Postgres, Swift, Swift Package, SwiftUI, TelemetryDeck, Vapor, analytics, backend, backend services, caching, development, development experience Keywords: Swift, distributed tracing, monitoring, multithreading, package, performance, scalability, server-side, tracing, type safety
swift.org 2 days ago
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429.
HN
We fixed Postgres connection pooling on serverless with PgDog
To tackle Postgres connection pooling challenges in their serverless architecture, a startup transitioned from using PgBouncer to PgDog after encountering performance issues during deployment spikes hosted on Vercel. The single-threaded design of PgBouncer proved inadequate under bursty traffic, leading to bottlenecks. Upon discovering PgDog at an event through its main contributor, the team found it adept at managing connection surges without necessitating a larger database infrastructure.
The startup implemented PgDog within an AWS environment using EKS, where it demonstrated robustness against real-world application demands, including Prisma's prepared statements. Key features like health-aware load balancing and integration with OpenMetrics facilitated comprehensive monitoring through Prometheus and Grafana, enhancing operational visibility and system stability. This transition resulted in significant improvements: the startup could downsize their Supabase host, remove a database replica, and secure cost efficiencies, allowing for seamless deployments during peak times without concerns about resource constraints.
Moreover, PgDog's focus on actual usage rather than preset connection limits optimized resource management, enhancing both operational efficiency and system reliability. This strategic shift not only addressed the immediate performance issues but also positioned the startup for better scalability and financial sustainability in their serverless setup.
Keywords: #phi4, AWS, EKS, Grafana, OpenMetrics, PgBouncer, PgDog, Postgres, Prisma, Prometheus, Supabase, Vercel, connection pooling, database connections, deploy spikes, health-aware load balancing, latency, metrics, multi-threaded pooler, operational efficiency, resource use, serverless
circleback.ai 2 days ago
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432.
HN
Building a High-Performance Postgres Time Series Stack with Iceberg
The article outlines the creation of an efficient time series data management system through the integration of PostgreSQL and Apache Iceberg. It emphasizes utilizing the strengths of both technologies to improve performance, scalability, and manageability when dealing with large volumes of time-series data. The goal is to harness PostgreSQL's robustness alongside Iceberg's proficiency in handling complex datasets, thereby constructing a powerful stack specifically designed for time series applications. This integration aims to deliver enhanced capabilities that address the challenges posed by extensive data management needs in time series contexts.
Keywords: #phi4, Building, Delimited, Duplicates, Extract, High-Performance, Iceberg, Keywords, List, Postgres, Relevant, Simple, Stack, Technical, Text, Time Series
www.snowflake.com 2 days ago
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452.
HN
Ctrl-C in psql gives me the heebie-jeebies
The text discusses the security implications of using `Ctrl-C` in PostgreSQL's command-line tool (`psql`) to send a `CancelRequest`, which by default is unencrypted, posing potential security risks. This request creates an additional connection with a unique protocol version (v1234.5678) and identifies the target query connection via a process ID and a secret key. Although newer PostgreSQL versions support encrypted `CancelRequest` messages through libpq, `psql` does not use this feature, leaving it vulnerable to Denial of Service attacks if intercepted on insecure networks. This vulnerability persists even with protocol v3.2, which allows for longer secret keys but requires explicit configuration to be effective.
Furthermore, the lack of encryption affects monitoring tools like Elephantshark that depend on TLS and Server Name Indication (SNI) for correct connection routing. Since `CancelRequest` messages do not include SNI, they complicate the process, although recent updates have started addressing this by mapping session identifiers to hostnames. To mitigate these security risks, it is recommended to use PostgreSQL 18 with a minimum protocol version of 3.2, employ VPNs for additional security, and avoid using `Ctrl-C` for cancellation in sensitive environments. Users should also verify if other Postgres clients or drivers support encrypted cancellations until `psql` implements this feature.
Keywords: #phi4, BackendKeyData, BunSQL, CancelRequest, Ctrl-C, Denial of Service, Elephantshark, Neon, PostgreSQL client, Postgres, SNI, SNI extension, TLS, VPN, cancellation, concurrent connections, connection, encryption, libpq, network traffic, plaintext, process ID, protocol v32, protocol version, proxy, psql, query, race condition, refactor, secret key, security, server handshake
neon.com 2 days ago
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472.
HN
Supertoast Tables
Hatchet developed a strategy known as "supertoast tables" to address the inefficiencies encountered when storing large JSONB payloads directly in PostgreSQL, which resulted in excessive database storage use and prolonged autovacuum processes due to TOAST table utilization. The core of this solution is a daily data partitioning system that separates recent payload data, stored locally within PostgreSQL, from older data offloaded to Amazon S3. This approach employs a "write-and-swap" technique where payloads from the previous day are migrated into new partitions with references to the corresponding S3-stored data instead of full payload copies, effectively reducing autovacuum loads and database bloat.
The implementation involves creating an empty partition template for each day, replicating write operations through triggers during offloading, and using batch processes that compress and transfer payloads to Amazon S3 in parallel. This method optimizes storage efficiency by ensuring only recent data remains within the local PostgreSQL environment while older entries are efficiently managed on S3. After transferring all necessary data to S3, old partitions are discarded and replaced with updated ones, maintaining system integrity through check constraints aligned with partition rules.
This innovative approach has enabled Hatchet to handle extensive daily payload volumes—hundreds of millions—with minimal CPU resource usage and reduced storage costs. By minimizing database operation overhead and leveraging PostgreSQL’s partitioning capabilities, the "supertoast tables" method significantly enhances data management efficiency compared to previous practices.
Keywords: #phi4, COPY operation, IOPS, NVMe disks, Postgres, S3 offloading, TOAST technique, WAL (Write-Ahead Log), autovacuum, batch processing, check constraint, compression algorithm, data replication, database storage, disk pressure, jsonb, latency-sensitive workloads, partitioning, payload processing, supertoast, task queues, throughput optimization, triggers, write-and-swap
hatchet.run 2 days ago
https://www.tigrisdata.com/ 2 days ago
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519.
HN
Show HN: DataQueryAI – Turn plain text into SQL locally
DataQueryAI is a versatile tool that allows users to query databases using plain language, eliminating the need for SQL knowledge. It operates on local machines through the Ollama engine, ensuring user data remains private by not leaving the device. The application supports multiple database systems, including Postgres, MySQL, and SQL Server, and offers result exports in CSV, Excel, or HTML formats. It accommodates a range of languages such as English, Vietnamese (with limited fluency), German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Available for Windows x86/x64 and macOS ARM64/x64 platforms, Linux support is forthcoming.
The pricing structure includes a free version that supports single database profiles with CSV export capabilities. For more advanced needs, the Pro Monthly plan costs $16 per month, allowing access to multiple databases and enhanced export options. Additionally, there is a one-time Pro Lifetime option priced at $79, offering all features. DataQueryAI emphasizes speed, privacy, and accessibility, targeting non-technical users with an interest in local-first AI tools that enhance data confidentiality by running queries without cloud involvement. The tool seeks user feedback on its utility and desired features to further improve its offerings.
Keywords: #phi4, CSV, DataQueryAI, Excel, HTML, MySQL, Ollama engine, Postgres, SQL, SQL Server, databases, local-first AI, non-technical users, plain language, privacy
www.dataqueryai.app 2 days ago
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525.
HN
Show HN: Agent-vfs – Virtual filesystem for AI agent memory
"Agent-vfs" is a virtual filesystem designed to abstract AI agents' memory using familiar file operations like reading and writing, rather than complex databases or APIs. It supports 11 operations including read, write, edit, list (ls), search (grep), and more, leveraging SQLite for development and Postgres in production settings. This approach addresses traditional filesystem limitations by offering isolation, backups, and scalability features essential for production environments. "Agent-vfs" integrates with popular AI SDKs such as Vercel AI SDK, OpenAI SDK, and Anthropic SDK, and can be installed via npm. It supports multi-tenant setups ensuring data isolation across users within a shared database. In production, the system provides integration flexibility through Drizzle for schema management, raw SQL execution, or custom adapters, with customizable table names. As an open-source tool under the MIT license, "agent-vfs" offers a persistent memory solution that is both easy to use and scalable across sessions.
Keywords: #phi4, AI agent memory, Agent-vfs, Drizzle, Postgres, SQLite, adapter, database table, file operations, multi-tenant, persistent memory, schema, tool access, virtual filesystem
github.com 2 days ago
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565.
HN
Show HN: Netwall
Netwall functions as an uncomplicated, text-based public message board where users engage without needing accounts or sign-ups. It allows anonymous posting of messages that are automatically deleted after one hour unless extended by community votes with the "+5m" option. Built using Vanilla JavaScript, Node/Express, and Postgres, Netwall includes a moderation system powered by OpenAI's API to prevent misuse. The platform attempts to estimate user locations via IP addresses and enforces several rules: users have a 10-minute interval between posts, limited to 15 per day, and messages cannot be duplicates or spam. Additionally, restricted word filtering is in place. Community reports can lead to the removal of posts, while an ethos of kindness is promoted among users. Netwall offers terminal-style themes for its interface and operates without maintaining a record of users' activity history, ensuring user anonymity and privacy throughout interactions on the platform.
Keywords: #phi4, +5m vote, Netwall, Node/Express, OpenAI Moderation API, Postgres, Solarized Dark, VPNs, Vanilla JS, community reports, country flags, duplicate messages, kindness, no accounts, post limit, private relays, public wall, self-deleting posts, spam prevention, terminal themes, text-only, time gifts
netwall.org 3 days ago
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623.
HN
Sub-10-Second Database Boot on Kubernetes with Full Isolation
The article outlines the development journey of Vela, a Postgres environment on Kubernetes designed to achieve sub-10-second boot times while ensuring complete isolation between databases. Initially employing KubeVirt to run virtual machines (VMs) as Kubernetes objects for robust isolation and live migration capabilities, the team encountered significant challenges with boot time variability primarily due to Docker image pulls. In response, they implemented pre-caching of Docker images during VM builds, which mitigated some issues but did not resolve all performance bottlenecks.
The ongoing struggles with KubeVirt's live migration, resource management, and network stability prompted the team to explore alternative approaches. They found a solution in Neon’s Autoscaling project, which offered a database-optimized scaling method that maintained TCP connections during CPU and memory adjustments. To better integrate this autoscaling capability within Kubernetes, modifications were made for improved PVC attachment and dynamic resource allocation inside VMs.
A pivotal improvement came with the replacement of Docker by a custom Linux image built using Buildroot. This change streamlined startup processes by eliminating unnecessary layers and ensuring determinism in boot times, ultimately allowing Vela to reach its sub-10-second target. The article highlights key lessons learned throughout this development process, including the importance of prioritizing determinism over convenience, mastering Kubernetes reconciliation, optimizing through component removal, understanding live migration's complexities, and opting for minimal OS images to decrease operational entropy.
The narrative concludes by acknowledging KubeVirt’s contributions to their work while expressing intentions for Vela to contribute its enhancements back to the open-source community, reinforcing a spirit of collaborative improvement within the ecosystem.
Keywords: #phi4, Autoscaling, Buildroot, CRDs, Docker, KubeVirt, Kubernetes, Neon, PVCs, Postgres, Prometheus, QEMU, VMs, Vela, VelaOS, containers, control plane, ephemeral environments, inittab, isolation, libvirt, live migration, reproducible builds, scalability, virtual machines
vela.simplyblock.io 3 days ago
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663.
HN
How we fixed Postgres connection pooling on serverless with PgDog
A startup facing challenges with Postgres connection pooling within its serverless architecture resolved these issues by transitioning from Supabase's default pooler, Supavisor, to PgBouncer, before discovering an optimal solution in PgDog. The primary issue was managing bursty traffic during deployments that led to connection spikes; this was inadequately addressed by the single-threaded nature of PgBouncer. Through exploration, they identified PgCat, a multi-threaded pooler suitable for such scenarios, which eventually evolved into PgDog, developed with contributions from a former PgCat developer. Implementing PgDog in their AWS EKS environment effectively handled connection spikes and resolved conflicts with Prisma's prepared statements, aided by the responsive support from the PgDog team.
PgDog offered several advantages beyond solving immediate issues, including health-aware load balancing that eliminated read downtime during database maintenance by Supabase. It also provided detailed real-time metrics through OpenMetrics, which improved visibility in incident management. With the integration of PgDog, the startup significantly reduced its dependence on overprovisioned resources, allowing for confident scaling down of their database infrastructure. This strategic shift led to cost savings and enhanced operational efficiency, enabling deployments during peak hours without connection-related disruptions.
Keywords: #phi4, AWS, EKS, Grafana, Kubernetes, OpenMetrics, PgBouncer, PgDog, Postgres, Prisma, Prometheus, Supabase, Vercel, connection pooling, database connections, deploy spikes, health-aware load balancing, latency, metrics, operational efficiency, replica, scaling, serverless
circleback.ai 3 days ago
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669.
HN
I built Fluxer, a Discord-like chat app by Hampus Kraft
Fluxer, developed by Hampus Kraft, emerges as an open-source alternative to Discord with a strong emphasis on European ownership and user control. Created in response to Discord's age-verification policy, Fluxer has attracted over 1,000 Visionaries through early sales of a $299 package to support its development. The platform aims for feature parity with popular communication tools like Discord and Slack while remaining free under the AGPLv3 license. It offers various support options including freemium hosting, donations, and paid support for self-hosted users. Built using TypeScript and Erlang/OTP, Fluxer supports both Cassandra and Postgres databases.
Kraft's motivation is rooted in his background with Discord's architecture and a desire to prioritize user privacy and control. Despite lacking features like end-to-end encryption at present, the platform focuses on replicating Discord’s familiar UX while allowing for custom client modifications. It also draws inspiration from technologies used by WhatsApp and Discord themselves. The project benefits from Kraft's educational foundation in computer engineering from KTH Royal Institute of Technology and his professional experiences.
Fluxer emphasizes a familiar user experience over novelty, contrasting with other platforms like Root which prioritize innovation at the cost of usability. Its API is compatible with Discord’s, enabling existing bots to function with minimal modifications. Although end-to-end encryption and federation are not current priorities due to their complexity, Fluxer plans to introduce a relay system for unified account views across instances and uses moderation tools from Project Arachnid's Shield for content detection.
Fluxer consciously relies on European service providers to minimize geopolitical dependencies despite its use of American technology. The platform is in public beta thanks to backing from Plutonium Visionary subscriptions, which sustain development without compromising independence. Future plans include enhancing moderation tools and improving data residency options, with potential age verification features if demand arises. Fluxer aspires to evolve into a community-driven communication platform that prioritizes user interests, inviting contributions and partnerships.
For collaboration or inquiries, contact is available via email at hampus@fluxer.app.
Keywords: #phi4, AGPLv3, API compatibility, CAPTCHA, CDN, Cassandra, Discord, Discord bot, E2EE, Electron, Erlang/OTP, European-owned, Flutter, Fluxer, GitHub Sponsors, KTH Royal Institute of Technology, LLMs, LiveKit, NSFW, OSS community, PWA, Plutonium, Postgres, RSS feeds, SDK, Sweden, Tauri, UX, Visionaries, WebSocket Gateway, age verification, beta, bootstrapped, community chat, customization, donations, federation, funding, hosted instance, independent, mobile web, moderation, open source, privacy-first, relays, roadmap, self-hostable
blog.fluxer.app 3 days ago
https://blog.fluxer.app/how-i-built-fluxer-a-discord-like-ch 3 days ago
https://news.ycombinator.com/item?id=46468725&ref=blog.f 3 days ago
https://fluxer.gg/crVKp7Rb 3 days ago
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678.
HN
Ctrl-C in psql gives me the heebie-jeebies
The article raises security concerns regarding the handling of `CancelRequest` messages when using `Ctrl-C` in `psql`, the PostgreSQL command-line interface, particularly due to their transmission over unencrypted connections. This vulnerability exposes users to potential Denial of Service (DoS) attacks since these requests are sent in plaintext and can be intercepted by malicious actors. Although newer PostgreSQL versions support encrypted cancellation requests and some drivers have implemented secure methods, `psql` itself has not been updated due to necessary architectural changes. The absence of encryption affects tools like Elephantshark, which cannot properly monitor network traffic without Server Name Indication (SNI) in cancellation messages. Until `psql` incorporates these security improvements, users are recommended to use PostgreSQL 18 or higher, enforce a minimum protocol version for longer secret keys, utilize VPNs, and avoid using `Ctrl-C`. The article anticipates updates to `psql` soon that will address encryption concerns for such requests and emphasizes the need to verify if other clients or drivers provide similar security measures.
Keywords: #phi4, CancelRequest, Ctrl-C, Denial of Service, Elephantshark, Neon, PostgreSQL client, Postgres, SNI, TLS, backendKeyData, cancellation, concurrent connections, connection, encryption, libpq, network traffic, process ID, protocol v32, proxy, psql, race condition, refactor, secret key, security, signal-safe
neon.com 3 days ago
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766.
HN
Top K is a deceptively hard problem in relational databases
Ming Ying's article examines the difficulties encountered when executing "Top K" queries in relational databases, particularly focusing on PostgreSQL (Postgres) and comparing it to specialized systems like ParadeDB. Top K queries aim to retrieve the top 'K' rows based on specific criteria such as recency or score; however, their execution can be intricate due to varying query conditions.
In PostgreSQL, B-tree indexes are employed for efficient retrieval when query conditions align with the index structure. However, challenges arise when filters not included in the index need to be applied, resulting in increased execution times due to additional filtering and sorting steps. The situation worsens with full-text search using GIN indexes, especially as dataset sizes grow, because maintaining efficiency across diverse query types becomes problematic.
To optimize PostgreSQL's performance, strategies like creating composite B-tree indexes or utilizing generated columns and partial GIN indexes are suggested. These methods offer some improvement but still face limitations when dealing with extensive result sets.
In contrast, ParadeDB introduces a distinct approach by using compound indexing that incorporates all necessary fields for filtering and sorting into a single index. This method circumvents the need for multiple tailored indexes. Moreover, ParadeDB employs columnar storage to facilitate efficient random access and batch processing of filters. For relevance-sorted queries, Block WAND is used to skip entire document blocks unlikely to qualify as top results.
ParadeDB's innovative indexing techniques lead to significant reductions in query execution time compared to PostgreSQL with GIN indexes, even for complex text search queries. Recent improvements in ParadeDB’s internal mechanisms further enhance performance by optimizing the advancement of document ID iterators during boolean queries.
The article concludes that while PostgreSQL struggles with efficiency and flexibility due to its reliance on B-tree structures for Top K queries, ParadeDB provides a more adaptable solution through integrated indexing and optimizations like columnar arrays and Block WAND. Future enhancements in systems like ParadeDB may include additional pruning strategies and support for complex joins, highlighting the potential of specialized search systems to overcome the limitations faced by traditional relational databases.
Keywords: #phi4, B-Tree, BM25, Block WAND, GIN index, ParadeDB, Postgres, Tantivy, Top K, columnar arrays, composite index, execution pipeline, filters, index, inverted index, optimization, query performance, relational databases, relevance score, sorting, text search
www.paradedb.com 3 days ago
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777.
HN
Using Rust and Postgres for everything: patterns learned over the years
The article provides an analysis of experiences and insights derived from employing Rust and PostgreSQL across multiple projects over several years. It highlights recurring patterns and valuable lessons learned in this context. Additionally, it mentions a technical requirement for users: the necessity of enabling JavaScript to fully access and interact with the website content where these insights are presumably detailed. This dual focus on both the software technologies and user accessibility underscores the article's comprehensive approach to discussing project development with Rust and PostgreSQL.
Keywords: #phi4, JavaScript, Postgres, Rust, doesn't work, enable, learned, patterns, properly, technical, website, years
kerkour.com 3 days ago
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820.
HN
Just Use Postgres
In the article "Just Use Postgres" by Stephan Schmidt, the author advocates for utilizing PostgreSQL as the primary tool in early-stage tech projects due to its adaptability and simplicity, which helps reduce operational complexity. By shifting complexities from DevOps into code, developers can expedite development and streamline system architecture. In a greenfield project example, Schmidt combined PostgreSQL with Elixir, Phoenix, and Liveview, alongside GitHub Actions for CI/CD, creating an efficient setup ideal for solo developers or small teams. This approach remained advantageous until the need arose for specialized services such as PDF generation and background job processing, at which point only minimal external tools were added.
Schmidt highlights PostgreSQL's ability to replace various components traditionally handled by separate technologies: it offers built-in full-text search instead of Elasticsearch, supports transactional job queues in lieu of Redis/RabbitMQ, uses JSONB columns for caching rather than Redis/Memcached, and functions as a key-value store without requiring services like MongoDB. With advancements in AI facilitating better interaction with PostgreSQL's features, including its JSONB syntax, the database becomes even more user-friendly.
The strategy emphasizes maintaining simplicity and speed during early development by leveraging available tools, allowing developers to focus on customer needs rather than managing complex infrastructure. While PostgreSQL may not be ideal for every task, it offers sufficient capability until scaling necessitates specialized solutions, thus supporting a streamlined development process in the initial stages of project growth.
Keywords: #phi4, AI/LLMs, CICD, Cache Invalidation, Deployment Simplicity, DevOps, Docker, Early Stage Startup, Elasticsearch, Elixir, Full-text Search, GitHub Actions, Infrastructure, JSONB, Job Queues, Kafka, Key-Value Store, Liveview, Materialized Views, Memcached, MongoDB, Oban, Operational Overhead, Phoenix, Postgres, RabbitMQ, Redis, SQS, Scalable Architectures, Speed of Iteration, System ReasoningKeywords: Postgres, Trigram Matching, Typesense, Unlogged Tables
amattn.com 4 days ago
|
872.
HN
Liberate yourself from infrastructure over-planning
The article challenges traditional views that backend systems should be hosted on the same cloud provider as their databases, advocating instead for cross-provider configurations to enhance flexibility and future-proofing strategies. It highlights findings from a benchmark study involving Cloudflare Workers and an AWS-hosted PostgreSQL database, which revealed unexpected outcomes concerning latency and performance.
Key insights include the significant role of geographic proximity in reducing latency—demonstrating that processing closer to data sources can drastically improve response times by up to 23x. Additionally, the choice of connection driver and strategy critically influences transaction latencies, with certain drivers offering faster performances when not handling interactive transactions.
Contrary to common assumptions, crossing provider boundaries incurs minimal penalties, which in some cases may even be negligible or advantageous compared to internal networking within a single cloud provider. These findings encourage teams to confidently select infrastructure options without excessive concern over latency issues associated with cross-provider setups, especially in co-located data center regions. However, variations could occur based on different providers, databases, and geographic locations.
Overall, the article advocates for greater flexibility in infrastructure planning by decoupling compute and database dependencies, underscoring the potential benefits of cross-provider environments.
Keywords: #phi4, AWS, Cloudflare Workers, Infrastructure, Postgres, TCP, WebSocket, benchmarking, connection strategies, cross-provider, drivers, geographic proximity, internal networking, latency, over-planning
www.lirbank.com 4 days ago
|
1003.
HN
Microsoft Shipped Pirated Harry Potter Books on Their Blog for 14 Months
The Microsoft developer blog incident involving the use of pirated Harry Potter books as demo data for 14 months underscores a broader issue where temporary solutions become entrenched due to lack of review—a situation paralleled by inadequate security practices such as utilizing shared passwords in production environments without stringent access controls. This oversight highlights how initial decisions made for convenience can inadvertently solidify into standard practice if not re-evaluated. In Microsoft's case, the use of copyrighted material likely stemmed from a failure to select legally safe alternatives rather than intentional infringement. Similarly, within database management, shared credentials are often set up with the intention of securing them later, though this rarely happens, resulting in persistent security risks.
The incident illustrates that using publicly available resources like Project Gutenberg's public domain texts could have avoided legal issues without additional effort. This example extends to broader practices in system design: establishing secure measures from inception—such as binding database access to individual identities instead of shared accounts—can mitigate future challenges and audit complications, making the process more efficient and cost-effective. The crux of this lesson is that better defaults should be established in system design, encouraging secure paths from the outset and preventing temporary fixes from evolving into long-term vulnerabilities. This principle applies universally across domains, including database access management, reinforcing the idea that prioritizing security at the beginning can prevent oversight and exposure to risks.
Keywords: #phi4, Audit Trail, Azure SQL, Copyrighted Text, Credential Rotation, Database Connection, Dataset, Default Settings Keywords: Microsoft, Identity-Based Access, Infrastructure, Kaggle, Microsoft, Password, Pirated Books, Postgres, Security, Shared Credentials, Tutorial, rmBug
chaosguru.substack.com 4 days ago
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1024.
HN
LLM Gateway: Budget enforcement, virtual API keys and usage analytics for LLMs
The any-llm-gateway is a FastAPI-based proxy server designed to enhance Large Language Model (LLM) management by incorporating budget enforcement, API key handling, and usage analytics into the multi-provider framework of any-llm. It acts as an intermediary between applications and LLM providers, offering robust cost control, access management, and observability features.
Key benefits include cost control through automatic or tracking-only budget limits, secure issuance and monitoring of API keys without exposing provider credentials, detailed logging of requests for full visibility into usage, including token counts and costs, and a production-ready deployment that supports Docker and PostgreSQL setups with minimal performance impact. The gateway functions transparently by authenticating application requests, checking budget constraints, routing to the appropriate LLM provider, and logging usage before returning responses.
The system offers smart budget management with shared or individual budgets, flexible API key systems for full access or scoped control, and comprehensive usage analytics. Deployment is straightforward using Docker, configurable via YAML or environment variables, optimized for PostgreSQL databases, and includes Kubernetes integration features like liveness and readiness probes. For setup instructions, users are directed to the Quick Start Guide.
Keywords: #phi4, API key management, Docker, FastAPI, Kubernetes, LLMs, Postgres, access management, budget enforcement, cost control, latency, observability, observability ``` FastAPI, observability ```Keywords: FastAPI, proxy server, usage analytics, visibility
mozilla-ai.github.io 4 days ago
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1081.
HN
The evolution of background job frameworks in Ruby
The evolution of background job frameworks in Ruby has been characterized by successive advancements addressing the limitations of previous systems. Initially, BackgroundDRb (2008) offered network communication and database persistence for jobs but lacked retry mechanisms. Delayed::Job (DJ), introduced by Shopify the same year, improved on this with job retries and scheduling, using a process isolation model that was memory-intensive. Resque emerged in 2010, leveraging Redis for efficient operations, though it struggled with transactional consistency due to enqueuing jobs outside database transactions.
Subsequently, Queue Classic & Que (2011-2013) utilized PostgreSQL's listen/notify and advisory locks but faced issues with table bloat impacting performance. Sidekiq, introduced in 2012, became popular for its advanced features such as periodic jobs and a web UI, enhancing Redis-based queue functionality. GoodJob, launched in 2020, focused on simplicity and compatibility with ActiveRecord using PostgreSQL features like listen/notify and advisory locks but avoided SKIP LOCKED for job locking.
The most recent development, Solid Queue (announced in 2023), represents the culmination of these innovations, offering a Rails-native solution that emphasizes transactional consistency with reduced Redis dependencies. It leverages modern PostgreSQL features such as SKIP LOCKED to efficiently manage concurrency, along with an integrated web UI, showcasing advancements from earlier frameworks and providing seamless integration into Ruby on Rails applications. Each framework's progression addressed specific scalability, concurrency, and operational efficiency challenges, paving the way for robust solutions like Solid Queue.
Keywords: #phi4, API, Active Job, Background jobs, DRb, Delayed::Job, GitHub, GoodJob, Heroku, Postgres, Que, Queue Classic, Rails, Redis, Resque, River, Ruby, SKIP LOCKED, Sidekiq, Solid Queue, advisory locks, async frameworks, concurrency, database-backed queues, distributed Ruby, job queue, listen/notify, multi-threaded model, transactional consistency
riverqueue.com 5 days ago
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1197.
HN
Drizzle Joins PlanetScale
On March 3, 2026, Drizzle and PlanetScale announced a strategic collaboration aimed at enhancing database tools specifically designed for JavaScript and TypeScript developers. This partnership is built upon shared principles such as performance optimization and an improved developer experience. Drizzle's ORM (Object-Relational Mapping) tool, renowned for its speed and user-friendliness, complements PlanetScale's mission to streamline database management processes. Notably, despite this new collaboration, Drizzle will maintain its status as an independent open-source project, ensuring continued community-driven development. The PlanetScale team has publicly acknowledged and expressed gratitude towards Drizzle for their valuable contributions to the broader developer community, highlighting a symbiotic relationship that promises mutual benefits in advancing database technology.
Keywords: #phi4, Drizzle, JavaScript, March 2026, ORM, PlanetScale, Postgres, Sam Lambert, TypeScript, cloud, colleagues, community, database tools, developer experience, goals, independent project, open source, performance, roadmap, support
planetscale.com 5 days ago
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1271.
HN
Ax: Supabase vs. PlanetScale
From the perspective of an AI agent's experience (AX), Supabase and PlanetScale offer distinct advantages and challenges for developers. Supabase excels in its comprehensive backend-as-a-service features that include a Postgres database, authentication, and storage. Its appeal lies in its rapid prototyping capabilities, with a straightforward sign-up process requiring no initial credit card information, which suits AI agents prioritizing quick setups. Despite limited CLI functionality restricted to local development, Supabase's robust training data allows for efficient solution recommendations without extensive searches.
PlanetScale, on the other hand, provides a managed MySQL/Postgres database platform emphasizing scalability and reliability through serverless scaling and Git-like branching capabilities. Its requirement of credit card information at sign-up contrasts with its flexible CLI (pscale), enabling AI agents to perform comprehensive database operations via terminal commands. However, Claude Code’s interactions reveal issues in PlanetScale's training data accuracy, such as outdated pricing and service assumptions.
The AX gaps highlight Supabase's advantage due to its up-to-date documentation and community resources, which support a smoother agent-driven development process. While PlanetScale offers flexible database management options, it demands more upfront decisions from users and suffers from AI recognition gaps that can hinder effective agent recommendations. Enhancing the overall user experience involves improving access to precise documentation and expanding CLI capabilities to facilitate automated workflows for agents. In summary, while both platforms have their strengths, Supabase is often favored by AI agents for rapid prototyping due to its all-in-one services and ease of use, whereas PlanetScale requires more initial investment but offers advanced database management features.
Keywords: #phi4, AI agents, CLI tools, CRUD functionality, JWT tokens, MCP servers, MySQL, PlanetScale, Postgres, Supabase, Vitess, agent experience (AX), authentication, bcrypt, databases, developer experience (DX), free tier, pricing plans, scalability, signup process, terminal access, uptime SLA, web search
techstackups.com 5 days ago
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1336.
HN
Spotify's take on ADRs is great, but how do you enforce them at scale?
Decision Guardian is an open-source tool developed as both a GitHub Action and a Command Line Interface (CLI), designed to enhance the visibility of architectural decision records (ADRs) by automatically posting them as comments on pull requests when protected files are modified. Originating from Spotify's 2020 guidance, it addresses the common issue of documentation being overlooked by presenting these decisions precisely when code changes occur.
The tool works by documenting architectural decisions in Markdown format, which aligns with existing ADR structures. It integrates seamlessly into GitHub workflows, triggering automatically during pull requests that alter protected files and posting pertinent decision comments without manual intervention. Decision Guardian boasts key features such as severity levels to block PRs based on criticality (Critical/Warning/Info), advanced matching capabilities using glob patterns and regex, compatibility with various CI systems like GitLab, Jenkins, CircleCI, and the ability to handle large pull requests efficiently. It also ensures idempotent comments to prevent comment spamming while allowing updates, all without requiring external network calls.
Complementing existing tools such as CODEOWNERS for reviewer assignment and Danger.js—particularly for non-JavaScript engineers due to its Markdown-based operation—Decision Guardian is distributed under the MIT license. Its setup can be accomplished with ease through a single-step GitHub Action or via the CLI command `npx decision-guardian`. The tool's repository is available on [GitHub - Decision Guardian](https://github.com/DecispherHQ/decision-guardian).
Keywords: #phi4, ACID compliance, ADRs, Architecture Decision Records, CI/CD, CLI, CODEOWNERS, Dangerjs, Decision Guardian, GitHub Action, MIT license, Markdown, MongoDB, PR comments, Postgres, ReDoS protection, path traversal protection Keywords: GitHub Action, protected files
news.ycombinator.com 6 days ago
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1339.
HN
Show HN: BridgeBase – one control plane for TigerBeetle,Redis,MySQL,ClickHouse
BridgeBase serves as an integrated control plane designed for managing various databases such as TigerBeetle, Redis, MySQL, ClickHouse, Postgres + PostGIS, and VectorDB. Developed to alleviate the complexities of operating multiple database systems, it introduces a unified authentication layer, dashboard, and tools for provisioning and monitoring. Currently supporting Redis and TigerBeetle, BridgeBase aims to streamline operations, reducing the necessity for platform engineering skills among users. The service employs an SDK-first strategy, providing compatibility with Node.js and Python through its availability on npm and PyPI. As it seeks feedback from those handling multi-database workloads in production environments, plans are underway to extend support for additional databases in the future.
Keywords: #phi4, BridgeBase, ClickHouse, MySQL, Node, PostGIS, Postgres, PyPI, Python, Redis, SDK-first approach, TigerBeetle, VectorDB, auth layer, control plane, dashboard, database workloads, feedback, infrastructure, monitor, multi-database stacks, npm, operational overhead, pain point Keywords: BridgeBase, platform engineers, provision
bridgebase.dev 6 days ago
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1378.
HN
Postgres Column Naming
In PostgreSQL, when selecting data without specifying column aliases, the system automatically assigns labels to columns based on specific rules. Raw values like `(1, 2)` are labeled as `column1` and `column2`. For rows created using expressions such as `(1, 2, 3) row (4, 5, 6)`, PostgreSQL names the column `row`. In case expressions lacking an `else` clause or featuring unnamed ones, the label defaults to `case`; however, if there is a named expression in the `else` clause, it uses that name as the column label. Simple select statements without aliases result in columns labeled with the inferred placeholder name `?column?`. For composite types like user-defined structures (e.g., an `employee` type), fields use their respective field names for labeling purposes.
Function calls typically label the resulting column using the function's name, although if nested, they default to `?column?`. Some functions and operators are internally translated into specific PostgreSQL functions during parsing. In cast expressions, columns are labeled with the destination type or, when available, the existing expression name. For arrays, the element type serves as the label. Additionally, SQL types may be converted into PostgreSQL-specific types during parsing, impacting column names. Overall, using explicit aliases is recommended to ensure clarity in query results and avoid potentially confusing automatic naming conventions.
Keywords: #phi4, Postgres, SQL types, alias, base expression, case expressions, casts, column naming, composite types, destination type, element type, expression, function name, functions, grammar, indexing, label, operators, parser, select, specific types, specific types Keywords: Postgres
steve.dignam.xyz 6 days ago
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1420.
HN
Making large Postgres migrations practical
PeerDB offers an efficient solution tailored for large-scale migrations from one PostgreSQL database to another, effectively addressing common challenges such as performance trade-offs and operational complexity. It achieves high-speed initial data loads and continuous change data capture (CDC) without necessitating significant alterations to the source database. The platform's architecture enables parallel snapshotting by logically partitioning tables using CTIDs, allowing concurrent streaming of partitions that significantly reduces load times compared to traditional methods like pg_dump/pg_restore or native logical replication.
In a benchmark evaluating 1TB table migrations using different tools—pg_dump/pg_restore, native logical replication, and PeerDB—the latter showcased superior performance. PeerDB completed the migration in just 1 hour and 49 minutes with eight threads, while pg_dump/pg_restore took approximately 17 hours and native logical replication required 8 hours and 40 minutes. This efficiency is achieved by leveraging PostgreSQL's binary format to preserve data fidelity and optimizing network bandwidth usage.
Additionally, PeerDB provides robust CDC capabilities, ensuring consistent synchronization with minimal downtime. It manages unchanged TOAST columns without the need for setting REPLICA IDENTITY FULL on source tables, employing caching techniques alongside the MERGE command to optimize data management. ClickHouse is working towards simplifying migration processes to become a one-click operation in the future.
PeerDB is available as an open-source project, facilitating quick setup with comprehensive guides for creating Postgres mirrors managed by ClickHouse. Users interested in exploring these capabilities can access private previews of PeerDB’s high-speed OLTP stack.
Keywords: #phi4, AWS DMS, CDC, CTID, OLTP, PeerDB, Postgres, TOAST, binary COPY, data fidelity, initial load, logical replication, migration, parallel snapshotting, pg_dump, replication slot
clickhouse.com 6 days ago
https://www.scrydata.com/ 6 days ago
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1436.
HN
Islets – The Spatial CMS
Islets Spatial CMS is an innovative headless content management system that emphasizes geographical organization by embedding spatial coordinates into a hierarchy that governs both its content structure and mapping capabilities. This design enables advanced spatial queries through PostgreSQL and the pgvector extension, allowing users to locate content based on proximity or along specific routes with enhanced vector search functionalities. Content within Islets can carry vector embeddings, revealing semantic similarities and hidden connections, which provides deeper insights into data relationships.
The system is built around a GraphQL-first API via Pothos, facilitating seamless spatial queries integration within its graph structure without relying on traditional RESTful approaches. Users benefit from easy importation of GeoJSON data from sources like OpenStreetMap or custom datasets, with the added ability to enrich this content using CMS features. A map-centric administrative interface is provided, allowing users to manage and visualize content contextually on a geographical canvas rather than through conventional spreadsheets.
Islets' design emphasizes extensibility; it supports sandboxed TypeScript plugins that allow customization of UI components, field types, API routes, and menu configurations. Additionally, Islets includes a mobile-first Progressive Web App (PWA) that can be installed across various devices, offering offline access with automatic data syncing upon reconnection to the internet, thus removing the necessity for app store installations.
Keywords: #phi4, GeoJSON, GraphQL-First API, Islets, OpenStreetMap, PWA, Postgres, Pothos, Progressive Web App, Spatial CMS, TypeScript plugins, content tree, headless CMS, latitude, longitude, map, mobile-first, pgvector, spatial hierarchy, spatial queries, vector search
islets.app 6 days ago
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1467.
HN
How does B-tree make your queries fast?
B-trees are efficient structures designed for managing large datasets within modern databases by balancing search efficiency and adapting to physical storage constraints. They extend the principles of Binary Search Trees (BST) by allowing multiple values per node and maintaining a balanced structure through self-balancing algorithms during insertions. While both B-trees and BSTs share a theoretical time complexity of \(O(\log n)\), their practical performance differs due to hardware considerations such as CPU caches, RAM, and disk storage. B-trees are optimized for sequential data access by organizing data in nodes that align with the characteristics of disk storage, thereby reducing expensive random disk accesses. When a node reaches its capacity, it is split into new nodes to maintain balance and optimize space usage, allowing efficient data retrieval and insertion as the dataset grows. This self-balancing nature makes B-trees especially suitable for database environments requiring rapid and reliable access to large volumes of data. Despite advancements in storage technologies like SSDs, B-tree designs remain integral to various databases, including PostgreSQL, due to their ability to leverage sequential access advantages.
Keywords: #phi4, B-tree, Binary Search Tree (BST), CPU caches, Disk storage, Postgres, RAM, data structure, database, hardware, height, index, metadata, nodes, pages, pointers, queries, random access, self-balancing algorithm, sequential access, split point, values, width
blog.allegro.tech 6 days ago
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1499.
HN
ai.embed() and ai.classify() as IMMUTABLE Postgres functions. AI-coded for $127
The `ai-native-pg` extension enhances PostgreSQL by integrating AI-based text embedding and classification directly into the database through two functions: `ai.embed()` and `ai.classify()`. These functions operate immutably within generated columns to enable automated data enrichment during write operations, utilizing ONNX Runtime for local inference without external API calls. This integration streamlines application architecture by shifting embedding logic from application code to the database schema, removing the need for managing external models or handling complex errors. Applications can perform AI-enriched tasks like semantic search seamlessly within existing PostgreSQL interactions.
Key benefits of this extension include improved transaction consistency, removal of external dependencies, and reduced latency in document processing (around 10.9ms per embedding), facilitating easy integration into PostgreSQL environments. However, the high memory usage per connection necessitates implementing connection pooling for scalable performance. Developed with AI-assisted coding under human oversight to ensure compliance with PostgreSQL standards, this extension represents an innovative approach to incorporating AI for code generation while preserving database reliability and functionality.
The project is hosted on GitHub under the Apache 2.0 license, with Docker images available for multiple PostgreSQL versions, and stability evaluations are ongoing prior to formal releases.
Keywords: #phi4, AI primitives, API calls, Apache 20 license, Docker, HNSW index, IMMUTABLE, ONNX Runtime, PostgreSQL extension, Postgres, Python services, SQL functions, aiclassify, aiembed, backend process, classification, connection pooling, embeddings, generated columns, inference engine, model loading, pgvector, schema logic, semantic search, token cost, transaction consistency, unit test suite, vector database
insert.dev 6 days ago
https://github.com/dmonroy/ai-native-pg 6 days ago
https://insert.dev/immutable-ai-functions-in-postgres/ 6 days ago
|
1550.
HN
Show HN: Open-source expense and budget tracker with SQL API for AI agents
The post introduces an open-source expense and budget tracker designed for AI agents using a SQL API, developed by Kirill Markin over five years of personal financial management experience. This tool allows users to input bank statements into an AI agent that categorizes transactions, inserts them into a database, and verifies account balances. Its standout feature is direct execution of SQL operations (SELECT, INSERT, UPDATE, DELETE) through a simple HTTP API, enabling seamless integration with large language models.
The application interface includes a budget table displaying past expenses, current spending against plans, and future financial projections, facilitating personal finance management akin to corporate strategies. Built using Next.js 16 and TypeScript on PostgreSQL 18, it emphasizes security via database-level row level security and hashed API keys. For deployment, Docker Compose is used locally while the production version leverages AWS CDK stack with ECS Fargate, RDS, ALB, and Cognito authentication, maintaining a straightforward architecture to avoid confusion for AI models.
Kirill invites feedback on the app's architecture and features, assuring security through open testing of its robustness. Access to a demo database is restricted to protect privacy, though users can sign up anonymously or self-host their data, encouraging engagement while prioritizing user confidentiality.
Keywords: #phi4, AI agents, ALB, AWS CDK, Cognito auth, Docker Compose, ECS Fargate, LLMsKeywords: Open-source, Nextjs, Open-source, Postgres, RDS, Row Level Security, SHA-256, SQL API, TypeScript, WAF, budget tracker, exchange rates, expense tracker, financial planning, security, transactions
github.com 6 days ago
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1556.
HN
Show HN: Omni – Open-source workplace search and chat, built on Postgres
Omni is an innovative open-source platform that enhances workplace efficiency by integrating with various tools such as Google Workspace, Slack, Confluence, and Jira, among others. It offers a unified search experience across these applications using both full-text (BM25) and semantic (pgvector) methods while relying solely on Postgres for data management instead of Elasticsearch or dedicated vector databases. A standout feature is its AI chat interface that allows users to search documents and execute Python/bash scripts securely within a sandboxed environment, ensuring user safety and data integrity.
Designed to be fully self-hosted, Omni ensures that no data exits the user's network, maintaining privacy and security. It implements permission inheritance for seamless access control, guaranteeing that users can only interact with data they are authorized to view. Additionally, it supports custom Large Language Models (LLMs) like Anthropic, OpenAI, and Gemini.
The architecture of Omni leverages ParadeDB on Postgres for efficient search and app data management, with core services crafted using Rust, Python, and SvelteKit. Each connector operates within lightweight containers, preserving independence across various languages and dependencies, which simplifies the platform's scalability and maintenance.
Deploying Omni is straightforward; it can be set up using Docker Compose or Terraform on cloud platforms such as AWS and GCP, with support for a wide range of integrations including Google Workspace, Slack, Confluence, Jira, Web sources, Fireflies, HubSpot, and local files. As an open-source project under the Apache License 2.0, Omni encourages community contributions, providing guidelines in its CONTRIBUTING.md document to facilitate collaboration.
Keywords: #phi4, AI tools, AWS/GCP deployments, Apache License 20, BM25 index, Confluence, Docker Compose, Google Drive, HNSW vector index, Jira, LLM provider, Omni, ParadeDB, Postgres, Python, Rust, Slack, SvelteKit, Terraform, chat platform, connectors, open-source, pgvector, sandboxed container, workplace search
github.com 6 days ago
https://github.com/getomnico/omni/tree/master 6 days ago
https://onyx.app/ 6 days ago
https://news.ycombinator.com/item?id=46045987 6 days ago
https://news.ycombinator.com/item?id=36667374 6 days ago
https://render.com/docs/postgresql-extensions 6 days ago
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1654.
HN
Rocks and Sand (capacity planning on Postgres)
The article delves into enhancing storage efficiency in PostgreSQL databases through strategic column alignment and capacity planning, focusing on how data types are aligned at 8 bytes due to internal design choices. This alignment can lead to unnecessary padding between columns of different sizes, increasing the overall row size. The discussion centers around several key concepts, including alignment basics, where fixed-size data types such as SMALLINT and BIGINT are padded to meet the 8-byte alignment requirement, resulting in inefficient storage usage.
The article highlights "Intensity Intervals & A Little Padded Room," illustrating how improper column ordering can lead to significant wasted space due to padding between columns. It introduces "Some Ground Rules & Column Tetris" as a strategy for minimizing this inefficiency by suggesting that NUMERIC and TEXT types, which do not require padding when placed at the end of a row, be strategically positioned there.
Optimal column arrangement is emphasized as crucial in reducing storage needs; an example showcases how reordering columns to place larger data types first can result in substantial table size reductions—up to 21% smaller. The article encourages manual optimization of column order to minimize wasted space, acknowledging that while PostgreSQL does not automatically reorder columns for optimal storage, users can achieve significant savings by applying these principles.
Although there is interest in automated solutions for this issue, the article notes that their complexity has prevented such features from being integrated into PostgreSQL. By understanding and implementing alignment strategies, users can effectively reduce storage requirements and enhance database efficiency.
Keywords: #phi4, Capacity planning, NUMERIC, Postgres, TEXT, alignment, column order, data types, optimization, padding, pg_column_size, storage, table size, variable length
www.enterprisedb.com 7 days ago
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1733.
HN
Show HN: OneCamp – Self-Hosted Slack/Asana/Zoom/Notion Alternative
OneCamp presents itself as a self-hosted alternative to popular platforms like Slack, Asana, Zoom, and Notion, with its launch scheduled for March 7. The project emphasizes modern software architecture through an open-source frontend developed using Next.js and React. Its collaboration features are powered by a custom Node microservice that employs Hocuspocus and Yjs for real-time editing capabilities facilitated via WebSockets. This functionality is further enhanced with Redis caching and integration with a Go backend, ensuring efficient data handling and synchronization.
In the realm of real-time AI, OneCamp integrates a self-hosted LiveKit SFU along with a Python agent that processes audio using Deepgram nova-2 for instantaneous transcription. This setup not only manages accurate timestamping but also broadcasts transcriptions in real-time, enhancing communication clarity and accessibility during live interactions.
For data management, OneCamp adopts a polyglot persistence approach by utilizing Postgres for relational data storage, Dgraph to manage graph relations, and OpenSearch for comprehensive full-text search capabilities. This diverse set of technologies ensures robust handling of various data types and complex queries efficiently.
Observability within the system is achieved through OpenTelemetry, which collects traces and logs that are subsequently sent to a self-hosted HyperDX on ClickHouse. This setup provides detailed insights into system performance and operational metrics, crucial for maintaining high reliability and facilitating troubleshooting processes.
While the frontend of OneCamp is accessible under an MIT license via GitHub, the backend remains closed-source. However, at launch, it will be offered as a paid managed service, allowing users to leverage its features without the need for self-hosting complexities. The project encourages community engagement through feedback, issue reporting, and contributions in the form of pull requests, fostering continuous improvement and collaboration.
Keywords: #phi4, Asana, Backend, CRDTs, ClickHouse, Collaboration, Deepgram, Dgraph, Feedback, Frontend, GitHub, Go, Hocuspocus, HyperDX, Issues, LiveKit, Nextjs, Node, Notion, Observability, OneCamp, Open Source, OpenSearch, OpenTelemetry, PRsKeywords: OneCamp, Polyglot Persistence, Postgres, Python, React, Real-time AI, Redis, Self-Hosted, Slack, Tiptap, WebRTC, WebSockets, Yjs, Zoom
news.ycombinator.com 7 days ago
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1734.
HN
The MySQL-to-Postgres Migration That Saved $480K/Year: A Step-by-Step Guide
The article details the strategic migration of two large codebases from MySQL 8 RDS to Postgres RDS, driven by issues like metadata locking and high costs associated with MySQL. The shift resulted in notable benefits: one company saw $480K annual savings through reduced instance size, while another achieved faster task execution and enhanced system responsiveness without altering their RDS configuration. A key component of the migration process was using AWS's Database Migration Service (DMS) for schema conversion and data transfer, which facilitated continuous replication during cutover periods to minimize downtime, achieving planned 30-minute outages in just a few minutes.
Addressing PostgreSQL's differing syntax from MySQL required updating the codebase. This challenge was effectively managed by employing Abstract Syntax Tree (AST) parsing tools rather than regular expressions for greater accuracy and reliability. The article highlights several lessons learned: backend migration work is intensive, necessitating automated testing prior to commencement; AST tools are recommended over regex for robustness; and rehearsing the cutover process can ensure a seamless transition with minimal unexpected issues. Overall, these migrations not only resulted in cost efficiency but also improved operational performance, underscoring the value of careful planning and advanced tooling in database transitions.
Keywords: #phi4, AWS, Automated Fixes, Case-Insensitive Matching, Code Migrations, Continuous Replication, Cost Reduction, DMS, Data, Deployment, Downtime Minimization, Edge Cases, End-to-End Tests, Go, Hibernate, Instance Scaling, JSON Operations, Java, Lit ORM, Locks, Migration, MySQL, Parameter Binding, Performance, Postgres, Query Differences, RDS, Realtime Platform, Rollback Strategy, Savings, Schema, Testing, Type Casting
medium.com 7 days ago
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1796.
HN
Show HN: AgentLookup – A public registry where AI agents find each other
AgentLookup serves as a public registry facilitating seamless interaction between AI agents by allowing them to register, discover capabilities, and access profiles of other agents without the need for an API key or human oversight. It operates through straightforward HTTP commands and provides a comprehensive API specification that agents can directly understand and use. Key functionalities include registration via POST requests to obtain agent IDs and secrets, capability-based searches using GET requests, and browsing options for new, active, or popular agents.
The system enhances usability as a public resource by implementing tiered rate limits based on user status ranging from anonymous to verified users. Developed with Next.js on Vercel and Postgres on Neon, AgentLookup addresses the needs of autonomous, long-running agents by offering an addressable identity and discovery mechanism similar to how websites use domains or DNS records. Additionally, it introduces a .well-known convention for domains to declare hosted agents, further integrating its functionality into web infrastructure.
Keywords: #phi4, AI agents, API spec, AgentLookup, Claude/Cursor, DNS SRV records, MCP server, Neon, Nextjs, POST, Postgres, Vercel, agentsjson, capabilities, discover, domains, endpoints, identity, registry, search, uptime, well-known
agentlookup.dev 8 days ago
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1839.
HN
Show HN: Runix, deploy any back end in seconds, no Dockerfile needed
Runix is a deployment platform tailored for backend applications that simplifies the deployment process by eliminating the need for Dockerfiles. Developed by bumahkib7, it features Runixcloud.dev, which automatically detects runtime environments including Rust, Java, Go, Python, Node, Ruby, Elixir, and .NET. The platform utilizes Claude AI to generate optimized Dockerfiles, builds containers, and provides a live URL with a unique subdomain for easy access. Additionally, Runix supports auto-provisioning of services such as Postgres, Redis, or Kafka when necessary.
Distinguishing itself from competitors like Vercel, Railway, and Render, Runix eliminates the need for Dockerfile configuration, extends support to more programming languages, offers a straightforward pricing model with no hidden fees, and operates on infrastructure located in Europe. Entirely developed using Rust, it encompasses all essential components such as API management and container orchestration. Currently in beta testing, Runix has already attracted its first paying customers and is actively seeking user feedback to enhance its offerings.
Keywords: #phi4, Claude AI, Docker, Dockerfile, Elixir, Europe, GitHub, Go, Java, Kafka, NET, Nodejs, Postgres, Python, Redis, Ruby, Runix, Rust, backend, beta, container, deployment, feedback, infrastructure, pricing, runtime, subdomain
news.ycombinator.com 8 days ago
https://runixcloud.dev 8 days ago
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2089.
HN
Show HN: Gas Town Control Plane – hosted monitoring for multi‑agent workspaces
The Gas Town Control Plane is an independent service designed to offer comprehensive monitoring for multi-agent workspaces within Gas Town environments. This hosted platform ingests events and snapshots from local rigs, storing them in a Postgres database, and provides a dashboard interface enabling operators to efficiently oversee their workspaces. The API server, developed using Go, supports event and snapshot ingestion and allows querying through various endpoints related to rigs, agents, and convoys. Additionally, the web UI offers visualization for different components such as rigs, agents, and hooks.
A quick start guide is available, which includes setting up a local Postgres instance via Docker Compose, running migrations, creating a workspace with specific commands, starting the API server, accessing the UI at `http://localhost:8080`, and configuring the local agent to ship data. The system comprises two primary components: the API server, serving both the dashboard and JSON API, and the local agent that operates within the workspace to ship data based on periodic schedules or specific events.
Configuration can be customized through various environment variables (e.g., `GTCP_HTTP_ADDR`, `GTCP_DATABASE_URL`), with Docker support for running both the API server and the agent. Security recommendations include using simple API key authentication for trusted networks, TLS termination for public exposure, and considering stronger methods like OAuth or JWT. Data retention settings are advised to manage sensitive information effectively within Postgres.
Release management can be handled via GitHub Actions by tagging and pushing releases, and integration testing is supported through embedded-postgres, although tests are skipped on Windows platforms. The project operates under the MIT License.
Keywords: #phi4, API Server, Agent, Authentication, Control Plane, Docker, Events, Gas Town, GitHub Actions, Integration Test, MIT License Keywords: Gas Town, MIT License Extracted Keywords: Gas Town, Monitoring, Multi-agent, Postgres, Snapshots, TLS, Telemetry, Web UI, Workspaces
github.com 9 days ago
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2106.
HN
The MySQL-to-Postgres Migration That Saved $480K/Year
The article details the successful migration of two large codebases from MySQL to PostgreSQL on AWS RDS, driven by metadata locking issues and high costs associated with MySQL. The first project encompassed approximately 350 tables using Hibernate with Java, while the second involved 80 tables utilizing Go's lit ORM. Both migrations employed AWS Database Migration Service (DMS) for transferring schema and data efficiently.
Key motivations for migrating to PostgreSQL included performance enhancements and cost savings, as PostgreSQL offers superior stability and efficiency at scale. The migration process encompassed three main stages: Schema Migration, Data Migration, and Code Migration. During Schema Migration, DMS was used to automatically generate and validate a compatible PostgreSQL schema. For Data Migration, parallel table-level transfers were conducted using a dedicated EC2 instance to ensure efficiency. Code Migration involved AST parsing instead of regex to accommodate differences in SQL syntax between MySQL and PostgreSQL, addressing variances in datetime functions, parameter binding, and JSON operations.
The deployments were executed with minimal downtime due to continuous replication by DMS, resulting in significant cost reductions—approximately $480K annually for the first codebase—and enhanced performance across both projects. The experiences underscored critical lessons such as the necessity of thorough backend work, automated testing, utilizing AST parsing, practicing strategies to minimize downtime, and adopting a proactive approach to resolving issues post-migration.
Keywords: #phi4, AST, AWS, Case-Insensitive Matching, Codebase, Continuous Replication, Cost Reduction, DMS, Data, Deployment, Edge Cases, End-to-End Tests, Go, Hibernate, Instance Scaling, JSON Operations, Java, Lit ORM, Locks, Migration, MySQL, Parameter Binding, Performance, Postgres, RDS, Realtime Platform, Rollback Strategy, Savings, Schema, Testing, Type Casting
medium.com 9 days ago
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2145.
HN
Soft-serve: a self-hostable Git SSH server for the command line
Soft Serve is a self-hostable Git SSH server tailored for command-line interaction with an accessible Text User Interface (TUI) over SSH. It facilitates cloning repositories through multiple protocols—SSH, HTTP, or the Git protocol—and includes support for Git Large File Storage (LFS). The server enables users to manage repositories via SSH commands, allowing on-demand creation or use of `git push` to establish them. Features include browsing repositories, files, and commits with an SSH-accessible UI that supports syntax-highlighted file printing and line numbering.
Installation is straightforward as Soft Serve is distributed as a single binary called `soft`, available through various package managers for multiple operating systems such as macOS, Linux, and Windows, or directly via Go. A Docker image is also provided for those preferring containerized deployment.
Setting up the server requires having Git installed and running the command `soft serve`, which creates a data directory containing repositories, SSH keys, and a database. Configuration can be customized through a `config.yaml` file or environment variables. Initial admin access necessitates an SSH authorized key set via an environment variable.
Soft Serve offers extensive configuration options, including server naming, logging formats, SSH settings (like listen address, public URL, and timeouts), Git daemon configurations, HTTP server setup with optional TLS support, cross-origin request security options, database driver selection between SQLite or Postgres, Git LFS arrangements, cron job setups for tasks such as mirror pulling, and stats server settings.
The server supports both SQLite and Postgres databases, with access control managed through SSH authentication and authorization. Permissions can be configured for anonymous access and keyless connections, while admin users possess the ability to modify server settings, manage user accounts, and adjust repository permissions via specific SSH commands.
User management allows admins to create users, add public keys, and set permissions through SSH commands. Once registered, users can self-manage their keys and settings. Repositories can be created, deleted, renamed, or imported from remote sources, with support for nested repositories, mirrors, and metadata management like descriptions and privacy settings.
Additional features of Soft Serve include a TUI for repository browsing over SSH or locally, server-side Git hooks for custom push event actions, and webhook functionality for handling repository events. It does not support new SSH RSA keys due to limitations in Go's SSH package, recommending SHA-1 RSA keys or alternative algorithms like Ed25519.
Soft Serve is part of the Charm project, emphasizing open-source collaboration and community involvement, and it operates under an MIT license.
Keywords: #phi4, Docker, Git, HTTP, LFS, Postgres, RSA keys, SQLite, SSH, TUI, access control, admin keys, authentication, authorization, clone, command line, configuration, contributing, database, environment variables, hooks, installation, license, repositories, server, user management, webhooks
github.com 9 days ago
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2193.
HN
Git in Postgres
The article examines using PostgreSQL as an alternative backend for storing git repository data to address inefficiencies associated with managing large datasets in traditional git setups. By leveraging a database like Postgres, the proposed solution stores git objects and references within two tables (`objects` and `refs`), indexed by their SHA1 hashes. The prototype "gitgres" integrates libgit2 with PostgreSQL using custom C code and PL/pgSQL functions to enhance data handling capabilities beyond what conventional git offers.
Several advantages of this approach are highlighted: unified storage consolidates all data in one system, simplifying backups; efficient querying enables complex joins between git commit data and other application information; PostgreSQL's features like real-time notifications and multi-tenant isolation offer enhanced functionality; improved performance is noted despite higher storage use due to the database's ability to handle large datasets effectively. Additionally, this integration can streamline operations in systems like Forgejo by embedding git data within the same database instance.
Despite these benefits, challenges remain, particularly regarding storage efficiency and implementing a server-side pack protocol. Nonetheless, using databases for version control expands possibilities for flexible and powerful data management, demonstrating potential advancements in how repositories are maintained and accessed.
Keywords: #phi4, Git, Postgres, TOAST, database, delta compression, libgit2, logical replication, objects table, packfiles, refs, repository, storage backend, triggers
nesbitt.io 10 days ago
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2194.
HN
Show HN: Gas Town Control Plane – hosted monitoring for multi‑agent workspaces
Gas Town Control Plane is a hosted monitoring service tailored for environments utilizing multiple agents within Gas Town workspaces, offering centralized event management through a web dashboard interface. This independent software solution comprises several key components: an API server developed in Go, a local telemetry-shipping binary known as gt-agent, and a Postgres schema designed with multi-tenant support and migration capabilities.
The service features an API Server that provides a JSON-based API for ingesting events and snapshots, accessible through endpoints such as `/v1/events` and `/v1/snapshots`. Authentication is managed using straightforward API keys. Additionally, the web UI component allows users to monitor various elements like rigs, agents, convoys, hooks, and activities, supported by telemetry data from local installations.
To initiate the service quickly, users can set up a Postgres instance locally or through Docker Compose, execute necessary migrations, create a workspace with specific commands, launch the API server (with an option for auto-migrations), authenticate via the web UI using an API key, and run the local agent to start data ingestion. Configuration is flexible through environment variables that adjust settings such as connection URLs, retention periods, polling intervals, and versioning.
The project supports Docker, offering detailed instructions for building and running images, including a script (`dev-up.sh`) that streamlines the initialization process of Postgres, migrations, and key generation. In terms of security, while simple API keys are used—ideal for trusted networks—it is recommended to implement TLS and more robust authentication methods if exposing services publicly. Users should also configure data retention settings carefully when handling sensitive information.
Development practices include integration testing facilitated by GitHub Actions with embedded-postgres support (excluding Windows), and the release process involves tagging and pushing binaries and Docker images via Git commands. The project welcomes community contributions, suggesting the use of GitHub CLI for creating issue labels to manage release notes efficiently. Operating under the MIT License, Gas Town Control Plane emphasizes ease of setup, configurability, and security in its development and deployment processes.
Keywords: #phi4, API Server, Agent, Authentication, Control Plane, Docker, Events, Gas Town, GitHub Actions, Integration Test, MIT License Keywords: Gas Town, MIT License Extracted Keywords: Gas Town, Monitoring, Multi-agent, Postgres, Snapshots, TLS, Telemetry, Web UI, Workspaces
github.com 10 days ago
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2259.
HN
AI powered migration from Postgres to ClickHouse
The article focuses on utilizing AI to enhance the migration of analytical workloads from Postgres to ClickHouse by circumventing typical challenges through a systematic approach using MooseStack. It underscores that relying solely on AI for migrations often leads to unsatisfactory outcomes unless integrated within an effective framework, such as MooseStack's "agent harness." This harness aids in managing intricate environments and ensuring seamless integration between Postgres for transactional workloads and ClickHouse for analytical tasks.
The migration process extends beyond simple SQL writing; it requires rearchitecting data models for optimal OLAP performance, query optimization, and precise propagation of changes within the system. MooseStack facilitates this by treating the application stack as code, thereby making dependencies explicit, allowing rapid iterations, and providing reliable feedback loops.
Successful migration hinges on several key components:
1. **Code as Interface**: Using typed objects in code allows agents to operate within familiar patterns, simplifying rollbacks and iterations.
2. **Fast Feedback Loops**: Immediate feedback on schema changes and runtime errors supports efficient development cycles.
3. **Rich Contextual Guidance**: Providing static context (data, documentation), skills (best practices), and reference implementations ensures the AI agent is well-informed and can avoid unnecessary trial and error.
By leveraging MooseStack, migrations become more manageable, reducing time from months to days while minimizing errors and ensuring high-quality results.
Keywords: #phi4, AI migration, ClickHouse, MooseStack, OLAP, Postgres, agent harness, analytics, dashboards, data stack, feedback loops, materialized views, schema evolution, semantic layer
clickhouse.com 10 days ago
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2269.
HN
Show HN: TechGrill – AI Interview Practice
TechGrill is an advanced application designed to replicate high-pressure senior-level technical interviews, emphasizing edge cases, and it utilizes Next.js 15 with Server Actions for its framework alongside Groq (Llama 3.3 70B) to deliver AI responses in sub-second latency, ensuring a smooth conversational experience. The app employs Neon (Postgres) coupled with Drizzle to store interview transcripts efficiently, supporting interviews across various levels from Junior to Senior. It also provides users with a performance scorecard post-session. TechGrill invites feedback on its innovative, AI-powered capabilities to enhance user engagement and effectiveness.
Keywords: #phi4, AI, Conversational, Drizzle, Groq, Interview, Junior, Latency, Llama 33, Mid-Level, Neon, Nextjs, Performance Scorecard, Postgres, Senior, Server Actions, TechGrill, Transcript Persistence
techgrill.vercel.app 10 days ago
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2298.
HN
You Just Need Postgres
The phrase "You Just Need Postgres" highlights how PostgreSQL can streamline database management by possibly integrating various databases into a single system. This consolidation leverages PostgreSQL's robust features and versatility to simplify operations and mitigate the complexities involved in managing multiple databases. By using PostgreSQL, organizations may eliminate the need for maintaining several distinct databases—such as seven different ones—as suggested in the text. The implication is that adopting PostgreSQL can lead to more efficient data management practices by reducing operational complexity and improving overall system efficiency through its comprehensive capabilities.
Keywords: #phi4, Postgres, backquotes, databases, extract, information, keyword, managing, need, stop, technical
youjustneedpostgres.com 10 days ago
https://old.reddit.com/r/PostgreSQL/comments/ 10 days ago
https://www.recall.ai/blog/postgres-listen-notify-does- 9 days ago
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2330.
HN
The MySQL-to-Postgres Migration That Saved $480K/Year: A Step-by-Step Guide
The article outlines a comprehensive strategy for migrating from MySQL to Postgres RDS, motivated by issues such as metadata locking and high costs, which resulted in significant savings for the involved companies. The migration process covered two distinct codebases: one large Java-based with 350 tables and a smaller Go-based with 80 tables. Both required schema and data migration facilitated by AWS Database Migration Service (DMS). Key steps included validating new schemas, executing parallel data transfers, and updating backend drivers and queries.
Challenges during the migration were mitigated by automating code adjustments through Abstract Syntax Tree (AST) parsers for Java and Go, addressing the most time-consuming task of updating query syntax. Continuous replication using DMS minimized downtime, with a planned short system shutdown ensuring data consistency and successful cutover. The outcomes included halving operational costs for the first codebase, resulting in $480k annual savings, while the second saw improved response times and enhanced real-time capabilities.
Lessons from the project highlighted that backend adjustments are central to migration success, emphasizing automated testing before migration, AST parsing over regex for precision, continuous replication to lessen downtime, and thorough practice of deployment sequences to prevent live transition issues.
Keywords: #phi4, AST Parser, AWS, Codebase, Continuous Replication, Cost Reduction, DMS, Data, Deployment, End-to-End Tests, Go, Hibernate, Instance Scaling, JSON Operations, Java, Lit ORM, Locks, Migration, MySQL, Parameter Binding, Performance, Postgres, Price/Performance, Query Differences, RDS, Rollback Strategy, Savings, Schema, Testing, Type Casting
medium.com 10 days ago
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2348.
HN
Show HN: Tiqd – a checklist library for life tasks
Tiqd is a comprehensive checklist library designed to assist with various life tasks such as visas, moving, travel, job interviews, and home buying. Developed using Next.js 14 SSG, the static site (tiqd.app) allows users to interact with checklists without requiring logins or backend management. These checklists are shareable through URL parameters that track checkbox status. Originally inspired by the creator's experiences as a digital nomad, Tiqd aims to simplify task management for significant life events. The platform is optimized for programmatic SEO and has unexpectedly attracted traffic from ChatGPT, while employing Claude Code for branding and distribution strategies. With 827 checklists available on topics like accessible travel planning, job integration, apartment hunting, immigration, financial optimization, and event planning, Tiqd focuses on learning more about effective content distribution methods.
Keywords: #phi4, Checklist library, Nextjs, Nextjs SSG, Postgres, SSG, Tiqd, Wedding Events, accessible travel, career planning, checklists, content site, digital nomad, distribution, finance, home buying, housing, immigration, job interviews, moving, programmatic SEO, travel, visas, wedding events Keywords: Checklist
www.tiqd.app 10 days ago
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2367.
HN
Show HN: Novyx – Memory API for AI agents (rollback, replay, semantic search)
Novyx is a specialized memory API designed to enhance AI agents' capabilities, particularly in maintaining data continuity across sessions and ensuring decision-making transparency. It features several key components aimed at improving memory management and operational integrity:
- **Store & Recall**: Employs semantic search using sentence embeddings to efficiently retrieve agent memories.
- **Rollback**: Offers the ability to revert operations to a previous state and preview changes without full redeployment.
- **Replay**: Enables time-travel debugging by reconstructing past knowledge states at specific timestamps, allowing comparison of memory over time.
- **Cortex**: Automates the curation of memories by consolidating duplicates, reinforcing significant information, and phasing out outdated data.
- **Audit Trail**: Ensures compliance with detailed logging using tamper-evident hash chains for all memory activities.
Technologically, Novyx is built on PostgreSQL and pgvector for storage and search capabilities, Redis for authentication and rate limiting, and relies on CPU-only embeddings (specifically all-MiniLM-L6-v2). It supports multi-tenancy with isolation at the application level across approximately 82 REST endpoints. The service offers SDKs in Python and JavaScript/TypeScript, integrating seamlessly with platforms like LangChain, CrewAI, and MCP.
The pricing model includes a free tier that provides access to up to 5K memories and API calls monthly. A Pro plan is available at $39 per month, offering unlimited features such as Replay and Cortex. An Enterprise option at $199 per month delivers advanced functionalities including counterfactual recall and drift analysis. Novyx operates in conjunction with trace debuggers like LangSmith or Langfuse by focusing on the agent's knowledge rather than its outputs. The service is accessible online, with comprehensive documentation available for further exploration at [novyxlabs.com](https://novyxlabs.com).
Keywords: #phi4, AI agents, Cortex, CrewAI, LangChain, MCP, Novyx, Postgres, REST endpoints, Redis, SDK, audit trail, compliance-grade logging, drift analysis, embeddings, insights Keywords: AI agents, knowledge graph, memory API, multi-tenant, persistent memory, pgvector, replay, rollback, semantic search, tamper-evident hash chains, time-travel debugging
www.novyxlabs.com 10 days ago
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2379.
HN
The real cost of random I/O
The article explores the impact of the `random_page_cost` parameter in PostgreSQL, which determines the relative expense of random I/O operations compared to sequential reads, historically set at a default value of 4.0. This setting is increasingly outdated due to advancements in storage technologies like SSDs that handle random I/O more efficiently than traditional disks. Through experiments using large tables and varying scan types, it was revealed that actual `random_page_cost` values could be as high as 25.2, indicating that random reads take significantly longer compared to sequential ones. This finding suggests that a simple reduction of this parameter due to SSD speed may not always yield optimal results.
The research showed that using the default `random_page_cost` can result in discrepancies between plan costs and actual execution durations, leading to suboptimal query plans. Aligning the parameter with experimentally measured values helps ensure that cost estimations match real performance, reducing inefficient planning decisions. However, lowering this parameter might still be advantageous under certain conditions: when a significant portion of accessed data is cached, queries are planned in isolation without considering overall cache impact, or if planner estimates are inaccurate for specific datasets.
To further improve PostgreSQL's costing model, the article recommends separating non-IO costs from `random_page_cost`, improving statistics on cached data availability, and incorporating prefetching into cost calculations. These enhancements could provide a more accurate reflection of actual performance dynamics in query planning, leading to better optimization strategies.
Keywords: #phi4, GUC parameters, Postgres, SSDs, bitmap scans, cache pressure, cost parameters, costing model, direct I/O, effective_cache_size, flash storage, index scan, latency, performance monitoring, pg_stat_statements, prefetching, query planning, random I/O, random_page_cost, sequential scan, shared buffers
vondra.me 10 days ago
https://dev.mysql.com/doc/refman/8.0/en/ 7 days ago
https://www.percona.com/blog/give-love-to-your-ssds-red 7 days ago
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2386.
HN
Git in Postgres
The article explores using PostgreSQL as a backend for Git repositories to address limitations faced by traditional package management systems such as Cargo's index or Homebrew's taps when handling large datasets. It proposes leveraging PostgreSQL databases for storing Git objects and references in SQL tables, enabling standard git clients to interact seamlessly without modification. A proof-of-concept named "gitgres" was developed using C with libgit2, demonstrating that a typical Git client can push or clone data while interfacing through the database. This setup allows direct querying of Git data within PostgreSQL, facilitating integrations such as joining commit data with issue trackers like Forgejo. Benefits include simplified deployment by centralizing data storage in one PostgreSQL instance, real-time notifications via database triggers, and multi-tenant isolation using row-level security. However, challenges arise from PostgreSQL's increased storage overhead compared to git’s delta compression, necessitating potential solutions like periodic repacking or offloading to services such as S3. Overall, the approach seeks to enhance performance and scalability in managing large sets of Git data by leveraging PostgreSQL's features for more integrated and efficient self-hosted Git management platforms.
Keywords: #phi4, Git, LFS, NOTIFY, Postgres, S3, SHA1, Spokes, TOAST, blame, database, delta-compression, diff, forge, libgit2, logical replication, merge, objects, packfiles, pg_trgm, recursive CTEs, refs, repository, row-level security, transport protocol, triggers
nesbitt.io 10 days ago
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2459.
HN
Postgres Jsonb Columns and Toast: A Performance Guide
PostgreSQL efficiently manages JSON data through its use of the `jsonb` data type in conjunction with the TOAST mechanism, addressing challenges inherent to its primarily row and column-oriented architecture. The `jsonb` type is favored for storing application data due to its flexibility, particularly when dealing with APIs and arrays, although it presents difficulties because PostgreSQL's design optimizes for fixed-size storage. To overcome these constraints, `jsonb` objects—being variable in length without size limits—are stored using TOAST (The Oversize Attribute Storage Technique). This method allows PostgreSQL to handle large `jsonb` entries by storing them outside the standard 8-KB page when they surpass page capacity. Consequently, this ensures that storage and retrieval processes remain efficient while sustaining overall performance for both row- and column-based operations.
Keywords: #phi4, APIs, Abstraction, Application Data, Arrays, Columns, Data Type, Database, Design Pattern, Efficiency, Engine, Fixed-size Pages, Jsonb, Oversize Attribute Storage Technique, Performance, Postgres, Rows, Storage Size Limit, Toast, User-facing Features, Variable Length
www.snowflake.com 11 days ago
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2467.
HN
Show HN: OpenTrace – Self-hosted observability server with 75 MCP tools
OpenTrace is a sophisticated, self-hosted observability server designed to integrate with Machine Comprehension Problem (MCP) clients like Claude Code or Cursor, offering over 75 tools for querying logs, databases, and server metrics using natural language directly within these interfaces. It streamlines workflows by eliminating the need to switch between dashboards and code editors, enhancing productivity in data management tasks. Key features include log ingestion through SQLite FTS5, providing full-text search capabilities with extensive filtering options; Postgres introspection offering read-only access with analytical insights like query statistics and index analysis; error management that groups errors akin to Sentry for detailed user impact assessments; comprehensive analytics tools such as session journeys and conversion funnels; and server monitoring capturing essential metrics via gopsutil. OpenTrace also integrates AI capabilities, facilitating actions like error resolution and health check setups.
Architecturally, the tool is built using Go and SQLite (employing WAL + FTS5), with HTTP routing handled by Chi and an HTMX-powered web interface. It operates as a single binary without requiring external dependencies, making it deployable on various platforms including DigitalOcean, Railway, or Render. Deployment options are flexible, supporting Docker Compose, direct binary downloads, or automated backups via Hetzner, all managed through environment variables for configuration.
The platform further enhances user efficiency with AI-assisted tools that suggest actions and pre-fill arguments, simplifying integration into investigations without the need for manual prompt engineering. Overall, OpenTrace delivers an extensive observability solution in a self-hosted format, integrating seamlessly within MCP clients to boost productivity by providing direct data querying capabilities.
Keywords: #phi4, AI assistant, CLI, Docker, Go, HTMX, MCP tools, OpenTrace, Postgres, REST API, Ruby gem, SQLite, VM monitoring, architecture, configuration, deployment, error grouping, logs, observability, self-hosted, server metrics, single binary, user analytics
github.com 11 days ago
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2480.
HN
pg_ash: See what your Postgres was busy with recently
pg_ash offers lightweight wait event sampling for PostgreSQL databases, providing a built-in session history feature not available in native Postgres versions 14 and above. It operates seamlessly without needing C extensions or special configurations, making it compatible with various managed database services like AWS RDS and Google Cloud SQL.
Key features of pg_ash include its ability to sample activity from `pg_stat_activity` every second, storing results compactly for easy querying using SQL. It provides functions to analyze top wait events, queries, and session activities in real-time or over specified intervals with pure SQL and PL/pgSQL. The tool uses a rotation system across three partitions to minimize data bloat, eliminating the need for VACUUM operations on sample tables. Additionally, it offers visualization tools such as stacked bar charts to provide insights into database performance issues.
Regarding performance and usage, pg_ash is straightforward to install using an SQL file, with sampling controls that are easy to manage through simple commands. The functions deliver high-resolution data with minimal overhead, making them suitable even under heavy server loads. Although it offers comprehensive historical analysis, achieving precise 1-second sampling may be challenging during extreme load due to the limitations of pg_cron.
Configuration requirements for using pg_ash include PostgreSQL version 14 or higher, with optional `pg_stat_statements` for enhanced query insights and `pg_cron` version 1.5+ for sub-minute scheduling. It is recommended to use pg_ash on the primary database since it involves write operations incompatible with physical replicas.
Overall, pg_ash stands out as a robust tool for diagnosing and resolving PostgreSQL performance issues by capturing detailed wait event data without significant server modifications or overhead.
Keywords: #phi4, ANSI colors, Activity, AlloyDB, BufferPin, CPU, Client, Cloud SQL, Extension, IO, IdleTx, LLM-assisted investigation, LWLock, Lock, Neon, PL/pgSQL, Postgres, RDS, SQL, Supabase, Timeout, WAL overhead, cron syntax, historical queries, idle-in-transaction sessions, install, lightweight, lock contention, performance issue, pg_ash, pg_cron, pgbench, pure SQL, ring buffer, rotation period, sampling, session history, timeline chart, top waits, uninstall, wait events, zero bloat
github.com 11 days ago
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2564.
HN
Money in Postgres
To effectively manage money data in a database while preserving its structure, this guide recommends using PostgreSQL's custom composite types. These types allow for bundling the amount and currency at the database level, enhancing data integrity and enabling seamless conversion of records into type-safe TypeScript objects through Drizzle ORM. This method is grounded in Martin Fowler’s money pattern, representing monetary values as integers in their lowest denominations both within application code and databases. While JavaScript's BigInt can manage amounts, PostgreSQL's bigint is used to simplify integration with Drizzle-ORM—a tool that focuses on providing a type-safe query builder rather than being a full-fledged ORM.
Traditionally, storing money involves separate columns for amount and currency, which complicates the handling of bundled JavaScript objects. An alternative approach uses JSONB in PostgreSQL to combine both fields into one column but at the cost of reduced SQL capabilities and validation checks. Custom composite types offer a more robust solution by enforcing data constraints directly within the database. These types ensure that amounts are stored as bigint while currency codes adhere to being three characters long, preserving familiar SQL syntax for tasks such as summing transaction totals.
With Drizzle ORM, converting between PostgreSQL's custom type strings and JavaScript Money objects is straightforward. Although native support in Drizzle’s query helpers for these custom types is limited, developers can create custom query helper functions to facilitate operations like comparing money columns across different tables. This approach simplifies development by providing database-level validation, maintaining easy SQL syntax, and ensuring efficient serialization/deserialization of Money objects. Ultimately, leveraging PostgreSQL's custom composite types minimizes developer friction, enhances code reliability, and supports complex query constructions.
Keywords: #phi4, Drizzle ORM, JSONB, JavaScript BigInt, Martin Fowler's money pattern, Money, Numeric, Postgres, SQL syntax, TypeScript, amount, bigint, composite type, currency code, custom types, deserialization, query helpers, serialization, transaction record, validation
numeric.substack.com 11 days ago
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2589.
HN
Show HN: Kinetic SQL – A lightweight, real-time SQL Engine for Node.js
Kinetic SQL is a lightweight real-time SQL engine tailored for Node.js applications, addressing limitations found in traditional ORMs like Prisma by offering efficient Change Data Capture (CDC) capabilities across Postgres, MySQL, and SQLite databases. It supports various frameworks including Express, Fastify, Vanilla Node.js, and NestJS, promoting framework agnosticism. Its key features include real-time subscriptions to database changes such as INSERT, UPDATE, DELETE; a CLI tool for auto-generating type-safe code from schemas; native execution of database functions via JavaScript methods; and seamless integration with NestJS through the KineticModule. Stress-testing is supported by high-frequency simulations like those in a live stock market simulator.
The installation process involves setting up Kinetic SQL alongside Drizzle ORM or other necessary plugins, followed by initialization based on the selected database type. Developers can use its RPC wrapper to call stored procedures as native methods and utilize Drizzle for complex queries. Compatibility requirements include Node.js 18+, PostgreSQL 12+ with LISTEN/NOTIFY, MySQL 5.7+ with binary logging enabled, and SQLite 3+. Although currently under development, plans are in place to add MSSQL support in future releases.
Kinetic SQL emphasizes type safety, connection pooling, and a zero-configuration setup for NestJS, aiming to simplify database interactions while minimizing ORM overhead. The library is actively maintained on GitHub, encouraging community feedback, feature requests, and contributions.
Keywords: #phi4, CLI, Drizzle ORM, Express, Kinetic SQL, KineticModule, LISTEN/NOTIFY, MSSQL, MySQL, NestJS, Nodejs, ORM, Postgres, RPC Wrapper, SQLite, TypeScript, auto-completion, binary logging, bugs, configuration, connection pooling, feature requests, issues, license, lightweight, live demo, real-time, schema generation, stock simulator, stored procedures, type safety
github.com 11 days ago
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2617.
HN
Implementing Stripe-Like Idempotency Keys in Postgres
The article explores the implementation of idempotency keys within a Postgres-based API framework to enhance robustness against failures and ensure data integrity during unreliable network conditions or server issues. Idempotency keys are unique values generated by clients for each request, allowing safe retries without causing unintended effects, as they enable the system to resume operations from where they were interrupted. The article uses Rocket Rides as a case study to illustrate the design of an idempotent API, emphasizing atomic phases—local transactions completed before any external state changes—to maintain consistency and allow safe retry mechanisms.
The proposed Postgres schema stores these keys with fields like `locked_at` to prevent concurrent request processing. By breaking down requests into atomic phases and utilizing serializable transactions, the system effectively handles concurrency and failure management by setting recovery points for retries. The article also suggests auxiliary processes such as enqueuers to initiate requests, completers for automatic retry attempts, and reapers to clean up outdated keys, ensuring long-term reliability.
These mechanisms collectively ensure that operations, like processing credit card charges akin to Stripe's model, are executed exactly once, even amid challenges such as network interruptions or server crashes. The system's architecture thus provides consistent operation under adverse conditions by combining idempotency with robust transaction management and auxiliary processes for maintaining operational integrity.
Keywords: #phi4, ACID database, APIs, Atomic Phases, Background Jobs, Database Constraints, Foreign State Mutations, Form Submission, Idempotency, Keys, MongoDB, Non-idempotent Operations, Passively Safe Systems, Postgres, Recovery Points, Retry Mechanism, Serialization Errors, Stripe API, Transactional Semantics, Unique Constraints
brandur.org 11 days ago
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2778.
HN
Show HN: Declarative open-source framework for MCPs with search and execute
Hyperterse, developed by Samrith, is an open-source framework facilitating the creation of Model Context Protocol (MCP) servers through a schema-first approach using declaration files. The latest version 2.0 aims to simplify the integration of AI agents with structured data from various databases such as Postgres, MySQL, MongoDB, and Redis. By allowing developers to define schemas, Hyperterse automatically generates secure, typed MCP tools for agents, eliminating the need for complex intermediary code and thereby streamlining access to structured data.
The framework's key features include its schema-first design, type-safe tool generation, and fine-grained query exposure capabilities. These elements collectively improve production-ready performance in version 2.0. Hyperterse is optimized to minimize token usage by focusing on two core functionalities: search and execute. This streamlined approach makes it particularly advantageous for building AI agents, enhancing large language model (LLM) features within SaaS applications, or securely exposing internal data.
Available under the Apache 2.0 license, Hyperterse can be self-hosted on personal infrastructure, with enterprise support offered upon request. The project encourages feedback from users who have deployed agents in production environments and is accessible via its GitHub repository at [GitHub](https://github.com/hyperterse/hyperterse).
Keywords: #phi4, AI agents, Apache 20, Hyperterse, LLM features, MCP, MongoDB, MySQL, Postgres, Redis, SaaS, auth, caching, declaration files, enterprise support, execute, framework, observability, open-source, production databases, schema-first, search, tool servers
hyperterse.com 12 days ago
https://medium.com/zar-engineering/code-mode-mcp-ac17c2 12 days ago
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2810.
HN
Show HN: QueryVeil – An AI data analyst that investigates your data
QueryVeil is an AI-driven data analysis platform designed for seamless investigation without depending on third-party services or merely converting queries into SQL. It operates entirely client-side using DuckDB WASM to execute SQL and WebLLM for local AI processing, ensuring user data remains private. The tool supports a variety of file formats including CSV, Excel, JSON, Parquet, and connects with databases such as Postgres, MySQL, and BigQuery.
Users can interact with QueryVeil in plain English, prompting the AI to execute multiple queries, correct errors up to three times autonomously, and generate reports complete with charts. The system proactively offers insights by identifying correlations, outliers, duplicates, and temporal gaps, providing a quick data overview before any specific query is executed. It features four modes: Chat, SQL editor, Jupyter-style notebooks, and a drag-and-drop report builder.
The tool allows users to share reports through public links or schedule email deliveries. A free tier includes local AI processing, unlimited file handling, all four operational modes, and automatic insights generation. The Pro version, priced at $19/month or $190/year, adds cloud models, database connections, enhanced sharing capabilities, and scheduled report delivery, with a 14-day free trial available.
Built using Nuxt 4, Vue 3, Pinia, and TailwindCSS, QueryVeil enables self-correction of SQL errors. Users can access a demo instantly at app.queryveil.com/demo without needing to sign up or provide an email address. The tool focuses on delivering insightful data analysis while prioritizing user privacy and seeks feedback regarding its AI behavior and the effectiveness of proactive insights. For further details, users are directed to visit www.queryveil.com.
Keywords: #phi4, AI, BigQuery, CSV, DuckDB WASM, Excel, JSON, Jupyter-style notebooks, LangGraph, Llama-32-3B, MySQL, Nuxt 4, OpenRouter proxy, Parquet, Pinia, Postgres, QueryVeil, SQL execution, Stripe billing, Supabase, TailwindCSS, Vue 3, WebGPU, WebLLM, anomaly detection, correlation detection, data analyst, drag-and-drop report builder, multi-step investigations, proactive insights, self-corrects
www.queryveil.com 12 days ago
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2861.
HN
Convex – The back end platform that keeps your app in sync
Convex is emerging as an innovative back-end platform that offers seamless synchronization capabilities, particularly appealing to developers working with TypeScript in conjunction with frameworks like React or Next.js. It stands out due to its strong integration features and enhanced developer experience (DX), providing key functionalities such as end-to-end type safety, real-time updates, and the ability to define database schemas directly within TypeScript. This makes it a more intuitive alternative compared to traditional setups that involve technologies like Angular, Django, and Postgres.
Developers find Convex advantageous in simplifying serverless and edge architectures, presenting itself as a preferable option over Firebase and MongoDB Realm by delivering comprehensive features at an attractive price point without the complexities typically associated with global state management. Additionally, it is well-regarded for enabling real-time application development using frameworks like Next.js and Clerk, further enhancing its appeal among React developers seeking robust backend solutions. Overall, Convex is praised for streamlining the development process, making it more efficient by allowing direct TypeScript-based database schema definitions, thus supporting a seamless integration into modern application workflows.
Keywords: #phi4, Angular, Auth0, Clerk, Convex, DB schema, DX, DX (Developer Experience) Keywords: Convex, Django, Edge, Firebase, IaC, MongoDB Realm, Nextjs, Postgres, Python, React, ReactHooks, S3, SQS, Serverless, TypeScript, Websocket, application state, documentation, e2e type safety, global state, integration, real-time updates, realtime application
www.convex.dev 12 days ago
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2888.
HN
Show HN: Omni – Open-source workplace search and chat, built on Postgres
Omni presents itself as an open-source, self-hosted workplace search and chat platform that integrates with a range of applications such as Google Drive, Slack, and Confluence. Designed to be a cost-effective alternative to solutions like Glean, Omni eschews the need for Elasticsearch or specialized vector databases by using Postgres, specifically ParadeDB and pgvector, to handle data indexing and search functionalities.
Key features include Unified Search, which syncs data from connected apps to build BM25 and HNSW vector indices, and Hybrid Search that blends traditional and vector-based results. The platform also offers a chat interface capable of harnessing Large Language Models (LLMs) for advanced functionality beyond basic Retrieval-Augmented Generation (RAG). Users benefit from flexibility in LLM providers, including OpenAI, Anthropic, or Gemini, and can opt for open-weight models via vLLM.
Omni emphasizes user privacy by ensuring data remains within the user's infrastructure, incorporates permission management to control data access inherited from source systems, and allows easy deployment through Docker Compose or Terraform templates across various cloud environments with minimal requirements. Currently in beta, Omni seeks feedback on its self-hosting capabilities and Postgres-only architecture. It supports numerous integrations like Google Workspace and Slack, with plans for more such as MS 365 and GitHub. The project is open to contributions under the Apache License 2.0, encouraging engagement through its [GitHub repository](https://github.com/getomnico/omni).
Keywords: #phi4, AWS, Apache 20 license, Azure, Confluence, Docker Compose, GCP, Gmail, Google Drive, HubSpot, Jira, LLMs, Omni, ParadeDB, Postgres, Slack, Terraform, chat platform, connectors, deployment, integration, open-source, permission inheritance, pgvector, privacy-first, self-hosted, workplace search
github.com 12 days ago
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2890.
HN
Show HN: TypeGraph – Type-safe graphs on Postgres/SQLite (no graph DB required)
TypeGraph is an innovative type-safe knowledge graph library that integrates with existing Postgres or SQLite databases, eliminating the need for a separate graph database. Created by Nicia-ai, it tackles challenges encountered when relational modeling surpasses the capabilities of standard ORMs. Key features include ontology reasoning, supporting advanced relationship concepts like `subClassOf`, `implies`, and `inverseOf`. It also enables vector plus graph queries, allowing embedding similarity searches in conjunction with graph traversals, facilitated by extensions such as `pgvector` for Postgres and `sqlite-vec` for SQLite. Built on Drizzle, TypeGraph allows incremental adoption within projects without replacing existing ORMs or databases.
Ideal for applications involving knowledge graphs in retrieval-augmented generation (RAG), access control based on relationships, recommendations, social features, and domains requiring multi-hop relationships, TypeGraph offers a universal schema defined with Zod. This ensures consistent data modeling across database, API validation, and TypeScript types, supporting AI-native structures that enhance large language models by providing structured context from ontologies. It features advanced relationship modeling capabilities beyond traditional foreign keys, facilitating logical rules like transitive relationships.
Developers benefit from starting with in-memory or SQLite graphs and scaling to Postgres without changing application logic. TypeGraph includes a type-safe query builder for constructing complex graph traversals using a fluent TypeScript API, enhancing error detection before execution. However, it is not designed for managing billions of edges or executing heavy graph algorithms like PageRank, where specialized tools are recommended.
For additional details, developers can access the [TypeGraph documentation](https://typegraph.dev) and its [GitHub repository](https://github.com/nicia-ai/typegraph/).
Keywords: #phi4, Drizzle, Postgres, RAG context, SQLite, TypeGraph, Zod, incremental adoption, knowledge graph, ontology reasoning, query builder, smart relationships, type-safe, vector queries
typegraph.dev 12 days ago
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3108.
HN
Agent Harness for Coding with ClickHouse
The article explores how AI can streamline the transition of analytical workloads from Postgres to ClickHouse through an agent harness system like MooseStack, ensuring efficient and successful migrations in complex production environments where standard AI agents fall short. It emphasizes a structured migration approach that begins with rearchitecting data models for OLAP performance by rewriting them, followed by converting read-time operations into materialized views at write time while optimizing queries within APIs and testing these changes across frontend components.
MooseStack serves as a critical framework in this process, offering agents the necessary tools, context, and interfaces to facilitate effective migration. This involves treating all data stack elements as code for AI agents to manage using familiar development practices such as Typescript patterns. By managing dependencies explicitly through version control systems like git, teams can iterate quickly with ease of rollback.
The strategy includes establishing rapid feedback loops via IDE errors, local dev environments, and cloud-based preview deployments, ensuring continuous validation and iteration throughout the migration process. Additionally, MooseStack provides AI agents with detailed contextual support regarding existing implementations and best practices, which is crucial for accurate and efficient migrations. Overall, integrating an agent harness like MooseStack enables fast, secure, and reliable transitions from Postgres to ClickHouse by viewing the data stack as code and supplying comprehensive feedback and contextual guidance.
Keywords: #phi4, AI Migration, Agent Harness, ClickHouse, Data Stack, Feedback Loops, Materialized Views, MooseStack, OLAP, Postgres, Query Optimization, Schema Evolution, Semantic Layer, Typescript
clickhouse.com 13 days ago
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3197.
HN
Is Postgres Enough in 2026?
As of 2026, PostgreSQL (Postgres) is highlighted as a comprehensive database solution suitable for early-stage development due to its wide-ranging features that address numerous core requirements. It supports ACID transactions, JSON document storage, full-text search, geospatial queries, and lightweight publish-subscribe functionalities. Additionally, Postgres benefits from an array of extensions that allow it to handle various workloads effectively.
The text advises against the premature integration of specialized tools such as Redis, Kafka, or Elasticsearch, which can complicate operations without yielding immediate advantages. Instead, early-stage teams are encouraged to exploit PostgreSQL's full capabilities, leveraging JSONB storage with GIN indexes, unlogged tables for caching, and materialized views for pre-computed results.
Specialized systems should be considered only when specific use cases—like needing extremely low latency (Redis), document-centric high-volume writes (MongoDB), advanced search functionalities (Elasticsearch), or reliable message brokering (Kafka)—cause measurable performance bottlenecks. The recommended approach is to begin with Postgres, optimize its capabilities using indexing strategies and other features, and expand the technology stack based on actual performance data. This strategy aims to maintain development speed by avoiding unnecessary complexity while efficiently delivering value.
Keywords: #phi4, ACID transactions, Elasticsearch, JSONB, Kafka, MongoDB, Postgres, Redis, connection pooling, full-text search, geospatial queries, materialized views, optimization, partitioning, pub/sub, scalability
xata.io 13 days ago
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3252.
HN
We Have Pgvector at Home
The article delves into utilizing the pgvector extension for implementing semantic search within PostgreSQL databases, focusing on identifying books related to themes such as "Megatron" or "teenage vampires." It addresses the challenge of integrating vector embeddings into PostgreSQL, which lacks native support for this data type. The built-in `cube` extension is highlighted as a potential solution for handling 2D data by allowing up to 100 dimensions with Euclidean distance calculations and indexing capabilities; however, its utility is limited due to constraints on dimensionality that may not suffice for modern applications. Additionally, the article discusses matryoshka embedding models, which aim to enhance efficiency by prioritizing crucial dimensions during training. Despite their potential, these models have yet to be effectively deployed. The piece appears to be part of a series from The Consensus, a subscription-based company, and concludes with unrelated text that might serve as placeholders or errors in the draft. Overall, it underscores PostgreSQL's limitations with high-dimensional vector data and explores various extensions and techniques aimed at overcoming these challenges.
Keywords: #phi4, Euclidean distance, Matryoshka, Postgres, Semantic search, books, cube extension, database, dimensions, embedding vector, pgvector, similarity search, vectors
theconsensus.dev 14 days ago
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3318.
HN
Postmortem: Clerk System Outage
On February 19, 2026, Clerk encountered a major service outage caused by an inefficient query plan due to Postgres' automatic analyze function. This led to severe database performance issues, resulting in over 95% of traffic returning HTTP status code 429 (Too Many Requests). The problem was traced back to a customer experiencing a traffic spike driven by an aggressive retry mechanism within Clerk's infrastructure and was identified at 16:15 UTC.
To address the outage, Clerk implemented several mitigation measures. These included manually blocking the problematic customer's traffic and activating a new failover mechanism for session token generation, which partially restored service. Full recovery required re-executing ANALYZE manually at 17:27 UTC, with normal database performance returning by 18:06 UTC.
The root cause was determined to be an unreliable statistic in Postgres' query planner that mistakenly assumed all values in a column were NULL, causing a "query plan flip." This incident underscored the need for improved monitoring and alerting mechanisms. In response, Clerk plans to enhance database resilience by adjusting statistics targets, refactoring queries, and auditing existing ones.
Additionally, Clerk aims to improve its incident communication process. This includes designating a communications lead, providing regular status updates, and improving tooling for broader information dissemination. The company recognizes the shortcomings in communication during this incident and is committed to enhancing system reliability through additional monitors, redundancies, and failovers.
Keywords: #phi4, 2026, Clerk System, Clerk System Outage, February 19, Outage, Postgres, Postmortem, Redundancy, Session API, database performance, failover, incident communication, inefficient query plan, query planner, redundancy Keywords: Postmortem, request handlers, retry mechanism, service outage, statistics target, traffic spike
clerk.com 14 days ago
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3322.
HN
Show HN: pgfence – catch dangerous Postgres migrations before they merge
pgfence is a command-line utility engineered specifically for detecting potentially hazardous PostgreSQL database migrations prior to their integration into the main codebase. Its primary objective is to bolster the safety and dependability of database modifications by proactively identifying operations that may pose risks. By analyzing migration scripts, pgfence helps developers recognize and mitigate issues before they manifest in a production environment. This preventative approach ensures smoother transitions during updates and maintains database integrity by addressing potential problems early in the development process.
Keywords: #phi4, CLI, Migration, Postgres, Safe, Show HN, catch, dangerous, database safety, merge, migrations, pgfence, tool
pgfence.com 14 days ago
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3357.
HN
Show HN: Pq – Simple, durable background tasks in Python using Postgres
PQ is a Python library tailored for executing durable background tasks leveraging PostgreSQL's robust features to overcome limitations found in existing solutions like python-rq. It facilitates task concurrency and durability management by utilizing PostgreSQL's `SELECT ... FOR UPDATE SKIP LOCKED` feature, ensuring atomic task claiming. The library offers several key features: transactional enqueueing ensures that tasks are only added if the enqueuing transaction commits successfully; fork isolation allows each task to run in a separate child process monitored by the parent worker, enhancing resilience against crashes and memory issues; periodic tasks can be scheduled with intervals or cron expressions and offer overlap control along with pause/resume functionality without requiring schedule deletion. PQ supports five priority levels through its priority queues feature, enabling dedicated workers to handle tasks based on their assigned importance. It also provides lifecycle hooks for executing code before and after task execution in a fork-safe manner, which is particularly useful for integrating libraries like OpenTelemetry. Additionally, it supports idempotency and upserts for client IDs, facilitating debouncing operations.
However, PQ has limitations; it is not designed for scenarios requiring high throughput (exceeding 10,000 tasks per second) or complex workflows, where systems such as Celery would be more appropriate due to their dedicated broker capabilities. Installation requires PostgreSQL and Python 3.13 or later, with the command `pip install python-pq`. Overall, PQ is best suited for teams that already utilize PostgreSQL and prefer an infrastructure setup with fewer components, eliminating the need for additional services like Redis or RabbitMQ.
Keywords: #phi4, Celery, DAGs, OOM safety, Postgres, Prefect, Python, RQ, RabbitMQ, Redis, Temporal, async support, background tasks, cron expressions, durability, enqueuing, error handling, fork isolation, high throughput, idempotency, lifecycle hooks, migration, periodic tasks, pip install, priority queues, process isolation, scheduling, task workers, transactional enqueueing, uv
github.com 14 days ago
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3366.
HN
What Is a Database Transaction?
The article delivers an extensive examination of database transactions, emphasizing their role and implementation in MySQL and Postgres. It outlines how transactions facilitate the execution of multiple SQL operations as a single atomic unit, ensuring data consistency through commands like `begin`, `commit`, and `rollback`. While most transactions conclude successfully, failures may arise due to physical issues such as hardware malfunctions or power outages, with databases utilizing disaster recovery techniques—like Postgres's write-ahead log (WAL)—to mitigate these risks.
The concept of "consistent reads" is introduced, enabling transactions to have an isolated database view during execution. Both MySQL and Postgres support this in `REPEATABLE READ` mode but employ different methods: Postgres uses multi-versioning, while MySQL relies on an undo log. Postgres's approach involves creating new row versions with metadata tracking transaction IDs (`xmin`, `xmax`), whereas MySQL overwrites old rows immediately and utilizes an undo log to recreate past versions as needed.
The article also explores four isolation levels—`Serializable`, `Repeatable Read`, `Read Committed`, and `Read Uncommitted`—each offering distinct balances between data consistency and performance, with `Serializable` being the most stringent. In handling write conflicts within the `SERIALIZABLE` mode, MySQL employs locks (shared for reading, exclusive for writing) to manage concurrent updates and detect deadlocks when they occur. Conversely, Postgres uses predicate locks and optimistic conflict resolution strategies, tracking row usage and dynamically resolving data conflicts to prevent deadlocks.
Transactions are highlighted as a fundamental component of database management systems, providing crucial consistency and isolation guarantees that support reliable application performance. The article concludes by inviting readers to suggest future topics of interest in the realm of database systems.
Keywords: #phi4, Atomic Operations, Commit, Database Transactions, Dirty Reads, Disaster Recovery, Isolation Levels, Locks, Multi-Versioning, MySQL, Optimistic Conflict Resolution, Phantom Reads, Postgres, Predicate Locks, Rollback, SERIALIZABLE Mode, SQL Databases, Undo Log, WAL (Write-Ahead Log), Write Conflicts
planetscale.com 14 days ago
https://aphyr.com/posts/327-jepsen-mariadb-galera-clust 14 days ago
https://visualdb.com/blog/concurrencycontrol/ 14 days ago
https://gsap.com 14 days ago
https://planetscale.com/blog/btrees-and-database-indexe 14 days ago
https://www.postgresql.org/docs/current/transactio 14 days ago
https://www.goodreads.com/book/show/23463279-desig 14 days ago
https://dev.mysql.com/doc/refman/8.4/en/ 14 days ago
https://mariadb.com/docs/server/reference/sql 14 days ago
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3373.
HN
Postgres Is Your Friend. ORM Is Not
The text presents a compelling case for using PostgreSQL as an all-encompassing tool for data management, highlighting its advanced features that extend beyond traditional relational database functions. It underscores the utility of JSONB fields, full-text search through the `pg_trgm` extension, automated partitioning with `pg_partman`, and concurrent processing via LISTEN/NOTIFY mechanisms. The text argues against employing Object-Relational Mappers (ORMs), citing them as sources of unnecessary complexity and inefficiency due to issues like N+1 queries and obscure behaviors. Instead, it advocates for the direct use of SQL, emphasizing its expressiveness, control, and efficiency in persisting aggregates.
By demonstrating plain SQL implementations within a repository pattern, the article illustrates how PostgreSQL can be effectively utilized without an ORM while maintaining key principles such as Separation of Concerns and Locality of Behaviour. It suggests accepting code repetition over excessive abstraction under DRY (Don't Repeat Yourself) to preserve clarity and control in complex queries, recommending Jinja templates for managing these queries.
Overall, the article positions PostgreSQL as a robust solution that simplifies infrastructure by eliminating the need for additional tools like Kafka or RabbitMQ. It encourages developers to leverage PostgreSQL's capabilities directly through SQL to achieve simplicity, efficiency, and transparency in data handling.
Keywords: #phi4, Django ORM, FOR UPDATE SKIP LOCKED, GIN index, JSONB, Jinja template, LISTEN/NOTIFY, Locality of Behaviour, ORM, Postgres, SQL DSL, SQLAlchemy, UUID, analytical queries, asyncpg, concurrency, full-text search, partitioning, pg_partman, pg_trgm, query optimization, scalability
hypha.pub 14 days ago
https://en.wikipedia.org/wiki/SQL_injection 14 days ago
https://en.wikipedia.org/wiki/SQL_injection#Parameteriz 14 days ago
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