---
title: "Postgres"
description: "308 posts about Postgres, summarised, each linking to the original."
---

# Postgres
> 308 posts about Postgres, summarised, each linking to the original.

## Articles

### [Branching Without Git Is Now The Default](https://yomu.fyi/post/branching-without-git-is-now-the-default.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: joshenlim, qiao, saxonf
- Published: May 4, 2026
- Type: Announcement

Supabase has made dashboard-based database branching the default for all projects, adding a GitHub-free alternative to its existing git-based workflow. Developers can create a branch from the dashboard, work in the SQL Editor or Table Editor against a separate Postgres instance, review a schema diff, and merge changes into production without Git configuration. The workflow uses pg-delta, a new schema-diffing engine in pg-toolbelt that covers tables, columns, RLS policies, functions, triggers, indexes, and extensions, replacing migra as dashboard branching’s default engine; pg-delta is alpha software and is also available in the CLI behind a flag. Git-based branching remains fully supported, and the two approaches can be switched between or used together, while branches created through the Supabase MCP server use dashboard branching automatically.


### [Backstage with Lakebase](https://yomu.fyi/post/backstage-with-lakebase.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cameron Casher, Kevin Hartman
- Published: Apr 30, 2026
- Type: Explainer

Backstage with Lakebase describes a proof of concept that moves Backstage’s stateful internal developer portal from standard Postgres to Databricks Lakebase, a serverless Postgres surface in the Databricks Data + AI Platform. Connection required an app-config.yaml change, PgSearchEngine instead of the default in-memory search, and short-lived OAuth JWT credentials generated with databricks postgres generate-database-credential; Knex migrations then ran cleanly. Using copy-on-write branches, the team cloned a roughly 63 MB catalog in 1.09 seconds and recovered 32 deleted entities in 3.78 seconds, while observing that point-in-time recovery snaps backward to the nearest WAL record. The post argues that cheap, isolated database state can replace much mocking and staging iteration with real-data development, branch-based testing, schema validation in CI, and automated cleanup, while noting that VS Code/Cursor synchronization tooling remains underway.


### [Databricks and Stripe projects: Infrastructure built for agents](https://yomu.fyi/post/databricks-and-stripe-projects-infrastructure-built-for-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Brad Van Vugt, Guillaume Rivals
- Published: Apr 29, 2026
- Type: Announcement

The post announces Databricks as a launch partner for Stripe Projects, an agent-first CLI that lets AI agents discover, provision, and pay for Neon databases without human interaction. It addresses the manual work still required to provision services, navigate interfaces, configure accounts, and enter payment details, which can slow autonomous app development. With Lakebase architecture, Neon can provide production-ready Postgres in under 350ms, while decoupled compute and storage support rapid creation and teardown of OLTP databases. Serverless scaling adjusts compute to traffic and can scale to zero, while zero-copy branching creates isolated production-data branches for testing, migrations, and prompt experiments without risking the primary environment. The post also notes Databricks’ Stripe Data Pipeline is available in the Databricks Marketplace for analyzing Stripe data without ETL.


### [How leading tech companies are killing the builder's tax with Lakebase](https://yomu.fyi/post/how-leading-tech-companies-are-killing-the-builder-s-tax-with-lakebase.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amey Banarse, Madelyn Mullen
- Published: Apr 27, 2026
- Type: Explainer

The post argues that AI-native applications face a “builder’s tax” because operational databases and lakehouse analytics are separated by CDC, ETL/ELT, and reverse ETL pipelines. This architecture can leave AI systems with stale data while duplicating governance and shifting engineering effort toward orchestration and failure management. It presents Lakebase, a fully managed serverless Postgres engine integrated with the Databricks Data + AI Platform, as a shared operational foundation where applications, agents, analytics, and governance use the same data. The proposed pattern combines a lakehouse intelligence layer, a low-latency Lakebase execution layer, and a continuous learning loop, with pgvector supporting agent memory and semantic search. Reported customer examples include faster feature onboarding, reduced on-call disruption, higher agent throughput, and consolidated data estates, though the results vary by organization.


### [Inside one of the first production deployments of Lakebase: LangGuard's agentic workflow governance engine](https://yomu.fyi/post/inside-one-of-the-first-production-deployments-of-lakebase-langguard-s.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Venkat Raghavan, Jason Keirstead, Ravi Srinivasan, Nina Williams, Amelia Westberg
- Published: Apr 27, 2026
- Type: Explainer

LangGuard is presented as a runtime enforcement layer for enterprise agentic workflows, monitoring actions, decisions, tools, credentials, and intent across connected systems. Its GRAIL data fabric records multidimensional trace data, builds a live knowledge graph, and evaluates allow/deny/modify decisions against policy before tools, datasets, or models are accessed. The deployment uses Databricks Lakebase as the operational system of record, relying on PostgreSQL, serverless autoscaling, scale-to-zero, compute-storage disaggregation, hot-data caching, and copy-on-write branching for policy testing. LangGuard chose this architecture to handle bursty trace writes and low-latency enforcement reads without provisioning for peak demand, while keeping operational data available to Databricks analytics and AI capabilities without additional ETL. The stated next step is predictive governance: training behavioral models on historical traces to flag anomalous agent behavior before a policy violation.


### [Operational databases: How they work and when to use them](https://yomu.fyi/post/operational-databases-how-they-work-and-when-to-use-them.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 24, 2026
- Type: Explainer

Operational databases, or online transaction processing (OLTP) databases, support day-to-day operations by processing real-time CRUD transactions with low latency, high concurrency, and ACID guarantees. The guide contrasts them with data warehouses and OLAP systems, which favor historical analysis, complex aggregations, and read-heavy workloads, while describing ETL, CDC, and operational data stores in the OLTP-to-OLAP pipeline. It argues that traditional OLTP systems can struggle with real-time analytics and AI because of siloed data, structured-data constraints, rigid schemas, scaling limits, and missing governance features. Databricks Lakebase is presented as a hybrid solution using separate storage and compute, serverless Postgres, elastic scaling, branching, shared lakehouse storage, and open formats. The proposed workflow connects existing OLTP systems through CDC or streaming into Delta Lake for fresh SQL, BI, ML, and AI access.


### [What's New in pg\_clickhouse - JSONB Support, SQL value functions, Streaming, and more](https://yomu.fyi/post/what-s-new-in-pg-clickhouse-jsonb-support-sql-value-functions-streamin.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: David Wheeler
- Published: Apr 24, 2026
- Type: Announcement

Recent releases of pg\_clickhouse, a Postgres foreign data wrapper for ClickHouse, introduce query pushdown enhancements and streaming result handling. Version 0.1.10 adds pushdown for JSONB accessor operators and extraction functions by mapping them to ClickHouse sub-column syntax and toJSONString calls outside SELECT clauses. In version 0.2.0, date and time functions such as CURRENT\_DATE, CURRENT\_TIMESTAMP, and clock\_timestamp() push down to ClickHouse while honoring the active Postgres session time zone and precision settings. To address memory pressure and out-of-memory risks from large foreign queries, the extension adds query result streaming to the HTTP driver with a default batch buffer of approximately 50MB. Container benchmarking against the NYC taxi dataset showed streaming reduced peak memory consumption from over 600 MiB to under 86 MiB.


### [Terraform & OpenAPI for ClickPipes is now Generally Available](https://yomu.fyi/post/terraform-openapi-for-clickpipes-is-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Marta Paes
- Published: Apr 20, 2026
- Type: Announcement

ClickHouse Cloud announced the general availability of ClickPipes resources in Terraform and OpenAPI, expanding infrastructure-as-code support across its managed data ingestion platform. The release resolves previous beta gaps by adding full connector coverage, including Change Data Capture for Postgres, MySQL, and MongoDB, alongside support for BigQuery, Azure Blob Storage, and reverse private endpoints. Operators can provision new data ingestion pipelines, pause or resume sync operations, and scale resources programmatically through OpenAPI endpoints or the ClickHouse Terraform provider starting at version 3.14.0. Existing ClickPipes can also be imported into Terraform state using native import blocks and automated configuration generation. Although SSH tunneling remains under active development, nearly all UI capabilities now map cleanly to programmatic interfaces.


### [What is pgvector?](https://yomu.fyi/post/what-is-pgvector.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 17, 2026
- Type: Explainer

pgvector is an open-source PostgreSQL extension that adds vector storage, indexing, and similarity search for embeddings alongside relational application data. It supports semantic search, recommendations, retrieval-augmented generation (RAG), image similarity, anomaly detection, and deduplication without requiring a separate vector database for many workloads. The extension uses the vector data type and distance metrics including L2, cosine similarity, and inner product, with HNSW and IVFFlat indexes offering different speed and memory trade-offs. Vector queries can also combine relational filters with Postgres full-text search for hybrid retrieval. pgvector is positioned as a practical choice for existing Postgres deployments, while pgvectorscale can extend its usefulness as data and query demands grow; dedicated vector databases become more relevant at larger scales, and Databricks AI Search serves complementary lakehouse workloads.


### [Agentic coding with ClickHouse. One person, one data stack, one full-stack application](https://yomu.fyi/post/agentic-coding-with-clickhouse-one-person-one-data-stack-one-full-stac.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Oussama Chakri
- Published: Apr 14, 2026
- Type: Problem & solution

A Solutions Architect at ClickHouse built ClickShop, a full-stack retail analytics platform, within a few days using Cursor to demonstrate production-level capabilities beyond static slides. The platform pairs ClickHouse for sub-second analytical queries across billions of rows with PostgreSQL for transactional workflows, using ClickPipes for real-time change data capture synchronization. LibreChat orchestrates 18 persona-tailored AI agents, while dual observability layers—ClickStack for OpenTelemetry infrastructure metrics and Langfuse for LLM prompt and cost tracking—store their telemetry directly in ClickHouse. Developing with an AI-assisted IDE was accelerated by open-source documentation and community skills, though it required continuous human review to handle unintended code refactoring and token consumption.


### [We're Transferring the Stripe Sync Engine to Stripe](https://yomu.fyi/post/we-re-transferring-the-stripe-sync-engine-to-stripe.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: raminder\_singh, matt\_linkous, gregor\_vand, ana\_mogul
- Published: Apr 14, 2026
- Type: Announcement

The Stripe Sync Engine repository is moving from supabase/stripe-sync-engine to stripe/sync-engine after growing from an open-source tool for syncing Stripe data into a Stripe schema in Postgres. The transfer follows collaboration that added cursor-based incremental sync, JSONB storage with generated columns, multi-account support for Stripe Connect platforms, event ordering in a new CLI, and automatic incremental backfilling using Supabase Cron and Queue. The latest release adds coupon synchronization, branching support, immediate syncing on installation, one-click upgrades, SSL enforcement, and tighter installation controls, along with clearer errors and dashboard visibility for Edge Function source code. The project remains open source under Apache 2.0, existing installations continue to work, and the Supabase dashboard integration is unchanged. With Stripe data in Postgres, users can join it with application data for subscription checks, feature gating, MRR, and churn analysis.


### [AI Agents Know About Supabase. They Don't Always Use It Right.](https://yomu.fyi/post/ai-agents-know-about-supabase-they-don-t-always-use-it-right.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: pedro\_rodrigues
- Published: Apr 9, 2026
- Type: Announcement

Supabase is releasing Supabase Agent Skills, an open-source instruction set for AI coding agents working with Supabase through Claude Code, Codex, GitHub Copilot, Cursor, and compatible Agent Skills implementations. The skill addresses recurring errors such as omitted RLS policies, hallucinated CLI commands, views missing security\_invoker = true, and reliance on outdated training data by requiring documentation checks and embedding critical security guidance in SKILL.md. It also guides CLI and MCP workflows and recommends direct schema changes on local or staging databases, followed by database advisors and a migration after the schema stabilizes. Early evaluations across six scenarios found MCP + Skill scores of 67% for Claude Opus 4.6, 71% for Claude Sonnet 4.6, 88% for Codex GPT-5.4, and 71% for GPT-5.4 Mini, though the sample was small.


### [How Respan is scaling LLM observability with ClickHouse Cloud](https://yomu.fyi/post/how-respan-is-scaling-llm-observability-with-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 6, 2026
- Type: Problem & solution

Respan scaled its LLM gateway and observability platform from hundreds of daily requests to 50 million daily events, causing its original Django and Postgres architecture to stall under transaction contention at 50 to 100 requests per second. To overcome Postgres write-ahead logging bottlenecks, the team migrated the ingestion and analytics pipelines to ClickHouse Cloud. Engineers minimized row sizes by truncating large prompt text and writing structured metrics to MergeTree tables, then built incremental minute- and hour-level materialized views alongside scheduled refreshable views for user metadata. Distributed tracing spans are ingested asynchronously with analytical parent-child resolution, utilizing functions like argMax rather than the FINAL modifier to prevent costly table merges. This architectural shift allows Respan to achieve sub-second search across tens of millions of records and support customer dashboards with predictable query performance.


### [100,000 GitHub stars](https://yomu.fyi/post/100-000-github-stars.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: paul\_copplestone
- Published: Apr 2, 2026
- Type: Announcement

Supabase reports reaching 100,000 GitHub stars and says eight million developers are building with the platform. Founded in 2020 by two people around a Postgres database, the project says it optimized for developers having a good time rather than for star counts, treating the milestone as evidence of a community that contributed, stayed involved, and built unexpected projects. Its open-source approach centers on supporting existing tools and communities: projects run unmodified Postgres, PostgREST handles billions of API requests daily, pgvector stores embeddings, Deno underpins Edge Functions, imgproxy handles image transformations, and Phoenix supports Realtime at almost 1.5 billion messages per day. The team says it aims to help make Postgres the default database for new internet projects, including by pursuing petabyte scale and pluggable storage.


### [PostgresBench: A Reproducible Benchmark for Postgres Services](https://yomu.fyi/post/postgresbench-a-reproducible-benchmark-for-postgres-services.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Lionel Palacin
- Published: Apr 2, 2026
- Type: Benchmark

PostgresBench is a public, reproducible benchmarking suite created to compare transactional performance across managed PostgreSQL providers. The benchmark uses the standard pgbench tool with a TPC-B-like write-intensive workload executing across 256 clients and 16 threads for ten-minute durations. Evaluators tested five services—including Postgres managed by ClickHouse, AWS Aurora, AWS RDS, Neon, and Crunchy Bridge—under 100 GB and 500 GB scale factors using default configurations in the us-east-2 region. Across tested configurations, Postgres managed by ClickHouse delivered higher transactions per second and lower P99 latencies due to physically co-located NVMe primary storage. These results demonstrate that remote network storage layers, such as EBS volumes, introduce significant latency overhead during sustained write and fsync operations.


### [Supabase Joins the Stripe Projects Developer Preview](https://yomu.fyi/post/supabase-joins-the-stripe-projects-developer-preview.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregor\_vand, ana\_mogul
- Published: Mar 19, 2026
- Type: Announcement

Supabase has joined the Stripe Projects developer preview, a Stripe CLI workflow for provisioning real services and obtaining working credentials with a single command. The integration provisions a complete Supabase project—including Postgres, Auth, Storage, Edge Functions, and Realtime—without requiring dashboard interaction, while supporting both developers and AI agents through deterministic, repeatable steps. After installing the Stripe CLI and adding supabase/project, stripe projects env --sync writes credentials to a local .env file and stores them in the Stripe Project for agent access; existing Supabase users can link their account. Projects remain in the user’s Supabase account with dashboard, connection-string, and data access, and database credentials can be rotated with stripe projects rotate supabase/project. The feature is a developer preview, and Stripe and Supabase are requesting feedback through Discord or GitHub.


### [March 2026 newsletter](https://yomu.fyi/post/march-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Mar 19, 2026
- Type: Announcement

The March 2026 ClickHouse newsletter details major platform updates, community deployments, and architectural practices for high-throughput analytics. Recent product announcements include ClickHouse 26.2 featuring time-based block flushing for streaming feeds, general availability of native inverted indexes for full-text search, and the release of chDB 4 with a Pandas-compatible DataStore API. Community implementations show Cloudflare managing 1.61 quadrillion daily events, while Trigger.dev isolated multi-tenant SQL execution using a domain-specific query language that compiles safe ClickHouse queries. In data optimization techniques, Hookdeck accelerated webhook payload search from over 30 seconds to under 400 milliseconds by partitioning payload hashes into typed bucket columns and scanning iterative time windows. Additional operational guidance emphasizes setting the ttl\_only\_drop\_parts parameter and aligning table partitioning with time units so background merges drop entire data parts cleanly without triggering mutation rewrites.


### [How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse](https://yomu.fyi/post/how-socialpruf-built-a-faster-more-reliable-data-stack-by-replacing-ne.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 17, 2026
- Type: Problem & solution

Socialpruf, a social media performance tracking platform ingesting hundreds of posts per second, experienced analytics latency, connection dropouts, and high network transfer costs while running Postgres on Neon alongside ClickHouse Cloud. To resolve these issues, the team migrated their transactional system of record to NVMe-backed Postgres managed by ClickHouse, colocating transactional and analytical workloads. Although standard Postgres logical replication failed on their 0.5 TB dataset, they achieved continuous synchronization using PeerDB before conducting a production cutover. The migration eliminated connection dropouts, reduced network transfer fees, and improved overall Postgres query performance by approximately 30 percent. To manage thousands of concurrent connections generated by ingestion workers, Socialpruf implemented ClickHouse's bundled, horizontally scalable PgBouncer instances.


### [AI is redrawing the database market](https://yomu.fyi/post/ai-is-redrawing-the-database-market.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tanya Bragin
- Published: Mar 17, 2026
- Type: Explainer

Emerging artificial intelligence workloads are fundamentally transforming data platform requirements across real-time analytics, data warehousing, and observability. Conversational analytics and autonomous agents replace periodic batch queries with rapid bursts of concurrent, low-latency requests, exposing the architectural limits of traditional cloud data warehouses and standalone transactional databases. To resolve these constraints, architectures are converging on engine-level integrations between Postgres for transactions and ClickHouse for analytical scale, while unifying operational observability and business event storage. ClickHouse also incorporates turnkey agent interfaces through its acquisition of LibreChat alongside dedicated execution tracing from tools like Langfuse. These combined capabilities ensure organizations can reliably support interactive agentic workflows without incurring severe latency penalties or compounding infrastructure costs.


### [Log Drains: Now available on Pro](https://yomu.fyi/post/log-drains-now-available-on-pro.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: steven\_eubank
- Published: Mar 5, 2026
- Type: Announcement

Supabase has launched Log Drains for its Pro tier, allowing users to send Supabase logs to their own logging backend and investigate them alongside the rest of their stack. The feature addresses the separation between Supabase logs and external observability tools by exporting events from Postgres, API Gateway, Auth, Storage, Edge Functions, and Realtime. Logs are sent over HTTP in batches of up to 250 entries or every second, whichever comes first, with Gzip compression when supported by the destination. Supported destinations include Sentry, Grafana Loki, Datadog, AWS S3, Axiom, and generic HTTP endpoints; users configure drains in the dashboard and can create multiple drains, with pricing of $60 per drain per project, $0.20 per million events, and $0.09 per GB of egress.


[Newer posts](https://yomu.fyi/topic/postgres/page/6.md) · [Older posts](https://yomu.fyi/topic/postgres/page/8.md)