---
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

### [Postgres on NVMe: performance and the convergence of transactions and analytics](https://yomu.fyi/post/postgres-on-nvme-performance-and-the-convergence-of-transactions-and-a.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 21, 2026
- Type: Benchmark

The post examines how local NVMe storage changes Postgres performance, particularly when working sets exceed memory and disk latency becomes the bottleneck. It argues that while faster storage accelerates transactional workloads, it does not alter the row-store layout, so analytical queries still require different approaches. Benchmark results show 9.2× more throughput on NVMe, with a median of 16,030 TPS versus 1,734 on EBS. The post introduces WalShadow, an open-source replication engine that reads the physical Postgres WAL to enable sub-second replication to ClickHouse without logical slots. It concludes that failover is solvable with tools like Patroni, and that storage latency is only part of the performance problem.


### [Database for AI Agents: 5 Evaluation Criteria](https://yomu.fyi/post/database-for-ai-agents-5-evaluation-criteria.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

The post defines a database for AI agents as infrastructure for short-term, episodic, procedural, and operational memory, rather than merely a place to retrieve documents. It proposes five evaluation criteria for moving agent workloads from prototypes to production: per-agent branch isolation, serverless scale-to-zero, hybrid search, ACID guarantees, and unified platform access. The technical rationale is that agents perform repeated reads and writes, resume tasks, share state concurrently, and need current data across vector, keyword, and metadata retrieval; zero-copy branches, sub-second wakeups, and single-query hybrid search address specific operational constraints. It concludes that a database missing more than one minimum bar becomes a production risk, while standard Postgres alone supplies ACID but not branching, scale-to-zero, or unified operational and analytical access.


### [How Databricks Helps Baseball Teams Gain an Edge with Data & AI](https://yomu.fyi/post/how-databricks-helps-baseball-teams-gain-an-edge-with-data-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nick Ragonese, Wesley Pasfield
- Published: Sep 18, 2026
- Type: Explainer

Baseball teams can use Databricks to turn Statcast and team-derived pitch-level data into game-day plans for hitters, bullpen changes, pinch-hit choices, advance scouting, and roster decisions. Genie, on Unity Catalog, queries governed Delta tables and reusable Unity Catalog SQL functions for tendencies by count, handedness, pitch type, location, and base-runner state. A Multi-Agent Supervisor built with Agent Bricks and deployed on Model Serving simulates matchup pockets, compares reliever arsenals with hitter profiles, and produces explanations that staff distill into printed decision cards. For advance work, AI Search indexes pitcher and hitter embeddings to find similar arms, while Lakebase Postgres keeps reports, tags, and decisions close to the source data for shared operational memory.


### [Zero downtime patching lakebase part-1 prewarming](https://yomu.fyi/post/zero-downtime-patching-lakebase-part-1-prewarming.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hans Norheim
- Published: Sep 18, 2026
- Type: Problem & solution

Lakebase addresses the workload disruption caused by planned PostgreSQL maintenance, where a restart clears in-memory buffer and local file caches. Although the database can return in about one second at P99, the source reports a roughly 70% pgbench TPS reduction while caches warm, potentially causing timeouts. Lakebase uses stateless Postgres compute replicas and disaggregated shared storage to prewarm a replacement compute before a scheduled restart. The control plane transfers cached-page information, loads pages from shared storage, and follows relevant WAL from Safekeepers, while the current primary continues serving traffic. After prewarming, Lakebase promotes the new compute with PostgreSQL’s pg\_promote without restarting the server; tests on 10 GB pgbench workloads found nearly instant throughput recovery, especially for read-only queries.


### [Database Branching in Postgres: Git](https://yomu.fyi/post/database-branching-in-postgres-git.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Susan Pierce
- Published: Sep 18, 2026
- Type: Problem & solution

Shared staging databases often drift as schemas diverge, test data accumulates, and developers avoid creating isolated environments because traditional pg\_dump copies are slow and expensive. Databricks Lakebase Postgres addresses this with database branching, which creates fully isolated environments from a parent’s exact schema and data at a point in time. Branches use copy-on-write: they share underlying versioned storage until a branch changes, then record only new data, while each branch has independent compute and a separate connection string. Idle branches scale compute to zero, and storage cost tracks changes rather than the full database size. This supports per-developer, per-pull-request, per-test-run, and recovery workflows, including point-in-time branching within the restore window, with the source describing branch creation through the console, CLI, or API in under a minute.


### [How agentic software development will change databases](https://yomu.fyi/post/how-agentic-software-development-will-change-databases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ippokratis Pandis, Nikita Shamgunov, Reynold Xin
- Published: Sep 18, 2026
- Type: Explainer

AI agents are changing software development from a slow, linear process into rapid evolutionary experimentation, creating many small, ephemeral applications and databases. The post identifies three resulting database requirements: near-zero marginal cost with elastic growth, native branching for rapid variants, and open ecosystems that agents can use reliably. Lakebase addresses branching through O(1) metadata copy-on-write at the storage layer, so agents can branch production or test state without physical data copying and pay only for experiment compute. Its decoupled, serverless compute can scale in sub-second intervals and down to zero, while standard open Postgres page formats in cloud object storage keep data accessible to external engines and tools. Lakebase telemetry reports about 10 branches per project on average, some nested beyond 500 iterations, and roughly four times as many databases created by agents as by humans.


### [Postgres week in the Netherlands: PGDay Lowlands & Percona Live 2026](https://yomu.fyi/post/postgres-week-in-the-netherlands-pgday-lowlands-percona-live-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 17, 2026
- Type: Explainer

An account of three days in the Netherlands covers talks at PGDay Lowlands in Utrecht and Percona Live 2026 in Amsterdam, including a five-minute presentation on ClickHouse integrations and a PostgreSQL 19 monitoring session. pg\_clickhouse uses a foreign data wrapper to expose ClickHouse tables as PostgreSQL foreign tables, allowing existing queries, ORMs, and dashboards to continue working while pushable work runs in ClickHouse; 15 of 22 TPC-H queries at scale factor 1 are fully pushed down. pg\_stat\_ch captures per-query execution events in a shared-memory ring buffer and sends batches to ClickHouse, dropping events rather than blocking PostgreSQL when ClickHouse is unavailable. The monitoring talk covered PostgreSQL 19 logging, WAL and I/O statistics, WAIT FOR, new system views, and multixact and wraparound changes. The post also reports talks on optimizer hints and plan stabilization, and says both conferences will return in 2027.


### [September 2026 newsletter](https://yomu.fyi/post/september-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 17, 2026
- Type: Announcement

The September 2026 ClickHouse newsletter gathers product updates, community contributions, technical articles, and upcoming events. Its main release coverage describes ClickHouse 26.8, including custom HTTP handlers, dynamic query filtering, pipelined SQL through the |> operator, current-user query logging, a URL database engine, and Japanese and Chinese text-index tokenizers. Other featured developments include On-Demand Compute in private preview with a cost-based optimizer and distributed query execution, PromQL and a TimeSeries engine in private preview, AI Functions, and Postgres integrations such as WalShadow and Managed Postgres on Google Cloud. The issue also reports CostBench’s first end-to-end performance-per-dollar results, explains replication-queue diagnosis, notes 57 breaking changes since 26.3, and lists training, webinars, roadshows, and other events.


### [Managed Postgres: What Lakebase Actually Takes Off Your Plate](https://yomu.fyi/post/managed-postgres-what-lakebase-actually-takes-off-your-plate.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 14, 2026
- Type: Explainer

Managed Postgres varies from OS patching alone to operating scaling, failover, backups, recovery, and related database tooling. Lakebase Postgres applies this model to serverless infrastructure, offering automatic scaling, scale-to-zero, PostgreSQL compatibility, automatic snapshots, point-in-time recovery, branching, built-in PgBouncer, and support for extensions including pgvector and PostGIS. Within a region, patching, scaling, failover, and backups run automatically, while encryption, access control, audit visibility, connection pooling, and lakehouse integrations are also addressed. The discussion also stresses checking PostgreSQL versions, configuration, extensions, and migration methods before moving an existing database. Cross-region disaster recovery remains the main exception: it is in Private Preview for AWS, with periodic replication, manual failover, and customer-managed recovery procedures.


### [Introducing WalShadow: Sub-second Postgres replication to ClickHouse from physical WAL](https://yomu.fyi/post/introducing-walshadow-sub-second-postgres-replication-to-clickhouse-fr.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 10, 2026
- Type: Announcement

ClickHouse announces WalShadow, an open-source engine that replicates Postgres data to ClickHouse directly from physical WAL, targeting sub-second analytical availability without logical replication. It consumes the physical WAL stream outside the source database, tracks schema in a shadow Postgres instance, decodes heap records through parallel Rust-based decoders, builds ClickHouse-native blocks, and inserts them concurrently. Because parallel blocks can arrive out of order, each row carries the source WAL position (\_lsn), while barriers preserve ordering for schema changes and truncations. In benchmarks using modest c8i.2xlarge instances, WalShadow reached approximately 200 ms commit-to-visible latency and 289,000 rows per second, compared with PeerDB’s roughly 10 seconds and 120,000 rows per second; it supports initial load, continuous replication, schema evolution, restart recovery, and planned switchovers.


### [Three principles for building a vector platform at Thumbtack](https://yomu.fyi/post/three-principles-for-building-a-vector-platform-at-thumbtack.md)
- Company: [Thumbtack](https://yomu.fyi/company/thumbtack.md)
- Author: John Zhu
- Published: Sep 10, 2026
- Type: Explainer

Thumbtack describes a vector platform that lets ML engineers stand up production vector search without separately negotiating database access, building ETL, or writing a query service. The design uses an existing Aurora Postgres cluster with pgvector, while Airflow jobs generate embeddings into BigQuery and managed reverse-ETL syncs them into vector tables; a query API validates inputs and returns ranked results. Three principles guide the layers: reuse trusted infrastructure, treat embeddings as ordinary data flowing through established pipelines, and lower adoption costs for subsequent teams. Production examples include semantic customer-support escalation using cosine similarity and an internal knowledge assistant combining vector retrieval, often with keyword search, before a language model responds; planned directions include self-serve provisioning, vector-backed feature retrieval, and framework-neutral agent memory.


### [Improving Lakebase Postgres compute cache](https://yomu.fyi/post/improving-lakebase-postgres-compute-cache.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Wein, Sunil Kamath, Haoyu Huang
- Published: Sep 10, 2026
- Type: Problem & solution

Lakebase Postgres uses disaggregated storage backed by object storage, so compute-side caching must deliver low latency and high throughput while avoiding inefficient memory use. Its original design paired a conservatively sized PostgreSQL shared buffer with a local file cache (LFC), but the roughly 1 GB shared-buffer cap pushed many hits to slower NVMe storage and could create double buffering. For fixed-size computes with CU >= 80, the new configuration disables the LFC and sets shared buffers to 75% of DRAM, using explicit 2 MB HugeTLB pages to control page-table and TLB overhead. Benchmark tests found huge pages reduced tail read latency by up to ~40% and CPU use by up to ~30%, while production examples reported doubled throughput, 5× fewer storage reads, or 5× lower CPU use. Work is underway to dynamically resize shared buffers and huge pages for autoscaling computes.


### [Announcing ClickHouse Managed Postgres on Google Cloud](https://yomu.fyi/post/announcing-clickhouse-managed-postgres-on-google-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 9, 2026
- Type: Announcement

ClickHouse Managed Postgres is entering Private Preview on Google Cloud, extending the fully managed, NVMe-backed Postgres service previously launched on AWS. The GCP deployment targets teams wanting Postgres for OLTP and ClickHouse for OLAP in one cloud, avoiding cross-cloud replication, egress costs, and separate networking and compliance management. It uses GCP Local SSD storage, native change data capture through ClickPipes, in-region networking, and pg\_clickhouse, which pushes filters, joins, and aggregations to ClickHouse behind a single Postgres connection. Preview capabilities include zonal high availability with two standbys and quorum replication, point-in-time recovery, read replicas, PgBouncer, more than 90 Postgres extensions, monitoring, Query Insights, scheduled upgrades, and infrastructure-as-code support through OpenAPI, Terraform, and clickhousectl. Access is invitation-only via a waitlist, with availability in every GCP region where ClickHouse Cloud runs and hands-on migration support from Cloud SQL, AlloyDB, RDS, Aurora, Neon, or self-managed Postgres.


### [Introducing Consort: Test-driven development on a branching database](https://yomu.fyi/post/introducing-consort-test-driven-development-on-a-branching-database.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kevin Hartman
- Published: Sep 9, 2026
- Type: Announcement

Consort is introduced as an open-source agentic development framework for test-driven development on branching Lakebase Postgres databases. It addresses the limitations of mocks, shared staging databases, and risky schema-change ceremonies by bringing integration testing into the inner loop. Its build lane runs red/green/refactor cycles against isolated copy-on-write branches of real data, while versioned migrations keep code and schema changes together and pull-request checks test migrations against fresh branches. Consort organizes work through role-based agents and a deterministic conductor, preserving feature artifacts in dedicated directories and supplying scoped context packages. A VS Code plugin pairs Git and Lakebase branches, while an observability dashboard exposes agent turns and human approval gates; the project is community-supported rather than a bundled platform feature with an SLA.


### [Build durable agents with Temporal and Lakebase](https://yomu.fyi/post/build-durable-agents-with-temporal-and-lakebase.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sam Ingbar
- Published: Sep 8, 2026
- Type: Explainer

Long-running personal-loan underwriting agents must preserve completed work, resume after worker failures, tolerate repeated tool calls, wait for reviewers, expose operational state, and retain an audit trail. The reference implementation combines Temporal durable Workflows and Activities with Lakebase Postgres, using Temporal Event History for replay and Lakebase schemas for run status, evidence, review state, messages, and metrics. Deterministic identifiers, constraints, guarded updates, and Postgres upserts make repeated Activity attempts target the same logical records, while Signals carry human decisions and long waits do not occupy workers. A Unity Catalog synced table supplies governed underwriting policy, and optional Change Data Feed publishes operational changes to Delta history tables. The implementation includes a React/FastAPI interface, mocked deterministic providers, OAuth credential refresh, and 21 passing tests, but does not validate production lending, compliance, security, scale, or an observed end-to-end feed.


### [Introducing chdb Postgres extension: High-performance imports from cloud storage](https://yomu.fyi/post/introducing-chdb-postgres-extension-high-performance-imports-from-clou.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 8, 2026
- Type: Announcement

The new chdb Postgres extension uses the chDB library, an in-process ClickHouse engine, to import and export data between Postgres and files on cloud storage. In benchmarks using the one-million-row NYC Taxi dataset, chdb ran on four-vCPU, 32-GB ClickHouse Managed Postgres instances and showed the most consistent performance; pg\_duckdb and pg\_lake took about two to three times longer for CSV, JSON, and Parquet, while aws\_s3 supported fewer formats. Its chdb\_query() function executes chDB queries, while chdb\_hook integrates with COPY for AWS S3, Google Cloud Storage, Azure Blob Storage, local files, and HTTP URLs, with wildcard expansion for multiple files. The extension launches a separate helper application on demand and exchanges ClickHouse Native results through file descriptors, limiting resource use and isolating the PostgreSQL cluster.


### [The 40-year-old database rule agents just broke: How LTAP unifies OLTP and OLAP workloads](https://yomu.fyi/post/the-40-year-old-database-rule-agents-just-broke-how-ltap-unifies-oltp.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 7, 2026
- Type: Explainer

LTAP (Lake Transactional/Analytical Processing) addresses the long-standing separation between operational row-oriented databases and analytical column-oriented systems, which traditionally required pipelines and copied data. It uses Lakebase’s stateless, ephemeral compute decoupled from durable lake storage to expose live operational data for analytical queries without moving it or loading the transaction-serving system. The storage layer preserves Postgres data types and physical representation while writing data into Parquet, with a hot row-format tier for operational access and a cooler columnar tier for analytical reads; Apache Spark and SQL can read that representation directly. By separating serverless operational and analytical compute, LTAP allows each workload to use specialized engines and independent capacity, while a unified catalog applies governance across both. The stated motivation is agentic workloads such as fraud detection, where stale copies miss fast events and uncontrolled analytical queries can degrade transactions.


### [Introducing Scheduled Upgrades in ClickHouse Managed Postgres](https://yomu.fyi/post/introducing-scheduled-upgrades-in-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 3, 2026
- Type: Announcement

ClickHouse Managed Postgres now supports scheduled maintenance windows, addressing the problem of routine platform upgrades landing at the same time for every customer, potentially during heavy workloads. Users can choose a two-hour UTC window, while Enterprise organizations can also specify the days of the week when maintenance is least disruptive. The setting covers ClickHouse-initiated Postgres minor-version upgrades, Managed Postgres feature updates, and patches to operating-system and system components, but not user-triggered changes or urgent storage scaling above 90% disk usage. Read replicas follow the schedule of their primary, and the feature is available without opt-in to Scale and Enterprise organizations; Basic users can see the setting and required plan.


### [From Neon Postgres to ClickHouse Managed Postgres](https://yomu.fyi/post/from-neon-postgres-to-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 2, 2026
- Type: Problem & solution

This user story describes three teams that moved production Postgres workloads from Neon to ClickHouse Managed Postgres after encountering reliability, performance, cost, and network-transfer problems as usage grew. ClickPipes handled schema migration, a parallel initial load, and change data capture, allowing teams to test synchronized databases before cutover. Reported outcomes included zero downtime or connection restarts, faster and more predictable queries, lower CPU or transfer costs, and p99 latency improving from seconds to single-digit milliseconds in one case. The post attributes these gains to local NVMe storage and eliminating transfer fees when analytics already runs on ClickHouse, while noting differences from Neon such as slower branch creation and manual application of several DDL changes.


### [Collaboration makes us all stronger](https://yomu.fyi/post/collaboration-makes-us-all-stronger.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aaron Kobayashi, Mehmet D. Ince, Anurag Srivastava, Alexey Kondratov
- Published: Sep 1, 2026
- Type: Incident / postmortem

An external researcher, Mehmet Ince, reported a memory-safety flaw in PostGIS’s address\_standardizer extension, where caller-controlled grammar data indexed a fixed-size array without bounds checking. Because the extension was available to ordinary tenant roles on managed Postgres services, Databricks treated the issue as a platform exposure rather than a third-party problem, while noting that its microVM architecture meant the exploit did not produce cross-customer impact on Lakebase Postgres and Neon. Detection alerted the Neon team during testing, enabling rapid validation and downstream patching while a durable fix was developed and deployed to protect tenants without customer action. The upstream fix initially missed cases; Mehmet supplied the remaining pieces, and the complete correction was returned to PostGIS, with his bounty donated and matched for the project.


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