---
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

### [New system views in PostgreSQL 19](https://yomu.fyi/post/new-system-views-in-postgresql-19.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 1, 2026
- Type: Explainer

PostgreSQL 19 adds four system views for lock contention, recovery, autovacuum priorities, and dynamic shared memory allocations. The article presents pg\_stat\_lock as cumulative, cluster-wide statistics with one row per lock type, reporting waits, total wait time, fastpath\_exceeded, and stats\_reset; waits and wait\_time count only successfully acquired locks that exceeded deadlock\_timeout, while fast-path overflow attempts are counted separately. Examples reset statistics, reproduce a 2.5-second table lock, and scan 140 partitions, producing 1 wait, 2201.783 milliseconds, and 422 fast-path overflows. The post describes recovery and autovacuum views, noting that autovacuum scores suggest likely priorities rather than guarantees because scoring and worker scheduling can differ. DSM registry entries expose name, type, and size; NULL means initialization failed.


### [Autoscaling Lakebase Postgres](https://yomu.fyi/post/autoscaling-lakebase-postgres.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Carlota Soto
- Published: Aug 31, 2026
- Type: Explainer

Lakebase Postgres addresses the waste and uncertainty of choosing a fixed database instance size before workload demand is known. Its decoupled architecture separates stateless compute from durable storage, allowing compute to start, stop, move, or resize without moving the database. The autoscaling algorithm combines CPU load, memory pressure, and a time-aware estimate of the Postgres working set, using modified HyperLogLog registers that store timestamps and estimate windows from one to 60 minutes. Scaling occurs live through autoscaler-agent, vm-monitor, Kubernetes scheduling, and NeonVM, with checks preventing unsafe downscales and scheduler approval for upscales. The system scales in both directions without dropping connections and operates across 100-millisecond, five-second, and 20-second timescales, including more than 32,000 resizes per month.


### [A new getting started experience for ClickHouse Managed Postgres](https://yomu.fyi/post/a-new-getting-started-experience-for-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 28, 2026
- Type: Announcement

ClickHouse Managed Postgres introduces a four-step onboarding experience designed to move users from provisioning a service to working with operational data and ClickHouse analytics. Existing onboarding placed new users on an empty Overview page, while the documentation quickstart required users to track progress outside the product. The new flow guides users through creating a service, migrating or ingesting data, running a query in the SQL Console, and configuring Postgres CDC to ClickHouse. Data can arrive through a managed Postgres-to-Postgres ClickPipe, a language client, a sample dataset, or a table created from scratch; pg\_clickhouse provides a PostgreSQL query layer over ClickHouse foreign tables. The article reports less than a minute of replication lag and benchmarks showing five aggregations falling from 555 ms to 164 ms and a JOIN with aggregations from 1,246 ms to 170 ms on one million-row data set.


### [Building for the AI Era: Lakebase, Streaming, and Lakehouse Innovations  at VLDB 2026](https://yomu.fyi/post/building-for-the-ai-era-lakebase-streaming-and-lakehouse-innovations-a.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Indrajit Roy, Ippokratis Pandis
- Published: Aug 27, 2026
- Type: Announcement

Databricks previews its VLDB 2026 keynote, four accepted papers, a demo, and a sponsor talk covering database architecture, streaming, and Lakehouse optimization. Reynold Xin will introduce Lakebase, which separates serverless PostgreSQL compute from storage, and LTAP, a transactional-analytical model intended for AI-agent workloads; Lakebase stores data and write-ahead logs in cloud object storage and supports sub-second cold starts and copy-on-write branching. The papers describe a decade of Spark Structured Streaming evolution, including microbatch pipelining that improved throughput by up to 3x, stateful APIs, and fine-grained access control, alongside AutoLiquid’s CLUSTER BY AUTO and Ultron’s history-based query optimization. AutoLiquid outperformed customer-selected clustering keys on over 95% of evaluated workloads, while Ultron improved median join latency by 25% in production workloads. The program also includes an Enzyme demonstration of incremental materialized-view maintenance and a sponsor presentation on LakehouseRT and the Reyden engine.


### [Vertical Advantage: Transforming Industries with Lakebase and Agentic AI](https://yomu.fyi/post/vertical-advantage-transforming-industries-with-lakebase-and-agentic-a.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amit Singh
- Published: Aug 27, 2026
- Type: Announcement

The post presents partner-built, industry-specific solutions on Databricks Lakebase for financial services, manufacturing and energy, retail and consumer sectors, travel and hospitality, healthcare and life sciences, communications and media, and the public sector. Lakebase is described as a serverless Postgres transactional database on the Databricks Data and AI Platform, combining operational and analytical workloads under unified governance and providing operational serving, agent memory, audit trails, and transactional runtime capabilities. Examples include regulation-change monitoring, AI-native claims operations, KYC and personalization accelerators, near-real-time fraud detection, telecom customer-lifecycle decisioning, and public-sector emergency Situation Reports, using components such as Unity Catalog, Lakeflow, Genie, Mosaic AI agents, and Agent Bricks. The stated outcome is production-ready acceleration from insight to action, including faster compliance response, claims adjudication in minutes, low-latency decisions, and reduced manual effort, with partners positioned to help scope pilots.


### [Object Storage + WAL: Lakebase Postgres for the agentic era](https://yomu.fyi/post/object-storage-wal-lakebase-postgres-for-the-agentic-era.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cassie Murray, Carlota Soto
- Published: Aug 27, 2026
- Type: Explainer

The post presents Lakebase Postgres as a design for agent workloads that need isolated copies, restores, replicas, historical queries, and many short-lived databases without repeatedly moving large datasets. It contrasts conventional data-centric OLTP with a transaction-centric model in which PostgreSQL’s write-ahead log (WAL) is the source of truth, with LSNs making database history addressable. Lakebase splits compute, which runs standard Postgres and uses RAM and local NVMe caches, from storage, where safekeepers replicate WAL through quorum acknowledgements, a pageserver materializes pages, and object storage retains immutable history. Commits wait for replicated WAL acknowledgement, not page uploads, while reads request pages at an LSN. The design supports point-in-time queries, cheap branch or read-only compute, scale-to-zero suspension, and shared transactional and analytical access, while still relying on RAM and NVMe for performance.


### [A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres](https://yomu.fyi/post/a-universal-interface-how-quintoandar-made-clickhouse-plug-and-play-wi.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 27, 2026
- Type: Problem & solution

QuintoAndar’s customer data platform was split between a Postgres-based hot layer for low-latency lookups and a Databricks-based cold layer for analytical processing, creating duplicate code and expensive MERGE INTO operations. The team consolidated the pipeline by having its event gateway write directly to ClickHouse Cloud, which also serves API requests and analytical queries while remaining accessible to Trino, Spark, and Superset. Hightouch’s Customer Studio did not support ClickHouse, so QuintoAndar used ClickHouse Managed Postgres and the pg\_clickhouse extension to expose ClickHouse tables as Postgres tables. Queries are pushed down to ClickHouse, while Hightouch’s planner and audit tables remain in NVMe-backed Postgres, allowing the existing activation workflow to continue with negligible round-trip impact for that use case.


### [Read your writes: WAIT FOR in PostgreSQL 19](https://yomu.fyi/post/read-your-writes-wait-for-in-postgresql-19.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 25, 2026
- Type: Explainer

PostgreSQL 19 introduces the top-level SQL command WAIT FOR, addressing stale reads that can occur when an application writes to a primary and immediately reads from an asynchronously replicated standby. After the write, the client or pooler obtains an LSN with pg\_current\_wal\_insert\_lsn() and sends it to the reading session, where WAIT FOR blocks until the requested WAL position reaches the selected stage. The default standby\_replay mode makes the change visible, while standby\_write, standby\_flush, primary\_flush, TIMEOUT, and NO\_THROW provide other durability and error-handling choices. The command waits without holding a snapshot, avoiding a replay self-deadlock, and exposes related wait events in pg\_stat\_activity. The feature preserves asynchronous writes by making only reads that require read-your-writes consistency wait, though timeline changes after promotion require caution.


### [What else runs on your Postgres server, and how do we stop it from taking the database down?](https://yomu.fyi/post/what-else-runs-on-your-postgres-server-and-how-do-we-stop-it-from-taki.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 21, 2026
- Type: Problem & solution

The Postgres VM described here runs PgBouncer instances, a backup agent, metrics exporters, a local Prometheus, a log collector, and watchdog timers alongside the database. Those processes share machine-wide resources outside Postgres’s huge-page-backed shared\_buffers, so leaks, CPU saturation, or disk exhaustion can threaten database availability. ClickHouse Managed Postgres places supporting services in a cgroup v2 slice and combines Go runtime GOMEMLIMIT targets with systemd memory.high and memory.max enforcement. GOMEMLIMIT encourages earlier garbage collection, memory.high applies reclaim and throttling, and memory.max provides a hard ceiling whose OOM scope excludes Postgres because it uses a separate cgroup. CPU weights, bounded buffers, log and metric controls, disk thresholds, and exemptions for replication and monitoring sessions extend isolation across other failure modes; these controls are enabled by default.


### [August 2026 newsletter](https://yomu.fyi/post/august-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 21, 2026
- Type: Announcement

The August 2026 newsletter rounds up ClickHouse product releases, research, managed services, observability, community work, and upcoming events. It introduces Andy Pavlo’s arrival to establish ClickHouse Labs, describes the 26.7 release’s join optimizations, token-position text indexes, and EXPLAIN ANALYZE, and reports a 40× phrase-search improvement on a Hacker News dataset. It also covers an interactive ClickBench playground for more than 100 database systems, running isolated workloads in Firecracker microVMs on AWS EC2, plus guidance on combining Postgres for mutable data with ClickHouse for immutable analytical workloads. Additional items address Managed Postgres notifications and backups, TTL-related disk growth, vector-search trade-offs, and hdx-evals, which found a purpose-built ClickStack MCP server scored 7–20 percentage points higher than direct SQL access in five reproducible scenarios.


### [How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code](https://yomu.fyi/post/how-hugging-face-inference-endpoints-jobs-and-buckets-power-search-on.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Niels Rogge
- Published: Aug 21, 2026
- Type: Explainer

Papers with Code needs a search engine that supports exact titles and arXiv identifiers, semantic requests, navigational queries, typos, and fast responses when model services are cold or unavailable. The system uses hybrid retrieval: PostgreSQL full-text search provides a lexical baseline, pgvector stores dense embeddings, and reciprocal rank fusion combines them. Full-corpus embeddings for more than 110,000 papers run in burstable GPU Jobs from repeatable-read database snapshots, with checksummed JSONL inputs and resumable Parquet outputs stored in Buckets. An Inference Endpoint serves low-latency query embeddings and bounded hourly updates for changed papers, while lexical retrieval remains a fallback. The embedding contract pins the model revision, dimension, prompts, normalization, and source hash; production uses Qwen/Qwen3-Embedding-0.6B with 256-dimensional L2-normalized vectors, and stored vectors also support related-paper retrieval without a request-time model call.


### [POSETTE Talk Recap - Postgres Isn't Slow. Your Storage Is](https://yomu.fyi/post/posette-talk-recap-postgres-isn-t-slow-your-storage-is.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 20, 2026
- Type: Benchmark

At POSETTE 2026, Sai Srirampur’s talk examines how storage limits can produce slow ingestion, erratic P95 reads, lagging autovacuum, slower checkpoints, and logical replication lag in large PostgreSQL workloads. The benchmark ran the same 3.3-billion-row, random-UPDATE workload on four local-NVMe clusters and four baseline gp3 EBS clusters, using m6id.4xlarge instances, 64 clients, and 16 worker threads. NVMe reached median 16,030 TPS versus 1,734 TPS on EBS, reduced median latency from 36.9 ms to 4.0 ms, and completed the tested VACUUM and replication-backlog tasks faster. The analysis attributes most of the gap to storage waits rather than CPU work, while the proposed production model combines local NVMe with quorum-based replication, external base backups, and archived WAL for recovery.


### [PyCharm for AI-assisted Django Workflows](https://yomu.fyi/post/pycharm-for-ai-assisted-django-workflows.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Will Vincent
- Published: Aug 20, 2026
- Type: Announcement

According to the 2026 Django Developers Survey, ninety percent of respondents incorporate artificial intelligence into their weekly or daily workflows. While agents generate code rapidly, developers remain accountable for understanding, evaluating, and shipping changes within their applications. PyCharm addresses these demands by integrating support for external agents, local models via Ollama and LM Studio, and customizable agent skills across codebases. The IDE maintains version-specific framework assistance, including auto-completion for Django 6.0 template partials, alongside architectural inspection tools and visual diffs. Furthermore, developers can trace application components, inspect API endpoints, execute HTTP requests, and query database migrations directly through built-in tooling.


### [How Databricks Feature Store serves features with sub-second freshness](https://yomu.fyi/post/how-databricks-feature-store-serves-features-with-sub-second-freshness.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ian Ackerman, Nick Joung, Abhay Bothra
- Published: Aug 17, 2026
- Type: Explainer

Databricks Feature Store addresses the lag between batch-computed machine-learning features and decisions that depend on events from seconds earlier. It lets a feature definition drive both offline batch processing and online streaming pipelines, orchestrating Kafka ingestion, Spark Real-Time Mode (RTM), Lakebase, and Model Serving. For a rolling 10-minute transaction sum, RTM updates per entity state in local RocksDB, expires contributions per event, and writes revised values to Lakebase through a streaming JDBC sink. The described path reports end-to-end p99 200ms latency from Kafka arrival to online feature availability, while Model Serving retrieves declared dependencies automatically at inference. Tumbling and sliding windows remain options when fewer, cheaper updates are acceptable.


### [What's new in pg\_clickhouse v0.10.0: Subqueries, TPC-H Speedups, C Driver, and Aggregates](https://yomu.fyi/post/what-s-new-in-pg-clickhouse-v0-10-0-subqueries-tpc-h-speedups-c-driver.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 11, 2026
- Type: Announcement

pg\_clickhouse v0.10.0 expands pushdown for analytic workloads, with full pushdown across the TPC-H suite remaining the stated target. The release moves the scoreboard from 12 of 22 fully pushed-down queries to 16, including Q17, which falls from 32.7 seconds to 37 milliseconds when its correlated aggregate subquery is shipped to ClickHouse. SubPlan handling now turns PostgreSQL subqueries into ClickHouse subqueries, while NOT IN and related operators receive guards that preserve PostgreSQL’s three-valued NULL semantics; compatibility requires ClickHouse 25.8 or later for the correlated-subquery shape. The binary driver was rebuilt on a plain-C client library and hardened against concurrency bugs. The release also broadens pushed-down functions and aggregates, adds clickhouse\_perform() and clickhouse\_server\_version(), and leaves six TPC-H queries plus other roadmap items unresolved.


### [Electric joins Databricks to bring WASM Postgres to AI agent sandboxes](https://yomu.fyi/post/electric-joins-databricks-to-bring-wasm-postgres-to-ai-agent-sandboxes.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stas Kelvich, Yan Leshinsky, Nikita Shamgunov, Reynold Xin
- Published: Aug 11, 2026
- Type: Announcement

Databricks says Electric is joining the company to extend its Postgres capabilities from the lakehouse to AI agent sandboxes. The post frames agentic applications as requiring both durable governed state and rapidly changing local context: agents choose data at runtime, operate in sandboxed environments where cloud databases are reached over the network, and collaborate while working in parallel. Electric’s PGlite is a lightweight Postgres database built for WebAssembly that runs inside an agent, application, browser tab, or device, while its real-time sync engine synchronizes distributed data with centralized Lakebase infrastructure. PGlite’s weekly downloads reportedly grew from 1 million to 13 million in twelve months, and the sync architecture is described as keeping agents’ shared context current. Combined, Lakebase, PGlite, and synchronization are presented as a Postgres-based foundation for collaborative agentic applications at production scale.


### [Supabase is now a connector on Perplexity Computer](https://yomu.fyi/post/supabase-is-now-a-connector-on-perplexity-computer.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: natalie\_roberge
- Published: Aug 7, 2026
- Type: Announcement

Supabase is now available as a connector on Perplexity Computer, allowing the agent to use a Supabase project as a persistent data layer across runs. Computer can read from and write to Postgres tables, answer questions by running queries, retrieve user and payment data for support debugging, and invoke Edge Functions with test payloads. Recurring queries can be scheduled, with results posted to Slack, while workflows can combine Supabase data with Stripe events and route fixes through GitHub pull requests. The announcement presents the connector as a way to keep database work inside Computer’s multi-step workflows across more than 400 apps, without custom glue code.


### [What's new in ClickHouse Managed Postgres: Customer notifications, better observability, faster backups, extensions, and more](https://yomu.fyi/post/what-s-new-in-clickhouse-managed-postgres-customer-notifications-bette.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 6, 2026
- Type: Announcement

ClickHouse reports a set of updates to its Managed Postgres beta, aimed at simplifying onboarding and advancing its unified Postgres-and-ClickHouse data stack. The redesigned flow supports sample-data loading or PostgreSQL migration through ClickPipes, built-in SQL exploration, replication to ClickHouse, and cross-system queries through pg\_clickhouse; services also notify users when disk usage remains above 85%, with console, email, and optional Slack channels. Observability now includes searchable PostgreSQL logs, CPU, memory, disk, IOPS, connection and per-table metrics, query insights, a Prometheus-compatible /metrics endpoint, and an OpenAPI slow-query API. Backups use more CPU and disk throughput on larger servers, making backup and restore times predictable even on 10+ TB servers, while extensions add pg\_re2 and improvements to pg\_clickhouse and pg\_stat\_ch, including regex pushdown, Arrow batching, sampling, and safer worker exits; Google Cloud Platform availability and further maintenance, configuration, and integration work are planned.


### [How Physical Intelligence unified its robotics data stack with Postgres managed by ClickHouse](https://yomu.fyi/post/how-physical-intelligence-unified-its-robotics-data-stack-with-postgre.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 6, 2026
- Type: Problem & solution

Physical Intelligence, which builds robotics foundation models, needed a data stack that could support both transactional operations and high-cardinality analytical workloads. Its previous single RDS Postgres instance could not handle those demands, so the team migrated to ClickHouse Cloud and adopted Postgres managed by ClickHouse for OLTP alongside ClickHouse for OLAP. Postgres is automatically replicated into ClickHouse through ClickPipes, while materialized views, full-text indices, and a query agent help researchers search across the company’s data. The resulting stack supports faster, easier exploration as data volume grows from the 10 billion to 100 billion row range, and also handles telemetry through ClickHouse with ClickStack and Grafana.


### [What is WAL backpressure, and why does ClickHouse Managed Postgres need it?](https://yomu.fyi/post/what-is-wal-backpressure-and-why-does-clickhouse-managed-postgres-need.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 5, 2026
- Type: Explainer

Postgres writes every change to the write-ahead log (WAL), and Managed Postgres uploads completed segments to object storage for point-in-time recovery; if uploads lag, retained WAL can fill the disk and cause a PANIC shutdown. ClickHouse Managed Postgres counters this with a data-plane systemd timer that checks pending segments every 15 seconds and uses cgroup v2 I/O controls to throttle client write bandwidth at 80%, 50%, or 20% of provisioned throughput as backlog thresholds rise. The drain path—including the archiver, checkpointer, and related processes—is placed in an immune cgroup, while client backends are throttled and reads remain uncapped. In a test with a 500 MB/s gp3 volume and an archive limited to 4 MB/s, the backlog reached 3,433 segments and 53 GiB before recovery. Once the limit was removed, the archiver cleared the queue, the throttle released automatically, and the disk stayed below 33% full.


[Newer posts](https://yomu.fyi/topic/postgres/page/2.md) · [Older posts](https://yomu.fyi/topic/postgres/page/4.md)