---
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 Changes gets AND filters, new operators, and column selection](https://yomu.fyi/post/postgres-changes-gets-and-filters-new-operators-and-column-selection.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: filipe
- Published: Aug 5, 2026
- Type: Announcement

Postgres Changes now supports filters composed across multiple columns, additional operators, and column selection for subscriptions over Postgres tables. It can receive insert, update, and delete events over WebSockets, while comma-separated conditions or the postgresChangesFilter builder combine conditions with AND. New operators include like, ilike, is, match, imatch, isdistinct, and negated forms, while selected payloads always include the table's primary key. Column selection is opt-in, requires @supabase/supabase-js 2.109.0 or newer, and columns must be selectable by the subscribing role; Row Level Security still applies. These changes can lower billable usage because rejected events avoid messages per subscriber and trimmed payloads send fewer egress bytes, although DELETE events carry only the primary key and OR composition is unsupported.


### [ClickHouse launches ClickHouse Labs with Andy Pavlo as VP of Database Research](https://yomu.fyi/post/clickhouse-launches-clickhouse-labs-with-andy-pavlo-as-vp-of-database.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 3, 2026
- Type: Announcement

ClickHouse announced the launch of ClickHouse Labs, a new research group led by Andy Pavlo, who joins as Vice President of Database Research. The group is intended to advance foundational work in database systems and share it openly with the broader community. Its research spans database and data architectures, query processing, systems performance, and infrastructure for both ClickHouse and PostgreSQL, with applications including real-time analytics, data warehousing, observability, and AI. Researchers will build and test ideas against demanding real-world workloads, collaborate with ClickHouse engineers, and publish findings in leading research venues so others can evaluate, extend, and build on them.


### [Andy Pavlo joins ClickHouse to establish ClickHouse Labs](https://yomu.fyi/post/andy-pavlo-joins-clickhouse-to-establish-clickhouse-labs.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 3, 2026
- Type: Announcement

Andy Pavlo announces that he is joining ClickHouse to establish and lead ClickHouse Labs, an industry research team focused on database systems. He describes ClickHouse’s early use of C++ and vectorized query execution with SIMD in 2016, and says its development has remained relevant to his academic research. Labs will work directly with ClickHouse engineers, customers, collaborators, and industry partners rather than operating separately, while also collaborating with the PostgreSQL team on performance and reliability for its managed service. An immediate priority is validating and pushing an existing backlog of ideas and optimizations into production, followed by research into hardware, algorithms, data structures, execution strategies, and DBMS development for AI and agentic workloads. The stated aim is to combine scientific research with technology that matters to users.


### [Backstage with Lakebase, part 3](https://yomu.fyi/post/backstage-with-lakebase-part-3.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cameron Casher, Shanil Anushka Fernando, Kevin Hartman
- Published: Jul 31, 2026
- Type: Explainer

This third part describes joining Backstage’s live operational Postgres catalog with analytical billing data so teams can query resource ownership and cloud spend together. Lakebase isolates compute per workload, allowing portal traffic and FinOps queries to use the same underlying storage; benchmarked catalog queries ran at 55–65 ms end-to-end and searches at two to four ms. Because Lakehouse Federation’s Postgres connector accepts static credentials while Lakebase applications use OAuth JWTs, the proof-of-concept creates a separate SCRAM-SHA-256 Postgres role for federation. This enables a single query to join Backstage resource names with Unity Catalog’s system.billing.usage without data movement, while branch billing remains visible per branch and endpoint. The operational considerations include password rotation, read-only grants, branch TTLs, and scale-to-zero endpoints that stop billing when idle.


### [Agentic media buying cannot scale without the right foundation. See how buyers and sellers get there on Databricks.](https://yomu.fyi/post/agentic-media-buying-cannot-scale-without-the-right-foundation-see-how.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Joe Hu, Mandy Baker, Luke Barnes
- Published: Jul 30, 2026
- Type: Explainer

Media buying remains slowed by fragmented coordination across emails, spreadsheets, PDFs, and phone calls, leaving teams to research inventory, compare pricing, negotiate, and issue orders manually. The post presents an agentic workflow in which buyer and seller agents discover inventory, negotiate prices, and book deals through IAB Tech Lab’s AAMP standards, including AdCOM, OpenDirect, OpenRTB deals, and registry-based discovery and trust. Its Databricks implementation runs self-contained applications on Databricks Apps, with CrewAI agent crews using Databricks Foundation Model APIs and Claude models, Lakebase providing transactional Postgres state, and MLflow tracing capturing decisions and tool calls. The example processes a $200,000 Q3 Brand Launch across CTV and Linear TV with a $38 CPM ceiling, and is packaged as a Databricks Automation Bundle accelerator deployable with a single command.


### [Simplify AI agent orchestration with Lakebase Postgres](https://yomu.fyi/post/simplify-ai-agent-orchestration-with-lakebase-postgres.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Li Yu, Michelle JanneyCoyle, Jon Cormack, Yarri Bryn, Alec Sorensen, Darshana Nair
- Published: Jul 22, 2026
- Type: Problem & solution

CLA and Databricks built a production document-processing application for auditing that reduces extraction time from hours to minutes without compromising quality, using Databricks-native services including Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. Lakebase serves as the orchestration layer’s single source of truth for tasks and execution attempts, coordinating long-running work, retries, leases, priorities, rate limits, costs, and status visibility. The queue uses Postgres patterns including FOR UPDATE SKIP LOCKED, priority and FIFO ordering, expiring leases for crash recovery, and database-backed concurrency controls. Databricks Jobs process PDFs through intelligent document processing and vision/LLM calls, while MLflow Tracing records execution and cost details and dashboard updates combine fast Postgres data with slower billing queries. In production, this architecture avoids external brokers and schedulers while providing durable task management, real-time visibility, and per-task cost attribution.


### [Supabase Pipelines is now in Public Alpha](https://yomu.fyi/post/supabase-pipelines-is-now-in-public-alpha.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: riccardo\_busetti
- Published: Jul 21, 2026
- Type: Announcement

Supabase Pipelines has moved into public alpha as a service for replicating Supabase Postgres data to external analytical systems in near real time. The release adds automatic application of supported schema changes, faster initial synchronization through parallelism, and a destination request form for ClickHouse, Snowflake, and DuckLake; Google BigQuery is the first destination available to everyone in public alpha. Powered by the open-source Rust-based Supabase ETL, Pipelines uses Postgres logical replication to copy existing tables, stream inserts, updates, deletes, and truncates, and resume from the last acknowledged position after interruptions. It provides at-least-once delivery and managed pipeline controls, while remaining an alpha product with ongoing work on throughput, batching, backpressure, broader schema support, and additional destinations.


### [Branching databases like code: a CI/CD pattern for Lakebase, in production at Glaspoort](https://yomu.fyi/post/branching-databases-like-code-a-ci-cd-pattern-for-lakebase-in-producti.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hadi Farhat, Gideon Spierings, Ricardo de Vries, Raymon Veldman
- Published: Jul 20, 2026
- Type: Problem & solution

Glaspoort replaced a sprawl of outdated, one-off BI reports with a custom application that combines Databricks analytical tools with Lakebase, a serverless Postgres OLTP database. The application keeps curated lakehouse data in a read-only schema while application state occupies a separate schema, and supports development, acceptance, and production environments. Its CI/CD pattern treats database branches like code: long-lived environments branch directly from production, each pull request receives an ephemeral production-shaped branch, migrations are replayed, and a staging app image runs the full test suite against it. This topology makes periodic resets cheap, avoids deleting dependent branches, and preserves fresh test data; the team chose stacked promotion for velocity, retaining a separate crisis pipeline for urgent fixes.


### [Eclipse Dataspace Components on AWS: Cost optimization strategies](https://yomu.fyi/post/eclipse-dataspace-components-on-aws-cost-optimization-strategies.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Jorge Hernández Suárez
- Published: Jul 17, 2026
- Type: Explainer

Deploying Eclipse Dataspace Components (EDC) connectors on AWS presents challenges in forecasting infrastructure expenses without clear benchmarks. Estimated costs differ substantially between business-critical setups and non-critical environments based on compute and database sizing. A business-critical configuration relying on Amazon Aurora PostgreSQL db.r6g.large and standard Amazon ECS with AWS Fargate totals an estimated 387.00 USD monthly in the eu-south-2 region. Sizing down to db.t4g.medium and switching to AWS Fargate Spot lowers the non-critical monthly expense to 164.00 USD, representing a 58% reduction. Core cost drivers remain baseline infrastructure components, while storage, API calls, and data transfers contribute marginally under baseline assumptions.


### [What happens in the milliseconds after you tap pay](https://yomu.fyi/post/what-happens-in-the-milliseconds-after-you-tap-pay.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Harsha Pasala, Subhadip Chanda
- Published: Jul 16, 2026
- Type: Tutorial

At checkout, fraud scoring must combine transaction inference with customer-specific rules while keeping latency low enough for an interactive payment. The retail-app sample pairs a FastAPI backend and React frontend in a Databricks App with Model Serving route optimization and Lakebase Postgres, where the model retrieves historical features and the backend reads profile controls. A transaction is scored first, then checked against daily spending, international-transaction, and country rules; pooled OAuth-authenticated connections and token-rotation handling avoid repeated handshakes and stale credentials. In the supplied benchmark, route-optimized calls reached 27.2 ms p50 and 37.3 ms p95 end-to-end, with feature lookup at 8.9 ms p50, CatBoost inference at 0.4 ms, and network overhead at 17.4 ms p50; actual performance varies by model.


### [Why Trainy migrated from Amazon RDS Postgres to ClickHouse Managed Postgres](https://yomu.fyi/post/why-trainy-migrated-from-amazon-rds-postgres-to-clickhouse-managed-pos.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Problem & solution

Trainy’s Pluto is an open-source experiment tracker that separates analytics metrics in ClickHouse Cloud from relational run metadata in Postgres. Its original metadata store, Amazon RDS Postgres, was too expensive for the volume observed and contributed to slower initial page loads, so Trainy migrated roughly 40–50 GB to ClickHouse Managed Postgres, which uses local NVMe storage and keeps both databases in one account. After migration, initial page loads became 2–3 times faster, while a complex query reported as taking 10–15 minutes previously returned in five seconds; visualization queries over runs with hundreds of thousands of steps typically return in 50–100 milliseconds at the P50. Trainy reports 100% reliable ingestion with zero dropped data points and says the unified stack could support future joins between GPU metrics and run metadata, as well as governed agent access.


### [How we configure huge pages in ClickHouse Managed Postgres](https://yomu.fyi/post/how-we-configure-huge-pages-in-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 14, 2026
- Type: Tutorial

ClickHouse Managed Postgres configures Linux huge pages to reduce the per-connection page-table cost and translation overhead of a large PostgreSQL shared\_buffers cache. The configuration reserves a quarter of machine memory before PostgreSQL starts, verifies HugePages\_Total, sets huge\_pages = 'on' to prevent silent fallback, and sizes shared\_buffers against the complete shared-memory segment, including buffer descriptors, WAL buffers, and lock tables. On an r7i.4xlarge with 32GB shared\_buffers, 4KB pages added 31.1MB of page tables per connection and reached 6.12GB at 200 connections, while 2MB huge pages reached 111MB. A select-only pgbench test at 100 clients also rose from 373,083 TPS to 418,087 TPS, a 12% increase, while startup failure without a sufficient pool made misconfiguration explicit.


### [Foundational context: Cross-industry & function-specific accelerators for Lakebase](https://yomu.fyi/post/foundational-context-cross-industry-function-specific-accelerators-for.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amit Singh
- Published: Jul 14, 2026
- Type: Announcement

Databricks presents Lakebase as a fully managed, serverless, standard Postgres database for combining operational and analytical workloads on its Data + AI Platform. The platform separates compute from storage, integrates with the lakehouse through Synced Tables and Lakebase CDF, and uses Unity Catalog for governance; copy-on-write branching and autoscaling to zero are described as core infrastructure primitives. The post showcases partner-built, ready-to-deploy accelerators spanning technology, finance, marketing, sales, supply chain, human resources, customer service, and operations. Examples include PostgreSQL migration assessment, multi-agent Genie orchestration, stateful enterprise agents, autonomous data reliability, governed contact-center intelligence, and project operations management. These offerings package Lakebase patterns into migration controls, domain-specific solutions, and agent frameworks intended to accelerate modernization and reduce transformation complexity.


### [How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows](https://yomu.fyi/post/how-trigger-dev-is-using-clickhouse-to-scale-observability-for-long-ru.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 9, 2026
- Type: Problem & solution

Trigger.dev, an open-source platform for serverless asynchronous tasks, needed to scale observability for increasingly complex, long-running AI workflows spanning thousands of tenants. Its initial Postgres design stored logs, traces, and events in a growing table, making indexes, analytics, and operational reliability increasingly difficult to manage. The team moved observability workloads to ClickHouse after evaluating performance, scalability, open-source compatibility, deployment flexibility, and support for dynamic telemetry, including its native JSON data type. Migration occurred gradually at the task-execution level, routing each run’s logs to either database without downtime or customer intervention. ClickHouse now supports real-time telemetry analysis, approximately 200-millisecond P95 latency for queries returning tens of thousands of rows, and a dramatically smaller telemetry storage footprint.


### [Searchable field-level encryption on Supabase with CipherStash](https://yomu.fyi/post/searchable-field-level-encryption-on-supabase-with-cipherstash.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: bilharmer
- Published: Jul 9, 2026
- Type: Announcement

CipherStash’s integration for Supabase adds searchable field-level encryption to Postgres applications without requiring a schema change. The encrypted Supabase SDK wrapper encrypts marked fields in the application, stores each value as a JSON payload containing ciphertext and Searchable Encrypted Metadata (SEM), and converts queries to SEM so Postgres can filter, sort, and join without plaintext access. ZeroKMS keeps keys under the customer’s control, while decryption policies determine who can read each value; the source states that neither CipherStash nor Supabase can access plaintext data. The integration supports Supabase.js, Drizzle, and Prisma Next, with a one-command setup, and CipherStash Proxy covers analytics, background workers, and other direct database access through the Postgres wire protocol.


### [Introducing pg\_re2, fast, RE2-powered regular expressions in Postgres](https://yomu.fyi/post/introducing-pg-re2-fast-re2-powered-regular-expressions-in-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 8, 2026
- Type: Announcement

pg\_re2 is a Postgres extension that brings RE2-powered regular expression functions and operators to Postgres, designed to address both execution speed and syntax compatibility with ClickHouse. It uses finite-automata-based RE2 rather than Postgres's backtracking POSIX engine, and version 0.4.0 adds the @~ operator plus btree and GIN index support. Benchmarks on an AWS m6g.2xlarge with PostgreSQL 18 report RE2's functions running at 1.8x to 8.6x the speed of native equivalents, while indexed operations are 1.1x to 1.8x faster. With pg\_clickhouse, using RE2-backed functions such as re2match() keeps regex behavior consistent between Postgres execution and ClickHouse pushdown, including patterns such as Quake\\b. The extension duplicates ClickHouse's suite for string search, replacement, extraction, and splitting.


### [AI needs the best-of-breed data stack: Postgres and ClickHouse](https://yomu.fyi/post/ai-needs-the-best-of-breed-data-stack-postgres-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 7, 2026
- Type: Explainer

AI-native applications generate large volumes of operational data, while agents and users increasingly require real-time answers from that data. This pressure raises both query concurrency and reliability requirements, with a cited sample of companies using Postgres and ClickHouse showing more than 1,000% data growth in six months and over 85 TB added. The proposed stack assigns Postgres low-latency transactional workloads and ClickHouse real-time analytics, connecting them through PeerDB and ClickPipes CDC with single-digit-second replication, pg\_clickhouse query pushdown, and pg\_stat\_ch telemetry streaming. The conclusion is that AI makes the OLTP/OLAP distinction more important, favoring an open best-of-breed stack over a single platform while preserving each database's specialized role.


### [98% faster queries, 50% lower cloud costs: Verihubs’ journey from Postgres to ClickHouse](https://yomu.fyi/post/98-faster-queries-50-lower-cloud-costs-verihubs-journey-from-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 6, 2026
- Type: Problem & solution

Verihubs rebuilt its data warehouse after a Postgres-based, daily-batch pipeline became slow, unreliable, and difficult to correct when late vendor callbacks required backfills. The new architecture uses Kafka, Kafka Connect, and Debezium for real-time ingestion into ClickHouse, with MergeTree for append-only logs and ReplacingMergeTree for datasets updated through change data capture. Materialized views create daily and monthly rollups, projections accelerate existing query patterns, and Dagster handles complex detection workflows that can notify Slack. The migration, completed in about six months, reduced query times from 20–30 minutes to two or three seconds on datasets up to 18 million rows, representing as much as 98% faster queries and 50% lower infrastructure costs. Verihubs says the resulting real-time OLAP system gives finance and operations fresher data for traffic analysis, reconciliation, performance reporting, and invoicing.


### [ClickHouse Agents is now available for Managed Postgres](https://yomu.fyi/post/clickhouse-agents-is-now-available-for-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 2, 2026
- Type: Announcement

ClickHouse Agents is now available for all Managed Postgres services in ClickHouse Cloud, extending its plain-English agent interface to operational data. The beta service, powered by Claude and built on LibreChat, combines an agent builder and chat interface with a sandboxed code interpreter; it is read-only by default and supports MCP-compatible systems. Agents can inspect Postgres schemas, run read-only queries, return the SQL used, and reason across Postgres and ClickHouse by querying each engine independently before merging results. Additional skills guide migration from Postgres to ClickHouse through discovery, access-pattern analysis, schema design, type mapping, CDC ingestion, and validation, while other workflows use metrics, Query Insights, and EXPLAIN to rank performance fixes. Planned extensions include scheduled recommendations and safe automated remediation, but the current release focuses on exploration, analysis, monitoring, and guided database work.


### [How we scale PgBouncer in ClickHouse Managed Postgres](https://yomu.fyi/post/how-we-scale-pgbouncer-in-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 1, 2026
- Type: Benchmark

PgBouncer is single-threaded, so a single process can bottleneck connection pooling on multi-vCPU machines even when Postgres has capacity. ClickHouse Managed Postgres runs a fleet of PgBouncer processes sized to available cores, sharing one port through so\_reuseport; peering forwards query-cancellation requests to the process that owns the session, while transaction pooling and per-process connection-budget splits limit Postgres connections. In tests on identical AWS EC2 c7i.4xlarge pooler instances, a 16-process fleet reached roughly 336,000 transactions per second at 256 clients, compared with 76,893 for one process, while using about 49% versus 8% in-guest CPU. The fleet's advantage emerged under concurrency, whereas the single process was slightly faster at eight clients, and the described setup ships by default with every ClickHouse Managed Postgres server.


[Newer posts](https://yomu.fyi/topic/postgres/page/3.md) · [Older posts](https://yomu.fyi/topic/postgres/page/5.md)