# SQL SQL is a standardized database language for defining, accessing, and manipulating data in relational databases. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Релиз OpenIDE 2026.2: одна IDE для Java, Kotlin, Go, Python и веб-разработки DevFeed: [Релиз OpenIDE 2026.2: одна IDE для Java, Kotlin, Go, Python и веб-разработки]() Original publisher: [Read original article]() Author: honest\_niceman (Haulmont) Published: 2026-09-17T09:12:16Z Content type: release Language: ru Sources: [Tagir Valeev]() Topics: [intellij-platform](), [Java](), [Git](), [Kotlin](), [Gradle](), [Databases](), [Docker](), [Go](), [Python](), [SQL](), [JSON]() Tags: [2](), [2026](), [2026-2](), [compose](), [db-eaa4bca15522](), [docker](), [docker-bdf52bf499c4](), [git](), [go](), [gradle](), [ide-1f5689fe989b](), [ide-af948443b041](), [ide-java-0ad4946a0e57](), [intellij-idea-27f15aabb340](), [intellij-platform](), [java](), [javascript](), [json](), [kotlin](), [openide](), [openide-2026-2](), [openide-pro](), [oracle](), [sql](), [structure]() ### AI overview OpenIDE 2026.2 updates its base platform, expands Java and Kotlin support, improves Git and Gradle workflows, and updates its Java profiler. The release also includes improvements to the DB client and Docker plugin, including SQL formatting, Greenplum support, JSON highlighting, and enhanced Compose service management. ### Source excerpt Обновили базовую платформу до версии 2026.2, расширили поддержку Java и Kotlin, улучшили работу с Git и обновили Java-профилировщик. Доработали редактор и подсказки в терминале, добавили проверки настроек Gradle и упростили переход к исходникам библиотек. Через Git worktrees теперь можно работать с несколькими ветками одновременно. Читать далее ## "Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE DevFeed: ["Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE]() Original publisher: [Read original article]() Author: Kent Marten; Sergei Fedorov Published: 2026-09-16T17:14:05Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [SQL](), [databricks](), [Cybersecurity](), [Security & Privacy]() Tags: [count](), [cybersecurity](), [databricks](), [failed](), [false-positives](), [functions](), [login](), [partition](), [product](), [real-time](), [regex](), [sql](), [window-functions]() ### AI overview This tutorial explains how Databricks supports SQL MATCH_RECOGNIZE for detecting ordered event patterns. It shows how the clause can simplify sequence detection, including identifying repeated login failures followed by a successful login, compared with complex SQL queries and window functions. ### Source excerpt Imagine you work in cybersecurity and you have a table that tracks login attempts... ## Plan Advice in PostgreSQL 19 DevFeed: [Plan Advice in PostgreSQL 19]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2026-09-15T16:27:53Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [version](), [SQL](), [Docker Compose]() Tags: [analyze](), [beta](), [docker-compose](), [pg-plan-advice](), [postgresql](), [sql]() ### AI overview This tutorial explains PostgreSQL 19's pg_plan_advice and pg_stash_advice modules, which represent query-plan decisions as reusable advice and can apply that advice by query ID. It also describes reconstructing comparable plan advice from ordinary plan output on earlier PostgreSQL versions. ### Source excerpt There is a conversation that happens in every PostgreSQL shop eventually. A query that has been fine for a year gets slow overnight. Nothing was deployed. The data grew a little, ANALYZE ran, and the planner -- entirely reasonably, on the numbers it had -- picked a different plan. The old plan was better. You would like it back. PostgreSQL 19 ships two new modules for exactly this: pg_plan_advice, which can read a plan back out as a string and enforce it later, and pg_stash_advice, which keeps those strings keyed by query id and applies them automatically. ▸ Every query below ran against the Lab, the free dataset bundle used throughout this blog, on PostgreSQL 19 Beta 3: POSTGRES_VERSION=19beta3 PG_MAJOR=19 docker compose up. Both modules are contrib, and the Lab image ships them; nothing below runs a LOAD to enable them, because the server already has them -- pg_plan_advice in session_preload_libraries, pg_stash_advice in shared_preload_libraries (it can survive a restart, which needs loading that way). One line each in postgresql.conf, or the equivalent server-start flag, and you're done. pg_stash_advice still needs its own CREATE EXTENSION, further down, for its SQL functions -- that's independent of how the module itself got loaded. ## PostgreSQL 19 graph queries fail the 'would you ship this?' test DevFeed: [PostgreSQL 19 graph queries fail the 'would you ship this?' test]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-15T09:42:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [Databases]() Tags: [bugs](), [concurrent](), [databases](), [graph](), [postgresql](), [sql]() ### AI overview The article reports that PostgreSQL 19's SQL/PGQ graph queries were rejected because of unresolved bugs. It also discusses concurrent REPACK as a way to reduce overnight maintenance calls for database administrators. ### Source excerpt SQL/PGQ gets bounced over unresolved bugs as concurrent REPACK promises fewer midnight calls for DBAs ## How to Design Gifting Features People Actually Use: Evidence from 58 Apps DevFeed: [How to Design Gifting Features People Actually Use: Evidence from 58 Apps]() Original publisher: [Read original article]() Author: Anamol Rajbhandari Published: 2026-09-14T13:58:38Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [App](), [SQL]() Tags: [apps](), [article](), [design](), [developer](), [ecommerce](), [research](), [sql](), [ux]() ### AI overview The article examines whether gifting features are worth building into consumer apps. Drawing on research across 58 apps, it identifies gifting scenarios, the triggers that prompt people to send gifts, and the costs and design work involved in implementing them. ### Source excerpt American shoppers spent about $29 billion on gift cards over the 2025 holiday season, and 43 percent of them bought at least one. This put gift cards at the top of what people said they wanted accordi ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection DevFeed: [Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection]() Original publisher: [Read original article]() Author: Osher Jacob Published: 2026-09-14T10:00:01Z Content type: article Language: en Sources: [Unit 42]() Topics: [AWS CloudTrail](), [Cloud](), [threat detection](), [Threat Research](), [SIEM, Security, Observability](), [Machine learning](), [Algorithms](), [SQL](), [IAM](), [identity and access management](), [Amazon Web Services]() Tags: [algorithms](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [cloud](), [cloud-cybersecurity-research](), [cloud-detection](), [devops](), [iam](), [identity-and-access-management](), [logs](), [machine-learning](), [post](), [sql](), [threat-detection](), [threat-research]() ### AI overview This article presents a behavioral clustering model for mapping cloud identities to functional roles using activity patterns from audit logs. It applies unsupervised machine learning with UMAP and HDBSCAN to data from more than 40,000 identities across 125 cloud environments, and shows how the resulting map can support automated threat detection. The article also explains how lightweight heuristics extracted from the map can classify identities at scale using standard SQL, reducing the need for continuous resource-intensive machine learning pipelines. ### Source excerpt We designed a behavioral clustering model to map cloud identity roles from audit logs, enabling continuous threat detection using standard SQL queries. The post Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection appeared first on Unit 42. ## Use Drizzle ORM with Appwrite Postgres DevFeed: [Use Drizzle ORM with Appwrite Postgres]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Drizzle](), [migration](), [Object-relational mapping](), [PostgreSQL](), [TypeScript](), [Databases](), [SQL]() Tags: [data](), [drizzle](), [migration](), [migrations](), [orm](), [postgres](), [postgresql](), [schema](), [sql](), [tutorials](), [typescript]() ### AI overview A tutorial showing how to connect Drizzle ORM to Appwrite Postgres, define tables and queries in TypeScript, generate and apply SQL migrations, seed data, and evolve a schema while preserving existing records. ### Source excerpt Connect Drizzle ORM to Appwrite Postgres, query data with TypeScript, and apply SQL migrations as your schema changes. ## Getting Ready for PostgreSQL 19 DevFeed: [Getting Ready for PostgreSQL 19]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2026-09-03T13:09:30Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [upgrade](), [Release notes](), [SQL](), [Docker Compose](), [Docker](), [Linux]() Tags: [beta](), [compatibility](), [docker](), [docker-compose](), [linux](), [postgresql](), [release-notes](), [sql](), [upgrade](), [version]() ### AI overview This article reviews PostgreSQL 19 Beta 3 ahead of its general availability, focusing on upgrade-relevant compatibility changes and useful SQL additions. It highlights changes involving JIT defaults, authentication, GiST indexing, naming restrictions, and configuration capacity, and includes queries tested against a PostgreSQL 19 Beta 3 instance. ### Source excerpt PostgreSQL 19 Beta 3 shipped on August 13, 2026, and the release notes have been filled in as of 2026-07-18 -- still marked subject to change, and the GA date isn't announced yet, but following the project's usual September/October cadence general availability should land within the next few weeks. That makes now the right time to read through what's changing, the same way I did for PostgreSQL 11 through 18 a few weeks ago. This is not a changelog dump. It's the subset of PG 19 I think is worth knowing about before you upgrade: a handful of compatibility breaks that will bite people who don't read release notes, and the SQL-level additions I found genuinely useful once I started poking at them. Every query below ran against a real PostgreSQL 19 Beta 3 instance -- no hand-waving about syntax that might work. ▸ Every query in this article ran against the Lab, the same free dataset bundle used in the rest of this blog (F1 data, geopolitical data, music data, and more), pinned to a real PostgreSQL 19 Beta 3 instance. PG 19 support isn't the Lab's default yet, but there is a prebuilt beta image on the registry, for both linux/amd64 and linux/arm64 -- so POSTGRES_VERSION=19beta3 PG_MAJOR=19 docker compose up pulls it rather than building anything, and every query below reproduces on it exactly as printed. (PG_MAJOR only matters if Compose ends up building; it costs nothing to set.) Plain docker compose up still pulls the PG 16 image, and stays the default until 19 reaches general availability. ## Postgres Calculations and the Ambiguity of NULL DevFeed: [Postgres Calculations and the Ambiguity of NULL]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-09-02T17:00:00Z Content type: article Language: en Sources: [CrunchyData Blog]() Topics: [SQL]() Tags: [fun-with-sql](), [postgres](), [schema](), [sql]() ### AI overview This article explains how Postgres handles NULL as an unknown marker in arithmetic, comparisons, concatenation, aggregates, window functions, predicates, joins, and sorting. It covers SQL three-valued logic, common surprises such as NOT IN and dropped rows, ways to test or handle missing values, and Postgres 19 IGNORE NULLS. ### Source excerpt How Postgres evaluates calculations when a value is unknown: three-valued logic, arithmetic and concatenation with NULL, NOT IN, aggregates and window functions, NULL sort order, and Postgres 19 IGNORE NULLS. ## How INTEGER and INT Produced Different Schemas in Debezium DevFeed: [How INTEGER and INT Produced Different Schemas in Debezium]() Original publisher: [Read original article]() Author: Prathit Malik Published: 2026-09-02T07:02:02Z Content type: article Language: en Sources: [Grofers]() Topics: [MySQL](), [migration](), [Streaming](), [Kafka](), [SQL](), [Databases]() Tags: [big-data](), [blinkit](), [change](), [database](), [debezium](), [jdbc](), [kafka](), [migration](), [mysql](), [open-source](), [schema](), [sql](), [streaming]() ### AI overview This article investigates a Debezium CDC pipeline failure caused by MySQL INT and INTEGER synonyms being treated as different types. The resulting schema mismatch produced an Integer where a downstream consumer expected a Long, causing a ClassCastException on every batch. It explains how streaming and snapshot schema handling differ and how routine migrations exposed the problem. ### Source excerpt A Debezium investigation: how MySQL synonyms INT and INTEGER were treated as different types. One of our CDC pipelines started failing with a ClassCastException on every batch. java.lang.ClassCastException: class java.lang.Integer cannot be cast to class java.lang.Long The pipeline was producing an Integer, but the downstream consumer expected a Long. Every run failed in the same way, which pointed us toward a schema mismatch rather than an issue with individual records. Background: how CDC works To see why a mismatch like that can hide for years, it helps to know how CDC actually works. Most companies replicate their transactional database (MySQL, Postgres, something similar) into a separate data lake for analytics, rather than querying the source directly, and Change Data Capture (CDC) is what keeps that copy in sync: it tails the database's transaction log and replays every insert, update, and delete downstream. Debezium is the most widely used open-source CDC tool for MySQL, and it builds a table's schema in one of two ways that are supposed to agree but do not always. Streaming mode: the first time it sees a CREATE TABLE or ALTER TABLE in the binlog, it parses the raw SQL text and writes the result to its own internal Kafka topic, database.history.kafka.topic. Every restart after that rebuilds the in-memory schema by replaying that topic, not by re-reading the binlog. Snapshot mode: reads the table definition fresh through MySQL's JDBC metadata interface, which normalizes types, every time it runs. Schema Registry sits downstream of both: each connector writes whatever schema it built into the registry, but neither connector reads its own schema back from it. Keep that in mind; it matters later. The trigger: a routine migration With that in mind, here's what actually happened to us. Rewind two years: one of our upstream service teams added a few columns to a source table as part of a standard schema change. ALTER TABLE ADD COLUMN length double NU ## \[webapps\] EasyAppointments 1.5.1 - Blind SQL Injection DevFeed: [\[webapps\] EasyAppointments 1.5.1 - Blind SQL Injection]() Original publisher: [Read original article]() Author: Michael Chesang Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Exploit-DB.com RSS Feed]() Topics: [SQL](), [Exploit]() Tags: [cve](), [cve-2025-50455](), [exploit](), [multiple](), [sql](), [webapps]() ### AI overview An Exploit Database entry describes a blind SQL injection affecting EasyAppointments 1.5.1, identified as CVE-2025-50455. ### Source excerpt EasyAppointments 1.5.1 - Blind SQL Injection ## Fix circular role dependencies before upgrading Amazon RDS and Amazon Aurora PostgreSQL DevFeed: [Fix circular role dependencies before upgrading Amazon RDS and Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Ravi Teja Adabala Published: 2026-08-31T23:42:55Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [RDS for PostgreSQL](), [Amazon Aurora](), [Amazon RDS](), [SQL](), [postgresql clusters](), [Graphs]() Tags: [amazon-aurora](), [amazon-rds](), [database](), [how-to](), [intermediate-200](), [postgresql](), [rds-for-postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains how circular PostgreSQL role dependencies can cause major version upgrades of Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL to stall or roll back when upgrading from PostgreSQL 14 or earlier to PostgreSQL 15 or later. It describes a diagnostic query and steps to resolve the dependencies before the upgrade. ### Source excerpt Circular role dependencies can stall or roll back a major version upgrade of Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL when you move from PostgreSQL 14 or earlier to 15 or later. Learn why this happens, how to detect it with a single pre-upgrade query, and how to clear it before you upgrade. ## \[webapps\] CubeCart 6.7.4 - SQL DevFeed: [\[webapps\] CubeCart 6.7.4 - SQL]() Original publisher: [Read original article]() Author: Mikail KOCADAĞ Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Exploit-DB.com RSS Feed]() Topics: [webapps](), [SQL](), [Exploit]() Tags: [cve-2026-54646](), [exploit](), [multiple](), [sql](), [webapps]() ### AI overview An exploit database entry identifying a SQL exploit for CubeCart 6.7.4, associated with CVE-2026-54646. ### Source excerpt CubeCart 6.7.4 - SQL ## \[webapps\] CubeCart 6.7.4 - SQL injection DevFeed: [\[webapps\] CubeCart 6.7.4 - SQL injection]() Original publisher: [Read original article]() Author: Mikail KOCADAĞ Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Exploit-DB.com RSS Feed]() Topics: [Exploit](), [SQL]() Tags: [cve](), [cve-2026-54647](), [exploit](), [multiple](), [webapps]() ### AI overview An exploit listing reports an SQL injection affecting CubeCart 6.7.4 and identifies it as CVE-2026-54647. ### Source excerpt CubeCart 6.7.4 - SQL injection ## 【kube-apiserver】选型收束与开放问题：排除树、Kine 与 events 分集群 DevFeed: [【kube-apiserver】选型收束与开放问题：排除树、Kine 与 events 分集群]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-08-28T00:00:00Z Content type: tutorial Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [Kubernetes](), [Caching](), [etcd](), [SQL](), [MySQL](), [PostgreSQL](), [SQLite](), [Raft]() Tags: [apiserver](), [churn](), [distributed](), [errcompacted](), [etcd](), [events](), [exclusion-tree](), [kine](), [kubernetes](), [mysql](), [open-questions](), [postgresql](), [quota](), [raft](), [selection](), [sql](), [sqlite](), [storage](), [ttl](), [v1-30-3](), [watch-cache]() ### AI overview This concluding article presents a mechanism-based decision tree for diagnosing Kubernetes control-plane issues across kube-apiserver and etcd layers. It explains Kine's differing Watch semantics when using SQLite, PostgreSQL, or MySQL backends, discusses routing Kubernetes Events to a separate etcd cluster, and identifies open questions around watch-cache SLOs, compaction, linear reads, and Lease fencing. ### Source excerpt 用机制排除树收束何时查 apiserver 轴、何时穿透 etcd 轴、何时两轴联查；回收 etcd/13 写下的 apiserver 停损线；Kine SQL backend 的 Watch 语义差与 Jepsen 覆盖空白；events --etcd-servers-overrides 分集群运维；列出 watch cache SLO 联合模型、线性读期望与 Lease+fencing 全链路三个开放问题；以 ADR 语言关闭系列边界。 ## CodeSOD: The Big Family DevFeed: [CodeSOD: The Big Family]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-08-27T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [PHP](), [Programming](), [Code](), [data](), [SQL](), [Localization (l10n)](), [formatting](), [HTML](), [Parsing]() Tags: [array](), [code](), [codesod](), [fetch](), [formatting](), [html](), [humor](), [php](), [programming](), [sql]() ### AI overview This commentary examines a large, generalized PHP code sample that builds hierarchical output from a presumed database source. It highlights probable SQL injection risk, unused array keys, dynamic localization fields, extensive number formatting and HTML string manipulation, string-based date-time parsing, and repeated code for child and sibling records. ### Source excerpt Some time ago, Charles shared with us some awful PHP, aka the most common sort. Today's code sample is maybe a little too big to sum up, but I'll let Charles take a crack at it. It's so bad that even analyzing and laughing at it feels impossible. But it's so bad, I couldn't not share it. I'm the only one handling all the IT-related tasks at my company, and I don't have anyone here to vent or laugh about this kind of thing with. So, I figured, why not share it here? I'm hoping it'll provide at least a little bit of catharsis or some dark humor. To make sure the confidentiality of the codebase was respected, I took the liberty of generalizing it. You might notice some inconsistencies, but that's just me trying to keep things neutral while protecting the original structure and functionality. Apologies if it looks a bit patchy - the goal was to avoid revealing any specific details or sensitive code. The whole block is north of 400 lines, and it's doing a lot. Or well, maybe it's not, as you'll see. Let's star with the outermost layer. $resm_data = $data_source->fetchData("group=" . $item_id); foreach ($resm_data as $key => $value) { // rest of the code here } We fetch data from a data source, presumably a database, passing our condition as a string, which reeks of probable SQL injection, but I don't know what library they're using. I also note they're using the key/value style of array iteration, but never actually check the key. $option_id = $value->option_id; $resm_details = $detail_source->fetch($option_id); if ($resm_details) { $label = $resm_details->{"label$lang"}; $description = $resm_details->{"description$lang"}; $category = $resm_details->category; Nice little bit of "meta" programming to get their localization working, it'll fetch labelen or labelde as needed. Definitely not a horrible, dangerous way to solve that problem. We use that again to get our currency figured out. That lets us do number formatting. So much number formatting code. if ($category == 0) ## Building an Operational Ontology: An E-Commerce Walkthrough DevFeed: [Building an Operational Ontology: An E-Commerce Walkthrough]() Original publisher: [Read original article]() Author: Togo YAMANAKA Published: 2026-08-26T14:29:30Z Content type: tutorial Language: en Sources: [Data Engineering Weekly]() Topics: [semantic-layer](), [SQL](), [TypeScript](), [Open Source](), [Databases]() Tags: [customer](), [data-pipeline](), [implementation](), [integration](), [open-source](), [semantic-layer](), [sql](), [typescript]() ### AI overview A walkthrough builds an operational ontology over integrated e-commerce order data from two systems with different schemas and status encodings. It models customers, orders, products, and relationships, then introduces named actions, business rules, and write-back to systems of record. ### Source excerpt The write side of the ontology conversation: named actions, business rules, and write-back to the systems of record -- a pattern already running at enterprise scale. ## Read your writes: WAIT FOR in PostgreSQL 19 DevFeed: [Read your writes: WAIT FOR in PostgreSQL 19]() Original publisher: [Read original article]() Author: Gülçin Yıldırım Jelínek Published: 2026-08-25T12:36:23Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [PostgreSQL](), [Replication](), [SQL](), [Latency](), [Databases]() Tags: [latency](), [postgresql](), [replication](), [sql]() ### AI overview PostgreSQL 19 introduces a `WAIT FOR` command that allows reads on asynchronous replicas to wait until a specified WAL position has been replayed. This provides read-your-writes consistency without synchronous replication or application-side polling. ### Source excerpt PostgreSQL 19's new `WAIT FOR` command enables read-your-writes consistency on asynchronous replicas by letting individual reads wait for a specific WAL position. ## How DuckDB Runs Recursive CTEs Faster DevFeed: [How DuckDB Runs Recursive CTEs Faster]() Original publisher: [Read original article]() Author: Denis Hirn Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [DuckDB]() Topics: [SQL](), [Databases]() Tags: [deep-dive](), [duckdb](), [performance](), [sql]() ### AI overview The article explains how DuckDB's upcoming v2.0 improves recursive CTE performance by retaining reusable state across iterations, selecting execution modes based on frontier size and physical work, and probing keyed state directly. A reachability benchmark reports a median runtime reduction from 4.051 seconds in DuckDB v1.5.5 to 0.095 seconds in the v2.0 preview, a 42.6x speedup without SQL changes. ### Source excerpt DuckDB's recursive CTE engine now treats recursion as one long-lived computation: it retains eligible epoch-invariant state, chooses execution modes from exact frontier cardinalities and physical work, probes keyed state directly and gives `USING KEY ... UNION` changed-key semantics. ## Access control for AI agents on Rails: gating SQL with Action Policy DevFeed: [Access control for AI agents on Rails: gating SQL with Action Policy]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-25T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Access Control](), [Authorization](), [Rails](), [SQL](), [agent observability]() Tags: [access-control](), [agents](), [ai](), [ai-agents](), [llms](), [open-source](), [prompt-injection](), [rails](), [ruby](), [sql]() ### AI overview This article explains how Evil Martians protected sensitive data in an internal Rails AI assistant that uses open-ended SQL analytics. It describes moving authorization from prompts into Action Policy so read-only database access also respects which rows and fields an employee may reveal. ### Source excerpt Our Rails AI assistant had read-only SQL access and could still return peer review scores. Learn how we kept open-ended analytics in an in-app AI assistant while making database access honor application permissions. ## Core Banking Modernization with Temenos Core and CockroachDB DevFeed: [Core Banking Modernization with Temenos Core and CockroachDB]() Original publisher: [Read original article]() Author: Nanda Badrappan,Muruga Balakrishnan Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Cockroach Labs](), [Data Infrastructure](), [Availability](), [real-time](), [Resilience](), [Transactions](), [legacy](), [SQL](), [Cloud](), [systems](), [on-prem]() Tags: [availability](), [cloud](), [cockroach-labs](), [cockroachdb](), [data-infrastructure](), [legacy](), [on-prem](), [real-time](), [resilience](), [sql](), [transactions]() ### AI overview This article explains why banks are modernizing legacy core banking infrastructure. It describes how Temenos Core and CockroachDB are positioned to support real-time transaction processing, continuous availability, distributed operations, resilience, and regulatory requirements. ### Source excerpt Banking has become an always-on business. Customers expect instant payments, accurate balances, and continuous access to financial services... ## Migrate multilingual full-text search from SQL Server to PostgreSQL DevFeed: [Migrate multilingual full-text search from SQL Server to PostgreSQL]() Original publisher: [Read original article]() Author: Ken Zhang Published: 2026-08-20T20:34:44Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [sql-server](), [SQL](), [Amazon Aurora](), [Amazon RDS](), [tokenization]() Tags: [advanced-300](), [amazon](), [amazon-aurora](), [amazon-rds](), [aws](), [blog](), [how-to](), [postgresql](), [postgresql-compatible](), [rds-for-postgresql](), [sql](), [technical-how-to](), [tokenization]() ### AI overview This tutorial explains how to migrate multilingual full-text search from SQL Server to PostgreSQL while preserving comparable behavior. It covers collations, text search configurations, language-specific tokenization, accent-insensitive search, synonym expansion, and validation testing on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL. ### Source excerpt Migrating full-text search from SQL Server to PostgreSQL can silently change results because the engines handle text, linguistics, and accents differently. This post shows how to reproduce SQL Server full-text search on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL, covering collation, tokenization, accent-insensitive search, and synonyms. ## How Poke Gives Every User Their Own Database, Without Them Ever Knowing DevFeed: [How Poke Gives Every User Their Own Database, Without Them Ever Knowing]() Original publisher: [Read original article]() Author: Jeff Olson Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [Turso](), [Architecture & Design](), [Security](), [AI Agent](), [SQL](), [cpu]() Tags: [ai-agent](), [ai-agents](), [code](), [database](), [databases](), [performance](), [security](), [software-architecture](), [turso]() ### AI overview Poke provisions a separate Turso database for each website generated by its iMessage personal assistant. The per-user isolation helps contain inefficient SQL, unexpected traffic, security vulnerabilities, and associated performance or data-leakage risks while keeping costs manageable for low-traffic sites. ### Source excerpt How Poke's iMessage assistant provisions a Turso database for every website it builds, giving each user full isolation without them ever noticing. [Next page]()