# Full-text Search Published articles for Full-text Search. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing TIN: full-text search for Postgres DevFeed: [Introducing TIN: full-text search for Postgres]() Original publisher: [Read original article]() Author: Patrick Reynolds Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [benchmarking]() Tags: [backups](), [benchmark](), [benchmarks](), [bm25](), [engineering](), [full-text-search](), [index](), [performance](), [postgres](), [reddit](), [replication](), [search](), [text](), [wikipedia]() ### AI overview PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance. ### Source excerpt TIN is a fast, full-featured, full-text search index for Postgres ## Live text search with Firestore pipelines and React DevFeed: [Live text search with Firestore pipelines and React]() Original publisher: [Read original article]() Author: Jeff Huleatt Published: 2026-05-28T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [React](), [Firebase](), [web applications](), [TypeScript]() Tags: [firebase](), [firestore](), [full-text-search](), [javascript](), [react](), [typescript](), [web](), [web-app]() ### AI overview This tutorial explains how to implement text search in a React web app using Firestore Enterprise text indexes and pipelines. It covers a reusable search-results component, React Suspense with TanStack Query, search on form submission, and debounced live search. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Realtime PostgreSQL: From Data Connect to SQL Connect DevFeed: [Realtime PostgreSQL: From Data Connect to SQL Connect]() Original publisher: [Read original article]() Author: Cynthia Wang Published: 2026-04-29T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [PostgreSQL](), [SQL](), [Firebase](), [GraphQL](), [Caching](), [reactive](), [SDKs]() Tags: [admin-sdk](), [caching](), [cloud-functions](), [firebase](), [full-text-search](), [graphql](), [postgresql](), [real-time](), [realtime](), [sdks](), [sql](), [sql-connect](), [updates]() ### AI overview Firebase announces that Firebase Data Connect is becoming Firebase SQL Connect, with a renewed focus on PostgreSQL and expanded SQL capabilities. The update includes Realtime Query Updates, Offline Caching, raw SQL alongside GraphQL, and lower-friction access for prototypes and testing. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Firestore adds generally available pipeline operations with full-text search, JOINs, geospatial queries, and data manipulation DevFeed: [Firestore adds generally available pipeline operations with full-text search, JOINs, geospatial queries, and data manipulation]() Original publisher: [Read original article]() Author: Morgan Chen; Minh Nguyen Published: 2026-04-27T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [NoSQL](), [Database](), [Serverless](), [serverless architecture]() Tags: [database](), [firebase](), [firestore](), [full-text-search](), [geo-queries](), [geospatial](), [javascript](), [nosql](), [real-time](), [serverless](), [serverless-architecture]() ### AI overview Firestore pipeline operations reached general availability on the enterprise edition, adding full-text search, geospatial queries, JOIN capabilities through subqueries, and data manipulation while retaining Firestore's NoSQL and serverless features. ### Source excerpt With support for full-text search, joins, and geospatial queries, Firestore is ready to meet all of your app's querying needs. ## What's new from Firebase at Cloud Next 2026 DevFeed: [What's new from Firebase at Cloud Next 2026]() Original publisher: [Read original article]() Author: Seba Gnagnarella; Arthur Thompson; Kristen Richards Published: 2026-04-22T11:30:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Cloud](), [Firestore](), [PostgreSQL](), [SQL](), [Mobile](), [Web](), [GraphQL]() Tags: [2026](), [ai](), [ai-logic](), [ai-studio](), [app-check](), [application-design-center](), [authentication](), [cloud-functions](), [cloud-next](), [dart-on-the-server](), [firebase](), [firestore](), [flutter](), [full-text-search](), [gemini](), [geo-queries](), [graphql](), [hybrid-inference](), [mobile](), [news](), [phone-number-verification](), [postgresql](), [sql](), [sql-connect](), [updates](), [web]() ### AI overview Firebase describes announcements from Cloud Next 2026, including the evolution of Firebase Data Connect into Firebase SQL Connect with realtime syncing, offline cache, and native SQL support. The article also covers Firestore Enterprise pipeline queries moving to general availability. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Six RAG strategies, explained simply (with code). DevFeed: [Six RAG strategies, explained simply (with code).]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-04-03T08:36:55Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Retrieval Augmented Generation (RAG)](), [Database](), [Large Language Model]() Tags: [database](), [embeddings](), [full-text-search](), [rag](), [retrieval](), [tutorials](), [vector-search]() ### AI overview This tutorial explains retrieval-augmented generation and compares retrieval strategies, including loading complete documents, full-text search, and vector search. It presents Postgres as a practical option for implementing these approaches and discusses their trade-offs. ### Source excerpt Six Ways to Retrieve Data for an LLM ## Full-text search and other searches with Data Connect DevFeed: [Full-text search and other searches with Data Connect]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-09-02T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Databases](), [Query (disambiguation)](), [AI search](), [Embeddings]() Tags: [database](), [embeddings](), [firebase](), [full-text-search](), [graphql](), [postgresql](), [search](), [sql-connect](), [vector](), [vector-search](), [vertex-ai]() ### AI overview Firebase Data Connect now supports PostgreSQL-powered full-text search. The article explains how it differs from string pattern filters and vector similarity search, using a movie app to show example use cases and query approaches. ### Source excerpt News, tutorials, and updates from the Firebase team. ## The 10 Most Popular Postgres Extensions on Neon DevFeed: [The 10 Most Popular Postgres Extensions on Neon]() Original publisher: [Read original article]() Author: Ben Hagan Published: 2025-08-21T16:04:10Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [Database](), [dashboards](), [datasets](), [Embeddings](), [Query (disambiguation)](), [observability](), [Monitoring](), [Latency](), [real-time](), [SQL](), [Code]() Tags: [data](), [database](), [embeddings](), [extensions](), [full-text-search](), [geospatial](), [insights](), [latency](), [monitoring](), [observability](), [performance](), [performance-monitoring](), [popular](), [postgres](), [real-time](), [search](), [sql]() ### AI overview The article presents the ten most popular Postgres extensions on Neon, using platform-wide installation and usage statistics. It explains how extensions can add full-text search, geospatial capabilities, vector storage for embeddings, database functions, query-performance monitoring, and UUID generation, and highlights an interactive real-time dashboard for exploring the data. ### Source excerpt Postgres is loved not just for what it is, but for what it can become: its extension system lets you transform your database into a full-text search engine, a geospatial powerhouse, or a vector store for embeddings - all without switching tools. With many thousands of active data... ## Inside ClickHouse full-text search: fast, native, and columnar DevFeed: [Inside ClickHouse full-text search: fast, native, and columnar]() Original publisher: [Read original article]() Author: Jimmy Aguilar, Elmi Ahmadov, and Robert Schulze Published: 2025-08-18T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Data structures](), [Parsing](), [Query (disambiguation)](), [tokenizers](), [Database]() Tags: [analytics](), [clickhouse](), [data](), [database](), [deep-dive](), [examples](), [full-text-search](), [how-to](), [performance](), [pipeline](), [search](), [token](), [tokenizers](), [tokens]() ### AI overview This deep dive explains ClickHouse's rebuilt full-text search implementation, including its column-oriented storage model, tokenization, inverted indexes, finite state transducers, posting lists, and redesigned query pipeline. It describes how the approach reduces I/O and improves search speed and space efficiency, while noting that the implementation described has been deprecated in favor of a newer implementation that was stated to be generally available and production ready as of March 2026. ### Source excerpt A deep dive into ClickHouse's built-in full-text search -- how it works, what's new, and how to use it for fast, precise text queries. ## Firebase Data Connect Adds Full-Text Search, Enums, SDK Downloads, and MCP Support DevFeed: [Firebase Data Connect Adds Full-Text Search, Enums, SDK Downloads, and MCP Support]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-07-23T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [data](), [Query (disambiguation)](), [MCP](), [SDK]() Tags: [enums](), [firebase](), [full-text-search](), [graphql](), [mcp](), [mcp-server](), [news](), [postgres](), [postgresql](), [sdk](), [sql-connect](), [updates]() ### AI overview Firebase announces new Data Connect features including Postgres-powered full-text search, enum support, SDK downloads from the console, and Firebase MCP server support. The article also describes search filters, language choices, query formats, and relevance thresholds. ### Source excerpt News, tutorials, and updates from the Firebase team. ## You Don't Need A Separate Vector Database DevFeed: [You Don't Need A Separate Vector Database]() Original publisher: [Read original article]() Author: Jamie Barton Published: 2024-11-04T00:00:00Z Content type: opinion Language: en Sources: [Turso Blog]() Topics: [Databases](), [Turso](), [libSQL](), [AI search](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [SQLite](), [SQL]() Tags: [architecture](), [data](), [database](), [databases](), [embeddings](), [full-text-search](), [libsql](), [rag](), [search](), [sql](), [sqlite](), [turso](), [vector]() ### AI overview The article argues that many applications do not need a separate vector database. Modern databases can combine vector search with SQL, full-text search, application data, caching, and background jobs, reducing synchronization and operational complexity. It presents Turso's native libSQL vector datatype as one way to store vectors alongside regular data, while acknowledging that specialized vector databases may still be justified when their additional features are required. ### Source excerpt Why you can skip Pinecone and friends: modern databases like Turso ship native vector search alongside SQL, full-text, and your application data in one place. ## Implement Full-Text Search over a GraphQL API in MongoDB Atlas DevFeed: [Implement Full-Text Search over a GraphQL API in MongoDB Atlas]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2021-12-16T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [GraphQL](), [MongoDB](), [Binance](), [CRUD](), [Serverless]() Tags: [full-text-search](), [function](), [graphql](), [javascript](), [mongodb](), [nosql](), [resolvers](), [schema](), [search](), [serverless](), [tutorial]() ### AI overview A tutorial showing how to implement full-text search in a GraphQL API for MongoDB Atlas using MongoDB Realm custom resolvers and Realm Functions. It explains how to extend the automatically generated CRUD API with Atlas Search. ### Source excerpt GraphQL can be an extremely powerful and efficient way to create APIs and MongoDB Realm makes it easy by allowing you to connect your collections to GraphQL schemas without writing a single line of co... The post Implement Full-Text Search over a GraphQL API in MongoDB Atlas appeared first on MongoDB. ## Vapor: UI Dashboard Package DevFeed: [Vapor: UI Dashboard Package]() Original publisher: [Read original article]() Author: Nuno Maduro Published: 2020-09-03T09:45:00Z Content type: article Language: en Sources: [Laravel Blog]() Topics: [Vapor](), [dashboards](), [Laravel](), [Amazon Web Services](), [Logging](), [AWS Lambda]() Tags: [aws](), [aws-lambda](), [full-text-search](), [gateway](), [http](), [laravel](), [logging](), [logs](), [monitor](), [open](), [open-source](), [payload](), [queue](), [retry](), [search](), [ui]() ### AI overview Laravel introduces Vapor UI, an open-source dashboard package for Vapor applications. It lets developers search and inspect application and infrastructure logs, review failed queue jobs, and retry or delete failed jobs. ### Source excerpt Today on Vapor, we're proud to introduce you to our new open-source package: Vapor UI. In short, this package provides a beautiful dashboard through your application that allows you to monitor your application's logs... ## How I Built a Serverless Search for My Blog DevFeed: [How I Built a Serverless Search for My Blog]() Original publisher: [Read original article]() Published: 2020-07-29T15:30:00Z Content type: tutorial Language: en Sources: [Gunnar Morling]() Topics: [Serverless](), [AWS Lambda](), [Quarkus](), [Java](), [Back end](), [Deployment](), [HTTP]() Tags: [aws-lambda](), [deployment](), [full-text-search](), [http](), [java](), [quarkus](), [search](), [serverless]() ### AI overview A developer describes building a custom search feature for a Hugo blog using Java and Apache Lucene. The search is compiled into a GraalVM native binary with Quarkus and deployed as a serverless application on AWS Lambda, with the article covering implementation, deployment, identity and access management, performance, and cost control. ### Source excerpt Table of Contents Why Serverless? Solution Overview Data Extraction Search Backend Implementation Apache Lucene in a GraalVM Native Binary The Search HTTP Service Wiring Things Up Deployment to AWS Lambda Building Quarkus Applications for AWS Lambda Identity and Access Management Performance Cost Control Wrap-Up and Outlook I have built a custom search functionality for this blog, based on Java and the Apache Lucene full-text search library, compiled into a native binary using the Quarkus framework and GraalVM. It is deployed as a Serverless application running on AWS Lambda, providing search results without any significant cold start delay. If you thought Java wouldn't be the right language for this job, keep reading; in this post I'm going to give an overview over the implementation of this feature and my learnings along the way. ## Ember CLI Addon Docs: Shared Documentation for the Ember Ecosystem DevFeed: [Ember CLI Addon Docs: Shared Documentation for the Ember Ecosystem]() Original publisher: [Read original article]() Author: pzuraq Published: 2018-03-07T21:00:37Z Content type: article Language: en Sources: [Addepar]() Topics: [Ember](), [Documentation](), [code productivity](), [JavaScript]() Tags: [automated](), [developer-productivity](), [documentation](), [ember](), [ember-cli](), [emberjs](), [examples](), [experimental](), [full-text-search]() ### AI overview Addepar describes the development of ember-cli-addon-docs, a shared documentation solution for Ember addons. The project addresses limitations in existing tools by providing Ember-focused API documentation, Markdown templates, live demos, versioning, automated deployments, search, and pluggable API documentation generators. ### Source excerpt On our road to modern Ember here at Addepar, we've been hard at work developing a new component library to replace a lot of our older components, such as the now deprecated Ember Widgets library. As anyone who has built a shared toolset of components will know, one of the more tedious tasks faced by a library developer is documentation -- users of the new components need to be able to reference the APIs to know how to use them, and having a single source of truth for your docs is invaluable to developer productivity, but setting up docs in the first place is a lot of hard work! For Ember, part of the problem is that there is no standard documentation solution as of yet. The core Ember and Ember-CLI API docs use YUIDoc to generate docs automatically, but it is an aging library which doesn't have much support for modern javascript features. JSDoc is also falling behind the times lately, and newer doc tools like ESDoc and Documentation.js are still too experimental for widespread use. Moreover, none of these tools targets Ember -- they are meant for generic Javascript usage and don't highlight things that Ember devs need to know, like what arguments a component receives, what actions it sends, or what values it yields. Worse yet, none of these tools has support for live examples and demos. To get those, most addon authors resort to building an Ember app, usually using the test application (or Dummy app) included with the addon. Building an app takes time and effort, and in the end this means that very few addons in the ecosystem are fully documented with good examples and well described APIs. When we started to set up our documentation we ran into all of these issues, and we began wondering where the shared solution was for Ember documentation. As often happens in the Ember community, it turned out others had been wondering this as well! We joined forces with Sam Selikoff, Dan Freeman, and the Ember Learning team to work on ember-cli-addon-docs, a project whose goal is t ## Regular Expression Search with Suffix Arrays DevFeed: [Regular Expression Search with Suffix Arrays]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2015-02-01T15:52:43Z Content type: tutorial Language: en Sources: [Nelson Elhage]() Topics: [Regular expression](), [Data structures](), [datasets](), [Code](), [Bioinformatics]() Tags: [algorithms](), [arrays](), [code-search](), [data-structure](), [full-text-search](), [google](), [library](), [open-source]() ### AI overview This technical writeup explains how livegrep uses suffix arrays for regular-expression search over source code. It introduces suffix arrays, describes substring lookup through binary search, and outlines livegrep's indexing process using a flattened source buffer, a file content map, and libdivsufsort. ### Source excerpt Back in January of 2012, Russ Cox posted an excellent blog post detailing how Google Code Search had worked, using a trigram index. By that point, I'd already implemented early versions of my own livegrep source-code search engine, using a different indexing approach that I developed independently, with input from a few friends. This post is my long-overdue writeup of how it works. Suffix Arrays A suffix array is a data structure used for full-text search and other applications, primarily these days in the field of bioinformatics. ## Rethinking full text search DevFeed: [Rethinking full text search]() Original publisher: [Read original article]() Author: Marek Published: 2012-07-01T22:00:00Z Content type: opinion Language: en Sources: [Marek Majkowski]() Topics: [Databases](), [scaling](), [NoSQL](), [data](), [coding]() Tags: [full-text-search](), [key-value-store](), [nosql](), [performance](), [real-time](), [scaling](), [search-engine]() ### AI overview The article explores the design of a full-text search engine for Twitter-like workloads, focusing on real-time index updates, horizontal scalability, and simple architecture. It reports experiments using a distributed key-value store and finds that the storage layer, rather than the Python indexer, became the main performance bottleneck as the dataset grew beyond available memory. ### Source excerpt Rethinking full text search In January 2009 I was wondering whether it is possible to build a full-text search engine that could handle the search for Twitter. At that time the search tool they provided was barely working. Later Twitter acquired Summize to solve this problem. A search engine for Twitter presents a unique set of constraints. Traditional full-text search engines assume that index once created won't be changed, updating is expensive by design. ## PostgreSQL: Data is Important DevFeed: [PostgreSQL: Data is Important]() Original publisher: [Read original article]() Author: Square Engineering Published: 2011-06-06T16:00:00Z Content type: article Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [PostgreSQL](), [SQL](), [Web](), [Replication](), [Geographic Information System]() Tags: [algorithms](), [architecture](), [consistency](), [data](), [durability](), [engineering](), [full-text-search](), [gis](), [postgresql](), [replication](), [search](), [sql](), [statement](), [types](), [web]() ### AI overview This article presents a case for using PostgreSQL in a modern web architecture. It describes PostgreSQL features including migration support, indexes, transactional DDL, constraints, streaming replication, query planning, PostGIS, and full-text search. ### Source excerpt A case for PostgreSQL in a modern web architecture.