# elasticsearch Elasticsearch is a distributed search and analytics engine, scalable data store, and vector database built on Apache Lucene. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Elastic and Tencent Cloud deepen strategic partnership to power AI-era search foundation DevFeed: [Elastic and Tencent Cloud deepen strategic partnership to power AI-era search foundation]() Original publisher: [Read original article]() Author: Yuki Jiang Published: 2026-09-03T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI search](), [elasticsearch](), [Large Language Model]() Tags: [ai-search](), [cloud](), [elasticsearch](), [large-language-models](), [partnership](), [platform-elasticsearch]() ### AI overview Elastic and Tencent Cloud expanded their collaboration and launched Tencent Cloud Elasticsearch Service Enterprise Edition, positioning AI search, model integration, and agent connectivity for enterprise data retrieval. ### Source excerpt Tencent Cloud and Elastic announced an expanded strategic collaboration in Shenzhen in August 2026. The companies will deepen their technical and product collaboration to help enterprises establish dedicated AI search for the AI era. ## The search multiplier: Driving revenue, productivity, and AI at scale DevFeed: [The search multiplier: Driving revenue, productivity, and AI at scale]() Original publisher: [Read original article]() Author: Nicole Volk Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [AI search](), [AI Infrastructure](), [Generative AI](), [idc](), [Resilience]() Tags: [accelerate](), [agentic-ai-cloud-search-context-engineering-customer-experience-deployment-end-user-experience](), [ai](), [ai-infrastructure](), [analysis](), [business-value](), [customer-story-elasticsearch-workplace-search](), [elasticsearch](), [elasticsearch-platform](), [financial-services](), [government](), [healthcare](), [resilience](), [retail](), [retrieval](), [search]() ### AI overview An IDC study of 11 large enterprises reports that the Elasticsearch Platform, used as a search and retrieval foundation, helped organizations deliver AI products faster while improving revenue, productivity, search relevance, and operational resilience. The study reports a 517% three-year ROI, $13.4 million in annual benefits per organization, and an 11-month payback period. ### Source excerpt An independent IDC study found organizations deploying the Elasticsearch Platform for enterprise search and agentic AI deliver AI products faster, drive higher revenue, boost productivity, and strengthen operational resilience across the business. ## Elastic community newsletter -- August 2026 DevFeed: [Elastic community newsletter -- August 2026]() Original publisher: [Read original article]() Author: Elastic DevRel team Published: 2026-08-13T00:00:00Z Content type: news Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [devrel](), [dashboards](), [Embeddings](), [multimodal](), [quantization](), [AI search](), [tracing]() Tags: [audio](), [dashboards](), [devrel](), [elasticsearch](), [embeddings](), [images](), [multimodal](), [pdf](), [quantization](), [tracing](), [video]() ### AI overview The Elastic community newsletter covers Elasticsearch 9.5, including native PromQL, the Dashboards API, Columnar Mode, vector search improvements, multimodal semantic search, and Elastic Agent Builder tracing in technical preview. ### Source excerpt In this newsletter, we cover version 9.5 of Elasticsearch, the latest blogs and videos, and upcoming events. ## Elastic 9.5: Columnar, VectorDB index mode & auto-calibration, and AI-driven alert triage DevFeed: [Elastic 9.5: Columnar, VectorDB index mode & auto-calibration, and AI-driven alert triage]() Original publisher: [Read original article]() Author: Sarah Leslie Published: 2026-08-04T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [elasticsearch](), [agent observability](), [Monitoring](), [Artificial Intelligence](), [Database]() Tags: [agent-observability](), [ai-agents](), [data](), [elasticsearch](), [monitoring](), [observability](), [observability-platform-search-security](), [performance](), [release]() ### AI overview Elastic 9.5 is generally available with Columnar Mode, VectorDB index mode and auto-calibration, native Prometheus and PromQL support, AI-driven alert triage, and enhancements to Elastic Agent Builder, including agent observability, monitoring, and human-in-the-loop approvals. ### Source excerpt Today, we are pleased to announce the GA of Elastic 9.5 as the latest version of the Elasticsearch Platform. This release includes a range of new features, including Columnar Mode, VectorDB index mode, and Agent Builder enhancements. Learn more. ## How to add autocomplete search to Webflow with Elasticsearch on Webflow Cloud DevFeed: [How to add autocomplete search to Webflow with Elasticsearch on Webflow Cloud]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-07-26T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [elasticsearch](), [Elastic Cloud](), [webflow](), [Nextra](), [API keys](), [Content Management System](), [Command-line interface](), [Front end](), [migration](), [Template]() Tags: [api-keys](), [cli](), [cloud](), [content](), [elastic-cloud](), [elasticsearch](), [frontend](), [getting-started](), [guide](), [guides](), [migration](), [next-js](), [webflow]() ### AI overview A guide to adding as-you-type autocomplete search to a Webflow site using Elasticsearch on Webflow Cloud. It covers updating the index mapping, re-indexing existing content, creating a Next.js Route Handler, and connecting a debounced, abort-controlled frontend input. ### Source excerpt Learn how to add as-you-type autocomplete search to Webflow using Elasticsearch's search_as_you_type field and a Webflow Cloud Route Handler ## The forbidden index: Building privacy-preserving search with OpenSearch DevFeed: [The forbidden index: Building privacy-preserving search with OpenSearch]() Original publisher: [Read original article]() Author: Kylie Wagar-Dirks Published: 2026-07-24T15:00:22Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Open Source](), [Access Control](), [pii](), [dashboards](), [tokenization](), [data](), [elasticsearch](), [Framework]() Tags: [analytics](), [analytics-stack](), [blog](), [compliance](), [dashboards](), [data](), [dynamic-data](), [open-source](), [opensearch](), [pii](), [plugin](), [privacy](), [search](), [tokenization]() ### AI overview The article describes a talk by Unnati Mishra and Akshat Khanna on building privacy-preserving search and analytics with OpenSearch. Their approach moves privacy controls into the ingest layer, using a custom plugin to redact PII at index time through tokenization and dynamic data masking. It also applies differential privacy to OpenSearch Dashboards by adding calibrated noise to aggregate query results. ### Source excerpt At OpenSearchCon Europe 2026, Unnati Mishra and Akshat Khanna (Angel One) argued that privacy in search must be handled during the ingest phase rather than retroactively through access controls. The post The forbidden index: Building privacy-preserving search with OpenSearch appeared first on OpenSearch. ## Palantir's Elasticsearch reindexing system supports online index rebuilds DevFeed: [Palantir's Elasticsearch reindexing system supports online index rebuilds]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-06-15T15:30:53Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [elasticsearch](), [Software Engineering](), [Database](), [observability]() Tags: [architecture](), [dashboards](), [database](), [elasticsearch](), [implementation](), [observability](), [parallel](), [rate-limiting]() ### AI overview The article describes Palantir's Elasticsearch reindexing system, which rebuilds large search indices while live traffic continues. It uses shadow indices, parallel database-to-index pipelines, multidimensional rate limiting, crash-safe state tracking, and support for multiple clusters. ### Source excerpt PLUS: Claude skills guide 👨💻, Call queue architecture ⚡, when NOT to use Kafka 👨💻 ## How to make the most of Elastic{ON} London 2026 DevFeed: [How to make the most of Elastic{ON} London 2026]() Original publisher: [Read original article]() Author: Ben Beeching Published: 2026-02-18T08:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI Agent](), [Large Language Model](), [Generative AI](), [elasticsearch](), [Security](), [observability](), [Microsoft]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [ai-agent](), [elasticsearch](), [engineering](), [event](), [events](), [genai](), [generative-ai](), [microsoft](), [security]() ### AI overview Elastic{ON} London 2026 will focus on agentic AI, context engineering, and practical applications across search, observability, and security. The article previews customer and partner use cases, technical workshops, networking opportunities, and a generative AI workshop on building applications with Elasticsearch. ### Source excerpt Elastic{ON} London is fast approaching. Here's how to get the most from the event. ## Effect of Lucene Segments on Elasticsearch DevFeed: [Effect of Lucene Segments on Elasticsearch]() Original publisher: [Read original article]() Published: 2026-01-09T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [elasticsearch](), [data](), [Databases]() Tags: [data](), [elasticsearch](), [performance](), [search-engine]() ### AI overview This deep dive explains how Elasticsearch relates to Lucene and how Lucene segments organize and represent data. It covers shards, inverted indexes, doc values, BKD trees, immutable segments, document updates, deletions, background segment merging, and reference counters used to keep searches consistent. ### Source excerpt . [How Lucene Segments Affect Elasticsearch](how-lucene-segments-affect-elasticsearch-cover... ## Disabling partial results in Elasticsearch DevFeed: [Disabling partial results in Elasticsearch]() Original publisher: [Read original article]() Published: 2026-01-02T00:00:00Z Content type: opinion Language: en Sources: [Gaurav Sarma's Blog]() Topics: [elasticsearch](), [distributed-systems](), [incident]() Tags: [distributed-systems](), [elasticsearch](), [incident](), [integrity](), [monitor](), [retry](), [search]() ### AI overview This commentary explains how disabling Elasticsearch partial search results can cause more query failures and longer-running queries in distributed workloads. It recommends monitoring returned row counts and shard failures, then retrying when appropriate, while reserving the setting for cases where data integrity cannot be compromised. ### Source excerpt . [Disabling allow_partial_search_results in Elasticsearch](disabling-allow-partial-results-in-elasticsearch-cover... ## Extracting a Chat Microservice from a Monolith: Lessons from hh.ru DevFeed: [Extracting a Chat Microservice from a Monolith: Lessons from hh.ru]() Original publisher: [Read original article]() Author: Ilrahil (hh.ru) Published: 2025-10-14T10:00:26Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Back end](), [event driven](), [PostgreSQL](), [Java](), [Databases](), [Kafka](), [elasticsearch]() Tags: [backend](), [elasticsearch](), [event-driven](), [hh-ru](), [highload-plus-plus](), [java](), [kafka](), [postgresql](), [tag-1e4ee1f65f5a](), [tag-75f84211cab4](), [tag-bfc1a5037c9b](), [tag-c2e9e71d738f](), [tag-d4be1d74f775]() ### AI overview A backend developer at hh.ru describes the staged extraction of a chat microservice from the Negotiations monolith. The article covers separating modules, monolith dependencies, SQL joins, transactions, asynchronous API delays, and the final service extraction, using event-driven architecture, data-model redesign, and an integration layer. ### Source excerpt Про выделение микросервисов из монолита рассказывали много, но у каждого свой путь - в статье расскажем про наш. От простейшего выделения сервиса в модуль в начале до решения проблем разрыва транзакций, SQL Join-запросов, задержек асинхронного API и непосредственно выделения нового сервиса. Использование event-driven архитектуры, редизайна модели данных и интеграционного слоя как основных подходов в процессе выделения. Читать далее ## Breaking free from rising observability costs with open, cost-efficient architectures DevFeed: [Breaking free from rising observability costs with open, cost-efficient architectures]() Original publisher: [Read original article]() Author: Mike Shi Published: 2025-09-18T08:27:13Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [observability](), [elasticsearch](), [Open Source](), [log management](), [systems]() Tags: [architecture](), [cost](), [elasticsearch](), [logs](), [observability](), [open](), [switching]() ### AI overview The article examines why observability costs increase as systems scale, linking the problem to legacy search-first architectures and pricing based on data ingestion or hosts. It traces the evolution from manual log processing and syslog through Splunk and Elasticsearch, then introduces an open, cost-efficient architecture centered on OTel-first collection, unified storage, and affordable long-term retention. ### Source excerpt Observability costs rise because legacy search-first architectures and ingest/per-host pricing models conflict with modern analytics workflows. Learn the root causes and the open blueprint: OTel-first collection, unified storage, and affordable long reten ## Scaling craft coffee: How ClickHouse powers Artly's barista bots DevFeed: [Scaling craft coffee: How ClickHouse powers Artly's barista bots]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-08-19T14:07:27Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Bots](), [telemetry](), [Data Infrastructure](), [Cloud Architecture](), [Amazon Web Services](), [Amazon S3](), [elasticsearch](), [Back end](), [Front end](), [Mobile](), [Web]() Tags: [architecture](), [aws](), [bots](), [clickhouse](), [cloud-architecture](), [data-infrastructure](), [elasticsearch](), [frontend](), [mobile](), [real-time](), [s3](), [telemetry](), [web]() ### AI overview This article explains how Artly uses ClickHouse within a hybrid AWS and GCP data infrastructure to support robotic baristas. The system processes telemetry, operational logs, and sales data in near real time, helping Artly monitor and improve consistent coffee production across multiple locations. ### Source excerpt "ClickHouse has a lot of great features. It's very flexible and great for our use cases. The pricing is also very affordable compared to other alternatives." - Tong Liu, Principal Software Architect ## How Discord Indexes Trillions of Messages DevFeed: [How Discord Indexes Trillions of Messages]() Original publisher: [Read original article]() Author: Vicki Niu Published: 2025-04-24T00:00:00Z Content type: article Language: en Sources: [Discord Blog]() Topics: [Discord](), [elasticsearch](), [Redis]() Tags: [cost](), [discord](), [elasticsearch](), [infrastructure](), [operations](), [performance](), [redis](), [search]() ### AI overview Discord describes how it built and scaled a message-search system using Elasticsearch, Redis, sharded message storage, queues, and indexing workers. The article highlights reliability and operational problems that emerged as Discord's message volume and Elasticsearch clusters grew. ### Source excerpt Learn how Discord scaled its search infrastructure to index trillions of messages and unlock new features. ## Databases in 2024: A Year in Review DevFeed: [Databases in 2024: A Year in Review]() Original publisher: [Read original article]() Author: Andy Pavlo Published: 2025-01-01T05:00:00Z Content type: opinion Language: en Sources: [Andy Pavlo - Carnegie Mellon University]() Topics: [Databases](), [Database](), [Open Source](), [elasticsearch](), [Redis](), [Cloud](), [MongoDB](), [amazon]() Tags: [amazon](), [blog](), [cloud](), [database](), [databases](), [elasticsearch](), [mongodb](), [open-source](), [redis]() ### AI overview A retrospective on the 2024 database ecosystem, focusing on database-company business models, cloud competition, and restrictive source-code license changes involving Redis and Elasticsearch, with additional discussion of MongoDB and hosted database services. ### Source excerpt Andy rises from the ashes of his dead startup and discusses what happened in 2024 in the database game. ## Blog: Falcosidekick 2.30.0 DevFeed: [Blog: Falcosidekick 2.30.0]() Original publisher: [Read original article]() Published: 2024-12-04T00:00:00Z Content type: release Language: en Sources: [Falco - Falco](), [Falco - The Falco blog]() Topics: [Falco](), [Cloud Native Ecosystem](), [elasticsearch](), [Prometheus]() Tags: [api](), [batching](), [cloud-native](), [compression](), [elasticsearch](), [falco](), [falcosidekick](), [http](), [prometheus](), [release]() ### AI overview Falcosidekick 2.30.0 adds Webex, OTLP Metrics, and Datadog Logs integrations. The release also improves throughput and Elasticsearch integration, updates Prometheus metric naming, supports multiple AlertManager hosts, and fixes several bugs. ### Source excerpt A few days after a new release of Falco Talon, our response engine, it's time for our favorite proxy forwarder to do the same. New outputs A new release means new integrations. Thanks to our contributors for their helps. Webex Notify your team on Webex with the integration developed by @k0rventen. OTLP Metrics The adoption of Open Telemetry is bigger and bigger in the Cloud Native ecosystem, @ekoops introduced the OTLP Metrics in Falcosidekick. Datalog Logs The Falco alerts can be forwarded to Datadog as events for a while in Falcosidekick, you can now use their Logs service thanks to @yohboy. New features Here's a non exhaustive list of the great features and enhancements which come with this new release: x3 throughput @alekmaus spotted a bottleneck with the http client used to forward the events to the outputs. His fix increases up to 300% the throughput!!! Better integration with Elasticsearch @alekmaus worked hard to improve the integration with Elasticsearch. In addition improvments for the clients, new settings have been introduced, like the possibility to specify an ingest pipeline or an api key, to enable batching and compression. See the docs to know them all. Better consistency for the Prometheus metrics Falco recently integrated a direct endpoint to expose metrics in the Prometheus format. After a lot of discussions between the maintainers and the community, a convention has been chosen for the names of the metrics. This release adapts the metrics exposed by Falcosidekick to follow this convention and have a consistency accross the different components of the ecosystem. Breaking changes: The renaming of the metrics might impact the queries for your alerts and dashboards. Multi hosts for AlertManager You can now specify a list of servers for the AlertManager output, which is a requirement when it's deployed in HA mode. Fixes The contributors fixed several bugs, here's a non exhaustive list of the more important ones: Fix PolicyReports created in the same n ## One timezone for everything: k8tz and the Jakarta timestamp problem DevFeed: [One timezone for everything: k8tz and the Jakarta timestamp problem]() Original publisher: [Read original article]() Published: 2024-11-20T00:00:00Z Content type: article Language: en Sources: [Andika Ahmad Ramadhan -- Blog]() Topics: [Kubernetes admission controller](), [timezone](), [certificates](), [TLS (Transport Layer Security)](), [elasticsearch]() Tags: [certificates](), [elasticsearch](), [kubernetes-admission-controller](), [timezone](), [tls]() ### AI overview The article explains how k8tz applies Asia/Jakarta to Kubernetes pods through an admission controller, allowing public images such as Fluentd and Elasticsearch to use the platform timezone without image changes. It also describes three upstream patches for cert-manager support, certificate rotation without restarts, and configurable initContainer names. ### Source excerpt How we used k8tz to force Asia/Jakarta onto public images like Fluentd and Elasticsearch, and the three patches I sent upstream. ## Reducing false positives with automated SIEM investigations from Elastic and Tines DevFeed: [Reducing false positives with automated SIEM investigations from Elastic and Tines]() Original publisher: [Read original article]() Author: Aaron Jewitt Published: 2024-05-31T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [SIEM, Security](), [SOC](), [Security](), [elasticsearch](), [Amazon Web Services](), [Software as a service](), [API]() Tags: [analysts](), [api](), [automated-threat-protection-anomaly-detection-cybersecurity-network-visibility-security-analytic](), [aws](), [blog-post](), [elastic](), [endpoint-security-security](), [false-positives](), [logs](), [security](), [server](), [soc](), [token](), [workflows]() ### AI overview Elastic's InfoSec team uses Tines to automate initial SIEM alert investigations, helping reduce false positives and analyst fatigue. The workflow queries Elasticsearch using alert data such as source.ip, closes alerts associated with trusted devices or known benign activity, and escalates cases that cannot be resolved automatically. ### Source excerpt Discover how Elastic's InfoSec team saves thousands of hours per month by using Tines to automate SIEM alert investigations while reducing false positives and detect compromised accounts. ## How the Guardian's Ophan Analytics Tool Moved from Elasticsearch Rollups to BigQuery DevFeed: [How the Guardian's Ophan Analytics Tool Moved from Elasticsearch Rollups to BigQuery]() Original publisher: [Read original article]() Author: Sam Hession Published: 2023-06-07T12:44:33Z Content type: article Language: en Sources: [Guardian]() Topics: [Monitoring](), [elasticsearch](), [real-time](), [BigQuery](), [data]() Tags: [analytics](), [bigquery](), [data](), [elasticsearch](), [monitoring](), [pipeline](), [real-time](), [warehouse]() ### AI overview The Guardian describes how Ophan, its real-time analytics tool, expanded from short-term pageview monitoring to historical data analysis. Because Elasticsearch Rollups remained in technical preview and lacked service-level guarantees, the team considered moving the long-term data pipeline to BigQuery. ### Source excerpt How the Guardian's real time analytics tool pivoted from ElasticSearch Rollups to BigQuery and what we learnt along the way Ophan is the Guardian's in-house developed real time analytics tool which allows us to see how our content is performing in real-time, providing our Editorial teams with the insights they need to curate and promote our journalism. Its intuitive ways of monitoring reader engagement with content help the teams to evaluate whether it's performing optimally. Ophan's distinguishing feature has always been to provide journalists with visualised real time pageview information, with features based on the previous two weeks. More recently we have expanded these capabilities to provide insights for wider timescales with years of data available at their fingertips. Continue reading... ## Temporal v1.20: Simpler dev environments & Workflow scheduling DevFeed: [Temporal v1.20: Simpler dev environments & Workflow scheduling]() Original publisher: [Read original article]() Author: Justin Pirie Published: 2023-03-20T07:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Development](), [releases](), [Command-line interface](), [distributed-systems](), [Automation](), [Containers](), [elasticsearch](), [datadog](), [cURL](), [Homebrew](), [Quartz]() Tags: [automation](), [cli](), [containers](), [curl](), [datadog](), [dev](), [distributed](), [distributed-systems](), [elasticsearch](), [homebrew](), [product-news](), [quartz](), [scheduling](), [v1](), [workflow]() ### AI overview Temporal v1.20 simplifies local development environments by packaging Temporalite into the core product and adding a Temporal CLI with installation options including curl, Homebrew, and binary downloads. The release also adds Advanced Visibility to the CLI, deprecates direct Advanced Visibility on Cassandra without Elasticsearch, and makes Scheduled Workflows generally available. ### Source excerpt Explore Temporal v1.20's improvements in dev environments and Workflow scheduling, making it easier to adopt distributed systems best practices. ## How we improved reporting and monitoring of test automation results DevFeed: [How we improved reporting and monitoring of test automation results]() Original publisher: [Read original article]() Author: Giuseppe Donati Web Test Automation Engineer Not a stereotypical Italian guy; Except Published: 2023-02-15T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Test automation](), [GitHub Actions](), [Selenium](), [CI/CD](), [Cloud](), [Kafka](), [elasticsearch](), [kibana](), [logstash](), [Grafana](), [Cucumber]() Tags: [article](), [elasticsearch](), [frontend](), [gcp](), [github-actions](), [grafana](), [kafka](), [kibana](), [logstash](), [monitoring](), [quality-assurance](), [selenium](), [test-automation]() ### AI overview The article describes trivago's updated system for executing, reporting, and monitoring Selenium-based end-to-end tests. It covers GitHub Actions workflows, custom runners on Google Cloud, cloud report storage, Kafka and Logstash processing, Elasticsearch and Kibana visualization, and Grafana and Slack alerting. ### Source excerpt Over the last few years, we completely refactored what was described in our previous article about how we use the ELK sta... ## Job Hound is Back DevFeed: [Job Hound is Back]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2022-05-27T10:58:00Z Content type: opinion Language: en Sources: [Scott Johnson]() Topics: [Job](), [Code](), [Docker](), [hosting](), [monitor](), [elasticsearch](), [test-coverage](), [Security]() Tags: [code](), [docker](), [elasticsearch](), [hosting](), [job](), [jobhound](), [monitor](), [security](), [ssl](), [test-coverage]() ### AI overview The author revisits Job Hound after learning how a user relied on it during a job search. The update describes new hosting, faster performance, Job Hound-specific blog filtering, content updates, uptime monitoring, analytics, and improved test coverage. Elasticsearch is currently offline, with plans to restore it. ### Source excerpt It is now 2022 and Job Hound has existed for 4 years now having happily served over 1,000 users to help apply for over 5,000 different jobs. Job Hound has been one of those bits of code that you write and it just runs happily in the background just plain working without fail. Other than a few restarts to the Docker containers to reclaim storage, I literally haven't touched it since I wrote it back in 2018. And, facepalm, I'm not happy about that. A chance set of interactions with a happy Job Hound user convinced me to re-focus on Job Hound: Scott, I used your Job Hound during my 7 month, 84 applications search and landed a gig at BLANK Health as a Security Engineer. Now I am using Job Hound again for my wife to track the same thing! What a fantastic site. Simple but more useful than just some Excel sheet. Just wanted to circle back and say thank you Marc Now here's the rub - he used Job Hound while the SSL was broken. When someone uses your site even when Chrome is telling you not to - that's powerful. I took Marc's advice to heart and dug back into the Job Hound source code: New Hosting (it is now on a cluster of machines, load balanced and all) Significantly Faster Blog link now gives you only posts about Job Hound A start on content updates Monitored for uptime issues On machines that I monitor every day for Pizza For Ukraine and Covid Near Me so I'll always know if something goes down New Analytics (useful for me although less so for you) Better test coverage (again useful for me) New features are planned and coming. I've also modified my personal schedule to allow me to put sustainable hours into Job Hound. Sustainable hours are key to side projects, particularly ones that aren't profitable (it was never supposed to be). The only downside right now is that ElasticSearch is being difficult so that's offline. It is, clearly, on the short list of things to bring back. My sincere thanks go out to Marc. Appreciated. So ... The Hound Is Back. ## Artsy Deprecates the KAWS Collections Service by Moving Its Functionality into Gravity DevFeed: [Artsy Deprecates the KAWS Collections Service by Moving Its Functionality into Gravity]() Original publisher: [Read original article]() Published: 2022-05-09T00:00:00Z Content type: article Language: en Sources: [Artsy]() Topics: [API](), [Node.js](), [Database](), [GraphQL](), [elasticsearch](), [TypeScript](), [Rails](), [Ruby]() Tags: [api](), [database](), [elasticsearch](), [graphql](), [kaws-refactoring-gravity](), [node-js](), [rails](), [ruby](), [typescript]() ### AI overview Artsy explains why it deprecated KAWS, a service for defining artwork collections, and moved its functionality into the main Gravity database and API. The article describes KAWS's Node.js, MongoDB, GraphQL, TypeScript, and Elasticsearch-based design and the lessons from retiring it. ### Source excerpt How do you tell when it's time to deprecate a system? If something mostly works OK, is it worth spending time and effort replacing its functionality? At Artsy, we realized several years ago that we needed to be able to group a bunch of artworks together. If we wanted to have a page with all of the ceramics by Lucio Fontana, or contemporary prints from the IFPDA Fair Sprint 2022, or a gift guide curated by Antwaun Sargent, we needed to have a way to make that happen. We decided to call these things "collections," a reasonable name for a collection of artworks. In order to create them, we developed a service called KAWS, named after the artist (whose works we wanted to put in several of these collections). Now, 4 years later, we've taken down the service and folded its functionality into Artsy's main database and API, Gravity. Let's talk about why and what we learned along the way. A little context KAWS has a somewhat unusual design. It's a server, a Node.js app with its own Mongo database, and it serves up a GraphQL API. It also relies on TypeORM to map TypeScript models to database records and TypeGraphQL to keep TS and GraphQL types in sync. This makes it a bit different from most services Artsy maintains. Most of our APIs are Ruby on Rails apps, and we don't have any other uses of TypeORM and TypeGraphQL. When it was created, it was a cool experiment with a possible direction for future APIs - one we decided not to pursue for the time being. KAWS doesn't store any artworks - or even any artwork IDs. Instead, it just stores a "query", a set of Elasticsearch criteria. This could be a list of artist IDs or gene IDs, a tag ID, a keyword, or any combination of those things. In other words, a KAWS artwork collection could be defined in human-readable terms as something like "artworks with the 'Black and White' gene by the artist Bridget Riley" or "artworks by Auguste Rodin with the keyword 'bronze' and the gene 'Sculpture'." This approach results in a somewhat odd flow ## How Temporal Auto-Setup works DevFeed: [How Temporal Auto-Setup works]() Original publisher: [Read original article]() Author: Shawn Wang Published: 2022-01-05T07:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Deployment](), [Docker](), [Databases](), [elasticsearch](), [Apache Cassandra](), [Kubernetes]() Tags: [cassandra](), [deployment](), [docker](), [elasticsearch](), [how-to](), [kubernetes](), [migrations](), [schema](), [self-hosted]() ### AI overview This article explains how Temporal's auto-setup process supports deployment. It describes the Docker entrypoint, schema setup for SQL databases and Cassandra, optional Elasticsearch visibility-schema setup, and Temporal Server namespace registration, with context for self-hosted users moving from auto-setup to production deployments. ### Source excerpt Discover how Temporal's auto-setup feature simplifies deployment, offering easy installation with Docker images and zipped binaries for the Temporal server [Next page]()