# ElasticSearch Published articles for ElasticSearch. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## See you at Elastic{ON} 2026 DevFeed: [See you at Elastic{ON} 2026]() Original publisher: [Read original article]() Author: Dan Courcy Published: 2026-09-08T00:00:00Z Content type: news Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI search](), [telemetry]() Tags: [ai](), [ai-agent](), [developer](), [elasticsearch](), [event](), [logging](), [metrics](), [observability](), [platform-observability-search-security](), [production](), [search](), [sre]() ### AI overview Elastic announces its Elastic{ON} 2026 global event tour, with sessions for developers on scaling AI agents with Elasticsearch and for SREs on observability, logging, metrics, and AI-assisted incident response. ### Source excerpt Elastic{ON} kicks off this September with seven stops in cities around the world. Elastic's CEO Ash Kulkarni will be appearing at stops across the tour, opening keynotes with a candid conversation about why your data has never been more valuable. ## Java Weekly, Issue 662 DevFeed: [Java Weekly, Issue 662]() Original publisher: [Read original article]() Author: baeldung Published: 2026-09-06T09:25:19Z Content type: article Language: en Sources: [Baeldung]() Topics: [IntelliJ IDEA](), [Concurrency](), [Tooling](), [Cryptography](), [elasticsearch]() Tags: [concurrency](), [cryptography](), [elasticsearch](), [intellij-idea](), [java](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [tooling](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview A Java ecosystem roundup led by a hands-on look at Project Loom in IntelliJ IDEA, covering virtual threads, scoped values, structured concurrency, and related tooling. It also links to Java and Spring articles, podcasts, release announcements, code-review commentary, and IntelliJ IDEA Conf 2026. ### Source excerpt The three Loom pillars and, more importantly, the tooling. Plus IntelliJ IDEA Conf 2026 getting close. The post Java Weekly, Issue 662 first appeared on Baeldung. ## 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. ## What's new in ECK 3.5 DevFeed: [What's new in ECK 3.5]() Original publisher: [Read original article]() Author: Kostas Stamatakis Published: 2026-08-20T00:00:00Z Content type: release Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Elastic Cloud]() Tags: [cloud](), [cloud-native-kubernetes](), [elastic-cloud](), [elasticsearch](), [feature](), [infrastructure](), [kubernetes](), [memory](), [onboarding](), [orchestration](), [platform](), [release](), [resource](), [scale](), [security](), [selectors](), [tls](), [workflows]() ### AI overview ECK 3.5 adds label-selector-based namespace management, pausable orchestration for maintenance, and automatic mutual TLS client certificates across Elasticsearch-connected components. ### Source excerpt ECK 3.5 brings dynamic namespace management via label selectors, pause orchestration for safe maintenance windows, mutual TLS across all stack components, simplified resource specs, and a reduced operator memory footprint. ## How Sony LIV uses ClickHouse Cloud to deliver live streaming analytics at billion-row scale DevFeed: [How Sony LIV uses ClickHouse Cloud to deliver live streaming analytics at billion-row scale]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-18T16:20:22Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [real-time](), [Streaming](), [data](), [telemetry](), [Platform Engineering](), [BigQuery](), [elasticsearch]() Tags: [bigquery](), [clickhouse](), [cloud](), [data](), [elasticsearch](), [latency](), [platform-engineering](), [real-time](), [streaming](), [telemetry]() ### AI overview Sony LIV rebuilt its streaming analytics infrastructure around ClickHouse Cloud, consolidating fragmented batch pipelines, Elasticsearch workflows, BigQuery workloads, and telemetry stores. The cloud-native platform processes billions of daily events and delivers sub-second analytics for operational visibility during live events. ### Source excerpt Sony LIV consolidated fragmented batch, Elasticsearch, and BigQuery workloads on ClickHouse Cloud, delivering sub-second analytics across billions of daily streaming events. ## Java Weekly, Issue 659 DevFeed: [Java Weekly, Issue 659]() Original publisher: [Read original article]() Author: baeldung Published: 2026-08-15T10:23:09Z Content type: article Language: en Sources: [Baeldung]() Topics: [Programming](), [intellij-platform](), [Model Context Protocol](), [elasticsearch]() Tags: [elasticsearch](), [github](), [gradle](), [idea](), [java](), [mcp](), [netflix](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [programming](), [releases](), [security](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview Java Weekly, Issue 659 is a roundup of Java ecosystem reading, including MCP server evolution, Project Valhalla, JVM security, DuckDB, HTTP extension design, AI features in IntelliJ IDEA, webinars, and software releases. ### Source excerpt Equality in Java is evolving. The post Java Weekly, Issue 659 first appeared on Baeldung. ## 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. ## Elastic and OpenAI collaborate to bring frontier intelligence to unstructured enterprise data DevFeed: [Elastic and OpenAI collaborate to bring frontier intelligence to unstructured enterprise data]() Original publisher: [Read original article]() Author: Gregory Tademoto,Hemant Malik Published: 2026-07-30T00:00:00Z Content type: news Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [AI search](), [AI, ML & Data Engineering](), [ai observability](), [telemetry]() Tags: [agents](), [ai](), [collaboration](), [elasticsearch](), [observability](), [openai](), [retrieval](), [telemetry]() ### AI overview Elastic and OpenAI announced an expanded collaboration to help organizations build production-ready AI applications and agents using OpenAI models with Elasticsearch. The article presents Elasticsearch as a governed retrieval and context layer for enterprise data, with observability and security operations among the stated use cases. ### Source excerpt Today, we announced an expanded collaboration to bridge the gap between AI agents and the enterprise context they need, helping organizations build production-ready AI applications and agents using OpenAI models with Elasticsearch. ## 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 ## 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 👨💻 ## May 2026 newsletter DevFeed: [May 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-05-21T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [telemetry](), [tracing](), [migration](), [Compression](), [incident](), [MCP Server](), [real-time](), [data](), [elasticsearch]() Tags: [ai](), [ai-agents](), [clickhouse](), [data](), [elasticsearch](), [mcp](), [mcp-server](), [newsletter](), [observability](), [real-time](), [tracing]() ### AI overview The May 2026 ClickHouse newsletter highlights observability work, including Qonto's migration from Grafana Tempo to ClickHouse Cloud for traces, logs, and events. It also covers an MCP-powered incident companion, agent telemetry integrations, agentic analytics in financial services, index-based pruning, and a critique of Elasticsearch for log analytics. The issue includes a community feature, Open House 2026 conference information, and the 26.4 release. ### Source excerpt Welcome to the May 2026 ClickHouse newsletter, which will round up what's happened in real-time data warehouses over the last month. ## Do you still need Elasticsearch for log analytics? ClickHouse says no. DevFeed: [Do you still need Elasticsearch for log analytics? ClickHouse says no.]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-04-23T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [benchmarking](), [elasticsearch](), [log management](), [observability](), [OpenTelemetry](), [telemetry](), [parquet](), [GitHub]() Tags: [analytics](), [benchmark](), [clickhouse](), [data](), [elasticsearch](), [github](), [logs](), [observability](), [opentelemetry](), [parquet]() ### AI overview ClickHouse presents redesigned full-text search integrated with its analytical engine and benchmarks it against Elasticsearch for log analytics. Across OpenTelemetry workloads of up to 50 billion rows, the article reports 2-6x faster full-text analytical workloads and more compact storage for ClickHouse. ### Source excerpt ClickHouse now brings full-text search and large-scale analytics together in one engine, making it a powerful alternative to Elasticsearch for log analytics. This benchmark shows why. ## Temporal Helm Charts 1.0.0 reaches stability and requires externally provided databases and optional components DevFeed: [Temporal Helm Charts 1.0.0 reaches stability and requires externally provided databases and optional components]() Original publisher: [Read original article]() Author: Tom Wheeler Published: 2026-04-21T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Helm charts](), [Kubernetes](), [configuration](), [Apache Cassandra](), [MySQL](), [PostgreSQL](), [elasticsearch](), [Grafana](), [Prometheus]() Tags: [cassandra](), [configuration](), [elasticsearch](), [grafana](), [helm](), [helm-charts](), [kubernetes](), [migrate](), [mysql](), [product-news](), [prometheus](), [stable]() ### AI overview Temporal's Helm Charts reached version 1.0.0 with a stable API and no expected breaking changes in subsequent releases in the series. The release removes bundled database and optional-component sub-charts, so users must provide those services and configure their connection details. ### Source excerpt Temporal's Helm Charts hit 1.0.0 with a stable API, streamlined architecture, and new configuration options. Learn what changed and how to migrate. ## From Static Catalogue to Smart Discovery: Engineering Lightning-Fast Search at DreamSetGo DevFeed: [From Static Catalogue to Smart Discovery: Engineering Lightning-Fast Search at DreamSetGo]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-03-09T13:50:30Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [PostgreSQL](), [Microservice](), [systems](), [elasticsearch]() Tags: [architecture](), [dream-sports](), [elasticsearch](), [engineering](), [open](), [performance](), [postgresql](), [search](), [search-engines](), [sports](), [tech](), [travel]() ### AI overview DreamSetGo's tech team explains how its growing sports-travel catalogue created a need for faster, more intuitive product discovery. The article describes rebuilding the catalogue as a microservice, considering PostgreSQL with Elasticsearch, and choosing PostgreSQL based on architectural and operational trade-offs. ### Source excerpt By the DreamSetGo Tech Team If you're excited about building high-performance systems that power premium sports travel experiences, explore our open roles at DreamSetGo. Why Search Became a Product Imperative Over the past couple of years, DreamSetGo's catalogue didn't just grow, it accelerated. We were introducing access to new tournaments, adding destinations across continents, and were expanding into different hospitality formats. What began as a few structured products quickly evolved into a layered catalogue of packages and experiences. And that's when we started noticing something subtle but important. Adding products was straightforward. Making them discoverable wasn't. Every new experience meant longer descriptions and richer metadata. On paper, this was definitely progress. But from a user perspective, it introduced friction. If a traveller already knew exactly where to click, they could navigate their way through the catalogue. But if they came in with intent like: "Wimbledon finals hospitality" "Hot air balloons in Cappadocia" "Scuba diving in the Andamans" ...there was no fast, intuitive way to show them the most relevant experience. Once we acknowledged this gap, the next step became clear: we needed a search system that was scalable, resilient, and fast enough to keep up with our growth. This blog takes you through how we designed that search, from architectural trade-offs to production optimisations, and why PostgreSQL turned out to be the best choice for this. The Obvious Architecture (That We Didn't Choose) We started revamping our architecture six months back and rebuilt our travel product catalogue as a dedicated microservice, one that would eventually power search, and filter it by date, price and more. At that point, we had roughly 1,000 products. But we weren't building for 1,000. We had a clear vision of significantly expanding the catalogue within the near future. Like most teams, our first instinct was predictable: PostgreSQL -> Source of Truth ## Faster OpenTelemetry migrations to ClickStack with Bindplane DevFeed: [Faster OpenTelemetry migrations to ClickStack with Bindplane]() Original publisher: [Read original article]() Author: The Bindplane Team Published: 2026-02-26T13:14:55Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [OpenTelemetry](), [clickhouse](), [telemetry](), [observability](), [Routing (disambiguation)](), [elasticsearch]() Tags: [architecture](), [backend](), [clickhouse](), [data](), [elasticsearch](), [getting-started](), [how-to](), [instrumentation](), [integration](), [learn](), [observability](), [opentelemetry](), [routing](), [storage](), [telemetry](), [traces]() ### AI overview This practical guide explains how to use Bindplane and OpenTelemetry to add ClickStack alongside existing observability destinations, route data selectively, and process telemetry before delivery. It presents a gradual migration approach for consolidating logs, metrics, and traces in a ClickHouse-powered backend without replacing the existing stack immediately. ### Source excerpt Already running telemetry pipelines? Learn how to add ClickStack alongside your existing observability backend using Bindplane -- no rip-and-replace required. One config change rolls out to your entire OpenTelemetry Collector fleet, so you can validate a ## 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. ## How HighLevel rebuilt its data platform for speed, scale, and simplicity on ClickHouse Cloud DevFeed: [How HighLevel rebuilt its data platform for speed, scale, and simplicity on ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-01-12T09:39:28Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [Cloud](), [Database](), [Databases](), [Low Latency](), [real-time](), [Back end](), [elasticsearch](), [MySQL]() Tags: [analytics](), [api](), [backend](), [clickhouse](), [cloud](), [data](), [database](), [databases](), [elasticsearch](), [latency](), [low-latency](), [mysql](), [real-time](), [scale](), [speed](), [storage](), [systems]() ### AI overview HighLevel rebuilt its data platform around ClickHouse Cloud to support real-time lead activity, workflow logs, notifications, and revenue dashboards at large scale. The migration consolidated workloads previously spread across MySQL, Elasticsearch, Firestore, and a document database, delivering major storage savings, faster queries, and capacity for billions of daily events. ### Source excerpt "With ClickHouse, we saw a whopping 88% reduction in storage, and P99 queries dropped from 6+ seconds to less than 200 milliseconds." - Pragnesh Bhavsar, Staff Engineer ## 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... ## Behind the music: How Chartmetric is scaling music analytics with ClickHouse DevFeed: [Behind the music: How Chartmetric is scaling music analytics with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-01-08T14:17:18Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [Time Series](), [real-time](), [Streaming](), [API](), [dashboards](), [Web app](), [airflow](), [elasticsearch](), [Web]() Tags: [airflow](), [analytics](), [api](), [app](), [apple](), [cache](), [clickhouse](), [cloud](), [dashboards](), [data](), [elasticsearch](), [music](), [scale](), [search](), [series](), [speed](), [streaming](), [time](), [time-series](), [web]() ### AI overview Chartmetric uses ClickHouse Cloud to scale real-time music analytics across billions of rows from streaming services, social media, and music charts. The migration from Postgres and Snowflake improved query speed and reduced storage costs, while ClickHouse now supports LLM-facing queries and a playlist cache ingesting more than 15 million rows daily. ### Source excerpt "ClickHouse works very well as part of our multi-system data stack. It's excellent for time-series data, and the VersionedCollapsingMergeTree engine was a game-changer for us, speeding up queries from 20 seconds in Snowflake to 1.5 seconds in ClickHouse." ## 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... ## Using Materialized Views and Derived Datasets to Optimize Data Queries DevFeed: [Using Materialized Views and Derived Datasets to Optimize Data Queries]() Original publisher: [Read original article]() Published: 2025-12-07T09:05:00Z Content type: article Language: en Sources: [Gunnar Morling]() Topics: [data-architecture](), [Data Management](), [Database](), [datasets](), [Latency](), [elasticsearch](), [parquet]() Tags: [data](), [data-lake](), [data-management](), [database](), [datasets](), [elasticsearch](), [latency](), [parquet](), [performance]() ### AI overview The article explains how pull-based queries retrieve matching records at query time and why this can create performance, data-format, data-shape, and data-location challenges. It presents materialized views and derived datasets as a way to precompute query results and store them in an optimized format, shape, and location. ### Source excerpt Table of Contents Materialized Views Embracing Data Duplication Streams for machines, tables for humans Historically, data management systems have been built around the notion of pull queries: users query data which, for instance, is stored in tables in an RDBMS, Parquet files in a data lake, or a full-text index in Elasticsearch. When a user issues a query, the engine will produce the result set at that point in time by churning through the data set and finding all matching records (oftentimes sped up by utilizing indexes). [Next page]()