Historically, Elasticsearch has relied on a schema on write approach to make searching data fast. We are now adding schema on read capabilities to Elasticsearch so that users have the flexibility to alter a document's schema after ingest and also generate fields that exist only as part of the search query. Together, schema on read and schema on write provides users with the choice to balance performance and flexibility based on their needs.

In 7.11, we’re excited to announce support for schema on read in the Elastic Stack. We now offer the best of both worlds on a single platform — the performance and scale of the existing schema on write mechanism that our users love and depend on, coupled with a new level of flexibility for defining and executing queries with schema on read. We call our implementation of schema on read runtime fields.

What does Netflix, eBay and Walmart have in common? They all use Elasticsearch. Elasticsearch is a real-time open-source distributed search and analytics engine built on top of Apache Lucene™, a fulltext search-engine library and developed in Java. Elasticsearch started as a scalable version of the Lucene open-source search framework that uses a structure based on documents instead of tables and schemas and comes with extensive REST APIs for storing and searching the data.

When we announced our license change for Elasticsearch and Kibana, moving the Apache 2.0-licensed source code to be dual licensed under both the Elastic License and SSPL, we also mentioned we would work closely with the community on a simplified and more permissive version of the Elastic License. I am happy to share the results with you. The Elastic License is already widely used.

Elastic is “an index”, “a search engine”, “a big data solution”, an analytics platform with advanced data visualizations and incredibly fast search capabilities. In short, it’s a solution for many problems. The Elasticsearch platform provides a distributed search cluster that enables large amounts of data to be indexed and searched at scale.

Elasticsearch's date_histogram aggregation is the cornerstone of Kibana's Discover. And the Logs Monitoring UI. I use it all the time to investigate trends in build failures, but when it is slow I get cranky. Four seconds to graph all of the failures of some test over the past six months! I don't have time for that! Who is going to give me my four seconds back?! So I spent the past six months speeding it up. On and off.

Shell International knows that it takes cutting-edge technology to thrive in the competitive, global energy industry. With projects around the world, in both renewable and non-renewable energy, Shell must always have insights into the future. From determining expected output to predicting equipment failures, there's no room for guessing in an industry where downtime is unacceptable.

The recent changes to the Elasticsearch license could have consequences on your intellectual property. On the 14th of January 2021, Elastic announced through their blog that Elasticsearch and Kibana will be moving over to a Server Side Public License (SSPL). This license change, effective from Elasticsearch version 7.11, has business owners that rely on the ELK stack rightly concerned.

With Elastic App Search, you can add scalable, relevant search experiences to all your apps and websites. It offers a host of search result personalization options out of the box, such as weights and boosts and curations. You could also add a these documents might interest you feature, which would surface additional content for users, similar to documents they’ve previously searched for. This post walks you through the process of creating this capability using the robust App Search APIs.

Let’s say you get an alert that one or more queries is slow. Or that your users complain, whichever comes first 🙂 We’ve all been there… How do you find the root cause for this slowness and then fix it? In this article, I’ll go through my usual thought process: first, I’d try to find which queries are slow. Then, I’d dig deeper: Let’s take a specific example and run through each step.