Elasticsearch is an open source, distributed document store and search engine that stores and retrieves data structures. As a distributed tool, Elasticsearch is highly scalable and offers advanced search capabilities. All of this adds up to a tool which can support a multitude of critical business needs and use cases. To follow are ten of the key Elasticsearch configurations are the most critical to get right when setting up and running your instance.

Signed search keys in Elastic App Search give you more control over a user's search experience. You can tailor the experience to show results you know are more relevant to the specific user while also controlling what data the user can see and search over.

Teams around the world are going through changes. With offices closed from Hong Kong to San Francisco, Zoom meetings are the new norm, and online platforms are the standard for collaborating and keeping businesses running as usual. We’ve written about distributed work and how doing distributed well requires the right tools. When a traditional office environment isn’t available, information naturally becomes fractured across multiple single-purpose platforms.

When building a full-text search experience such as an FAQ search or Wiki search, there are a number of ways to tackle the challenge using the Elasticsearch Query DSL. For full-text search there’s a relatively long list of possible query types to use, ranging from the simplest match query up to the powerful intervals query.

Elasticsearch was designed to allow its users to get up and running quickly, without having to understand all of its inner workings. However, more often than not, it’s only a matter of time before you run into configuration troubles. Elasticsearch is open-source software that indexes and stores information in a NoSQL database and is based on the Lucene search engine. Elasticsearch is also part of the ELK Stack.

Have you ever had trouble working with Elasticsearch clusters? You’re not alone. In this post, I will discuss a problem I’ve encountered working with large Elasticsearch clusters and how I solved it. I will share a lot of knowhow on major technical Elasticsearch concepts, some diagrams for illustration, and of course a cool solution! In particular, I will go into Elasticsearch nodes, indices, and shards.

Let’s start off with some good news: since 7.9.0, your Elastic Workplace Search deployment has been collecting and logging product usage data for you and your team. Usage data like, what your users are searching for, what links they're actually clicking on, and which searches are falling short. And better yet, in a future release we’ll be putting a prebuilt Workplace Search analytics dashboard at your fingertips in Kibana, one of the most powerful visualization tools available.

We're excited to announce that indices created in Elasticsearch 7.10 will be smaller. Bigger isn't always better, and our internal benchmarks reported space reductions up to 10%. This may not seem like much for small use cases, but it's huge for teams handling (and paying for cloud storage of) petabytes of data.

Whether you consume App Search from Elastic or from Swiftype, you’re getting a set of robust APIs and unprecedented relevance controls to deliver amazing search experiences. But what if you could have that same powerful set of search tools, only better, faster, more flexible, and still built on the powerful, scalable foundation of Elasticsearch? We’d like to invite you to migrate your Swiftype App Search deployment over to App Search on Elastic Cloud.