blue medora

Elasticsearch Query Performance | Metrics to Watch

If you’re using the Elasticsearch query functionality, for mainly front-facing client search, there are 3 important metrics to monitor performance. Your cluster can be putting up with any number of queries at a time. The volume of queries over time will align roughly to the load of requests laying a potential burden. Unexpected peaks and valley in a time series of query load could be signs of a problem or potential optimization opportunities.


42 Elasticsearch Query Examples - Hands-on Tutorial

Elasticsearch provides a powerful set of options for querying documents for various use cases so it’s useful to know which query to apply to a specific case. The following is a hands-on tutorial to help you take advantage of the most important queries that Elasticsearch has to offer. In this guide, you’ll learn 42 popular query examples with detailed explanations, but before we get started, here’s a summary of what the types of queries we’ll tackle.


Transparent, resource-based pricing with Elastic Enterprise Search

Until now, standard search solution pricing has been based on models that are difficult to understand, expensive to scale, and/or beneficial to only the search vendor. At Elastic, we’re taking a different approach based on the principles of transparency, fairness, and scalability, and have introduced resource-based pricing for our products running on Elastic Cloud. And we believe that this pricing approach will revolutionize Enterprise Search buying and ownership.


Creating meta engines in App Search to scale your search experiences

We introduced meta engines for Elastic App Search on Elastic Cloud and self-managed versions in the 7.6 release and have been thrilled to see the response to the new feature. Meta engines provide the ability to search across multiple existing or new engines. Think of adding a new search box to a page that then goes off and searches the documents in the sub-engines of your choosing.

blue matador

AWS Elasticsearch Health Monitoring: 8 Things to Watch

If you have ever used a search bar on a website, you've probably used Elasticsearch. Elasticsearch is an open-source search and analytics engine used for full-text search as well as analyzing logs and metrics. It allows websites to use autocomplete in text fields, search suggestions, location or geospatial search. Tons of companies use Elasticsearch, including Nike, SportsEngine, Autodesk, and Expedia.


Multilingual search using language identification in Elasticsearch

We’re pleased to announce that along with the release of the machine learning inference ingest processor, we are releasing language identification in Elasticsearch 7.6. With this release, we wanted to take the opportunity to describe some use cases and strategies for searching in multilingual corpora, and how language identification plays a part. We’ve covered some of these topics in the past, and we’ll build on these in some of the examples that follow.


Getting started with Elastic App Search on Elastic Cloud

With Elastic App Search, you can easily add rich, powerful search to your website, applications, or mobile apps. And now you can deploy directly from the Elastic Cloud. App Search is built on top of Elasticsearch, meaning that it’s highly scalable and fast. It comes out of the box with pre-tuned relevance, but gives you plenty of user-friendly options for fine-tuning results to customize the search experience.


Elastic App Search: Now available on Elasticsearch Service

We're excited to announce that Elastic App Search is now generally available on Elasticsearch Service. App Search is a ready-to-use, fully complete search solution with user-friendly relevance tuning and analytics built in. And starting today, you can deploy App Search instances with the click of a button right from the Elasticsearch Service dashboard. Now you can get all the tooling needed for a powerfully relevant search experience with the operational flexibility and scale of Elastic Cloud.