If you’re looking for a short answer on OpenSearch vs Solr, here’s a flow chart: We normally recommend the one you (or your team) already know or the prefer because, for most projects, there’s not that much in it in terms of features. Both search engines are well supported and have strong communities behind them. That said, there are significant differences, too.

Elastic Enterprise Search 8.8 seamlessly bundles new Elastic developed semantic search capabilities with an expanding catalog of open code database and storage connectors. Additionally this release adds rich capabilities to measure and simplify adding features to your search application. These new capabilities allow customers to: Elastic Enterprise Search 8.8 is available now on Elastic Cloud — the only hosted Elasticsearch offering to include all of the new features in this latest release.

Elasticsearch is a search and analytics engine that allows for complex searches on large datasets of different types and formats. Elasticsearch mappings are the blueprints that define how data is indexed and searched to support these data-related features. Understanding how Elasticsearch mappings work is essential to an effective Elasticsearch deployment. In this article, we’ll explore the key concepts of Elasticsearch mappings and common Elasticsearch mapping pitfalls to avoid.

The Elasticsearch reindex API copies data from one index to another. You can use reindex to change the index mapping, copy data to another cluster, or copy only a subset of data to another index. For example, suppose you want to reindex all the data in index1 into index2. In that case, you run the following example in Kibana dev tools: In this article, we dive into some common issues solved by reindexing as well as troubleshooting issues with reindexing itself.

Starting the journey for Elasticsearch monitoring is crucial to get the right visibility and transparency over its behavior. Elasticsearch is the most used search and analytics engine. It provides both scalability and redundancy to provide a high-availability search. As of 2023, more than sixty thousand companies of all sizes and backgrounds are using it as their search solution to track a diverse range of data, like analytics, logging, or business information.

Engines in Elastic App Search enable you to index documents and provide out-of-the-box, tunable search capabilities. By default, engines support a predefined list of languages. If your language is not on that list, this blog explains how you can add support for additional languages. We’ll do this by creating an App Search engine that has analyzers set up for that language.

Logit.io is pleased to introduce the latest version of OpenSearch onto the platform, with an OpenTelemetry-compliant data schema that unlocks a host of future analytics and observability capabilities. Also included in this release are improvements in threat detection for security analytics workloads, visualization tools, and machine learning (ML) models.

Elasticsearch has made a name for itself as a powerful, scalable, and easy-to-use search and analytics engine, enabling organizations to derive valuable insights from their data in real-time. However, to truly unlock the potential of Elasticsearch, it is essential that the right data in the right format is provisioned to Elasticsearch. This is where integrating a telemetry pipeline can add value to Elasticsearch.