Thousands of customers call the official Elasticsearch Service (ESS) on Elastic Cloud their home for running not only Elasticsearch, but exclusive products such as Elastic Logs, Elastic APM, Elastic SIEM, and more.
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.
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.
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.
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.
If you’re running Solr and have to troubleshoot it (or maybe you just want a good overview!), then you’d probably want to collect logs, configs, maybe a snapshot of metrics and some system data, like top or netstat. We created a small tool for this exact task, creatively named solr-diagnostics. It’s been out there for almost two years, and we found it useful in our Solr consulting and production support engagements. So we thought it’s about time to spread the word.
As part of Elasticsearch 7.5.0, we introduced a couple of ways to control the index age math that’s used by index lifecycle management (ILM) for phase timings calculations using the origination_date index lifecycle settings. This means you can now tell Elasticsearch how old your data is, which is pretty handy if you’re indexing data that’s older than today-days-old.
As another year is coming to an end, let's take time to look at what happened in Lucene land in 2019.
With the addition of new data structures in Lucene 6.0, the Elasticsearch 5.0 release delivered massive indexing and search performance improvements for one-dimension numeric, date, and IP fields, and two-dimension (lat, lon) geo_point fields. Building on this work, the Elasticsearch 6.0 release further improved usability and simplicity of the geo_point API by setting the default indexing structure to the new block k-d tree (BKD) and removing all support for legacy prefix tree encoding.
As part of Elasticsearch 7.5.0, a new ingest processor — named enrich processor — was released. This new processor allows ingest node to enrich documents being ingested with additional data from reference data sets. This opens up a new world of possibilities for ingest nodes.
