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.
What does the revamped, modern technology stack of a 185-year-old Australian news corporation look like? In an industry that faces serious competition from anyone with a WordPress site and something to say, Nine Publishing, producer of Australia’s three largest news sites, sought to preserve a competitive edge by understanding how to enrich the experiences of their readers and journalists, while simultaneously identifying pain points and spotting problems before they happen.
This is a technical deep dive into the authentication process — a necessary first step before addressing the authorization decisions that are at the core of Elasticsearch security. The following will be a very detailed explanation of the inner workings of a key part of the authentication process: realms. If you'd prefer to start with a broader view of authentication (and authorization) in Elasticsearch, you may want to check out Demystifying authentication and authorization in Elasticsearch.
