I spoke at Container World 2019 in Santa Clara and shared insights on what LogDNA has learned in scaling Elastic Search using Kubernetes over the years. Here are some highlights from the talk and you can also find the slide deck below.
Recently Amazon launched Open Distro for Elasticsearch, a distribution of Elasticsearch with a number of additional features. The project was created out of concern that Elasticsearch was starting to include proprietary features, and that Elastic was straying from its open source roots.
Entity extraction is, in the context of search, the process of figuring out which fields a query should target, as opposed to always hitting all fields. The reason we may want to involve entity extraction in search is to improve precision. For example: how do we tell that, when the user typed in Apple iPhone, the intent was to run company:Apple AND product:iPhone? And not bring back phone stickers in the shape of an apple?
Entity extraction is, in the context of search, the process of figuring out which fields a query should target, as opposed to always hitting all fields. The reason we may want to involve entity extraction in search is to improve precision. For example: how do we tell that, when the user typed in Apple iPhone, the intent was to run company:Apple AND product:iPhone? And not bring back phone stickers in the shape of an apple?
