Whether an active member of the ELK community or just a happy user, you’ve probably heard of a recent data breach involving Elasticsearch. Indeed, not a month goes by where we don’t come across an article or research showcasing a set of sensitive information exposed on an Elasticsearch cluster.
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?
As shown in Elasticsearch Key Metrics, the setup, tuning, and operations of Elasticsearch require deep insights into the performance metrics such as index rate, query rate, query latency, merge times, and many more. Sematext provides an excellent alternative to other Elasticsearch monitoring tools.
Over the years the adoption of Elasticsearch and its ecosystem of tools positioned them as the leaders in the time series data management and analysis market. With strong search capabilities, great analytical engine, Kibana as the flexible frontend and a number of data shippers enable building of end to end data processing pipeline using components designed to work with each other. Very simple setup and configuration resulted in high adoption rates and the whole stack gaining more and more users.
Entity extraction is the process of figuring out which fields a query should target, as opposed to always hitting all fields. For example: how to tell, when the user typed in Apple iPhone, that the intent was to run company:Apple AND product:iPhone?
