As the first part of the three-part series on monitoring Apache Solr, this article explores which Solr metrics are important to monitor and why. The second part of the series covers Solr open source monitoring tools and identify the tools and techniques you need to help you monitor and administer Solr and SolrCloud in production.
As shown in Part 1 Solr Key Metrics to Monitor, the setup, tuning, and operations of Solr require deep insights into the performance metrics such as request rate and latency, JVM memory utilization, garbage collector work time and count and many more. Sematext provides an excellent alternative to other Solr monitoring tools.
Open source software adoption continues to grow. Tools like Kafka and Solr are widely used in small startups, ones that are using cloud ready tools from the start, but also in large enterprises, where legacy software is getting faster by incorporating new tools. In this second part of our Solr monitoring series (see the first part discussing Solr metrics to monitor), we will explore some of the open source tools available to monitor Solr nodes and clusters.
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.
