Real User Monitoring, or RUM, is a type of monitoring technology for digital businesses that analyzes customers’ digital experiences by looking at exactly how online visitors are interacting with a website or application, analyzing everything from page load events to AJAX requests to frontend application c
So you’ve got a ClickHouse DBMS, and you’re looking for a tool to monitor it. You’ve come to the right place. In this blog post (which is part 2 of a three-part series on ClickHouse monitoring), we’ll identify and discuss the various ClickHouse monitoring tools available today.
Lucene has a lot of options for configuring similarity. By extension, Solr and Elasticsearch have the same options. Similarity makes the base of your relevancy score: how similar is this document (actually, this field in this document) to the query?
More than likely you’re here because you’ve made the leap or are thinking of making the leap in investing in a Real Monitoring Solution. Congrats. You’re one step closer to having the power of user metrics working in your favor.
Looking for ways to measure your users’ experience? You’ve come to the right place. In this article, we will be diving into several ways to measure your users’ satisfaction as well as ways to enhance & expand your real user monitoring strategy.
During the Entity Extraction For Product Searches talk that Radu Gheorghe and I gave at Activate conference in Montreal last year, we talked about various natural language processing and machine learning algorithms. We showed entity extraction both on top of Solr and using external libraries.
RUM and APM are two important acronyms to know if you work in software development or DevOps today. Moreover, not only is it important to be able to define RUM and APM — It’s also critical to understand the similarities and differences between each type of software monitoring process.
We’ve been working with Elasticsearch since its inception, either with clients on consulting for Elasticsearch products and Elasticsearch production support, or by building our own hosted log management solution.
During our Activate presentation, we talked about how to do query expansion by dynamically generating synonyms. Instead of statically defining synonyms lists, we showed a demo of how you could use word2vec to derive synonyms from a dataset.
