Recent Linux kernel releases are coming weaponized with built-in instrumentation framework that has its roots in what historically was approached as BPF (Berkeley Packet Filter) – a very efficient network packet filtering mechanism which aims to avoid unnecessary user space allocations and operate on packet’s data directly in kernel land. The most familiar application of BPF powers is related to filter expressions used in tcpdump tool.
Over the years, natural language processing, in the world of search, went from interesting detail to a must have, especially in areas such as e-commerce. Engineers started incorporating classification, synonym generation, named entity recognition and much more into their search systems giving users better search results and in some cases leading to more revenue.
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 crashes. The most commonly known example of RUM would be Google Analytics, or GA, which tracks certain spectrums of the interaction between your user and your website or webapp.
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? I’m saying the base of the score because, on top of this score, you can apply per-field boosts, function scoring (e.g. boost more recent documents) and re-ranking (e.g. Learning to Rank).
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. Real User Monitoring is a way for your users to communicate with you how satisfied they were when they interacted with your website or webapp, so how can you be sure you’re listening correctly?
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. In this post we dig into Learning to Rank with Solr Streaming Expressions.
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. For the last 4 years, we’ve also been sharing our knowledge through Elasticsearch training classes. In 2018, we had remote public training classes on a fixed quarterly schedule, so you can more easily plan your learning time and budget.
