Operations | Monitoring | ITSM | DevOps | Cloud

May 2019

Solr Monitoring Made Easy with Sematext

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.

Solr Open Source 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.

Node.js Monitoring Made Easy with Sematext

Node.js monitoring is a tricky task. There are certain challenges to look out for. Because Node.js is a dynamically typed programming language and single-threaded you give the interpreter and runtime a lot of freedom to make decisions. This can easily result in memory leaks and high CPU loads. Parallel execution is simulated in Node.js by using asynchronous execution of functions. But, if a single function blocks the thread or event queue, the application performance will take a huge hit.

Top Node.js Metrics to Monitor

Making Node.js applications quick and sturdy is a tricky task to get right. Nailing the performance just right with the V8 engine Node.js is built on is not at all as simple as one would think. JavaScript is a dynamically typed language, where you let the interpreter assign types to variables. If you’re not careful this can lead to memory leaks.