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.
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.
What is the most important feature your Node.js application can have? Do you think it’s having fancy fuzzy logic for your full-text search, or maybe using sockets for real-time chats? You tell me. What’s the fanciest, most amazing and sexy feature you can add to your Node.js application?
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.
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?
When managing cloud-native applications, it’s essential to have end-to-end visibility into what’s happening at any given time. This is especially true because of the distributed and dynamic nature of cloud-native apps, which are often deployed using ephemeral technologies like containers and serverless functions.
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 the first part of a three-part series on Apache Kafka monitoring, this article explores which Kafka metrics are important to monitor and why. When monitoring Kafka, it’s important to also monitor ZooKeeper as Kafka depends on it. The second part will cover Kafka open source monitoring tools, and identify the tools and techniques you need to further help monitor and administer Kafka in production.
Open source software adoption continues to grow within enterprises (even for legacy applications), beyond just startups and born-in-the-cloud software. In this second part of our Kafka monitoring series (see the first part discussing Kafka metrics to monitor), we’ll take a look at some open source tools available to monitor Kafka clusters. We’ll explore what it takes to install, configure, and actually use each tool in a meaningful way.
Monitoring Kafka is a tricky task. As you can see in the first chapter, Kafka Key Metrics to Monitor, the setup, tuning, and operations of Kafka require deep insights into performance metrics such as consumer lag, I/O utilization, garbage collection and many more. Sematext provides an excellent alternative to other Kafka monitoring tools because it’s quick and simple to use.
