Sematext

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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.

Entity Extraction for Product Searches, Sematext

A user looking for “awesome smartphone 2018” is likely really after “+review:awesome +category:smartphone +release_date:2018”. A clever use of (e)dismax might get us pretty close to where we want, but it’s not real query understanding. There are other ways, of course, like training a model that will, based on the keyword, guess which field it’s looking into.
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Entity Extraction for Product Searches

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?

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Best Practices for Efficient Log Management and Monitoring

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.

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Entity Extraction with spaCy

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?

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Kafka Metrics to Monitor

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.

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Kafka Open Source Monitoring Tools

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.