As Elasticsearch is gradually becoming the standard for textual data indexing (specifically log data) more companies struggle to scale their ELK stack. We decided to pick up the glove and create a series of posts to help you tackle the most common Elasticsearch performance and functional issues. This post will help you in understanding and solving one of the most frustrating Elasticsearch issues – Mapping exceptions.
Elasticsearch, Logstash, and Kibana — the trio better known as Elastic Stack (or ELK, if you prefer a term that is now going out of style), make up a powerful set of tools for searching and analyzing data. Their power derives not just from their technical features, but also the fact that Elastic Stack is an open source platform that anyone can download and set up anywhere.
Elastic Stack Features (formerly X-Pack) is an Elastic Stack extension that bundles security, alerting, monitoring, reporting, and graph capabilities. One could use either all or specific components.
A few weeks ago, BubbleUp came out of Beta. We’ve been getting fantastic user feedback on how BubbleUp helps users speed through the Core Analysis Loop and lets people find things they never could have found before. We’ve also been learning more about how BubbleUp works with Tracing, which unearthed some difficult issues. Today, we’re taking those head on.
A couple of months ago we released the all-new Anodot.com. Following the release, I explored our Google Analytics account to see what had happened post-launch. I have always been ambivalent about Google Analytics. On the one hand, the service has helped shape web analytics as we know it today and is used by nearly every website. Not to mention it’s free and rather easy to consume. On the other hand, GA is never a slam dunk.
Even if you haven't heard of data described as a "time-series," you've probably seen examples out in the wild. As the name suggests, a time-series is a representation of an event over a period of time. That could mean representing many different changes: your application usage data, such as error rates over time or the growing number of activations per day.
C# logs can be an invaluable resource for optimizing application performance and debugging errors. But it’s not easy to extract the full potential of your logs if they’re not providing enough context around each error, or if they’re written in a format that’s difficult to parse.
RSA 2019 is finally here and we’re super-excited to participate this year in this great gathering of security experts where we will be demoing Logz.io Security Analytics — our new app for helping organizations combat security threats and meet compliance requirements.
