Windows logs can be very informative, providing a perfect picture of the activities happening on an endpoint. Unfortunately, the logs can also be hard to decipher when you first start examining them. Graylog uses Pipelines and Data Adapters to enrich logs to make them more functional and easier for you to read.
Java programs running on JVM create objects on the heap. At some stage, these objects are no longer used and can pile up as “garbage” needlessly taking up memory. Replacing the manual process of explicitly allocating and freeing memory, the Java Garbage Collection process was designed to take care of this problem automatically.
Don’t you just hate it when APIs are failing and you have absolutely no clue why? Now imagine you don’t have access to the VM, cluster or container where your software is running. Want me to continue with this nightmare? Yes, that’s what debugging AWS Lambda functions tends to seem like. A horrid nightmare of not knowing what is happening nor why things are failing. This article will show you a way of logging function invocations.
They say knowledge is power and today, there is no greater source of knowledge than data. It is well documented that businesses produce a vast amount of data whether it be in manufacturing, IT, UX, Marketing, Sales or Finance. Collectively, all of this data can provide you with business intelligence, enabling you to optimize decisions, improve collaboration, and increase overall performance.
Alerts are a core part of monitoring systems. Using alerts keeps you aware of changes within your infrastructure and applications, helping you identify and respond to issues faster. Log management solutions like LogDNA provide an ideal environment for configuring alerts, since it allows you to create detailed alerts based on your log data. Rather than manually search for problems, you can use alerts to scan your log data in real-time and receive immediate notifications on potential problems.
One of the unintended side-effects of the public cloud is the enablement of a platform-agnostic cloud architecture. This means that it’s now easier than ever to mix offerings from multiple vendors (cloud vendors in this case) to form a single application or service-based solution. There is one gotcha, however: log management.
Many of our users are responsible for monitoring logs to detect sudden changes in volume or to control the budget. To help you with this goal, we’re excited to announce new enhancements to our usage dashboard.
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.
