Netdata-Monitoring on Netdata

Cost Transparency: The True Cost Of Monitoring

Fri, 01 Dec 2023 00:00:00 +0000

Businesses are increasingly reliant on monitoring tools to ensure the seamless performance and reliability of their systems. However, the true cost of implementing and maintaining these tools is often obscured by hidden expenses. Our previous blog delved into the concealed costs associated with various monitoring solutions, such as Prometheus & Grafana (Open Source Monitoring) and commercial platforms like Datadog, Dynatrace, and NewRelic. These costs can manifest in various forms - from complex setups and maintenance to additional charges for advanced features.

Hidden Costs Of Monitoring: Uncovering Expenses & Solutions

Fri, 07 Jul 2023 00:00:00 +0000

When it comes to monitoring IT infrastructure, the costs you see on the price tag of the tool are often just the tip of the iceberg. Below the waterline, a mass of hidden costs can lurk, which can significantly affect the total cost of ownership.

In this blogpost we will cover the analysis of two traditional monitoring domains, Open Source observability and Commercial Centralized observability solutions, focusing the direct and indirect impacts when implementing these solution. In summary:

Netdata Parents (Streaming and Replication)

Fri, 30 Jun 2023 00:00:00 +0000

What are they and why do we need them?

A “Parent” is a Netdata Agent, like the ones we install on all our systems, but is configured as a central node that receives, stores and processes metrics data from other Netdata “Child” nodes in our infrastructure.

Netdata Parents are flexible. You can have one big active-active cluster of Netdata Parents, or you can spread a lot of independent Parents across the infrastructure.

Extending Netdata's anomaly detection training window

Thu, 02 Feb 2023 00:00:00 +0000

We have been busy at work under the hood of the Netdata agent to introduce new capabilities that let you extend the “training window” used by Netdata’s native anomaly detection capabilities.

This blog post will discuss one of these improvements to help you reduce “false positives” by essentially extending the training window by using the new (beautifully named) number of models per dimension configuration parameter.

Background

One of the most important considerations of our native anomaly detection capabilities is the overhead of running the training and scoring computations required to train thousands of models (one per metric) and produce anomaly bits every second based on those trained models.

Anomaly Advisor: Unsupervised Anomaly Detection

Thu, 26 May 2022 00:00:00 +0000

Today we are excited to launch one of our flagship ML assisted troubleshooting features in Netdata – the Anomaly Advisor.

The Anomaly Advisor builds on earlier work to introduce unsupervised anomaly detection capabilities into the Netdata Agent from v1.32.0 onwards.

Getting Started

Once you enable ML on your nodes, each node will begin producing an “Anomaly Bit” every second in addition to raw metric values. This anomaly bit will be 1 when the trained ML models consider recent raw data for a metric to look anomalous or 0 when things look ’normal’. The Anomaly Advisor leverages this information to enable seamless space or room level anomaly detection out of the box with minimal configuration.