Fluentd vs Nagios: What are the differences?
Fluentd: Unified logging layer. Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure; Nagios: Complete monitoring and alerting for servers, switches, applications, and services. Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
Fluentd and Nagios are primarily classified as "Log Management" and "Monitoring" tools respectively.
Some of the features offered by Fluentd are:
- Open source
- Minimum resources
On the other hand, Nagios provides the following key features:
- Monitor your entire IT infrastructure
- Spot problems before they occur
- Know immediately when problems arise
Fluentd and Nagios are both open source tools. It seems that Fluentd with 8.04K GitHub stars and 938 forks on GitHub has more adoption than Nagios with 60 GitHub stars and 36 GitHub forks.
According to the StackShare community, Nagios has a broader approval, being mentioned in 177 company stacks & 40 developers stacks; compared to Fluentd, which is listed in 64 company stacks and 18 developer stacks.
What is Fluentd?
What is Nagios?
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Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:
By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.
To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...
(GitHub : https://github.com/m3db/m3)
We use Nagios to monitor our stack and alert us when problems arise. Nagios allows us to monitor every aspect of each of our servers such as running processes, CPU usage, disk usage, and more. This means that as soon as problems arise, we can detect them and call out an engineer to resolve the issues as soon as possible.
We use Nagios to monitor customer instances of Bridge and proactively alert us about issues like queue sizes, downed services, errors in logs, etc.
We use nagios based OpsView to monitor our server farm and keep everything running smoothly.