Fluentd vs Metricbeat

Overview

Fluentd

Stacks630

Followers688

Votes39

GitHub Stars13.4K

Forks1.4K

Metricbeat

Stacks48

Followers125

Votes3

Fluentd vs Metricbeat: What are the differences?

Key Differences Between Fluentd and Metricbeat

Fluentd and Metricbeat are both popular open-source tools used for collecting and forwarding log data, but there are several key differences between them. Here are the main disparities:

Architecture: Fluentd is a log collector, aggregator, and integrator, while Metricbeat focuses specifically on collecting metrics about the system and services. Fluentd can process both structured and unstructured data, whereas Metricbeat focuses solely on metric data.
Data Sources: Fluentd is designed to collect data from various sources, such as log files, syslog, and application logs. On the other hand, Metricbeat primarily collects data from system resources like CPU usage, memory, disk IO, and network traffic.
Integration: Fluentd offers a wide range of plugins for integrating with various services and platforms, enabling easy data analysis and storage. In contrast, Metricbeat is more focused on integration with the Elastic Stack, providing seamless integration with Elasticsearch, Kibana, and Logstash.
Monitoring: Fluentd provides monitoring capabilities, allowing users to monitor the throughput, latency, and error rates of their log data pipelines. Metricbeat, on the other hand, focuses on monitoring system metrics like CPU usage, memory utilization, and network traffic.
Configuration: Fluentd uses a flexible and powerful configuration language that allows users to define complex data processing and filtering rules, making it highly configurable. Metricbeat, on the other hand, uses configuration files written in YAML to specify the metrics to be collected and exported.
Community and Ecosystem: Fluentd has a larger community and a broader ecosystem with a wide range of plugins and extensions available for various use cases. Metricbeat, being a part of the Elastic Stack, benefits from the extensive community and ecosystem around Elastic products.

In summary, Fluentd and Metricbeat differ in architecture, data sources, integration capabilities, monitoring features, configuration methods, and community/ecosystem support.

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Advice on Fluentd, Metricbeat

Sunil

Team Lead at XYZ

Jun 15, 2020

Needs adviceon

Prometheus

Grafana

Linux

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Comments

Detailed Comparison

Fluentd	Metricbeat
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.	Collect metrics from your systems and services. From CPU to memory, Redis to NGINX, and much more, It is a lightweight way to send system and service statistics.
Open source; Flexible; Minimum resources; Reliable	System-Level Monitoring; system-level CPU usage statistics; Network IO statistics
Statistics
GitHub Stars 13.4K	GitHub Stars -
GitHub Forks 1.4K	GitHub Forks -
Stacks 630	Stacks 48
Followers 688	Followers 125
Votes 39	Votes 3
Pros & Cons
Pros 11 Open-source 10 Great for Kubernetes node container log forwarding 9 Easy 9 Lightweight	Pros 2 Simple 1 Easy to setup
Integrations
No integrations available	Redis Linux NGINX Windows

What are some alternatives to Fluentd, Metricbeat?

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

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Fluentd vs Metricbeat: What are the differences?

Key Differences Between Fluentd and Metricbeat

Fluentd and Metricbeat are both popular open-source tools used for collecting and forwarding log data, but there are several key differences between them. Here are the main disparities:

Architecture: Fluentd is a log collector, aggregator, and integrator, while Metricbeat focuses specifically on collecting metrics about the system and services. Fluentd can process both structured and unstructured data, whereas Metricbeat focuses solely on metric data.
Data Sources: Fluentd is designed to collect data from various sources, such as log files, syslog, and application logs. On the other hand, Metricbeat primarily collects data from system resources like CPU usage, memory, disk IO, and network traffic.
Integration: Fluentd offers a wide range of plugins for integrating with various services and platforms, enabling easy data analysis and storage. In contrast, Metricbeat is more focused on integration with the Elastic Stack, providing seamless integration with Elasticsearch, Kibana, and Logstash.
Monitoring: Fluentd provides monitoring capabilities, allowing users to monitor the throughput, latency, and error rates of their log data pipelines. Metricbeat, on the other hand, focuses on monitoring system metrics like CPU usage, memory utilization, and network traffic.
Configuration: Fluentd uses a flexible and powerful configuration language that allows users to define complex data processing and filtering rules, making it highly configurable. Metricbeat, on the other hand, uses configuration files written in YAML to specify the metrics to be collected and exported.
Community and Ecosystem: Fluentd has a larger community and a broader ecosystem with a wide range of plugins and extensions available for various use cases. Metricbeat, being a part of the Elastic Stack, benefits from the extensive community and ecosystem around Elastic products.

In summary, Fluentd and Metricbeat differ in architecture, data sources, integration capabilities, monitoring features, configuration methods, and community/ecosystem support.