Filebeat vs Metricbeat

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Filebeat vs Metricbeat: What are the differences?

Filebeat and Metricbeat are two data shippers in the Elastic Stack that serve different purposes and have distinct functionalities. The key differences between Filebeat and Metricbeat are as follows:

  1. Data Collection: Filebeat is primarily used for harvesting log files, allowing you to collect, parse, and send log file data to Elasticsearch or Logstash for further analysis. On the other hand, Metricbeat focuses on collecting and shipping system and application-level metrics to Elasticsearch, making it suitable for monitoring infrastructure and services.

  2. Data Types: Filebeat is designed to handle text-based log files, extracting structured information from various sources such as log messages or standard output streams. It parses logs into discrete events and forwards them to the output for indexing. In contrast, Metricbeat is specifically built for capturing numeric metrics and statistical data from various applications or services in real-time, providing insights into system performance, CPU usage, memory utilization, network statistics, and more.

  3. Parsing and Formatting: While Filebeat emphasizes parsing, collecting, and shipping log events, Metricbeat comes with built-in metric modules for various platforms that simplify metric collection. It automatically collects and formats metrics from different sources, reducing the need for complex parsing or data extraction configurations.

  4. Flexibility: Filebeat offers more customization options when it comes to log parsing and filtering. It allows you to define patterns and rules to extract relevant information and filter out noise from log files. On the other hand, Metricbeat is designed for metric collection and provides predefined modules for a wide range of popular monitoring targets, making it easier to configure and collect metrics without extensive customization.

  5. Use Cases: Filebeat is commonly used in scenarios where log files are the primary source of data, such as analyzing application logs, security logs, or system logs for troubleshooting and monitoring purposes. In contrast, Metricbeat is suitable for monitoring and analyzing infrastructure and services by collecting metrics related to system performance, application usage, or network traffic.

  6. Visualizations: Filebeat mainly focuses on collecting and forwarding log events to Elasticsearch, where you can visualize and analyze the log data using Kibana. Metricbeat, on the other hand, targets metric collection for monitoring and analysis, providing prebuilt metric dashboards in Kibana. These dashboards allow you to visualize and gain insights into system performance, network metrics, or application health.

In summary, Filebeat primarily deals with collecting and parsing log files, while Metricbeat is focused on collecting system and application-level metrics. Filebeat offers more flexibility for log parsing and customization, whereas Metricbeat provides predefined metric modules for easier metric collection and visualization.

Advice on Filebeat and Metricbeat
Sunil Chaudhari
Needs advice
on
MetricbeatMetricbeat
and
PrometheusPrometheus

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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Replies (2)
Matthew Rothstein
Recommends
on
PrometheusPrometheus

If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.

Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.

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Recommends
on
InstanaInstana

Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose

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Pros of Filebeat
Pros of Metricbeat
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    What is Filebeat?

    It helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files.

    What is Metricbeat?

    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.

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    What are some alternatives to Filebeat and Metricbeat?
    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.
    Fluentd
    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.
    Rsyslog
    It offers high-performance, great security features and a modular design. It is able to accept inputs from a wide variety of sources, transform them, and output to the results to diverse destinations.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    SLF4J
    It is a simple Logging Facade for Java (SLF4J) serves as a simple facade or abstraction for various logging frameworks allowing the end user to plug in the desired logging framework at deployment time.
    See all alternatives