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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Network Monitoring
  5. Fluentd vs Packetbeat

Fluentd vs Packetbeat

OverviewComparisonAlternatives

Overview

Packetbeat
Packetbeat
Stacks15
Followers44
Votes4
Fluentd
Fluentd
Stacks630
Followers688
Votes39
GitHub Stars13.4K
Forks1.4K

Fluentd vs Packetbeat: What are the differences?

Fluentd vs Packetbeat

Fluentd and Packetbeat are two popular log management and analysis tools that are used to collect and process data. While both tools are widely used, they have some key differences in terms of features and functionality that set them apart.

  1. Data Collection: Fluentd is primarily used for log data collection, where it can gather logs from various sources and forward them to multiple destinations. On the other hand, Packetbeat is a network packet analyzer that captures network traffic and extracts important information from the packets, such as HTTP requests, DNS queries, and MySQL queries.

  2. Focus: Fluentd is a general-purpose log collector and aggregator, focusing on collecting logs and forwarding them to different systems for further processing. It provides a flexible and scalable approach for log management. Packetbeat, on the other hand, is specifically designed for real-time network monitoring and analysis, focusing on providing visibility into application-level protocols and metrics.

  3. Supported Protocols: Fluentd supports a wide range of log data formats and protocols, including syslog, JSON, Apache logs, and more. It can integrate with various data sources and systems. Packetbeat, on the other hand, is focused on network traffic analysis and supports protocols like HTTP, MySQL, DNS, PostgreSQL, and more. It captures specific parameters and metrics from these protocols for analysis.

  4. Deployment: Fluentd can be deployed as a standalone service or as part of a larger logging infrastructure. It has extensive integration capabilities and can work with various logging frameworks and tools. Packetbeat is typically deployed as an agent on individual servers or network devices, where it captures and analyzes the network traffic at the source.

  5. Real-time analysis: Packetbeat provides real-time analysis of network protocols and metrics, allowing users to monitor the health and performance of their applications in real-time. It can be used for troubleshooting and detecting anomalies in the network traffic. Fluentd, while it can handle real-time logs, is more focused on log collection and aggregation.

  6. Community and Ecosystem: Fluentd has a large and active community, with a wide range of plugins and extensions available for customization and integration. It has been adopted by many organizations and has a strong ecosystem of tools and services built around it. Packetbeat, while it has a smaller community compared to Fluentd, is part of the Elastic Stack and integrates well with other tools like Elasticsearch, Logstash, and Kibana.

In summary, Fluentd is a versatile log management tool with a broad focus on log collection and forwarding, while Packetbeat is a specialized network packet analyzer that provides real-time visibility into application-level protocols. Both tools have their strengths and are suited for different use cases in data collection and analysis.

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Detailed Comparison

Packetbeat
Packetbeat
Fluentd
Fluentd

Packetbeat agents sniff the traffic between your application processes, parse on the fly protocols like HTTP, MySQL, Postgresql or REDIS and correlate the messages into transactions.

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.

Packetbeat Statistics: Contains high-level views like the network topology, the application layer protocols repartition, the response times repartition, and others;Packetbeat Search: This page enables you to do full text searches over the indexed network messages;Packetbeat Query Analysis: This page demonstrates more advanced statistics like the top N slow SQL queries, the database throughput or the most common MySQL erro
Open source; Flexible; Minimum resources; Reliable
Statistics
GitHub Stars
-
GitHub Stars
13.4K
GitHub Forks
-
GitHub Forks
1.4K
Stacks
15
Stacks
630
Followers
44
Followers
688
Votes
4
Votes
39
Pros & Cons
Pros
  • 2
    Easy setup
  • 2
    Works well with ELK stack
Pros
  • 11
    Open-source
  • 10
    Great for Kubernetes node container log forwarding
  • 9
    Easy
  • 9
    Lightweight

What are some alternatives to Packetbeat, Fluentd?

Papertrail

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.

Logmatic

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

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

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

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.

Graylog

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Sematext

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

ELK

ELK

It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

Sumo Logic

Sumo Logic

Cloud-based machine data analytics platform that enables companies to proactively identify availability and performance issues in their infrastructure, improve their security posture and enhance application rollouts. Companies using Sumo Logic reduce their mean-time-to-resolution by 50% and can save hundreds of thousands of dollars, annually. Customers include Netflix, Medallia, Orange, and GoGo Inflight.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

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