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

Logstash vs Packetbeat

OverviewComparisonAlternatives

Overview

Packetbeat
Packetbeat
Stacks15
Followers44
Votes4
Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K

Logstash vs Packetbeat: What are the differences?

Developers describe Logstash as "Collect, Parse, & Enrich Data". 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. On the other hand, Packetbeat is detailed as "Open Source application monitoring & packet tracing system". 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.

Logstash can be classified as a tool in the "Log Management" category, while Packetbeat is grouped under "Network Monitoring".

Some of the features offered by Logstash are:

  • Centralize data processing of all types
  • Normalize varying schema and formats
  • Quickly extend to custom log formats

On the other hand, Packetbeat provides the following key features:

  • 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

Logstash and Packetbeat are both open source tools. Logstash with 10.3K GitHub stars and 2.78K forks on GitHub appears to be more popular than Packetbeat with 7.48K GitHub stars and 2.54K GitHub forks.

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Detailed Comparison

Packetbeat
Packetbeat
Logstash
Logstash

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.

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.

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
Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
Statistics
GitHub Stars
-
GitHub Stars
14.7K
GitHub Forks
-
GitHub Forks
3.5K
Stacks
15
Stacks
12.3K
Followers
44
Followers
8.8K
Votes
4
Votes
103
Pros & Cons
Pros
  • 2
    Easy setup
  • 2
    Works well with ELK stack
Pros
  • 69
    Free
  • 18
    Easy but powerful filtering
  • 12
    Scalable
  • 2
    Kibana provides machine learning based analytics to log
  • 1
    Well Documented
Cons
  • 4
    Memory-intensive
  • 1
    Documentation difficult to use
Integrations
No integrations available
Kibana
Kibana
Elasticsearch
Elasticsearch
Beats
Beats

What are some alternatives to Packetbeat, Logstash?

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.

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.

Fluentd

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.

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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