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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Logstash vs logagent

Logstash vs logagent

OverviewComparisonAlternatives

Overview

Logstash
Logstash
Stacks12.3K
Followers8.8K
Votes103
GitHub Stars14.7K
Forks3.5K
Logagent
Logagent
Stacks4
Followers5
Votes0
GitHub Stars390
Forks78

Logstash vs logagent: What are the differences?

<Write Introduction here>
  1. Data Collection: Logstash is a data processing pipeline, while logagent is a lightweight Logstash alternative. Logstash utilizes plugins for data collection from various sources, including logs, metrics, web applications, datastores, etc. On the other hand, logagent is specifically designed for collecting logs and metrics with a focus on simplicity and efficiency.

  2. Resource Consumption: Logstash is known to be resource-intensive due to its Java runtime environment and plugin system. It can consume significant amounts of memory and CPU resources, especially in complex data processing scenarios. In contrast, logagent is built with efficiency in mind and is lightweight, consuming fewer resources compared to Logstash.

  3. Ease of Use: Logstash offers a wide range of configuration options and features, making it a powerful tool for complex data processing tasks. However, this can also make it more complex to configure and use for users with simpler requirements. Logagent, on the other hand, is designed to be easy to set up and use, with a focus on simplicity and quick deployment.

  4. Community Support: Logstash, being a part of the Elastic Stack, benefits from a large and active community of users and developers. This results in extensive documentation, plugins, and community support for users facing issues or seeking guidance. Logagent, while not as widely used as Logstash, also has a supportive community and regular updates from its developers.

  5. Scalability: Logstash is designed to handle large-scale data processing tasks and can be scaled horizontally by adding more nodes to the processing pipeline. It provides features like load balancing, high availability, and parallel processing to ensure efficient data handling. Logagent, while capable of scaling to a certain extent, may not offer the same level of scalability and advanced clustering options as Logstash.

  6. Integration with Ecosystem: Logstash is tightly integrated with the Elastic Stack, allowing seamless integration with Elasticsearch, Kibana, Beats, and other components for end-to-end log processing and visualization. Logagent, while having integration options with various systems and datastores, may not offer the same level of ecosystem integration as Logstash within the Elastic ecosystem.

In Summary, Logstash and logagent offer differences in terms of data collection, resource consumption, ease of use, community support, scalability, and integration with the ecosystem. 

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Detailed Comparison

Logstash
Logstash
Logagent
Logagent

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.

Open-source, light-weight data shipper with out of the box and extensible log parsing, on-disk buffering, secure transport and bulk indexing.

Centralize data processing of all types;Normalize varying schema and formats;Quickly extend to custom log formats;Easily add plugins for custom data source
Open source - Apache Licence; Log parsing; On-disk buffering; Bulk indexing; Low memory overhead; Elasticsearch integration; Log routing; Container/Docker/Kubernetes integration; Log structure parsing; Log enrichment; Log rotation; Secure and reliable data transfer; Masking sensitive data; Syslog integration; Heroku integration; CloudFoundry integration; Filtering and aggregation; Two-way SSL auth
Statistics
GitHub Stars
14.7K
GitHub Stars
390
GitHub Forks
3.5K
GitHub Forks
78
Stacks
12.3K
Stacks
4
Followers
8.8K
Followers
5
Votes
103
Votes
0
Pros & Cons
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
No community feedback yet
Integrations
Kibana
Kibana
Elasticsearch
Elasticsearch
Beats
Beats
GitHub
GitHub
Node.js
Node.js
Docker
Docker
Kubernetes
Kubernetes
JavaScript
JavaScript
Git
Git

What are some alternatives to Logstash, Logagent?

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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