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

Fluentd vs logagent

OverviewComparisonAlternatives

Overview

Fluentd
Fluentd
Stacks630
Followers688
Votes39
GitHub Stars13.4K
Forks1.4K
Logagent
Logagent
Stacks4
Followers5
Votes0
GitHub Stars390
Forks78

Fluentd vs logagent: What are the differences?

<Introduction>
  1. Data Parsing and Transformation: Fluentd offers a powerful parser engine allowing users to extract, filter, and remap data easily, whereas Logagent focuses on lightweight log parsing and forwarding without extensive transformation capabilities.
  2. Supported Services: Fluentd has a wider range of supported services and plugins, enabling seamless integration with various data sources like databases, cloud services, and more, while Logagent is more focused on log processing for a limited number of environments.
  3. Scalability: Fluentd is known for its scalability and robustness, often used in large-scale deployments due to its proven performance in handling high volumes of data compared to Logagent, which may not be as efficient in handling massive data streams.
  4. Community Support: Fluentd enjoys a larger and more active community, providing extensive documentation, resources, and community-contributed plugins and configurations, whereas Logagent may have a smaller user base with fewer resources and support options.
  5. Backend Storage Integration: Fluentd has better integration with various backend storage solutions like Elasticsearch, Kafka, and others, making it a versatile option for data storage and retrieval compared to Logagent, which may have limitations in backend storage compatibility.
  6. Configuration Complexity: Fluentd can be more complex to configure and manage due to its extensive feature set and customization options, whereas Logagent aims to provide a simpler configuration process for quick setup and deployment for straightforward log processing tasks.

In Summary, Fluentd offers a more versatile and feature-rich solution with advanced data parsing capabilities, scalability, extensive plugin support, and backend storage integration, while Logagent focuses on lightweight log processing with simpler configuration and may be more suitable for specific use cases.

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Detailed Comparison

Fluentd
Fluentd
Logagent
Logagent

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.

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

Open source; Flexible; Minimum resources; Reliable
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
13.4K
GitHub Stars
390
GitHub Forks
1.4K
GitHub Forks
78
Stacks
630
Stacks
4
Followers
688
Followers
5
Votes
39
Votes
0
Pros & Cons
Pros
  • 11
    Open-source
  • 10
    Great for Kubernetes node container log forwarding
  • 9
    Easy
  • 9
    Lightweight
No community feedback yet
Integrations
No integrations available
GitHub
GitHub
Node.js
Node.js
Docker
Docker
Kubernetes
Kubernetes
JavaScript
JavaScript
Git
Git

What are some alternatives to Fluentd, 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.

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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