Fluentd
Developer(s) | Treasure Data |
---|---|
Initial release | October 2011 |
Stable release |
v1.2
|
Repository |
|
Written in | C, Ruby |
Operating system | Linux (Amazon Linux, CentOS, RHEL), Mac OS X (10.9 and above), Ruby, Windows (7 and above) |
Type | Logging Tool |
License | opensource under Apache 2.0 |
Website |
fluentd |
Fluentd is a cross platform open source data collection software project originally developed at Treasure Data. It is written primarily in the Ruby programming language.
Overview
Fluentd is a Big Data tool for semi- or un-structured data sets. Like Apache Kafka, it analyzes event logs, application logs, and clickstreams.[1] According to Suonsyrjä and Mikkonen, the "core idea of Fluentd is to be the unifying layer between different types of log inputs and outputs.",[2] Fluentd is available on Linux, Mac OSX, and Windows.[3]
History
Fluentd was created by Sadayuki Furuhashi as a project of the Mountain View-based firm Treasure Data. Written primarily in Ruby, its source code was released as open-source software in October 2011.[4][5] The company announced $5 million of funding in 2013.[6]
Users
Fluentd was one of the data collection tools recommended by Amazon Web Services in 2013, when it was said to be similar to Apache Flume or Scribe.[7] Google Cloud Platform's BigQuery recommends Fluentd as default real-time data-ingestion tool, and uses Google's customized version of Fluentd, called google-fluentd, as a default logging agent.[8][9]
References
- ↑ Pasupuleti, Pradeep and Purra, Beulah Salome (2015). Data Lake Development with Big Data. pp. 44–45; 48. Packt. ISBN 1785881663
- ↑ Suonsyrjä, Sampo and Mikkonen, Tommi "Designing an Unobtrusive Analytics Framework for Monitoring Java Applications", pp. 170–173 in Software Measurement. Springer. ISBN 3319242857
- ↑ Fluentd.org. "Download Fluentd". Retrieved 10 March 2016.
- ↑ Mayer, Chris (30 October 2013). "Treasure Data: Breaking down the Hadoop barrier". JAXenter
- ↑ Fluentd.org. "What is Fluentd?". Retrieved 10 March 2016.
- ↑ Derrick Harris (July 23, 2013). "Treasure Data raises $5M, fuses Hadoop and data warehouse in Amazon's cloud". GigaOm.
- ↑ Parviz Deyhim (August 2013). "Best Practices for Amazon EMR" (PDF). Amazon Web Services. p. 12. Archived from the original (PDF) on 2016-03-26. Retrieved March 24, 2017.
- ↑ Google Cloud Platform (2016). "Real-time logs analysis using Fluentd and BigQuery". Retrieved 10 March 2016.
- ↑ Google Cloud Platform (2016). "The Logging Agent". Retrieved 10 March 2016.
Further reading
- Goasguen, Sébastien (2014). 60 Recipes for Apache CloudStack: Using the CloudStack Ecosystem, "Chapter 6: Advanced Recipes". O'Reilly Media. ISBN 1491910127