FluentD is a free and open-source data collector. With its decentralized ecosystem, it’s known for its built-in reliability and cross-platform compatibility. One of the biggest challenges in big data collection is the lack of standardization between collection sources. They just aren’t able to talk to each other. With FluentD, you can address one of the biggest challenges to big data log collection.
If you’re running your services and applications on Kubernetes and you need a way to seamlessly collect logs out of your applications, FluentD in Kubernetes is a great solution for your needs. FluentD is positioned to support big data as well as unstructured and semi-structured data sets for you to better use, understand, and analyze your log data.
In this post, we’ll define FluentD, show some examples of how it’s used in business today, and provide tips on how you can get started with FluentD in your company. Read on to learn more about FluentD.
What is FluentD?
FluentD is a cross-platform software project originally developed at Treasure Data. The program was designed to solve the challenge of big data log collection. It’s licensed under Apache License v2.0 and written in Ruby programming language.
With FluentD, you can better unify collecting and consuming your data, so you can understand it and use it effectively for your business. It bridges the gap between data collection sources by supporting Linux and Windows so you can track Windows event logs with the latest versions. You can read and match the logs you want with the tail input plug-in and send them to Elasticsearch, Cloudwatch, or S3. FluentD can collect, parse, and distribute those log files.
FluentD also supports a seamless sync between Kubernetes, so you can better monitor and manage your services and infrastructure. Kubernetes allows you to fine-tune your performance as you look for faults.
However, FluentD is difficult to configure. It uses Fluent Bit and FluentD Forwarder for leaf machines, which means it uses less memory.
Who uses FluentD?
Companies such as Amazon Web Services, Change.org, CyberAgent, DeNA, Drecom, GREE, and GungHo use FluentD for its easy installation and use, visualization of metrics, log monitoring, and log collection. As open-source software, the community of users and its dedication to improving the software is a big benefit.
What is a data collector?
A data collector is an application that collects the metadata about your storage systems and delivers it to be analyzed. You can install this lightweight application on your server.
How does FluentD log?
FluentD in Kubernetes allows you to collect log data from your data source. Its components compile the data from Kubernetes (or another source), transform those logs, and then redirect it to the appropriate data output result. Your data output plug-ins allow you to collect and repurpose the data to better analyze and understand your log data.
Why is FluentD important?
FluentD allows you to use your logs as they are generated. With its unified logging layer, you can decouple data sources to iterate data more quickly for more effective and efficient use. Here are a few reasons you should use FluentD in Kubernetes.
- Simple and easy to use: You can set up FluentD in 10 minutes, with more than 500 plug-ins to support your preferred use-case scenarios.
- Free and open source: Use FluentD in Kubernetes without restriction. It’s flexible so you can use it for your company’s needs.
- Reliability and high performance: With so many companies already using FluentD, it’s proven dependable and high-quality.
- Community: FluentD in Kubernetes has a large and dedicated community to support its growth and development.
- Compatible: It works to standardize and support cross-platform syncing of data for big data compatibility, analysis, and reuse.
In addition to all these reasons, FluentD is flexible. You can unify your data while you collect, filter, buffer, and output your data logs. The power is in its flexibility but also the widespread community support. FluentD will continue to evolve and improve.
Tips and reminders for FluentD
FluentD is already easier to maintain and install than Scribe, Flume, or other data collection tools. Whether you’re already using FluentD or considering it, here are tips for setting up and optimizing your FluentD logs and processing. The goal is to get you the fastest and most streamlined experience for your data.
Avoid extra computations
It’s typically well-advised to streamline your data as much as possible throughout data processing, and it’s the same with FluentD. While FluentD is flexible enough to handle even demanding data requirements, you should avoid adding extra computations to the configuration.
More data could make the system less robust as it struggles to maintain and read everything. FluentD is designed to be simple and easy to use, so you should focus on maintaining that simplicity.
Use multi-process
If you find that you’re overloading your CPU, try a multi-process. The multi-process input plug-in allows you to spin off multiple processes with additional configuration requirements. While the multiple child processes may take more time to set up, it will also help you better manage and prevent CPU (over)usage in the node. Therefore, you can avoid bottlenecking billions of incoming FluentD records.
Reduce memory usage
You can use the Ruby GC parameters to tune your performance and configure your parameters. Use RUBY_GC_HEAP_OLDOBJECT_LIMIT_FACTOR to lower the value and improve your memory usage (the default is 2.0).
How does FluentD work with Kubernetes?
You can deploy FluentD in Kubernetes as a DaemonSet so that each node will have one pod. As a result, you can collect logs from K8s clusters. Then, you can read logs from the directory created for Kubernetes namesakes. You can scrap logs, convert them to structured JSON data, and push them in Elasticsearch via the tail input plug-in.
So, what is FluentD in Kubernetes? FluentD is designed to be a flexible and light solution with hundreds of plug-in options to support outputs and sources. FluentD in Kubernetes offers a unifying layer between the log types. As you build and use plug-ins with FluentD, you can analyze application logs, clickstreams, and event logs.
FluentD allows you to analyze a myriad of logs no matter what your role is within your organization. With its flexibility and seamless cross-platform compatibility, you can better communicate real-time data analysis without the danger of integrating bad data or experiencing the torture of slow down. It’s a data sync you can rely on.
With so much on the line, you need a company that will support your IT operation needs. You can see the importance of FluentD in Kubernetes. That’s why it’s important to find out how LogicMonitor can help with your log analysis.
FluentD + LogicMonitor
LogicMonitor offers log analysis with LM Logs, which allows you to manage your log data even if you don’t know proprietary query language often required for searches. Fluentd can collect logs from multiple sources and structure the data in JSON format. This allows for a unified log data processing including collecting, filtering, buffering, and outputting logs across multiple sources and destinations. With LogicMonitor, the log data is easier to read because it enhances FluentD’s data collection by showing cohesive output. Find out how you can use LM Logs to identify and address issues of critical importance within your IT environment.
Contact LogicMonitor to learn more about our Log Analysis today!