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1.4 Fluent Bit v1.4 Documentation

High Performance Logs Processor

Fluent Bit is a Fast and Lightweight Log Processor, Stream Processor and Forwarder for Linux, OSX, Windows and BSD family operating systems. It has been made with a strong focus on performance to allow the collection of events from different sources without complexity.

Features

High Performance
Data Parsing
- Convert your unstructured messages using our parsers: JSON, Regex, LTSV and Logfmt
Reliability and Data Integrity
- Backpressure Handling
- Data Buffering in memory and file system
Networking
- Security: built-in TLS/SSL support
- Asynchronous I/O
Pluggable Architecture and Extensibility: Inputs, Filters and Outputs
- More than 50 built-in plugins available
- Extensibility
  - Write any input, filter or output plugin in C language
  - Bonus: write Filters in Lua or Output plugins in Golang
Monitoring: expose internal metrics over HTTP in JSON and Prometheus format
Stream Processing: Perform data selection and transformation using simple SQL queries
- Create new streams of data using query results
- Aggregation Windows
- Data analysis and prediction: Timeseries forecasting
Portable: runs on Linux, MacOS, Windows and BSD systems

Fluent Bit, Fluentd and CNCF

Fluent Bit is a sub-component of the Fluentd project ecosystem, it's licensed under the terms of the Apache License v2.0. This project was created by Treasure Data and is its current primary sponsor.

Nowadays Fluent Bit get contributions from several companies and individuals and same as Fluentd, it's hosted as a CNCF subproject.

About

What is Fluent Bit ?

Fluent Bit is a CNCF sub-project under the umbrella of Fluentd

Fluent Bit is an open source and multi-platform log processor tool which aims to be a generic Swiss knife for logs processing and distribution.

Nowadays the number of sources of information in our environments is ever increasing. Handling data collection at scale is complex, and collecting and aggregating diverse data requires a specialized tool that can deal with:

Different sources of information
Different data formats
Data Reliability
Security
Flexible Routing
Multiple destinations

Fluent Bit has been designed with performance and low resources consumption in mind.

A Brief History of Fluent Bit

Every project has a story

On 2014, the Fluentd team at Treasure Data forecasted the need of a lightweight log processor for constraint environments like Embedded Linux and Gateways, the project aimed to be part of the Fluentd Ecosystem and we called it Fluent Bit, fully open source and available under the terms of the Apache License v2.0.

After the project was around for some time, it got some traction in the Embedded market but we also started getting requests for several features from the Cloud community like more inputs, filters, and outputs. Not so long after that, Fluent Bit becomes one of the preferred solutions to solve the logging challenges in Cloud environments.

Fluentd & Fluent Bit

The Production Grade Ecosystem

Logging and data processing in general can be complex, and at scale a bit more, that's why was born. But now is more than a simple tool, it's a full ecosystem that contains SDKs for different languages and sub projects like .

On this page, we will describe the relationship between the and open source projects, as a summary we can say both are:

Licensed under the terms of Apache License v2.0
Hosted projects by the
Production Grade solutions: deployed thousands of times every single day, millions per month.
Community driven projects
Widely Adopted by the Industry: trusted by all major companies like AWS, Microsoft, Google Cloud and hundred of others.
Originally created by .

Both projects share a lot of similarities, is fully designed and built on top of the best ideas of architecture and general design. Choosing which one to use depends on the end-user needs.

The following table describes a comparison in different areas of the projects:

Both and can work as Aggregators or Forwarders, they both can complement each other or use them as standalone solutions.

License

Strong Commitment to the Openness and Collaboration

, including it core, plugins and tools are distributed under the terms of the :

Concepts

Buffering

Performance and Data Safety

When process data, it uses the system memory (heap) as a primary and temporal place to store the record logs before they get delivered, on this private memory area the records are processed.

Buffering refers to the ability to store the records somewhere, and while they are processed and delivered, still be able to store more. Buffering in memory is the fastest mechanism, but there are certain scenarios where the mechanism requires special strategies to deal with , data safety or reduce memory consumption by the service in constraint environments.

Network failures or latency on third party service is pretty common, and on scenarios where we cannot deliver data fast enough as we receive new data to process, we likely will face backpressure. Our buffering strategies are designed to solve problems associated with backpressure and general delivery failures.

Fluent Bit as buffering strategies, offers a primary buffering mechanism in memory and an optional secondary one using the file system. With this hybrid solution you can adjust to any use case safety and keep a high performance while processing your data.

Both mechanisms are not exclusive and when the data is ready to be processed or delivered it will be always in memory, while other data in the queue might be in the file system until is ready to be processed and moved up to memory.

To learn more about the buffering configuration in Fluent Bit, please jump to the section.

Data Pipeline

Input

The way to gather data from your sources

Fluent Bit provides different Input Plugins to gather information from different sources, some of them just collect data from log files while others can gather metrics information from the operating system. There are many plugins for different needs.

When an input plugin is loaded, an internal instance is created. Every instance has its own and independent configuration. Configuration keys are often called properties.

Every input plugin has its own documentation section where it's specified how it can be used and what properties are available.

For more details, please refer to the Input Plugins section.

Parser

Convert Unstructured to Structured messages

Dealing with raw strings or unstructured messages is a constant pain; having a structure is highly desired. Ideally we want to set a structure to the incoming data by the Input Plugins as soon as they are collected:

The Parser allows you to convert from unstructured to structured data. As a demonstrative example consider the following Apache (HTTP Server) log entry:

The above log line is a raw string without format, ideally we would like to give it a structure that can be processed later easily. If the proper configuration is used, the log entry could be converted to:

Buffer

Data processing with reliability

Previously defined in the Buffering concept section, the buffer phase in the pipeline aims to provide a unified and persistent mechanism to store your data, either using the primary in-memory model or using the filesystem based mode.

The buffer phase already contains the data in an immutable state, meaning, no other filter can be applied.

Note that buffered data is not longer a raw text, instead it's in Fluent Bit internal binary representation.

Fluent Bit offers a buffering mechanism in the file system that acts as a backup system to avoid data loss in case of system failures.

Router

Create flexible routing rules

Routing is a core feature that allows to route your data through Filters and finally to one or multiple destinations. The router relies on the concept of and rules

There are two important concepts in Routing:

Tag
Match

When the data is generated by the input plugins, it comes with a Tag (most of the time the Tag is configured manually), the Tag is a human-readable indicator that helps to identify the data source.

In order to define where the data should be routed, a Match rule must be specified in the output configuration.

Consider the following configuration example that aims to deliver CPU metrics to an Elasticsearch database and Memory metrics to the standard output interface:

Note: the above is a simple example demonstrating how Routing is configured.

Routing works automatically reading the Input Tags and the Output Match rules. If some data has a Tag that doesn't match upon routing time, the data is deleted.

Routing with Wildcard

Routing is flexible enough to support wildcard in the Match pattern. The below example defines a common destination for both sources of data:

The match rule is set to my_* which means it will match any Tag that starts with my_.

Output

Destinations for your data: databases, cloud services and more!

The output interface allows us to define destinations for the data. Common destinations are remote services, local file system or standard interface with others. Outputs are implemented as plugins and there are many available.

When an output plugin is loaded, an internal instance is created. Every instance has its own independent configuration. Configuration keys are often called properties.

Every output plugin has its own documentation section specifying how it can be used and what properties are available.

Installation

Sources

Linux Packages

Amazon

Administration

Configuring Fluent Bit

Data Pipeline

Inputs

Parsers

Filters

Outputs

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