Logging and data processing in general can be complex, and at scale a bit more, that's why Fluentd was born. Fluentd has become more than a simple tool, it has grown into a fullscale ecosystem that contains SDKs for different languages and sub-projects like Fluent Bit.

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

• Licensed under the terms of Apache License v2.0

• Hosted projects by the Cloud Native Computing Foundation (CNCF)

• 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 Treasure Data.

Both projects share a lot of similarities, Fluent Bit is fully designed and built on top of the best ideas of Fluentd 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 Fluentd and Fluent Bit can work as Aggregators or Forwarders, they both can complement each other or use them as standalone solutions.

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

Features

• High Performance

• Data Parsing
• Metrics Collection (Prometheus compatible)

• Reliability and Data Integrity
• Networking

  • Security: built-in TLS/SSL support

  • Asynchronous I/O

• • More than 80 built-in plugins available

  • Extensibility

    • Write any input, filter or output plugin in C language
• • 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

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.

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.

In production environments we want to have full control of the data we are collecting, filtering is an important feature that allows us to alter the data before delivering it to some destination.

Filtering is implemented through plugins, so each filter available could be used to match, exclude or enrich your logs with some specific metadata.

We support many filters, A common use case for filtering is Kubernetes deployments. Every Pod log needs to get the proper metadata associated

Very similar to the input plugins, Filters run in an instance context, which has its own independent configuration. Configuration keys are often called properties.