Fluent Bit, including it core, plugins and tools are distributed under the terms of the Apache License v2.0:

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

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Before diving into Fluent Bit it’s good to get acquainted with some of the key concepts of the service. This document provides a gentle introduction to those concepts and common Fluent Bit terminology. We’ve provided a list below of all the terms we’ll cover, but we recommend reading this document from start to finish to gain a more general understanding of our log and stream processor.

• Event or Record

• Filtering

• Tag

• Timestamp

• Match

• Structured Message

Event or Record

Every incoming piece of data that belongs to a log or a metric that is retrieved by Fluent Bit is considered an Event or a Record.

As an example consider the following content of a Syslog file:

Jan 18 12:52:16 flb systemd[2222]: Starting GNOME Terminal Server
Jan 18 12:52:16 flb dbus-daemon[2243]: [session uid=1000 pid=2243] Successfully activated service 'org.gnome.Terminal'
Jan 18 12:52:16 flb systemd[2222]: Started GNOME Terminal Server.
Jan 18 12:52:16 flb gsd-media-keys[2640]: # watch_fast: "/org/gnome/terminal/legacy/" (establishing: 0, active: 0)

It contains four lines and all of them represents four independent Events.

Internally, an Event always has two components (in an array form):

[TIMESTAMP, MESSAGE]

Filtering

In some cases is required to perform modifications on the Events content, the process to alter, enrich or drop Events is called Filtering.

There are many use cases when Filtering is required like:

• Append specific information to the Event like an IP address or metadata.

• Select a specific piece of the Event content.

• Drop Events that matches certain pattern.

Tag

Every Event that gets into Fluent Bit gets assigned a Tag. This tag is an internal string that is used in a later stage by the Router to decide which Filter or Output phase it must go through.

Most of the tags are assigned manually in the configuration. If a tag is not specified, Fluent Bit will assign the name of the Input plugin instance from where that Event was generated from.

A Tagged record must always have a Matching rule. To learn more about Tags and Matches check the Routing section.

Timestamp

The Timestamp represents the time when an Event was created. Every Event contains a Timestamp associated. The Timestamp is a numeric fractional integer in the format:

SECONDS.NANOSECONDS

Seconds

It is the number of seconds that have elapsed since the Unix epoch.

Nanoseconds

Fractional second or one thousand-millionth of a second.

Match

Fluent Bit allows to deliver your collected and processed Events to one or multiple destinations, this is done through a routing phase. A Match represent a simple rule to select Events where it Tags matches a defined rule.

To learn more about Tags and Matches check the Routing section.

Structured Messages

Source events can have or not have a structure. A structure defines a set of keys and values inside the Event message. As an example consider the following two messages:

No structured message

"Project Fluent Bit created on 1398289291"

Structured Message

{"project": "Fluent Bit", "created": 1398289291}

At a low level both are just an array of bytes, but the Structured message defines keys and values, having a structure helps to implement faster operations on data modifications.

Logging and data processing in general can be complex, and at scale a bit more, that's why Fluentd 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 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.

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
• Reliability and Data Integrity
• Networking

  • Security: built-in TLS/SSL support

  • Asynchronous I/O

• • More than 50 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

​ 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

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

When processes 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.

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.

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:

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.

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.

The following article cover the relevant notes for users upgrading from previous Fluent Bit versions. We aim to cover compatibility changes that you must be aware of.

For more details about changes on each release please refer to the Official Release Notes.

Fluent Bit v1.6

If you are migrating from previous version of Fluent Bit please review the following important changes:

Tail Input Plugin

Now by default the plugin follows a file from the end once the service starts (old behavior was always read from the beginning). For every file found at start, its followed from it last position, for new files discovered at runtime or rotated, they are read from the beginning.

If you desire to keep the old behavior you can set the option read_from_head to true.

Stackdriver Output Plugin

The project_id of resource in LogEntry sent to Google Cloud Logging would be set to the project ID rather than the project number. To learn the difference between Project ID and project number, see this for more details.

If you have any existing queries based on the resource's project_id, please update your query accordingly.

Fluent Bit v1.5

The migration from v1.4 to v1.5 is pretty straightforward.

• If you enabled keepalive mode in your configuration, note that this configuration property has been renamed to net.keepalive. Now all Network I/O keepalive is enabled by default, to learn more about this and other associated configuration properties read the Networking Administration section.

• If you use the Elasticsearch output plugin, note the default value of type changed from flb_type to _doc. Many versions of Elasticsearch will tolerate this, but ES v5.6 through v6.1 require a type without a leading underscore. See the Elasticsearch output plugin documentation FAQ entry for more.

Fluent Bit v1.4

If you are migrating from Fluent Bit v1.3, there are no breaking changes. Just new exciting features to enjoy :)

Fluent Bit v1.3

If you are migrating from Fluent Bit v1.2 to v1.3, there are no breaking changes. If you are upgrading from an older version please review the incremental changes below.

Fluent Bit v1.2

Docker, JSON, Parsers and Decoders

On Fluent Bit v1.2 we have fixed many issues associated with JSON encoding and decoding, for hence when parsing Docker logs is no longer necessary to use decoders. The new Docker parser looks like this:

[PARSER]
    Name         docker
    Format       json
    Time_Key     time
    Time_Format  %Y-%m-%dT%H:%M:%S.%L
    Time_Keep    On

Note: again, do not use decoders.

Kubernetes Filter

We have done improvements also on how Kubernetes Filter handle the stringified log message. If the option Merge_Log is enabled, it will try to handle the log content as a JSON map, if so, it will add the keys to the root map.

In addition, we have fixed and improved the option called Merge_Log_Key. If a merge log succeed, all new keys will be packaged under the key specified by this option, a suggested configuration is as follows:

[FILTER]
    Name             Kubernetes
    Match            kube.*
    Kube_Tag_Prefix  kube.var.log.containers.
    Merge_Log        On
    Merge_Log_Key    log_processed

As an example, if the original log content is the following map:

{"key1": "val1", "key2": "val2"}

the final record will be composed as follows:

{
    "log": "{\"key1\": \"val1\", \"key2\": \"val2\"}",
    "log_processed": {
        "key1": "val1",
        "key2": "val2"
    }
}

Fluent Bit v1.1

If you are upgrading from Fluent Bit <= 1.0.x you should take in consideration the following relevant changes when switching to Fluent Bit v1.1 series:

Kubernetes Filter

We introduced a new configuration property called Kube_Tag_Prefix to help Tag prefix resolution and address an unexpected behavior that landed in previous versions.

During 1.0.x release cycle, a commit in Tail input plugin changed the default behavior on how the Tag was composed when using the wildcard for expansion generating breaking compatibility with other services. Consider the following configuration example:

[INPUT]
    Name  tail
    Path  /var/log/containers/*.log
    Tag   kube.*

The expected behavior is that Tag will be expanded to:

kube.var.log.containers.apache.log

but the change introduced in 1.0 series switched from absolute path to the base file name only:

kube.apache.log

On Fluent Bit v1.1 release we restored to our default behavior and now the Tag is composed using the absolute path of the monitored file.

Having absolute path in the Tag is relevant for routing and flexible configuration where it also helps to keep compatibility with Fluentd behavior.

This behavior switch in Tail input plugin affects how Filter Kubernetes operates. As you know when the filter is used it needs to perform local metadata lookup that comes from the file names when using Tail as a source. Now with the new Kube_Tag_Prefix option you can specify what's the prefix used in Tail input plugin, for the configuration example above the new configuration will look as follows:

[INPUT]
    Name  tail
    Path  /var/log/containers/*.log
    Tag   kube.*

[FILTER]
    Name             kubernetes
    Match            *
    Kube_Tag_Prefix  kube.var.log.containers.

So the proper for Kube_Tag_Prefix value must be composed by Tag prefix set in Tail input plugin plus the converted monitored directory replacing slashes with dots.

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 Tags and Matching 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:

[INPUT]
    Name cpu
    Tag  my_cpu

[INPUT]
    Name mem
    Tag  my_mem

[OUTPUT]
    Name   es
    Match  my_cpu

[OUTPUT]
    Name   stdout
    Match  my_mem

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:

[INPUT]
    Name cpu
    Tag  my_cpu

[INPUT]
    Name mem
    Tag  my_mem

[OUTPUT]
    Name   stdout
    Match  my_*

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

The following operating systems and architectures are supported in Fluent Bit.

From an architecture support perspective, Fluent Bit is fully functional on x86_64, Arm64v8 and Arm32v7 based processors.

Fluent Bit can work also on OSX and *BSD systems, but not all plugins will be available on all platforms. Official support will be expanding based on community demand.

Previously defined in the 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.

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.

uses very low CPU and Memory consumption, it's compatible with most of x86, x86_64, arm32v7 and arm64v8 based platforms. In order to build it you need the following components in your system for the build process:

• Compiler: GCC or clang

• CMake

• Flex & Bison: only if you enable the Stream Processor or Record Accessor feature (both enabled by default)

In the core there are not other dependencies, For certain features that depends on third party components like output plugins with special backend libraries (e.g: kafka), those are included in the main source code repository.

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.

Install on Redhat / CentOS

Fluent Bit is distributed as td-agent-bit package and is available for the latest stable CentOS system. The following architectures are supported

• x86_64

• aarch64 / arm64v8

Configure Yum

We provide td-agent-bit through a Yum repository. In order to add the repository reference to your system, please add a new file called td-agent-bit.repo in /etc/yum.repos.d/ with the following content:

[td-agent-bit]
name = TD Agent Bit
baseurl = https://packages.fluentbit.io/centos/7/$basearch/
gpgcheck=1
gpgkey=https://packages.fluentbit.io/fluentbit.key
enabled=1

note: we encourage you always enable the gpgcheck for security reasons. All our packages are signed.

The GPG Key fingerprint is F209 D876 2A60 CD49 E680 633B 4FF8 368B 6EA0 722A

Install

Once your repository is configured, run the following command to install it:

$ yum install td-agent-bit

Now the following step is to instruct Systemd to enable the service:

$ sudo service td-agent-bit start

If you do a status check, you should see a similar output like this:

$ service td-agent-bit status
Redirecting to /bin/systemctl status  td-agent-bit.service
● td-agent-bit.service - TD Agent Bit
   Loaded: loaded (/usr/lib/systemd/system/td-agent-bit.service; disabled; vendor preset: disabled)
   Active: active (running) since Thu 2016-07-07 02:08:01 BST; 9s ago
 Main PID: 3820 (td-agent-bit)
   CGroup: /system.slice/td-agent-bit.service
           └─3820 /opt/td-agent-bit/bin/td-agent-bit -c etc/td-agent-bit/td-agent-bit.conf
...

The default configuration of td-agent-bit is collecting metrics of CPU usage and sending the records to the standard output, you can see the outgoing data in your /var/log/messages file.

Fluent Bit is distributed as td-agent-bit package and is available for the latest (and old) stable Debian systems: Buster, Stretch and Jessie.

Server GPG key

The first step is to add our server GPG key to your keyring, on that way you can get our signed packages:

Update your sources lists

On Debian, you need to add our APT server entry to your sources lists, please add the following content at bottom of your /etc/apt/sources.list file:

Debian 10 (Buster)

Debian 9 (Stretch)

Debian 8 (Jessie)

Update your repositories database

Now let your system update the apt database:

Install TD Agent Bit

Using the following apt-get command you are able now to install the latest td-agent-bit:

Now the following step is to instruct systemd to enable the service:

If you do a status check, you should see a similar output like this:

The default configuration of td-agent-bit is collecting metrics of CPU usage and sending the records to the standard output, you can see the outgoing data in your /var/log/syslog file.

Learn how to install Fluent Bit and the AWS output plugins on Amazon Linux 2 via AWS Systems Manager.

Fluent Bit is distributed as td-agent-bit package and is available for the latest stable Ubuntu system: Focal Fossa.

Server GPG key

The first step is to add our server GPG key to your keyring, on that way you can get our signed packages:

$ wget -qO - https://packages.fluentbit.io/fluentbit.key | sudo apt-key add -

Update your sources lists

On Ubuntu, you need to add our APT server entry to your sources lists, please add the following content at bottom of your /etc/apt/sources.list file:

Ubuntu 20.04 LTS (Focal Fossa)

deb https://packages.fluentbit.io/ubuntu/focal focal main

Ubuntu 18.04 LTS (Bionic Beaver)

deb https://packages.fluentbit.io/ubuntu/bionic bionic main

Ubuntu 16.04 LTS (Xenial Xerus)

deb https://packages.fluentbit.io/ubuntu/xenial xenial main

Update your repositories database

Now let your system update the apt database:

$ sudo apt-get update

Install TD-Agent Bit

Using the following apt-get command you are able now to install the latest td-agent-bit:

$ sudo apt-get install td-agent-bit

Now the following step is to instruct systemd to enable the service:

$ sudo service td-agent-bit start

If you do a status check, you should see a similar output like this:

sudo service td-agent-bit status
● td-agent-bit.service - TD Agent Bit
   Loaded: loaded (/lib/systemd/system/td-agent-bit.service; disabled; vendor preset: enabled)
   Active: active (running) since mié 2016-07-06 16:58:25 CST; 2h 45min ago
 Main PID: 6739 (td-agent-bit)
    Tasks: 1
   Memory: 656.0K
      CPU: 1.393s
   CGroup: /system.slice/td-agent-bit.service
           └─6739 /opt/td-agent-bit/bin/td-agent-bit -c /etc/td-agent-bit/td-agent-bit.conf
...

The default configuration of td-agent-bit is collecting metrics of CPU usage and sending the records to the standard output, you can see the outgoing data in your /var/log/syslog file.

Fluent Bit in normal operation mode allows to be configurable through text files or using specific arguments in the command line, while this is the ideal deployment case, there are scenarios where a more restricted configuration is required: static configuration mode.

Static configuration mode aims to include a built-in configuration in the final binary of Fluent Bit, disabling the usage of external files or flags at runtime.

Getting Started

Requirements

The following steps assumes you are familiar with configuring Fluent Bit using text files and you have experience building it from scratch as described in the Build and Install section.

Configuration Directory

In your file system prepare a specific directory that will be used as an entry point for the build system to lookup and parse the configuration files. It is mandatory that this directory contain as a minimum one configuration file called fluent-bit.conf containing the required SERVICE, INPUT and OUTPUT sections. As an example create a new fluent-bit.conf file with the following content:

[SERVICE]
    Flush     1
    Daemon    off
    Log_Level info

[INPUT]
    Name      cpu

[OUTPUT]
    Name      stdout
    Match     *

the configuration provided above will calculate CPU metrics from the running system and print them to the standard output interface.

Build with Custom Configuration

Inside Fluent Bit source code, get into the build/ directory and run CMake appending the FLB_STATIC_CONF option pointing the configuration directory recently created, e.g:

$ cd fluent-bit/build/
$ cmake -DFLB_STATIC_CONF=/path/to/my/confdir/

then build it:

$ make

At this point the fluent-bit binary generated is ready to run without necessity of further configuration:

$ bin/fluent-bit 
Fluent-Bit v0.15.0
Copyright (C) Treasure Data

[2018/10/19 15:32:31] [ info] [engine] started (pid=15186)
[0] cpu.local: [1539984752.000347547, {"cpu_p"=>0.750000, "user_p"=>0.500000, "system_p"=>0.250000, "cpu0.p_cpu"=>1.000000, "cpu0.p_user"=>1.000000, "cpu0.p_system"=>0.000000, "cpu1.p_cpu"=>0.000000, "cpu1.p_user"=>0.000000, "cpu1.p_system"=>0.000000, "cpu2.p_cpu"=>0.000000, "cpu2.p_user"=>0.000000, "cpu2.p_system"=>0.000000, "cpu3.p_cpu"=>1.000000, "cpu3.p_user"=>1.000000, "cpu3.p_system"=>0.000000}]

Stable

For production systems, we strongly suggest that you always get the latest stable release from our web site, you can get the official tarballs (.tar.gz) from the following link:

https://fluentbit.io/download/

Development

For people who aims to contribute to the project testing or extending the code base, can get the development version from our GIT repository:

$ git clone https://github.com/fluent/fluent-bit

Note that our master branch is where the development of Fluent Bit happens. Since it's a development version, expect issues when compiling or at run time.

We encourage everybody to help us testing every development version, at the end this is what will become stable.

Fluent Bit uses CMake as it build system. The suggested procedure to prepare the build system consists on the following steps:

Prepare environment

In the following steps you can find exact commands to build and install the project with the default options. If you already know how CMake works you can skip this part and look at the build options available. Note that Fluent Bit requires CMake 3.x. You may need to use cmake3 instead of cmake to complete the following steps on your system.

Change to the build/ directory inside the Fluent Bit sources:

$ cd build/

Let CMake configure the project specifying where the root path is located:

$ cmake ../
-- The C compiler identification is GNU 4.9.2
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- The CXX compiler identification is GNU 4.9.2
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
...
-- Could NOT find Doxygen (missing:  DOXYGEN_EXECUTABLE)
-- Looking for accept4
-- Looking for accept4 - not found
-- Configuring done
-- Generating done
-- Build files have been written to: /home/edsiper/coding/fluent-bit/build

Now you are ready to start the compilation process through the simple make command:

$ make
Scanning dependencies of target msgpack
[  2%] Building C object lib/msgpack-1.1.0/CMakeFiles/msgpack.dir/src/unpack.c.o
[  4%] Building C object lib/msgpack-1.1.0/CMakeFiles/msgpack.dir/src/objectc.c.o
[  7%] Building C object lib/msgpack-1.1.0/CMakeFiles/msgpack.dir/src/version.c.o
...
[ 19%] Building C object lib/monkey/mk_core/CMakeFiles/mk_core.dir/mk_file.c.o
[ 21%] Building C object lib/monkey/mk_core/CMakeFiles/mk_core.dir/mk_rconf.c.o
[ 23%] Building C object lib/monkey/mk_core/CMakeFiles/mk_core.dir/mk_string.c.o
...
Scanning dependencies of target fluent-bit-static
[ 66%] Building C object src/CMakeFiles/fluent-bit-static.dir/flb_pack.c.o
[ 69%] Building C object src/CMakeFiles/fluent-bit-static.dir/flb_input.c.o
[ 71%] Building C object src/CMakeFiles/fluent-bit-static.dir/flb_output.c.o
...
Linking C executable ../bin/fluent-bit
[100%] Built target fluent-bit-bin

to continue installing the binary on the system just do:

$ make install

it's likely you may need root privileges so you can try to prefixing the command with sudo.

Build Options

Fluent Bit provides certain options to CMake that can be enabled or disabled when configuring, please refer to the following tables under the General Options, Development Options, Input Plugins and _Output Plugins sections.

General Options

Development Options

Input Plugins

The input plugins provides certain features to gather information from a specific source type which can be a network interface, some built-in metric or through a specific input device, the following input plugins are available:

Filter Plugins

The filter plugins allows to modify, enrich or drop records. The following table describes the filters available on this version:

Output Plugins

The output plugins gives the capacity to flush the information to some external interface, service or terminal, the following table describes the output plugins available as of this version:

Fluent Bit container images are available on Docker Hub ready for production usage. Current available images can be deployed in multiple architectures.

Tags and Versions

The following table describe the tags are available on Docker Hub fluent/fluent-bit repository:

It's strongly suggested that you always use the latest image of Fluent Bit.

Multi Architecture Images

Our x86_64 stable image is based in Distroless focusing on security containing just the Fluent Bit binary and minimal system libraries and basic configuration. Optionally, we provide debug images for x86_64 which contains Busybox that can be used to troubleshoot or testing purposes.

In addition, the main manifest provides images for arm64v8 and arm32v7 architectures. From a deployment perspective there is no need to specify an architecture, the container client tool that pulls the image gets the proper layer for the running architecture.

For every architecture we build the layers using the following base images:

Getting Started

Download the last stable image from 1.6 series:

$ docker pull fluent/fluent-bit:1.6

Once the image is in place, now run the following (useless) test which makes Fluent Bit measure CPU usage by the container:

$ docker run -ti fluent/fluent-bit:1.6 /fluent-bit/bin/fluent-bit -i cpu -o stdout -f 1

That command will let Fluent Bit measure CPU usage every second and flush the results to the standard output, e.g:

Fluent-Bit v1.6.x
Copyright (C) Treasure Data

[2019/10/01 12:29:02] [ info] [engine] started
[0] cpu.0: [1504290543.000487750, {"cpu_p"=>0.750000, "user_p"=>0.250000, "system_p"=>0.500000, "cpu0.p_cpu"=>0.000000, "cpu0.p_user"=>0.000000, "cpu0.p_system"=>0.000000, "cpu1.p_cpu"=>1.000000, "cpu1.p_user"=>0.000000, "cpu1.p_system"=>1.000000, "cpu2.p_cpu"=>1.000000, "cpu2.p_user"=>1.000000, "cpu2.p_system"=>0.000000, "cpu3.p_cpu"=>0.000000, "cpu3.p_user"=>0.000000, "cpu3.p_system"=>0.000000}]

F.A.Q

Why there is no Fluent Bit Docker image based on Alpine Linux ?

Alpine Linux uses Musl C library instead of Glibc. Musl is not fully compatible with Glibc which generated many issues in the following areas when used with Fluent Bit:

• Memory Allocator: to run Fluent Bit properly in high-load environments, we use Jemalloc as a default memory allocator which reduce fragmentation and provides better performance for our needs. Jemalloc cannot run smoothly with Musl and requires extra work.

• Alpine Linux Musl functions bootstrap have a compatibility issue when loading Golang shared libraries, this generate problems when trying to load Golang output plugins in Fluent Bit.

• Alpine Linux Musl Time format parser does not support Glibc extensions

• Maintainers preference in terms of base image due to security and maintenance reasons are Distroless and Debian.

Where 'latest' Tag points to ?

Our Docker containers images are deployed thousands of times per day, we take security and stability very seriously.

The latest tag most of the time points to the latest stable image. When we release a major update to Fluent Bit like for example from v1.3.x to v1.4.0, we don't move latest tag until 2 weeks after the release. That give us extra time to verify with our community that everything works as expected.

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.

Fluent Bit supports the usage of environment variables in any value associated to a key when using a configuration file.

The variables are case sensitive and can be used in the following format:

${MY_VARIABLE}

When Fluent Bit starts, the configuration reader will detect any request for ${MY_VARIABLE} and will try to resolve its value.

Example

Create the following configuration file (fluent-bit.conf):

[SERVICE]
    Flush        1
    Daemon       Off
    Log_Level    info

[INPUT]
    Name cpu
    Tag  cpu.local

[OUTPUT]
    Name  ${MY_OUTPUT}
    Match *

Open a terminal and set the environment variable:

$ export MY_OUTPUT=stdout

The above command set the 'stdout' value to the variable MY_OUTPUT.

Run Fluent Bit with the recently created configuration file:

$ bin/fluent-bit -c fluent-bit.conf
Fluent Bit v1.4.0
* Copyright (C) 2019-2020 The Fluent Bit Authors
* Copyright (C) 2015-2018 Treasure Data
* Fluent Bit is a CNCF sub-project under the umbrella of Fluentd
* https://fluentbit.io

[2020/03/03 12:25:25] [ info] [engine] started
[0] cpu.local: [1491243925, {"cpu_p"=>1.750000, "user_p"=>1.750000, "system_p"=>0.000000, "cpu0.p_cpu"=>3.000000, "cpu0.p_user"=>2.000000, "cpu0.p_system"=>1.000000, "cpu1.p_cpu"=>0.000000, "cpu1.p_user"=>0.000000, "cpu1.p_system"=>0.000000, "cpu2.p_cpu"=>4.000000, "cpu2.p_user"=>4.000000, "cpu2.p_system"=>0.000000, "cpu3.p_cpu"=>1.000000, "cpu3.p_user"=>1.000000, "cpu3.p_system"=>0.000000}]

As you can see the service worked properly as the configuration was valid.

One of the ways to configure Fluent Bit is using a main configuration file. Fluent Bit allows to use one configuration file which works at a global scope and uses the Format and Schema defined previously.

The main configuration file supports four types of sections:

• Service

• Input

• Filter

• Output

In addition, it's also possible to split the main configuration file in multiple files using the feature to include external files:

• Include File

Service

The Service section defines global properties of the service, the keys available as of this version are described in the following table:

The following is an example of a SERVICE section:

[SERVICE]
    Flush           5
    Daemon          off
    Log_Level       debug

Input

An INPUT section defines a source (related to an input plugin), here we will describe the base configuration for each INPUT section. Note that each input plugin may add it own configuration keys:

The Name is mandatory and it let Fluent Bit know which input plugin should be loaded. The Tag is mandatory for all plugins except for the input forward plugin (as it provides dynamic tags).

Example

The following is an example of an INPUT section:

[INPUT]
    Name cpu
    Tag  my_cpu

Filter

A FILTER section defines a filter (related to an filter plugin), here we will describe the base configuration for each FILTER section. Note that each filter plugin may add it own configuration keys:

The Name is mandatory and it let Fluent Bit know which filter plugin should be loaded. The Match or Match_Regex is mandatory for all plugins. If both are specified, Match_Regex takes precedence.

Example

The following is an example of an FILTER section:

[FILTER]
    Name  stdout
    Match *

Output

The OUTPUT section specify a destination that certain records should follow after a Tag match. The configuration support the following keys:

Example

The following is an example of an OUTPUT section:

[OUTPUT]
    Name  stdout
    Match my*cpu

Example: collecting CPU metrics

The following configuration file example demonstrates how to collect CPU metrics and flush the results every five seconds to the standard output:

[SERVICE]
    Flush     5
    Daemon    off
    Log_Level debug

[INPUT]
    Name  cpu
    Tag   my_cpu

[OUTPUT]
    Name  stdout
    Match my*cpu

Visualize

You can also visualize Fluent Bit INPUT, FILTER, and OUTPUT configuration via https://config.calyptia.com

Include File

To avoid complicated long configuration files is better to split specific parts in different files and call them (include) from one main file.

Starting from Fluent Bit 0.12 the new configuration command @INCLUDE has been added and can be used in the following way:

@INCLUDE somefile.conf

The configuration reader will try to open the path somefile.conf, if not found, it will assume it's a relative path based on the path of the base configuration file, e.g:

• Main configuration file path: /tmp/main.conf

• Included file: somefile.conf

• Fluent Bit will try to open somefile.conf, if it fails it will try /tmp/somefile.conf.

The @INCLUDE command only works at top-left level of the configuration line, it cannot be used inside sections.

Wildcard character (*) is supported to include multiple files, e.g:

@INCLUDE input_*.conf

Fluent Bit is distributed as td-agent-bit package for Windows. Fluent Bit has two flavours of Windows installers: a ZIP archive (for quick testing) and an EXE installer (for system installation).

Installation Packages

The latest stable version is 1.6.10:

To check the integrity, use Get-FileHash commandlet on PowerShell.

PS> Get-FileHash td-agent-bit-1.6.10-win32.exe

Installing from ZIP archive

Download a ZIP archive from the download page. There are installers for 32-bit and 64-bit environments, so choose one suitable for your environment.

Then you need to expand the ZIP archive. You can do this by clicking "Extract All" on Explorer, or if you're using PowerShell, you can use Expand-Archive commandlet.

PS> Expand-Archive td-agent-bit-1.6.10-win64.zip

The ZIP package contains the following set of files.

td-agent-bit
├── bin
│   ├── fluent-bit.dll
│   └── fluent-bit.exe
├── conf
│   ├── fluent-bit.conf
│   ├── parsers.conf
│   └── plugins.conf
└── include
    │   ├── flb_api.h
    │   ├── ...
    │   └── flb_worker.h
    └── fluent-bit.h

Now, launch cmd.exe or PowerShell on your machine, and execute fluent-bit.exe as follows.

PS> .\bin\fluent-bit.exe -i dummy -o stdout

If you see the following output, it's working fine!

PS> .\bin\fluent-bit.exe  -i dummy -o stdout
Fluent Bit v1.6.x
* Copyright (C) 2019-2020 The Fluent Bit Authors
* Copyright (C) 2015-2018 Treasure Data
* Fluent Bit is a CNCF sub-project under the umbrella of Fluentd
* https://fluentbit.io

[2019/06/28 10:13:04] [ info] [storage] initializing...
[2019/06/28 10:13:04] [ info] [storage] in-memory
[2019/06/28 10:13:04] [ info] [storage] normal synchronization mode, checksum disabled, max_chunks_up=128
[2019/06/28 10:13:04] [ info] [engine] started (pid=10324)
[2019/06/28 10:13:04] [ info] [sp] stream processor started
[0] dummy.0: [1561684385.443823800, {"message"=>"dummy"}]
[1] dummy.0: [1561684386.428399000, {"message"=>"dummy"}]
[2] dummy.0: [1561684387.443641900, {"message"=>"dummy"}]
[3] dummy.0: [1561684388.441405800, {"message"=>"dummy"}]

To halt the process, press CTRL-C in the terminal.

Installing from EXE installer

Download an EXE installer from the download page. It has both 32-bit and 64-bit builds. Choose one which is suitable for you.

Then, double-click the EXE installer you've downloaded. Installation wizard will automatically start.

Click Next and proceed. By default, Fluent Bit is installed into C:\Program Files\td-agent-bit\, so you should be able to launch fluent-bit as follow after installation.

PS> C:\Program Files\td-agent-bit\bin\fluent-bit.exe -i dummy -o stdout

Windows Service Support

Windows services are equivalent to "daemons" in UNIX (i.e. long-running background processes). Since v1.5.0, Fluent Bit has the native support for Windows Service.

Suppose you have the following installation layout:

C:\fluent-bit\
├── conf
│   ├── fluent-bit.conf
│   └── parsers.conf
└── bin
    ├── fluent-bit.dll
    └── fluent-bit.exe

To register Fluent Bit as a Windows service, you need to execute the following command on Command Prompt. Please be careful that a single space is required after binpath=.

% sc.exe create fluent-bit binpath= "\fluent-bit\bin\fluent-bit.exe -c \fluent-bit\conf\fluent-bit.conf"

Now Fluent Bit can be started and managed as a normal Windows service.

% sc.exe start fluent-bit
% sc.exe query fluent-bit
SERVICE_NAME: fluent-bit
    TYPE               : 10  WIN32_OWN_PROCESS
    STATE              : 4 Running
    ...

To halt the Fluent Bit service, just execute the "stop" command.

% sc.exe stop fluent-bit

The end-goal of Fluent Bit is to collect, parse, filter and ship logs to a central place. In this workflow there are many phases and one of the critical pieces is the ability to do buffering : a mechanism to place processed data into a temporal location until is ready to be shipped.

By default when Fluent Bit process data, it uses Memory as a primary and temporary place to store the records, but there are certain scenarios where would be ideal to have a persistent buffering mechanism based in the filesystem to provide aggregation and data safety capabilities.

Choosing the right configuration is critical and the behavior of the service can be conditioned based in the backpressure settings. Before to jump into the configuration properties let's understand the relationship between Chunks, Memory, Filesystem and Backpressure.

Chunks, Memory, Filesystem and Backpressure

Understanding the chunks, buffering and backpressure concepts is critical for a proper configuration. Let's do a recap of the meaning of these concepts.

Chunks

When an input plugin (source) emit records, the engine group the records together in a Chunk. A Chunk size usually is around 2MB. By configuration, the engine decide where to place this Chunk, the default is that all chunks are created only in memory.

Buffering and Memory

As mentioned above, the Chunks generated by the engine are placed in memory but this is configurable.

If memory is the only mechanism set for the input plugin, it will just store data as much as it can there (memory). This is the fastest mechanism with less system overhead, but if the service is not able to deliver the records fast enough because of a slow network or an unresponsive remote service, Fluent Bit memory usage will increase since it will accumulate more data than it can deliver.

On a high load environment with backpressure the risks of having high memory usage is the chance to get killed by the Kernel (OOM Killer). A workaround for this backpressure scenario is to limit the amount of memory in records that an input plugin can register, this configuration property is called mem_buf_limit: if a plugin have enqueued more than mem_buf_limit, it won't be able to ingest more until it data can be delivered or flushed properly. On this scenario the input plugin in question is paused.

The workaround of mem_buf_limit is good for certain scenarios and environments, it helps to control the memory usage of the service, but at the costs that if a file gets rotated while paused, you might lose that data since it won't be able to register new records. This can happen with any input source plugin. The goal of mem_buf_limit is memory control and survival of the service.

For full data safety guarantee, use filesystem buffering.

Filesystem buffering to the rescue

Filesystem buffering enabled helps with backpressure and overall memory control.

Behind the scenes, Memory and Filesystem buffering mechanisms are not mutual exclusive, indeed when enabling filesystem buffering for your input plugin (source) you are getting the best of the two worlds: performance and data safety.

When the Filesystem buffering is enabled, the behavior of the engine is different, upon Chunk creation, it stores the content in memory but also it maps a copy on disk (through mmap(2)), this Chunk is active in memory and backed up in disk is called to be up which means "the chunk content is up in memory".

How this Filesystem buffering mechanism deals with high memory usage and backpressure ?: Fluent Bit controls the number of Chunks that are up in memory.

By default, the engine allows to have 128 Chunks up in memory in total (considering all Chunks), this value is controlled by service property storage.max_chunks_up. The active Chunks that are up are ready for delivery and the ones that still are receiving records. Any other remaining Chunk is in a down state, which means that's only in the filesystem and won't be up in memory unless is ready to be delivered.

If the input plugin has enabled mem_buf_limit and storage.type as filesystem, when reaching the mem_buf_limit threshold, instead of the plugin being paused, all new data will go to Chunks that are down in the filesystem. This allows to control the memory usage by the service but also providing a a guarantee that the service won't lose any data.

Limiting Filesystem space for Chunks

Fluent Bit implements the concept of logical queues: a Chunk based on its Tag, can be routed to multiple destinations, so internally we keep a reference from where a Chunk was created and where it needs to go.

It's common to find cases that if we have multiple destinations for a Chunk, one of the destination might be slower than the other, and maybe one of the destinations is generating backpressure and not all of them. On this scenario how do we limit the amount of filesystem Chunks that we are logically queueing ?.

Starting from Fluent Bit v1.6, we introduced the new configuration property for output plugins called storage.total_limit_size which limits the number of Chunks that exists in the file system for a certain logical output destination. If one destinations reaches the storage.total_limit_size limit, the oldest Chunk from it queue for that logical output destination will be discarded.

Configuration

The storage layer configuration takes place in three areas:

• Service Section

• Input Section

• Output Section

The known Service section configure a global environment for the storage layer, the Input sections defines which buffering mechanism to use and the output the limits for the logical queues.

Service Section Configuration

The Service section refers to the section defined in the main configuration file:

a Service section will look like this:

[SERVICE]
    flush                     1
    log_Level                 info
    storage.path              /var/log/flb-storage/
    storage.sync              normal
    storage.checksum          off
    storage.backlog.mem_limit 5M

that configuration configure an optional buffering mechanism where it root for data is /var/log/flb-storage/, it will use normal synchronization mode, without checksum and up to a maximum of 5MB of memory when processing backlog data.

Input Section Configuration

Optionally, any Input plugin can configure their storage preference, the following table describe the options available:

The following example configure a service that offers filesystem buffering capabilities and two Input plugins being the first based in filesystem and the second with memory only.

[SERVICE]
    flush                     1
    log_Level                 info
    storage.path              /var/log/flb-storage/
    storage.sync              normal
    storage.checksum          off
    storage.backlog.mem_limit 5M

[INPUT]
    name          cpu
    storage.type  filesystem

[INPUT]
    name          mem
    storage.type  memory

Output Section Configuration

If certain chunks are filesystem storage.type based, it's possible to control the size of the logical queue for an output plugin. The following table describe the options available:

The following example create records with CPU usage samples in the filesystem and then they are delivered to Google Stackdriver service limiting the logical queue (buffering) to 5M:

[SERVICE]
    flush                     1
    log_Level                 info
    storage.path              /var/log/flb-storage/
    storage.sync              normal
    storage.checksum          off
    storage.backlog.mem_limit 5M

[INPUT]
    name                      cpu
    storage.type              filesystem 

[OUTPUT]
    name                      stackdriver
    match                     *
    storage.total_limit_size  5M

If for some reason Fluent Bit get's offline because of a network issue, it will continuing buffering CPU samples but just keeping a maximum of 5M of the newest data.

Fluent Bit source code provides Bitbake recipes to configure, build and package the software for a Yocto based image. Note that specific steps of usage of these recipes in your Yocto environment (Poky) is out of the scope of this documentation.

We distribute two main recipes, one for testing/dev purposes and other with the latest stable release.

It's strongly recommended to always use the stable release of Fluent Bit recipe and not the one from GIT master for production deployments.

Fluent Bit and other architectures

Fluent Bit >= v1.1.x fully supports x86_64, x86, arm32v7 and arm64v8.

Fluent Bit is a lightweight and extensible Log Processor that comes with full support for Kubernetes:

• Process Kubernetes containers logs from the file system or Systemd/Journald.

• Enrich logs with Kubernetes Metadata.

• Centralize your logs in third party storage services like Elasticsearch, InfluxDB, HTTP, etc.

Concepts

Before getting started it is important to understand how Fluent Bit will be deployed. Kubernetes manages a cluster of nodes, so our log agent tool will need to run on every node to collect logs from every POD, hence Fluent Bit is deployed as a DaemonSet (a POD that runs on every node of the cluster).

When Fluent Bit runs, it will read, parse and filter the logs of every POD and will enrich each entry with the following information (metadata):

• Pod Name

• Pod ID

• Container Name

• Container ID

• Labels

• Annotations

To obtain this information, a built-in filter plugin called kubernetes talks to the Kubernetes API Server to retrieve relevant information such as the pod_id, labels and annotations, other fields such as pod_name, container_id and container_name are retrieved locally from the log file names. All of this is handled automatically, no intervention is required from a configuration aspect.

Our Kubernetes Filter plugin is fully inspired on the Fluentd Kubernetes Metadata Filter written by Jimmi Dyson.

Installation

Fluent Bit must be deployed as a DaemonSet, so on that way it will be available on every node of your Kubernetes cluster. To get started run the following commands to create the namespace, service account and role setup:

$ kubectl create namespace logging
$ kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/fluent-bit-service-account.yaml
$ kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/fluent-bit-role.yaml
$ kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/fluent-bit-role-binding.yaml

The next step is to create a ConfigMap that will be used by our Fluent Bit DaemonSet:

$ kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/output/elasticsearch/fluent-bit-configmap.yaml

Note for Kubernetes < v1.16

For Kubernetes versions older than v1.16, the DaemonSet resource is not available on apps/v1 , the resource is available on apiVersion: extensions/v1beta1 . Our current Daemonset Yaml files uses the new apiVersion.

If you are using and older Kubernetes version, manually grab a copy of your Daemonset Yaml file and replace the value of apiVersion from:

apiVersion: apps/v1

to

apiVersion: extensions/v1beta1

You can read more about this deprecation on Kubernetes v1.14 Changelog here:

https://github.com/kubernetes/kubernetes/blob/master/CHANGELOG-1.14.md#deprecations

Fluent Bit to Elasticsearch

Fluent Bit DaemonSet ready to be used with Elasticsearch on a normal Kubernetes Cluster:

$ kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/output/elasticsearch/fluent-bit-ds.yaml

Fluent Bit to Elasticsearch on Minikube

If you are using Minikube for testing purposes, use the following alternative DaemonSet manifest:

$ kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/output/elasticsearch/fluent-bit-ds-minikube.yaml

Installing with Helm Chart

Helm is a package manager for Kubernetes and allows you to quickly deploy application packages into your running cluster. Fluent Bit is distributed via a helm chart found in the Fluent Helm Charts repo: https://github.com/fluent/helm-charts.

To add the Fluent Helm Charts repo use the following command

helm repo add fluent https://fluent.github.io/helm-charts

To validate that the repo was added you can run helm search repo fluent to ensure the charts were added. The default chart can then be installed by running the following

helm install fluent-bit fluent/fluent-bit

Default Values

The default chart values include configuration to read container logs, with Docker parsing, systemd logs apply Kubernetes metadata enrichment and finally output to an Elasticsearch cluster. You can modify the values file included https://github.com/fluent/helm-charts/blob/master/charts/fluent-bit/values.yaml to specify additional outputs, health checks, monitoring endpoints, or other configuration options.

Details

The default configuration of Fluent Bit makes sure of the following:

• Consume all containers logs from the running Node.

• The Tail input plugin will not append more than 5MB into the engine until they are flushed to the Elasticsearch backend. This limit aims to provide a workaround for backpressure scenarios.

• The Kubernetes filter will enrich the logs with Kubernetes metadata, specifically labels and annotations. The filter only goes to the API Server when it cannot find the cached info, otherwise it uses the cache.

• The default backend in the configuration is Elasticsearch set by the Elasticsearch Ouput Plugin. It uses the Logstash format to ingest the logs. If you need a different Index and Type, please refer to the plugin option and do your own adjustments.

• There is an option called Retry_Limit set to False, that means if Fluent Bit cannot flush the records to Elasticsearch it will re-try indefinitely until it succeed.

Container Runtime Interface (CRI) parser

Fluent Bit by default assumes that logs are formatted by the Docker interface standard. However, when using CRI you can run into issues with malformed JSON if you do not modify the parser used. Fluent Bit includes a CRI log parser that can be used instead. An example of the parser is seen below:

# CRI Parser
[PARSER]
    # http://rubular.com/r/tjUt3Awgg4
    Name cri
    Format regex
    Regex ^(?<time>[^ ]+) (?<stream>stdout|stderr) (?<logtag>[^ ]*) (?<message>.*)$
    Time_Key    time
    Time_Format %Y-%m-%dT%H:%M:%S.%L%z

To use this parser change the Input section for your configuration from docker to cri

[INPUT]
    Name tail
    Path /var/log/containers/*.log
    Parser cri
    Tag kube.*
    Mem_Buf_Limit 5MB
    Skip_Long_Lines On

Windows Deployment

Since v1.5.0, Fluent Bit supports deployment to Windows pods.

Log files overview

When deploying Fluent Bit to Kubernetes, there are three log files that you need to pay attention to.

C:\k\kubelet.err.log

• This is the error log file from kubelet daemon running on host.

• You will need to retain this file for future troubleshooting (to debug deployment failures etc.)

C:\var\log\containers\<pod>_<namespace>_<container>-<docker>.log

• This is the main log file you need to watch. Configure Fluent Bit to follow this file.

• It is actually a symlink to the Docker log file in C:\ProgramData\, with some additional metadata on its file name.

C:\ProgramData\Docker\containers\<docker>\<docker>.log

• This is the log file produced by Docker.

• Normally you don't directly read from this file, but you need to make sure that this file is visible from Fluent Bit.

Typically, your deployment yaml contains the following volume configuration.

spec:
  containers:
  - name: fluent-bit
    image: my-repo/fluent-bit:1.5.0
    volumeMounts:
    - mountPath: C:\k
      name: k
    - mountPath: C:\var\log
      name: varlog
    - mountPath: C:\ProgramData
      name: progdata
  volumes:
  - name: k
    hostPath:
      path: C:\k
  - name: varlog
    hostPath:
      path: C:\var\log
  - name: progdata
    hostPath:
      path: C:\ProgramData

Configure Fluent Bit

Assuming the basic volume configuration described above, you can apply the following config to start logging. You can visualize this configuration here

fluent-bit.conf: |
    [SERVICE]
      Parsers_File      C:\\fluent-bit\\parsers.conf

    [INPUT]
      Name              tail
      Tag               kube.*
      Path              C:\\var\\log\\containers\\*.log
      Parser            docker
      DB                C:\\fluent-bit\\tail_docker.db
      Mem_Buf_Limit     7MB
      Refresh_Interval  10

    [INPUT]
      Name              tail
      Tag               kubelet.err
      Path              C:\\k\\kubelet.err.log
      DB                C:\\fluent-bit\\tail_kubelet.db

    [FILTER]
      Name              kubernetes
      Match             kube.*
      Kube_URL          https://kubernetes.default.svc.cluster.local:443

    [OUTPUT]
      Name  stdout
      Match *

parsers.conf: |
    [PARSER]
        Name         docker
        Format       json
        Time_Key     time
        Time_Format  %Y-%m-%dT%H:%M:%S.%L
        Time_Keep    On

Mitigate unstable network on Windows pods

Windows pods often lack working DNS immediately after boot (#78479). To mitigate this issue, filter_kubernetes provides a built-in mechanism to wait until the network starts up:

• DNS_Retries - Retries N times until the network start working (6)

• DNS_Wait_Time - Lookup interval between network status checks (30)

By default, Fluent Bit waits for 3 minutes (30 seconds x 6 times). If it's not enough for you, tweak the configuration as follows.

[filter]
    Name kubernetes
    ...
    DNS_Retries 10
    DNS_Wait_Time 30

Fluent Bit might optionally use a configuration file to define how the service will behave, and before proceeding we need to understand how the configuration schema works.

The schema is defined by three concepts:

• Sections

• Entries: Key/Value

• Indented Configuration Mode

A simple example of a configuration file is as follows:

Sections

A section is defined by a name or title inside brackets. Looking at the example above, a Service section has been set using [SERVICE] definition. Section rules:

• All section content must be indented (4 spaces ideally).

• Multiple sections can exist on the same file.

• A section is expected to have comments and entries, it cannot be empty.

• Any commented line under a section, must be indented too.

Entries: Key/Value

A section may contain Entries, an entry is defined by a line of text that contains a Key and a Value, using the above example, the [SERVICE] section contains two entries, one is the key Daemon with value off and the other is the key Log_Level with the value debug. Entries rules:

• An entry is defined by a key and a value.

• A key must be indented.

• A key must contain a value which ends in the breakline.

• Multiple keys with the same name can exist.

Also commented lines are set prefixing the # character, those lines are not processed but they must be indented too.

Indented Configuration Mode

Fluent Bit configuration files are based in a strict Indented Mode, that means that each configuration file must follow the same pattern of alignment from left to right when writing text. By default an indentation level of four spaces from left to right is suggested. Example:

As you can see there are two sections with multiple entries and comments, note also that empty lines are allowed and they do not need to be indented.

Install on Amazon Linux 2

Fluent Bit is distributed as td-agent-bit package and is available for the latest Amazon Linux 2. The following architectures are supported

• x86_64

• aarch64 / arm64v8

Configure Yum

We provide td-agent-bit through a Yum repository. In order to add the repository reference to your system, please add a new file called td-agent-bit.repo in /etc/yum.repos.d/ with the following content:

note: we encourage you always enable the gpgcheck for security reasons. All our packages are signed.

The GPG Key fingerprint is F209 D876 2A60 CD49 E680 633B 4FF8 368B 6EA0 722A

Install

Once your repository is configured, run the following command to install it:

Now the following step is to instruct systemd to enable the service:

If you do a status check, you should see a similar output like this:

The default configuration of td-agent-bit is collecting metrics of CPU usage and sending the records to the standard output, you can see the outgoing data in your /var/log/messages file.

AWS maintains a distribution of Fluent Bit combining the latest official release with a set of Go Plugins for sending logs to AWS services. AWS and Fluent Bit are working together to rewrite their plugins for inclusion in the official Fluent Bit distribution.

Plugins

Currently, the image contains Go Plugins for:

Versions and Regional Repositories

AWS vends their container image via , and a set of highly available regional Amazon ECR repositories. For more information, see the .

The AWS for Fluent Bit image uses a custom versioning scheme because it contains multiple projects. To see what each release contains, check out the .

SSM Public Parameters

AWS vends SSM Public Parameters with the regional repository link for each image. These parameters can be queried by any AWS account.

To see a list of available version tags in a given region, run the following command:

To see the ECR repository URI for a given image tag in a given region, run the following:

You can use these SSM public parameters as parameters in your CloudFormation templates:

Fluent Bit is distributed as td-agent-bit package and is available for the Raspberry, specifically for distribution, the following versions are supported:

• Raspbian Buster (10)

• Raspbian Stretch (9)

• Raspbian Jessie (8)

Server GPG key

The first step is to add our server GPG key to your keyring, on that way you can get our signed packages:

Update your sources lists

On Debian and derivated systems such as Raspbian, you need to add our APT server entry to your sources lists, please add the following content at bottom of your /etc/apt/sources.list file:

Raspbian 10 (Buster)

Raspbian 9 (Stretch)

Raspbian 8 (Jessie)

Update your repositories database

Now let your system update the apt database:

Install TD-Agent Bit

Using the following apt-get command you are able now to install the latest td-agent-bit:

Now the following step is to instruct systemd to enable the service:

If you do a status check, you should see a similar output like this:

The default configuration of td-agent-bit is collecting metrics of CPU usage and sending the records to the standard output, you can see the outgoing data in your /var/log/syslog file.

has an Engine that helps to coordinate the data ingestion from input plugins and call the Scheduler to decide when is time to flush the data through one or multiple output plugins. The Scheduler flush new data every a fixed time of seconds and Schedule retries when asked.

Once an output plugin gets call to flush some data, after processing that data it can notify the Engine three possible return statuses:

• OK

• Retry

• Error

If the return status was OK, it means it was successfully able to process and flush the data, if it returned an Error status, means that an unrecoverable error happened and the engine should not try to flush that data again. If a Retry was requested, the Engine will ask the Scheduler to retry to flush that data, the Scheduler will decide how many seconds to wait before that happen.

Configuring Retries

The Scheduler provides a simple configuration option called Retry_Limit which can be set independently on each output section. This option allows to disable retries or impose a limit to try N times and then discard the data after reaching that limit:

Example

The following example configure two outputs where the HTTP plugin have an unlimited number of retries and the Elasticsearch plugin have a limit of 5 times:

Fluent Bit works internally with structured records and it can be composed of an unlimited number of keys and values. Values can be anything like a number, string, array, or a map.

Having a way to select a specific part of the record is critical for certain core functionalities or plugins, this feature is called Record Accessor.

consider Record Accessor a simple grammar to specify record content and other miscellaneus values.

Format

A record accessor rule starts with the character $. Using the structured content above as an example the following table describes how to access a record:

{
    "log": "some message",
    "stream": "stdout",
    "labels": {
        "color": "blue",
        "unset": null,
        "project": {
            "env": "production"
        }
    }
}

The following table describe some accessing rules and the expected returned value:

If the accessor key does not exist in the record like the last example $labels['undefined'] , the operation is simply omitted, no exception will occur.

Usage Example

The feature is enabled on a per plugin basis, not all plugins enable this feature. As an example consider a configuration that aims to filter records using grep that only matches where labels have a color blue:

[SERVICE]
    flush        1
    log_level    info
    parsers_file parsers.conf

[INPUT]
    name      tail
    path      test.log
    parser    json

[FILTER]
    name      grep
    match     *
    regex     $labels['color'] ^blue$

[OUTPUT]
    name      stdout
    match     *
    format    json_lines