1 of 44

Outputs

Amazon CloudWatch

Send logs and metrics to Amazon CloudWatch

The Amazon CloudWatch output plugin allows to ingest your records into the CloudWatch Logs service. Support for CloudWatch Metrics is also provided via EMF.

This is the documentation for the core Fluent Bit CloudWatch plugin written in C. It can replace the aws/amazon-cloudwatch-logs-for-fluent-bit Golang Fluent Bit plugin released last year. The Golang plugin was named cloudwatch; this new high performance CloudWatch plugin is called cloudwatch_logs to prevent conflicts/confusion. Check the amazon repo for the Golang plugin for details on the deprecation/migration plan for the original plugin.

See here for details on how AWS credentials are fetched.

Configuration Parameters

Getting Started

In order to send records into Amazon Cloudwatch, you can run the plugin from the command line or through the configuration file:

Command Line

The cloudwatch plugin, can read the parameters from the command line through the -p argument (property), e.g:

$ fluent-bit -i cpu -o cloudwatch_logs -p log_group_name=group -p log_stream_name=stream -p region=us-west-2 -m '*' -f 1

Configuration File

In your main configuration file append the following Output section:

[OUTPUT]
    Name cloudwatch_logs
    Match   *
    region us-east-1
    log_group_name fluent-bit-cloudwatch
    log_stream_prefix from-fluent-bit-
    auto_create_group On

Permissions

The following AWS IAM permissions are required to use this plugin:

{
	"Version": "2012-10-17",
	"Statement": [{
		"Effect": "Allow",
		"Action": [
			"logs:CreateLogStream",
			"logs:CreateLogGroup",
			"logs:PutLogEvents"
		],
		"Resource": "*"
	}]
}

Worker support

Fluent Bit 1.7 adds a new feature called workers which enables outputs to have dedicated threads. This cloudwatch_logs plugin has partial support for workers in Fluent Bit 2.1.11 and prior. 2.1.11 and prior, the plugin can support a single worker; enabling multiple workers will lead to errors/indeterminate behavior. Starting from Fluent Bit 2.1.12, the cloudwatch_logs plugin added full support for workers, meaning that more than one worker can be configured.

Example:

[OUTPUT]
    Name cloudwatch_logs
    Match   *
    region us-east-1
    log_group_name fluent-bit-cloudwatch
    log_stream_prefix from-fluent-bit-
    auto_create_group On
    workers 1

If you enable workers, you are enabling one or more dedicated threads for your CloudWatch output. We recommend starting with 1 worker, evaluating the performance, and then enabling more workers if needed. For most users, the plugin can provide sufficient throughput with 0 or 1 workers.

Log Stream and Group Name templating using record_accessor syntax

Sometimes, you may want the log group or stream name to be based on the contents of the log record itself. This plugin supports templating log group and stream names using Fluent Bit record_accessor syntax.

Here is an example usage, for a common use case- templating log group and stream names based on Kubernetes metadata.

Recall that the kubernetes filter can add metadata which will look like the following:

kubernetes: {
    annotations: {
        "kubernetes.io/psp": "eks.privileged"
    },
    container_hash: "<some hash>",
    container_name: "myapp",
    docker_id: "<some id>",
    host: "ip-10-1-128-166.us-east-2.compute.internal",
    labels: {
        app: "myapp",
        "pod-template-hash": "<some hash>"
    },
    namespace_name: "my-namespace",
    pod_id: "198f7dd2-2270-11ea-be47-0a5d932f5920",
    pod_name: "myapp-5468c5d4d7-n2swr"
}

Using record_accessor, we can build a template based on this object.

Here is our output configuration:

[OUTPUT]
    Name cloudwatch_logs
    Match   *
    region us-east-1
    log_group_name fallback-group
    log_stream_prefix fallback-stream
    auto_create_group On
    log_group_template application-logs-$kubernetes['host'].$kubernetes['namespace_name']
    log_stream_template $kubernetes['pod_name'].$kubernetes['container_name']

With the above kubernetes metadata, the log group name will be application-logs-ip-10-1-128-166.us-east-2.compute.internal.my-namespace. And the log stream name will be myapp-5468c5d4d7-n2swr.myapp.

If the kubernetes structure is not found in the log record, then the log_group_name and log_stream_prefix will be used instead, and Fluent Bit will log an error like:

[2022/06/30 06:09:29] [ warn] [record accessor] translation failed, root key=kubernetes

Limitations of record_accessor syntax

Notice in the example above, that the template values are separated by dot characters. This is important; the Fluent Bit record_accessor library has a limitation in the characters that can separate template variables- only dots and commas (. and ,) can come after a template variable. This is because the templating library must parse the template and determine the end of a variable.

Assume that your log records contain the metadata keys container_name and task. The following would be invalid templates because the two template variables are not separated by commas or dots:

$task-$container_name
$task/$container_name
$task_$container_name
$taskfooo$container_name

However, the following are valid:

$task.$container_name
$task.resource.$container_name
$task.fooo.$container_name

And the following are valid since they only contain one template variable with nothing after it:

fooo$task
fooo____$task
fooo/bar$container_name

Metrics Tutorial

Fluent Bit has different input plugins (cpu, mem, disk, netif) to collect host resource usage metrics. cloudwatch_logs output plugin can be used to send these host metrics to CloudWatch in Embedded Metric Format (EMF). If data comes from any of the above mentioned input plugins, cloudwatch_logs output plugin will convert them to EMF format and sent to CloudWatch as JSON log. Additionally, if we set json/emf as the value of log_format config option, CloudWatch will extract custom metrics from embedded JSON payload.

Note: Right now, only cpu and mem metrics can be sent to CloudWatch.

For using the mem input plugin and sending memory usage metrics to CloudWatch, we can consider the following example config file. Here, we use the aws filter which adds ec2_instance_id and az (availability zone) to the log records. Later, in the output config section, we set ec2_instance_id as our metric dimension.

[SERVICE]
    Log_Level info

[INPUT]
    Name mem
    Tag mem

[FILTER]
    Name aws
    Match *

[OUTPUT]
    Name cloudwatch_logs
    Match *
    log_stream_name fluent-bit-cloudwatch
    log_group_name fluent-bit-cloudwatch
    region us-west-2
    log_format json/emf
    metric_namespace fluent-bit-metrics
    metric_dimensions ec2_instance_id
    auto_create_group true

The following config will set two dimensions to all of our metrics- ec2_instance_id and az.

[FILTER]
    Name aws
    Match *

[OUTPUT]
    Name cloudwatch_logs
    Match *
    log_stream_name fluent-bit-cloudwatch
    log_group_name fluent-bit-cloudwatch
    region us-west-2
    log_format json/emf
    metric_namespace fluent-bit-metrics
    metric_dimensions ec2_instance_id,az
    auto_create_group true

AWS for Fluent Bit

Amazon distributes a container image with Fluent Bit and these plugins.

GitHub

github.com/aws/aws-for-fluent-bit

Amazon ECR Public Gallery

aws-for-fluent-bit

Our images are available in Amazon ECR Public Gallery. You can download images with different tags by following command:

docker pull public.ecr.aws/aws-observability/aws-for-fluent-bit:<tag>

For example, you can pull the image with latest version by:

docker pull public.ecr.aws/aws-observability/aws-for-fluent-bit:latest

If you see errors for image pull limits, try log into public ECR with your AWS credentials:

aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws

You can check the Amazon ECR Public official doc for more details

Docker Hub

amazon/aws-for-fluent-bit

Amazon ECR

You can use our SSM Public Parameters to find the Amazon ECR image URI in your region:

aws ssm get-parameters-by-path --path /aws/service/aws-for-fluent-bit/

For more see the AWS for Fluent Bit github repo.

Amazon Kinesis Data Firehose

Send logs to Amazon Kinesis Firehose

The Amazon Kinesis Data Firehose output plugin allows to ingest your records into the service.

This is the documentation for the core Fluent Bit Firehose plugin written in C. It can replace the Golang Fluent Bit plugin released last year. The Golang plugin was named firehose; this new high performance and highly efficient firehose plugin is called kinesis_firehose to prevent conflicts/confusion.

See for details on how AWS credentials are fetched.

Configuration Parameters

Getting Started

In order to send records into Amazon Kinesis Data Firehose, you can run the plugin from the command line or through the configuration file:

Command Line

The firehose plugin, can read the parameters from the command line through the -p argument (property), e.g:

Configuration File

In your main configuration file append the following Output section:

Permissions

The following AWS IAM permissions are required to use this plugin:

Worker support

Fluent Bit 1.7 adds a new feature called workers which enables outputs to have dedicated threads. This kinesis_firehose plugin fully supports workers.

Example:

If you enable a single worker, you are enabling a dedicated thread for your Firehose output. We recommend starting with without workers, evaluating the performance, and then adding workers one at a time until you reach your desired/needed throughput. For most users, no workers or a single worker will be sufficient.

AWS for Fluent Bit

Amazon distributes a container image with Fluent Bit and these plugins.

GitHub

Amazon ECR Public Gallery

Our images are available in Amazon ECR Public Gallery. You can download images with different tags by following command:

For example, you can pull the image with latest version by:

If you see errors for image pull limits, try log into public ECR with your AWS credentials:

Docker Hub

Amazon ECR

You can use our SSM Public Parameters to find the Amazon ECR image URI in your region:

Amazon Kinesis Data Streams

Send logs to Amazon Kinesis Streams

The Amazon Kinesis Data Streams output plugin allows to ingest your records into the Kinesis service.

This is the documentation for the core Fluent Bit Kinesis plugin written in C. It has all the core features of the aws/amazon-kinesis-streams-for-fluent-bit Golang Fluent Bit plugin released in 2019. The Golang plugin was named kinesis; this new high performance and highly efficient kinesis plugin is called kinesis_streams to prevent conflicts/confusion.

Currently, this kinesis_streams plugin will always use a random partition key when uploading records to kinesis via the PutRecords API.

See here for details on how AWS credentials are fetched.

Configuration Parameters

Getting Started

In order to send records into Amazon Kinesis Data Streams, you can run the plugin from the command line or through the configuration file:

Command Line

The kinesis_streams plugin, can read the parameters from the command line through the -p argument (property), e.g:

$ fluent-bit -i cpu -o kinesis_streams -p stream=my-stream -p region=us-west-2 -m '*' -f 1

Configuration File

In your main configuration file append the following Output section:

[OUTPUT]
    Name  kinesis_streams
    Match *
    region us-east-1
    stream my-stream

Permissions

The following AWS IAM permissions are required to use this plugin:

{
	"Version": "2012-10-17",
	"Statement": [{
		"Effect": "Allow",
		"Action": [
			"kinesis:PutRecords"
		],
		"Resource": "*"
	}]
}

Worker support

Fluent Bit 1.7 adds a new feature called workers which enables outputs to have dedicated threads. This kinesis_streams plugin fully supports workers.

Example:

[OUTPUT]
    Name  kinesis_streams
    Match *
    region us-east-1
    stream my-stream
    workers 2

If you enable a single worker, you are enabling a dedicated thread for your Kinesis output. We recommend starting with without workers, evaluating the performance, and then adding workers one at a time until you reach your desired/needed throughput. For most users, no workers or a single worker will be sufficient.

AWS for Fluent Bit

Amazon distributes a container image with Fluent Bit and these plugins.

GitHub

github.com/aws/aws-for-fluent-bit

Amazon ECR Public Gallery

aws-for-fluent-bit

Our images are available in Amazon ECR Public Gallery. You can download images with different tags by following command:

docker pull public.ecr.aws/aws-observability/aws-for-fluent-bit:<tag>

For example, you can pull the image with latest version by:

docker pull public.ecr.aws/aws-observability/aws-for-fluent-bit:latest

If you see errors for image pull limits, try log into public ECR with your AWS credentials:

aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws

You can check the Amazon ECR Public official doc for more details.

Docker Hub

amazon/aws-for-fluent-bit

Amazon ECR

You can use our SSM Public Parameters to find the Amazon ECR image URI in your region:

aws ssm get-parameters-by-path --path /aws/service/aws-for-fluent-bit/

For more see the AWS for Fluent Bit github repo.

Amazon S3

Send logs, data, metrics to Amazon S3

The Amazon S3 output plugin allows you to ingest your records into the cloud object store.

The plugin can upload data to S3 using the or using S3 . Multipart is the default and is recommended; Fluent Bit will stream data in a series of 'parts'. This limits the amount of data it has to buffer on disk at any point in time. By default, every time 5 MiB of data have been received, a new 'part' will be uploaded. The plugin can create files up to gigabytes in size from many small chunks/parts using the multipart API. All aspects of the upload process are configurable using the configuration options.

The plugin allows you to specify a maximum file size, and a timeout for uploads. A file will be created in S3 when the max size is reached, or the timeout is reached- whichever comes first.

Records are stored in files in S3 as newline delimited JSON.

Configuration Parameters

TLS / SSL

Permissions

The plugin requires the following AWS IAM permissions:

Differences between S3 and other Fluent Bit outputs

The s3 output plugin is special because its use case is to upload files of non-trivial size to an Amazon S3 bucket. This is in contrast to most other outputs which send many requests to upload data in batches of a few Megabytes or less.

When Fluent Bit recieves logs, it stores them in chunks, either in memory or the filesystem depending on your settings. A chunk is usually around 2 MB in size. Fluent Bit sends the chunks in order to each output that matches their tag. Most outputs then send the chunk immediately to their destination. A chunk is sent to the output's "flush callback function", which must return one of FLB_OK, FLB_RETRY, or FLB_ERROR. Fluent Bit keeps count of the return values from each outputs "flush callback function"; these counters are the data source for Fluent Bit's error, retry, and success metrics available in prometheus format via its monitoring interface.

The S3 output plugin is a Fluent Bit output plugin and thus it conforms to the Fluent Bit output plugin specification. However, since the S3 use case is to upload large files, generally much larger than 2 MB, its behavior is different. The S3 "flush callback function" simply buffers the incoming chunk to the filesystem, and returns an FLB_OK. Consequently, the prometheus metrics available via the Fluent Bit http server are meaningless for S3. In addition, the storage.total_limit_size parameter is not meaningful for S3 since it has its own buffering system in the store_dir. Instead, use store_dir_limit_size. Finally, S3 always requires a write-able filesystem; running Fluent Bit on a read-only filesystem will not work with the S3 output.

S3 uploads are primarily initiated via the S3 "timer callback function", which runs separately from its "flush callback function". Because S3 has its own system of buffering and its own callback to upload data, the normal sequential data ordering of chunks provided by the Fluent Bit engine may be compromised. Consequently, S3 has the presevere_data_ordering option which will ensure data is uploaded in the original order it was collected by Fluent Bit.

Summary: Uniqueness in S3 Plugin

You must use store_dir_limit_size to limit the space on disk used by S3 buffer files.
The original ordering of data inputted to Fluent Bit may not be preserved unless you enable preserve_data_ordering On.

S3 Key Format and Tag Delimiters

In Fluent Bit, all logs have an associated tag. The s3_key_format option lets you inject the tag into the s3 key using the following syntax:

$TAG => the full tag
$TAG[n] => the nth part of the tag (index starting at zero). This syntax is copied from the rewrite tag filter. By default, “parts” of the tag are separated with dots, but you can change this with s3_key_format_tag_delimiters.

In the example below, assume the date is January 1st, 2020 00:00:00 and the tag associated with the logs in question is my_app_name-logs.prod.

With the delimiters as . and -, the tag will be split into parts as follows:

$TAG[0] = my_app_name
$TAG[1] = logs
$TAG[2] = prod

So the key in S3 will be /prod/my_app_name/2020/01/01/00/00/00/bgdHN1NM.gz.

Allowing a file extension in the S3 Key Format with $UUID

The Fluent Bit S3 output was designed to ensure that previous uploads will never be over-written by a subsequent upload. Consequently, the s3_key_format supports time formatters, $UUID, and $INDEX. $INDEX is special because it is saved in the store_dir; if you restart Fluent Bit with the same disk, then it can continue incrementing the index from its last value in the previous run.

For files uploaded with the PutObject API, the S3 output requires that a unique random string be present in the S3 key. This is because many of the use cases for PutObject uploads involve a short time period between uploads such that a timestamp in the S3 key may not be unique enough between uploads. For example, if you only specify minute granularity timestamps in the S3 key, with a small upload size, it is possible to have two uploads that have timestamps set in the same minute. This "requirement" can be disabled with static_file_path On.

There are three cases where the PutObject API is used:

When you explicitly set use_put_object On
On startup when the S3 output finds old buffer files in the store_dir from a previous run and attempts to send all of them at once.
On shutdown, when to prevent data loss the S3 output attempts to send all currently buffered data at once.

Consequently, you should always specify $UUID somewhere in your S3 key format. Otherwise, if the PutObject API is used, S3 will append a random 8 character UUID to the end of your S3 key. This means that a file extension set at the end of an S3 key will have the random UUID appended to it. This behavior can be disabled with static_file_path On.

Let's walk through this via an example. First case, we attempt to set a .gz extension without specifying $UUID.

In the case where pending data is uploaded on shutdown, if the tag was app, the S3 key in the S3 bucket might be:

The S3 output appended a random string to the "extension", since this upload on shutdown used the PutObject API.

There are two ways of disabling this behavior. Option 1, use static_file_path:

Option 2, explicitly define where the random UUID will go in the S3 key format:

Reliability

The store_dir is used to temporarily store data before it is uploaded. If Fluent Bit is stopped suddenly it will try to send all data and complete all uploads before it shuts down. If it can not send some data, on restart it will look in the store_dir for existing data and will try to send it.

Multipart uploads are ideal for most use cases because they allow the plugin to upload data in small chunks over time. For example, 1 GB file can be created from 200 5MB chunks. While the file size in S3 will be 1 GB, only 5 MB will be buffered on disk at any one point in time.

Using S3 without persisted disk

In these situations, we recommend using the PutObject API, and sending data frequently, to avoid local buffering as much as possible. This will limit data loss in the event Fluent Bit is killed unexpectedly.

The following settings are recommended for this use case:

S3 Multipart Uploads

Fallback to PutObject

Uploads are triggered by three settings:

total_file_size and upload_chunk_size: When S3 has buffered data in the store_dir that meets the desired total_file_size (for use_put_object On) or the upload_chunk_size (for Multipart), it will trigger an upload operation.

If you encounter this frequently, use the numbers in the messages to guess your compression factor. For example, in this case, the buffered data was reduced from 5,630,650 bytes to 1,063,320 bytes. The compressed size is 1/5 the actual data size, so configuring upload_chunk_size 30M should ensure each part is large enough after compression to be over the min required part size of 5,242,880 bytes.

The S3 API allows the last part in an upload to be less than the 5,242,880 byte minimum. Therefore, if a part is too small for an existing upload, the S3 output will upload that part and then complete the upload.

upload_timeout constrains total multipart upload time for a single file

Completing uploads

Worker support

Fluent Bit 1.7 adds a new feature called workers which enables outputs to have dedicated threads. This s3 plugin has partial support for workers. The plugin can only support a single worker; enabling multiple workers will lead to errors/indeterminate behavior.

Example:

If you enable a single worker, you are enabling a dedicated thread for your S3 output. We recommend starting without workers, evaluating the performance, and then enabling a worker if needed. For most users, the plugin can provide sufficient throughput without workers.

Usage with MinIO

Example:

Then, the records will be stored into the MinIO server.

Getting Started

In order to send records into Amazon S3, you can run the plugin from the command line or through the configuration file.

Command Line

The s3 plugin, can read the parameters from the command line through the -p argument (property), e.g:

Configuration File

In your main configuration file append the following Output section:

An example that using PutObject instead of multipart:

AWS for Fluent Bit

Amazon distributes a container image with Fluent Bit and this plugins.

GitHub

Amazon ECR Public Gallery

Our images are available in Amazon ECR Public Gallery. You can download images with different tags by following command:

For example, you can pull the image with latest version by:

If you see errors for image pull limits, try log into public ECR with your AWS credentials:

Docker Hub

Amazon ECR

You can use our SSM Public Parameters to find the Amazon ECR image URI in your region:

Advanced usage

Use Apache Arrow for in-memory data processing

To use this feature, FLB_ARROW must be turned on at compile time:

Once compiled, Fluent Bit can upload incoming data to S3 in Apache Arrow format. For example:

As shown in this example, setting Compression to arrow makes Fluent Bit to convert payload into Apache Arrow format.

The stored data is very easy to load, analyze and process using popular data processing tools (such as Python pandas, Apache Spark and Tensorflow). The following code uses pyarrow to analyze the uploaded data:

Azure Blob

Official and Microsoft Certified Azure Storage Blob connector

The Azure Blob output plugin allows ingesting your records into service. This connector is designed to use the Append Blob and Block Blob API.

Our plugin works with the official Azure Service and also can be configured to be used with a service emulator such as .

Azure Storage Account

Before getting started, make sure you already have an Azure Storage account. As a reference, the following link explains step-by-step how to set up your account:

Configuration Parameters

We expose different configuration properties. The following table lists all the options available, and the next section has specific configuration details for the official service or the emulator.

Key

Description

default

Getting Started

As mentioned above, you can either deliver records to the official service or an emulator. Below we have an example for each use case.

Configuration for Azure Storage Service

The following configuration example generates a random message with a custom tag:

After you run the configuration file above, you will be able to query the data using the Azure Storage Explorer. The example above will generate the following content in the explorer:

Configuring and using Azure Emulator: Azurite

Install and run Azurite

The quickest way to get started is to install Azurite using npm:

then run the service:

Configuring Fluent Bit for Azurite

after running that Fluent Bit configuration you will see the data flowing into Azurite:

Azure Data Explorer

Send logs to Azure Data Explorer (Kusto)

The Kusto output plugin allows to ingest your logs into an Azure Data Explorer cluster, via the Queued Ingestion mechanism.

Creating a Kusto Cluster and Database

You can create an Azure Data Explorer cluster in one of the following ways:

Creating an Azure Registered Application

Fluent-Bit will use the application's credentials, to ingest data into your cluster.

Creating a Table

Fluent-Bit ingests the event data into Kusto in a JSON format, that by default will include 3 properties:

log - the actual event payload.
tag - the event tag.
timestamp - the event timestamp.

A table with the expected schema must exist in order for data to be ingested properly.

.create table FluentBit (log:dynamic, tag:string, timestamp:datetime)

Optional - Creating an Ingestion Mapping

By default, Kusto will insert incoming ingestions into a table by inferring the mapped table columns, from the payload properties. However, this mapping can be customized by creatng a JSON ingestion mapping. The plugin can be configured to use an ingestion mapping via the ingestion_mapping_reference configuration key.

Configuration Parameters

Configuration File

Get started quickly with this configuration file:

[OUTPUT]
    Match *
    Name azure_kusto
    Tenant_Id <app_tenant_id>
    Client_Id <app_client_id>
    Client_Secret <app_secret>
    Ingestion_Endpoint https://ingest-<cluster>.<region>.kusto.windows.net
    Database_Name <database_name>
    Table_Name <table_name>
    Ingestion_Mapping_Reference <mapping_name>

Troubleshooting

403 Forbidden

If you get a 403 Forbidden error response, make sure that:

You provided the correct AAD registered application credentials.
You authorized the application to ingest into your database or table.

Azure Log Analytics

Send logs, metrics to Azure Log Analytics

Azure output plugin allows to ingest your records into service.

To get more details about how to setup Azure Log Analytics, please refer to the following documentation:

Configuration Parameters

Getting Started

In order to insert records into an Azure Log Analytics instance, you can run the plugin from the command line or through the configuration file:

Command Line

The azure plugin, can read the parameters from the command line in two ways, through the -p argument (property), e.g:

Configuration File

In your main configuration file append the following Input & Output sections:

Azure Logs Ingestion API

Send logs to Azure Log Analytics using Logs Ingestion API with DCE and DCR

Azure Logs Ingestion plugin allows you ingest your records using to supported or to that you create.

The Logs ingestion API requires the following components:

A Data Collection Endpoint (DCE)
A Data Collection Rule (DCR) and
A Log Analytics Workspace

To get more details about how to setup these components, please refer to the following documentations:

Configuration Parameters

Getting Started

To send records into an Azure Log Analytics using Logs Ingestion API the following resources needs to be created:

A Data Collection Endpoint (DCE) for ingestion
A Data Collection Rule (DCR) for data transformation
An app registration with client secrets (for DCR access).

Configuration File

Use this configuration to quickly get started:

Setup your DCR transformation accordingly based on the json output from fluent-bit's pipeline (input, parser, filter, output).

Counter

Counter is a very simple plugin that counts how many records it's getting upon flush time. Plugin output is as follows:

Getting Started

You can run the plugin from the command line or through the configuration file:

Command Line

From the command line you can let Fluent Bit count up a data with the following options:

Configuration File

In your main configuration file append the following Input & Output sections:

Testing

Once Fluent Bit is running, you will see the reports in the output interface similar to this:

Datadog

Send logs to Datadog

The Datadog output plugin allows to ingest your logs into Datadog.

Before you begin, you need a Datadog account, a Datadog API key, and you need to activate Datadog Logs Management.

Configuration Parameters

Key

Description

Default

Configuration File

Get started quickly with this configuration file:

[OUTPUT]
    Name        datadog
    Match       *
    Host        http-intake.logs.datadoghq.com
    TLS         on
    compress    gzip
    apikey      <my-datadog-api-key>
    dd_service  <my-app-service>
    dd_source   <my-app-source>
    dd_tags     team:logs,foo:bar

Troubleshooting

403 Forbidden

If you get a 403 Forbidden error response, double check that you have a valid Datadog API key and that you have activated Datadog Logs Management.

Elasticsearch

Send logs to Elasticsearch (including Amazon OpenSearch Service)

The es output plugin, allows to ingest your records into an Elasticsearch database. The following instructions assumes that you have a fully operational Elasticsearch service running in your environment.

Configuration Parameters

Key

Description

default

The parameters index and type can be confusing if you are new to Elastic, if you have used a common relational database before, they can be compared to the database and table concepts. Also see the FAQ below

TLS / SSL

Elasticsearch output plugin supports TLS/SSL, for more details about the properties available and general configuration, please refer to the TLS/SSL section.

write_operation

The write_operation can be any of:

Please note, Id_Key or Generate_ID is required in update, and upsert scenario.

Getting Started

In order to insert records into a Elasticsearch service, you can run the plugin from the command line or through the configuration file:

Command Line

The es plugin, can read the parameters from the command line in two ways, through the -p argument (property) or setting them directly through the service URI. The URI format is the following:

es://host:port/index/type

Using the format specified, you could start Fluent Bit through:

$ fluent-bit -i cpu -t cpu -o es://192.168.2.3:9200/my_index/my_type \
    -o stdout -m '*'

which is similar to do:

$ fluent-bit -i cpu -t cpu -o es -p Host=192.168.2.3 -p Port=9200 \
    -p Index=my_index -p Type=my_type -o stdout -m '*'

Configuration File

In your main configuration file append the following Input & Output sections. You can visualize this configuration here

[INPUT]
    Name  cpu
    Tag   cpu

[OUTPUT]
    Name  es
    Match *
    Host  192.168.2.3
    Port  9200
    Index my_index
    Type  my_type

About Elasticsearch field names

Some input plugins may generate messages where the field names contains dots, since Elasticsearch 2.0 this is not longer allowed, so the current es plugin replaces them with an underscore, e.g:

{"cpu0.p_cpu"=>17.000000}

becomes

{"cpu0_p_cpu"=>17.000000}

FAQ

Elasticsearch rejects requests saying "the final mapping would have more than 1 type"

Since Elasticsearch 6.0, you cannot create multiple types in a single index. This means that you cannot set up your configuration as below anymore.

[OUTPUT]
    Name  es
    Match foo.*
    Index search
    Type  type1

[OUTPUT]
    Name  es
    Match bar.*
    Index search
    Type  type2

If you see an error message like below, you'll need to fix your configuration to use a single type on each index.

Rejecting mapping update to [search] as the final mapping would have more than 1 type

For details, please read the official blog post on that issue.

Elasticsearch rejects requests saying "Document mapping type name can't start with '_'"

Fluent Bit v1.5 changed the default mapping type from flb_type to _doc, which matches the recommendation from Elasticsearch from version 6.2 forwards (see commit with rationale). This doesn't work in Elasticsearch versions 5.6 through 6.1 (see Elasticsearch discussion and fix). Ensure you set an explicit map (such as doc or flb_type) in the configuration, as seen on the last line:

[OUTPUT]
    Name  es
    Match *
    Host  vpc-test-domain-ke7thhzoo7jawsrhmm6mb7ite7y.us-west-2.es.amazonaws.com
    Port  443
    Index my_index
    AWS_Auth On
    AWS_Region us-west-2
    tls   On
    Type  doc

Fluent Bit + Amazon OpenSearch Service

The Amazon OpenSearch Service adds an extra security layer where HTTP requests must be signed with AWS Sigv4. Fluent Bit v1.5 introduced full support for Amazon OpenSearch Service with IAM Authentication.

See here for details on how AWS credentials are fetched.

Example configuration:

[OUTPUT]
    Name  es
    Match *
    Host  vpc-test-domain-ke7thhzoo7jawsrhmm6mb7ite7y.us-west-2.es.amazonaws.com
    Port  443
    Index my_index
    Type  my_type
    AWS_Auth On
    AWS_Region us-west-2
    tls     On

Notice that the Port is set to 443, tls is enabled, and AWS_Region is set.

Fluent Bit + Elastic Cloud

Fluent Bit supports connecting to Elastic Cloud providing just the cloud_id and the cloud_auth settings. cloud_auth uses the elastic user and password provided when the cluster was created, for details refer to the Cloud ID usage page.

Example configuration:

[OUTPUT]
    Name es
    Include_Tag_Key true
    Tag_Key tags
    tls On
    tls.verify Off
    Suppress_Type_Name On
    cloud_id elastic-obs-deployment:ZXVybxxxxxxxxxxxg==
    cloud_auth elastic:2vxxxxxxxxYV

Validation Failed: 1: an id must be provided if version type or value are set

Since v1.8.2, Fluent Bit started using create method (instead of index) for data submission. This makes Fluent Bit compatible with Datastream introduced in Elasticsearch 7.9.

If you see action_request_validation_exception errors on your pipeline with Fluent Bit >= v1.8.2, you can fix it up by turning on Generate_ID as follows:

[OUTPUT]
    Name es
    Match *
    Host  192.168.12.1
    Generate_ID on

Action/metadata contains an unknown parameter type

Elastic Cloud is now on version 8 so the type option must be removed by setting Suppress_Type_Name On as indicated above.

Without this you will see errors like:

{"error":{"root_cause":[{"type":"illegal_argument_exception","reason":"Action/metadata line [1] contains an unknown parameter [_type]"}],"type":"illegal_argument_exception","reason":"Action/metadata line [1] contains an unknown parameter [_type]"},"status":400}

Logstash_Prefix_Key

The following snippet demonstrates using the namespace name as extracted by the kubernetes filter as logstash prefix:

[OUTPUT]
    Name es
    Match *
    # ...
    Logstash_Prefix logstash
    Logstash_Prefix_Key $kubernetes["namespace_name"]
    # ...

For records that do nor have the field kubernetes.namespace_name, the default prefix, logstash will be used.

File

The file output plugin allows to write the data received through the input plugin to file.

Configuration Parameters

The plugin supports the following configuration parameters:

Key

Description

Default

Format

out_file format

Output time, tag and json records. There is no configuration parameters for out_file.

tag: [time, {"key1":"value1", "key2":"value2", "key3":"value3"}]

plain format

Output the records as JSON (without additional tag and timestamp attributes). There is no configuration parameters for plain format.

{"key1":"value1", "key2":"value2", "key3":"value3"}

csv format

Output the records as csv. Csv supports an additional configuration parameter.

time[delimiter]"value1"[delimiter]"value2"[delimiter]"value3"

ltsv format

Output the records as LTSV. LTSV supports an additional configuration parameter.

field1[label_delimiter]value1[delimiter]field2[label_delimiter]value2\n

template format

Output the records using a custom format template.

This accepts a formatting template and fills placeholders using corresponding values in a record.

For example, if you set up the configuration as below:

[INPUT]
  Name mem

[OUTPUT]
  Name file
  Format template
  Template {time} used={Mem.used} free={Mem.free} total={Mem.total}

You will get the following output:

1564462620.000254 used=1045448 free=31760160 total=32805608

Getting Started

You can run the plugin from the command line or through the configuration file:

Command Line

From the command line you can let Fluent Bit count up a data with the following options:

$ fluent-bit -i cpu -o file -p path=output.txt

Configuration File

In your main configuration file append the following Input & Output sections:

[INPUT]
    Name cpu
    Tag  cpu

[OUTPUT]
    Name file
    Match *
    Path output_dir

FlowCounter

FlowCounter is the protocol to count records. The flowcounter output plugin allows to count up records and its size.

Configuration Parameters

The plugin supports the following configuration parameters:

Key

Description

Default

Getting Started

You can run the plugin from the command line or through the configuration file:

Command Line

From the command line you can let Fluent Bit count up a data with the following options:

$ fluent-bit -i cpu -o flowcounter

Configuration File

In your main configuration file append the following Input & Output sections:

[INPUT]
    Name cpu
    Tag  cpu

[OUTPUT]
    Name flowcounter
    Match *
    Unit second

Testing

Once Fluent Bit is running, you will see the reports in the output interface similar to this:

$ fluent-bit -i cpu -o flowcounter  
Fluent Bit v1.x.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

[2016/12/23 11:01:20] [ info] [engine] started
[out_flowcounter] cpu.0:[1482458540, {"counts":60, "bytes":7560, "counts/minute":1, "bytes/minute":126 }]

Forward

Forward is the protocol used by Fluentd to route messages between peers. The forward output plugin provides interoperability between Fluent Bit and Fluentd. There are no configuration steps required besides specifying where Fluentd is located, which can be a local or a remote destination.

This plugin offers two different transports and modes:

Forward (TCP): It uses a plain TCP connection.
Secure Forward (TLS): when TLS is enabled, the plugin switch to Secure Forward mode.

Configuration Parameters

The following parameters are mandatory for either Forward for Secure Forward modes:

Key

Description

Default

Secure Forward Mode Configuration Parameters

When using Secure Forward mode, the TLS mode requires to be enabled. The following additional configuration parameters are available:

Forward Setup

Before proceeding, make sure that Fluentd is installed, if it's not the case please refer to the following Fluentd Installation document and go ahead with that.

Once Fluentd is installed, create the following configuration file example that will allow us to stream data into it:

<source>
  type forward
  bind 0.0.0.0
  port 24224
</source>

<match fluent_bit>
  type stdout
</match>

That configuration file specifies that it will listen for TCP connections on the port 24224 through the forward input type. Then for every message with a fluent_bit TAG, will print the message to the standard output.

In one terminal launch Fluentd specifying the new configuration file created:

$ fluentd -c test.conf
2017-03-23 11:50:43 -0600 [info]: reading config file path="test.conf"
2017-03-23 11:50:43 -0600 [info]: starting fluentd-0.12.33
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-mixin-config-placeholders' version '0.3.1'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-docker' version '0.1.0'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-elasticsearch' version '1.4.0'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-flatten-hash' version '0.2.0'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-flowcounter-simple' version '0.0.4'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-influxdb' version '0.2.8'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-json-in-json' version '0.1.4'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-mongo' version '0.7.10'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-out-http' version '0.1.3'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-parser' version '0.6.0'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-record-reformer' version '0.7.0'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-rewrite-tag-filter' version '1.5.1'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-stdin' version '0.1.1'
2017-03-23 11:50:43 -0600 [info]: gem 'fluent-plugin-td' version '0.10.27'
2017-03-23 11:50:43 -0600 [info]: adding match pattern="fluent_bit" type="stdout"
2017-03-23 11:50:43 -0600 [info]: adding source type="forward"
2017-03-23 11:50:43 -0600 [info]: using configuration file: <ROOT>
  <source>
    type forward
    bind 0.0.0.0
    port 24224
  </source>
  <match fluent_bit>
    type stdout
  </match>
</ROOT>
2017-03-23 11:50:43 -0600 [info]: listening fluent socket on 0.0.0.0:24224

Fluent Bit + Forward Setup

Now that Fluentd is ready to receive messages, we need to specify where the forward output plugin will flush the information using the following format:

bin/fluent-bit -i INPUT -o forward://HOST:PORT

If the TAG parameter is not set, the plugin will retain the tag. Keep in mind that TAG is important for routing rules inside Fluentd.

Using the CPU input plugin as an example we will flush CPU metrics to Fluentd with tag fluent_bit:

$ bin/fluent-bit -i cpu -t fluent_bit -o forward://127.0.0.1:24224

Now on the Fluentd side, you will see the CPU metrics gathered in the last seconds:

2017-03-23 11:53:06 -0600 fluent_bit: {"cpu_p":0.0,"user_p":0.0,"system_p":0.0,"cpu0.p_cpu":0.0,"cpu0.p_user":0.0,"cpu0.p_system":0.0,"cpu1.p_cpu":0.0,"cpu1.p_user":0.0,"cpu1.p_system":0.0,"cpu2.p_cpu":0.0,"cpu2.p_user":0.0,"cpu2.p_system":0.0,"cpu3.p_cpu":1.0,"cpu3.p_user":1.0,"cpu3.p_system":0.0}
2017-03-23 11:53:07 -0600 fluent_bit: {"cpu_p":2.25,"user_p":2.0,"system_p":0.25,"cpu0.p_cpu":3.0,"cpu0.p_user":3.0,"cpu0.p_system":0.0,"cpu1.p_cpu":1.0,"cpu1.p_user":1.0,"cpu1.p_system":0.0,"cpu2.p_cpu":1.0,"cpu2.p_user":1.0,"cpu2.p_system":0.0,"cpu3.p_cpu":3.0,"cpu3.p_user":2.0,"cpu3.p_system":1.0}
2017-03-23 11:53:08 -0600 fluent_bit: {"cpu_p":1.75,"user_p":1.0,"system_p":0.75,"cpu0.p_cpu":2.0,"cpu0.p_user":1.0,"cpu0.p_system":1.0,"cpu1.p_cpu":3.0,"cpu1.p_user":1.0,"cpu1.p_system":2.0,"cpu2.p_cpu":3.0,"cpu2.p_user":2.0,"cpu2.p_system":1.0,"cpu3.p_cpu":2.0,"cpu3.p_user":1.0,"cpu3.p_system":1.0}
2017-03-23 11:53:09 -0600 fluent_bit: {"cpu_p":4.75,"user_p":3.5,"system_p":1.25,"cpu0.p_cpu":4.0,"cpu0.p_user":3.0,"cpu0.p_system":1.0,"cpu1.p_cpu":5.0,"cpu1.p_user":4.0,"cpu1.p_system":1.0,"cpu2.p_cpu":3.0,"cpu2.p_user":2.0,"cpu2.p_system":1.0,"cpu3.p_cpu":5.0,"cpu3.p_user":4.0,"cpu3.p_system":1.0}

So we gathered CPU metrics and flushed them out to Fluentd properly.

Fluent Bit + Secure Forward Setup

DISCLAIMER: the following example does not consider the generation of certificates for best practice on production environments.

Secure Forward aims to provide a secure channel of communication with the remote Fluentd service using TLS.

Fluent Bit

Paste this content in a file called flb.conf:

[SERVICE]
    Flush      5
    Daemon     off
    Log_Level  info

[INPUT]
    Name       cpu
    Tag        cpu_usage

[OUTPUT]
    Name          forward
    Match         *
    Host          127.0.0.1
    Port          24284
    Shared_Key    secret
    Self_Hostname flb.local
    tls           on
    tls.verify    off

Fluentd

Paste this content in a file called fld.conf:

<source>
  @type         secure_forward
  self_hostname myserver.local
  shared_key    secret
  secure no
</source>

<match **>
 @type stdout
</match>

If you're using Fluentd v1, set up it as below:

<source>
  @type forward
  <transport tls>
    cert_path /etc/td-agent/certs/fluentd.crt
    private_key_path /etc/td-agent/certs/fluentd.key
    private_key_passphrase password
  </transport>
  <security>
    self_hostname myserver.local
    shared_key secret
  </security>
</source>

<match **>
 @type stdout
</match>

Test Communication

Start Fluentd:

$ fluentd -c fld.conf

Start Fluent Bit:

$ fluent-bit -c flb.conf

After five seconds, Fluent Bit will write records to Fluentd. In Fluentd output you will see a message like this:

2017-03-23 13:34:40 -0600 [info]: using configuration file: <ROOT>
  <source>
    @type secure_forward
    self_hostname myserver.local
    shared_key xxxxxx
    secure no
  </source>
  <match **>
    @type stdout
  </match>
</ROOT>
2017-03-23 13:34:41 -0600 cpu_usage: {"cpu_p":1.0,"user_p":0.75,"system_p":0.25,"cpu0.p_cpu":1.0,"cpu0.p_user":1.0,"cpu0.p_system":0.0,"cpu1.p_cpu":2.0,"cpu1.p_user":1.0,"cpu1.p_system":1.0,"cpu2.p_cpu":1.0,"cpu2.p_user":1.0,"cpu2.p_system":0.0,"cpu3.p_cpu":2.0,"cpu3.p_user":1.0,"cpu3.p_system":1.0}
2017-03-23 13:34:42 -0600 cpu_usage: {"cpu_p":1.75,"user_p":1.75,"system_p":0.0,"cpu0.p_cpu":3.0,"cpu0.p_user":3.0,"cpu0.p_system":0.0,"cpu1.p_cpu":2.0,"cpu1.p_user":2.0,"cpu1.p_system":0.0,"cpu2.p_cpu":0.0,"cpu2.p_user":0.0,"cpu2.p_system":0.0,"cpu3.p_cpu":1.0,"cpu3.p_user":1.0,"cpu3.p_system":0.0}
2017-03-23 13:34:43 -0600 cpu_usage: {"cpu_p":1.75,"user_p":1.25,"system_p":0.5,"cpu0.p_cpu":3.0,"cpu0.p_user":3.0,"cpu0.p_system":0.0,"cpu1.p_cpu":2.0,"cpu1.p_user":2.0,"cpu1.p_system":0.0,"cpu2.p_cpu":0.0,"cpu2.p_user":0.0,"cpu2.p_system":0.0,"cpu3.p_cpu":1.0,"cpu3.p_user":0.0,"cpu3.p_system":1.0}
2017-03-23 13:34:44 -0600 cpu_usage: {"cpu_p":5.0,"user_p":3.25,"system_p":1.75,"cpu0.p_cpu":4.0,"cpu0.p_user":2.0,"cpu0.p_system":2.0,"cpu1.p_cpu":8.0,"cpu1.p_user":5.0,"cpu1.p_system":3.0,"cpu2.p_cpu":4.0,"cpu2.p_user":3.0,"cpu2.p_system":1.0,"cpu3.p_cpu":4.0,"cpu3.p_user":2.0,"cpu3.p_system":2.0}

GELF

GELF is Graylog Extended Log Format. The GELF output plugin allows to send logs in GELF format directly to a Graylog input using TLS, TCP or UDP protocols.

The following instructions assumes that you have a fully operational Graylog server running in your environment.

Configuration Parameters

According to GELF Payload Specification, there are some mandatory and optional fields which are used by Graylog in GELF format. These fields are determined with Gelf\*_Key_ key in this plugin.

Key

Description

default

TLS / SSL

GELF output plugin supports TLS/SSL, for more details about the properties available and general configuration, please refer to the TLS/SSL section.

Notes

If you're using Fluent Bit to collect Docker logs, note that Docker places your log in JSON under key log. So you can set log as your Gelf_Short_Message_Key to send everything in Docker logs to Graylog. In this case, you need your log value to be a string; so don't parse it using JSON parser.
The order of looking up the timestamp in this plugin is as follows:
1. Value of Gelf_Timestamp_Key provided in configuration
2. Value of timestamp key
3. If you're using Docker JSON parser, this parser can parse time and use it as timestamp of message. If all above fail, Fluent Bit tries to get timestamp extracted by your parser.
4. Timestamp does not set by Fluent Bit. In this case, your Graylog server will set it to the current timestamp (now).
Your log timestamp has to be in UNIX Epoch Timestamp format. If the Gelf_Timestamp_Key value of your log is not in this format, your Graylog server will ignore it.
If you're using Fluent Bit in Kubernetes and you're using Kubernetes Filter Plugin, this plugin adds host value to your log by default, and you don't need to add it by your own.
The version of GELF message is also mandatory and Fluent Bit sets it to 1.1 which is the current latest version of GELF.
If you use udp as transport protocol and set Compress to true, Fluent Bit compresses your packets in GZIP format, which is the default compression that Graylog offers. This can be used to trade more CPU load for saving network bandwidth.

Configuration File Example

If you're using Fluent Bit for shipping Kubernetes logs, you can use something like this as your configuration file:

[INPUT]
    Name                    tail
    Tag                     kube.*
    Path                    /var/log/containers/*.log
    Parser                  docker
    DB                      /var/log/flb_kube.db
    Mem_Buf_Limit           5MB
    Refresh_Interval        10

[FILTER]
    Name                    kubernetes
    Match                   kube.*
    Merge_Log_Key           log
    Merge_Log               On
    Keep_Log                Off
    Annotations             Off
    Labels                  Off

[FILTER]
    Name                    nest
    Match                   *
    Operation               lift
    Nested_under            log

[OUTPUT]
    Name                    gelf
    Match                   kube.*
    Host                    <your-graylog-server>
    Port                    12201
    Mode                    tcp
    Gelf_Short_Message_Key  data

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

By default, GELF tcp uses port 12201 and Docker places your logs in /var/log/containers directory. The logs are placed in value of the log key. For example, this is a log saved by Docker:

{"log":"{\"data\": \"This is an example.\"}","stream":"stderr","time":"2019-07-21T12:45:11.273315023Z"}

If you use Tail Input and use a Parser like the docker parser shown above, it decodes your message and extracts data (and any other present) field. This is how this log in stdout looks like after decoding:

[0] kube.log: [1565770310.000198491, {"log"=>{"data"=>"This is an example."}, "stream"=>"stderr", "time"=>"2019-07-21T12:45:11.273315023Z"}]

Now, this is what happens to this log:

Fluent Bit GELF plugin adds "version": "1.1" to it.
The Nest Filter, unnests fields inside log key. In our example, it puts data alongside stream and time.
We used this data key as Gelf_Short_Message_Key; so GELF plugin changes it to short_message.
Kubernetes Filter adds host name.
Timestamp is generated.
Any custom field (not present in GELF Payload Specification.) is prefixed by an underline.

Finally, this is what our Graylog server input sees:

{"version":"1.1", "short_message":"This is an example.", "host": "<Your Node Name>", "_stream":"stderr", "timestamp":1565770310.000199}

Google Chronicle

Chronicle

The Chronicle output plugin allows ingesting security logs into Google Chronicle service. This connector is designed to send unstructured security logs.

Google Cloud Configuration

Fluent Bit streams data into an existing Google Chronicle tenant using a service account that you specify. Therefore, before using the Chronicle output plugin, you must create a service account, create a Google Chronicle tenant, authorize the service account to write to the tenant, and provide the service account credentials to Fluent Bit.

Creating a Service Account

To stream security logs into Google Chronicle, the first step is to create a Google Cloud service account for Fluent Bit:

Creating a Google Cloud Service Account

Creating a Tenant of Google Chronicle

Fluent Bit does not create a tenant of Google Chronicle for your security logs, so you must create this ahead of time.

Retrieving Service Account Credentials

Fluent Bit's Chronicle output plugin uses a JSON credentials file for authentication credentials. Download the credentials file by following these instructions:

Creating and Managing Service Account Keys

Configurations Parameters

See Google's official documentation for further details.

Configuration File

If you are using a Google Cloud Credentials File, the following configuration is enough to get you started:

[INPUT]
    Name  dummy
    Tag   dummy

[OUTPUT]
    Name       chronicle
    Match      *
    customer_id my_customer_id
    log_type my_super_awesome_type

Google Cloud BigQuery

BigQuery output plugin is an experimental plugin that allows you to stream records into service. The implementation does not support the following, which would be expected in a full production version:

.
using insertId.
using templateSuffix.

Google Cloud Configuration

Fluent Bit streams data into an existing BigQuery table using a service account that you specify. Therefore, before using the BigQuery output plugin, you must create a service account, create a BigQuery dataset and table, authorize the service account to write to the table, and provide the service account credentials to Fluent Bit.

Creating a Service Account

To stream data into BigQuery, the first step is to create a Google Cloud service account for Fluent Bit:

Creating a BigQuery Dataset and Table

Fluent Bit does not create datasets or tables for your data, so you must create these ahead of time. You must also grant the service account WRITER permission on the dataset:

Within the dataset you will need to create a table for the data to reside in. You can follow the following instructions for creating your table. Pay close attention to the schema. It must match the schema of your output JSON. Unfortunately, since BigQuery does not allow dots in field names, you will need to use a filter to change the fields for many of the standard inputs (e.g, mem or cpu).

Retrieving Service Account Credentials

Fluent Bit BigQuery output plugin uses a JSON credentials file for authentication credentials. Download the credentials file by following these instructions:

Workload Identity Federation

Using identity federation, you can grant on-premises or multi-cloud workloads access to Google Cloud resources, without using a service account key. It can be used as a more secure alternative to service account credentials. Google Cloud's workload identity federation supports several identity providers (see documentation) but Fluent Bit BigQuery plugin currently supports Amazon Web Services (AWS) only.

You must configure workload identity federation in GCP before using it with Fluent Bit.

Configurations Parameters

Configuration File

If you are using a Google Cloud Credentials File, the following configuration is enough to get you started:

HTTP

The http output plugin allows to flush your records into a HTTP endpoint. For now the functionality is pretty basic and it issues a POST request with the data records in (or JSON) format.

Configuration Parameters

Key

Description

default

TLS / SSL

Getting Started

In order to insert records into a HTTP server, you can run the plugin from the command line or through the configuration file:

Command Line

The http plugin, can read the parameters from the command line in two ways, through the -p argument (property) or setting them directly through the service URI. The URI format is the following:

Using the format specified, you could start Fluent Bit through:

Configuration File

In your main configuration file, append the following Input & Output sections:

By default, the URI becomes tag of the message, the original tag is ignored. To retain the tag, multiple configuration sections have to be made based and flush to different URIs.

Another approach we also support is the sending the original message tag in a configurable header. It's up to the receiver to do what it wants with that header field: parse it and use it as the tag for example.

To configure this behaviour, add this config:

Provided you are using Fluentd as data receiver, you can combine in_http and out_rewrite_tag_filter to make use of this HTTP header.

Notice how we override the tag, which is from URI path, with our custom header

Example : Add a header

Example : Sumo Logic HTTP Collector

Suggested configuration for Sumo Logic using json_lines with iso8601 timestamps. The PrivateKey is specific to a configured HTTP collector.

Kafka

Kafka output plugin allows to ingest your records into an service. This plugin use the official (built-in dependency)

Configuration Parameters

Key

Description

default

Setting rdkafka.log.connection.close to false and rdkafka.request.required.acks to 1 are examples of recommended settings of librdfkafka properties.

Getting Started

In order to insert records into Apache Kafka, you can run the plugin from the command line or through the configuration file:

Command Line

The kafka plugin can read parameters through the -p argument (property), e.g:

Configuration File

In your main configuration file append the following Input & Output sections:

Avro Support

Fluent-bit comes with support for avro encoding for the out_kafka plugin. Avro support is optional and must be activated at build-time by using a build def with cmake: -DFLB_AVRO_ENCODER=On such as in the following example which activates:

out_kafka with avro encoding
fluent-bit's prometheus
metrics via an embedded http endpoint
debugging support
builds the test suites

Kafka Configuration File with Avro Encoding

This is example fluent-bit config tails kubernetes logs, decorates the log lines with kubernetes metadata via the kubernetes filter, and then sends the fully decorated log lines to a kafka broker encoded with a specific avro schema.

Kafka REST Proxy

The kafka-rest output plugin, allows to flush your records into a server. The following instructions assumes that you have a fully operational Kafka REST Proxy and Kafka services running in your environment.

Configuration Parameters

Key

Description

default

TLS / SSL

Kafka REST Proxy output plugin supports TLS/SSL, for more details about the properties available and general configuration, please refer to the section.

Getting Started

In order to insert records into a Kafka REST Proxy service, you can run the plugin from the command line or through the configuration file:

Command Line

The kafka-rest plugin, can read the parameters from the command line in two ways, through the -p argument (property), e.g:

Configuration File

In your main configuration file append the following Input & Output sections:

LogDNA

is an intuitive cloud based log management system that provides you an easy interface to query your logs once they are stored.

The Fluent Bit logdna output plugin allows you to send your log or events to a compliant service like:

Before to get started with the plugin configuration, make sure to obtain the proper account to get access to the service. You can start with a free trial in the following link:

Configuration Parameters

Key

Description

Default

Auto Enrichment & Data Discovery

One of the features of Fluent Bit + LogDNA integration is the ability to auto enrich each record with further context.

When the plugin process each record (or log), it tries to lookup for specific key names that might contain specific context for the record in question, the following table describe the keys and the discovery logic:

Getting Started

The following configuration example, will emit a dummy example record and ingest it on LogDNA. Copy and paste the following content in a file called logdna.conf:

run Fluent Bit with the new configuration file:

Fluent Bit output:

Your record will be available and visible in your LogDNA dashboard after a few seconds.

Query your Data in LogDNA

In your LogDNA dashboard, go to the top filters and mark the Tags aa and bb, then you will be able to see your records as the example below:

Amazon S3

Send logs, data, metrics to Amazon S3

The Amazon S3 output plugin allows you to ingest your records into the cloud object store.

The plugin allows you to specify a maximum file size, and a timeout for uploads. A file will be created in S3 when the max size is reached, or the timeout is reached- whichever comes first.

Records are stored in files in S3 as newline delimited JSON.

See for details on how AWS credentials are fetched.

NOTE: The outputted by Fluent Bit's built-in http server are meaningless for the S3 output. This is because S3 has its own buffering and retry mechanisms. The Fluent Bit AWS S3 maintainers apologize for this feature gap; you can .

Configuration Parameters

Key

Description

Default

TLS / SSL

To skip TLS verification, set tls.verify as false. For more details about the properties available and general configuration, please refer to the section.

Permissions

The plugin requires the following AWS IAM permissions:

{
	"Version": "2012-10-17",
	"Statement": [{
		"Effect": "Allow",
		"Action": [
			"s3:PutObject"
		],
		"Resource": "*"
	}]
}

Differences between S3 and other Fluent Bit outputs

Summary: Uniqueness in S3 Plugin

The HTTP Monitoring interface output metrics are not meaningful for S3: AWS understands that this is non-ideal; we have that will allow S3 to manage its own output metrics.
You must use store_dir_limit_size to limit the space on disk used by S3 buffer files.
The original ordering of data inputted to Fluent Bit may not be preserved unless you enable preserve_data_ordering On.

S3 Key Format and Tag Delimiters

In Fluent Bit, all logs have an associated tag. The s3_key_format option lets you inject the tag into the s3 key using the following syntax:

$TAG => the full tag
$TAG[n] => the nth part of the tag (index starting at zero). This syntax is copied from the rewrite tag filter. By default, “parts” of the tag are separated with dots, but you can change this with s3_key_format_tag_delimiters.

In the example below, assume the date is January 1st, 2020 00:00:00 and the tag associated with the logs in question is my_app_name-logs.prod.

[OUTPUT]
    Name                         s3
    Match                        *
    bucket                       my-bucket
    region                       us-west-2
    total_file_size              250M
    s3_key_format                /$TAG[2]/$TAG[0]/%Y/%m/%d/%H/%M/%S/$UUID.gz
    s3_key_format_tag_delimiters .-

With the delimiters as . and -, the tag will be split into parts as follows:

$TAG[0] = my_app_name
$TAG[1] = logs
$TAG[2] = prod

So the key in S3 will be /prod/my_app_name/2020/01/01/00/00/00/bgdHN1NM.gz.

Allowing a file extension in the S3 Key Format with $UUID

There are three cases where the PutObject API is used:

When you explicitly set use_put_object On
On startup when the S3 output finds old buffer files in the store_dir from a previous run and attempts to send all of them at once.
On shutdown, when to prevent data loss the S3 output attempts to send all currently buffered data at once.

Let's walk through this via an example. First case, we attempt to set a .gz extension without specifying $UUID.

[OUTPUT]
    Name                         s3
    Match                        *
    bucket                       my-bucket
    region                       us-west-2
    total_file_size              50M
    use_put_object               Off
    compression                  gzip
    s3_key_format                /$TAG/%Y/%m/%d/%H_%M_%S.gz

In the case where pending data is uploaded on shutdown, if the tag was app, the S3 key in the S3 bucket might be:

/app/2022/12/25/00_00_00.gz-apwgylqg

The S3 output appended a random string to the "extension", since this upload on shutdown used the PutObject API.

There are two ways of disabling this behavior. Option 1, use static_file_path:

[OUTPUT]
    Name                         s3
    Match                        *
    bucket                       my-bucket
    region                       us-west-2
    total_file_size              50M
    use_put_object               Off
    compression                  gzip
    s3_key_format                /$TAG/%Y/%m/%d/%H_%M_%S.gz
    static_file_path             On

Option 2, explicitly define where the random UUID will go in the S3 key format:

[OUTPUT]
    Name                         s3
    Match                        *
    bucket                       my-bucket
    region                       us-west-2
    total_file_size              50M
    use_put_object               Off
    compression                  gzip
    s3_key_format                /$TAG/%Y/%m/%d/%H_%M_%S/$UUID.gz

Reliability

There is one minor drawback to multipart uploads- the file and data will not be visible in S3 until the upload is completed with a call. The plugin will attempt to make this call whenever Fluent Bit is shut down to ensure your data is available in s3. It will also store metadata about each upload in the store_dir, ensuring that uploads can be completed when Fluent Bit restarts (assuming it has access to persistent disk and the store_dir files will still be present on restart).

Using S3 without persisted disk

If you run Fluent Bit in an environment without persistent disk, or without the ability to restart Fluent Bit and give it access to the data stored in the store_dir from previous executions- some considerations apply. This might occur if you run Fluent Bit on .

The following settings are recommended for this use case:

[OUTPUT]
     Name s3
     Match *
     bucket your-bucket
     region us-east-1
     total_file_size 1M
     upload_timeout 1m
     use_put_object On

S3 Multipart Uploads

With use_put_object Off (default), S3 will attempt to send files using multipart uploads. For each file, S3 first calls , then a series of calls to for each fragment (targeted to be upload_chunk_size bytes), and finally to create the final file in S3.

Fallback to PutObject

S3 each fragment to be at least 5,242,880 bytes, otherwise the upload is rejected.

Consequently, the S3 output must sometimes fallback to the .

Uploads are triggered by three settings:

total_file_size and upload_chunk_size: When S3 has buffered data in the store_dir that meets the desired total_file_size (for use_put_object On) or the upload_chunk_size (for Multipart), it will trigger an upload operation.
upload_timeout: Whenever locally buffered data has been present on the filesystem in the store_dir longer than the configured upload_timeout, it will be sent. This happens regardless of whether or not the desired byte size has been reached. Consequently, if you configure a small upload_timeout, your files may be smaller than the total_file_size. The timeout is evaluated against the time at which S3 started buffering data for each unqiue tag (that is, the time when new data was buffered for the unique tag after the last upload). The timeout is also evaluated against the time, so a multipart upload will be completed after upload_timeout has elapsed, even if the desired size has not yet been reached.

If your upload_timeout triggers an upload before the pending buffered data reaches the upload_chunk_size, it may be too small for a multipart upload. S3 will consequently fallback to use the .

When you enable compression, S3 applies the compression algorithm at send time. The size settings noted above trigger uploads based on the size of buffered data, not the final compressed size. Consequently, it is possible that after compression, buffered data no longer meets the required minimum S3 size. If this occurs, you will see a log message like:

[ info] [output:s3:s3.0] Pre-compression upload_chunk_size= 5630650, After compression, chunk is only 1063320 bytes, the chunk was too small, using PutObject to upload

upload_timeout constrains total multipart upload time for a single file

The upload_timeout is evaluated against the time. So a multipart upload will be completed after upload_timeout has elapsed, even if the desired size has not yet been reached.

Completing uploads

When is called, an UploadID is returned. S3 stores these IDs for active uploads in the store_dir. Until is called, the uploaded data will not be visible in S3.

On shutdown, S3 output will attempt to complete all pending uploads. If it fails to complete an upload, the ID will remain buffered in the store_dir in a directory called multipart_upload_metadata. If you restart the S3 output with the same store_dir it will discover the old UploadIDs and complete the pending uploads. The also has suggestions on discovering and deleting/completing dangling uploads in your buckets.

Worker support

Example:

[OUTPUT]
     Name s3
     Match *
     bucket your-bucket
     region us-east-1
     total_file_size 1M
     upload_timeout 1m
     use_put_object On
     workers 1

Usage with MinIO

is a high-performance, S3 compatible object storage and you can build your app with S3 functionality without S3.

Assume you run at localhost:9000, and create a bucket of your-bucket by referring .

Example:

[OUTPUT]
     Name s3
     Match *
     bucket your-bucket
     endpoint http://localhost:9000

Then, the records will be stored into the MinIO server.

Getting Started

In order to send records into Amazon S3, you can run the plugin from the command line or through the configuration file.

Command Line

The s3 plugin, can read the parameters from the command line through the -p argument (property), e.g:

$ fluent-bit -i cpu -o s3 -p bucket=my-bucket -p region=us-west-2 -p -m '*' -f 1

Configuration File

In your main configuration file append the following Output section:

[OUTPUT]
     Name s3
     Match *
     bucket your-bucket
     region us-east-1
     store_dir /home/ec2-user/buffer
     total_file_size 50M
     upload_timeout 10m

An example that using PutObject instead of multipart:

[OUTPUT]
     Name s3
     Match *
     bucket your-bucket
     region us-east-1
     store_dir /home/ec2-user/buffer
     use_put_object On
     total_file_size 10M
     upload_timeout 10m

AWS for Fluent Bit

Amazon distributes a container image with Fluent Bit and this plugins.

GitHub

Amazon ECR Public Gallery

Our images are available in Amazon ECR Public Gallery. You can download images with different tags by following command:

docker pull public.ecr.aws/aws-observability/aws-for-fluent-bit:<tag>

For example, you can pull the image with latest version by:

docker pull public.ecr.aws/aws-observability/aws-for-fluent-bit:latest

If you see errors for image pull limits, try log into public ECR with your AWS credentials:

aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws

You can check the for more details.

Docker Hub

Amazon ECR

You can use our SSM Public Parameters to find the Amazon ECR image URI in your region:

aws ssm get-parameters-by-path --path /aws/service/aws-for-fluent-bit/

For more see .

Advanced usage

Use Apache Arrow for in-memory data processing

Starting from Fluent Bit v1.8, the Amazon S3 plugin includes the support for . The support is currently not enabled by default, as it depends on a shared version of libarrow as the prerequisite.

To use this feature, FLB_ARROW must be turned on at compile time:

$ cd build/
$ cmake -DFLB_ARROW=On ..
$ cmake --build .

Once compiled, Fluent Bit can upload incoming data to S3 in Apache Arrow format. For example:

[INPUT]
  Name cpu

[OUTPUT]
  Name s3
  Bucket your-bucket-name
  total_file_size 1M
  use_put_object On
  upload_timeout 60s
  Compression arrow

As shown in this example, setting Compression to arrow makes Fluent Bit to convert payload into Apache Arrow format.

>>> import pyarrow.feather as feather
>>> import pyarrow.fs as fs
>>>
>>> s3 = fs.S3FileSystem()
>>> file = s3.open_input_file("my-bucket/fluent-bit-logs/cpu.0/2021/04/27/09/36/15-object969o67ZF")
>>> df = feather.read_feather(file)
>>> print(df.head())
                          date  cpu_p  user_p  system_p  cpu0.p_cpu  cpu0.p_user  cpu0.p_system
0  2021-04-27T09:33:53.539346Z    1.0     1.0       0.0         1.0          1.0            0.0
1  2021-04-27T09:33:54.539330Z    0.0     0.0       0.0         0.0          0.0            0.0
2  2021-04-27T09:33:55.539305Z    1.0     0.0       1.0         1.0          0.0            1.0
3  2021-04-27T09:33:56.539430Z    0.0     0.0       0.0         0.0          0.0            0.0
4  2021-04-27T09:33:57.539803Z    0.0     0.0       0.0         0.0          0.0            0.0