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Outputs

Amazon CloudWatch

Send logs and metrics to Amazon CloudWatch

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

This is the documentation for the core Fluent Bit CloudWatch plugin written in C. It can replace the 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 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:

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 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:

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

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:

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

Here is our output configuration:

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:

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.

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

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 Firehose

Send logs to Amazon Kinesis Firehose

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

This is the documentation for the core Fluent Bit Firehose plugin written in C. It can replace the aws/amazon-kinesis-firehose-for-fluent-bit 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 here 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:

$ fluent-bit -i cpu -o kinesis_firehose -p delivery_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_firehose
    Match *
    region us-east-1
    delivery_stream my-stream

Permissions

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

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

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:

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

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

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 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 Azure Blob Storage 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 Azurite.

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:

Azure Blob Storage Tutorial (Video)

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:

[SERVICE]
    flush     1
    log_level info

[INPUT]
    name      dummy
    dummy     {"name": "Fluent Bit", "year": 2020}
    samples   1
    tag       var.log.containers.app-default-96cbdef2340.log

[OUTPUT]
    name                  azure_blob
    match                 *
    account_name          YOUR_ACCOUNT_NAME
    shared_key            YOUR_SHARED_KEY
    path                  kubernetes
    container_name        logs
    auto_create_container on
    tls                   on

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:

$ npm install -g azurite

then run the service:

$ azurite
Azurite Blob service is starting at http://127.0.0.1:10000
Azurite Blob service is successfully listening at http://127.0.0.1:10000
Azurite Queue service is starting at http://127.0.0.1:10001
Azurite Queue service is successfully listening at http://127.0.0.1:10001

Configuring Fluent Bit for Azurite

Azurite comes with a default account_name and shared_key, so make sure to use the specific values provided in the example below (do an exact copy/paste):

[SERVICE]
    flush     1
    log_level info

[INPUT]
    name      dummy
    dummy     {"name": "Fluent Bit", "year": 2020}
    samples   1
    tag       var.log.containers.app-default-96cbdef2340.log

[OUTPUT]
    name                  azure_blob
    match                 *
    account_name          devstoreaccount1
    shared_key            Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==
    path                  kubernetes
    container_name        logs
    auto_create_container on
    tls                   off
    emulator_mode         on
    endpoint              http://127.0.0.1:10000

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

$ azurite
Azurite Blob service is starting at http://127.0.0.1:10000
Azurite Blob service is successfully listening at http://127.0.0.1:10000
Azurite Queue service is starting at http://127.0.0.1:10001
Azurite Queue service is successfully listening at http://127.0.0.1:10001
127.0.0.1 - - [03/Sep/2020:17:40:03 +0000] "GET /devstoreaccount1/logs?restype=container HTTP/1.1" 404 -
127.0.0.1 - - [03/Sep/2020:17:40:03 +0000] "PUT /devstoreaccount1/logs?restype=container HTTP/1.1" 201 -
127.0.0.1 - - [03/Sep/2020:17:40:03 +0000] "PUT /devstoreaccount1/logs/kubernetes/var.log.containers.app-default-96cbdef2340.log?comp=appendblock HTTP/1.1" 404 -
127.0.0.1 - - [03/Sep/2020:17:40:03 +0000] "PUT /devstoreaccount1/logs/kubernetes/var.log.containers.app-default-96cbdef2340.log HTTP/1.1" 201 -
127.0.0.1 - - [03/Sep/2020:17:40:04 +0000] "PUT /devstoreaccount1/logs/kubernetes/var.log.containers.app-default-96cbdef2340.log?comp=appendblock HTTP/1.1" 201 -

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. This output plugin can also be used to ingest logs into an Eventhouse cluster in Microsoft Fabric Real Time Analytics.

For ingesting into Azure Data Explorer: Creating a Kusto Cluster and Database

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

For ingesting into Microsoft Fabric Real Time Analytics : Creating an Eventhouse Cluster and KQL Database

You can create an Eventhouse cluster and a KQL database follow the following steps:

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 .

Before you begin, you need a , a , and you need to .

Configuration Parameters

Key

Description

Default

Configuration File

Get started quickly with this configuration file:

Troubleshooting

403 Forbidden

Standard Output

The stdout output plugin allows to print to the standard output the data received through the input plugin. Their usage is very simple as follows:

Configuration Parameters

Key

Description

default

Command Line

$ bin/fluent-bit -i cpu -o stdout -v

We have specified to gather CPU usage metrics and print them out to the standard output in a human readable way:

$ bin/fluent-bit -i cpu -o stdout -p format=msgpack -v
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

[2016/10/07 21:52:01] [ info] [engine] started
[0] cpu.0: [1475898721, {"cpu_p"=>0.500000, "user_p"=>0.250000, "system_p"=>0.250000, "cpu0.p_cpu"=>0.000000, "cpu0.p_user"=>0.000000, "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"=>0.000000, "cpu3.p_system"=>1.000000}]
[1] cpu.0: [1475898722, {"cpu_p"=>0.250000, "user_p"=>0.250000, "system_p"=>0.000000, "cpu0.p_cpu"=>0.000000, "cpu0.p_user"=>0.000000, "cpu0.p_system"=>0.000000, "cpu1.p_cpu"=>1.000000, "cpu1.p_user"=>1.000000, "cpu1.p_system"=>0.000000, "cpu2.p_cpu"=>0.000000, "cpu2.p_user"=>0.000000, "cpu2.p_system"=>0.000000, "cpu3.p_cpu"=>0.000000, "cpu3.p_user"=>0.000000, "cpu3.p_system"=>0.000000}]
[2] cpu.0: [1475898723, {"cpu_p"=>0.750000, "user_p"=>0.250000, "system_p"=>0.500000, "cpu0.p_cpu"=>2.000000, "cpu0.p_user"=>1.000000, "cpu0.p_system"=>1.000000, "cpu1.p_cpu"=>0.000000, "cpu1.p_user"=>0.000000, "cpu1.p_system"=>0.000000, "cpu2.p_cpu"=>1.000000, "cpu2.p_user"=>0.000000, "cpu2.p_system"=>1.000000, "cpu3.p_cpu"=>0.000000, "cpu3.p_user"=>0.000000, "cpu3.p_system"=>0.000000}]
[3] cpu.0: [1475898724, {"cpu_p"=>1.000000, "user_p"=>0.750000, "system_p"=>0.250000, "cpu0.p_cpu"=>1.000000, "cpu0.p_user"=>1.000000, "cpu0.p_system"=>0.000000, "cpu1.p_cpu"=>2.000000, "cpu1.p_user"=>1.000000, "cpu1.p_system"=>1.000000, "cpu2.p_cpu"=>1.000000, "cpu2.p_user"=>1.000000, "cpu2.p_system"=>0.000000, "cpu3.p_cpu"=>1.000000, "cpu3.p_user"=>1.000000, "cpu3.p_system"=>0.000000}]

No more, no less, it just works.

Amazon CloudWatch

Send logs and metrics to Amazon CloudWatch

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

See for details on how AWS credentials are fetched.

Configuration Parameters

Key

Description

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

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

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 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']

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

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

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

[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

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 .

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:

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:

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.

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.

Loki

Loki is multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate.

The Fluent Bit loki built-in output plugin allows you to send your log or events to a Loki service. It supports data enrichment with Kubernetes labels, custom label keys and Tenant ID within others.

Be aware there is a separate Golang output plugin provided by Grafana with different configuration options.

Configuration Parameters

Key

Description

Default

Labels

Loki store the record logs inside Streams, a stream is defined by a set of labels, at least one label is required.

Fluent Bit implements a flexible mechanism to set labels by using fixed key/value pairs of text but also allowing to set as labels certain keys that exists as part of the records that are being processed. Consider the following JSON record (pretty printed for readability):

{
    "key": 1,
    "sub": {
        "stream": "stdout",
        "id": "some id"
    },
    "kubernetes": {
        "labels": {
            "team": "Santiago Wanderers"
        }
    }
}

If you decide that your Loki Stream will be composed by two labels called job and the value of the record key called stream , your labels configuration properties might look as follows:

[OUTPUT]
    name   loki
    match  *
    labels job=fluentbit, $sub['stream']

As you can see the label job has the value fluentbit and the second label is configured to access the nested map called sub targeting the value of the key stream . Note that the second label name must starts with a $, that means that's a Record Accessor pattern so it provide you the ability to retrieve values from nested maps by using the key names.

When processing above's configuration, internally the ending labels for the stream in question becomes:

job="fluentbit", stream="stdout"

Another feature of Labels management is the ability to provide custom key names, using the same record accessor pattern we can specify the key name manually and let the value to be populated automatically at runtime, e.g:

[OUTPUT]
    name   loki
    match  *
    labels job=fluentbit, mystream=$sub['stream']

When processing that new configuration, the internal labels will be:

job="fluentbit", mystream="stdout"

Using the `label_keys` property

The additional configuration property called label_keys allow to specify multiple record keys that needs to be placed as part of the outgoing Stream Labels, yes, this is a similar feature than the one explained above in the labels property. Consider this as another way to set a record key in the Stream, but with the limitation that you cannot use a custom name for the key value.

The following configuration examples generate the same Stream Labels:

[OUTPUT]
    name       loki
    match      *
    labels     job=fluentbit
    label_keys $sub['stream']

the above configuration accomplish the same than this one:

[OUTPUT]
    name   loki
    match  *
    labels job=fluentbit, $sub['stream']

both will generate the following Streams label:

job="fluentbit", stream="stdout"

Using the `label_map_path` property

The configuration property label_map_path is to read a JSON file that defines how to extract labels from each record.

The file should contain a JSON object. Each keys define how to get label value from a nested record. Each values are used as label names.

The following configuration examples generate the same Stream Labels:

map.json:

{
    "sub": {
           "stream": "stream"
    }
}

The following configuration examples generate the same Stream Labels:

[OUTPUT]
    name   loki
    match  *
    label_map_path /path/to/map.json

the above configuration accomplish the same than this one:

[OUTPUT]
    name   loki
    match  *
    labels job=fluentbit, $sub['stream']

both will generate the following Streams label:

job="fluentbit", stream="stdout"

Kubernetes & Labels

Note that if you are running in a Kubernetes environment, you might want to enable the option auto_kubernetes_labels which will auto-populate the streams with the Pod labels for you. Consider the following configuration:

[OUTPUT]
    name                   loki
    match                  *
    labels                 job=fluentbit
    auto_kubernetes_labels on

Based in the JSON example provided above, the internal stream labels will be:

job="fluentbit", team="Santiago Wanderers"

Networking and TLS Configuration

This plugin inherit core Fluent Bit features to customize the network behavior and optionally enable TLS in the communication channel. For more details about the specific options available refer to the following articles:

Networking Setup: timeouts, keepalive and source address
Security & TLS: all about TLS configuration and certificates

Note that all options mentioned in the articles above must be enabled in the plugin configuration in question.

Fluent Bit + Grafana Cloud

Fluent Bit supports sending logs (and metrics) to Grafana Cloud by providing the appropriate URL and ensuring TLS is enabled.

An example configuration - make sure to set the credentials and ensure the host URL matches the correct one for your deployment:

    [OUTPUT]
        Name        loki
        Match       *
        Host        logs-prod-eu-west-0.grafana.net
        port        443
        tls         on
        tls.verify  on
        http_user   XXX
        http_passwd XXX

Getting Started

The following configuration example, will emit a dummy example record and ingest it on Loki . Copy and paste the following content into a file called out_loki.conf:

[SERVICE]
    flush     1
    log_level info

[INPUT]
    name      dummy
    dummy     {"key": 1, "sub": {"stream": "stdout", "id": "some id"}, "kubernetes": {"labels": {"team": "Santiago Wanderers"}}}
    samples   1

[OUTPUT]
    name                   loki
    match                  *
    host                   127.0.0.1
    port                   3100
    labels                 job=fluentbit
    label_keys             $sub['stream']
    auto_kubernetes_labels on

run Fluent Bit with the new configuration file:

$ fluent-bit -c out_loki.conf

Fluent Bit output:

Fluent Bit v1.7.0
* 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

[2020/10/14 20:57:45] [ info] [engine] started (pid=809736)
[2020/10/14 20:57:45] [ info] [storage] version=1.0.6, initializing...
[2020/10/14 20:57:45] [ info] [storage] in-memory
[2020/10/14 20:57:45] [ info] [storage] normal synchronization mode, checksum disabled, max_chunks_up=128
[2020/10/14 20:57:45] [ info] [output:loki:loki.0] configured, hostname=127.0.0.1:3100
[2020/10/14 20:57:45] [ info] [sp] stream processor started
[2020/10/14 20:57:46] [debug] [http] request payload (272 bytes)
[2020/10/14 20:57:46] [ info] [output:loki:loki.0] 127.0.0.1:3100, HTTP status=204