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.

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:

• Create a free-tier cluster

• Create a fully-featured cluster

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:

• Create an Eventhouse cluster

• Create a KQL database

Creating an Azure Registered Application

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

• Register an Application

• Add a client secret

• Authorize the app in your database

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.

The Amazon S3 output plugin lets you ingest records into the S3 cloud object store.

The plugin can upload data to S3 using the multipart upload API or PutObject. Multipart is the default and is recommended. Fluent Bit will stream data in a series of parts. This limits the amount of data buffered 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 or parts using the multipart API. All aspects of the upload process are configurable.

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

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

See AWS Credentials for details about fetching AWS credentials.

Configuration Parameters

TLS / SSL

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

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

The S3 output plugin is used to upload large files to an Amazon S3 bucket, while most other outputs which send many requests to upload data in batches of a few megabytes or less.

When Fluent Bit receives logs, it stores them in chunks, either in memory or the filesystem depending on your settings. Chunks are usually around 2 MB in size. Fluent Bit sends 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 output's flush callback function. These counters are the data source for Fluent Bit's error, retry, and success metrics available in Prometheus format through its monitoring interface.

The S3 output plugin conforms to the Fluent Bit output plugin specification. Since S3's use case is to upload large files (over 2 MB), its behavior is different. S3's flush callback function buffers the incoming chunk to the filesystem, and returns an FLB_OK. This means Prometheus metrics available from 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. S3 requires a writeable filesystem. Running Fluent Bit on a read-only filesystem won't work with the S3 output.

S3 uploads primarily initiate using the S3 timer callback function, which runs separately from its flush.

S3 has its own buffering system and its own callback to upload data, so the normal sequential data ordering of chunks provided by the Fluent Bit engine may be compromised. S3 has the presevere_data_ordering option which ensures data is uploaded in the original order it was collected by Fluent Bit.

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 opened an issue with a design to 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 following example, 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 splits into parts as follows:

• $TAG[0] = my_app_name

• $TAG[1] = logs

• $TAG[2] = prod

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 overwritten by a subsequent upload. 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, 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. Many of the use cases for PutObject uploads involve a short time period between uploads, so 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.

The PutObject API is used in these cases:

• 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. To prevent data loss the S3 output attempts to send all currently buffered data at once.

You should always specify $UUID somewhere in your S3 key format. Otherwise, if the PutObject API is used, S3 appends a random eight-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. Disabled this with static_file_path On.

For example, 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 file extension, since this upload on shutdown used the PutObject API.

There are two ways of disabling this behavior:

• Use static_file_path:

  Copy

  [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

• Explicitly define where the random UUID will go in the S3 key format:

  Copy

  [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

The store_dir is used to temporarily store data before upload. If Fluent Bit stops 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 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 5 MB 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.

One drawback to multipart uploads is that the file and data aren't visible in S3 until the upload is completed with a CompleteMultipartUpload call. The plugin attempts to make this call whenever Fluent Bit is shut down to ensure your data is available in S3. It also stores 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 AWS Fargate.

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:

[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 CreateMultipartUpload, then a series of calls to UploadPart for each fragment (targeted to be upload_chunk_size bytes), and finally CompleteMultipartUpload to create the final file in S3.

Fallback to PutObject

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

The S3 output must sometimes fallback to the PutObject API.

Uploads are triggered by these 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 even when the desired byte size hasn't been reached. 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 CreateMultipartUpload 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 fallback to use the PutObject API.

When you enable compression, S3 applies the compression algorithm at send time. The size settings trigger uploads based on the size of buffered data, not the final compressed size. It's possible that after compression, buffered data no longer meets the required minimum S3 UploadPart 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

If you encounter this frequently, use the numbers in the messages to guess your compression factor. In this example, the buffered data was reduced from 5,630,650 bytes to 1,063,320 bytes. The compressed size is one-fifth the actual data size. Configuring upload_chunk_size 30M should ensure each part is large enough after compression to be over the minimum 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. 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

The upload_timeout evaluated against the CreateMultipartUpload time. A multipart upload will be completed after upload_timeout elapses, even if the desired size has not yet been reached.

Completing uploads

When CreateMultipartUpload is called, an UploadID is returned. S3 stores these IDs for active uploads in the store_dir. Until CompleteMultipartUpload is called, the uploaded data isn't visible in S3.

On shutdown, S3 output attempts to complete all pending uploads. If an upload fails to complete, the ID remains 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 S3 documentation has suggestions on discovering and deleting or completing dangling uploads in your buckets.

Usage with MinIO

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

The following example runs a MinIO server at localhost:9000, and create a bucket of your-bucket.

Example:

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

The records store in the MinIO server.

Get Started

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 reads parameters from the command line through the -p argument:

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 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 plugins.

GitHub

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

Amazon ECR Public Gallery

Our images are available in the Amazon ECR Public Gallery as aws-for-fluent-bit.

You can download images with different tags using the following command:

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

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

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

If you see errors for image pull limits, try signing in to 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

See the Amazon ECR Public official documentation for more details.

Docker Hub

amazon/aws-for-fluent-bit is also available from the Docker Hub.

Amazon ECR

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 information, see the AWS for Fluent Bit GitHub repo.

Advanced usage

Use Apache Arrow for in-memory data processing

With Fluent Bit v1.8 or greater, the Amazon S3 plugin includes the support for Apache Arrow. Support isn't enabled by default, and has a dependency on a shared version of libarrow.

To use this feature, FLB_ARROW must be turned on at compile time. Use the following commands:

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

After being 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

Setting Compression to arrow makes Fluent Bit convert payload into Apache Arrow format.

Load, analyze, and process stored data using popular data processing tools such as Python pandas, Apache Spark and Tensorflow.

The following example uses pyarrow to analyze the uploaded data:

>>> 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

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.

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 -

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:

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:

Format

out_file format

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

plain format

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

csv format

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

ltsv format

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

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:

You will get the following output:

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:

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:

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:

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:

Intergration with Localstack (Cloudwatch Logs)

For an instance of Localstack running at http://localhost:4566, the following configuration needs to be added to the [OUTPUT] section:

Any testing credentials can be exported as local variables, such as AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.

Permissions

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

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:

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

Configuration Parameters

TLS / SSL

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:

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

which is similar to do:

Configuration File

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:

becomes

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.

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

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

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.

Example configuration:

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

Fluent Bit + Elastic Cloud

Example configuration:

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:

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:

Logstash_Prefix_Key

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

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

The Datadog output plugin allows to ingest your logs into .

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

Configuration Parameters

Configuration File

Get started quickly with this configuration file:

Troubleshooting

403 Forbidden

GELF is 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 , 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.

TLS / SSL

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

  4. Timestamp does not set by Fluent Bit. In this case, your Graylog server will set it to the current timestamp (now).

• 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:

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:

Now, this is what happens to this log:

1. Fluent Bit GELF plugin adds "version": "1.1" to it.

3. We used this data key as Gelf_Short_Message_Key; so GELF plugin changes it to short_message.

5. Timestamp is generated.

Finally, this is what our Graylog server input sees:

The Chronicle output plugin allows ingesting security logs into 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 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:

Configurations Parameters

Configuration File

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

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

This is the documentation for the core Fluent Bit Kinesis plugin written in C. It has all the core features of the 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 .

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:

Configuration File

In your main configuration file append the following Output section:

Permissions

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

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:

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).

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: