Can I customize partitioning in Kinesis Firehose before delivering to S3? - amazon-s3

Question

I have a Firehose stream that is intended to ingest millions of events from different sources and of different event-types. The stream should deliver all data to one S3 bucket as a store of raw\unaltered data.
I was thinking of partitioning this data in S3 based on metadata embedded within the event message like event-souce, event-type and event-date.
However, Firehose follows its default partitioning based on record arrival time. Is it possible to customize this partitioning behavior to fit my needs?
Update: Accepted answer updated as a new answer suggests the feature is available as of Sep 2021

Answer 1

As of writing this, the dynamic partitioning feature Vlad has mentioned is still pretty new. I needed it to be a part of CloudFormation template, which was still not properly documented. I had to add in DynamicPartitioningConfiguration to get it working properly. MetadataExtractionQuery syntax was also not properly documented.
MyKinesisFirehoseStream:
Type: AWS::KinesisFirehose::DeliveryStream
...
Properties:
ExtendedS3DestinationConfiguration:
Prefix: "clients/client_id=!{client_id}/dt=!{timestamp:yyyy-MM-dd}/"
ErrorOutputPrefix: "errors/!{firehose:error-output-type}/"
DynamicPartitioningConfiguration:
Enabled: "true"
RetryOptions:
DurationInSeconds: "300"
ProcessingConfiguration:
Enabled: "true"
Processors:
- Type: AppendDelimiterToRecord
- Type: MetadataExtraction
Parameters:
- ParameterName: MetadataExtractionQuery
ParameterValue: "{client_id:.client_id}"
- ParameterName: JsonParsingEngine
ParameterValue: JQ-1.6

Answer 2

Since September 1st, 2021, AWS Kinesis Firehose supports this feature. Read the announcement blog post here.
From the documentation:
You can use the Key and Value fields to specify the data record parameters to be used as dynamic partitioning keys and jq queries to generate dynamic partitioning key values. ...
Here is how it looks like from UI:

Answer 3

No. You cannot 'partition' based upon event content.
Some options are:
Send to separate Firehose streams
Send to a Kinesis Data Stream (instead of Firehose) and write your own custom Lambda function to process and save the data (See: AWS Developer Forums: Athena and Kinesis Firehose)
Use Kinesis Analytics to process the message and 'direct' it to different Firehose streams
If you are going to use the output with Amazon Athena or Amazon EMR, you could also consider converting it into Parquet format, which has much better performance. This would require post-processing of the data in S3 as a batch rather than converting the data as it arrives in a stream.

Answer 4

To build on John's answer, if you don't have the near real-time streaming requirements, we've found batch-processing with Athena to be a simple solution for us.
Kinesis streams to a given table unpartitioned_event_data, which can make use of the native record arrival time partitioning.
We define another Athena table partitioned_event_table which can be defined with custom partition keys and make use of the INSERT INTO capabilities that Athena has. Athena will automatically repartition your data in the format you want without requiring any custom consumers or new infrastructure to manage. This can be scheduled with a cron, SNS, or something like Airflow.
What's cool is you can create a view that does a UNION of the two tables to query historical and real-time data in one place.
We actually dealt with this problem at Radar and talk about more trade-offs in this blog post.

Answer 5

To expand on Murali's answer, we have implemented it in CDK:
Our incomming json data looks something like this:
{
"data":
{
"timestamp":1633521266990,
"defaultTopic":"Topic",
"data":
{
"OUT1":"Inactive",
"Current_mA":3.92
}
}
}
The CDK code looks as following:
const DeliveryStream = new CfnDeliveryStream(this, 'deliverystream', {
deliveryStreamName: 'deliverystream',
extendedS3DestinationConfiguration: {
cloudWatchLoggingOptions: {
enabled: true,
},
bucketArn: Bucket.bucketArn,
roleArn: deliveryStreamRole.roleArn,
prefix: 'defaultTopic=!{partitionKeyFromQuery:defaultTopic}/!{timestamp:yyyy/MM/dd}/',
errorOutputPrefix: 'error/!{firehose:error-output-type}/',
bufferingHints: {
intervalInSeconds: 60,
},
dynamicPartitioningConfiguration: {
enabled: true,
},
processingConfiguration: {
enabled: true,
processors: [
{
type: 'MetadataExtraction',
parameters: [
{
parameterName: 'MetadataExtractionQuery',
parameterValue: '{Topic: .data.defaultTopic}',
},
{
parameterName: 'JsonParsingEngine',
parameterValue: 'JQ-1.6',
},
],
},
{
type: 'AppendDelimiterToRecord',
parameters: [
{
parameterName: 'Delimiter',
parameterValue: '\\n',
},
],
},
],
},
},
})

Answer 6

My scenario is:
Firehose needs to send data to s3, which is tied to glue table, parquet as format, and dynamic partitioning enabled since I want to consider the year, month, and day from the data I push to firehose instead of the default.
Below is the working code
rawdataFirehose:
Type: AWS::KinesisFirehose::DeliveryStream
Properties:
DeliveryStreamName: !Join ["-", [rawdata, !Ref AWS::StackName]]
DeliveryStreamType: DirectPut
ExtendedS3DestinationConfiguration:
BucketARN: !GetAtt rawdataS3bucket.Arn
Prefix: parquetdata/year=!{partitionKeyFromQuery:year}/month=!{partitionKeyFromQuery:month}/day=!{partitionKeyFromQuery:day}/
BufferingHints:
IntervalInSeconds: 300
SizeInMBs: 128
ErrorOutputPrefix: errors/
RoleARN: !GetAtt FirehoseRole.Arn
DynamicPartitioningConfiguration:
Enabled: true
ProcessingConfiguration:
Enabled: true
Processors:
- Type: MetadataExtraction
Parameters:
- ParameterName: MetadataExtractionQuery
ParameterValue: "{year:.year,month:.month,day:.day}"
- ParameterName: "JsonParsingEngine"
ParameterValue: "JQ-1.6"
DataFormatConversionConfiguration:
Enabled: true
InputFormatConfiguration:
Deserializer:
HiveJsonSerDe: {}
OutputFormatConfiguration:
Serializer:
ParquetSerDe: {}
SchemaConfiguration:
CatalogId: !Ref AWS::AccountId
RoleARN: !GetAtt FirehoseRole.Arn
DatabaseName: !Ref rawDataDB
TableName: !Ref rawDataTable
Region:
Fn::ImportValue: AWSRegion
VersionId: LATEST
FirehoseRole:
Type: AWS::IAM::Role
Properties:
AssumeRolePolicyDocument:
Version: 2012-10-17
Statement:
- Effect: Allow
Principal:
Service: firehose.amazonaws.com
Action: sts:AssumeRole
Policies:
- PolicyName: !Sub firehose-glue-${Envname}
PolicyDocument: |
{
"Version": "2012-10-17",
"Statement":
[
{
"Effect": "Allow",
"Action":
[
"glue:*",
"iam:ListRolePolicies",
"iam:GetRole",
"iam:GetRolePolicy",
"tag:GetResources",
"s3:*",
"cloudwatch:*",
"ssm:*"
],
"Resource": "*"
}
]
}
Note:
rawDataDB is a reference to glue database
rawDataTable is a reference to table
rawdataS3bucket is a reference to s3 bucket