Actor-based library to help you more effectively and easily push data into an Amazon Kinesis stream by taking care of the heavy lifting for you.
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Actor-based library to help you push data into an Amazon Kinesis stream by taking care of the heavy lifting for you.

This guide contains the following sections:

  • The Basics - how to get started using this library
  • Features - what you can expect from this library

You can check out the release note here, and report issues here.

The Basics

Before you start

Please familiarize yourself with how Amazon Kinesis works by looking through its online documentations, in particular its Key Concepts and Limitations.

Getting Started

Download and install the library from Nuget here.

This library enables you to write a data producing application on top of Amazon Kinesis.

To start, you need an instance of IProducer, which you can create using one of the overloads of the static method Producer.CreateNew. To send data to Kinesis, wrap the data (a byte[]) into a Record and call IProducer.Send and that's it!

The built-in implementation of IProducer can operate in either Blocking or Background modes:


Data are saved into Kinesis straight away, and the task will complete as soon as the Kinesis request has completed.


Data are saved into Kinesis by a number of concurrent background workers, the the task will complete as soon as it's submitted into a managed backlog.

At the time of writing, Amazon Kinesis has a put latency of 103ms which renders blocking sends rather undesirable in most use cases, hence by default, the built-in IProducer will operate in the Background mode.

Configuring the Producer

The built-in IProducer accepts a number of configuration options:

Configuration Default Value Description

Background :

  • HighWaterMarks = 1000
  • HighWaterMarksMode = DropData
How to process the send requests.
LevelOfConcurrency 10 The amount of concurrency for writing to the Kinesis.
MaxPutRecordAttempts 3 Max number of attempts for putting a record into Kinesis.
  • MaxThrottlePerMinute = 10
  • ConsecutiveMinutes = 3
Threshold for the throttled calls before splitting shards.

To configure the producer, pass in an instance of DarkseidConfig when creating the producer using the static method Producer.CreateNew.

Error Handling

When operating in the Blocking mode, any exceptions will be reported via the returned Task.

However, when operating in the Background mode, since the exception will be caught by the background worker, so to find out about the exception and the Record in question (so that you can resend, or fall-back to Amazon SQS perhaps?) you'll need to listen to the IProducer.OnError event.


When operating in the Background mode, in order to avoid losing any data that are still sitting in the backlog, it's important for you to ensure that you dispose of the IProducer instance when your application is shutting down.

F# Example

let awsKey      = "AKIAI5Y767DTOFBUSYAA"
let awsSecret   = "zollLGekGcjIdFvCzvtbyf9OfCI1R3nvjtkSQgSM"
let region      = RegionEndpoint.USEast1
let appName     = "TestApp"
let streamName  = "TestStream"

// provide a custom configuration for the producer
let bgConfig  = BackgroundProcessingConfig(HighWaterMarks = 100000, HighWaterMarksMode = HighWaterMarksMode.Block)
let mode      = Background bgConfig
let config    = new DarkseidConfig(Mode = mode, LevelOfConcurrency = 100u)

let producer  = Producer.CreateNew(awsKey, awsSecret, region, appName, streamName, config)

let payload = [| 1..3 |] |> (fun _ -> "42") |> Array.reduce (+) |> System.Text.Encoding.UTF8.GetBytes

let send () =
    let record = { Data = payload; PartitionKey = Guid.NewGuid().ToString() }

let loop = 
    async {
        while true do
            do! Async.Sleep(1)

printfn "Starting 10 send loops..."

for i = 1 to 10 do
    do Async.Start(loop)

printfn "Started."

You can find both C# and F# examples under the examples folder, you just need to update the AWS credentials and set the stream name to the one you have created in your AWS environment to run the examples.


Auto-splitting shards

The producer will automatically publishes shard-specific CloudWatch metrics under the namespace Darkseid, which allows you to see the number of successful/throttled send requests, as well as the total amount of data sent (in bytes) by shard ID.

When the number of throttled requests in the stream exceeds the configured threshold (see Configuring the Producer) the library will use the aforementioned metrics to find the most appropriate shard and split its hash key range down the middle into two shards (please refer to the SplitShard page in the Kinesis docs).

Multiple send modes

You are able to send data to Amazon Kinesis using this library in two modes:

  • Blocking - send data to Kinesis straight away, and complete the task when the request succeeds
  • Background - send data to Kinesis via an internal queue using a number of concurrent background workers, the task will complete as soon as the data is submitted into the internal queue

both modes allow you to wait for the request to complete asynchronously using a .Net Task.

High water marks in the backlog

When operating in the Background mode, data is pushed to Kinesis in the background by a number of workers (the number is determined by the configured level of concurrency), if left unmanaged there is however a risk of the backlog growing indefinitely until we run out of memory.

To mitigate this risk, I borrowed the idea of high water marks from the brilliant 0MQ library so that the backlog is allowed to grow only to a certain size before actions are taken to either:

  • Drop Data - the last send request will complete straight away without committing the data into the backlog.
  • Block Caller - the last send request will go into a non-blocking wait loop and will complete only when the size of the backlog drops below the high water mark and the data is committed into the backlog.

until the size of backlog drops down below our specified high water mark.

The default behaviour is to drop data, because blocking the caller runs the risk of thread starvation. If the caller is synchronously waiting for the send request to complete, then as more callers are blocked less threads will be available to carry out the work for the background workers hence negatively impacting the throughput and causing even more callers to be blocked..

Fully Asynchronous

This library is fully asynchronous, all the I/O operations are performed in a non-blocking fashion.

Highly configurable

Many aspects of the library's operations can be configured, see the Configuring the Producer section for more details.

Monitoring via CloudWatch

The library provides shard specific CloudWatch metrics for successful and throttled requests to Kinesis as well as the total size of data (in bytes) sent, down to the minute interval, which fills a gap in the current set of CloudWatch metrics that the service itself is reporting.