Confluent's .NET Client for Apache Kafka^TM

confluent-kafka-dotnet is Confluent's .NET client for Apache Kafka and the Confluent Platform.

Features:

• High performance - confluent-kafka-dotnet is a lightweight wrapper around librdkafka, a finely tuned C client.

• Reliability - There are a lot of details to get right when writing an Apache Kafka client. We get them right in one place (librdkafka) and leverage this work across all of our clients (also confluent-kafka-python and confluent-kafka-go).

• Supported - Commercial support is offered by Confluent.

• Future proof - Confluent, founded by the original creator/co-creator of Kafka, is building a streaming platform with Apache Kafka at its core. It's high priority for us that client features keep pace with core Apache Kafka and components of the Confluent Platform.

confluent-kafka-dotnet is derived from Andreas Heider's rdkafka-dotnet. We're fans of his work and were very happy to have been able to leverage rdkafka-dotnet as the basis of this client. Thanks Andreas!

Referencing

confluent-kafka-dotnet is distributed via NuGet. We provide the following packages:

• Confluent.Kafka [net462, netstandard1.3, netstandard2.0] - The core client library.
• Confluent.SchemaRegistry.Serdes.Avro [netstandard2.0] - Provides a serializer and deserializer for working with Avro serialized data with Confluent Schema Registry integration.
• Confluent.SchemaRegistry.Serdes.Protobuf [netstandard2.0] - Provides a serializer and deserializer for working with Protobuf serialized data with Confluent Schema Registry integration.
• Confluent.SchemaRegistry.Serdes.Json [netstandard2.0] - Provides a serializer and deserializer for working with Json serialized data with Confluent Schema Registry integration.
• Confluent.SchemaRegistry [netstandard2.0] - Confluent Schema Registry client (a dependency of the Confluent.SchemaRegistry.Serdes packages).
• Confluent.SchemaRegistry.Encryption [netcoreapp3.1, net6.0] - Confluent Schema Registry client-side field-level encryption client (a dependency of the other Confluent.SchemaRegistry.Encryption.* packages).
• Confluent.SchemaRegistry.Encryption.Aws [netcoreapp3.1, net6.0] - Confluent Schema Registry client-side field-level encryption client for AWS KMS.
• Confluent.SchemaRegistry.Encryption.Azure [netcoreapp3.1, net6.0] - Confluent Schema Registry client-side field-level encryption client for Azure Key Vault.
• Confluent.SchemaRegistry.Encryption.Gcp [netcoreapp3.1, net6.0] - Confluent Schema Registry client-side field-level encryption client for Google Cloud KMS.
• Confluent.SchemaRegistry.Encryption.HcVault [netcoreapp3.1, net6.0] - Confluent Schema Registry client-side field-level encryption client for Hashicorp Vault.
• Confluent.SchemaRegistry.Rules [net6.0] - Confluent Schema Registry client-side support for data quality rules (via the Common Expression Language) and schema migration rules (via JSONata).

To install Confluent.Kafka from within Visual Studio, search for Confluent.Kafka in the NuGet Package Manager UI, or run the following command in the Package Manager Console:

Install-Package Confluent.Kafka -Version 2.5.0

To add a reference to a dotnet core project, execute the following at the command line:

dotnet add package -v 2.5.0 Confluent.Kafka

Note:

depends on the

librdkafka.redist

package which provides a number of different builds of

librdkafka

that are compatible with common platforms. If you are on one of these platforms this will all work seamlessly (and you don't need to explicitly reference

librdkafka.redist

). If you are on a different platform, you may need to build librdkafka manually (or acquire it via other means) and load it using the Library.Load method.

Branch builds

Nuget packages corresponding to all commits to release branches are available from the following nuget package source (Note: this is not a web URL - you should specify it in the nuget package manager): https://ci.appveyor.com/nuget/confluent-kafka-dotnet. The version suffix of these nuget packages matches the appveyor build number. You can see which commit a particular build number corresponds to by looking at the AppVeyor build history

Usage

For a step-by-step guide and code samples, see Getting Started with Apache Kafka and .NET on Confluent Developer.

You can also take the free self-paced training course Apache Kafka for .NET Developers on Confluent Developer.

Take a look in the examples directory and at the integration tests for further examples.

For an overview of configuration properties, refer to the librdkafka documentation.

Basic Producer Examples

You should use the

method if you would like to wait for the result of your produce requests before proceeding. You might typically want to do this in highly concurrent scenarios, for example in the context of handling web requests. Behind the scenes, the client will manage optimizing communication with the Kafka brokers for you, batching requests as appropriate.

Note that a server round-trip is slow (3ms at a minimum; actual latency depends on many factors). In highly concurrent scenarios you will achieve high overall throughput out of the producer using the above approach, but there will be a delay on each

call. In stream processing applications, where you would like to process many messages in rapid succession, you would typically use the

Produce

method instead:

Basic Consumer Example

IHostedService and Web Application Integration

The Web example demonstrates how to integrate Apache Kafka with a web application, including how to implement

to realize a long running consumer poll loop, how to register a producer as a singleton service, and how to bind configuration from an injected

IConfiguration

instance.

Exactly Once Processing

The .NET Client has full support for transactions and idempotent message production, allowing you to write horizontally scalable stream processing applications with exactly once semantics. The ExactlyOnce example demonstrates this capability by way of an implementation of the classic "word count" problem, also demonstrating how to use the FASTER Key/Value store (similar to RocksDb) to materialize working state that may be larger than available memory, and incremental rebalancing to avoid stop-the-world rebalancing operations and unnecessary reloading of state when you add or remove processing nodes.

Schema Registry Integration

The three "Serdes" packages provide serializers and deserializers for Avro, Protobuf and JSON with Confluent Schema Registry integration. The

nuget package provides a client for interfacing with Schema Registry's REST API.

Note: All three serialization formats are supported across Confluent Platform. They each make different tradeoffs, and you should use the one that best matches to your requirements. Avro is well suited to the streaming data use-case, but the quality and maturity of the non-Java implementations lags that of Java - this is an important consideration. Protobuf and JSON both have great support in .NET.

Error Handling

Errors delivered to a client's error handler should be considered informational except when the

flag is set to

true

, indicating that the client is in an un-recoverable state. Currently, this can only happen on the producer, and only when

enable.idempotence

has been set to

true

. In all other scenarios, clients will attempt to recover from all errors automatically.

Although calling most methods on the clients will result in a fatal error if the client is in an un-recoverable state, you should generally only need to explicitly check for fatal errors in your error handler, and handle this scenario there.

Producer

When using

, to determine whether a particular message has been successfully delivered to a cluster, check the

Error

field of the

DeliveryReport

during the delivery handler callback.

When using

, any delivery result other than

NoError

will cause the returned

Task

to be in the faulted state, with the

Task.Exception

field set to a

ProduceException

containing information about the message and error via the

DeliveryResult

and

Error

fields. Note: if you

await

the call, this means a

ProduceException

will be thrown.

Consumer

All

errors will result in a

ConsumeException

with further information about the error and context available via the

Error

and

ConsumeResult

fields.

3rd Party

There are numerous libraries that expand on the capabilities provided by Confluent.Kafka, or use Confluent.Kafka to integrate with Kafka. For more information, refer to the 3rd Party Libraries page.

Confluent Cloud

For a step-by-step guide on using the .NET client with Confluent Cloud see Getting Started with Apache Kafka and .NET on Confluent Developer.

You can also refer to the Confluent Cloud example which demonstrates how to configure the .NET client for use with Confluent Cloud.

Developer Notes

Instructions on building and testing confluent-kafka-dotnet can be found here.

Copyright (c) 2016-2019 Confluent Inc. 2015-2016 Andreas Heider

KAFKA is a registered trademark of The Apache Software Foundation and has been licensed for use by confluent-kafka-dotnet. confluent-kafka-dotnet has no affiliation with and is not endorsed by The Apache Software Foundation.