Confluent's .NET Client for Apache Kafka^TM

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

Features:

• High performance - confluent-kafka-dotnet is a lightweight wrapper aroundlibrdkafka, a finely tuned Cclient.

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

• Supported - Commercial support is offered byConfluent.

• Future proof - Confluent, founded by thecreators of Kafka, is building a streaming platformwith Apache Kafka at its core. It's high priority for us that client features keeppace 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 thisclient. Thanks Andreas!

Referencing

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

• Confluent.Kafka [net45, 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 [netstandard1.4, netstandard2.0] - Confluent Schema Registry client (a dependency of the Confluent.SchemaRegistry.Serdes packages).

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 1.8.1

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

dotnet add package -v 1.8.1 Confluent.Kafka

Note: Confluent.Kafka 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 - youshould specify it in the nuget package manager):https://ci.appveyor.com/nuget/confluent-kafka-dotnet. The version suffix of these nuget packagesmatches the appveyor build number. You can see which commit a particular build number corresponds to by looking at theAppVeyor build history

Usage

Take a look in the examples directory for example usage. The integration tests also serve as good examples.

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

Basic Producer Examples

You should use the ProduceAsync method if you would like to wait for the result of your producerequests 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 manageoptimizing communication with the Kafka brokers for you, batching requests as appropriate.

using System;
using System.Threading.Tasks;
using Confluent.Kafka;

class Program
{
    public static async Task Main(string[] args)
    {
        var config = new ProducerConfig { BootstrapServers = "localhost:9092" };

        // If serializers are not specified, default serializers from
        // `Confluent.Kafka.Serializers` will be automatically used where
        // available. Note: by default strings are encoded as UTF8.
        using (var p = new ProducerBuilder<Null, string>(config).Build())
        {
            try
            {
                var dr = await p.ProduceAsync("test-topic", new Message<Null, string> { Value="test" });
                Console.WriteLine($"Delivered '{dr.Value}' to '{dr.TopicPartitionOffset}'");
            }
            catch (ProduceException<Null, string> e)
            {
                Console.WriteLine($"Delivery failed: {e.Error.Reason}");
            }
        }
    }
}

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 usingthe above approach, but there will be a delay on each await call. In stream processingapplications, where you would like to process many messages in rapid succession, you would typicallyuse the Produce method instead:

using System;
using Confluent.Kafka;

class Program
{
    public static void Main(string[] args)
    {
        var conf = new ProducerConfig { BootstrapServers = "localhost:9092" };

        Action<DeliveryReport<Null, string>> handler = r => 
            Console.WriteLine(!r.Error.IsError
                ? $"Delivered message to {r.TopicPartitionOffset}"
                : $"Delivery Error: {r.Error.Reason}");

        using (var p = new ProducerBuilder<Null, string>(conf).Build())
        {
            for (int i=0; i<100; ++i)
            {
                p.Produce("my-topic", new Message<Null, string> { Value = i.ToString() }, handler);
            }

            // wait for up to 10 seconds for any inflight messages to be delivered.
            p.Flush(TimeSpan.FromSeconds(10));
        }
    }
}

Basic Consumer Example

using System;
using System.Threading;
using Confluent.Kafka;

class Program
{
    public static void Main(string[] args)
    {
        var conf = new ConsumerConfig
        { 
            GroupId = "test-consumer-group",
            BootstrapServers = "localhost:9092",
            // Note: The AutoOffsetReset property determines the start offset in the event
            // there are not yet any committed offsets for the consumer group for the
            // topic/partitions of interest. By default, offsets are committed
            // automatically, so in this example, consumption will only start from the
            // earliest message in the topic 'my-topic' the first time you run the program.
            AutoOffsetReset = AutoOffsetReset.Earliest
        };

        using (var c = new ConsumerBuilder<Ignore, string>(conf).Build())
        {
            c.Subscribe("my-topic");

            CancellationTokenSource cts = new CancellationTokenSource();
            Console.CancelKeyPress += (_, e) => {
                e.Cancel = true; // prevent the process from terminating.
                cts.Cancel();
            };

            try
            {
                while (true)
                {
                    try
                    {
                        var cr = c.Consume(cts.Token);
                        Console.WriteLine($"Consumed message '{cr.Value}' at: '{cr.TopicPartitionOffset}'.");
                    }
                    catch (ConsumeException e)
                    {
                        Console.WriteLine($"Error occured: {e.Error.Reason}");
                    }
                }
            }
            catch (OperationCanceledException)
            {
                // Ensure the consumer leaves the group cleanly and final offsets are committed.
                c.Close();
            }
        }
    }
}

IHostedService and Web Application Integration

The Web example demonstrates how to integrateApache Kafka with a web application, including how to implement IHostedService to realize a long running consumer poll loop, how toregister 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 streamprocessing applications with exactly once semantics. The ExactlyOnce example demonstrates this capability by wayof an implementation of the classic "word count" problem, also demonstrating how to use the FASTERKey/Value store (similar to RocksDb) to materialize working state that may be larger than available memory, and incremental rebalancingto 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 Confluent.SchemaRegistry nuget package provides a client for interfacing withSchema 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 IsFatal flagis set to true, indicating that the client is in an un-recoverable state. Currently, this can only happen onthe producer, and only when enable.idempotence has been set to true. In all other scenarios, clients willattempt 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-recoverablestate, you should generally only need to explicitly check for fatal errors in your error handler, and handlethis scenario there.

Producer

When using Produce, 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 ProduceAsync, any delivery result other than NoError will cause the returned Task to be in thefaulted state, with the Task.Exception field set to a ProduceException containing information about the messageand error via the DeliveryResult and Error fields. Note: if you await the call, this means a ProduceExceptionwill be thrown.

Consumer

All Consume errors will result in a ConsumeException with further information about the error and contextavailable via the Error and ConsumeResult fields.

Confluent Cloud

The Confluent Cloud example demonstrates how to configure the .NET client for use withConfluent 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 useby confluent-kafka-dotnet. confluent-kafka-dotnet has no affiliation with and is not endorsed byThe Apache Software Foundation.