confluent-kafka-dotnet is Confluent's .NET client for Apache Kafka and the Confluent Platform.
Features:
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High performance - confluent-kafka-dotnet is a lightweight wrapper around librdkafka, a finely tuned C client.
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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).
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Supported - Commercial support is offered by Confluent.
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Future proof - Confluent, founded by the creators 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!
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 1.5.2-RC1
To add a reference to a dotnet core project, execute the following at the command line:
dotnet add package -v 1.5.2-RC1 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.
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 manger): 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
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.
You should use the ProduceAsync 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.
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 using
the above approach, but there will be a delay on each await call. In stream processing
applications, where you would like to process many messages in rapid succession, you would typically
use 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}"
: $"Delivery Error: {r.Error.Reason}");
using (var p = new ProducerBuilder<Null, string>(conf).Build())
{
for (int i=0; i<100; ++i)
{
p.Produce("my-topic", new Message<Null, string> { Value = i.ToString() }, handler);
}
// wait for up to 10 seconds for any inflight messages to be delivered.
p.Flush(TimeSpan.FromSeconds(10));
}
}
}using System;
using System.Threading;
using Confluent.Kafka;
class Program
{
public static void Main(string[] args)
{
var conf = new ConsumerConfig
{
GroupId = "test-consumer-group",
BootstrapServers = "localhost:9092",
// Note: The AutoOffsetReset property determines the start offset in the event
// there are not yet any committed offsets for the consumer group for the
// topic/partitions of interest. By default, offsets are committed
// automatically, so in this example, consumption will only start from the
// earliest message in the topic 'my-topic' the first time you run the program.
AutoOffsetReset = AutoOffsetReset.Earliest
};
using (var c = new ConsumerBuilder<Ignore, string>(conf).Build())
{
c.Subscribe("my-topic");
CancellationTokenSource cts = new CancellationTokenSource();
Console.CancelKeyPress += (_, e) => {
e.Cancel = true; // prevent the process from terminating.
cts.Cancel();
};
try
{
while (true)
{
try
{
var cr = c.Consume(cts.Token);
Console.WriteLine($"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();
}
}
}
}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 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 maturity of the non-Java implementations lags that of Java - this is an important consideration. Protobuf and JSON both have great support in .NET.
You can use the Avro serializer and deserializer with the GenericRecord class or with specific classes generated
using the avrogen tool, available via Nuget (.NET Core 2.1 required):
dotnet tool install --global Apache.Avro.Tools
Usage:
avrogen -s your_schema.avsc .
For more information about working with Avro in .NET, refer to the the blog post Decoupling Systems with Apache Kafka, Schema Registry and Avro
Errors delivered to a client's error handler should be considered informational except when the IsFatal 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.
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 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.
All Consume errors will result in a ConsumeException with further information about the error and context
available via the Error and ConsumeResult fields.
The Confluent Cloud example demonstrates how to configure the .NET client for use with Confluent Cloud.
Instructions on building and testing confluent-kafka-dotnet can be found here.
Copyright (c) 2016-2019 Confluent Inc. 2015-2016 Andreas Heider