How to Download io.confluent.connect.avro.avroconverter
If you are using Kafka Connect to stream data between Kafka and other systems, you might want to use a data serialization format that is compact, efficient, and schema-aware. One such format is Avro, which is a binary format that can encode complex data types and schemas in JSON. To use Avro with Kafka Connect, you need a plugin called io.confluent.connect.avro.avroconverter, which integrates with Schema Registry to convert data to and from Avro format.
In this article, we will explain what io.confluent.connect.avro.avroconverter is, why you need it, and how to download, install, configure, and test it. By the end of this article, you will be able to use Avro as a common data format for your Kafka Connect connectors.
download io.confluent.connect.avro.avroconverter
Prerequisites
Before you download the avroconverter plugin, you need to have the following:
A running Kafka cluster with at least one broker and one zookeeper.
A running Kafka Connect worker in standalone or distributed mode.
A running Schema Registry service that stores and validates schemas for Avro data.
A basic understanding of Apache Kafka, Kafka Connect, Avro format, and Schema Registry.
Downloading the Plugin
There are two ways to download the avroconverter plugin:
From Confluent Hub: Confluent Hub is a web-based repository of connectors and plugins for Kafka Connect. You can browse, search, download, and install plugins from Confluent Hub using a command-line interface (CLI) or a graphical user interface (GUI). To download the avroconverter plugin from Confluent Hub using CLI, run the following command:
$ confluent-hub install confluentinc/kafka-connect-avro-converter:latest
This will download the latest version of the plugin and extract it into one of the directories that is listed on the Connect worker's plugin.path configuration property. You can also specify a different version or a different directory if you want.
How to download io.confluent.connect.avro.avroconverter for Kafka Connect
Download io.confluent.connect.avro.avroconverter from Confluent Hub
io.confluent.connect.avro.avroconverter installation and configuration guide
io.confluent.connect.avro.avroconverter schema registry integration
io.confluent.connect.avro.avroconverter example usage and code
io.confluent.connect.avro.avroconverter vs other Kafka Connect converters
Benefits of using io.confluent.connect.avro.avroconverter for data serialization
Troubleshooting io.confluent.connect.avro.avroconverter issues and errors
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Download io.confluent.connect.avro.avroconverter ZIP file
Download io.confluent.connect.avro.avroconverter JAR file
Download io.confluent.connect.avro.AvroConverter class file
Download io.confluent.kafka.serializers.AbstractKafkaAvroSerDe class file
Download org.apache.kafka.common.serialization.Deserializer interface file
Download org.apache.kafka.common.serialization.Serializer interface file
Download org.apache.kafka.common.config.ConfigDef class file
Download org.apache.kafka.common.config.ConfigException class file
Download org.apache.kafka.common.config.Configurable interface file
Download org.apache.kafka.common.errors.SerializationException class file
Download org.apache.kafka.common.utils.Utils class file
Download org.apache.kafka.common.utils.AppInfoParser class file
Download org.apache.kafka.common.utils.Version class file
Download org.apache.kafka.common.utils.Time interface file
Download org.apache.kafka.common.utils.SystemTime class file
Download org.apache.kafka.common.utils.NanoClock interface file
Download org.apache.kafka.common.utils.SystemNanoClock class file
Download org.apache.kafka.common.utils.Clock interface file
Download org.apache.kafka.common.utils.SystemClock class file
Download org.apache.kafka.common.utils.UtilsTest class file
Download org.apache.kafka.common.utils.AppInfoParserTest class file
Download org.apache.kafka.common.utils.VersionTest class file
Download org.apache.kafka.common.utils.TimeTest class file
Download org.apache.kafka.common.utils.SystemTimeTest class file
Download org.apache.kafka.common.utils.NanoClockTest class file
Download org.apache.kafka.common.utils.SystemNanoClockTest class file
Download org.apache.kafka.common.utils.ClockTest class file
Download org.apache.kafka.common.utils.SystemClockTest class file
From GitHub: You can also download the source code of the plugin from GitHub and build it yourself using Maven. To do so, clone the schema-registry repository from GitHub and run mvn package in the avro-converter directory:
$ git clone $ cd schema-registry/avro-converter $ mvn package
-avro-converter-<version>.jar in the target directory. You can copy this JAR file to any directory that is on the Connect worker's plugin.path configuration property.
Installing the Plugin
After you download the avroconverter plugin, you need to install it on your Kafka Connect worker. This is a simple process that involves restarting the Connect worker with the plugin on its classpath. To do so, follow these steps:
Stop the Connect worker if it is running.
Copy the plugin JAR file (or the entire plugin directory if you downloaded it from Confluent Hub) to a directory that is on the Connect worker's plugin.path configuration property. By default, this property is set to /usr/share/java, but you can change it to any directory you want.
Start the Connect worker again. The worker will scan the directories on its plugin.path and load the plugin classes.
You can verify that the plugin is installed correctly by running the following command:
$ confluent local services connect connector list
This will list all the available connectors and plugins on your Connect worker. You should see io.confluent.connect.avro.AvroConverter among them.
Configuring the Plugin
Now that you have installed the avroconverter plugin, you need to configure it to use Schema Registry and Avro format for your Kafka Connect connectors. To do so, you need to set some properties in your connector configuration file or in your REST API request. The most important properties are:
key.converter: This specifies the converter class for the key of the records. You need to set this to io.confluent.connect.avro.AvroConverter.
value.converter: This specifies the converter class for the value of the records. You need to set this to io.confluent.connect.avro.AvroConverter.
key.converter.schema.registry.url: This specifies the URL of the Schema Registry service that stores and validates schemas for Avro data. You need to set this to the same URL that you used to start your Schema Registry service.
value.converter.schema.registry.url: This specifies the URL of the Schema Registry service that stores and validates schemas for Avro data. You need to set this to the same URL that you used to start your Schema Registry service.
key.converter.enhanced.avro.schema.support: This specifies whether to enable enhanced Avro schema support, such as logical types, default values, and union types. You can set this to true or false.
value.converter.enhanced.avro.schema.support: This specifies whether to enable enhanced Avro schema support, such as logical types, default values, and union types. You can set this to true or false.
key.converter.schemas.enable: This specifies whether to use schemas for serializing and deserializing keys. You can set this to true or false.
value.converter.schemas.enable: This specifies whether to use schemas for serializing and deserializing values. You can set this to true or false.
You can also set other properties that are specific to your connector type, such as topics, tasks, transforms, etc. For example, here is a sample configuration file for a file source connector that uses Avro format:
"name": "file-source-avro", "config": "connector.class": "org.apache.kafka.connect.file.FileStreamSourceConnector", "tasks.max": "1", "file": "/tmp/test.txt", "topic": "test-avro", "key.converter": "io.confluent.connect.avro.AvroConverter", "value.converter": "io.confluent.connect.avro.AvroConverter", "key.converter.schema.registry.url": " "value.converter.schema.registry.url": " "key.converter.enhanced.avro.schema.support": "true", "value.converter.enhanced.avro.schema.support": "true", .converter.schemas.enable": "false", "value.converter.schemas.enable": "true"
You can save this file as file-source-avro.json and use it to create the connector using the REST API:
$ curl -X POST -H "Content-Type: application/json" --data @file-source-avro.json
Testing the Plugin
To test the avroconverter plugin, you can use a Kafka console consumer to consume the data from the topic that your connector is writing to. You need to specify the same converter and schema registry properties as your connector, and also set the print.key and print.value properties to true. For example, to consume the data from the test-avro topic, run the following command:
$ kafka-avro-console-consumer --bootstrap-server localhost:9092 --topic test-avro --property print.key=true --property print.value=true --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer --property value.deserializer=io.confluent.kafka.serializers.KafkaAvroDeserializer --property schema.registry.url=
This will print the key and value of each record in JSON format, along with the schema ID and version from Schema Registry. You can verify that the data is in Avro format by checking the schema ID and version, or by using a tool like avro-tools to inspect the binary data.
Conclusion
In this article, we have learned how to download, install, configure, and test the avroconverter plugin for Kafka Connect. This plugin allows us to use Avro as a common data format for our Kafka Connect connectors, which has many benefits such as compactness, efficiency, and schema-awareness. We have also learned how to use Schema Registry to store and validate schemas for Avro data, and how to use Confluent Hub or GitHub to download plugins for Kafka Connect. We hope you found this article useful and informative. If you have any questions or feedback, please let us know in the comments below.
FAQs
Here are some frequently asked questions and answers about the avroconverter plugin:
What is Avro?
Avro is a binary data serialization format that can encode complex data types and schemas in JSON. It is widely used in big data applications, such as Apache Hadoop, Apache Spark, and Apache Kafka.
What is Schema Registry?
Schema Registry is a service that stores and validates schemas for Avro data. It provides a RESTful interface for registering and retrieving schemas, as well as compatibility checks and schema evolution support.
What is Confluent Hub?
Confluent Hub is a web-based repository of connectors and plugins for Kafka Connect. It allows users to browse, search, download, and install plugins from Confluent Hub using a command-line interface (CLI) or a graphical user interface (GUI).
What is Kafka Connect?
Kafka Connect is a framework for streaming data between Kafka and other systems. It provides a scalable and reliable way to connect Kafka with various sources and sinks, such as databases, APIs, files, etc.
How do I create my own plugin for Kafka Connect?
You can create your own plugin for Kafka Connect by implementing the Converter, Connector, or Transform interfaces. You can also use existing libraries or frameworks that provide these interfaces, such as Apache Camel or Spring Boot. You can find more information on how to create your own plugin in the .
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