Apache Hudi: Ingesting Protobuf Data from Kafka – A Step-by-Step Guide

Step 2: Define the Protobuf Schema

Next, define the protobuf schema for your data. Create a new file called `my_data.proto` with the following content:

syntax = "proto3";

message MyData {
  string id = 1;
  string name = 2;
  int32 age = 3;
}

Compile the protobuf schema using the following command:

protoc --java_out=. my_data.proto

This will generate a `MyData.java` file in the same directory.

Step 3: Produce Protobuf Data to Kafka

Now, let’s produce some protobuf data to Kafka using the `MyData` protobuf schema. Create a new Java or Scala program that uses the `MyData` class to create protobuf messages and publish them to a Kafka topic.

// Java example
importafka.producer.KafkaProducer;
import kafka.producer.ProducerConfig;

public class ProtobufProducer {
  public static void main(String[] args) {
    Properties props = new Properties();
    props.put("bootstrap.servers", "localhost:9092");
    props.put("acks", "all");
    props.put("retries", 0);
    props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
    props.put("value.serializer", "com.example.MyDataSerializer");

    KafkaProducer<String, MyData> producer = new KafkaProducer<>(props);

    MyData myData = MyData.newBuilder()
      .setId("1")
      .setName("John Doe")
      .setAge(30)
      .build();

    producer.send(new ProducerRecord<String, MyData>("my_topic", myData));
  }
}

Run the program to produce protobuf data to the Kafka topic `my_topic`.

Step 4: Ingest Protobuf Data from Kafka with Apache Hudi

Now, let’s ingest the protobuf data from Kafka using Apache Hudi. Create a new Apache Hudi ingestion pipeline that consumes data from the Kafka topic and writes it to a Hudi table.

// Scala example
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.hive_sync.config.HoodieHiveConfig

object IngestProtobufData {
  def main(args: Array[String]): Unit = {
    val hoodieConfig = HoodieWriteConfig.newBuilder()
      .withPath("hdfs://localhost:9000/path/to/hudi/table")
      .withTable-name("my_hudi_table")
      .withSchema(Utilities.schemaToString(scala.util.Try"class MyData").get))
      .build()

    val kafkaConfig = new KafkaConfig("my_topic", "localhost:9092")

    val dataSource = DataSource.builder()
      .options(kafkaConfig)
      .load()
      .format("kafka")
      .option("bootstrap.servers", "localhost:9092")
      .option("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
      .option("value.deserializer", "com.example.MyDataDeserializer")
      .load()

    val hudiWriter = new HoodieWriter(hoodieConfig, dataSource)

    hudiWriter.write()
  }
}

Run the Apache Hudi ingestion pipeline to ingest the protobuf data from Kafka and write it to a Hudi table.

Step 5: Query the Ingested Data

Finally, let’s query the ingested data using Apache Hudi’s query engine. Create a new Apache Hudi query that reads data from the Hudi table and prints the results.

// Scala example
import org.apache.hudi.DataSourceReadOptions
import org.apache.hudi.config.HoodieReadConfig

object QueryIngestedData {
  def main(args: Array[String]): Unit = {
    val hoodieConfig = HoodieReadConfig.newBuilder()
      .withPath("hdfs://localhost:9000/path/to/hudi/table")
      .withTable-name("my_hudi_table")
      .build()

    val dataSource = DataSource.builder()
      .options(hoodieConfig)
      .load()
      .format("hudi")
      .load()

    val data = dataSource.collect()

    data.foreach(println(_))
  }
}

Run the Apache Hudi query to read the ingested data from the Hudi table and print the results.

Conclusion

In this article, we’ve demonstrated how to ingest protobuf data from Kafka using Apache Hudi. We’ve covered the preparation of the environment, definition of the protobuf schema, production of protobuf data to Kafka, ingestion of protobuf data from Kafka with Apache Hudi, and querying of the ingested data. With Apache Hudi, you can now efficiently ingest and process protobuf data from Kafka, enabling real-time data analytics and decision-making.

Note: This article is for educational purposes only and is not intended to be used in production without further testing and validation. Additionally, the code examples provided are simplified and may require modifications to work in your specific environment.

Frequently Asked Question

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