What is the method for Spark to read from Kafka and write into Hive?

Spark has the ability to utilize Spark Streaming to read data from Kafka and write it into Hive.

Here is the method for using Spark Streaming to read from Kafka and write data into Hive:

1. Import the necessary libraries and dependencies.

import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka._

1. Set up the Spark Streaming context and configure the Kafka parameters.

val sparkConf = new SparkConf().setMaster("local[2]").setAppName("KafkaToHive")
val ssc = new StreamingContext(sparkConf, Seconds(5))

val kafkaParams = Map("metadata.broker.list" -> "localhost:9092",
                      "zookeeper.connect" -> "localhost:2181",
                      "group.id" -> "spark-streaming")

1. Create a DStream to read data from Kafka.

val topics = Set("topic1")
val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)

1. Process data in Kafka and write it to Hive.

kafkaStream.foreachRDD { rdd =>
  if (!rdd.isEmpty()) {
    val hiveContext = new HiveContext(rdd.sparkContext)
    import hiveContext.implicits._
    
    val dataFrame = rdd.map(_._2).toDF("value")
    
    dataFrame.write.mode(SaveMode.Append).saveAsTable("hive_table")
  }
}

In the above code, we first create a HiveContext to connect to Hive. Next, we convert the data in the RDD into a DataFrame and use the write method of the DataFrame to save the data to a Hive table.

1. Start Spark Streaming and wait for it to finish.

ssc.start()
ssc.awaitTermination()

This will start Spark Streaming and wait for it to read data from Kafka and write it into Hive.

Please make sure to correctly configure the connection parameters for Hive and Kafka in your Spark application, and add the relevant libraries and dependencies in the Spark startup command.

This is a basic example that you can modify and expand according to your needs.