spark读取oracle写入kafka,sparkStreaming读取kafka写入hive表

可以按照以下步骤实现：

1. 首先，需要在Spark中使用JDBC连接Oracle数据库，读取数据。可以使用以下代码：

val jdbcUrl = "jdbc:oracle:thin:@localhost:1521:ORCL"
val jdbcUsername = "username"
val jdbcPassword = "password"
val jdbcDriverClass = "oracle.jdbc.driver.OracleDriver"

val connectionProperties = new Properties()
connectionProperties.put("user", jdbcUsername)
connectionProperties.put("password", jdbcPassword)
connectionProperties.put("driver", jdbcDriverClass)

val oracleDF = spark.read.jdbc(jdbcUrl, "table_name", connectionProperties)

2. 接下来，将数据写入Kafka。可以使用以下代码：

import org.apache.spark.sql.functions._
import org.apache.spark.sql.streaming.Trigger

val kafkaBrokers = "localhost:9092"
val kafkaTopic = "topic_name"

val kafkaDF = oracleDF.select(to_json(struct("*")).alias("value"))
  .selectExpr("CAST(NULL AS STRING) AS key", "value")
  .writeStream
  .format("kafka")
  .option("kafka.bootstrap.servers", kafkaBrokers)
  .option("topic", kafkaTopic)
  .trigger(Trigger.ProcessingTime("10 seconds"))
  .start()

3. 最后，使用Spark Streaming从Kafka读取数据，并将其写入Hive表。可以使用以下代码：

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructType}
import org.apache.spark.sql.functions._

val spark = SparkSession.builder
  .appName("KafkaToHive")
  .enableHiveSupport()
  .getOrCreate()

val kafkaBrokers = "localhost:9092"
val kafkaTopic = "topic_name"

val schema = new StructType().add("column_name", StringType)

val kafkaDF = spark.readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", kafkaBrokers)
  .option("subscribe", kafkaTopic)
  .option("startingOffsets", "earliest")
  .load()
  .select(from_json(col("value").cast("string"), schema).alias("data"))
  .select("data.*")

kafkaDF.writeStream
  .outputMode("append")
  .foreachBatch { (batchDF, _) =>
    batchDF.write.mode("append").insertInto("hive_table")
  }
  .start()
  .awaitTermination()

注意：在执行代码之前，需要先在Hive中创建相应的表，以便可以将数据写入其中。