spark.read
    .format("jdbc")
    .option("driver","ru.yandex.clickhouse.ClickHouseDriver")
    .option("url", "jdbc:clickhouse://hadoop:8124/tutorial")
	.option("numPartitions", "2")
    .option("dbtable", "test")
	.load().show()

//clickhouse客户端配置
val pro = new java.util.Properties
pro.put("driver", "ru.yandex.clickhouse.ClickHouseDriver")
df.write
    .mode(SaveMode.Append)
    .option("batchsize", "20000")
    .option("isolationLevel", "NONE")
    .option("numPartitions", "2")
    .jdbc("jdbc:clickhouse://hadoop:8124/tutorial","test",pro)

import java.util.Random

import org.apache.spark.SparkConf
import org.apache.spark.sql.types.{DoubleType, LongType, StringType}
import org.apache.spark.sql.{SaveMode, SparkSession}
import ru.yandex.clickhouse.ClickHouseDataSource

object OfficialJDBCDriver {

  val chDriver = "ru.yandex.clickhouse.ClickHouseDriver"
  val chUrls = Array(
    "jdbc:clickhouse://1.1.1.1:8123/default",
    "jdbc:clickhouse://2.2.2.2:8123/default")

  def main(args: Array[String]): Unit = {
    if (args.length < 3) {
      System.err.println("Usage: OfficialJDBCDriver <tableName> <partitions> <batchSize>\n" +
        "  <tableName> is the hive table name \n" +
        "  <partitions> is the partitions which want insert into clickhouse, like 20200516,20200517\n" +
        "  <batchSize> is JDBC batch size, may be 1000\n\n")
      System.exit(1)
    }

    val (tableName, partitions, batchSize) = (args(0), args(1).split(","),  args(2).toInt)
    val sparkConf: SparkConf =
      new SparkConf()
        .setAppName("OfficialJDBCDriver ")

    val spark: SparkSession =
      SparkSession
        .builder()
        .enableHiveSupport()
        .config(sparkConf)
        .getOrCreate()

    val pro = new java.util.Properties
    pro.put("driver", chDriver)
    pro.setProperty("user", "default")
    pro.setProperty("password", "123456")
    var chShardIndex = 1
    for (partition <- partitions) {
      val chUrl = chUrls((chShardIndex - 1) % chUrls.length)
      val sql = s"select * from tmp.$tableName where dt = $partition"
      val df = spark.sql(sql)
      val (fieldNames, placeholders) = df.schema.fieldNames.foldLeft("", "")(
        (str, name) =>
          if (str._1.nonEmpty && str._2.nonEmpty)
            (str._1 + ", " + name, str._2 + ", " + "?")
          else (name, "?")
      )
      val insertSQL = s"insert into my_table ($fieldNames) values ($placeholders)"
      df.foreachPartition(records => {
        try {
          var count = 0;
          val chDatasource = new ClickHouseDataSource(chUrl, pro)
          val chConn = chDatasource.getConnection("default", "123456")
          val psmt = chConn.prepareStatement(insertSQL)
          while (records.hasNext) {
            val record = records.next()
            var fieldIndex = 1
            record.schema.fields.foreach(field => {
              // 匹配类型，获取对应类型的字段值 
              field.dataType match {
                case StringType =>
                  psmt.setString(fieldIndex, record.getAs[String](field.name))
                case LongType =>
                  psmt.setLong(fieldIndex, record.getAs[Long](field.name))
                case DoubleType =>
                  psmt.setDouble(fieldIndex, record.getAs[Double](field.name))
                // 这里可以新增自己需要的type
                case _ => println(s" other type: ${field.dataType}")
              }
              fieldIndex += 1
            })
            psmt.addBatch()
            // 批量写入
            if (count % batchSize == 0) {
              psmt.executeBatch()
              psmt.clearBatch()
            }
            count += 1
          }
          psmt.executeBatch()
          psmt.close()
          chConn.close()
        } catch {
          case e: Exception =>
            e.printStackTrace()
        }
      })
      chShardIndex += 1
    }
    spark.close()
  }
}