clickhouse使用Spark导入数据

本文主要介绍如何通过Spark程序写入数据到Clickhouse中。

操作步骤

1. 准备Spark程序目录结构。
    

    find .
   .
   ./build.sbt
   ./src
   ./src/main
   ./src/main/scala
   ./src/main/scala/com
   ./src/main/scala/com/spark
   ./src/main/scala/com/spark/test
   ./src/main/scala/com/spark/test/WriteToCk.scala

2. 编辑build.sbt配置文件添加依赖。
    

   name := "Simple Project"
   
   version := "1.0"
   
   scalaVersion := "2.12.10"
   
   libraryDependencies += "org.apache.spark" %% "spark-sql" % "3.0.0"
   
   libraryDependencies += "ru.yandex.clickhouse" % "clickhouse-jdbc" % "0.2.4"

3. 创建WriteToCk.scala数据写入程序文件。
    

   package com.spark.test
   
   import java.util
   import java.util.Properties
   
   import org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions
   import org.apache.spark.SparkConf
   import org.apache.spark.sql.{SaveMode, SparkSession}
   import org.apache.spark.storage.StorageLevel
   
   object WriteToCk {
     val properties = new Properties()
     properties.put("driver", "ru.yandex.clickhouse.ClickHouseDriver")
     properties.put("user", "<your-user-name>")
     properties.put("password", "<your-password>")
     properties.put("batchsize","100000")
     properties.put("socket_timeout","300000")
     properties.put("numPartitions","8")
     properties.put("rewriteBatchedStatements","true")
   
     val url = "jdbc:clickhouse://<you-url>:8123/default"
     val table = "<your-table-name>"
   
     def main(args: Array[String]): Unit = {
       val sc = new SparkConf()
       sc.set("spark.driver.memory", "1G")
       sc.set("spark.driver.cores", "4")
       sc.set("spark.executor.memory", "1G")
       sc.set("spark.executor.cores", "2")
   
       val session = SparkSession.builder().master("local[*]").config(sc).appName("write-to-ck").getOrCreate()
   
       val df = session.read.format("csv")
         .option("header", "true")
         .option("sep", ",")
         .option("inferSchema", "true")
         .load("</your/path/to/test/data/a.txt>")
         .selectExpr(
           "Year",
           "Quarter",
           "Month"
         )
         .persist(StorageLevel.MEMORY_ONLY_SER_2)
       println(s"read done")
   
       df.write.mode(SaveMode.Append).option(JDBCOptions.JDBC_BATCH_INSERT_SIZE, 100000).jdbc(url, table, properties)
       println(s"write done")
   
       df.unpersist(true)
     }
   }

   参数说明

   • your-user-name：目标ClickHouse集群中创建的数据库账号名。
   • your-pasword：数据库账号名对应的密码。
   • your-url：目标ClickHouse集群地址。
   • /your/path/to/test/data/a.txt：要导入的数据文件的路径，包含文件地址和文件名。
      

     说明 文件中的数据及schema，需要与ClickHouse中目标表的结构保持一致。
   • your-table-name：ClickHouse集群中的目标表名称。
4. 编译打包。
    

   sbt package

5. 运行。
    

   ${SPARK_HOME}/bin/spark-submit  --class "com.spark.test.WriteToCk"  --master local[4] --conf "spark.driver.extraClassPath=${HOME}/.m2/repository/ru/yandex/clickhouse/clickhouse-jdbc/0.2.4/clickhouse-jdbc-0.2.4.jar" --conf "spark.executor.extraClassPath=${HOME}/.m2/repository/ru/yandex/clickhouse/clickhouse-jdbc/0.2.4/clickhouse-jdbc-0.2.4.jar" target/scala-2.12/simple-project_2.12-1.0.jar