Spark-Sql快速入门系列(5) | Hive数据库

一.hive和spark sql的集成方式(面试可能会问到)

hive on spark(版本兼容)
官网https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started

spark on hive(版本兼容)
官网
http://spark.apache.org/docs/2.1.1/sql-programming-guide.html#hive-tables

二.spark_shell和spark_sql操作

spark_shell

如果你在集群上使用了tez，你需要在spark/conf下spark-defaults.conf添加lzo的路径

spark.jars=/export/servers/hadoop-2.7.7/share/hadoop/common/hadoop-lzo-0.4.20.jar

spark-yarn模式启动

bin/spark-shell --master yarn

spark_sql

完全跟sql一样

使用hiveserver2 + beeline

spark-sql 得到的结果不够友好, 所以可以使用hiveserver2 + beeline
1.启动thriftserver(后台)

sbin/start-thriftserver.sh

2.启动beeline

bin/beeline
# 然后输入
!connect jdbc:hive2://hadoop102:10000
# 然后按照提示输入用户名和密码

三.脚本使用spark-sql

四.idea中读写Hive数据

1.从hive中读数据

添加依赖

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-hive_2.11</artifactId>
    <version>2.1.1</version>
</dependency>

代码实现

import org.apache.spark.sql.SparkSession

object HiveRead {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("HiveRead")
      //添加支持外置hive
      .enableHiveSupport()
      .getOrCreate()

    spark.sql("show databases")
    spark.sql("use guli")
    spark.sql("select count(*) from gulivideo_orc").show()

    spark.close()
  }

}

结果

2.从hive中写数据

使用hive的insert语句去写

import org.apache.spark.sql.SparkSession

object HiveWrite {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "root");
    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("HiveRead")
      //添加支持外置hive
      .enableHiveSupport()
      .config("spark.sql.warehouse.dir","hdfs://hadoop102:9000/user/hive/warehouse")
      .getOrCreate()

    //先创建一个数据库
    spark.sql("create database spark1602")
    spark.sql("use spark1602")
    spark.sql("create table user1(id int,name string)")
    spark.sql("insert into user1 values(10,'lisi')").show()

    spark.close()

  }
}

使用df.write.saveAsTable(“表名”)(常用)

import org.apache.spark.sql.SparkSession

object HiveWrite {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "root");
    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("HiveRead")
      //添加支持外置hive
      .enableHiveSupport()
      .config("spark.sql.warehouse.dir","hdfs://hadoop102:9000/user/hive/warehouse")
      .getOrCreate()


    val df = spark.read.json("D:\\idea\\spark-sql\\input\\user.json")
    spark.sql("use spark1602")
    //直接把数据写入到hive中,表可以存在也可以不存在
    df.write.saveAsTable("user2")
    //也可以进行追加
   //df.write.mode("append").saveAsTable("user2")
    spark.close()

  }

}

使用df.write.insertInto(“表名”)

import org.apache.spark.sql.SparkSession

object HiveWrite {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "root");
    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("HiveRead")
      //添加支持外置hive
      .enableHiveSupport()
      .config("spark.sql.warehouse.dir","hdfs://hadoop102:9000/user/hive/warehouse")
      .getOrCreate()


    val df = spark.read.json("D:\\idea\\spark-sql\\input\\user.json")
    spark.sql("use spark1602")
    df.write.insertInto("user2")

    spark.close()
  }

}

3.saveAsTable和insertInto的原理

saveAsTable
使用列名进行分配值

insertInto
按照位置进行1对1

五.聚合后的分区数

import org.apache.spark.sql.SparkSession

object HiveWrite {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "root");
    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("HiveRead")
      //添加支持外置hive
      .enableHiveSupport()
      .config("spark.sql.warehouse.dir","hdfs://hadoop102:9000/user/hive/warehouse")
      .getOrCreate()

    val df = spark.read.json("D:\\idea\\spark-sql\\input\\user.json")
    df.createOrReplaceTempView("a")

    spark.sql("use spark1602")
    val df1 = spark.sql("select * from a ")
    val df2 = spark.sql("select sum(age) sum_age from a group by name")
    println(df1.rdd.getNumPartitions)
    println(df2.rdd.getNumPartitions)
   df1.write.saveAsTable("a3")
   df2.write.saveAsTable("a4")

    spark.close()
  }

}

结果：聚合函数分区数默认200个

如果数据量小，没必要200两个分区，简直浪费
将

 df2.write.saveAsTable("a4")

修改为

 df2.coalesce(1).write.saveAsTable("a4")