SparkSQL语法及API

SparkSQL语法及API

1、SparkSql基础语法

一、经过方法来使用

1．查询

df.select("id","name").show();

1>带条件的查询

df.select($"id",$"name").where($"name" === "bbb").show()

2>排序查询

    orderBy/sort($"列名")  升序排列

    orderBy/sort($"列名".desc)  降序排列

    orderBy/sort($"列1" , $"列2".desc) 按两列排序

    例如：

df.select($"id",$"name").orderBy($"name".desc).show
df.select($"id",$"name").sort($"name".desc).show
tabx.select($"id",$"name").sort($"id",$"name".desc).show

3>分组查询

    groupBy("列名", ...).max(列名) 求最大值

    groupBy("列名", ...).min(列名) 求最小值

    groupBy("列名", ...).avg(列名) 求平均值

    groupBy("列名", ...).sum(列名) 求和

    groupBy("列名", ...).count() 求个数

    groupBy("列名", ...).agg 能够将多个方法进行聚合

    例如：

scala>val rdd = sc.makeRDD(List((1,"a","bj",100),(2,"b","sh",80),(3,"c","gz",50),(4,"d","bj",45),(5,"e","gz",90)));
scala>val df = rdd.toDF("id","name","addr","score");
scala>df.groupBy("addr").count().show()
scala>df.groupBy("addr").agg(max($"score"), min($"score"), count($"*")).show

4>链接查询

scala>val dept=sc.parallelize(List((100,"caiwubu"),(200,"yanfabu"))).toDF("deptid","deptname")
scala>val emp=sc.parallelize(List((1,100,"zhang"),(2,200,"li"),(3,300,"wang"))).toDF("id","did","name")
scala>dept.join(emp,$"deptid" === $"did").show
scala>dept.join(emp,$"deptid" === $"did","left").show

    左向外联接的结果集包括  LEFT OUTER子句中指定的左表的全部行，而不单单是联接列所匹配的行。若是左表的某行在右表中没有匹配行，则在相关联的结果集行中右表的全部选择列表列均为空值。

scala>dept.join(emp,$"deptid" === $"did","right").show

 

2．执行运算

val df = sc.makeRDD(List(1,2,3,4,5)).toDF("num");
df.select($"num" * 100).show

3．使用列表

val df = sc.makeRDD(List(("zhang",Array("bj","sh")),("li",Array("sz","gz")))).toDF("name","addrs")
df.selectExpr("name","addrs[0]").show

    使用结构体：

{"name":"王二小","address":{"city":"大土坡","street":"南二环甲字1号"}}
{"name":"流放","address":{"city":"天涯海角","street":"南二环甲字2号"}}

val df = sqlContext.read.json("file:///root/work/users.json")
dfs.select("name","address.street").show

    其余

df.count//获取记录总数
val row = df.first()//获取第一条记录
val value = row.getString(1)//获取该行指定列的值
df.collect //获取当前df对象中的全部数据为一个Array 其实就是调用了df对象对应的底层的rdd的collect方法

二、经过sql语句来调用

1．针对表的操做

1>建立表

df.registerTempTable("tabName")

2>查看表

sqlContext.sql("show tables").show

2．查询

val sqc = new org.apache.spark.sql.SQLContext(sc);
val df = sc.makeRDD(List((1,"a","bj"),(2,"b","sh"),(3,"c","gz"),(4,"d","bj"),(5,"e","gz"))).toDF("id","name","addr");
df.registerTempTable("stu");
sqc.sql("select * from stu").show()

1>带条件的查询

val df = sc.makeRDD(List((1,"a","bj"),(2,"b","sh"),(3,"c","gz"),(4,"d","bj"),(5,"e","gz"))).toDF("id","name","addr");
df.registerTempTable("stu");
sqc.sql("select * from stu where addr = 'bj'").show()

2>排序查询

val sqlContext = new org.apache.spark.sql.SQLContext(sc);
val df = sc.makeRDD(List((1,"a","bj"),(2,"b","sh"),(3,"c","gz"),(4,"d","bj"),(5,"e","gz"))).toDF("id","name","addr");
df.registerTempTable("stu");
sqlContext.sql("select * from stu order by addr").show()
sqlContext.sql("select * from stu order by addr  desc").show()

3>分组查询

val sqlContext = new org.apache.spark.sql.SQLContext(sc);
val df = sc.makeRDD(List((1,"a","bj"),(2,"b","sh"),(3,"c","gz"),(4,"d","bj"),(5,"e","gz"))).toDF("id","name","addr");
df.registerTempTable("stu");
sqlContext.sql("select addr,count(*) from stu group by addr").show()

4>链接查询

val sqlContext = new org.apache.spark.sql.SQLContext(sc);
val dept=sc.parallelize(List((100,"财务部"),(200,"研发部"))).toDF("deptid","deptname")
val emp=sc.parallelize(List((1,100,"张财务"),(2,100,"李会计"),(3,300,"王研发"))).toDF("id","did","name")
dept.registerTempTable("deptTab");
emp.registerTempTable("empTab");
sqlContext.sql("select deptname,name from deptTab inner join empTab on deptTab.deptid = empTab.did").show()

5>分页查询

val sqlContext = new org.apache.spark.sql.SQLContext(sc);
val df = sc.makeRDD(List(1,2,3,4,5)).toDF("num");
df.registerTempTable("tabx")
sqlContext.sql("select * from tabx limit 3").show();

3．执行运算

val sqlContext = new org.apache.spark.sql.SQLContext(sc);
val df = sc.makeRDD(List(1,2,3,4,5)).toDF("num");
df.registerTempTable("tabx")
sqlContext.sql("select num * 100 from tabx").show();

4．相似hive方式的操做

scala>val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
scala>hiveContext.sql("create table if not exists zzz (key int, value string) row format delimited fields terminated by '|'")
scala>hiveContext.sql("load data local inpath 'file:///home/software/hdata.txt' into table zzz")
scala>hiveContext.sql("select key,value from zzz").show

5．案例

val sqlContext = new org.apache.spark.sql.SQLContext(sc);
val df = sc.textFile("file:///root/work/words.txt").flatMap{ _.split(" ") }.toDF("word")
df.registerTempTable("wordTab")
sqlContext.sql("select word,count(*) from wordTab group by word").show

2、SparkSql API

    能够经过java API使用sparksql。

一、建立工程

    打开scala IDE开发环境，建立一个scala工程。

二、导入jar包

    导入spark相关依赖jar包。

 

三、建立类

    建立包路径以object类。

四、代码示意

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext

object Driver {
  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setMaster("local").setAppName("sql")
    val sc = new SparkContext(conf)
    //获取Sparksql上下文对象
    val sqc = new SQLContext(sc)

    val r1 = sc.makeRDD(List(("tom", 23), ("rose", 25), ("jim", 15), ("jary", 30)))
    //导入sql上下文对象的隐藏类，目的是让rdd具备toDF方法
    import sqc.implicits._
    val t1 = r1.toDF("name", "age")

    t1.registerTempTable("stu")
    val result = sqc.sql("select * from stu")
    //DataFrame转成RDD，通常用于结果的存储
    val resultRDD = result.toJavaRDD
    resultRDD.saveAsTextFile("D://sqlresult")

  }
}

五、部署到服务器

    打jar包，并上传到linux虚拟机上，在spark的bin目录下执行以下命令：

sh spark-submit --class cn.tedu.sparksql.Demo01 ./sqlDemo01.jar

    最后检验。

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