今下午在课上没有将实验做完,课下进行了补充,最终完成。下面附上厦门大学数据库实验室中spark实验官网提供的标准答案,以供参考。
将下列 json 数据复制到你的 ubuntu 系统/usr/local/spark 下,并保存命名为 employee.json。 { "id":1 ,"name":" Ella","age":36 } { "id":2,"name":"Bob","age":29 }
{ "id":3 ,"name":"Jack","age":29 }
{ "id":4 ,"name":"Jim","age":28 }
{ "id":5 ,"name":"Damon" }
{ "id":5 ,"name":"Damon" }
首先为 employee.json 创建 DataFrame,并写出 Scala 语句完成下列操作:创建 DataFrame
答案:
scala> import org.apache.spark.sql.SparkSession scala> val spark=SparkSession.builder().getOrCreate() scala> import spark.implicits._
scala> val df = spark.read.json("file:///usr/local/spark/test/employee.json")
(1) 查询 DataFrame 的所有数据答案:scala> df.show()
(2) 查询所有数据,并去除重复的数据
答案:scala> df.distinct().show()
(3) 查询所有数据,打印时去除 id 字段
答案:scala> df.drop("id").show() (4) 筛选age>20的记录答案:scala> df.filter(df("age") > 30 ).show()
(5) 将数据按 name 分组
答案:scala> df.groupBy("name").count().show()
(6) 将数据按 name 升序排列
答案:scala> df.sort(df("name").asc).show()
(7) 取出前 3 行数据
答案:scala> df.take(3) 或scala> df.head(3) (8) 查询所有记录的 name 列,并为其取别名为 username
答案:scala> df.select(df("name").as("username")).show()
(9) 查询年龄 age 的平均值
答案:scala> df.agg("age"->"avg") (10) 查询年龄 age 的最小值
答案:scala> df.agg("age"->"min")
源文件内容如下(包含 id,name,age),将数据复制保存到 ubuntu 系统/usr/local/spark 下,命名为 employee.txt,实现从 RDD 转换得到 DataFrame,并按 id:1,name:Ella,age:36 的格式
打印出 DataFrame 的所有数据。请写出程序代码。(任选一种方法即可)
1,Ella,36
2,Bob,29
3,Jack,29
答案:
假设当前目录为/usr/local/spark/mycode/rddtodf,在当前目录下新建一个目录 mkdir -p src/main/scala ,然后在目录 /usr/local/spark/mycode/rddtodf/src/main/scala 下新建一个
rddtodf.scala,复制下面代码;(下列两种方式任选其一)
方法一:利用反射来推断包含特定类型对象的RDD的schema,适用对已知数据结构的RDD 转换;
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder import org.apache.spark.sql.Encoder import spark.implicits._ object RDDtoDF { def main(args: Array[String]) { case class Employee(id:Long,name: String, age: Long) val employeeDF = spark.sparkContext.textFile("file:///usr/local/spark/employee.txt").map(_.split(",")).map(at tributes => Employee(attributes(0).trim.toInt,attributes(1), attributes(2).trim.toInt)).toDF() employeeDF.createOrReplaceTempView("employee") val employeeRDD = spark.sql("select id,name,age from employee") employeeRDD.map(t => "id:"+t(0)+","+"name:"+t(1)+","+"age:"+t(2)).show() } } |
方法二:使用编程接口,构造一个 schema 并将其应用在已知的 RDD 上。
import org.apache.spark.sql.types._import org.apache.spark.sql.Encoder import org.apache.spark.sql.Row object RDDtoDF { def main(args: Array[String]) { val employeeRDD = spark.sparkContext.textFile("file:///usr/local/spark/employee.txt") val schemaString = "id name age" val fields = schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, nullable = true)) val schema = StructType(fields) val rowRDD = employeeRDD.map(_.split(",")).map(attributes => Row(attributes(0).trim, attributes(1), attributes(2).trim)) val employeeDF = spark.createDataFrame(rowRDD, schema) employeeDF.createOrReplaceTempView("employee") val results = spark.sql("SELECT id,name,age FROM employee") results.map(t => "id:"+t(0)+","+"name:"+t(1)+","+"age:"+t(2)).show() } } |
在目录/usr/local/spark/mycode/rddtodf 目录下新建 simple.sbt,复制下面代码:
name := "Simple Project" version := "1.0" scalaVersion := "2.11.8" libraryDependencies += "org.apache.spark" % "spark-core" % "2.1.0" |
在目录/usr/local/spark/mycode/rddtodf 下执行下面命令打包程序
/usr/local/sbt/sbt package |
|
最后在目录/usr/local/spark/mycode/rddtodf 下执行下面命令提交程序 |
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/usr/local/spark/bin/spark-submit --class " RDDtoDF /usr/local/spark/mycode/rddtodf/target/scala-2.11/simple-project_2.11-1.0.jar |
" |
在终端即可看到输出结果。
(1) 在 MySQL 数据库中新建数据库 sparktest,再建表 employee,包含下列两行数据;表 1 employee 表原有数据
id |
name |
gender |
|
age |
1 |
Alice |
F |
|
22 |
2 |
John |
M |
|
25 |
答案:
mysql> create database sparktest; mysql> use sparktest;
mysql> create table employee (id int(4), name char(20), gender char(4), age int(4)); mysql> insert into employee values(1,'Alice','F',22); mysql> insert into employee values(2,'John','M',25);
(2) 配置 Spark通过 JDBC 连接数据库MySQL,编程实现利用 DataFrame 插入下列数据到 MySQL,最后打印出 age 的最大值和 age 的总和。表 2 employee 表新增数据
id |
|
name |
|
gender |
|
age |
3 |
|
Mary |
|
F |
|
26 |
4 |
|
Tom |
|
M |
|
23 |
答案:假设当前目录为/usr/local/spark/mycode/testmysql,在当前目录下新建一个目录 mkdir -p src/main/scala ,然后在目录 /usr/local/spark/mycode/testmysql/src/main/scala 下新建一个 testmysql.scala,复制下面代码;
import java.util.Properties import org.apache.spark.sql.types._ import org.apache.spark.sql.Row object TestMySQL { def main(args: Array[String]) { val employeeRDD = spark.sparkContext.parallelize(Array("3 Mary F 26","4 Tom M 23")).map(_.split(" ")) val schema = StructType(List(StructField("id", IntegerType, true),StructField("name", StringType, true),StructField("gender", StringType, true),StructField("age", IntegerType, true))) val rowRDD = employeeRDD.map(p => Row(p(0).toInt,p(1).trim, p(2).trim,p(3).toInt)) val employeeDF = spark.createDataFrame(rowRDD, schema) val prop = new Properties() prop.put("user", "root") prop.put("password", "hadoop") prop.put("driver","com.mysql.jdbc.Driver") employeeDF.write.mode("append").jdbc("jdbc:mysql://localhost:3306/sparktest", sparktest.employee", prop) val jdbcDF = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/sparktest").option("driver","com.mysql.jdbc.Driver").optio n("dbtable","employee").option("user","root").option("password", "hadoop").load() jdbcDF.agg("age" -> "max", "age" -> "sum") } } |
在目录/usr/local/spark/mycode/testmysql 目录下新建 simple.sbt,复制下面代码:
name := "Simple Project" version := "1.0" scalaVersion := "2.11.8"
libraryDependencies += "org.apache.spark" % "spark-core" % "2.1.0" |
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在目录/usr/local/spark/mycode/testmysql 下执行下面命令打包程序 |
|
/usr/local/sbt/sbt package |
|
最后在目录/usr/local/spark/mycode/testmysql 下执行下面命令提交程序 |
|
/usr/local/spark/bin/spark-submit --class " TestMySQL /usr/local/spark/mycode/testmysql/target/scala-2.11/simple-project_2.11-1.0.jar |
" |
在终端即可看到输出结果。
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