Spark session available as 'spark'.
Welcome to Spark version 2.0.1
Using Scala version 2.11.8
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.DataFrame
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.Encoder
scala>
scala> val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate()
16/11/05 15:40:31 WARN SparkSession$Builder: Use an existing SparkSession, some configuration may not take effect.
spark: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@68d97cfb
scala>
scala> // For implicit conversions like converting RDDs to DataFrames
scala> import spark.implicits._
scala>
scala> // 创建数据框
scala> // val data1:DataFrame=spark.read.csv("hdfs://ns1/datafile/wangxiao/Affairs.csv")
scala>
scala> val data1: DataFrame = spark.read.option("header", true).format("csv").load("hdfs://ns1/datafile/wangxiao/Affairs.csv")
data1: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
scala>
scala> data1.printSchema()
root
|-- affairs: string (nullable = true)
|-- gender: string (nullable = true)
|-- age: string (nullable = true)
|-- yearsmarried: string (nullable = true)
|-- children: string (nullable = true)
|-- religiousness: string (nullable = true)
|-- education: string (nullable = true)
|-- occupation: string (nullable = true)
|-- rating: string (nullable = true)
scala>
scala> data1.limit(10).show
scala>
scala> //##############################################
scala> // 转换字符类型
scala> val res1 = data1.select(
| data1("affairs").cast("Double"),
| data1("age").cast("Double"),
| data1("yearsmarried").cast("Double"),
| data1("religiousness").cast("Double"),
| data1("education").cast("Double"),
| data1("occupation").cast("Double"),
| data1("rating").cast("Double"),
| data1("gender").cast("String"),
| data1("children").cast("String"))
res1: org.apache.spark.sql.DataFrame = [affairs: double, age: double ... 7 more fields]
scala>
scala> res1.printSchema()
root
|-- affairs: double (nullable = true)
|-- age: double (nullable = true)
|-- yearsmarried: double (nullable = true)
|-- religiousness: double (nullable = true)
|-- education: double (nullable = true)
|-- occupation: double (nullable = true)
|-- rating: double (nullable = true)
|-- gender: string (nullable = true)
|-- children: string (nullable = true)
scala>
scala> //################################################
scala> //创建RDD
scala> val data2: RDD[String] = spark.sparkContext.textFile("hdfs://ns1/datafile/wangxiao/Affairs.txt")
scala>
scala> case class Affairs1(affairs: Int, gender: String, age: Int,
| yearsmarried: Double, children: String, religiousness: Int,
| education: Double, occupation: Double, rating: Int)
defined class Affairs1
scala>
scala> // RDD转换成数据框
scala> val res2 = data2.map { _.split(" ") }.map { line =>
| Affairs1(line(0).toInt, line(1).trim.toString(), line(2).toInt,
| line(3).toDouble, line(4).trim.toString(), line(5).toInt,
| line(6).toDouble, line(7).toDouble, line(8).toInt)
| }.toDF()
res2: org.apache.spark.sql.DataFrame = [affairs: int, gender: string ... 7 more fields]
scala>
scala> res2.printSchema()
root
|-- affairs: integer (nullable = false)
|-- gender: string (nullable = true)
|-- age: integer (nullable = false)
|-- yearsmarried: double (nullable = false)
|-- children: string (nullable = true)
|-- religiousness: integer (nullable = false)
|-- education: double (nullable = false)
|-- occupation: double (nullable = false)
|-- rating: integer (nullable = false)
scala>
scala> //###############################################
scala> // 创建视图
scala> res1.createOrReplaceTempView("Affairs")
scala>
scala> // 子查询
scala> //val df1 = spark.sql("SELECT * FROM Affairs WHERE age BETWEEN 20 AND 25")
scala> val df1 = spark.sql("select gender, age,rating from ( SELECT * FROM Affairs WHERE age BETWEEN 20 AND 25 ) t ")
df1: org.apache.spark.sql.DataFrame = [gender: string, age: double ... 1 more field]
scala>
scala> df1.limit(10).show
+------+----+------+
|gender| age|rating|
+------+----+------+
| male|22.0| 3.0|
|female|22.0| 3.0|
| male|22.0| 5.0|
|female|22.0| 4.0|
|female|22.0| 4.0|
|female|22.0| 5.0|
|female|22.0| 5.0|
|female|22.0| 5.0|
|female|22.0| 5.0|
|female|22.0| 5.0|
+------+----+------+
scala>
scala> // 保存数据框到文件
scala> data1.select("gender", "age", "education").write.format("csv").save("hdfs://ns1/datafile/wangxiao/data123.csv")

ITPub博客

Spark2 DataFrame数据框常用操作

最新文章