Spark---创建DataFrame的方式

1、读取json格式的文件创建DataFrame

注意：

1、可以两种方式读取json格式的文件。

2、df.show()默认显示前20行数据。

3、DataFrame原生API可以操作DataFrame。

4、注册成临时表时，表中的列默认按ascii顺序显示列。

df.createTempView("mytable")
df.createOrReplaceTempView("mytable")
df.createGlobalTempView("mytable")
df.createOrReplaceGlobalTempView("mytable")
Session.sql("select * from global_temp.mytable").show()

5、DataFrame是一个Row类型的RDD，df.rdd()/df.javaRdd()。

java

SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("jsonfile");
SparkContext sc = new SparkContext(conf);

//创建sqlContext
SQLContext sqlContext = new SQLContext(sc);

/**
 * DataFrame的底层是一个一个的RDD  RDD的泛型是Row类型。
 * 以下两种方式都可以读取json格式的文件
 */
DataFrame df = sqlContext.read().format("json").load("sparksql/json");
// DataFrame df2 = sqlContext.read().json("sparksql/json.txt");
// df2.show();

/**
 * DataFrame转换成RDD
 */
RDD<Row> rdd = df.rdd();
/**
 * 显示 DataFrame中的内容，默认显示前20行。如果现实多行要指定多少行show(行数)
 * 注意：当有多个列时，显示的列先后顺序是按列的ascii码先后显示。
 */
// df.show();
/**
 * 树形的形式显示schema信息
 */
df.printSchema();
/**
  * dataFram自带的API 操作DataFrame
  */
  //select name from table
 // df.select("name").show();
 //select name age+10 as addage from table
	 df.select(df.col("name"),df.col("age").plus(10).alias("addage")).show();
 //select name ,age from table where age>19
	 df.select(df.col("name"),df.col("age")).where(df.col("age").gt(19)).show();
 //select count(*) from table group by age
 df.groupBy(df.col("age")).count().show();
		
 /**
   * 将DataFrame注册成临时的一张表，这张表临时注册到内存中，是逻辑上的表，不会雾化到磁盘
  */
 df.registerTempTable("jtable");
		
 DataFrame sql = sqlContext.sql("select age,count(1) from jtable group by age");
 DataFrame sql2 = sqlContext.sql("select * from jtable");
		
 sc.stop();

scala:

1.val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
2.// val frame: DataFrame = session.read.json("./data/json")
3.val frame = session.read.format("json").load("./data/json")
4.frame.show(100)
5.frame.printSchema()
6.
7./**
8.* DataFrame API 操作
9.*/
10.//select name ,age from table
11.frame.select("name","age").show(100)
12.
13.//select name,age + 10 as addage from table
14.frame.select(frame.col("name"),frame.col("age").plus(10).as("addage")).show(100)
15.
16.//select name,age from table where age >= 19
17.frame.select("name","age").where(frame.col("age").>=(19)).show(100)
18.frame.filter("age>=19").show(100)
19.
20.//select name ,age from table order by name asc ,age desc
21.import session.implicits._
22.frame.sort($"name".asc,frame.col("age").desc).show(100)
23.
24.//select name ,age from table where age is not null
25.frame.filter("age is not null").show()
26.
27./**
28.* 创建临时表
29.*/
30.frame.createTempView("mytable")
31.session.sql("select name ,age from mytable where age >= 19").show()
32.frame.createOrReplaceTempView("mytable")
33.frame.createGlobalTempView("mytable")
34.frame.createOrReplaceGlobalTempView("mytable")
35.
36./**
37.* dataFrame 转换成RDD
38.*/
39.val rdd: RDD[Row] = frame.rdd
40.rdd.foreach(row=>{
41.  val name = row.getAs[String]("name")
42.  val age = row.getAs[Long]("age")
43.  println(s"name is $name ,age is $age")
44.})

2、通过json格式的RDD创建DataFrame

java:

SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("jsonRDD");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<String> nameRDD = sc.parallelize(Arrays.asList(
	"{\"name\":\"zhangsan\",\"age\":\"18\"}",
	"{\"name\":\"lisi\",\"age\":\"19\"}",
	"{\"name\":\"wangwu\",\"age\":\"20\"}"
));
JavaRDD<String> scoreRDD = sc.parallelize(Arrays.asList(
"{\"name\":\"zhangsan\",\"score\":\"100\"}",
"{\"name\":\"lisi\",\"score\":\"200\"}",
"{\"name\":\"wangwu\",\"score\":\"300\"}"
));

DataFrame namedf = sqlContext.read().json(nameRDD);
DataFrame scoredf = sqlContext.read().json(scoreRDD);
namedf.registerTempTable("name");
scoredf.registerTempTable("score");

DataFrame result = sqlContext.sql("select name.name,name.age,score.score from name,score where name.name = score.name");
result.show();

sc.stop();

scala:

1.val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
2.val jsonList = List[String](
3.  "{'name':'zhangsan','age':'18'}",
4.  "{'name':'lisi','age':'19'}",
5.  "{'name':'wangwu','age':'20'}",
6.  "{'name':'maliu','age':'21'}",
7.  "{'name':'tainqi','age':'22'}"
8.)
9.
10.import session.implicits._
11.val jsds: Dataset[String] = jsonList.toDS()
12.val df = session.read.json(jsds)
13.df.show()
14.
15./**
16.* Spark 1.6
17.*/
18.val jsRDD: RDD[String] = session.sparkContext.parallelize(jsonList)
19.val frame: DataFrame = session.read.json(jsRDD)
20.frame.show()

3、非json格式的RDD创建DataFrame

1）、通过反射的方式将非json格式的RDD转换成DataFrame（不建议使用）

• 自定义类要可序列化

• 自定义类的访问级别是Public

• RDD转成DataFrame后会根据映射将字段按Assci码排序

• 将DataFrame转换成RDD时获取字段两种方式,一种是df.getInt(0)下标获取（不推荐使用），另一种是df.getAs("列名")获取（推荐使用）

  /**

  • 注意：

  • 1.自定义类必须是可序列化的

  • 2.自定义类访问级别必须是Public

  • 3.RDD转成DataFrame会把自定义类中字段的名称按assci码排序
    */
    SparkConf conf = new SparkConf();
    conf.setMaster("local").setAppName("RDD");
    JavaSparkContext sc = new JavaSparkContext(conf);
    SQLContext sqlContext = new SQLContext(sc);
    JavaRDD<String> lineRDD = sc.textFile("sparksql/person.txt");
    JavaRDD<Person> personRDD = lineRDD.map(new Function<String, Person>() {

    /**
    *
    */
    private static final long serialVersionUID = 1L;

    @Override
    public Person call(String s) throws Exception {
    Person p = new Person();
    p.setId(s.split(",")[0]);
    p.setName(s.split(",")[1]);
    p.setAge(Integer.valueOf(s.split(",")[2]));
    return p;
    }
    });
    /**

  • 传入进去Person.class的时候，sqlContext是通过反射的方式创建DataFrame

  • 在底层通过反射的方式获得Person的所有field，结合RDD本身，就生成了DataFrame
    */
    DataFrame df = sqlContext.createDataFrame(personRDD, Person.class);
    df.show();
    df.registerTempTable("person");
    sqlContext.sql("select name from person where id = 2").show();

  /**

  • 将DataFrame转成JavaRDD
  • 注意：
  • 1.可以使用row.getInt(0),row.getString(1)...通过下标获取返回Row类型的数据，但是要注意列顺序问题---不常用
  • 2.可以使用row.getAs("列名")来获取对应的列值。

  */
  JavaRDD<Row> javaRDD = df.javaRDD();
  JavaRDD<Person> map = javaRDD.map(new Function<Row, Person>() {

    /**
    * 
    */
    private static final long serialVersionUID = 1L;
  
    @Override
    public Person call(Row row) throws Exception {
            Person p = new Person();
            //p.setId(row.getString(1));
            //p.setName(row.getString(2));
            //p.setAge(row.getInt(0));
  
            p.setId((String)row.getAs("id"));
            p.setName((String)row.getAs("name"));
            p.setAge((Integer)row.getAs("age"));
            return p;
    }

  });
  map.foreach(new VoidFunction<Person>() {

    /**
    * 
    */
    private static final long serialVersionUID = 1L;
  
    @Override
    public void call(Person t) throws Exception {
          System.out.println(t);
    }

  });

  sc.stop();

scala：

1.case class MyPerson(id:Int,name:String,age:Int,score:Double)
2.
3.object Test {
4.  def main(args: Array[String]): Unit = {
5.    val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
6.    val peopleInfo: RDD[String] = session.sparkContext.textFile("./data/people.txt")
7.    val personRDD : RDD[MyPerson] = peopleInfo.map(info =>{
8.MyPerson(info.split(",")(0).toInt,info.split(",")(1),info.split(",")(2).toInt,info.split(",")(3).toDouble)
9.    })
10.    import session.implicits._
11.    val ds = personRDD.toDS()
12.    ds.createTempView("mytable")
13.    session.sql("select * from mytable ").show()
14.  }
15.}

2）、动态创建Schema将非json格式的RDD转换成DataFrame

java:

SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("rddStruct");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<String> lineRDD = sc.textFile("./sparksql/person.txt");
/**
 * 转换成Row类型的RDD
 */
JavaRDD<Row> rowRDD = lineRDD.map(new Function<String, Row>() {

	/**
	 * 
	 */
	private static final long serialVersionUID = 1L;

	@Override
	public Row call(String s) throws Exception {
          return RowFactory.create(
                String.valueOf(s.split(",")[0]),
                String.valueOf(s.split(",")[1]),
                Integer.valueOf(s.split(",")[2])
	);
	}
});
/**
 * 动态构建DataFrame中的元数据，一般来说这里的字段可以来源自字符串，也可以来源于外部数据库
 */
List<StructField> asList =Arrays.asList(
	DataTypes.createStructField("id", DataTypes.StringType, true),
	DataTypes.createStructField("name", DataTypes.StringType, true),
	DataTypes.createStructField("age", DataTypes.IntegerType, true)
);

StructType schema = DataTypes.createStructType(asList);
DataFrame df = sqlContext.createDataFrame(rowRDD, schema);

df.show();
sc.stop();

scala:

1.val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
2.val peopleInfo: RDD[String] = session.sparkContext.textFile("./data/people.txt")
3.
4.val rowRDD: RDD[Row] = peopleInfo.map(info => {
5.  val id = info.split(",")(0).toInt
6.  val name = info.split(",")(1)
7.  val age = info.split(",")(2).toInt
8.  val score = info.split(",")(3).toDouble
9.  Row(id, name, age, score)
10.})
11.val structType: StructType = StructType(Array[StructField](
12.  StructField("id", IntegerType),
13.  StructField("name", StringType),
14.  StructField("age", IntegerType),
15.  StructField("score", DoubleType)
16.))
17.val frame: DataFrame = session.createDataFrame(rowRDD,structType)
18.frame.createTempView("mytable")
19.session.sql("select * from mytable ").show()

4、读取parquet文件创建DataFrame

注意：

• 可以将DataFrame存储成parquet文件。保存成parquet文件的方式有两种

  df.write().mode(SaveMode.Overwrite)format("parquet")
  .save("./sparksql/parquet");
  df.write().mode(SaveMode.Overwrite).parquet("./sparksql/parquet");

• SaveMode指定文件保存时的模式。

Overwrite：覆盖

Append：追加

ErrorIfExists：如果存在就报错

Ignore：如果存在就忽略

java:

SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("parquet");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
JavaRDD<String> jsonRDD = sc.textFile("sparksql/json");
DataFrame df = sqlContext.read().json(jsonRDD);
/**
 * 将DataFrame保存成parquet文件，SaveMode指定存储文件时的保存模式
 * 保存成parquet文件有以下两种方式：
 */
df.write().mode(SaveMode.Overwrite).format("parquet").save("./sparksql/parquet");
df.write().mode(SaveMode.Overwrite).parquet("./sparksql/parquet");
df.show();
/**
 * 加载parquet文件成DataFrame	
 * 加载parquet文件有以下两种方式：	
 */

DataFrame load = sqlContext.read().format("parquet").load("./sparksql/parquet");
load = sqlContext.read().parquet("./sparksql/parquet");
load.show();

sc.stop();

scala:

1.val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
2.val frame: DataFrame = session.read.json("./data/json")
3.frame.show()
4.frame.write.mode(SaveMode.Overwrite).parquet("./data/parquet")
5.
6.val df: DataFrame = session.read.format("parquet").load("./data/parquet")
7.df.createTempView("mytable")
8.session.sql("select count(*) from mytable ").show()

5、读取JDBC中的数据创建DataFrame(MySql为例)

两种方式创建DataFrame

java:

SparkConf conf = new SparkConf();
conf.setMaster("local").setAppName("mysql");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
/**
 * 第一种方式读取MySql数据库表，加载为DataFrame
 */
Map<String, String> options = new HashMap<String,String>();
options.put("url", "jdbc:mysql://192.168.179.4:3306/spark");
options.put("driver", "com.mysql.jdbc.Driver");
options.put("user", "root");
options.put("password", "123456");
options.put("dbtable", "person");
DataFrame person = sqlContext.read().format("jdbc").options(options).load();
person.show();
person.registerTempTable("person");
/**
 * 第二种方式读取MySql数据表加载为DataFrame
 */
DataFrameReader reader = sqlContext.read().format("jdbc");
reader.option("url", "jdbc:mysql://192.168.179.4:3306/spark");
reader.option("driver", "com.mysql.jdbc.Driver");
reader.option("user", "root");
reader.option("password", "123456");
reader.option("dbtable", "score");
DataFrame score = reader.load();
score.show();
score.registerTempTable("score");

DataFrame result = 
sqlContext.sql("select person.id,person.name,score.score from person,score where person.name = score.name");
result.show();
/**
 * 将DataFrame结果保存到Mysql中
 */
Properties properties = new Properties();
properties.setProperty("user", "root");
properties.setProperty("password", "123456");
result.write().mode(SaveMode.Overwrite).jdbc("jdbc:mysql://192.168.179.4:3306/spark", "result", properties);

sc.stop();

scala:

1.val session = SparkSession.builder().appName("jsonData").master("local").getOrCreate()
2.
3.val prop = new Properties()
4.prop.setProperty("user","root")
5.prop.setProperty("password","123456")
6./**
7.* 第一种方式
8.*/
9.val df1 = session.read.jdbc("jdbc:mysql://192.168.179.14:3306/spark","person",prop)
10.df1.show()
11.df1.createTempView("person")
12.
13./**
14.* 第二种方式
15.*/
16.val map = Map[String,String](
17. "url" -> "jdbc:mysql://192.168.179.14:3306/spark",
18. "driver " -> "com.mysql.jdbc.Driver",
19. "user" -> "root",
20. "password" -> "123456",
21. "dbtable" -> "score"
22.)
23.val df2 = session.read.format("jdbc").options(map).load()
24.df2.show()
25.
26./**
27.* 第三种方式
28.*/
29.val df3 = session.read.format("jdbc")
30. .option("url", "jdbc:mysql://192.168.179.14:3306/spark")
31. .option("driver", "com.mysql.jdbc.Driver")
32. .option("user", "root")
33. .option("password", "123456")
34. .option("dbtable", "score")
35. .load()
36.df3.show()
37.df3.createTempView("score")
38.
39.val result = session.sql("select person.id,person.name,person.age,score.score from person ,score where person.id = score.id")
40.
41.result.show()
42.//将结果保存到mysql中
43.result.write.mode(SaveMode.Overwrite).jdbc("jdbc:mysql://192.168.179.14:3306/spark","result",prop)
44.