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 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 nameRDD = sc.parallelize(Arrays.asList(
"{\"name\":\"zhangsan\",\"age\":\"18\"}",
"{\"name\":\"lisi\",\"age\":\"19\"}",
"{\"name\":\"wangwu\",\"age\":\"20\"}"
));
JavaRDD 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 lineRDD = sc.textFile("sparksql/person.txt");
JavaRDD personRDD = lineRDD.map(new Function() {
/**
*
*/
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 javaRDD = df.javaRDD();
JavaRDD map = javaRDD.map(new Function() {
/**
*
*/
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() {
/**
*
*/
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 lineRDD = sc.textFile("./sparksql/person.txt");
/**
* 转换成Row类型的RDD
*/
JavaRDD rowRDD = lineRDD.map(new Function() {
/**
*
*/
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 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 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 options = new HashMap();
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.