[英]How to convert DataFrame to Dataset in Apache Spark in Java?
I can convert DataFrame to Dataset in Scala very easy: 我可以很容易地将Scala中的DataFrame转换为Dataset:
case class Person(name:String, age:Long)
val df = ctx.read.json("/tmp/persons.json")
val ds = df.as[Person]
ds.printSchema
but in Java version I don't know how to convert Dataframe to Dataset? 但在Java版本中我不知道如何将Dataframe转换为Dataset? Any Idea? 任何的想法?
my effort is: 我的努力是:
DataFrame df = ctx.read().json(logFile);
Encoder<Person> encoder = new Encoder<>();
Dataset<Person> ds = new Dataset<Person>(ctx,df.logicalPlan(),encoder);
ds.printSchema();
but the compiler say: 但是编译器说:
Error:(23, 27) java: org.apache.spark.sql.Encoder is abstract; cannot be instantiated
solution based on @Leet-Falcon answers: 基于@Leet-Falcon答案的解决方案:
DataFrame df = ctx.read().json(logFile);
Encoder<Person> encoder = Encoders.bean(Person.class);
Dataset<Person> ds = new Dataset<Person>(ctx, df.logicalPlan(), encoder);
Official Spark docs suggest in Dataset API the following: 官方Spark文档在Dataset API中建议如下:
Java Encoders are specified by calling static methods on Encoders . 通过在编码器上调用静态方法来指定Java编码器 。
List<String> data = Arrays.asList("abc", "abc", "xyz");
Dataset<String> ds = context.createDataset(data, Encoders.STRING());
Encoders can be composed into tuples: 编码器可以组成元组:
Encoder<Tuple2<Integer, String>> encoder2 = Encoders.tuple(Encoders.INT(), Encoders.STRING());
List<Tuple2<Integer, String>> data2 = Arrays.asList(new scala.Tuple2(1, "a");
Dataset<Tuple2<Integer, String>> ds2 = context.createDataset(data2, encoder2);
Or constructed from Java Beans by Encoders#bean : 或者由编码器编写的 Java Beans #bean :
Encoders.bean(MyClass.class);
If you want to convert a generic DF to a Dataset in Java, you can use RowEncoder class like below 如果要将通用DF转换为Java中的数据集,可以使用如下所示的RowEncoder类
DataFrame df = sql.read().json(sc.parallelize(ImmutableList.of(
"{\"id\": 0, \"phoneNumber\": 109, \"zip\": \"94102\"}"
)));
Dataset<Row> dataset = df.as(RowEncoder$.MODULE$.apply(df.schema()));
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