Question

我有一个json数据文件，其中包含一个属性，即“tags”的字符串数组。 Apache Spark DataFrame 架构如下所示：

root
 |-- acceptedAnswerId: long (nullable = true)
 |-- answerCount: long (nullable = true)
 |-- body: string (nullable = true)
 |-- score: long (nullable = true)
 |-- <b>tags</b>: array (nullable = true)
 |    |-- element: string (containsNull = true)
 |-- title: string (nullable = true)
 |-- viewCount: long (nullable = true)

我想在 Java 中将每一行分成几行。我可以使用Scala找到类似的答案，但无法在Java中转换解决方案。有什么建议吗？

JSON中的“tags”属性如下所示：

"tags":["c#",".net","compression","decompression"]

Answer 1

为了使解决方案更具可视性，示例json数据如下所示：

{"id":4,"score":358,"viewCount":24247,"answerCount":13,"commentCount":1,"favoriteCount":28,"tags":["c#","winforms","type-conversion","opacity"]}

以下是用于将json数据读取为＆＃34; DataFrame＆＃34;的Java代码段。对象：

JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);

String jsonData = "{\"id\":4,\"score\":358,\"viewCount\":24247,\"tags\":[\"c#\",\"winforms\",\"type-conversion\",\"opacity\"]}";    

List dataSet = Arrays.asList(jsonData);

JavaRDD distData = sc.parallelize(dataSet);

DataFrame stackoverflow_Posts = sqlContext.read().json(distData);

stackoverflow_Posts.printSchema(); //let's print out the DataFrame schema (Output#1)

stackoverflow_Posts.show(); //let's show the DataFrame content (Ouput#2)

架构：输出＃1 如下所示：

root
 |-- id: long (nullable = true)
 |-- score: long (nullable = true)
 |-- tags: array (nullable = true)
 |    |-- element: string (containsNull = true)
 |-- viewCount: long (nullable = true)

数据：输出＃2 如下所示：

+---+-----+--------------------+---------+
| id|score|                tags|viewCount|
+---+-----+--------------------+---------+
|  4|  358|[c#, winforms, ty...|    24247|
+---+-----+--------------------+---------+

根据zero323的信息，我会继续处理：

DataFrame expanded = stackoverflow_Posts.withColumn("tag", org.apache.spark.sql.functions.explode(stackoverflow_Posts.col("tags")));

expanded.printSchema(); //let's print out the DataFrame schema again (Output#3)

expanded.show(); //let's show the DataFrame content (Output#4)

架构：输出＃3 如下所示：

root
 |-- id: long (nullable = true)
 |-- score: long (nullable = true)
 |-- tags: array (nullable = true)
 |    |-- element: string (containsNull = true)
 |-- viewCount: long (nullable = true)
 |-- tag: string (nullable = true)

爆炸后的数据：输出＃4

+---+-----+--------------------+---------+---------------+
| id|score|                tags|viewCount|            tag|
+---+-----+--------------------+---------+---------------+
|  4|  358|[c#, winforms, ty...|    24247|             c#|
|  4|  358|[c#, winforms, ty...|    24247|       winforms|
|  4|  358|[c#, winforms, ty...|    24247|type-conversion|
|  4|  358|[c#, winforms, ty...|    24247|        opacity|
+---+-----+--------------------+---------+---------------+

结果看起来非常类似于使用SQL连接两个表。

Answer 2

您只需使用explode功能。

DataFrame df = ...
DataFrame expanded = df.withColumn(
  "tag", org.apache.spark.sql.functions.explode(df.col("tags"))).drop("tags");

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