如何在DataFrame中保持键值顺序与JSON相同? | 如何在DataFrame中保持键值顺序与JSON相同

本文介绍了如何在DataFrame中保持键值顺序与JSON相同?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

示例JSON数据:

{"name": "dev","salary": 100,"occupation": "engg","address": "noida"}
{"name": "karthik","salary": 200,"occupation": "engg","address": "blore"}

Spark Java代码:

Spark Java code:

DataFrame df = sqlContext.read().json(jsonPath);
df.printSchema();
df.show(false);

输出:

root
 |-- address: string (nullable = true)
 |-- name: string (nullable = true)
 |-- occupation: string (nullable = true)
 |-- salary: long (nullable = true)


+-------+-------+----------+------+
|address|name   |occupation|salary|
+-------+-------+----------+------+
|noida  |dev    |engg      |10000 |
|blore  |karthik|engg      |20000 |
+-------+-------+----------+------+

各列按字母顺序排列. 有什么方法可以维持自然秩序?

Columns are arranged in the alphabetical order. Is there any way to maintain natural order?

推荐答案

您可以在阅读json的同时提供schema，它会保持顺序.

You can provide schema while reading the json and it will maintain the order.

StructType schema = DataTypes.createStructType(new StructField[] {
    DataTypes.createStructField("name", DataTypes.StringType, true),
    DataTypes.createStructField("salary", DataTypes.IntegerType, true),
    DataTypes.createStructField("occupation", DataTypes.StringType, true),
    DataTypes.createStructField("address", DataTypes.StringType, true)});

DataFrame df = sqlContext.read().schema(schema).json(jsonPath);
df.printSchema();
df.show(false);

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