阅读spark

Question

我正在尝试在pysaprk中使用spark-xml jar读取xml / nested xml。

df = sqlContext.read \
  .format("com.databricks.spark.xml")\
   .option("rowTag", "hierachy")\
   .load("test.xml"

执行时，数据帧无法正常创建。

    +--------------------+
    |                 att|
    +--------------------+
    |[[1,Data,[Wrapped...|
    +--------------------+

我有的xml格式如下：

Answer 1

heirarchy应为 rootTag ，而att应为 rowTag

df = spark.read \
    .format("com.databricks.spark.xml") \
    .option("rootTag", "hierarchy") \
    .option("rowTag", "att") \
    .load("test.xml")

你应该

+-----+------+----------------------------+
|Order|attval|children                    |
+-----+------+----------------------------+
|1    |Data  |[[[1, Studyval], [2, Site]]]|
|2    |Info  |[[[1, age], [2, gender]]]   |
+-----+------+----------------------------+

和schema

root
 |-- Order: long (nullable = true)
 |-- attval: string (nullable = true)
 |-- children: struct (nullable = true)
 |    |-- att: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- Order: long (nullable = true)
 |    |    |    |-- attval: string (nullable = true)

查找有关databricks xml

的更多信息

Answer 2

Databricks发布了新版本，可将XML读取到Spark DataFrame

<dependency>
     <groupId>com.databricks</groupId>
     <artifactId>spark-xml_2.12</artifactId>
     <version>0.6.0</version>
 </dependency>

我在此示例中使用的输入XML文件可在GitHub存储库中找到。

val df = spark.read
      .format("com.databricks.spark.xml")
      .option("rowTag", "person")
      .xml("persons.xml")

模式

root
 |-- _id: long (nullable = true)
 |-- dob_month: long (nullable = true)
 |-- dob_year: long (nullable = true)
 |-- firstname: string (nullable = true)
 |-- gender: string (nullable = true)
 |-- lastname: string (nullable = true)
 |-- middlename: string (nullable = true)
 |-- salary: struct (nullable = true)
 |    |-- _VALUE: long (nullable = true)
 |    |-- _currency: string (nullable = true)

输出：

+---+---------+--------+---------+------+--------+----------+---------------+
|_id|dob_month|dob_year|firstname|gender|lastname|middlename|         salary|
+---+---------+--------+---------+------+--------+----------+---------------+
|  1|        1|    1980|    James|     M|   Smith|      null|  [10000, Euro]|
|  2|        6|    1990|  Michael|     M|    null|      Rose|[10000, Dollor]|
+---+---------+--------+---------+------+--------+----------+---------------+

请注意，Spark XML API有一些局限性，这里Spark-XML API Limitations

希望这会有所帮助！

Answer 3

您可以使用Databricks jar将xml解析为数据框。您可以使用maven或sbt来编译依赖关系，也可以直接将jar与spark提交一起使用。

pyspark --jars /home/sandipan/Downloads/spark_jars/spark-xml_2.11-0.6.0.jar

df = spark.read \
    .format("com.databricks.spark.xml") \
    .option("rootTag", "SmsRecords") \
    .option("rowTag", "sms") \
    .load("/home/sandipan/Downloads/mySMS/Sms/backupinfo.xml")

Schema>>> df.printSchema()
root
 |-- address: string (nullable = true)
 |-- body: string (nullable = true)
 |-- date: long (nullable = true)
 |-- type: long (nullable = true)

>>> df.select("address").distinct().count()
530 

关注此 http://www.thehadoopguy.com/2019/09/how-to-parse-xml-data-to-saprk-dataframe.html