我正在使用Python中的spark.xml读取XML文件,并且遇到了一个看似非常具体的问题。
我能够缩小导致问题的XML部分的范围,但不能弄清为什么会发生这种情况。
这是要在XML文件中读取的代码:
src = sqlContext.read.format("com.databricks.spark.xml").options(rowTag="root").load("file.xml")
这里是使用spark.xml库从XML读取的模式,其中rowTag =“ root”:
root
|-- AID: long (nullable = true)
|-- RID: long (nullable = true)
|-- XmlData: struct (nullable = true)
| |-- NC: struct (nullable = true)
| | |-- RS: struct (nullable = true)
| | | |-- RD: struct (nullable = true)
| | | | |-- CR: struct (nullable = true)
| | | | | |-- Addr1: string (nullable = true)
| | | | | |-- Addr2: string (nullable = true)
| | | | | |-- City: string (nullable = true)
| | | | | |-- InFile: string (nullable = true)
| | | | | |-- Name: string (nullable = true)
| | | | | |-- Phone: long (nullable = true)
| | | | | |-- State: string (nullable = true)
| | | | | |-- Zip: long (nullable = true)
| | | | |-- SC: struct (nullable = true)
| | | | | |-- Class: string (nullable = true)
| | | | | |-- ClassType: string (nullable = true)
| | | | | |-- Message: string (nullable = true)
| | | | | |-- SC: long (nullable = true)
| | | | |-- NC: long (nullable = true)
| | | | |-- CRR: string (nullable = true)
| | | | |-- RM: struct (nullable = true)
| | | | | |-- Addr1: string (nullable = true)
| | | | | |-- City: string (nullable = true)
| | | | | |-- MemberNo: string (nullable = true)
| | | | | |-- Name: string (nullable = true)
| | | | | |-- State: string (nullable = true)
| | | | | |-- Zip: long (nullable = true)
| | | | |-- TL: array (nullable = true)
| | | | | |-- element: struct (containsNull = true)
| | | | | | |-- _AvgDays: long (nullable = true)
| | | | | | |-- _Comment: string (nullable = true)
| | | | | | |-- _Current: long (nullable = true)
| | | | | | |-- _HC: long (nullable = true)
| | | | | | |-- _IndType: string (nullable = true)
| | | | | | |-- _LS: long (nullable = true)
| | | | | | |-- _OpenDate: long (nullable = true)
| | | | | | |-- _PD120Day: long (nullable = true)
| | | | | | |-- _PD30Day: long (nullable = true)
| | | | | | |-- _PD60Day: long (nullable = true)
| | | | | | |-- _PD90Day: long (nullable = true)
| | | | | | |-- _Region: string (nullable = true)
| | | | | | |-- _ReportDate: string (nullable = true)
| | | | | | |-- _SourceID: long (nullable = true)
| | | | | | |-- _TD: long (nullable = true)
| | | | | | |-- _VALUE: string (nullable = true)
| | | | |-- Trends: array (nullable = true)
| | | | | |-- element: struct (containsNull = true)
| | | | | | |-- _CurrentPct: double (nullable = true)
| | | | | | |-- _LineCnt: long (nullable = true)
| | | | | | |-- _PD120DayPct: double (nullable = true)
| | | | | | |-- _PD30DayPct: double (nullable = true)
| | | | | | |-- _PD60DayPct: double (nullable = true)
| | | | | | |-- _PD90DayPct: double (nullable = true)
| | | | | | |-- _PD: string (nullable = true)
| | | | | | |-- _TD: long (nullable = true)
| | | | | | |-- _VALUE: string (nullable = true)
该模式还有更多内容,但是由于某种原因,XML解析器无法超越这一点。 我认为XML中的'趋势'标签存在问题,但找不到。
以下是“趋势”标签条目的示例:
<Trends PD="4205" LineCnt="0" TD="0" CurrentPct="0" PD30DayPct="0" PD60DayPct="0" PD90DayPct="0" PD120DayPct="0" />
我知道在XML解析器的早期版本中无法读取这样的关闭标签,但是在Databricks中使用它时,它可以正常工作,并且其他带有关闭标签的文件也可以正确读取。
这是进一步说明我正在解释的最终结果(我的XML中有13条记录):
+---------+--------+-------+
|AID |RID |XmlData|
+---------+--------+-------+
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
| null| null| null|
+---------+--------+-------+
(此顶层视图很好-我的代码稍后遍历“ XMLData”中的结构/数组-但这当然需要首先填充)
有人知道为什么这会导致解析时停止加载架构吗?
请注意,我无法显式定义架构。这将破坏我正在从事的项目的目的-我必须能够动态推断模式。同样,它对于我正在使用的其他文件也能正常工作。
答案 0 :(得分:1)
原因:
所以我能够弄清楚为什么会这样。当您尝试转换为数据框的xml的值不一致时,您可能会看到此问题。例如,类似下面的内容将出现此问题:
<?xml version="1.0"?>
<Company>
<Employee id="1">
<Email>tp@xyz.com</Email>
<Measures id="id32" type="AttributesInContext">
<Dimensions value="7in" title="Height"></Dimensions>
<Dimensions value="" title="Weight"></Dimensions></Measures>
</Employee>
<Employee id="2">
<Email>tp@xyz.com</Email>
<Measures id="id33" type="AttributesInContext">
<Dimensions value="6in" title="Height"></Dimensions>
<Dimensions value="" title="Weight"></Dimensions></Measures>
</Employee>
<Employee id="3">
<Email>tp@xyz.com</Email>
<Measures id="id34" type="AttributesInContext">
<Dimensions value="4in" title="Height"></Dimensions>
<Dimensions value="" title="Weight"></Dimensions></Measures>
</Employee>
</Company>
在这里,由于每个rowTag
条目中都有value =“”,因此我们很可能在所有行的数据框中都得到null,因为它无法推断数据类型。但是,如果将所有value=""
字段替换为某个实际值,则会看到此问题不会发生。
解决方案:
根据databricks link,可以在读取xml文件时使用option("nullValue", "")
选项解决此问题。因此,您的命令将如下所示(我在scala中尝试过此操作,在python中应该与此类似):
var xmldf = sparkSession.read.format("com.databricks.spark.xml")
.option("rowTag", rootTag).option("nullValue", "").load("/path/to/xml")