用于附加 xml 节点的 Groovy 脚本代码需要 15 多个小时才能处理 6K 员工记录 [英] Groovy script code to append xml node taking 15+ hours for 6K employee records

问题描述

下面的代码可以工作，但需要 15 多个小时来执行 6000 条员工记录，是否有任何改进的可能?

Below code is working but taking 15+ hours to execute 6000 employee records, any improvements possible?

对于 6000 名员工，我有两个员工记录结构(员工数据和员工福利)，我已使用人员编号将它们合并为单个 xml(要检查 xml 结构，请查看我之前的问题 - https://stackoverflow.com/questions/65174244/multiple-different-xml-structures-to-one-using-xml-using-xsl).

I have two employee record structures (employee data and employee benefits) for each of 6000 employees I have merged them into single xml using personnel number (to check the xml structure please check my previous question - https://stackoverflow.com/questions/65174244/multiple-different-xml-structures-to-one-using-xml-using-xsl).

现在我必须在 xml 员工记录中附加一个节点/子节点，当 ID(multimap:Message1 中的 personIdExternal 在 multimap:Message2 中找到相同的 ID/PERNR.

Now I have to append a node/subnode in xml employee record when ID (personIdExternal in multimap:Message1 finds same ID / PERNR in multimap:Message2.

 xml.'**'.findAll{it.name() == 'EmpEmployment'}.each{ p->

 def perID = xml.'**'.find{it.personIdExternal.text() == p.personIdExternal.text()} 
 def pernr = xml.'**'.find{it.PERNR.text() == '000'+perID.personIdExternal.text()}
 if(pernr != null)
 {    
       perID.appendNode {
       erpBenEligibility(pernr.PARDT.text()) }
  }

}  
 message.setBody(groovy.xml.XmlUtil.serialize(xml))

示例 XML:

<?xml version='1.0' encoding='UTF-8'?>
<multimap:Messages xmlns:multimap="http://sap.com/xi/XI/SplitAndMerge">
<multimap:Message1>
<person>
 <person>
    <street>test_stree1</street>
    <city>test_city1</city>
    <state>test_state1</state>
    <EmpEmployment>
     <personIdExternal> 001 </personIdExternal>
    </EmpEmployment>
 </person>
 <person>
    <street>test_stree2</street>
    <city>test_city2</city>
    <state>test_state2</state>
     <EmpEmployment>
     <personIdExternal> 002 </personIdExternal>
    </EmpEmployment>
 </person>
 <person>
    <street>test_stree3</street>
    <city>test_city3</city>
    <state>test_state3</state>
     <EmpEmployment>
     <personIdExternal> 003</personIdExternal>
     </EmpEmployment>
 </person>
</person>
</multimap:Message1>
<multimap:Message2>
<rfc:ZHR_GET_EMP_BENEFIT_DETAILS.Response xmlns:rfc="urn:sap- 
    com:document:sap:rfc:functions"> 
 <phone>
  <home>
   <phone>number1</phone>
  </home> 
  <PERNR> 001 </PERNR>
  <PARDT>#### 1 ####</PARDT>
  <home>
   <phone>number2</phone>
  </home> 
  <PERNR> 002 </PERNR>
  <PARDT>#### 2 ####</PARDT>
  <home>
   <phone>number3</phone>
  </home> 
  <PERNR> 003 </PERNR>
  <PARDT>#### 3 ####</PARDT>
</phone> 
</rfc:ZHR_GET_EMP_BENEFIT_DETAILS.Response xmlns:rfc="urn:sap-com:document:sap:rfc:functions">    
</multimap:Message2>
</multimap:Messages>

推荐答案

代码中的一些主要问题:

some major issues in your code:

• 使用 .** 访问器.如果 message1 中有 10000 个 persons，则 xml.** 将返回一个数组，其中 count(person)+count(EmpEmployment)+count(personIdExternal) =10000*3 个元素.并在这个数组上调用 findAll 应该扫描所有这些元素
• 在主循环 xml.'**'.findAll{it.name() == 'EmpEmployment'}.each{ 中，您无缘无故地构建嵌套的大数组.例如在这个表达式之后 def perID = xml.'**'.find{it.personIdExternal.text() == p.personIdExternal.text()} 你有 perID 等于 p

• using .** accessors. if you have 10000 persons in message1, then xml.** will return an array with count(person)+count(EmpEmployment)+count(personIdExternal) = 10000*3 elements. and calling findAll on this array should scan all those elements
• inside the main loop xml.'**'.findAll{it.name() == 'EmpEmployment'}.each{ you are building nested large arrays for no reason. for example after this expression def perID = xml.'**'.find{it.personIdExternal.text() == p.personIdExternal.text()} you have perID equals to p

您的代码仍然与 xml 示例不对应.

your code still does not correspond to the xml sample.

所以，我将做一些假设来展示如何在没有 .**. 的情况下构建 gpath:

so, i'm going to make some assumptions to show how you could build gpath without .**.:

让我们有这样的xml:

let we have xml like this:

<?xml version='1.0' encoding='UTF-8'?>
<multimap:Messages xmlns:multimap="http://sap.com/xi/XI/SplitAndMerge">
<multimap:Message1>
  <person>
      <person>
        <EmpEmployment>
          <personIdExternal>001</personIdExternal>
        </EmpEmployment>
      </person>
  </person>
</multimap:Message1>
<multimap:Message2>
  <phone>
      <xyz>
        <PERNR>000001</PERNR>
        <PARDT>#### 1 ####</PARDT>
      </xyz>
  </phone>
</multimap:Message2>
</multimap:Messages>

这是构建大型xml消息的代码部分:

this is a code part to build large xml message:

def count = 60000 //just for test let's create xml with 60K elements
def msg = '''<?xml version='1.0' encoding='UTF-8'?>
<multimap:Messages xmlns:multimap="http://sap.com/xi/XI/SplitAndMerge">
<multimap:Message1>
  <person>
'''+
(1..count).collect{"""\
      <person>
        <EmpEmployment>
          <personIdExternal>${String.format('%03d',it)}</personIdExternal>
        </EmpEmployment>
      </person>
"""}.join()+
'''  </person>
</multimap:Message1>
<multimap:Message2>
  <phone>
'''+
(1..count).collect{"""\
      <xyz>
        <PERNR>${String.format('%06d',it)}</PERNR>
        <PARDT>#### ${it} ####</PARDT>
      </xyz>
"""}.join()+
'''  </phone>
</multimap:Message2>
</multimap:Messages>
'''

现在是修改后的变换算法:

and now the modified transforming algorithm:

def xml = new XmlParser().parseText(msg)

def t = System.currentTimeMillis()
def ns = new groovy.xml.Namespace('http://sap.com/xi/XI/SplitAndMerge')

//for fast search let map PERNR value to a node that contains it
def pernrMap=xml[ns.Message2][0].phone[0].children().collectEntries{ [it.PERNR.text(), it] }

//itearte msg1 -> find entry in pernrMap -> add node
xml[ns.Message1][0].person[0].person.each{p->
    def emp = p.EmpEmployment[0]
    def pernr = pernrMap['000'+emp.personIdExternal.text()]
    if(pernr) emp.appendNode('erpBenEligibility', null, pernr.PARDT.text() )
}
groovy.xml.XmlUtil.serialize(xml)
println "t = ${(System.currentTimeMillis()-t)/1000} sec"

即使对于 msg1 & 中的 60k 个元素msg2 它在不到 1 秒的时间内完成转换.

even for 60k elements in msg1 & msg2 it does transformation in less then 1 sec.

这篇关于用于附加 xml 节点的 Groovy 脚本代码需要 15 多个小时才能处理 6K 员工记录的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！