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MongoDB中，列的类型不同（Array类型，Object类型），在转换成为二维表的时候，就会有不同的处理方式。

从MongoDB数据集插件8.34+版本，提供接口可自行开发插件，处理更为复杂的类型的列。

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Code Block

title	ColumnResolver
collapse	true

package com.fr.plugin.db.mongo.expand;

import com.fr.stable.fun.mark.Mutable;
import org.bson.Document;

import java.util.List;

public interface ColumnResolver extends Mutable {

    String MARK_STRING = "ColumnResolver";

    int CURRENT_LEVEL = 1;


    boolean checkIfConditionMeet(ColumnResolver resolver);

    /**
     * 是否需要处理该类型的元素
     *
     * @param cell doc查询出来的对象
     * @return 需要做扩展处理则返回true，否则返回false
     */
    boolean accept(Object cell);

    /**
     * 增加新的列
     *
     * @param key  列名
     * @param cell 内容
     */
    List<FieldObject> expandColumn(String key, Object cell);


    /**
     * 扩展特殊列，增加新的行
     *
     * @param doc                 MongoDB的文档查询对象
     * @param columnNames         列民集合列名集合
     * @param waitingColumnsIndexcolumnResolverMap 需要特殊处理的列集合需要特殊处理的列以及处理器集合
     * @param rowDataCollections  扩展出来的行数据集合
     * @param rowData             当前行数据
     */
    void expandData(Document doc, List<String> columnNames,
                    Map<FieldObject, List<Integer>ColumnResolver> waitingColumnsIndex, columnResolverMap,
                    List<List<Object>> rowDataCollections,
                    List<Object> rowData);
}

示例实现（数组类型）

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Code Block

title	处理数组元素的示例代码
collapse	true

import org.bson.Document;

import java.util.*;

public class ArrayColumnResolver extends AbstractColumnResolver {

    @Override
    public boolean accept(Object cell) {
        return cell instanceof List ArrayList
                && !(((ArrayList) cell).get(0) instanceof Document);
    }


    @Override
    public voidList<FieldObject> expandDataexpandColumn(DocumentString dockey, List<String> columnNames, List<Integer> waitingColumnsIndex, List<List<Object>> rowDataCollections, List<Object> rowData) {
 Object cell) {
        List<FieldObject> data = new ArrayList<FieldObject>();
         fillRowDataByArray(doc, columnNames, waitingColumnsIndex, rowDataCollections, rowData)data.add(FieldObject.create(key));
         return data;
    }

    private@Override
    public void fillRowDataByArrayexpandData(Document doc, List<String> columnNames, Map<FieldObject, List<Integer>ColumnResolver> waitingColumnsIndexcolumnResolverMap, List<List<Object>> rowDataCollections, List<Object> rowData) {
        Map<Integer, List<Object>> group = new HashMap<Integer, List<Object>>();
        List<Object> standard = null;
        int maxLength = 0;
        for (int index : waitingColumnsIndex) {Map.Entry<FieldObject, ColumnResolver> entry : columnResolverMap.entrySet()) {
            if (!checkIfConditionMeet(entry.getValue())) {
                continue;
            }
            FieldObject fieldObject = entry.getKey();
            Object data = doc.get(columnNamesfieldObject.getgetOriginalName(index));
            if (data instanceof ListArrayList) {
                List<Object> array = (List<Object>) data;
                if (standard == null) {
                    standard = array;
                }
                maxLength = Math.max(maxLength, array.size());
                group.put(indexfieldObject.getIndex(), array);
            }
        }

        if (standard != null) {
            for (int i = 0; i < maxLength; i++) {
                List<Object> row = new ArrayList<Object>(Arrays.asList(new Object[rowData.size()]));
                Collections.copy(row, rowData);
                for (intFieldObject indexfieldObject : waitingColumnsIndexcolumnResolverMap.keySet()) {
                    List<Object> array = group.get(fieldObject.getIndex(index));
                    row.set(indexfieldObject.getIndex(), array.size() > i ? array.get(i) : null);
                }
                rowDataCollections.add(row);
            }
        }
    }
}

示例实现（对象类型）

示例实现（数组对象类型）

面对这样的数据结构：我们以数组元素为例子，面对这样的数据结构：

Code Block

title	对象类型的数据结构带数组对象元素的JSON结构
collapse	true

{ 
    "TEST_ID" : "8376", 
    "valueSection_Name" : "大三李", 
    "ItemList" : [
        {
            "Item_Name" : "AAA", 
            "Item_Value" : "123"
        }, 
        {
            "Item_Name" : "wordBBB", 
            "nameItem_Value" : "456"
        }, 
        {
            "OItem_Name" : "XXXCCC", 
            "PItem_Value" : "YYY789"
        }
    ]
}

当需要把表格转换为二维表的时候，就需要处理name这样的Object类型的列，可以看出name是一个HashMap类型的结构，所以可以实现一个MapColumnResolver处理器，用于处理这一类的列：

希望展现成下面形式的二维表：

Image Added

同样可以通过实现接口com.fr.plugin.db.mongo.expand.ColumnResolver来实现，示例代码如下：

Code Block

title	处理Map元素的示例代码数据中包含对象的JSON结构处理代码
collapse	true

import org.bson.Document;

import java.util.*;

public class MapColumnResolverArrayMapColumnResolver extends AbstractColumnResolver {

    @Override
    public boolean accept(Object cell) {
        return cell instanceof ArrayList && ((ArrayList) cell).get(0) instanceof Document;
    }

    @Override
    public voidList<FieldObject> expandDataexpandColumn(DocumentString dockey, List<String> columnNames, List<Integer> waitingColumnsIndex, List<List<Object>> rowDataCollections, List<Object> rowDataObject cell) {
        Map<Integer, List<Object>> groupList<FieldObject> expand = new HashMap<Integer, List<Object>>ArrayList<FieldObject>();
        if List<Object>(cell standardinstanceof =ArrayList) null;{
           int Document maxLengthdocument = (Document) ((ArrayList) cell).get(0);
            for (intString indexname : waitingColumnsIndexdocument.keySet()) {
            Object  data = docexpand.getadd(columnNamesFieldObject.get(indexcreate(key, name));
            if}
 (data instanceof Document) {
    }
        return expand;
    }

 Document document = (Document)data;@Override
    public void expandData(Document doc,
         List<Object>  array = new ArrayList<Object>();
             List<String> columnNames,
  if (standard == null) {
                    for (Map.Entry<StringMap<FieldObject, Object>ColumnResolver> columnResolverMap,
 entry : document.entrySet()) {
                        array.add(entry);List<List<Object>> rowDataCollections,
                    }
       List<Object> rowData) {
        boolean expanded  standard = arrayfalse;
        List<List<Object>> rowList = new ArrayList<List<Object>>();
        }
      for (Map.Entry<FieldObject, ColumnResolver> entry : columnResolverMap.entrySet()) {
          maxLength  = Math.max(maxLength, array.sizeif (!checkIfConditionMeet(entry.getValue());) {
                group.put(index, array)continue;
            }

             }

FieldObject fieldObject = entry.getKey();
            ifList<Document> (standardchildren != null(ArrayList<Document>) {doc.get(fieldObject.getOriginalName());
            for (int i = 0, size = children.size(); i < maxLengthsize; i ++) {
                Document document = children.get(i);
                if (!expanded) {
                    List<Object> row = new ArrayList<Object>(Arrays.asList(new Object[rowData.size()]));
                    Collections.copy(row, rowData);
                for (int index : waitingColumnsIndex) { row.set(fieldObject.getIndex(), document.get(fieldObject.getCurrentName()));
                    List<Object> array = group.get(indexrowDataCollections.add(row);
                    Map.Entry<String, Object> el = (Map.Entry<String, Object>)array.get(i);rowList.add(row);
                } else {
                    rowrowList.get(i).set(indexfieldObject.getIndex(), arraydocument.size() > i ? el.getValue() : nullget(fieldObject.getCurrentName()));
                }
              }
  rowDataCollections.add(row);
          expanded = }true;
        }
    }
}

注册方式

Code Block
<dependence> <Item key="com.fr.solution.plugin.db.mongo" type="plugin"/> </dependence> <extra-core> <ColumnResolver class="com.fr.plugin.db.mongo.expand.impl.ArrayColumnResolver"/> </extra-core>

使用方式

同时安装MongoDB插件和列处理插件，就可以实现自已的要求了。

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示例实现（数组类型）

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