【仅供内部供应商使用,不提供对外解答和培训】
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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8.4+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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public interface ColumnResolver extends Mutable { String MARK_STRING = "ColumnResolver"; int CURRENT_LEVEL = 1; boolean checkIfConditionMeet(ColumnResolver resolver); package com.fr.plugin.db.mongo.expand; import com.fr.stable.fun.mark.Mutable; import org.bson.Document; import java.util.List; /** * Created by richie on 2017/5/8. * 不是通常的基本类型的列的处理 */ public interface ColumnResolver extends Mutable { * 是否需要处理该类型的元素 * * @param cell doc查询出来的对象 String MARK_STRING* = "ColumnResolver"; @return 需要做扩展处理则返回true,否则返回false */ int CURRENT_LEVEL = 1boolean accept(Object cell); /** * 是否需要处理该类型的元素增加新的列 * * @param key cell doc查询出来的对象列名 * @return@param cell 需要做扩展处理则返回true,否则返回false内容 */ booleanList<FieldObject> acceptexpandColumn(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, ColumnResolver> columnResolverMap, List<List<Object>> rowDataCollections, List<Object> rowData); } |
我们以数组元素为例子,面对这样的数据结构:
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{
"name" : "李四",
"age" : 15,
"language" : [
"英语",
"数学",
"物理"
],
"region" : [
"新疆",
"海南",
"台湾"
],
"country" : "US"
} |
当需要把表格转换为二维表的时候,就需要处理language和region这样的数组元素,可以通过实现接口com.fr.plugin.db.mongo.expand.ColumnResolver,来把数组自动扩张开,示例代码如下:
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public class ArrayColumnResolver extends AbstractColumnResolver {
@Override
public boolean accept(Object cell) {
return cell instanceof ArrayList
&& !(((ArrayList) cell).get(0) instanceof Document);
}
@Override
public List<FieldObject> expandColumn(String key, Object cell) {
List<FieldObject> data = new ArrayList<FieldObject>();
data.add(FieldObject.create(key));
return data;
}
@Override
public void expandData(Document doc, List<String> columnNames, Map<FieldObject, ColumnResolver> columnResolverMap, 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 (Map.Entry<FieldObject, ColumnResolver> entry : columnResolverMap.entrySet()) {
if (!checkIfConditionMeet(entry.getValue())) {
continue;
}
FieldObject fieldObject = entry.getKey();
Object data = doc.get(fieldObject.getOriginalName());
if (data instanceof ArrayList) {
List<Object> array = (List<Object>) data;
if (standard == null) {
standard = array;
}
maxLength = Math.max(maxLength, array.size());
group.put(fieldObject.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 (FieldObject fieldObject : columnResolverMap.keySet()) {
List<Object> array = group.get(fieldObject.getIndex());
row.set(fieldObject.getIndex(), array.size() > i ? array.get(i) : null);
}
rowDataCollections.add(row);
}
}
}
} |
我们以数组元素为例子,面对这样的数据结构:
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{
"TEST_ID" : "8376",
"Section_Name" : "大三李",
"ItemList" : [
{
"Item_Name" : "AAA",
"Item_Value" : "123"
},
{
"Item_Name" : "BBB",
"Item_Value" : "456"
},
{
"Item_Name" : "CCC",
"Item_Value" : "789"
}
]
} |
希望展现成下面形式的二维表:
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:
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public class ArrayMapColumnResolver extends AbstractColumnResolver { @Override public boolean accept(Object cell) { return cell instanceof ArrayList && ((ArrayList) cell).get(0) instanceof Document; } @Override public List<FieldObject> expandColumn(String key, Object cell) { List<FieldObject> expand = new ArrayList<FieldObject>(); if (cell instanceof ArrayList) { Document document = (Document) ((ArrayList) cell).get(0); for (String name : document.keySet()) { expand.add(FieldObject.create(key, name)); } } return expand; } @Override public void expandData(Document doc, List<String> columnNames, |
...
Map<FieldObject, ColumnResolver> columnResolverMap,
List<List<Object>> rowDataCollections,
List<Object> rowData) {
boolean expanded = false;
List<List<Object>> rowList = new ArrayList<List<Object>>();
for (Map.Entry<FieldObject, ColumnResolver> entry : columnResolverMap.entrySet()) {
if (!checkIfConditionMeet(entry.getValue())) {
continue;
}
FieldObject fieldObject = entry.getKey();
List<Document> children = (ArrayList<Document>) doc.get(fieldObject.getOriginalName());
for (int i = 0, size = children.size(); i < size; i ++) {
Document document = children.get(i);
if (!expanded) {
List<Object> row = new ArrayList<Object>(Arrays.asList(new Object[rowData.size()]));
Collections.copy(row, rowData);
row.set(fieldObject.getIndex(), document.get(fieldObject.getCurrentName()));
rowDataCollections.add(row);
rowList.add(row);
} else {
rowList.get(i).set(fieldObject.getIndex(), document.get(fieldObject.getCurrentName()));
}
}
expanded = true;
}
}
} |
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<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> |
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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