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。
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 列名集合 * @param columnResolverMap 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:
{ "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:
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); } } } } |
<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。