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配置里用json怎麼改為sql

發布時間: 2022-09-03 19:16:08

㈠ json格式的數組，怎麼轉成其他格式資料庫比如xls csv sql

利用代碼將其轉化，不同語言不同操作也

㈡ 如何用C#將序列化為json的字元串導入到sql server中資料庫

將序列化為json的字元串tostring，然後，直接當成字元存入資料庫即可。

㈢ 如何把多種數據轉為json存儲到sql

這個 一般就是 一個拼接字元串的問題吧你把 String aaa ="aaa"; int bbb = 101;就可以這樣拼接啊 StringBuffer sb sb。apand（「｛」）；sb。apand（「aaa:"+""+aaa);sb。apand（「,bbb:"+""+bbb);sb。apand（「｝」）；封裝成一個字元串 就可以 存了 json只是一種數據格式 本質 可以理解為 字元串 而已 可以用字元串保存到資料庫 很簡的啊

㈣ java將json數據解析為sql語句

importjava.util.Iterator;
importjava.util.Set;
importjava.util.Map.Entry;

importcom.google.gson.JsonArray;
importcom.google.gson.JsonElement;
importcom.google.gson.JsonObject;
importcom.google.gson.JsonParser;

publicclassSql
{
	publicstaticStringparseSQL(Stringjson)
	{
		JsonParserparser=newJsonParser();
		JsonObjectobj=(JsonObject)parser.parse(json);;
		Stringtable=obj.get("table").getAsString();
		Stringop_type=obj.get("op_type").getAsString();
		Stringsql="";
		if("I".equals(op_type))
		{
			sql+="INSERTINTO"+table+"(";
			JsonObjectafter=(JsonObject)obj.get("after");
			Set<Entry<String,JsonElement>>entry=after.entrySet();
			Iterator<Entry<String,JsonElement>>it=entry.iterator();
			Stringvs="values(";
			while(it.hasNext())
			{
				Entry<String,JsonElement>elem=it.next();
				Stringkey=elem.getKey();
				Stringval=elem.getValue().toString();
				sql+=key+",";
				vs+=val+",";
			}
			sql=sql.replaceAll(",\s*$","");
			vs=vs.replaceAll(",\s*$","");
			sql+=")"+vs+")";
		}
		elseif("U".equals(op_type))
		{
			sql+="UPDATE"+table+"SET";
			JsonObjectafter=(JsonObject)obj.get("after");
			Set<Entry<String,JsonElement>>entry=after.entrySet();
			Iterator<Entry<String,JsonElement>>it=entry.iterator();
			while(it.hasNext())
			{
				Entry<String,JsonElement>elem=it.next();
				Stringkey=elem.getKey();
				Stringval=elem.getValue().toString();
				sql+=key+"="+val+",";
			}
			sql=sql.replaceAll(",\s*$","");
			sql+="WHERE";
			after=(JsonObject)obj.get("before");
			entry=after.entrySet();
			it=entry.iterator();
			while(it.hasNext())
			{
				Entry<String,JsonElement>elem=it.next();
				Stringkey=elem.getKey();
				Stringval=elem.getValue().toString();
				sql+=key+"="+val+"AND";
			}
			sql=sql.replaceAll("\s*AND\s*$","");
		}
		elseif("D".equals(op_type))
		{
			sql+="DELETEFROM"+table+"WHERE";
			JsonObjectafter=(JsonObject)obj.get("before");
			Set<Entry<String,JsonElement>>entry=after.entrySet();
			Iterator<Entry<String,JsonElement>>it=entry.iterator();
			while(it.hasNext())
			{
				Entry<String,JsonElement>elem=it.next();
				Stringkey=elem.getKey();
				Stringval=elem.getValue().toString();
				sql+=key+"="+val+"AND";
			}
			sql=sql.replaceAll("\s*AND\s*$","");
		}
		returnsql;
	}

	publicstaticvoidmain(String[]args)
	{
		Stringinsert=
		"{"table":"GG.TCUSTORD","op_type":"I","op_ts":"2013-06-0222:14:36.000000","current_ts":"2015-09-18T13:39:35.447000","pos":"00000000000000001444","tokens":{"R":"AADPkvAAEAAEqL2AAA"},"after":{"CUST_CODE":"WILL","ORDER_DATE":"1994-09-30:15:33:00","PRODUCT_CODE":"CAR","ORDER_ID":"144","PRODUCT_PRICE":17520.00,"PRODUCT_AMOUNT":3,"TRANSACTION_ID":"100"}}";
		Stringupdate=
		"{"table":"GG.TCUSTORD","op_type":"U","op_ts":"2013-06-0222:14:41.000000","current_ts":"2015-09-18T13:39:35.748000","pos":"00000000000000002891","tokens":{"L":"206080450","6":"9.0.80330","R":"AADPkvAAEAAEqLzAAC"},"before":{"CUST_CODE":"BILL","ORDER_DATE":"1995-12-31:15:00:00","PRODUCT_CODE":"CAR","ORDER_ID":"765","PRODUCT_PRICE":15000.00,"PRODUCT_AMOUNT":3,"TRANSACTION_ID":"100"},"after":{"CUST_CODE":"BILL","ORDER_DATE":"1995-12-31:15:00:00","PRODUCT_CODE":"CAR","ORDER_ID":"765","PRODUCT_PRICE":14000.00,"PRODUCT_AMOUNT":3,"TRANSCATION_ID":"100"}}";
		Stringdelete=
		"{"table":"GG.TCUSTORD","op_type":"D","op_ts":"2013-06-0222:14:41.000000","current_ts":"2015-09-18T13:39:35.766000","pos":"00000000000000004338","tokens":{"L":"206080450","6":"9.0.80330","R":"AADPkvAAEAAEqLzAAC"},"before":{"CUST_CODE":"DAVE","ORDER_DATE":"1993-11-03:07:51:35","PRODUCT_CODE":"PLANE","ORDER_ID":"600"}}";
		System.out.println(parseSQL(insert));
		System.out.println(parseSQL(update));
		System.out.println(parseSQL(delete));
	}
}

㈤ 如何將拼湊的json串存入sqlserver中

先檢查資料庫中保存這個數據的欄位的數據類型是否設置有誤，這種大量字元的欄位，數據類型可以使用text或者varchar(max),如果資料庫的欄位設置沒有問題，請檢查存儲前的代碼，是否有字元串截斷操作。

㈥ 如何將從介面取到的json數據存入mysql資料庫

mysql資料庫建立表，存儲json欄位用text類型
然後從介面中獲取JSON數據，轉成STRING格式，直接插入到這個欄位就可以了。

㈦ 如何將網上json資料庫解析到本地的sql資料庫中啊

取得數據最開始是json字元串->轉化為json數據->保存到資料庫

json數據知道的話，很簡單了

㈧ java將json格式轉換嬡雖ysql的java腳本，有沒有

執行環境
需要以下類庫支持
commons-lang 2.5
commons-beanutils 1.8.0
commons-collections 3.2.1
commons-logging 1.1.1
ezmorph 1.0.6
4.功能示例
這里通過JUnit-Case例子給出代碼示例

復制代碼代碼如下:

package com.mai.json;

import static org.junit.Assert.assertEquals;
import java.util.ArrayList;
import java.util.Date;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import net.sf.ezmorph.Morpher;
import net.sf.ezmorph.MorpherRegistry;
import net.sf.ezmorph.bean.BeanMorpher;
import net.sf.json.JSONArray;
import net.sf.json.JSONObject;
import net.sf.json.util.JSONUtils;
import org.apache.commons.beanutils.PropertyUtils;
import org.junit.Test;
public class JsonLibTest {
/*
* 普通類型、List、Collection等都是用JSONArray解析
*
* Map、自定義類型是用JSONObject解析
* 可以將Map理解成一個對象，裡面的key/value對可以理解成對象的屬性/屬性值
* 即{key1:value1,key2,value2......}
*
* 1.JSONObject是一個name:values集合，通過它的get(key)方法取得的是key後對應的value部分(字元串)
* 通過它的getJSONObject(key)可以取到一個JSONObject，--> 轉換成map,
* 通過它的getJSONArray(key) 可以取到一個JSONArray ，
*
*
*/

//一般數組轉換成JSON
@Test
public void testArrayToJSON(){
boolean[] boolArray = new boolean[]{true,false,true};
JSONArray jsonArray = JSONArray.fromObject( boolArray );
System.out.println( jsonArray );
// prints [true,false,true]
}

//Collection對象轉換成JSON
@Test
public void testListToJSON(){
List list = new ArrayList();
list.add( "first" );
list.add( "second" );
JSONArray jsonArray = JSONArray.fromObject( list );
System.out.println( jsonArray );
// prints ["first","second"]
}

//字元串json轉換成json， 根據情況是用JSONArray或JSONObject
@Test
public void testJsonStrToJSON(){
JSONArray jsonArray = JSONArray.fromObject( "['json','is','easy']" );
System.out.println( jsonArray );
// prints ["json","is","easy"]
}

//Map轉換成json， 是用jsonObject
@Test
public void testMapToJSON(){
Map map = new HashMap();
map.put( "name", "json" );
map.put( "bool", Boolean.TRUE );
map.put( "int", new Integer(1) );
map.put( "arr", new String[]{"a","b"} );
map.put( "func", "function(i){ return this.arr[i]; }" );

JSONObject jsonObject = JSONObject.fromObject( map );
System.out.println( jsonObject );
}

//復合類型bean轉成成json
@Test
public void testBeadToJSON(){
MyBean bean = new MyBean();
bean.setId("001");
bean.setName("銀行卡");
bean.setDate(new Date());

List cardNum = new ArrayList();
cardNum.add("農行");
cardNum.add("工行");
cardNum.add("建行");
cardNum.add(new Person("test"));

bean.setCardNum(cardNum);

JSONObject jsonObject = JSONObject.fromObject(bean);
System.out.println(jsonObject);

}

//普通類型的json轉換成對象
@Test
public void testJSONToObject() throws Exception{
String json = "{name=\"json\",bool:true,int:1,double:2.2,func:function(a){ return a; },array:[1,2]}";
JSONObject jsonObject = JSONObject.fromObject( json );
System.out.println(jsonObject);
Object bean = JSONObject.toBean( jsonObject );
assertEquals( jsonObject.get( "name" ), PropertyUtils.getProperty( bean, "name" ) );
assertEquals( jsonObject.get( "bool" ), PropertyUtils.getProperty( bean, "bool" ) );
assertEquals( jsonObject.get( "int" ), PropertyUtils.getProperty( bean, "int" ) );
assertEquals( jsonObject.get( "double" ), PropertyUtils.getProperty( bean, "double" ) );
assertEquals( jsonObject.get( "func" ), PropertyUtils.getProperty( bean, "func" ) );
System.out.println(PropertyUtils.getProperty(bean, "name"));
System.out.println(PropertyUtils.getProperty(bean, "bool"));
System.out.println(PropertyUtils.getProperty(bean, "int"));
System.out.println(PropertyUtils.getProperty(bean, "double"));
System.out.println(PropertyUtils.getProperty(bean, "func"));
System.out.println(PropertyUtils.getProperty(bean, "array"));

List arrayList = (List)JSONArray.toCollection(jsonObject.getJSONArray("array"));
for(Object object : arrayList){
System.out.println(object);
}

}

//將json解析成復合類型對象, 包含List
@Test
public void testJSONToBeanHavaList(){
String json = "{list:[{name:'test1'},{name:'test2'}],map:{test1:{name:'test1'},test2:{name:'test2'}}}";
// String json = "{list:[{name:'test1'},{name:'test2'}]}";
Map classMap = new HashMap();
classMap.put("list", Person.class);
MyBeanWithPerson diyBean = (MyBeanWithPerson)JSONObject.toBean(JSONObject.fromObject(json),MyBeanWithPerson.class , classMap);
System.out.println(diyBean);

List list = diyBean.getList();
for(Object o : list){
if(o instanceof Person){
Person p = (Person)o;
System.out.println(p.getName());
}
}
}

//將json解析成復合類型對象, 包含Map
@Test
public void testJSONToBeanHavaMap(){
//把Map看成一個對象
String json = "{list:[{name:'test1'},{name:'test2'}],map:{testOne:{name:'test1'},testTwo:{name:'test2'}}}";
Map classMap = new HashMap();
classMap.put("list", Person.class);
classMap.put("map", Map.class);
//使用暗示，直接將json解析為指定自定義對象，其中List完全解析,Map沒有完全解析
MyBeanWithPerson diyBean = (MyBeanWithPerson)JSONObject.toBean(JSONObject.fromObject(json),MyBeanWithPerson.class , classMap);
System.out.println(diyBean);

System.out.println("do the list release");
List<Person> list = diyBean.getList();
for(Person o : list){
Person p = (Person)o;
System.out.println(p.getName());
}

System.out.println("do the map release");

//先往注冊器中注冊變換器，需要用到ezmorph包中的類
MorpherRegistry morpherRegistry = JSONUtils.getMorpherRegistry();
Morpher dynaMorpher = new BeanMorpher( Person.class, morpherRegistry);
morpherRegistry.registerMorpher( dynaMorpher );

Map map = diyBean.getMap();
/*這里的map沒進行類型暗示，故按默認的，裡面存的為net.sf.ezmorph.bean.MorphDynaBean類型的對象*/
System.out.println(map);
/*輸出:
{testOne=net.sf.ezmorph.bean.MorphDynaBean@f73c1[
{name=test1}
], testTwo=net.sf.ezmorph.bean.MorphDynaBean@186c6b2[
{name=test2}
]}
*/
List<Person> output = new ArrayList();
for( Iterator i = map.values().iterator(); i.hasNext(); ){
//使用注冊器對指定DynaBean進行對象變換
output.add( (Person)morpherRegistry.morph( Person.class, i.next() ) );
}

for(Person p : output){
System.out.println(p.getName());
/*輸出:
test1
test2
*/
}

}
}

㈨ 如何獲取從his系統json的數據解析為xml插入sql用c#實現

序列化只是將數據序列化為完整的准確無誤的json格式的數據！
解析指的就是將你上面的json數據一一從json格式中分解出來，的到字元串格式的便於封裝bean對像
建立一對多的對象
將value setter給你的bean對象！
最後將bean存資料庫

㈩ 如何解析JSON數組到SQL表

例如：

源JSON數據：

{
"item1":{
"title":"2",
"value":null,
"visible":true,
"name":"item1",
"enabled":true,
"readonly":false,
"id":"f1f46ce6-9d0b-4eaf-88b7-d35b23a4d2e4"
},
"item2":{
"title":null,
"value":null
"visible":true,
"name":"item2",
"enabled":true,
"readonly":false,
"id":"da2b8a02-cfbd-4de8-8a33-74e2a484475a"
},
"item3":{
"title":"",
"value":null,
"visible":true,
"name":"item3",
"enabled":true,
"readonly":false,
"id":"57ee45d6-41d7-45c2-b022-13220e31d2d2"
}}

SQL查詢

SELECT[Key].[key]AS[ItemName],[Value].*FROMOPENJSON(@json,'$')AS[Key]CROSSAPPLYOPENJSON([Key].value)
WITH(
TitleVARCHAR(100)'$.title',
ValueVARCHAR(100)'$.value',
VisibleVARCHAR(100)'$.visible',
NameVARCHAR(100)'$.name',
EnabledVARCHAR(100)'$.enabled',
ReadOnlyVARCHAR(100)'$.readonly',
IdVARCHAR(500)'$.id'
)AS[Value]
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