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sql解析雙層json

發布時間: 2022-06-17 23:29:19

1. spark sql多層json怎麼查

spark sql多層json怎麼查
Spark SQL是支持在Spark中使用Sql、HiveSql、Scala中的關系型查詢表達式。它的...jsonRdd - 從一個已存在的RDD中載入數據,其中每一個RDD元素都是一個

2. sql中json解析

你好！

withtas(select'a:[{f:,h:,checindate:''month1：,year:,day:'',checkoutdate:''month：,year:,day:'',},
{checindate:''month2：,year:,day:,'',checkoutdate:''month：,year:,day},
{checindate:''month3：,year:,day:,'',checkoutdate:''month：,year:,day}]'strfromal)
,t1as(SELECTsubstr(str,instr(str,'[')+1,instr(str,']')-instr(str,'[')-1)strFROMT)
,t2as(selectsubstr(str,instr(str,'{')+1,instr(str,'}')-instr(str,'{')-1)strfromt1)
selectstr,substr(str,instr(str,'checindate')+12,instr(str,'checkoutdate')-instr(str,'checindate')-12)fromt2;

得到第一個checindate，直接截取字元串就可以了

別搞得那麼復雜了

3. sql 處理 json

json的數據json.loads進來以後會變成一個json的對象，你需要自己把python對象中的欄位值取出來，拼成sql語句你可以把這個過程封裝成一個函數importjsondefsave_json(json_str):obj=json.loads(json_str)sql='insertintotblvalues("%s")'%obj['id']#這里注意編碼，要轉成資料庫的編碼格式#blabla

4. 請教一個SparkSQL解析內嵌有json的csv的問題

一個SparkSQL解析內嵌有json的csv的問題
用excel打開，粘貼出來數據是這樣：
uid,uuid,event_type,event_data,created_at
123456,abcdefabcdefabcdef,some-action,"{""ration"": ""20"", ""questionId"": ""123456""}",1476201605

這里的event_data是一個json，使用下面的代碼讀取：
val usereventDF = spark.read.format("csv").option("header", "true").option("inferSchema", "true")
.load("/Users/xxx/Desktop/event.csv")
.createOrReplaceTempView("t_event")

當select *的時候，會發現created_at欄位的輸出被按照前面json當中的逗號識別了：
+-------+--------------------+--------------+--------------------+--------------------+
| uid| uuid| event_type| event_data| created_at|
+-------+--------------------+--------------+--------------------+--------------------+
|123456|abcdefabcdefabcdef...|some-action|"{""ration"": "...| ""questionId"": ...|

即便使用get_json_object，也會發現直接輸出'$'就是個 "{"

5. SQL server存儲過程實現JSON數據解析，然後插入資料庫表求高手指點

兩種方式
1、SQL有個charindex 函數，可以用這個函數配合substr實現 split功能實現循環插入
2、sql 2008以上存儲過程支持表值參數，json反序列化在程序里更方便，所以反序列化之後通過表值參數傳遞

6. 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));
	}
}

7. db2 sql怎麼解析json

jsp頁面的數據轉換成json格式可以採用js來解析：
例如在ation中：
bookList = new ArrayList<Books>();
JSONObject json = new JSONObject();
json.accumulate("bookList", bookList);
json.accumulate("pageNum", pageNum);
json.accumulate("totalPages", totalPages);
json.accumulate("totalNum", totalNum);
jsonObj = json.toString();

8. 求救，mysql怎麼解析json

DELIMITER $$ USE `dw`$$ DROP FUNCTION IF EXISTS `fn_Json_getKeyValue`$$ CREATE DEFINER=`data`@`%` FUNCTION `fn_Json_getKeyValue`( in_JsonArray VARCHAR(4096),#JSON數組字元串 in_Index TINYINT, #JSON對象序號，序號從1開始 in_KeyName VARCHAR(64)#鍵名 ) RETURNS VARCHAR(512) CHARSET utf8 BEGIN DECLARE vs_return VARCHAR(4096); DECLARE vs_JsonArray, vs_Json, vs_KeyName VARCHAR(4096); #declare vs_Json varchar(4096); DECLARE vi_pos1, vi_pos2 SMALLINT UNSIGNED; #寫監控日誌 #insert into dw.t_etl_log(sp_name, title, description) #values('dw.fn_Json_getKeyValue', '通過Json鍵名取鍵值', concat('in_JsonArray=', in_JsonArray)); SET vs_JsonArray = TRIM(in_JsonArray); SET vs_KeyName = TRIM(in_KeyName); IF vs_JsonArray = '' OR vs_JsonArray IS NULL OR vs_KeyName = '' OR vs_KeyName IS NULL OR in_Index 0 THEN #如果鍵名存在 SET vi_pos1 = vi_pos1 + CHAR_LENGTH(vs_KeyName); SET vi_pos2 = LOCATE(',', vs_json, vi_pos1); IF vi_pos2 = 0 THEN #最後一個元素沒有','分隔符，也沒有結束符'}' SET vi_pos2 = CHAR_LENGTH(vs_json) + 1; END IF; SET vs_return = REPLACE(MID(vs_json, vi_pos1, vi_pos2 - vi_pos1), '"', ''); END IF; END IF; END IF; RETURN(vs_return); END$$ DELIMITER ; 測試： {"old_current_score":"2","new_current_score":"0","old_grade_id":"1","new_grade_id":"1","grade_time":"2016-04-09 00:43:26","grade_upgrade_time":"2017-04-09 00:43:26"} select fn_Json_getKeyValue(reason,1,'old_grade_id')

9. 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

10. sql中對json數據欄位的查詢

先取出string，再在內存里轉換為對象並檢查。
ps：存json是沒問題，但又想存json又想直接查，違反了資料庫的範式。

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